mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 10:54:36 +00:00
99c5b27654
* Refactor quantized processing functions * ggml : minor --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
10253 lines
308 KiB
C
10253 lines
308 KiB
C
// Defines CLOCK_MONOTONIC and asprintf on Linux
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#define _GNU_SOURCE
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#include "ggml.h"
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#if defined(_MSC_VER) || defined(__MINGW32__)
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#include <malloc.h> // using malloc.h with MSC/MINGW
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#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
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#include <alloca.h>
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#endif
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#include <assert.h>
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#include <errno.h>
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#include <time.h>
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#include <math.h>
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#include <stdlib.h>
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#include <string.h>
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#include <stdint.h>
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#include <stdio.h>
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#include <float.h>
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// if C99 - static_assert is noop
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// ref: https://stackoverflow.com/a/53923785/4039976
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#ifndef static_assert
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#define static_assert(cond, msg) struct global_scope_noop_trick
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#endif
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#if defined _MSC_VER || defined(__MINGW32__)
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#if !defined(__MINGW32__)
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#include <Windows.h>
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#else
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// ref: https://github.com/ggerganov/whisper.cpp/issues/168
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#include <windows.h>
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#endif
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typedef volatile LONG atomic_int;
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typedef atomic_int atomic_bool;
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static void atomic_store(atomic_int* ptr, LONG val) {
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InterlockedExchange(ptr, val);
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}
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static LONG atomic_load(atomic_int* ptr) {
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return InterlockedCompareExchange(ptr, 0, 0);
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}
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static LONG atomic_fetch_add(atomic_int* ptr, LONG inc) {
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return InterlockedExchangeAdd(ptr, inc);
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}
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static LONG atomic_fetch_sub(atomic_int* ptr, LONG dec) {
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return atomic_fetch_add(ptr, -(dec));
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}
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typedef HANDLE pthread_t;
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typedef DWORD thread_ret_t;
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static int pthread_create(pthread_t* out, void* unused, thread_ret_t(*func)(void*), void* arg) {
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HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL);
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if (handle == NULL)
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{
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return EAGAIN;
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}
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*out = handle;
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return 0;
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}
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static int pthread_join(pthread_t thread, void* unused) {
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return (int) WaitForSingleObject(thread, INFINITE);
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}
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static int sched_yield (void) {
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Sleep (0);
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return 0;
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}
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#else
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#include <pthread.h>
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#include <stdatomic.h>
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typedef void* thread_ret_t;
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#endif
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#ifdef __HAIKU__
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#define static_assert(cond, msg) _Static_assert(cond, msg)
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#endif
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#define GGML_MLOCK_SUPPORT 0
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#ifdef __has_include
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#if __has_include(<sys/mman.h>)
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#undef GGML_MLOCK_SUPPORT
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#define GGML_MLOCK_SUPPORT 1
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#include <sys/mman.h>
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#endif
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#endif
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/*#define GGML_PERF*/
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#define GGML_DEBUG 0
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#define GGML_GELU_FP16
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#define GGML_SILU_FP16
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#define GGML_SOFT_MAX_UNROLL 4
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#define GGML_VEC_DOT_UNROLL 2
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#ifdef GGML_USE_ACCELERATE
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// uncomment to use vDSP for soft max computation
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// note: not sure if it is actually faster
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//#define GGML_SOFT_MAX_ACCELERATE
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#endif
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#if UINTPTR_MAX == 0xFFFFFFFF
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#define GGML_MEM_ALIGN 4
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#else
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#define GGML_MEM_ALIGN 16
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#endif
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#define UNUSED(x) (void)(x)
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#define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0)
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#define GGML_ASSERT(x) \
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do { \
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if (!(x)) { \
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fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
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abort(); \
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} \
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} while (0)
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#ifdef GGML_USE_ACCELERATE
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#include <Accelerate/Accelerate.h>
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#elif GGML_USE_OPENBLAS
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#include <cblas.h>
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#endif
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#undef MIN
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#undef MAX
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#define MIN(a, b) ((a) < (b) ? (a) : (b))
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#define MAX(a, b) ((a) > (b) ? (a) : (b))
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// floating point type used to accumulate sums
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typedef double ggml_float;
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// 16-bit float
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// on Arm, we use __fp16
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// on x86, we use uint16_t
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#ifdef __ARM_NEON
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// if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
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//
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// $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/
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//
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#include <arm_neon.h>
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#define GGML_COMPUTE_FP16_TO_FP32(x) ((float) (x))
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#define GGML_COMPUTE_FP32_TO_FP16(x) (x)
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#define GGML_FP16_TO_FP32(x) ((float) (x))
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#define GGML_FP32_TO_FP16(x) (x)
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#else
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#ifdef __wasm_simd128__
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#include <wasm_simd128.h>
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#else
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#ifdef __POWER9_VECTOR__
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#include <altivec.h>
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#undef bool
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#define bool _Bool
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#else
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#include <immintrin.h>
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#endif
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#endif
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#ifdef __F16C__
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#define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x)
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#define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0)
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#elif defined(__POWER9_VECTOR__)
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#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
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#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
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/* the inline asm below is about 12% faster than the lookup method */
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#define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x)
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#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
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static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
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register float f;
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register double d;
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__asm__(
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"mtfprd %0,%2\n"
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"xscvhpdp %0,%0\n"
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"frsp %1,%0\n" :
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/* temp */ "=d"(d),
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/* out */ "=f"(f):
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/* in */ "r"(h));
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return f;
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}
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static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
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register double d;
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register ggml_fp16_t r;
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__asm__( /* xscvdphp can work on double or single precision */
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"xscvdphp %0,%2\n"
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"mffprd %1,%0\n" :
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/* temp */ "=d"(d),
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/* out */ "=r"(r):
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/* in */ "f"(f));
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return r;
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}
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#else
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// FP16 <-> FP32
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// ref: https://github.com/Maratyszcza/FP16
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static inline float fp32_from_bits(uint32_t w) {
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union {
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uint32_t as_bits;
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float as_value;
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} fp32;
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fp32.as_bits = w;
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return fp32.as_value;
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}
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static inline uint32_t fp32_to_bits(float f) {
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union {
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float as_value;
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uint32_t as_bits;
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} fp32;
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fp32.as_value = f;
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return fp32.as_bits;
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}
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static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
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const uint32_t w = (uint32_t) h << 16;
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const uint32_t sign = w & UINT32_C(0x80000000);
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const uint32_t two_w = w + w;
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const uint32_t exp_offset = UINT32_C(0xE0) << 23;
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#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)
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const float exp_scale = 0x1.0p-112f;
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#else
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const float exp_scale = fp32_from_bits(UINT32_C(0x7800000));
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#endif
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const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale;
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const uint32_t magic_mask = UINT32_C(126) << 23;
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const float magic_bias = 0.5f;
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const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias;
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const uint32_t denormalized_cutoff = UINT32_C(1) << 27;
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const uint32_t result = sign |
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(two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value));
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return fp32_from_bits(result);
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}
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static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
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#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)
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const float scale_to_inf = 0x1.0p+112f;
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const float scale_to_zero = 0x1.0p-110f;
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#else
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const float scale_to_inf = fp32_from_bits(UINT32_C(0x77800000));
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const float scale_to_zero = fp32_from_bits(UINT32_C(0x08800000));
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#endif
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float base = (fabsf(f) * scale_to_inf) * scale_to_zero;
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const uint32_t w = fp32_to_bits(f);
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const uint32_t shl1_w = w + w;
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const uint32_t sign = w & UINT32_C(0x80000000);
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uint32_t bias = shl1_w & UINT32_C(0xFF000000);
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if (bias < UINT32_C(0x71000000)) {
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bias = UINT32_C(0x71000000);
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}
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base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base;
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const uint32_t bits = fp32_to_bits(base);
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const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00);
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const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF);
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const uint32_t nonsign = exp_bits + mantissa_bits;
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return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign);
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}
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#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
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#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
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#endif // __F16C__
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#endif // __ARM_NEON
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//
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// global data
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//
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// precomputed gelu table for f16 (128 KB)
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static ggml_fp16_t table_gelu_f16[1 << 16];
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// precomputed silu table for f16 (128 KB)
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static ggml_fp16_t table_silu_f16[1 << 16];
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// precomputed exp table for f16 (128 KB)
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static ggml_fp16_t table_exp_f16[1 << 16];
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// precomputed f32 table for f16 (256 KB)
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static float table_f32_f16[1 << 16];
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// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32,
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// so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON.
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// This is also true for POWER9.
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#if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16)
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inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
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uint16_t s;
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memcpy(&s, &f, sizeof(uint16_t));
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return table_f32_f16[s];
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}
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#define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x)
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#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
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#endif
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// note: do not use these inside ggml.c
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// these are meant to be used via the ggml.h API
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float ggml_fp16_to_fp32(ggml_fp16_t x) {
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return (float) GGML_FP16_TO_FP32(x);
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}
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ggml_fp16_t ggml_fp32_to_fp16(float x) {
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return GGML_FP32_TO_FP16(x);
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}
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//
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// timing
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//
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#if defined(_MSC_VER) || defined(__MINGW32__)
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static int64_t timer_freq;
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void ggml_time_init(void) {
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LARGE_INTEGER frequency;
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QueryPerformanceFrequency(&frequency);
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timer_freq = frequency.QuadPart;
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}
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int64_t ggml_time_ms(void) {
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LARGE_INTEGER t;
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QueryPerformanceCounter(&t);
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return (t.QuadPart * 1000) / timer_freq;
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}
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int64_t ggml_time_us(void) {
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LARGE_INTEGER t;
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QueryPerformanceCounter(&t);
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return (t.QuadPart * 1000000) / timer_freq;
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}
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#else
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void ggml_time_init(void) {}
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int64_t ggml_time_ms(void) {
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struct timespec ts;
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clock_gettime(CLOCK_MONOTONIC, &ts);
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return (int64_t)ts.tv_sec*1000 + (int64_t)ts.tv_nsec/1000000;
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}
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int64_t ggml_time_us(void) {
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struct timespec ts;
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clock_gettime(CLOCK_MONOTONIC, &ts);
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return (int64_t)ts.tv_sec*1000000 + (int64_t)ts.tv_nsec/1000;
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}
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#endif
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int64_t ggml_cycles(void) {
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return clock();
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}
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int64_t ggml_cycles_per_ms(void) {
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return CLOCKS_PER_SEC/1000;
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}
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#ifdef GGML_PERF
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#define ggml_perf_time_ms() ggml_time_ms()
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#define ggml_perf_time_us() ggml_time_us()
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#define ggml_perf_cycles() ggml_cycles()
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#define ggml_perf_cycles_per_ms() ggml_cycles_per_ms()
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#else
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#define ggml_perf_time_ms() 0
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#define ggml_perf_time_us() 0
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#define ggml_perf_cycles() 0
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#define ggml_perf_cycles_per_ms() 0
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#endif
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//
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// cache line
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//
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#if defined(__cpp_lib_hardware_interference_size)
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#define CACHE_LINE_SIZE hardware_destructive_interference_size
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#else
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#if defined(__POWER9_VECTOR__)
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#define CACHE_LINE_SIZE 128
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#else
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#define CACHE_LINE_SIZE 64
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#endif
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#endif
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static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float);
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//
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// quantization
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//
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#define QK 32
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// AVX routines provided by GH user Const-me
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// ref: https://github.com/ggerganov/ggml/pull/27#issuecomment-1464934600
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#if __AVX2__ || __AVX512F__
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// Unpack 32 4-bit fields into 32 bytes
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// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval
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static inline __m256i bytesFromNibbles( const uint8_t* rsi )
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{
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// Load 16 bytes from memory
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__m128i tmp = _mm_loadu_si128( ( const __m128i* )rsi );
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// Expand bytes into uint16_t values
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__m256i bytes = _mm256_cvtepu8_epi16( tmp );
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// Unpack values into individual bytes
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const __m256i lowMask = _mm256_set1_epi8( 0xF );
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__m256i high = _mm256_andnot_si256( lowMask, bytes );
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__m256i low = _mm256_and_si256( lowMask, bytes );
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high = _mm256_slli_epi16( high, 4 );
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bytes = _mm256_or_si256( low, high );
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return bytes;
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}
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static inline __m128i packNibbles( __m256i bytes )
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{
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// Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh
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const __m256i lowByte = _mm256_set1_epi16( 0xFF );
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__m256i high = _mm256_andnot_si256( lowByte, bytes );
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__m256i low = _mm256_and_si256( lowByte, bytes );
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high = _mm256_srli_epi16( high, 4 );
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bytes = _mm256_or_si256( low, high );
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// Compress uint16_t lanes into bytes
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__m128i r0 = _mm256_castsi256_si128( bytes );
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__m128i r1 = _mm256_extracti128_si256( bytes, 1 );
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return _mm_packus_epi16( r0, r1 );
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}
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#endif
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// method 5
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// blocks of QK elements
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// represented with a single float (delta) and QK/2 8-bit ints (i.e QK 4-bit signed integer factors)
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typedef struct {
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float d; // delta
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uint8_t qs[QK / 2]; // nibbles / quants
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} block_q4_0;
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static_assert(sizeof(block_q4_0) == sizeof(float) + QK / 2, "wrong q4_0 block size/padding");
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// method 4
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// blocks of QK elements
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// represented with 2 floats (delta + min) and QK/2 8-bit ints (i.e QK 4-bit unsigned integer factors)
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typedef struct {
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float d;
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float m;
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uint8_t qs[QK / 2]; // nibbles / quants
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} block_q4_1;
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static_assert(sizeof(block_q4_1) == sizeof(float) * 2 + QK / 2, "wrong q4_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;
|
|
|
|
uint8_t pp[QK/2];
|
|
|
|
for (int i = 0; i < nb; i++) {
|
|
float amax = 0.0f; // absolute max
|
|
|
|
for (int l = 0; l < QK; l++) {
|
|
const float v = x[i*QK + l];
|
|
amax = MAX(amax, fabsf(v));
|
|
}
|
|
|
|
const float d = amax / ((1 << 3) - 1);
|
|
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;
|
|
|
|
const uint8_t vi0 = (int8_t)roundf(v0) + 8;
|
|
const uint8_t vi1 = (int8_t)roundf(v1) + 8;
|
|
|
|
assert(vi0 >= 0 && vi0 < 16);
|
|
assert(vi1 >= 0 && vi1 < 16);
|
|
|
|
pp[l/2] = vi0 | (vi1 << 4);
|
|
}
|
|
|
|
memcpy(y[i].qs, pp, sizeof(pp));
|
|
}
|
|
}
|
|
|
|
static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int k) {
|
|
assert(k % QK == 0);
|
|
const int nb = k / QK;
|
|
|
|
block_q4_0 * restrict y = vy;
|
|
|
|
#if defined(__POWER9_VECTOR__)
|
|
const vector float v85 = vec_splats(8.5f);
|
|
for (int i = 0; i < nb; i++) {
|
|
float amax = 0.0f; // absolute max
|
|
|
|
vector float srcv [8];
|
|
vector float asrcv[8];
|
|
vector float amaxv[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 < 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]);
|
|
|
|
amax = MAX(
|
|
MAX(vec_extract(amaxv[0], 0), vec_extract(amaxv[0], 1)),
|
|
MAX(vec_extract(amaxv[0], 2), vec_extract(amaxv[0], 3)));
|
|
|
|
const float d = amax / ((1 << 3) - 1);
|
|
const float id = d ? 1.0/d : 0.0;
|
|
|
|
y[i].d = d;
|
|
|
|
const vector float vid = vec_splats(id);
|
|
uint8_t * restrict pb = y[i].qs;
|
|
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);
|
|
|
|
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);
|
|
}
|
|
}
|
|
#elif __ARM_NEON
|
|
uint8_t pp[QK/2];
|
|
for (int i = 0; i < nb; i++) {
|
|
float amax = 0.0f; // absolute max
|
|
|
|
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]);
|
|
|
|
amax = MAX(
|
|
MAX(vgetq_lane_f32(amaxv[0], 0), vgetq_lane_f32(amaxv[0], 1)),
|
|
MAX(vgetq_lane_f32(amaxv[0], 2), vgetq_lane_f32(amaxv[0], 3)));
|
|
|
|
const float d = amax / ((1 << 3) - 1);
|
|
const float id = d ? 1.0f/d : 0.0f;
|
|
|
|
y[i].d = d;
|
|
|
|
for (int l = 0; l < 8; l++) {
|
|
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);
|
|
|
|
pp[2*l + 0] = vgetq_lane_s32(vi, 0) | (vgetq_lane_s32(vi, 1) << 4);
|
|
pp[2*l + 1] = vgetq_lane_s32(vi, 2) | (vgetq_lane_s32(vi, 3) << 4);
|
|
}
|
|
|
|
memcpy(y[i].qs, pp, sizeof(pp));
|
|
}
|
|
#elif defined(__AVX2__)
|
|
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 / 7.0f;
|
|
y[i].d = d;
|
|
const float id = ( maxScalar != 0.0f ) ? 7.0f / 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 );
|
|
|
|
// 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 );
|
|
|
|
// Apply offset to translate the range from [ -7 .. +7 ] into [ +1 .. +15 ]
|
|
const __m256i off = _mm256_set1_epi8( 8 );
|
|
i0 = _mm256_add_epi8( i0, off );
|
|
|
|
// Compress the vector into 4 bit/value, and store
|
|
__m128i res = packNibbles( i0 );
|
|
_mm_storeu_si128( ( __m128i* )y[i].qs, res );
|
|
}
|
|
#elif defined(__wasm_simd128__)
|
|
uint8_t pp[QK/2];
|
|
for (int i = 0; i < nb; i++) {
|
|
float amax = 0.0f; // absolute max
|
|
|
|
v128_t srcv [8];
|
|
v128_t asrcv[8];
|
|
v128_t amaxv[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]);
|
|
|
|
amax = MAX(
|
|
MAX(wasm_f32x4_extract_lane(amaxv[0], 0), wasm_f32x4_extract_lane(amaxv[0], 1)),
|
|
MAX(wasm_f32x4_extract_lane(amaxv[0], 2), wasm_f32x4_extract_lane(amaxv[0], 3)));
|
|
|
|
const float d = amax / ((1 << 3) - 1);
|
|
const float id = d ? 1.0/d : 0.0;
|
|
|
|
y[i].d = d;
|
|
|
|
for (int l = 0; l < 8; l++) {
|
|
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);
|
|
|
|
pp[2*l + 0] = wasm_i32x4_extract_lane(vi, 0) | (wasm_i32x4_extract_lane(vi, 1) << 4);
|
|
pp[2*l + 1] = wasm_i32x4_extract_lane(vi, 2) | (wasm_i32x4_extract_lane(vi, 3) << 4);
|
|
}
|
|
|
|
memcpy(y[i].qs, pp, sizeof(pp));
|
|
}
|
|
#else
|
|
// scalar
|
|
quantize_row_q4_0_reference(x, y, k);
|
|
#endif
|
|
}
|
|
|
|
static void quantize_row_q4_1(const float * restrict x, void * restrict vy, int k) {
|
|
assert(k % QK == 0);
|
|
const int nb = k / QK;
|
|
|
|
block_q4_1 * restrict y = vy;
|
|
|
|
uint8_t pp[QK/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];
|
|
if (v < min) min = v;
|
|
if (v > max) max = v;
|
|
}
|
|
|
|
const float d = (max - min) / ((1 << 4) - 1);
|
|
const float id = d ? 1.0f/d : 0.0f;
|
|
|
|
y[i].d = 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;
|
|
|
|
const uint8_t vi0 = roundf(v0);
|
|
const uint8_t vi1 = roundf(v1);
|
|
|
|
assert(vi0 >= 0 && vi0 < 16);
|
|
assert(vi1 >= 0 && vi1 < 16);
|
|
|
|
pp[l/2] = vi0 | (vi1 << 4);
|
|
}
|
|
|
|
memcpy(y[i].qs, pp, sizeof(pp));
|
|
}
|
|
}
|
|
|
|
static void dequantize_row_q4_0(const void * restrict vx, float * restrict y, int k) {
|
|
assert(k % QK == 0);
|
|
const int nb = k / QK;
|
|
|
|
const block_q4_0 * restrict x = vx;
|
|
|
|
#if defined(__AVX2__)
|
|
for (int i = 0; i < nb; i++) {
|
|
// scale factor
|
|
const __m256 d_v = _mm256_broadcast_ss(&x[i].d);
|
|
|
|
const uint8_t * restrict pp = x[i].qs;
|
|
|
|
for (int l = 0; l < QK; l += 32) {
|
|
// Load 32x4-bit integers into 32x8-bit integers
|
|
__m256i vx8 = bytesFromNibbles(pp+l/2);
|
|
|
|
// Subtract 8 from the integers
|
|
vx8 = _mm256_sub_epi8(vx8, _mm256_set1_epi8(8));
|
|
|
|
// Convert to 16-bit int
|
|
const __m256i vx16_lo = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vx8, 0));
|
|
const __m256i vx16_hi = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vx8, 1));
|
|
|
|
// Convert to 32-bit int -> float 32
|
|
const __m256 vf[4] = {
|
|
_mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_lo, 0))),
|
|
_mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_lo, 1))),
|
|
_mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_hi, 0))),
|
|
_mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_hi, 1)))
|
|
};
|
|
|
|
// 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);
|
|
}
|
|
}
|
|
}
|
|
#elif defined(__ARM_NEON)
|
|
for (int i = 0; i < nb; i++) {
|
|
const float32x4_t vd = vdupq_n_f32(x[i].d);
|
|
|
|
const uint8_t * restrict pp = x[i].qs;
|
|
|
|
for (int l = 0; l < QK; 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 v1 = vshr_n_u8(v8, 4);
|
|
|
|
// Convert to signed 8-bit integers
|
|
const int8x8_t vs_0 = vreinterpret_s8_u8(v0);
|
|
const int8x8_t vs_1 = vreinterpret_s8_u8(v1);
|
|
|
|
// Subtract 8 from each byte
|
|
const int8x8_t vb_0 = vsub_s8(vs_0, vdup_n_s8(8));
|
|
const int8x8_t vb_1 = vsub_s8(vs_1, vdup_n_s8(8));
|
|
|
|
// Interleave and combine
|
|
const int8x8_t vx_0 = vzip1_s8(vb_0, vb_1);
|
|
const int8x8_t vx_1 = vzip2_s8(vb_0, vb_1);
|
|
|
|
const int8x16_t vq = vcombine_s8(vx_0, vx_1);
|
|
|
|
// convert to 2x int16x8_t
|
|
const int16x8_t vi_0 = vmovl_s8(vget_low_s8 (vq));
|
|
const int16x8_t vi_1 = vmovl_s8(vget_high_s8(vq));
|
|
|
|
// convert to 4x float32x4_t
|
|
const float32x4_t vf_0 = vcvtq_f32_s32(vmovl_s16(vget_low_s16 (vi_0)));
|
|
const float32x4_t vf_1 = vcvtq_f32_s32(vmovl_s16(vget_high_s16(vi_0)));
|
|
const float32x4_t vf_2 = vcvtq_f32_s32(vmovl_s16(vget_low_s16 (vi_1)));
|
|
const float32x4_t vf_3 = vcvtq_f32_s32(vmovl_s16(vget_high_s16(vi_1)));
|
|
|
|
// Multiply by d
|
|
const float32x4_t r0 = vmulq_f32(vf_0, vd);
|
|
const float32x4_t r1 = vmulq_f32(vf_1, vd);
|
|
const float32x4_t r2 = vmulq_f32(vf_2, vd);
|
|
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);
|
|
}
|
|
}
|
|
#else
|
|
// scalar
|
|
for (int i = 0; i < nb; i++) {
|
|
const float d = x[i].d;
|
|
|
|
const uint8_t * restrict pp = x[i].qs;
|
|
|
|
for (int l = 0; l < QK; 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;
|
|
|
|
//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;
|
|
|
|
assert(!isnan(y[i*QK + l + 0]));
|
|
assert(!isnan(y[i*QK + 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;
|
|
|
|
const block_q4_1 * restrict x = vx;
|
|
|
|
#if defined(__AVX2__)
|
|
for (int i = 0; i < nb; i++) {
|
|
const __m256 d_v = _mm256_broadcast_ss(&x[i].d);
|
|
const __m256 d_m = _mm256_broadcast_ss(&x[i].m);
|
|
|
|
const uint8_t * restrict pp = x[i].qs;
|
|
|
|
for (int l = 0; l < QK; l += 32) {
|
|
// Load 32x4-bit integers into 32x8-bit integers
|
|
__m256i vx8 = bytesFromNibbles(pp+l/2);
|
|
|
|
// Convert to 16-bit int
|
|
const __m256i vx16_lo = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vx8, 0));
|
|
const __m256i vx16_hi = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vx8, 1));
|
|
|
|
// Convert to 32-bit int -> float 32
|
|
const __m256 vf[4] = {
|
|
_mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_lo, 0))),
|
|
_mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_lo, 1))),
|
|
_mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_hi, 0))),
|
|
_mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_hi, 1)))
|
|
};
|
|
|
|
// 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);
|
|
}
|
|
}
|
|
}
|
|
#else
|
|
for (int i = 0; i < nb; i++) {
|
|
const float d = x[i].d;
|
|
const float m = x[i].m;
|
|
|
|
const uint8_t * restrict pp = x[i].qs;
|
|
|
|
for (int l = 0; l < QK; 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*QK + l + 0] = v0;
|
|
y[i*QK + l + 1] = v1;
|
|
|
|
assert(!isnan(y[i*QK + l + 0]));
|
|
assert(!isnan(y[i*QK + l + 1]));
|
|
}
|
|
}
|
|
#endif
|
|
}
|
|
|
|
//
|
|
// simd mappings
|
|
//
|
|
|
|
// we define a common set of C macros which map to specific intrinsics based on the current architecture
|
|
// we then implement the fundamental computation operations below using only these macros
|
|
// adding support for new architectures requires to define the corresponding SIMD macros
|
|
//
|
|
// GGML_F32_STEP / GGML_F16_STEP
|
|
// number of elements to process in a single step
|
|
//
|
|
// GGML_F32_EPR / GGML_F16_EPR
|
|
// number of elements to fit in a single register
|
|
//
|
|
|
|
#if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA)
|
|
|
|
#define GGML_SIMD
|
|
|
|
// F32 NEON
|
|
|
|
#define GGML_F32_STEP 16
|
|
#define GGML_F32_EPR 4
|
|
|
|
#define GGML_F32x4 float32x4_t
|
|
#define GGML_F32x4_ZERO vdupq_n_f32(0.0f)
|
|
#define GGML_F32x4_SET1(x) vdupq_n_f32(x)
|
|
#define GGML_F32x4_LOAD vld1q_f32
|
|
#define GGML_F32x4_STORE vst1q_f32
|
|
#define GGML_F32x4_FMA(a, b, c) vfmaq_f32(a, b, c)
|
|
#define GGML_F32x4_ADD vaddq_f32
|
|
#define GGML_F32x4_MUL vmulq_f32
|
|
#if defined(__ARM_FEATURE_QRDMX)
|
|
#define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x)
|
|
#else
|
|
#define GGML_F32x4_REDUCE_ONE(x) \
|
|
(vgetq_lane_f32(x, 0) + \
|
|
vgetq_lane_f32(x, 1) + \
|
|
vgetq_lane_f32(x, 2) + \
|
|
vgetq_lane_f32(x, 3))
|
|
#endif
|
|
#define GGML_F32x4_REDUCE(res, x) \
|
|
{ \
|
|
for (int i = 0; i < GGML_F32_ARR/2; ++i) { \
|
|
x[2*i] = vaddq_f32(x[2*i], x[2*i+1]); \
|
|
} \
|
|
for (int i = 0; i < GGML_F32_ARR/4; ++i) { \
|
|
x[4*i] = vaddq_f32(x[4*i], x[4*i+2]); \
|
|
} \
|
|
for (int i = 0; i < GGML_F32_ARR/8; ++i) { \
|
|
x[8*i] = vaddq_f32(x[8*i], x[8*i+4]); \
|
|
} \
|
|
res = GGML_F32x4_REDUCE_ONE(x[0]); \
|
|
}
|
|
|
|
#define GGML_F32_VEC GGML_F32x4
|
|
#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO
|
|
#define GGML_F32_VEC_SET1 GGML_F32x4_SET1
|
|
#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD
|
|
#define GGML_F32_VEC_STORE GGML_F32x4_STORE
|
|
#define GGML_F32_VEC_FMA GGML_F32x4_FMA
|
|
#define GGML_F32_VEC_ADD GGML_F32x4_ADD
|
|
#define GGML_F32_VEC_MUL GGML_F32x4_MUL
|
|
#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
|
|
|
|
// F16 NEON
|
|
|
|
#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
|
|
#define GGML_F16_STEP 32
|
|
#define GGML_F16_EPR 8
|
|
|
|
#define GGML_F16x8 float16x8_t
|
|
#define GGML_F16x8_ZERO vdupq_n_f16(0.0f)
|
|
#define GGML_F16x8_SET1(x) vdupq_n_f16(x)
|
|
#define GGML_F16x8_LOAD vld1q_f16
|
|
#define GGML_F16x8_STORE vst1q_f16
|
|
#define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c)
|
|
#define GGML_F16x8_ADD vaddq_f16
|
|
#define GGML_F16x8_MUL vmulq_f16
|
|
#define GGML_F16x8_REDUCE(res, x) \
|
|
{ \
|
|
for (int i = 0; i < GGML_F16_ARR/2; ++i) { \
|
|
x[2*i] = vaddq_f16(x[2*i], x[2*i+1]); \
|
|
} \
|
|
for (int i = 0; i < GGML_F16_ARR/4; ++i) { \
|
|
x[4*i] = vaddq_f16(x[4*i], x[4*i+2]); \
|
|
} \
|
|
for (int i = 0; i < GGML_F16_ARR/8; ++i) { \
|
|
x[8*i] = vaddq_f16(x[8*i], x[8*i+4]); \
|
|
} \
|
|
const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 (x[0])); \
|
|
const float32x4_t t1 = vcvt_f32_f16(vget_high_f16(x[0])); \
|
|
res = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1)); \
|
|
}
|
|
|
|
#define GGML_F16_VEC GGML_F16x8
|
|
#define GGML_F16_VEC_ZERO GGML_F16x8_ZERO
|
|
#define GGML_F16_VEC_SET1 GGML_F16x8_SET1
|
|
#define GGML_F16_VEC_LOAD(p, i) GGML_F16x8_LOAD(p)
|
|
#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE(p, r[i])
|
|
#define GGML_F16_VEC_FMA GGML_F16x8_FMA
|
|
#define GGML_F16_VEC_ADD GGML_F16x8_ADD
|
|
#define GGML_F16_VEC_MUL GGML_F16x8_MUL
|
|
#define GGML_F16_VEC_REDUCE GGML_F16x8_REDUCE
|
|
#else
|
|
// if FP16 vector arithmetic is not supported, we use FP32 instead
|
|
// and take advantage of the vcvt_ functions to convert to/from FP16
|
|
|
|
#define GGML_F16_STEP 16
|
|
#define GGML_F16_EPR 4
|
|
|
|
#define GGML_F32Cx4 float32x4_t
|
|
#define GGML_F32Cx4_ZERO vdupq_n_f32(0.0f)
|
|
#define GGML_F32Cx4_SET1(x) vdupq_n_f32(x)
|
|
#define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16(x))
|
|
#define GGML_F32Cx4_STORE(x, y) vst1_f16(x, vcvt_f16_f32(y))
|
|
#define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c)
|
|
#define GGML_F32Cx4_ADD vaddq_f32
|
|
#define GGML_F32Cx4_MUL vmulq_f32
|
|
#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE
|
|
|
|
#define GGML_F16_VEC GGML_F32Cx4
|
|
#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO
|
|
#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1
|
|
#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p)
|
|
#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i])
|
|
#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA
|
|
#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD
|
|
#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL
|
|
#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE
|
|
#endif
|
|
|
|
#elif defined(__AVX__)
|
|
|
|
#define GGML_SIMD
|
|
|
|
// F32 AVX
|
|
|
|
#define GGML_F32_STEP 32
|
|
#define GGML_F32_EPR 8
|
|
|
|
#define GGML_F32x8 __m256
|
|
#define GGML_F32x8_ZERO _mm256_setzero_ps()
|
|
#define GGML_F32x8_SET1(x) _mm256_set1_ps(x)
|
|
#define GGML_F32x8_LOAD _mm256_loadu_ps
|
|
#define GGML_F32x8_STORE _mm256_storeu_ps
|
|
#if defined(__FMA__)
|
|
#define GGML_F32x8_FMA(a, b, c) _mm256_fmadd_ps(b, c, a)
|
|
#else
|
|
#define GGML_F32x8_FMA(a, b, c) _mm256_add_ps(_mm256_mul_ps(b, c), a)
|
|
#endif
|
|
#define GGML_F32x8_ADD _mm256_add_ps
|
|
#define GGML_F32x8_MUL _mm256_mul_ps
|
|
#define GGML_F32x8_REDUCE(res, x) \
|
|
{ \
|
|
for (int i = 0; i < GGML_F32_ARR/2; ++i) { \
|
|
x[2*i] = _mm256_add_ps(x[2*i], x[2*i+1]); \
|
|
} \
|
|
for (int i = 0; i < GGML_F32_ARR/4; ++i) { \
|
|
x[4*i] = _mm256_add_ps(x[4*i], x[4*i+2]); \
|
|
} \
|
|
for (int i = 0; i < GGML_F32_ARR/8; ++i) { \
|
|
x[8*i] = _mm256_add_ps(x[8*i], x[8*i+4]); \
|
|
} \
|
|
const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), \
|
|
_mm256_extractf128_ps(x[0], 1)); \
|
|
const __m128 t1 = _mm_hadd_ps(t0, t0); \
|
|
res = _mm_cvtss_f32(_mm_hadd_ps(t1, t1)); \
|
|
}
|
|
// TODO: is this optimal ?
|
|
|
|
#define GGML_F32_VEC GGML_F32x8
|
|
#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO
|
|
#define GGML_F32_VEC_SET1 GGML_F32x8_SET1
|
|
#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD
|
|
#define GGML_F32_VEC_STORE GGML_F32x8_STORE
|
|
#define GGML_F32_VEC_FMA GGML_F32x8_FMA
|
|
#define GGML_F32_VEC_ADD GGML_F32x8_ADD
|
|
#define GGML_F32_VEC_MUL GGML_F32x8_MUL
|
|
#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE
|
|
|
|
// F16 AVX
|
|
|
|
#define GGML_F16_STEP 32
|
|
#define GGML_F16_EPR 8
|
|
|
|
// F16 arithmetic is not supported by AVX, so we use F32 instead
|
|
|
|
#define GGML_F32Cx8 __m256
|
|
#define GGML_F32Cx8_ZERO _mm256_setzero_ps()
|
|
#define GGML_F32Cx8_SET1(x) _mm256_set1_ps(x)
|
|
|
|
#if defined(__F16C__)
|
|
// the _mm256_cvt intrinsics require F16C
|
|
#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((__m128i *)(x)))
|
|
#define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0))
|
|
#else
|
|
static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) {
|
|
float tmp[8];
|
|
|
|
for (int i = 0; i < 8; i++)
|
|
tmp[i] = GGML_FP16_TO_FP32(x[i]);
|
|
|
|
return _mm256_loadu_ps(tmp);
|
|
}
|
|
static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) {
|
|
float arr[8];
|
|
|
|
_mm256_storeu_ps(arr, y);
|
|
|
|
for (int i = 0; i < 8; i++)
|
|
x[i] = GGML_FP16_TO_FP32(arr[i]);
|
|
}
|
|
#define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x)
|
|
#define GGML_F32Cx8_STORE(x, y) __avx_f32cx8_store(x, y)
|
|
#endif
|
|
|
|
#define GGML_F32Cx8_FMA GGML_F32x8_FMA
|
|
#define GGML_F32Cx8_ADD _mm256_add_ps
|
|
#define GGML_F32Cx8_MUL _mm256_mul_ps
|
|
#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE
|
|
|
|
#define GGML_F16_VEC GGML_F32Cx8
|
|
#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO
|
|
#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1
|
|
#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p)
|
|
#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i])
|
|
#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA
|
|
#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD
|
|
#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL
|
|
#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE
|
|
|
|
#elif defined(__POWER9_VECTOR__)
|
|
|
|
#define GGML_SIMD
|
|
|
|
// F32 POWER9
|
|
|
|
#define GGML_F32_STEP 32
|
|
#define GGML_F32_EPR 4
|
|
|
|
#define GGML_F32x4 vector float
|
|
#define GGML_F32x4_ZERO 0.0f
|
|
#define GGML_F32x4_SET1 vec_splats
|
|
#define GGML_F32x4_LOAD(p) vec_xl(0, p)
|
|
#define GGML_F32x4_STORE(p, r) vec_xst(r, 0, p)
|
|
#define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a)
|
|
#define GGML_F32x4_ADD vec_add
|
|
#define GGML_F32x4_MUL vec_mul
|
|
#define GGML_F32x4_REDUCE(res, x) \
|
|
{ \
|
|
for (int i = 0; i < GGML_F32_ARR/2; ++i) { \
|
|
x[2*i] = vec_add(x[2*i], x[2*i+1]); \
|
|
} \
|
|
for (int i = 0; i < GGML_F32_ARR/4; ++i) { \
|
|
x[4*i] = vec_add(x[4*i], x[4*i+2]); \
|
|
} \
|
|
for (int i = 0; i < GGML_F32_ARR/8; ++i) { \
|
|
x[8*i] = vec_add(x[8*i], x[8*i+4]); \
|
|
} \
|
|
res = vec_extract(x[0], 0) + \
|
|
vec_extract(x[0], 1) + \
|
|
vec_extract(x[0], 2) + \
|
|
vec_extract(x[0], 3); \
|
|
}
|
|
|
|
#define GGML_F32_VEC GGML_F32x4
|
|
#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO
|
|
#define GGML_F32_VEC_SET1 GGML_F32x4_SET1
|
|
#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD
|
|
#define GGML_F32_VEC_STORE GGML_F32x4_STORE
|
|
#define GGML_F32_VEC_FMA GGML_F32x4_FMA
|
|
#define GGML_F32_VEC_ADD GGML_F32x4_ADD
|
|
#define GGML_F32_VEC_MUL GGML_F32x4_MUL
|
|
#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
|
|
|
|
// F16 POWER9
|
|
#define GGML_F16_STEP GGML_F32_STEP
|
|
#define GGML_F16_EPR GGML_F32_EPR
|
|
#define GGML_F16_VEC GGML_F32x4
|
|
#define GGML_F16_VEC_ZERO GGML_F32x4_ZERO
|
|
#define GGML_F16_VEC_SET1 GGML_F32x4_SET1
|
|
#define GGML_F16_VEC_FMA GGML_F32x4_FMA
|
|
#define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE
|
|
// Use vec_xl, not vec_ld, in case the load address is not aligned.
|
|
#define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ? \
|
|
vec_extract_fp32_from_shorth(vec_xl(0, p - GGML_F16_EPR)) : \
|
|
vec_extract_fp32_from_shortl(vec_xl(0, p))
|
|
#define GGML_ENDIAN_BYTE(i) ((unsigned char *)&(uint16_t){1})[i]
|
|
#define GGML_F16_VEC_STORE(p, r, i) \
|
|
if (i & 0x1) \
|
|
vec_xst(vec_pack_to_short_fp32(r[i - GGML_ENDIAN_BYTE(1)], \
|
|
r[i - GGML_ENDIAN_BYTE(0)]), \
|
|
0, p - GGML_F16_EPR)
|
|
|
|
#elif defined(__wasm_simd128__)
|
|
|
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#define GGML_SIMD
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|
|
|
// F32 WASM
|
|
|
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#define GGML_F32_STEP 16
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#define GGML_F32_EPR 4
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|
|
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#define GGML_F32x4 v128_t
|
|
#define GGML_F32x4_ZERO wasm_f32x4_splat(0.0f)
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#define GGML_F32x4_SET1(x) wasm_f32x4_splat(x)
|
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#define GGML_F32x4_LOAD wasm_v128_load
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#define GGML_F32x4_STORE wasm_v128_store
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#define GGML_F32x4_FMA(a, b, c) wasm_f32x4_add(wasm_f32x4_mul(b, c), a)
|
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#define GGML_F32x4_ADD wasm_f32x4_add
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#define GGML_F32x4_MUL wasm_f32x4_mul
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#define GGML_F32x4_REDUCE(res, x) \
|
|
{ \
|
|
for (int i = 0; i < GGML_F32_ARR/2; ++i) { \
|
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x[2*i] = wasm_f32x4_add(x[2*i], x[2*i+1]); \
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|
} \
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for (int i = 0; i < GGML_F32_ARR/4; ++i) { \
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x[4*i] = wasm_f32x4_add(x[4*i], x[4*i+2]); \
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|
} \
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for (int i = 0; i < GGML_F32_ARR/8; ++i) { \
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x[8*i] = wasm_f32x4_add(x[8*i], x[8*i+4]); \
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|
} \
|
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res = wasm_f32x4_extract_lane(x[0], 0) + \
|
|
wasm_f32x4_extract_lane(x[0], 1) + \
|
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wasm_f32x4_extract_lane(x[0], 2) + \
|
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wasm_f32x4_extract_lane(x[0], 3); \
|
|
}
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|
|
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#define GGML_F32_VEC GGML_F32x4
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#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO
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#define GGML_F32_VEC_SET1 GGML_F32x4_SET1
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#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD
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#define GGML_F32_VEC_STORE GGML_F32x4_STORE
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#define GGML_F32_VEC_FMA GGML_F32x4_FMA
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#define GGML_F32_VEC_ADD GGML_F32x4_ADD
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#define GGML_F32_VEC_MUL GGML_F32x4_MUL
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#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
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|
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// F16 WASM
|
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|
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#define GGML_F16_STEP 16
|
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#define GGML_F16_EPR 4
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|
|
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inline static v128_t __wasm_f16x4_load(const ggml_fp16_t * p) {
|
|
float tmp[4];
|
|
|
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tmp[0] = GGML_FP16_TO_FP32(p[0]);
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|
tmp[1] = GGML_FP16_TO_FP32(p[1]);
|
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tmp[2] = GGML_FP16_TO_FP32(p[2]);
|
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tmp[3] = GGML_FP16_TO_FP32(p[3]);
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|
|
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return wasm_v128_load(tmp);
|
|
}
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|
|
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inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) {
|
|
float tmp[4];
|
|
|
|
wasm_v128_store(tmp, x);
|
|
|
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p[0] = GGML_FP32_TO_FP16(tmp[0]);
|
|
p[1] = GGML_FP32_TO_FP16(tmp[1]);
|
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p[2] = GGML_FP32_TO_FP16(tmp[2]);
|
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p[3] = GGML_FP32_TO_FP16(tmp[3]);
|
|
}
|
|
|
|
#define GGML_F16x4 v128_t
|
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#define GGML_F16x4_ZERO wasm_f32x4_splat(0.0f)
|
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#define GGML_F16x4_SET1(x) wasm_f32x4_splat(x)
|
|
#define GGML_F16x4_LOAD(x) __wasm_f16x4_load(x)
|
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#define GGML_F16x4_STORE(x, y) __wasm_f16x4_store(x, y)
|
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#define GGML_F16x4_FMA GGML_F32x4_FMA
|
|
#define GGML_F16x4_ADD wasm_f32x4_add
|
|
#define GGML_F16x4_MUL wasm_f32x4_mul
|
|
#define GGML_F16x4_REDUCE(res, x) \
|
|
{ \
|
|
for (int i = 0; i < GGML_F16_ARR/2; ++i) { \
|
|
x[2*i] = wasm_f32x4_add(x[2*i], x[2*i+1]); \
|
|
} \
|
|
for (int i = 0; i < GGML_F16_ARR/4; ++i) { \
|
|
x[4*i] = wasm_f32x4_add(x[4*i], x[4*i+2]); \
|
|
} \
|
|
for (int i = 0; i < GGML_F16_ARR/8; ++i) { \
|
|
x[8*i] = wasm_f32x4_add(x[8*i], x[8*i+4]); \
|
|
} \
|
|
res = wasm_f32x4_extract_lane(x[0], 0) + \
|
|
wasm_f32x4_extract_lane(x[0], 1) + \
|
|
wasm_f32x4_extract_lane(x[0], 2) + \
|
|
wasm_f32x4_extract_lane(x[0], 3); \
|
|
}
|
|
|
|
#define GGML_F16_VEC GGML_F16x4
|
|
#define GGML_F16_VEC_ZERO GGML_F16x4_ZERO
|
|
#define GGML_F16_VEC_SET1 GGML_F16x4_SET1
|
|
#define GGML_F16_VEC_LOAD(p, i) GGML_F16x4_LOAD(p)
|
|
#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x4_STORE(p, r[i])
|
|
#define GGML_F16_VEC_FMA GGML_F16x4_FMA
|
|
#define GGML_F16_VEC_ADD GGML_F16x4_ADD
|
|
#define GGML_F16_VEC_MUL GGML_F16x4_MUL
|
|
#define GGML_F16_VEC_REDUCE GGML_F16x4_REDUCE
|
|
|
|
#elif defined(__SSE3__)
|
|
|
|
#define GGML_SIMD
|
|
|
|
// F32 SSE
|
|
|
|
#define GGML_F32_STEP 32
|
|
#define GGML_F32_EPR 4
|
|
|
|
#define GGML_F32x4 __m128
|
|
#define GGML_F32x4_ZERO _mm_setzero_ps()
|
|
#define GGML_F32x4_SET1(x) _mm_set1_ps(x)
|
|
#define GGML_F32x4_LOAD _mm_loadu_ps
|
|
#define GGML_F32x4_STORE _mm_storeu_ps
|
|
#if defined(__FMA__)
|
|
// TODO: Does this work?
|
|
#define GGML_F32x4_FMA(a, b, c) _mm_fmadd_ps(b, c, a)
|
|
#else
|
|
#define GGML_F32x4_FMA(a, b, c) _mm_add_ps(_mm_mul_ps(b, c), a)
|
|
#endif
|
|
#define GGML_F32x4_ADD _mm_add_ps
|
|
#define GGML_F32x4_MUL _mm_mul_ps
|
|
#define GGML_F32x4_REDUCE(res, x) \
|
|
{ \
|
|
for (int i = 0; i < GGML_F32_ARR/2; ++i) { \
|
|
x[2*i] = _mm_add_ps(x[2*i], x[2*i+1]); \
|
|
} \
|
|
for (int i = 0; i < GGML_F32_ARR/4; ++i) { \
|
|
x[4*i] = _mm_add_ps(x[4*i], x[4*i+2]); \
|
|
} \
|
|
for (int i = 0; i < GGML_F32_ARR/8; ++i) { \
|
|
x[8*i] = _mm_add_ps(x[8*i], x[8*i+4]); \
|
|
} \
|
|
const __m128 t0 = _mm_hadd_ps(x[0], x[0]); \
|
|
res = _mm_cvtss_f32(_mm_hadd_ps(t0, t0)); \
|
|
}
|
|
// TODO: is this optimal ?
|
|
|
|
#define GGML_F32_VEC GGML_F32x4
|
|
#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO
|
|
#define GGML_F32_VEC_SET1 GGML_F32x4_SET1
|
|
#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD
|
|
#define GGML_F32_VEC_STORE GGML_F32x4_STORE
|
|
#define GGML_F32_VEC_FMA GGML_F32x4_FMA
|
|
#define GGML_F32_VEC_ADD GGML_F32x4_ADD
|
|
#define GGML_F32_VEC_MUL GGML_F32x4_MUL
|
|
#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
|
|
|
|
// F16 SSE
|
|
|
|
#define GGML_F16_STEP 32
|
|
#define GGML_F16_EPR 4
|
|
|
|
static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) {
|
|
float tmp[4];
|
|
|
|
tmp[0] = GGML_FP16_TO_FP32(x[0]);
|
|
tmp[1] = GGML_FP16_TO_FP32(x[1]);
|
|
tmp[2] = GGML_FP16_TO_FP32(x[2]);
|
|
tmp[3] = GGML_FP16_TO_FP32(x[3]);
|
|
|
|
return _mm_loadu_ps(tmp);
|
|
}
|
|
|
|
static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) {
|
|
float arr[4];
|
|
|
|
_mm_storeu_ps(arr, y);
|
|
|
|
x[0] = GGML_FP32_TO_FP16(arr[0]);
|
|
x[1] = GGML_FP32_TO_FP16(arr[1]);
|
|
x[2] = GGML_FP32_TO_FP16(arr[2]);
|
|
x[3] = GGML_FP32_TO_FP16(arr[3]);
|
|
}
|
|
|
|
#define GGML_F32Cx4 __m128
|
|
#define GGML_F32Cx4_ZERO _mm_setzero_ps()
|
|
#define GGML_F32Cx4_SET1(x) _mm_set1_ps(x)
|
|
#define GGML_F32Cx4_LOAD(x) __sse_f16x4_load(x)
|
|
#define GGML_F32Cx4_STORE(x, y) __sse_f16x4_store(x, y)
|
|
#define GGML_F32Cx4_FMA GGML_F32x4_FMA
|
|
#define GGML_F32Cx4_ADD _mm_add_ps
|
|
#define GGML_F32Cx4_MUL _mm_mul_ps
|
|
#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE
|
|
|
|
#define GGML_F16_VEC GGML_F32Cx4
|
|
#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO
|
|
#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1
|
|
#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p)
|
|
#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i])
|
|
#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA
|
|
#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD
|
|
#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL
|
|
#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE
|
|
|
|
#endif
|
|
|
|
// GGML_F32_ARR / GGML_F16_ARR
|
|
// number of registers to use per step
|
|
#ifdef GGML_SIMD
|
|
#define GGML_F32_ARR (GGML_F32_STEP/GGML_F32_EPR)
|
|
#define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR)
|
|
#endif
|
|
|
|
//
|
|
// fundamental operations
|
|
//
|
|
|
|
inline static void ggml_vec_set_i8(const int n, int8_t * x, const int8_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
|
|
|
|
inline static void ggml_vec_set_i16(const int n, int16_t * x, const int16_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
|
|
|
|
inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
|
|
|
|
inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
|
|
|
|
inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] + y[i]; }
|
|
inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] += x[i]; }
|
|
inline static void ggml_vec_acc1_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] += v; }
|
|
inline static void ggml_vec_sub_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] - y[i]; }
|
|
inline static void ggml_vec_set_f32 (const int n, float * x, const float v) { for (int i = 0; i < n; ++i) x[i] = v; }
|
|
inline static void ggml_vec_cpy_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]; }
|
|
inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = -x[i]; }
|
|
inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]*y[i]; }
|
|
inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; }
|
|
|
|
inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) {
|
|
#ifdef GGML_SIMD
|
|
float sumf = 0.0f;
|
|
const int np = (n & ~(GGML_F32_STEP - 1));
|
|
|
|
GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
|
|
|
|
GGML_F32_VEC ax[GGML_F32_ARR];
|
|
GGML_F32_VEC ay[GGML_F32_ARR];
|
|
|
|
for (int i = 0; i < np; i += GGML_F32_STEP) {
|
|
for (int j = 0; j < GGML_F32_ARR; j++) {
|
|
ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR);
|
|
ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
|
|
|
|
sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]);
|
|
}
|
|
}
|
|
|
|
// reduce sum0..sum3 to sum0
|
|
GGML_F32_VEC_REDUCE(sumf, sum);
|
|
|
|
// leftovers
|
|
for (int i = np; i < n; ++i) {
|
|
sumf += x[i]*y[i];
|
|
}
|
|
#else
|
|
// scalar
|
|
ggml_float sumf = 0.0;
|
|
for (int i = 0; i < n; ++i) {
|
|
sumf += (ggml_float)(x[i]*y[i]);
|
|
}
|
|
#endif
|
|
|
|
*s = sumf;
|
|
}
|
|
|
|
#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;
|
|
|
|
#if defined(GGML_SIMD)
|
|
const int np = (n & ~(GGML_F16_STEP - 1));
|
|
|
|
GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO };
|
|
|
|
GGML_F16_VEC ax[GGML_F16_ARR];
|
|
GGML_F16_VEC ay[GGML_F16_ARR];
|
|
|
|
for (int i = 0; i < np; i += GGML_F16_STEP) {
|
|
for (int j = 0; j < GGML_F16_ARR; j++) {
|
|
ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j);
|
|
ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
|
|
|
|
sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]);
|
|
}
|
|
}
|
|
|
|
// reduce sum0..sum3 to sum0
|
|
GGML_F16_VEC_REDUCE(sumf, sum);
|
|
|
|
// leftovers
|
|
for (int i = np; i < n; ++i) {
|
|
sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]));
|
|
}
|
|
#else
|
|
for (int i = 0; i < n; ++i) {
|
|
sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]));
|
|
}
|
|
#endif
|
|
|
|
*s = sumf;
|
|
}
|
|
|
|
static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
|
|
const int nb = n / QK;
|
|
|
|
assert(n % QK == 0);
|
|
assert(nb % 2 == 0);
|
|
|
|
const block_q4_0 * restrict x = vx;
|
|
const block_q4_0 * restrict y = vy;
|
|
|
|
ggml_float sumf = 0.0;
|
|
|
|
#if defined(__ARM_NEON)
|
|
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 uint8x16_t m4b = vdupq_n_u8(0xf);
|
|
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_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_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);
|
|
|
|
#if defined(__ARM_FEATURE_DOTPROD)
|
|
// dot product into int16x8_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);
|
|
|
|
p_0 = vdotq_s32(p_0, v0_0hs, v1_0hs);
|
|
p_1 = vdotq_s32(p_1, v0_1hs, v1_1hs);
|
|
|
|
// scalar
|
|
#if defined(__ARM_FEATURE_QRDMX)
|
|
sum0 += x0->d * y0->d * vaddvq_s32(p_0);
|
|
sum1 += x1->d * y1->d * vaddvq_s32(p_1);
|
|
#else
|
|
sum0 += x0->d * y0->d * (vgetq_lane_s32(p_0, 0) + vgetq_lane_s32(p_0, 1) + vgetq_lane_s32(p_0, 2) + vgetq_lane_s32(p_0, 3));
|
|
sum1 += x1->d * y1->d * (vgetq_lane_s32(p_1, 0) + vgetq_lane_s32(p_1, 1) + vgetq_lane_s32(p_1, 2) + vgetq_lane_s32(p_1, 3));
|
|
#endif
|
|
#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));
|
|
|
|
const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hs), vget_low_s8 (v1_0hs));
|
|
const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hs), vget_high_s8(v1_0hs));
|
|
|
|
const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1ls), vget_low_s8 (v1_1ls));
|
|
const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1ls), vget_high_s8(v1_1ls));
|
|
|
|
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 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);
|
|
|
|
// scalar
|
|
#if defined(__ARM_FEATURE_QRDMX)
|
|
sum0 += x0->d * y0->d * vaddvq_s16(p_0);
|
|
sum1 += x1->d * y1->d * vaddvq_s16(p_1);
|
|
#else
|
|
sum0 += x0->d * y0->d * (vgetq_lane_s16(p_0, 0) + vgetq_lane_s16(p_0, 1) + vgetq_lane_s16(p_0, 2) + vgetq_lane_s16(p_0, 3) + vgetq_lane_s16(p_0, 4) + vgetq_lane_s16(p_0, 5) + vgetq_lane_s16(p_0, 6) + vgetq_lane_s16(p_0, 7));
|
|
sum1 += x1->d * y1->d * (vgetq_lane_s16(p_1, 0) + vgetq_lane_s16(p_1, 1) + vgetq_lane_s16(p_1, 2) + vgetq_lane_s16(p_1, 3) + vgetq_lane_s16(p_1, 4) + vgetq_lane_s16(p_1, 5) + vgetq_lane_s16(p_1, 6) + vgetq_lane_s16(p_1, 7));
|
|
#endif
|
|
#endif
|
|
}
|
|
|
|
sumf = (ggml_float)(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;
|
|
const int remainder = 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 );
|
|
#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 ) );
|
|
|
|
// 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 );
|
|
|
|
// 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 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
|
|
x16 = _mm256_cvtepi8_epi16( _mm256_extracti128_si256( bx, 1 ) );
|
|
y16 = _mm256_cvtepi8_epi16( _mm256_extracti128_si256( by, 1 ) );
|
|
// Accumulate products of int16_t integers
|
|
i32 = _mm256_add_epi32( i32, _mm256_madd_epi16( x16, y16 ) );
|
|
|
|
// Convert int32_t to float
|
|
__m256 p = _mm256_cvtepi32_ps( i32 );
|
|
// Apply the scale, and accumulate
|
|
acc = _mm256_fmadd_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 = &px[i + 0];
|
|
const block_q4_0 * restrict y0 = &py[i + 0];
|
|
const block_q4_0 * restrict x1 = &px[i + 1];
|
|
const block_q4_0 * restrict y1 = &py[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;
|
|
#else
|
|
// scalar
|
|
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;
|
|
|
|
for (int j = 0; j < QK/2; j++) {
|
|
const uint8_t v0 = p0[j];
|
|
const uint8_t v1 = p1[j];
|
|
|
|
const float f0 = d0*((int8_t) (v0 & 0xf) - 8);
|
|
const float f1 = d0*((int8_t) (v0 >> 4) - 8);
|
|
|
|
const float f2 = d1*((int8_t) (v1 & 0xf) - 8);
|
|
const float f3 = d1*((int8_t) (v1 >> 4) - 8);
|
|
|
|
sumf += f0*f2 + f1*f3;
|
|
}
|
|
}
|
|
#endif
|
|
|
|
*s = sumf;
|
|
}
|
|
|
|
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;
|
|
|
|
const block_q4_1 * restrict x = vx;
|
|
const block_q4_1 * restrict y = vy;
|
|
|
|
float sumf = 0.0;
|
|
|
|
#if defined(__AVX2__)
|
|
// Initialize accumulator with zeros
|
|
__m256 acc = _mm256_setzero_ps();
|
|
// Accumulator for constant offsets
|
|
float acc_offset = 0.0f;
|
|
|
|
// 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;
|
|
|
|
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, 0b10101010 );
|
|
|
|
// 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 );
|
|
|
|
// Now we have a vector with bytes in [ 0 .. 15 ] interval.
|
|
|
|
// 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);
|
|
}
|
|
|
|
// 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;
|
|
#else
|
|
// scalar
|
|
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;
|
|
|
|
const uint8_t * restrict p0 = x[i].qs;
|
|
const uint8_t * restrict p1 = y[i].qs;
|
|
|
|
for (int j = 0; j < QK/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 f2 = d1*(v1 & 0xf) + m1;
|
|
const float f3 = d1*(v1 >> 4) + m1;
|
|
|
|
sumf += f0*f2 + f1*f3;
|
|
}
|
|
}
|
|
#endif
|
|
|
|
*s = sumf;
|
|
}
|
|
|
|
// compute GGML_VEC_DOT_UNROLL dot products at once
|
|
// xs - x row stride in bytes
|
|
inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * restrict s, void * restrict xv, ggml_fp16_t * restrict y) {
|
|
ggml_float sumf[GGML_VEC_DOT_UNROLL] = { 0.0 };
|
|
|
|
ggml_fp16_t * restrict x[GGML_VEC_DOT_UNROLL];
|
|
|
|
for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) {
|
|
x[i] = (ggml_fp16_t *) ((char *) xv + i*xs);
|
|
}
|
|
|
|
#if defined(GGML_SIMD)
|
|
const int np = (n & ~(GGML_F16_STEP - 1));
|
|
|
|
GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } };
|
|
|
|
GGML_F16_VEC ax[GGML_F16_ARR];
|
|
GGML_F16_VEC ay[GGML_F16_ARR];
|
|
|
|
for (int i = 0; i < np; i += GGML_F16_STEP) {
|
|
for (int j = 0; j < GGML_F16_ARR; j++) {
|
|
ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
|
|
|
|
for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) {
|
|
ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j);
|
|
|
|
sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]);
|
|
}
|
|
}
|
|
}
|
|
|
|
// reduce sum0..sum3 to sum0
|
|
for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) {
|
|
GGML_F16_VEC_REDUCE(sumf[k], sum[k]);
|
|
}
|
|
|
|
// leftovers
|
|
for (int i = np; i < n; ++i) {
|
|
for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
|
|
sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i]));
|
|
}
|
|
}
|
|
#else
|
|
for (int i = 0; i < n; ++i) {
|
|
for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
|
|
sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i]));
|
|
}
|
|
}
|
|
#endif
|
|
|
|
for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) {
|
|
s[i] = sumf[i];
|
|
}
|
|
}
|
|
|
|
inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float * restrict x, const float v) {
|
|
#if defined(GGML_SIMD)
|
|
const int np = (n & ~(GGML_F32_STEP - 1));
|
|
|
|
GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
|
|
|
|
GGML_F32_VEC ax[GGML_F32_ARR];
|
|
GGML_F32_VEC ay[GGML_F32_ARR];
|
|
|
|
for (int i = 0; i < np; i += GGML_F32_STEP) {
|
|
for (int j = 0; j < GGML_F32_ARR; j++) {
|
|
ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR);
|
|
ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
|
|
ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx);
|
|
|
|
GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
|
|
}
|
|
}
|
|
|
|
// leftovers
|
|
for (int i = np; i < n; ++i) {
|
|
y[i] += x[i]*v;
|
|
}
|
|
#else
|
|
// scalar
|
|
for (int i = 0; i < n; ++i) {
|
|
y[i] += x[i]*v;
|
|
}
|
|
#endif
|
|
}
|
|
|
|
//inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] *= v; }
|
|
inline static void ggml_vec_scale_f32(const int n, float * y, const float v) {
|
|
#if defined(GGML_SIMD)
|
|
const int np = (n & ~(GGML_F32_STEP - 1));
|
|
|
|
GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
|
|
|
|
GGML_F32_VEC ay[GGML_F32_ARR];
|
|
|
|
for (int i = 0; i < np; i += GGML_F32_STEP) {
|
|
for (int j = 0; j < GGML_F32_ARR; j++) {
|
|
ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
|
|
ay[j] = GGML_F32_VEC_MUL(ay[j], vx);
|
|
|
|
GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
|
|
}
|
|
}
|
|
|
|
// leftovers
|
|
for (int i = np; i < n; ++i) {
|
|
y[i] *= v;
|
|
}
|
|
#else
|
|
// scalar
|
|
for (int i = 0; i < n; ++i) {
|
|
y[i] *= v;
|
|
}
|
|
#endif
|
|
}
|
|
|
|
inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, x, x); *s = sqrtf(*s); }
|
|
inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; }
|
|
inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); }
|
|
inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); }
|
|
inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); }
|
|
inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; }
|
|
inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; }
|
|
|
|
static const float GELU_COEF_A = 0.044715f;
|
|
static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
|
|
|
|
inline static float ggml_gelu_f32(float x) {
|
|
return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
|
|
}
|
|
|
|
inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
|
|
const uint16_t * i16 = (const uint16_t *) x;
|
|
for (int i = 0; i < n; ++i) {
|
|
y[i] = table_gelu_f16[i16[i]];
|
|
}
|
|
}
|
|
|
|
#ifdef GGML_GELU_FP16
|
|
inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) {
|
|
uint16_t t;
|
|
for (int i = 0; i < n; ++i) {
|
|
ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
|
|
memcpy(&t, &fp16, sizeof(uint16_t));
|
|
y[i] = GGML_FP16_TO_FP32(table_gelu_f16[t]);
|
|
}
|
|
}
|
|
#else
|
|
inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) {
|
|
for (int i = 0; i < n; ++i) {
|
|
y[i] = ggml_gelu_f32(x[i]);
|
|
}
|
|
}
|
|
#endif
|
|
|
|
// Sigmoid Linear Unit (SiLU) function
|
|
inline static float ggml_silu_f32(float x) {
|
|
return x/(1.0f + expf(-x));
|
|
}
|
|
|
|
inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
|
|
const uint16_t * i16 = (const uint16_t *) x;
|
|
for (int i = 0; i < n; ++i) {
|
|
y[i] = table_silu_f16[i16[i]];
|
|
}
|
|
}
|
|
|
|
#ifdef GGML_SILU_FP16
|
|
inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) {
|
|
uint16_t t;
|
|
for (int i = 0; i < n; ++i) {
|
|
ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
|
|
memcpy(&t, &fp16, sizeof(uint16_t));
|
|
y[i] = GGML_FP16_TO_FP32(table_silu_f16[t]);
|
|
}
|
|
}
|
|
#else
|
|
inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) {
|
|
for (int i = 0; i < n; ++i) {
|
|
y[i] = ggml_silu_f32(x[i]);
|
|
}
|
|
}
|
|
#endif
|
|
|
|
inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) {
|
|
#ifndef GGML_USE_ACCELERATE
|
|
ggml_float sum = 0.0;
|
|
for (int i = 0; i < n; ++i) {
|
|
sum += (ggml_float)x[i];
|
|
}
|
|
*s = sum;
|
|
#else
|
|
vDSP_sve(x, 1, s, n);
|
|
#endif
|
|
}
|
|
|
|
inline static void ggml_vec_max_f32(const int n, float * s, const float * x) {
|
|
#ifndef GGML_USE_ACCELERATE
|
|
float max = -INFINITY;
|
|
for (int i = 0; i < n; ++i) {
|
|
max = MAX(max, x[i]);
|
|
}
|
|
*s = max;
|
|
#else
|
|
vDSP_maxv(x, 1, s, n);
|
|
#endif
|
|
}
|
|
|
|
inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) {
|
|
ggml_vec_norm_f32(n, s, x);
|
|
*s = 1.f/(*s);
|
|
}
|
|
|
|
//
|
|
// logging
|
|
//
|
|
|
|
#if (GGML_DEBUG >= 1)
|
|
#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
|
|
#else
|
|
#define GGML_PRINT_DEBUG(...)
|
|
#endif
|
|
|
|
#if (GGML_DEBUG >= 5)
|
|
#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__)
|
|
#else
|
|
#define GGML_PRINT_DEBUG_5(...)
|
|
#endif
|
|
|
|
#if (GGML_DEBUG >= 10)
|
|
#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__)
|
|
#else
|
|
#define GGML_PRINT_DEBUG_10(...)
|
|
#endif
|
|
|
|
#define GGML_PRINT(...) printf(__VA_ARGS__)
|
|
|
|
//
|
|
// data types
|
|
//
|
|
|
|
static const int GGML_BLCK_SIZE[GGML_TYPE_COUNT] = {
|
|
QK,
|
|
QK,
|
|
1,
|
|
1,
|
|
1,
|
|
1,
|
|
1,
|
|
};
|
|
|
|
static_assert(GGML_TYPE_COUNT == 7, "GGML_TYPE_COUNT != 5");
|
|
|
|
static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
|
|
sizeof(block_q4_0),
|
|
sizeof(block_q4_1),
|
|
sizeof(int8_t ),
|
|
sizeof(int16_t),
|
|
sizeof(int32_t),
|
|
sizeof(ggml_fp16_t),
|
|
sizeof(float ),
|
|
};
|
|
|
|
// don't forget to update the array above when adding new types
|
|
static_assert(GGML_TYPE_COUNT == 7, "GGML_TYPE_COUNT != 5");
|
|
|
|
static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
|
|
"NONE",
|
|
|
|
"DUP",
|
|
"ADD",
|
|
"SUB",
|
|
"MUL",
|
|
"DIV",
|
|
"SQR",
|
|
"SQRT",
|
|
"SUM",
|
|
"MEAN",
|
|
"REPEAT",
|
|
"ABS",
|
|
"SGN",
|
|
"NEG",
|
|
"STEP",
|
|
"RELU",
|
|
"GELU",
|
|
"SILU",
|
|
"NORM",
|
|
"RMS_NORM",
|
|
|
|
"MUL_MAT",
|
|
|
|
"SCALE",
|
|
"CPY",
|
|
"RESHAPE",
|
|
"VIEW",
|
|
"PERMUTE",
|
|
"TRANSPOSE",
|
|
"GET_ROWS",
|
|
"DIAG_MASK_INF",
|
|
"SOFT_MAX",
|
|
"ROPE",
|
|
"CONV_1D_1S",
|
|
"CONV_1D_2S",
|
|
|
|
"FLASH_ATTN",
|
|
"FLASH_FF",
|
|
};
|
|
|
|
static_assert(GGML_OP_COUNT == 35, "GGML_OP_COUNT != 35");
|
|
|
|
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
|
|
"none",
|
|
|
|
"x",
|
|
"x+y",
|
|
"x-y",
|
|
"x*y",
|
|
"x/y",
|
|
"x^2",
|
|
"√x",
|
|
"Σx",
|
|
"Σx/n",
|
|
"repeat(x)",
|
|
"abs(x)",
|
|
"sgn(x)",
|
|
"-x",
|
|
"step(x)",
|
|
"relu(x)",
|
|
"gelu(x)",
|
|
"silu(x)",
|
|
"norm(x)",
|
|
"rms_norm(x)",
|
|
|
|
"X*Y",
|
|
|
|
"x*v",
|
|
"x-\\>y",
|
|
"reshape(x)",
|
|
"view(x)",
|
|
"permute(x)",
|
|
"transpose(x)",
|
|
"get_rows(x)",
|
|
"diag_mask_inf(x)",
|
|
"soft_max(x)",
|
|
"rope(x)",
|
|
"conv_1d_1s(x)",
|
|
"conv_1d_2s(x)",
|
|
|
|
"flash_attn(x)",
|
|
"flash_ff(x)",
|
|
};
|
|
|
|
static_assert(GGML_OP_COUNT == 35, "GGML_OP_COUNT != 35");
|
|
|
|
//
|
|
// ggml object
|
|
//
|
|
|
|
struct ggml_object {
|
|
size_t offs;
|
|
size_t size;
|
|
|
|
struct ggml_object * next;
|
|
|
|
char padding[8];
|
|
};
|
|
|
|
static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object);
|
|
|
|
static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
|
|
static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
|
|
|
|
//
|
|
// ggml context
|
|
//
|
|
|
|
struct ggml_context {
|
|
size_t mem_size;
|
|
void * mem_buffer;
|
|
bool mem_buffer_owned;
|
|
bool mem_buffer_mlocked;
|
|
|
|
int n_objects;
|
|
|
|
struct ggml_object * objects_begin;
|
|
struct ggml_object * objects_end;
|
|
|
|
struct ggml_scratch scratch;
|
|
struct ggml_scratch scratch_save;
|
|
};
|
|
|
|
struct ggml_context_container {
|
|
bool used;
|
|
|
|
struct ggml_context context;
|
|
};
|
|
|
|
//
|
|
// compute types
|
|
//
|
|
|
|
enum ggml_task_type {
|
|
GGML_TASK_INIT = 0,
|
|
GGML_TASK_COMPUTE,
|
|
GGML_TASK_FINALIZE,
|
|
};
|
|
|
|
struct ggml_compute_params {
|
|
enum ggml_task_type type;
|
|
|
|
int ith, nth;
|
|
|
|
// work buffer for all threads
|
|
size_t wsize;
|
|
void * wdata;
|
|
};
|
|
|
|
//
|
|
// ggml state
|
|
//
|
|
|
|
struct ggml_state {
|
|
struct ggml_context_container contexts[GGML_MAX_CONTEXTS];
|
|
};
|
|
|
|
// global state
|
|
static struct ggml_state g_state;
|
|
static atomic_int g_state_barrier = 0;
|
|
|
|
// barrier via spin lock
|
|
inline static void ggml_critical_section_start(void) {
|
|
int processing = atomic_fetch_add(&g_state_barrier, 1);
|
|
|
|
while (processing > 0) {
|
|
// wait for other threads to finish
|
|
atomic_fetch_sub(&g_state_barrier, 1);
|
|
sched_yield(); // TODO: reconsider this
|
|
processing = atomic_fetch_add(&g_state_barrier, 1);
|
|
}
|
|
}
|
|
|
|
// TODO: make this somehow automatically executed
|
|
// some sort of "sentry" mechanism
|
|
inline static void ggml_critical_section_end(void) {
|
|
atomic_fetch_sub(&g_state_barrier, 1);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
void ggml_print_object(const struct ggml_object * obj) {
|
|
GGML_PRINT(" - ggml_object: offset = %zu, size = %zu, next = %p\n",
|
|
obj->offs, obj->size, (const void *) obj->next);
|
|
}
|
|
|
|
void ggml_print_objects(const struct ggml_context * ctx) {
|
|
struct ggml_object * obj = ctx->objects_begin;
|
|
|
|
GGML_PRINT("%s: objects in context %p:\n", __func__, (const void *) ctx);
|
|
|
|
while (obj != NULL) {
|
|
ggml_print_object(obj);
|
|
obj = obj->next;
|
|
}
|
|
|
|
GGML_PRINT("%s: --- end ---\n", __func__);
|
|
}
|
|
|
|
int ggml_nelements(const struct ggml_tensor * tensor) {
|
|
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
|
|
|
return tensor->ne[0]*tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
|
|
}
|
|
|
|
int ggml_nrows(const struct ggml_tensor * tensor) {
|
|
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
|
|
|
return tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
|
|
}
|
|
|
|
size_t ggml_nbytes(const struct ggml_tensor * tensor) {
|
|
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
|
|
|
return (ggml_nelements(tensor)*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type];
|
|
}
|
|
|
|
int ggml_blck_size(enum ggml_type type) {
|
|
return GGML_BLCK_SIZE[type];
|
|
}
|
|
|
|
size_t ggml_type_size(enum ggml_type type) {
|
|
return GGML_TYPE_SIZE[type];
|
|
}
|
|
|
|
float ggml_type_sizef(enum ggml_type type) {
|
|
return ((float)(GGML_TYPE_SIZE[type]))/GGML_BLCK_SIZE[type];
|
|
}
|
|
|
|
size_t ggml_element_size(const struct ggml_tensor * tensor) {
|
|
return GGML_TYPE_SIZE[tensor->type];
|
|
}
|
|
|
|
static inline bool ggml_is_scalar(const struct ggml_tensor * tensor) {
|
|
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
|
|
|
return tensor->ne[0] == 1 && tensor->ne[1] == 1 && tensor->ne[2] == 1 && tensor->ne[3] == 1;
|
|
}
|
|
|
|
static inline bool ggml_is_vector(const struct ggml_tensor * tensor) {
|
|
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
|
|
|
return tensor->ne[1] == 1 && tensor->ne[2] == 1 && tensor->ne[3] == 1;
|
|
}
|
|
|
|
static inline bool ggml_is_matrix(const struct ggml_tensor * tensor) {
|
|
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
|
|
|
return tensor->ne[2] == 1 && tensor->ne[3] == 1;
|
|
}
|
|
|
|
static inline bool ggml_can_mul_mat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
|
|
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
|
|
|
return
|
|
(t0->ne[0] == t1->ne[0]) &&
|
|
(t0->ne[2] == t1->ne[2]) &&
|
|
(t0->ne[3] == t1->ne[3]);
|
|
}
|
|
|
|
static inline bool ggml_is_transposed(const struct ggml_tensor * tensor) {
|
|
return tensor->nb[0] > tensor->nb[1];
|
|
}
|
|
|
|
static inline bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
|
|
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
|
|
|
return
|
|
tensor->nb[0] == GGML_TYPE_SIZE[tensor->type] &&
|
|
tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/GGML_BLCK_SIZE[tensor->type] &&
|
|
tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
|
|
tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
|
|
}
|
|
|
|
static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) {
|
|
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
|
|
|
return
|
|
tensor->nb[0] == GGML_TYPE_SIZE[tensor->type] &&
|
|
tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
|
|
tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
|
|
}
|
|
|
|
static inline bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
|
|
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
|
|
|
return
|
|
(t0->ne[0] == t1->ne[0] ) &&
|
|
(t0->ne[1] == t1->ne[1] ) &&
|
|
(t0->ne[2] == t1->ne[2] ) &&
|
|
(t0->ne[3] == t1->ne[3] );
|
|
}
|
|
|
|
// check if t1 can be represented as a repeatition of t0
|
|
static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
|
|
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
|
|
|
return
|
|
(t1->ne[0]%t0->ne[0] == 0) &&
|
|
(t1->ne[1]%t0->ne[1] == 0) &&
|
|
(t1->ne[2]%t0->ne[2] == 0) &&
|
|
(t1->ne[3]%t0->ne[3] == 0);
|
|
}
|
|
|
|
static inline int ggml_up32(int n) {
|
|
return (n + 31) & ~31;
|
|
}
|
|
|
|
static inline int ggml_up64(int n) {
|
|
return (n + 63) & ~63;
|
|
}
|
|
|
|
static inline int ggml_up(int n, int m) {
|
|
// assert m is a power of 2
|
|
GGML_ASSERT((m & (m - 1)) == 0);
|
|
return (n + m - 1) & ~(m - 1);
|
|
}
|
|
|
|
// assert that pointer is aligned to GGML_MEM_ALIGN
|
|
#define ggml_assert_aligned(ptr) \
|
|
GGML_ASSERT(((uintptr_t) (ptr))%GGML_MEM_ALIGN == 0)
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
struct ggml_context * ggml_init(struct ggml_init_params params) {
|
|
// make this function thread safe
|
|
ggml_critical_section_start();
|
|
|
|
static bool is_first_call = true;
|
|
|
|
if (is_first_call) {
|
|
// initialize GELU, SILU and EXP F32 tables
|
|
{
|
|
const uint64_t t_start = ggml_time_us(); UNUSED(t_start);
|
|
|
|
ggml_fp16_t ii;
|
|
for (int i = 0; i < (1 << 16); ++i) {
|
|
uint16_t ui = i;
|
|
memcpy(&ii, &ui, sizeof(ii));
|
|
const float f = table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(ii);
|
|
table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f));
|
|
table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f));
|
|
table_exp_f16[i] = GGML_FP32_TO_FP16(expf(f));
|
|
}
|
|
|
|
const uint64_t t_end = ggml_time_us(); UNUSED(t_end);
|
|
|
|
GGML_PRINT_DEBUG("%s: GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f);
|
|
}
|
|
|
|
// initialize g_state
|
|
{
|
|
const uint64_t t_start = ggml_time_us(); UNUSED(t_start);
|
|
|
|
g_state = (struct ggml_state) {
|
|
/*.contexts =*/ { { 0 } },
|
|
};
|
|
|
|
for (int i = 0; i < GGML_MAX_CONTEXTS; ++i) {
|
|
g_state.contexts[i].used = false;
|
|
}
|
|
|
|
const uint64_t t_end = ggml_time_us(); UNUSED(t_end);
|
|
|
|
GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f);
|
|
}
|
|
|
|
is_first_call = false;
|
|
}
|
|
|
|
// find non-used context in g_state
|
|
struct ggml_context * ctx = NULL;
|
|
|
|
for (int i = 0; i < GGML_MAX_CONTEXTS; i++) {
|
|
if (!g_state.contexts[i].used) {
|
|
g_state.contexts[i].used = true;
|
|
ctx = &g_state.contexts[i].context;
|
|
|
|
GGML_PRINT_DEBUG("%s: found unused context %d\n", __func__, i);
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (ctx == NULL) {
|
|
GGML_PRINT_DEBUG("%s: no unused context found\n", __func__);
|
|
|
|
ggml_critical_section_end();
|
|
|
|
return NULL;
|
|
}
|
|
|
|
*ctx = (struct ggml_context) {
|
|
/*.mem_size =*/ params.mem_size,
|
|
/*.mem_buffer =*/ params.mem_buffer ? params.mem_buffer : malloc(params.mem_size),
|
|
/*.mem_buffer_owned =*/ params.mem_buffer ? false : true,
|
|
/*.mem_buffer_mlocked =*/ false,
|
|
/*.n_objects =*/ 0,
|
|
/*.objects_begin =*/ NULL,
|
|
/*.objects_end =*/ NULL,
|
|
/*.scratch =*/ { 0, 0, NULL, },
|
|
/*.scratch_save =*/ { 0, 0, NULL, },
|
|
};
|
|
|
|
GGML_ASSERT(ctx->mem_buffer != NULL); // check for allocation failure
|
|
|
|
ggml_assert_aligned(ctx->mem_buffer);
|
|
|
|
GGML_PRINT_DEBUG("%s: context initialized\n", __func__);
|
|
|
|
ggml_critical_section_end();
|
|
|
|
return ctx;
|
|
}
|
|
|
|
void ggml_free(struct ggml_context * ctx) {
|
|
// make this function thread safe
|
|
ggml_critical_section_start();
|
|
|
|
bool found = false;
|
|
|
|
for (int i = 0; i < GGML_MAX_CONTEXTS; i++) {
|
|
if (&g_state.contexts[i].context == ctx) {
|
|
g_state.contexts[i].used = false;
|
|
|
|
GGML_PRINT_DEBUG("%s: context %d with %d objects has been freed. memory used = %zu\n",
|
|
__func__, i, ctx->n_objects, ctx->objects_end->offs + ctx->objects_end->size);
|
|
|
|
#if GGML_MLOCK_SUPPORT
|
|
if (ctx->mem_buffer_mlocked) {
|
|
if (munlock(ctx->mem_buffer, ctx->mem_size)) {
|
|
fprintf(stderr, "%s: failed to munlock buffer: %s\n", __func__, strerror(errno));
|
|
}
|
|
}
|
|
#endif
|
|
|
|
if (ctx->mem_buffer_owned) {
|
|
free(ctx->mem_buffer);
|
|
}
|
|
|
|
found = true;
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (!found) {
|
|
GGML_PRINT_DEBUG("%s: context not found\n", __func__);
|
|
}
|
|
|
|
ggml_critical_section_end();
|
|
}
|
|
|
|
size_t ggml_used_mem(const struct ggml_context * ctx) {
|
|
return ctx->objects_end->offs + ctx->objects_end->size;
|
|
}
|
|
|
|
size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch) {
|
|
const size_t result = ctx->scratch.data ? ctx->scratch.offs : 0;
|
|
|
|
ctx->scratch = scratch;
|
|
|
|
return result;
|
|
}
|
|
|
|
bool ggml_mlock_supported(void) {
|
|
return GGML_MLOCK_SUPPORT;
|
|
}
|
|
|
|
#if GGML_MLOCK_SUPPORT
|
|
#ifdef __APPLE__
|
|
#define MLOCK_SUGGESTION "Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or\n" \
|
|
"decreasing 'vm.global_no_user_wire_amount'. Also try increasing RLIMIT_MLOCK (ulimit -l)."
|
|
#else
|
|
#define MLOCK_SUGGESTION "Try increasing RLIMIT_MLOCK (ulimit -l)."
|
|
#endif
|
|
bool ggml_mlock(struct ggml_context * ctx, char ** err_p) {
|
|
if (ctx->mem_buffer_mlocked) {
|
|
return true;
|
|
}
|
|
if (mlock(ctx->mem_buffer, ctx->mem_size)) {
|
|
int ret = asprintf(err_p, "failed to mlock %zu-byte buffer: %s\n" MLOCK_SUGGESTION,
|
|
ctx->mem_size, strerror(errno));
|
|
GGML_ASSERT(ret >= 0);
|
|
return false;
|
|
}
|
|
ctx->mem_buffer_mlocked = true;
|
|
return true;
|
|
}
|
|
#else // GGML_MLOCK_SUPPORT
|
|
bool ggml_mlock(struct ggml_context * ctx, char ** err_p) {
|
|
*err_p = strdup("can't mlock because it's not supported on this system");
|
|
return false;
|
|
}
|
|
#endif // GGML_MLOCK_SUPPORT
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
struct ggml_tensor * ggml_new_tensor_impl(
|
|
struct ggml_context * ctx,
|
|
enum ggml_type type,
|
|
int n_dims,
|
|
const int* ne,
|
|
void* data) {
|
|
// always insert objects at the end of the context's memory pool
|
|
struct ggml_object * obj_cur = ctx->objects_end;
|
|
|
|
const size_t cur_offs = obj_cur == NULL ? 0 : obj_cur->offs;
|
|
const size_t cur_size = obj_cur == NULL ? 0 : obj_cur->size;
|
|
const size_t cur_end = cur_offs + cur_size;
|
|
|
|
size_t size_needed = 0;
|
|
|
|
if (data == NULL) {
|
|
size_needed += GGML_TYPE_SIZE[type]*(ne[0]/GGML_BLCK_SIZE[type]);
|
|
for (int i = 1; i < n_dims; i++) {
|
|
size_needed *= ne[i];
|
|
}
|
|
// align to GGML_MEM_ALIGN
|
|
size_needed = ((size_needed + GGML_MEM_ALIGN - 1)/GGML_MEM_ALIGN)*GGML_MEM_ALIGN;
|
|
}
|
|
|
|
char * const mem_buffer = ctx->mem_buffer;
|
|
struct ggml_object * const obj_new = (struct ggml_object *)(mem_buffer + cur_end);
|
|
|
|
if (ctx->scratch.data == NULL || data != NULL) {
|
|
size_needed += sizeof(struct ggml_tensor);
|
|
|
|
if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) {
|
|
GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n",
|
|
__func__, cur_end + size_needed + GGML_OBJECT_SIZE, ctx->mem_size);
|
|
assert(false);
|
|
return NULL;
|
|
}
|
|
|
|
*obj_new = (struct ggml_object) {
|
|
.offs = cur_end + GGML_OBJECT_SIZE,
|
|
.size = size_needed,
|
|
.next = NULL,
|
|
};
|
|
} else {
|
|
if (ctx->scratch.offs + size_needed > ctx->scratch.size) {
|
|
GGML_PRINT("%s: not enough space in the scratch memory\n", __func__);
|
|
assert(false);
|
|
return NULL;
|
|
}
|
|
|
|
if (cur_end + sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE > ctx->mem_size) {
|
|
GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n",
|
|
__func__, cur_end + sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE, ctx->mem_size);
|
|
assert(false);
|
|
return NULL;
|
|
}
|
|
|
|
data = (char * const) ctx->scratch.data + ctx->scratch.offs;
|
|
|
|
*obj_new = (struct ggml_object) {
|
|
.offs = cur_end + GGML_OBJECT_SIZE,
|
|
.size = sizeof(struct ggml_tensor),
|
|
.next = NULL,
|
|
};
|
|
|
|
//printf("scratch offs = %zu, size_needed = %zu\n", ctx->scratch.offs, size_needed);
|
|
|
|
ctx->scratch.offs += size_needed;
|
|
}
|
|
|
|
if (obj_cur != NULL) {
|
|
obj_cur->next = obj_new;
|
|
} else {
|
|
// this is the first object in this context
|
|
ctx->objects_begin = obj_new;
|
|
}
|
|
|
|
ctx->objects_end = obj_new;
|
|
|
|
//printf("%s: inserted new object at %zu, size = %zu\n", __func__, cur_end, obj_new->size);
|
|
|
|
struct ggml_tensor * const result = (struct ggml_tensor *)(mem_buffer + obj_new->offs);
|
|
|
|
ggml_assert_aligned(result);
|
|
|
|
*result = (struct ggml_tensor) {
|
|
/*.type =*/ type,
|
|
/*.n_dims =*/ n_dims,
|
|
/*.ne =*/ { 1, 1, 1, 1 },
|
|
/*.nb =*/ { 0, 0, 0, 0 },
|
|
/*.op =*/ GGML_OP_NONE,
|
|
/*.is_param =*/ false,
|
|
/*.grad =*/ NULL,
|
|
/*.src0 =*/ NULL,
|
|
/*.src1 =*/ NULL,
|
|
/*.opt =*/ { NULL },
|
|
/*.n_tasks =*/ 0,
|
|
/*.perf_runs =*/ 0,
|
|
/*.perf_cycles =*/ 0,
|
|
/*.perf_time_us =*/ 0,
|
|
/*.data =*/ data == NULL ? (void *)(result + 1) : data,
|
|
/*.pad =*/ { 0 },
|
|
};
|
|
|
|
ggml_assert_aligned(result->data);
|
|
|
|
for (int i = 0; i < n_dims; i++) {
|
|
result->ne[i] = ne[i];
|
|
}
|
|
|
|
result->nb[0] = GGML_TYPE_SIZE[type];
|
|
result->nb[1] = result->nb[0]*(result->ne[0]/GGML_BLCK_SIZE[type]);
|
|
for (int i = 2; i < GGML_MAX_DIMS; i++) {
|
|
result->nb[i] = result->nb[i - 1]*result->ne[i - 1];
|
|
}
|
|
|
|
ctx->n_objects++;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_new_tensor(
|
|
struct ggml_context * ctx,
|
|
enum ggml_type type,
|
|
int n_dims,
|
|
const int * ne) {
|
|
return ggml_new_tensor_impl(ctx, type, n_dims, ne, NULL);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_new_tensor_1d(
|
|
struct ggml_context * ctx,
|
|
enum ggml_type type,
|
|
int ne0) {
|
|
return ggml_new_tensor(ctx, type, 1, &ne0);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_new_tensor_2d(
|
|
struct ggml_context * ctx,
|
|
enum ggml_type type,
|
|
int ne0,
|
|
int ne1) {
|
|
const int ne[2] = { ne0, ne1 };
|
|
return ggml_new_tensor(ctx, type, 2, ne);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_new_tensor_3d(
|
|
struct ggml_context * ctx,
|
|
enum ggml_type type,
|
|
int ne0,
|
|
int ne1,
|
|
int ne2) {
|
|
const int ne[3] = { ne0, ne1, ne2 };
|
|
return ggml_new_tensor(ctx, type, 3, ne);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_new_tensor_4d(
|
|
struct ggml_context * ctx,
|
|
enum ggml_type type,
|
|
int ne0,
|
|
int ne1,
|
|
int ne2,
|
|
int ne3) {
|
|
const int ne[4] = { ne0, ne1, ne2, ne3 };
|
|
return ggml_new_tensor(ctx, type, 4, ne);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) {
|
|
ctx->scratch_save = ctx->scratch;
|
|
ctx->scratch.data = NULL;
|
|
|
|
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1);
|
|
|
|
ctx->scratch = ctx->scratch_save;
|
|
|
|
ggml_set_i32(result, value);
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) {
|
|
ctx->scratch_save = ctx->scratch;
|
|
ctx->scratch.data = NULL;
|
|
|
|
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
|
|
|
|
ctx->scratch = ctx->scratch_save;
|
|
|
|
ggml_set_f32(result, value);
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_dup_tensor(struct ggml_context * ctx, const struct ggml_tensor * src) {
|
|
return ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, NULL);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) {
|
|
memset(tensor->data, 0, ggml_nbytes(tensor));
|
|
return tensor;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) {
|
|
const int n = ggml_nrows(tensor);
|
|
const int nc = tensor->ne[0];
|
|
const size_t n1 = tensor->nb[1];
|
|
|
|
char * const data = tensor->data;
|
|
|
|
switch (tensor->type) {
|
|
case GGML_TYPE_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));
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value);
|
|
}
|
|
} break;
|
|
case GGML_TYPE_I16:
|
|
{
|
|
assert(tensor->nb[0] == sizeof(int16_t));
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value);
|
|
}
|
|
} break;
|
|
case GGML_TYPE_I32:
|
|
{
|
|
assert(tensor->nb[0] == sizeof(int32_t));
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value);
|
|
}
|
|
} break;
|
|
case GGML_TYPE_F16:
|
|
{
|
|
assert(tensor->nb[0] == sizeof(ggml_fp16_t));
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), value);
|
|
}
|
|
} break;
|
|
case GGML_TYPE_F32:
|
|
{
|
|
assert(tensor->nb[0] == sizeof(float));
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_set_f32(nc, (float *)(data + i*n1), value);
|
|
}
|
|
} break;
|
|
case GGML_TYPE_COUNT:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
|
|
return tensor;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) {
|
|
const int n = ggml_nrows(tensor);
|
|
const int nc = tensor->ne[0];
|
|
const size_t n1 = tensor->nb[1];
|
|
|
|
char * const data = tensor->data;
|
|
|
|
switch (tensor->type) {
|
|
case GGML_TYPE_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));
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value);
|
|
}
|
|
} break;
|
|
case GGML_TYPE_I16:
|
|
{
|
|
assert(tensor->nb[0] == sizeof(int16_t));
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value);
|
|
}
|
|
} break;
|
|
case GGML_TYPE_I32:
|
|
{
|
|
assert(tensor->nb[0] == sizeof(int32_t));
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value);
|
|
}
|
|
} break;
|
|
case GGML_TYPE_F16:
|
|
{
|
|
assert(tensor->nb[0] == sizeof(ggml_fp16_t));
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), value);
|
|
}
|
|
} break;
|
|
case GGML_TYPE_F32:
|
|
{
|
|
assert(tensor->nb[0] == sizeof(float));
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_set_f32(nc, (float *)(data + i*n1), value);
|
|
}
|
|
} break;
|
|
case GGML_TYPE_COUNT:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
|
|
return tensor;
|
|
}
|
|
|
|
int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) {
|
|
switch (tensor->type) {
|
|
case GGML_TYPE_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));
|
|
return ((int8_t *)(tensor->data))[i];
|
|
} break;
|
|
case GGML_TYPE_I16:
|
|
{
|
|
GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
|
|
return ((int16_t *)(tensor->data))[i];
|
|
} break;
|
|
case GGML_TYPE_I32:
|
|
{
|
|
GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
|
|
return ((int32_t *)(tensor->data))[i];
|
|
} break;
|
|
case GGML_TYPE_F16:
|
|
{
|
|
GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
|
|
return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
|
|
} break;
|
|
case GGML_TYPE_F32:
|
|
{
|
|
GGML_ASSERT(tensor->nb[0] == sizeof(float));
|
|
return ((float *)(tensor->data))[i];
|
|
} break;
|
|
case GGML_TYPE_COUNT:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
|
|
return 0.0f;
|
|
}
|
|
|
|
void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) {
|
|
switch (tensor->type) {
|
|
case GGML_TYPE_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));
|
|
((int8_t *)(tensor->data))[i] = value;
|
|
} break;
|
|
case GGML_TYPE_I16:
|
|
{
|
|
GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
|
|
((int16_t *)(tensor->data))[i] = value;
|
|
} break;
|
|
case GGML_TYPE_I32:
|
|
{
|
|
GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
|
|
((int32_t *)(tensor->data))[i] = value;
|
|
} break;
|
|
case GGML_TYPE_F16:
|
|
{
|
|
GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
|
|
((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value);
|
|
} break;
|
|
case GGML_TYPE_F32:
|
|
{
|
|
GGML_ASSERT(tensor->nb[0] == sizeof(float));
|
|
((float *)(tensor->data))[i] = value;
|
|
} break;
|
|
case GGML_TYPE_COUNT:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) {
|
|
switch (tensor->type) {
|
|
case GGML_TYPE_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));
|
|
return ((int8_t *)(tensor->data))[i];
|
|
} break;
|
|
case GGML_TYPE_I16:
|
|
{
|
|
GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
|
|
return ((int16_t *)(tensor->data))[i];
|
|
} break;
|
|
case GGML_TYPE_I32:
|
|
{
|
|
GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
|
|
return ((int32_t *)(tensor->data))[i];
|
|
} break;
|
|
case GGML_TYPE_F16:
|
|
{
|
|
GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
|
|
return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
|
|
} break;
|
|
case GGML_TYPE_F32:
|
|
{
|
|
GGML_ASSERT(tensor->nb[0] == sizeof(float));
|
|
return ((float *)(tensor->data))[i];
|
|
} break;
|
|
case GGML_TYPE_COUNT:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
|
|
return 0.0f;
|
|
}
|
|
|
|
void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) {
|
|
switch (tensor->type) {
|
|
case GGML_TYPE_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));
|
|
((int8_t *)(tensor->data))[i] = value;
|
|
} break;
|
|
case GGML_TYPE_I16:
|
|
{
|
|
GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
|
|
((int16_t *)(tensor->data))[i] = value;
|
|
} break;
|
|
case GGML_TYPE_I32:
|
|
{
|
|
GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
|
|
((int32_t *)(tensor->data))[i] = value;
|
|
} break;
|
|
case GGML_TYPE_F16:
|
|
{
|
|
GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
|
|
((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value);
|
|
} break;
|
|
case GGML_TYPE_F32:
|
|
{
|
|
GGML_ASSERT(tensor->nb[0] == sizeof(float));
|
|
((float *)(tensor->data))[i] = value;
|
|
} break;
|
|
case GGML_TYPE_COUNT:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
void * ggml_get_data(const struct ggml_tensor * tensor) {
|
|
return tensor->data;
|
|
}
|
|
|
|
float * ggml_get_data_f32(const struct ggml_tensor * tensor) {
|
|
assert(tensor->type == GGML_TYPE_F32);
|
|
return (float *)(tensor->data);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_view_tensor(
|
|
struct ggml_context * ctx,
|
|
const struct ggml_tensor * src) {
|
|
return ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, src->data);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// ggml_dup
|
|
|
|
struct ggml_tensor * ggml_dup_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
bool inplace) {
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad)) {
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
|
|
|
result->op = GGML_OP_DUP;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_dup(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_dup_impl(ctx, a, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_dup_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_dup_impl(ctx, a, true);
|
|
}
|
|
|
|
// ggml_add
|
|
|
|
struct ggml_tensor * ggml_add_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
bool inplace) {
|
|
GGML_ASSERT(ggml_are_same_shape(a, b));
|
|
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad || b->grad)) {
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
|
|
|
result->op = GGML_OP_ADD;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = b;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_add(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
return ggml_add_impl(ctx, a, b, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_add_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
return ggml_add_impl(ctx, a, b, true);
|
|
}
|
|
|
|
// ggml_sub
|
|
|
|
struct ggml_tensor * ggml_sub_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
bool inplace) {
|
|
GGML_ASSERT(ggml_are_same_shape(a, b));
|
|
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad || b->grad)) {
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
|
|
|
result->op = GGML_OP_SUB;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = b;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_sub(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
return ggml_sub_impl(ctx, a, b, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_sub_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
return ggml_sub_impl(ctx, a, b, true);
|
|
}
|
|
|
|
// ggml_mul
|
|
|
|
struct ggml_tensor * ggml_mul_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
bool inplace) {
|
|
GGML_ASSERT(ggml_are_same_shape(a, b));
|
|
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad || b->grad)) {
|
|
is_node = true;
|
|
}
|
|
|
|
if (inplace) {
|
|
GGML_ASSERT(is_node == false);
|
|
}
|
|
|
|
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
|
|
|
result->op = GGML_OP_MUL;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = b;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_mul(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
return ggml_mul_impl(ctx, a, b, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_mul_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
return ggml_mul_impl(ctx, a, b, true);
|
|
}
|
|
|
|
// ggml_div
|
|
|
|
struct ggml_tensor * ggml_div_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
bool inplace) {
|
|
GGML_ASSERT(ggml_are_same_shape(a, b));
|
|
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad || b->grad)) {
|
|
is_node = true;
|
|
}
|
|
|
|
if (inplace) {
|
|
GGML_ASSERT(is_node == false);
|
|
}
|
|
|
|
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
|
|
|
result->op = GGML_OP_DIV;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = b;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_div(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
return ggml_div_impl(ctx, a, b, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_div_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
return ggml_div_impl(ctx, a, b, true);
|
|
}
|
|
|
|
// ggml_sqr
|
|
|
|
struct ggml_tensor * ggml_sqr_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
bool inplace) {
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad)) {
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
|
|
|
result->op = GGML_OP_SQR;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_sqr(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_sqr_impl(ctx, a, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_sqr_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_sqr_impl(ctx, a, true);
|
|
}
|
|
|
|
// ggml_sqrt
|
|
|
|
struct ggml_tensor * ggml_sqrt_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
bool inplace) {
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad)) {
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
|
|
|
result->op = GGML_OP_SQRT;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_sqrt(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_sqrt_impl(ctx, a, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_sqrt_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_sqrt_impl(ctx, a, true);
|
|
}
|
|
|
|
// ggml_sum
|
|
|
|
struct ggml_tensor * ggml_sum(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
bool is_node = false;
|
|
|
|
if (a->grad) {
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1);
|
|
|
|
result->op = GGML_OP_SUM;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
// ggml_mean
|
|
|
|
struct ggml_tensor * ggml_mean(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
bool is_node = false;
|
|
|
|
if (a->grad) {
|
|
GGML_ASSERT(false); // TODO: implement
|
|
is_node = true;
|
|
}
|
|
|
|
int ne[GGML_MAX_DIMS] = { 1, a->ne[1], a->ne[2], a->ne[3] };
|
|
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, a->n_dims, ne);
|
|
|
|
result->op = GGML_OP_MEAN;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
// ggml_repeat
|
|
|
|
struct ggml_tensor * ggml_repeat(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
GGML_ASSERT(ggml_can_repeat(a, b));
|
|
|
|
bool is_node = false;
|
|
|
|
if (a->grad) {
|
|
is_node = true;
|
|
}
|
|
|
|
if (ggml_are_same_shape(a, b) && !is_node) {
|
|
return a;
|
|
}
|
|
|
|
struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, b->n_dims, b->ne);
|
|
|
|
result->op = GGML_OP_REPEAT;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = b;
|
|
|
|
return result;
|
|
}
|
|
|
|
// ggml_abs
|
|
|
|
struct ggml_tensor * ggml_abs_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
bool inplace) {
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad)) {
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
|
|
|
result->op = GGML_OP_ABS;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_abs(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_abs_impl(ctx, a, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_abs_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_abs_impl(ctx, a, true);
|
|
}
|
|
|
|
|
|
// ggml_sgn
|
|
|
|
struct ggml_tensor * ggml_sgn_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
bool inplace) {
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad)) {
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
|
|
|
result->op = GGML_OP_SGN;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_sgn(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_sgn_impl(ctx, a, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_sgn_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_sgn_impl(ctx, a, true);
|
|
}
|
|
|
|
// ggml_neg
|
|
|
|
struct ggml_tensor * ggml_neg_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
bool inplace) {
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad)) {
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
|
|
|
result->op = GGML_OP_NEG;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_neg(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_neg_impl(ctx, a, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_neg_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_neg_impl(ctx, a, true);
|
|
}
|
|
|
|
// ggml_step
|
|
|
|
struct ggml_tensor * ggml_step_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
bool inplace) {
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad)) {
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
|
|
|
result->op = GGML_OP_STEP;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_step(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_step_impl(ctx, a, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_step_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_step_impl(ctx, a, true);
|
|
}
|
|
|
|
// ggml_relu
|
|
|
|
struct ggml_tensor * ggml_relu_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
bool inplace) {
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad)) {
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
|
|
|
result->op = GGML_OP_RELU;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_relu(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_relu_impl(ctx, a, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_relu_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_relu_impl(ctx, a, true);
|
|
}
|
|
|
|
// ggml_gelu
|
|
|
|
struct ggml_tensor * ggml_gelu_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
bool inplace) {
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad)) {
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
|
|
|
result->op = GGML_OP_GELU;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_gelu(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_gelu_impl(ctx, a, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_gelu_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_gelu_impl(ctx, a, true);
|
|
}
|
|
|
|
// ggml_silu
|
|
|
|
struct ggml_tensor * ggml_silu_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
bool inplace) {
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad)) {
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
|
|
|
result->op = GGML_OP_SILU;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_silu(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_silu_impl(ctx, a, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_silu_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_silu_impl(ctx, a, true);
|
|
}
|
|
|
|
// ggml_norm
|
|
|
|
struct ggml_tensor * ggml_norm_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
bool inplace) {
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad)) {
|
|
GGML_ASSERT(false); // TODO: implement backward
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
|
|
|
result->op = GGML_OP_NORM;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL; // TODO: maybe store epsilon here?
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_norm(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_norm_impl(ctx, a, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_norm_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_norm_impl(ctx, a, true);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_rms_norm_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
bool inplace) {
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad)) {
|
|
GGML_ASSERT(false); // TODO: implement backward
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
|
|
|
result->op = GGML_OP_RMS_NORM;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL; // TODO: maybe store epsilon here?
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_rms_norm(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_rms_norm_impl(ctx, a, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_rms_norm_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
return ggml_rms_norm_impl(ctx, a, true);
|
|
}
|
|
|
|
// ggml_mul_mat
|
|
|
|
struct ggml_tensor * ggml_mul_mat(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
GGML_ASSERT(ggml_can_mul_mat(a, b));
|
|
GGML_ASSERT(!ggml_is_transposed(a));
|
|
|
|
bool is_node = false;
|
|
|
|
if (a->grad || b->grad) {
|
|
is_node = true;
|
|
}
|
|
|
|
const int ne[4] = { a->ne[1], b->ne[1], a->ne[2], b->ne[3] };
|
|
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, MIN(a->n_dims, b->n_dims), ne);
|
|
|
|
result->op = GGML_OP_MUL_MAT;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = b;
|
|
|
|
return result;
|
|
}
|
|
|
|
// ggml_scale
|
|
|
|
struct ggml_tensor * ggml_scale_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
bool inplace) {
|
|
GGML_ASSERT(ggml_is_scalar(b));
|
|
GGML_ASSERT(ggml_is_padded_1d(a));
|
|
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad || b->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);
|
|
|
|
result->op = GGML_OP_SCALE;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = b;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_scale(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
return ggml_scale_impl(ctx, a, b, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_scale_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
return ggml_scale_impl(ctx, a, b, true);
|
|
}
|
|
|
|
// ggml_cpy
|
|
|
|
struct ggml_tensor * ggml_cpy_impl(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
bool inplace) {
|
|
GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b));
|
|
|
|
bool is_node = false;
|
|
|
|
if (!inplace && (a->grad || b->grad)) {
|
|
GGML_ASSERT(false); // TODO: implement backward
|
|
is_node = true;
|
|
}
|
|
|
|
// make a view of the destination
|
|
struct ggml_tensor * result = ggml_view_tensor(ctx, b);
|
|
|
|
result->op = GGML_OP_CPY;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = b;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_cpy(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
return ggml_cpy_impl(ctx, a, b, false);
|
|
}
|
|
|
|
struct ggml_tensor * ggml_cpy_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
return ggml_cpy_impl(ctx, a, b, true);
|
|
}
|
|
|
|
// ggml_reshape
|
|
|
|
struct ggml_tensor * ggml_reshape(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
GGML_ASSERT(ggml_is_contiguous(a));
|
|
GGML_ASSERT(ggml_is_contiguous(b));
|
|
GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b));
|
|
|
|
bool is_node = false;
|
|
|
|
if (a->grad || b->grad) {
|
|
GGML_ASSERT(false); // TODO: implement backward
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, b->n_dims, b->ne, a->data);
|
|
|
|
result->op = GGML_OP_RESHAPE;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_reshape_2d(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int ne0,
|
|
int ne1) {
|
|
GGML_ASSERT(ggml_is_contiguous(a));
|
|
GGML_ASSERT(ggml_nelements(a) == ne0*ne1);
|
|
|
|
bool is_node = false;
|
|
|
|
if (a->grad) {
|
|
GGML_ASSERT(false); // TODO: implement backward
|
|
is_node = true;
|
|
}
|
|
|
|
const int ne[2] = { ne0, ne1 };
|
|
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, a->data);
|
|
|
|
result->op = GGML_OP_RESHAPE;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * ggml_reshape_3d(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int ne0,
|
|
int ne1,
|
|
int ne2) {
|
|
GGML_ASSERT(ggml_is_contiguous(a));
|
|
GGML_ASSERT(ggml_nelements(a) == ne0*ne1*ne2);
|
|
|
|
bool is_node = false;
|
|
|
|
if (a->grad) {
|
|
GGML_ASSERT(false); // TODO: implement backward
|
|
is_node = true;
|
|
}
|
|
|
|
const int ne[3] = { ne0, ne1, ne2 };
|
|
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, a->data);
|
|
|
|
result->op = GGML_OP_RESHAPE;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
// ggml_view_1d
|
|
|
|
struct ggml_tensor * ggml_view_1d(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int ne0,
|
|
size_t offset) {
|
|
if (a->grad) {
|
|
GGML_ASSERT(false); // gradient propagation is not supported
|
|
}
|
|
|
|
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, &ne0, (char *) a->data + offset);
|
|
|
|
result->op = GGML_OP_VIEW;
|
|
result->grad = NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL; // TODO: maybe store the offset here?
|
|
|
|
return result;
|
|
}
|
|
|
|
// ggml_view_2d
|
|
|
|
struct ggml_tensor * ggml_view_2d(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int ne0,
|
|
int ne1,
|
|
size_t nb1,
|
|
size_t offset) {
|
|
if (a->grad) {
|
|
GGML_ASSERT(false); // gradient propagation is not supported
|
|
}
|
|
|
|
const int ne[GGML_MAX_DIMS] = { ne0, ne1, 1, 1 };
|
|
|
|
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, (char *) a->data + offset);
|
|
|
|
result->nb[1] = nb1;
|
|
result->nb[2] = result->nb[1]*ne1;
|
|
result->nb[3] = result->nb[2];
|
|
|
|
result->op = GGML_OP_VIEW;
|
|
result->grad = NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL; // TODO: maybe store the offset here?
|
|
|
|
return result;
|
|
}
|
|
|
|
// ggml_permute
|
|
|
|
struct ggml_tensor * ggml_permute(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int axis0,
|
|
int axis1,
|
|
int axis2,
|
|
int axis3) {
|
|
GGML_ASSERT(axis0 >= 0 && axis0 < GGML_MAX_DIMS);
|
|
GGML_ASSERT(axis1 >= 0 && axis1 < GGML_MAX_DIMS);
|
|
GGML_ASSERT(axis2 >= 0 && axis2 < GGML_MAX_DIMS);
|
|
GGML_ASSERT(axis3 >= 0 && axis3 < GGML_MAX_DIMS);
|
|
|
|
GGML_ASSERT(axis0 != axis1);
|
|
GGML_ASSERT(axis0 != axis2);
|
|
GGML_ASSERT(axis0 != axis3);
|
|
GGML_ASSERT(axis1 != axis2);
|
|
GGML_ASSERT(axis1 != axis3);
|
|
GGML_ASSERT(axis2 != axis3);
|
|
|
|
bool is_node = false;
|
|
|
|
if (a->grad) {
|
|
GGML_ASSERT(false); // TODO: implement backward
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
|
|
|
|
int ne[GGML_MAX_DIMS];
|
|
int nb[GGML_MAX_DIMS];
|
|
|
|
ne[axis0] = a->ne[0];
|
|
ne[axis1] = a->ne[1];
|
|
ne[axis2] = a->ne[2];
|
|
ne[axis3] = a->ne[3];
|
|
|
|
nb[axis0] = a->nb[0];
|
|
nb[axis1] = a->nb[1];
|
|
nb[axis2] = a->nb[2];
|
|
nb[axis3] = a->nb[3];
|
|
|
|
result->ne[0] = ne[0];
|
|
result->ne[1] = ne[1];
|
|
result->ne[2] = ne[2];
|
|
result->ne[3] = ne[3];
|
|
|
|
result->nb[0] = nb[0];
|
|
result->nb[1] = nb[1];
|
|
result->nb[2] = nb[2];
|
|
result->nb[3] = nb[3];
|
|
|
|
result->op = GGML_OP_PERMUTE;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL; // TODO: maybe store the permutation here?
|
|
|
|
return result;
|
|
}
|
|
|
|
// ggml_transpose
|
|
|
|
struct ggml_tensor * ggml_transpose(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
bool is_node = false;
|
|
|
|
if (a->grad) {
|
|
GGML_ASSERT(false); // TODO: implement backward
|
|
is_node = true;
|
|
}
|
|
|
|
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
|
|
|
|
result->ne[0] = a->ne[1];
|
|
result->ne[1] = a->ne[0];
|
|
|
|
result->nb[0] = a->nb[1];
|
|
result->nb[1] = a->nb[0];
|
|
|
|
result->op = GGML_OP_TRANSPOSE;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
// ggml_get_rows
|
|
|
|
struct ggml_tensor * ggml_get_rows(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
GGML_ASSERT(ggml_is_matrix(a) && ggml_is_vector(b) && b->type == GGML_TYPE_I32);
|
|
|
|
bool is_node = false;
|
|
|
|
if (a->grad || b->grad) {
|
|
GGML_ASSERT(false); // TODO: implement backward
|
|
is_node = true;
|
|
}
|
|
|
|
// TODO: implement non F32 return
|
|
//struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]);
|
|
struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, a->ne[0], b->ne[0]);
|
|
|
|
result->op = GGML_OP_GET_ROWS;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = b;
|
|
|
|
return result;
|
|
}
|
|
|
|
// ggml_diag_mask_inf
|
|
|
|
struct ggml_tensor * ggml_diag_mask_inf(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int n_past) {
|
|
bool is_node = false;
|
|
|
|
if (a->grad) {
|
|
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_i32(ctx, n_past);
|
|
|
|
result->op = GGML_OP_DIAG_MASK_INF;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = b;
|
|
|
|
return result;
|
|
}
|
|
|
|
// ggml_soft_max
|
|
|
|
struct ggml_tensor * ggml_soft_max(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a) {
|
|
bool is_node = false;
|
|
|
|
if (a->grad) {
|
|
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);
|
|
|
|
result->op = GGML_OP_SOFT_MAX;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = NULL;
|
|
|
|
return result;
|
|
}
|
|
|
|
// ggml_rope
|
|
|
|
struct ggml_tensor * ggml_rope(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int n_past,
|
|
int n_dims,
|
|
int mode) {
|
|
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, 3);
|
|
((int32_t *) b->data)[0] = n_past;
|
|
((int32_t *) b->data)[1] = n_dims;
|
|
((int32_t *) b->data)[2] = mode;
|
|
|
|
result->op = GGML_OP_ROPE;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = b;
|
|
|
|
return result;
|
|
}
|
|
|
|
// ggml_conv_1d_1s
|
|
|
|
struct ggml_tensor * ggml_conv_1d_1s(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
GGML_ASSERT(ggml_is_matrix(b));
|
|
GGML_ASSERT(a->ne[1] == b->ne[1]);
|
|
GGML_ASSERT(a->ne[3] == 1);
|
|
bool is_node = false;
|
|
|
|
if (a->grad || b->grad) {
|
|
GGML_ASSERT(false); // TODO: implement backward
|
|
is_node = true;
|
|
}
|
|
|
|
const int ne[4] = { b->ne[0], a->ne[2], 1, 1, };
|
|
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
|
|
|
|
result->op = GGML_OP_CONV_1D_1S;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = b;
|
|
|
|
return result;
|
|
}
|
|
|
|
// ggml_conv_1d_2s
|
|
|
|
struct ggml_tensor * ggml_conv_1d_2s(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b) {
|
|
GGML_ASSERT(ggml_is_matrix(b));
|
|
GGML_ASSERT(a->ne[1] == b->ne[1]);
|
|
GGML_ASSERT(a->ne[3] == 1);
|
|
bool is_node = false;
|
|
|
|
if (a->grad || b->grad) {
|
|
GGML_ASSERT(false); // TODO: implement backward
|
|
is_node = true;
|
|
}
|
|
|
|
const int ne[4] = { b->ne[0]/2, a->ne[2], 1, 1, };
|
|
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
|
|
|
|
result->op = GGML_OP_CONV_1D_2S;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = b;
|
|
|
|
return result;
|
|
}
|
|
|
|
// ggml_flash_attn
|
|
|
|
struct ggml_tensor * ggml_flash_attn(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * q,
|
|
struct ggml_tensor * k,
|
|
struct ggml_tensor * v,
|
|
bool masked) {
|
|
GGML_ASSERT(ggml_can_mul_mat(k, q));
|
|
// TODO: check if vT can be multiplied by (k*qT)
|
|
|
|
bool is_node = false;
|
|
|
|
if (q->grad || k->grad || v->grad) {
|
|
GGML_ASSERT(false); // TODO: implement backward
|
|
is_node = true;
|
|
}
|
|
|
|
//struct ggml_tensor * result = ggml_dup_tensor(ctx, q);
|
|
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, q->ne);
|
|
|
|
result->op = GGML_OP_FLASH_ATTN;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = q;
|
|
result->src1 = k;
|
|
result->opt[0] = v;
|
|
result->opt[1] = ggml_new_i32(ctx, masked ? 1 : 0);
|
|
|
|
return result;
|
|
}
|
|
|
|
// ggml_flash_ff
|
|
|
|
struct ggml_tensor * ggml_flash_ff(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b0,
|
|
struct ggml_tensor * b1,
|
|
struct ggml_tensor * c0,
|
|
struct ggml_tensor * c1) {
|
|
GGML_ASSERT(ggml_can_mul_mat(b0, a));
|
|
// TODO: more checks
|
|
|
|
bool is_node = false;
|
|
|
|
if (a->grad || b0->grad || b1->grad || c0->grad || c1->grad) {
|
|
GGML_ASSERT(false); // TODO: implement backward
|
|
is_node = true;
|
|
}
|
|
|
|
//struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
|
|
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, a->ne);
|
|
|
|
result->op = GGML_OP_FLASH_FF;
|
|
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
|
result->src0 = a;
|
|
result->src1 = b0;
|
|
result->opt[0] = b1;
|
|
result->opt[1] = c0;
|
|
result->opt[2] = c1;
|
|
|
|
return result;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
void ggml_set_param(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * tensor) {
|
|
tensor->is_param = true;
|
|
|
|
GGML_ASSERT(tensor->grad == NULL);
|
|
tensor->grad = ggml_dup_tensor(ctx, tensor);
|
|
}
|
|
|
|
// ggml_compute_forward_dup
|
|
|
|
static void ggml_compute_forward_dup_f16(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
GGML_ASSERT(params->ith == 0);
|
|
GGML_ASSERT(ggml_is_contiguous(dst));
|
|
GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int ne00 = src0->ne[0];
|
|
const int ne01 = src0->ne[1];
|
|
const int ne02 = src0->ne[2];
|
|
const int ne03 = src0->ne[3];
|
|
|
|
const size_t nb00 = src0->nb[0];
|
|
const size_t nb01 = src0->nb[1];
|
|
const size_t nb02 = src0->nb[2];
|
|
const size_t nb03 = src0->nb[3];
|
|
|
|
if (ggml_is_contiguous(src0) && src0->type == dst->type) {
|
|
memcpy(dst->data, src0->data, ggml_nelements(dst) * GGML_TYPE_SIZE[src0->type]);
|
|
return;
|
|
}
|
|
|
|
if (src0->nb[0] == sizeof(ggml_fp16_t)) {
|
|
if (dst->type == GGML_TYPE_F16) {
|
|
size_t id = 0;
|
|
const size_t rs = ne00*nb00;
|
|
|
|
for (int i03 = 0; i03 < ne03; i03++) {
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
for (int i01 = 0; i01 < ne01; i01++) {
|
|
const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
|
|
char * dst_ptr = (char *) dst->data + id*rs;
|
|
|
|
memcpy(dst_ptr, src0_ptr, rs);
|
|
|
|
id++;
|
|
}
|
|
}
|
|
}
|
|
} else if (dst->type == GGML_TYPE_F32) {
|
|
size_t id = 0;
|
|
float * dst_ptr = (float *) dst->data;
|
|
|
|
for (int i03 = 0; i03 < ne03; i03++) {
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
for (int i01 = 0; i01 < ne01; i01++) {
|
|
for (int i00 = 0; i00 < ne00; i00++) {
|
|
const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
|
|
|
|
dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr);
|
|
id++;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
GGML_ASSERT(false); // TODO: implement
|
|
}
|
|
} else {
|
|
//printf("%s: this is not optimal - fix me\n", __func__);
|
|
|
|
if (dst->type == GGML_TYPE_F32) {
|
|
size_t id = 0;
|
|
float * dst_ptr = (float *) dst->data;
|
|
|
|
for (int i03 = 0; i03 < ne03; i03++) {
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
for (int i01 = 0; i01 < ne01; i01++) {
|
|
for (int i00 = 0; i00 < ne00; i00++) {
|
|
const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
|
|
|
|
dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr);
|
|
id++;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else if (dst->type == GGML_TYPE_F16) {
|
|
size_t id = 0;
|
|
ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
|
|
|
|
for (int i03 = 0; i03 < ne03; i03++) {
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
for (int i01 = 0; i01 < ne01; i01++) {
|
|
for (int i00 = 0; i00 < ne00; i00++) {
|
|
const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
|
|
|
|
dst_ptr[id] = *src0_ptr;
|
|
id++;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
GGML_ASSERT(false); // TODO: implement
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_dup_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
GGML_ASSERT(params->ith == 0);
|
|
GGML_ASSERT(ggml_is_contiguous(dst));
|
|
GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int ne00 = src0->ne[0];
|
|
const int ne01 = src0->ne[1];
|
|
const int ne02 = src0->ne[2];
|
|
const int ne03 = src0->ne[3];
|
|
|
|
const size_t nb00 = src0->nb[0];
|
|
const size_t nb01 = src0->nb[1];
|
|
const size_t nb02 = src0->nb[2];
|
|
const size_t nb03 = src0->nb[3];
|
|
|
|
if (ggml_is_contiguous(src0) && src0->type == dst->type) {
|
|
memcpy(dst->data, src0->data, ggml_nelements(dst) * GGML_TYPE_SIZE[src0->type]);
|
|
return;
|
|
}
|
|
|
|
if (src0->nb[0] == sizeof(float)) {
|
|
if (dst->type == GGML_TYPE_F32) {
|
|
size_t id = 0;
|
|
const size_t rs = ne00*nb00;
|
|
|
|
for (int i03 = 0; i03 < ne03; i03++) {
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
for (int i01 = 0; i01 < ne01; i01++) {
|
|
const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
|
|
char * dst_ptr = (char *) dst->data + id*rs;
|
|
|
|
memcpy(dst_ptr, src0_ptr, rs);
|
|
|
|
id++;
|
|
}
|
|
}
|
|
}
|
|
} else if (dst->type == GGML_TYPE_F16) {
|
|
size_t id = 0;
|
|
ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
|
|
|
|
for (int i03 = 0; i03 < ne03; i03++) {
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
for (int i01 = 0; i01 < ne01; i01++) {
|
|
for (int i00 = 0; i00 < ne00; i00++) {
|
|
const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
|
|
|
|
dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
|
|
id++;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
GGML_ASSERT(false); // TODO: implement
|
|
}
|
|
} else {
|
|
//printf("%s: this is not optimal - fix me\n", __func__);
|
|
|
|
if (dst->type == GGML_TYPE_F32) {
|
|
size_t id = 0;
|
|
float * dst_ptr = (float *) dst->data;
|
|
|
|
for (int i03 = 0; i03 < ne03; i03++) {
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
for (int i01 = 0; i01 < ne01; i01++) {
|
|
for (int i00 = 0; i00 < ne00; i00++) {
|
|
const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
|
|
|
|
dst_ptr[id] = *src0_ptr;
|
|
id++;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else if (dst->type == GGML_TYPE_F16) {
|
|
size_t id = 0;
|
|
ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
|
|
|
|
for (int i03 = 0; i03 < ne03; i03++) {
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
for (int i01 = 0; i01 < ne01; i01++) {
|
|
for (int i00 = 0; i00 < ne00; i00++) {
|
|
const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
|
|
|
|
dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
|
|
id++;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
GGML_ASSERT(false); // TODO: implement
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_dup(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F16:
|
|
{
|
|
ggml_compute_forward_dup_f16(params, src0, dst);
|
|
} break;
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_dup_f32(params, src0, dst);
|
|
} break;
|
|
case GGML_TYPE_Q4_0:
|
|
case GGML_TYPE_Q4_1:
|
|
case GGML_TYPE_I8:
|
|
case GGML_TYPE_I16:
|
|
case GGML_TYPE_I32:
|
|
case GGML_TYPE_COUNT:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_add
|
|
|
|
static void ggml_compute_forward_add_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int ith = params->ith;
|
|
const int nth = params->nth;
|
|
|
|
const int n = ggml_nrows(src0);
|
|
const int nc = src0->ne[0];
|
|
|
|
const size_t nb00 = src0->nb[0];
|
|
const size_t nb01 = src0->nb[1];
|
|
|
|
const size_t nb10 = src1->nb[0];
|
|
const size_t nb11 = src1->nb[1];
|
|
|
|
const size_t nb0 = dst->nb[0];
|
|
const size_t nb1 = dst->nb[1];
|
|
|
|
GGML_ASSERT( nb0 == sizeof(float));
|
|
GGML_ASSERT(nb00 == sizeof(float));
|
|
|
|
if (nb10 == sizeof(float)) {
|
|
const int j0 = (n/nth)*ith;
|
|
const int j1 = ith == nth - 1 ? n : (n/nth)*(ith + 1);
|
|
|
|
for (int j = j0; j < j1; j++) {
|
|
ggml_vec_add_f32(nc,
|
|
(float *) ((char *) dst->data + j*nb1),
|
|
(float *) ((char *) src0->data + j*nb01),
|
|
(float *) ((char *) src1->data + j*nb11));
|
|
}
|
|
} else {
|
|
// src1 is not contiguous
|
|
for (int j = ith; j < n; j += nth) {
|
|
float * dst_ptr = (float *) ((char *) dst->data + j*nb1);
|
|
float * src0_ptr = (float *) ((char *) src0->data + j*nb01);
|
|
for (int i = 0; i < nc; i++) {
|
|
float * src1_ptr = (float *) ((char *) src1->data + j*nb11 + i*nb10);
|
|
|
|
dst_ptr[i] = src0_ptr[i] + *src1_ptr;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_add(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_add_f32(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_sub
|
|
|
|
static void ggml_compute_forward_sub_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int n = ggml_nrows(src0);
|
|
const int nc = src0->ne[0];
|
|
|
|
assert( dst->nb[0] == sizeof(float));
|
|
assert(src0->nb[0] == sizeof(float));
|
|
assert(src1->nb[0] == sizeof(float));
|
|
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_sub_f32(nc,
|
|
(float *) ((char *) dst->data + i*( dst->nb[1])),
|
|
(float *) ((char *) src0->data + i*(src0->nb[1])),
|
|
(float *) ((char *) src1->data + i*(src1->nb[1])));
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_sub(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_sub_f32(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_mul
|
|
|
|
static void ggml_compute_forward_mul_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int n = ggml_nrows(src0);
|
|
const int nc = src0->ne[0];
|
|
|
|
assert( dst->nb[0] == sizeof(float));
|
|
assert(src0->nb[0] == sizeof(float));
|
|
assert(src1->nb[0] == sizeof(float));
|
|
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_mul_f32(nc,
|
|
(float *) ((char *) dst->data + i*( dst->nb[1])),
|
|
(float *) ((char *) src0->data + i*(src0->nb[1])),
|
|
(float *) ((char *) src1->data + i*(src1->nb[1])));
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_mul(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_mul_f32(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_div
|
|
|
|
static void ggml_compute_forward_div_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int n = ggml_nrows(src0);
|
|
const int nc = src0->ne[0];
|
|
|
|
assert( dst->nb[0] == sizeof(float));
|
|
assert(src0->nb[0] == sizeof(float));
|
|
assert(src1->nb[0] == sizeof(float));
|
|
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_div_f32(nc,
|
|
(float *) ((char *) dst->data + i*( dst->nb[1])),
|
|
(float *) ((char *) src0->data + i*(src0->nb[1])),
|
|
(float *) ((char *) src1->data + i*(src1->nb[1])));
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_div(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_div_f32(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_sqr
|
|
|
|
static void ggml_compute_forward_sqr_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
assert(ggml_are_same_shape(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int n = ggml_nrows(src0);
|
|
const int nc = src0->ne[0];
|
|
|
|
assert( dst->nb[0] == sizeof(float));
|
|
assert(src0->nb[0] == sizeof(float));
|
|
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_sqr_f32(nc,
|
|
(float *) ((char *) dst->data + i*( dst->nb[1])),
|
|
(float *) ((char *) src0->data + i*(src0->nb[1])));
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_sqr(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_sqr_f32(params, src0, dst);
|
|
} break;
|
|
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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_sqrt
|
|
|
|
static void ggml_compute_forward_sqrt_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
assert(ggml_are_same_shape(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int n = ggml_nrows(src0);
|
|
const int nc = src0->ne[0];
|
|
|
|
assert( dst->nb[0] == sizeof(float));
|
|
assert(src0->nb[0] == sizeof(float));
|
|
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_sqrt_f32(nc,
|
|
(float *) ((char *) dst->data + i*( dst->nb[1])),
|
|
(float *) ((char *) src0->data + i*(src0->nb[1])));
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_sqrt(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_sqrt_f32(params, src0, dst);
|
|
} break;
|
|
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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_sum
|
|
|
|
static void ggml_compute_forward_sum_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
assert(ggml_is_scalar(dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
assert(ggml_is_scalar(dst));
|
|
assert(src0->nb[0] == sizeof(float));
|
|
|
|
const int ne00 = src0->ne[0];
|
|
const int ne01 = src0->ne[1];
|
|
const int ne02 = src0->ne[2];
|
|
const int ne03 = src0->ne[3];
|
|
|
|
const size_t nb01 = src0->nb[1];
|
|
const size_t nb02 = src0->nb[2];
|
|
const size_t nb03 = src0->nb[3];
|
|
|
|
for (int i03 = 0; i03 < ne03; i03++) {
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
for (int i01 = 0; i01 < ne01; i01++) {
|
|
ggml_vec_sum_f32(ne00,
|
|
(float *) (dst->data),
|
|
(float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_sum(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_sum_f32(params, src0, dst);
|
|
} break;
|
|
case GGML_TYPE_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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_mean
|
|
|
|
static void ggml_compute_forward_mean_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
assert(src0->nb[0] == sizeof(float));
|
|
|
|
const int ne00 = src0->ne[0];
|
|
const int ne01 = src0->ne[1];
|
|
const int ne02 = src0->ne[2];
|
|
const int ne03 = src0->ne[3];
|
|
|
|
const size_t nb01 = src0->nb[1];
|
|
const size_t nb02 = src0->nb[2];
|
|
const size_t nb03 = src0->nb[3];
|
|
|
|
const int ne0 = dst->ne[0];
|
|
const int ne1 = dst->ne[1];
|
|
const int ne2 = dst->ne[2];
|
|
const int ne3 = dst->ne[3];
|
|
|
|
assert(ne0 == 1);
|
|
assert(ne1 == ne01);
|
|
assert(ne2 == ne02);
|
|
assert(ne3 == ne03);
|
|
|
|
UNUSED(ne0);
|
|
UNUSED(ne1);
|
|
UNUSED(ne2);
|
|
UNUSED(ne3);
|
|
|
|
const size_t nb1 = dst->nb[1];
|
|
const size_t nb2 = dst->nb[2];
|
|
const size_t nb3 = dst->nb[3];
|
|
|
|
for (int i03 = 0; i03 < ne03; i03++) {
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
for (int i01 = 0; i01 < ne01; i01++) {
|
|
ggml_vec_sum_f32(ne00,
|
|
(float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3),
|
|
(float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03));
|
|
|
|
*(float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3) /= (float) ne00;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_mean(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_mean_f32(params, src0, dst);
|
|
} break;
|
|
case GGML_TYPE_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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_repeat
|
|
|
|
static void ggml_compute_forward_repeat_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
assert(ggml_can_repeat(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
// TODO: implement support for rank > 2 tensors
|
|
assert(src0->ne[2] == 1);
|
|
assert(src0->ne[3] == 1);
|
|
assert( dst->ne[2] == 1);
|
|
assert( dst->ne[3] == 1);
|
|
|
|
const int nc = dst->ne[0];
|
|
const int nr = dst->ne[1];
|
|
const int nc0 = src0->ne[0];
|
|
const int nr0 = src0->ne[1];
|
|
const int ncr = nc/nc0; // guaranteed to be an integer due to the check in ggml_can_repeat
|
|
const int nrr = nr/nr0; // guaranteed to be an integer due to the check in ggml_can_repeat
|
|
|
|
// TODO: support for transposed / permuted tensors
|
|
assert( dst->nb[0] == sizeof(float));
|
|
assert(src0->nb[0] == sizeof(float));
|
|
|
|
// TODO: maybe this is not optimal?
|
|
for (int i = 0; i < nrr; i++) {
|
|
for (int j = 0; j < ncr; j++) {
|
|
for (int k = 0; k < nr0; k++) {
|
|
ggml_vec_cpy_f32(nc0,
|
|
(float *) ((char *) dst->data + (i*nr0 + k)*( dst->nb[1]) + j*nc0*( dst->nb[0])),
|
|
(float *) ((char *) src0->data + ( k)*(src0->nb[1])));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_repeat(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_repeat_f32(params, src0, dst);
|
|
} break;
|
|
case GGML_TYPE_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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_abs
|
|
|
|
static void ggml_compute_forward_abs_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
assert(ggml_are_same_shape(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int n = ggml_nrows(src0);
|
|
const int nc = src0->ne[0];
|
|
|
|
assert(dst->nb[0] == sizeof(float));
|
|
assert(src0->nb[0] == sizeof(float));
|
|
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_abs_f32(nc,
|
|
(float *) ((char *) dst->data + i*( dst->nb[1])),
|
|
(float *) ((char *) src0->data + i*(src0->nb[1])));
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_abs(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_abs_f32(params, src0, dst);
|
|
} break;
|
|
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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_sgn
|
|
|
|
static void ggml_compute_forward_sgn_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
assert(ggml_are_same_shape(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int n = ggml_nrows(src0);
|
|
const int nc = src0->ne[0];
|
|
|
|
assert(dst->nb[0] == sizeof(float));
|
|
assert(src0->nb[0] == sizeof(float));
|
|
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_sgn_f32(nc,
|
|
(float *) ((char *) dst->data + i*( dst->nb[1])),
|
|
(float *) ((char *) src0->data + i*(src0->nb[1])));
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_sgn(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_sgn_f32(params, src0, dst);
|
|
} break;
|
|
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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_neg
|
|
|
|
static void ggml_compute_forward_neg_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
assert(ggml_are_same_shape(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int n = ggml_nrows(src0);
|
|
const int nc = src0->ne[0];
|
|
|
|
assert(dst->nb[0] == sizeof(float));
|
|
assert(src0->nb[0] == sizeof(float));
|
|
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_neg_f32(nc,
|
|
(float *) ((char *) dst->data + i*( dst->nb[1])),
|
|
(float *) ((char *) src0->data + i*(src0->nb[1])));
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_neg(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_neg_f32(params, src0, dst);
|
|
} break;
|
|
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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_step
|
|
|
|
static void ggml_compute_forward_step_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
assert(ggml_are_same_shape(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int n = ggml_nrows(src0);
|
|
const int nc = src0->ne[0];
|
|
|
|
assert(dst->nb[0] == sizeof(float));
|
|
assert(src0->nb[0] == sizeof(float));
|
|
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_step_f32(nc,
|
|
(float *) ((char *) dst->data + i*( dst->nb[1])),
|
|
(float *) ((char *) src0->data + i*(src0->nb[1])));
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_step(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_step_f32(params, src0, dst);
|
|
} break;
|
|
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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_relu
|
|
|
|
static void ggml_compute_forward_relu_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
assert(ggml_are_same_shape(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int n = ggml_nrows(src0);
|
|
const int nc = src0->ne[0];
|
|
|
|
assert(dst->nb[0] == sizeof(float));
|
|
assert(src0->nb[0] == sizeof(float));
|
|
|
|
for (int i = 0; i < n; i++) {
|
|
ggml_vec_relu_f32(nc,
|
|
(float *) ((char *) dst->data + i*( dst->nb[1])),
|
|
(float *) ((char *) src0->data + i*(src0->nb[1])));
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_relu(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_relu_f32(params, src0, dst);
|
|
} break;
|
|
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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_gelu
|
|
|
|
static void ggml_compute_forward_gelu_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
GGML_ASSERT(ggml_is_contiguous(src0));
|
|
GGML_ASSERT(ggml_is_contiguous(dst));
|
|
GGML_ASSERT(ggml_are_same_shape(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int ith = params->ith;
|
|
const int nth = params->nth;
|
|
|
|
const int nc = src0->ne[0];
|
|
const int nr = ggml_nrows(src0);
|
|
|
|
// rows per thread
|
|
const int dr = (nr + nth - 1)/nth;
|
|
|
|
// row range for this thread
|
|
const int ir0 = dr*ith;
|
|
const int ir1 = MIN(ir0 + dr, nr);
|
|
|
|
for (int i1 = ir0; i1 < ir1; i1++) {
|
|
ggml_vec_gelu_f32(nc,
|
|
(float *) ((char *) dst->data + i1*( dst->nb[1])),
|
|
(float *) ((char *) src0->data + i1*(src0->nb[1])));
|
|
|
|
#ifndef NDEBUG
|
|
for (int k = 0; k < nc; k++) {
|
|
const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k];
|
|
UNUSED(x);
|
|
assert(!isnan(x));
|
|
assert(!isinf(x));
|
|
}
|
|
#endif
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_gelu(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_gelu_f32(params, src0, dst);
|
|
} break;
|
|
case GGML_TYPE_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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
|
|
//printf("XXXXXXXX gelu\n");
|
|
}
|
|
|
|
// ggml_compute_forward_silu
|
|
|
|
static void ggml_compute_forward_silu_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
GGML_ASSERT(ggml_is_contiguous(src0));
|
|
GGML_ASSERT(ggml_is_contiguous(dst));
|
|
GGML_ASSERT(ggml_are_same_shape(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int ith = params->ith;
|
|
const int nth = params->nth;
|
|
|
|
const int nc = src0->ne[0];
|
|
const int nr = ggml_nrows(src0);
|
|
|
|
// rows per thread
|
|
const int dr = (nr + nth - 1)/nth;
|
|
|
|
// row range for this thread
|
|
const int ir0 = dr*ith;
|
|
const int ir1 = MIN(ir0 + dr, nr);
|
|
|
|
for (int i1 = ir0; i1 < ir1; i1++) {
|
|
ggml_vec_silu_f32(nc,
|
|
(float *) ((char *) dst->data + i1*( dst->nb[1])),
|
|
(float *) ((char *) src0->data + i1*(src0->nb[1])));
|
|
|
|
#ifndef NDEBUG
|
|
for (int k = 0; k < nc; k++) {
|
|
const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k];
|
|
UNUSED(x);
|
|
assert(!isnan(x));
|
|
assert(!isinf(x));
|
|
}
|
|
#endif
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_silu(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_silu_f32(params, src0, dst);
|
|
} break;
|
|
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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
|
|
// ggml_compute_forward_norm
|
|
|
|
static void ggml_compute_forward_norm_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
GGML_ASSERT(ggml_are_same_shape(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
GGML_ASSERT(src0->nb[0] == sizeof(float));
|
|
|
|
const int ith = params->ith;
|
|
const int nth = params->nth;
|
|
|
|
const int ne00 = src0->ne[0];
|
|
const int ne01 = src0->ne[1];
|
|
const int ne02 = src0->ne[2];
|
|
const int ne03 = src0->ne[3];
|
|
|
|
const size_t nb01 = src0->nb[1];
|
|
const size_t nb02 = src0->nb[2];
|
|
const size_t nb03 = src0->nb[3];
|
|
|
|
const size_t nb1 = dst->nb[1];
|
|
const size_t nb2 = dst->nb[2];
|
|
const size_t nb3 = dst->nb[3];
|
|
|
|
const float eps = 1e-5f; // TODO: make this a parameter
|
|
|
|
// TODO: optimize
|
|
for (int i03 = 0; i03 < ne03; i03++) {
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
for (int i01 = ith; i01 < ne01; i01 += nth) {
|
|
const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
|
|
|
|
ggml_float sum = 0.0;
|
|
for (int i00 = 0; i00 < ne00; i00++) {
|
|
sum += (ggml_float)x[i00];
|
|
}
|
|
|
|
float mean = sum/ne00;
|
|
|
|
float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
|
|
|
|
ggml_float sum2 = 0.0;
|
|
for (int i00 = 0; i00 < ne00; i00++) {
|
|
float v = x[i00] - mean;
|
|
y[i00] = v;
|
|
sum2 += (ggml_float)(v*v);
|
|
}
|
|
|
|
float variance = sum2/ne00;
|
|
const float scale = 1.0f/sqrtf(variance + eps);
|
|
|
|
ggml_vec_scale_f32(ne00, y, scale);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_norm(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_norm_f32(params, src0, dst);
|
|
} break;
|
|
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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_rms_norm_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
GGML_ASSERT(ggml_are_same_shape(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
GGML_ASSERT(src0->nb[0] == sizeof(float));
|
|
|
|
const int ith = params->ith;
|
|
const int nth = params->nth;
|
|
|
|
const int ne00 = src0->ne[0];
|
|
const int ne01 = src0->ne[1];
|
|
const int ne02 = src0->ne[2];
|
|
const int ne03 = src0->ne[3];
|
|
|
|
const size_t nb01 = src0->nb[1];
|
|
const size_t nb02 = src0->nb[2];
|
|
const size_t nb03 = src0->nb[3];
|
|
|
|
const size_t nb1 = dst->nb[1];
|
|
const size_t nb2 = dst->nb[2];
|
|
const size_t nb3 = dst->nb[3];
|
|
|
|
const float eps = 1e-6f; // TODO: make this a parameter
|
|
|
|
// TODO: optimize
|
|
for (int i03 = 0; i03 < ne03; i03++) {
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
for (int i01 = ith; i01 < ne01; i01 += nth) {
|
|
const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
|
|
|
|
ggml_float sum = 0.0;
|
|
for (int i00 = 0; i00 < ne00; i00++) {
|
|
sum += (ggml_float)(x[i00] * x[i00]);
|
|
}
|
|
|
|
float mean = sum/ne00;
|
|
|
|
float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
|
|
|
|
memcpy(y, x, ne00 * sizeof(float));
|
|
// for (int i00 = 0; i00 < ne00; i00++) {
|
|
// y[i00] = x[i00];
|
|
// }
|
|
|
|
const float scale = 1.0f/sqrtf(mean + eps);
|
|
|
|
ggml_vec_scale_f32(ne00, y, scale);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_rms_norm(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_rms_norm_f32(params, src0, dst);
|
|
} break;
|
|
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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
|
|
// ggml_compute_forward_mul_mat
|
|
|
|
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
|
// helper function to determine if it is better to use BLAS or not
|
|
// for large matrices, BLAS is faster
|
|
static bool ggml_compute_forward_mul_mat_use_blas(
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
//const int ne00 = src0->ne[0];
|
|
//const int ne01 = src0->ne[1];
|
|
|
|
const int ne10 = src1->ne[0];
|
|
|
|
const int ne0 = dst->ne[0];
|
|
const int ne1 = dst->ne[1];
|
|
|
|
// TODO: find the optimal values for these
|
|
if (ggml_is_contiguous(src0) &&
|
|
ggml_is_contiguous(src1) && ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32))) {
|
|
|
|
/*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/
|
|
return true;
|
|
}
|
|
|
|
return false;
|
|
}
|
|
#endif
|
|
|
|
static void ggml_compute_forward_mul_mat_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
int64_t t0 = ggml_perf_time_us();
|
|
UNUSED(t0);
|
|
|
|
const int ne00 = src0->ne[0];
|
|
const int ne01 = src0->ne[1];
|
|
const int ne02 = src0->ne[2];
|
|
const int ne03 = src0->ne[3];
|
|
|
|
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
|
const int ne10 = src1->ne[0];
|
|
#endif
|
|
const int ne11 = src1->ne[1];
|
|
#ifndef NDEBUG
|
|
const int ne12 = src1->ne[2];
|
|
const int ne13 = src1->ne[3];
|
|
|
|
const int ne0 = dst->ne[0];
|
|
const int ne1 = dst->ne[1];
|
|
const int ne2 = dst->ne[2];
|
|
const int ne3 = dst->ne[3];
|
|
|
|
const int nb00 = src0->nb[0];
|
|
#endif
|
|
const int nb01 = src0->nb[1];
|
|
const int nb02 = src0->nb[2];
|
|
const int nb03 = src0->nb[3];
|
|
|
|
#ifndef NDEBUG
|
|
const int nb10 = src1->nb[0];
|
|
#endif
|
|
const int nb11 = src1->nb[1];
|
|
const int nb12 = src1->nb[2];
|
|
const int nb13 = src1->nb[3];
|
|
|
|
const int nb0 = dst->nb[0];
|
|
const int nb1 = dst->nb[1];
|
|
const int nb2 = dst->nb[2];
|
|
const int nb3 = dst->nb[3];
|
|
|
|
const int ith = params->ith;
|
|
const int nth = params->nth;
|
|
|
|
assert(ne02 == ne12);
|
|
assert(ne03 == ne13);
|
|
assert(ne2 == ne12);
|
|
assert(ne3 == ne13);
|
|
|
|
// we don't support permuted src0 or src1
|
|
assert(nb00 == sizeof(float));
|
|
assert(nb10 == sizeof(float));
|
|
|
|
// dst cannot be transposed or permuted
|
|
assert(nb0 == sizeof(float));
|
|
assert(nb0 <= nb1);
|
|
assert(nb1 <= nb2);
|
|
assert(nb2 <= nb3);
|
|
|
|
assert(ne0 == ne01);
|
|
assert(ne1 == ne11);
|
|
assert(ne2 == ne02);
|
|
assert(ne3 == ne03);
|
|
|
|
// nb01 >= nb00 - src0 is not transposed
|
|
// compute by src0 rows
|
|
|
|
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
|
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
|
|
if (params->ith != 0) {
|
|
return;
|
|
}
|
|
|
|
if (params->type == GGML_TASK_INIT) {
|
|
return;
|
|
}
|
|
|
|
if (params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
for (int i03 = 0; i03 < ne03; i03++) {
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03);
|
|
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
|
|
|
|
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
|
|
|
|
// zT = y * xT
|
|
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
|
|
ne11, ne01, ne10,
|
|
1.0f, y, ne10,
|
|
x, ne10,
|
|
0.0f, d, ne01);
|
|
}
|
|
}
|
|
|
|
//printf("CBLAS F32 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
|
|
|
|
return;
|
|
}
|
|
#endif
|
|
|
|
if (params->type == GGML_TASK_INIT) {
|
|
return;
|
|
}
|
|
|
|
if (params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
// parallelize by src0 rows using ggml_vec_dot_f32
|
|
|
|
// total rows in src0
|
|
const int nr = ne01*ne02*ne03;
|
|
|
|
// rows per thread
|
|
const int dr = (nr + nth - 1)/nth;
|
|
|
|
// row range for this thread
|
|
const int ir0 = dr*ith;
|
|
const int ir1 = MIN(ir0 + dr, nr);
|
|
|
|
for (int ir = ir0; ir < ir1; ++ir) {
|
|
// src0 indices
|
|
const int i03 = ir/(ne02*ne01);
|
|
const int i02 = (ir - i03*ne02*ne01)/ne01;
|
|
const int i01 = (ir - i03*ne02*ne01 - i02*ne01);
|
|
|
|
for (int ic = 0; ic < ne11; ++ic) {
|
|
// src1 indices
|
|
const int i13 = i03;
|
|
const int i12 = i02;
|
|
const int i11 = ic;
|
|
|
|
// dst indices
|
|
const int i0 = i01;
|
|
const int i1 = i11;
|
|
const int i2 = i02;
|
|
const int i3 = i03;
|
|
|
|
ggml_vec_dot_f32(ne00,
|
|
(float *) ((char *) dst->data + (i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3)),
|
|
(float *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)),
|
|
(float *) ((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)));
|
|
}
|
|
}
|
|
|
|
//int64_t t1 = ggml_perf_time_us();
|
|
//static int64_t acc = 0;
|
|
//acc += t1 - t0;
|
|
//if (t1 - t0 > 10) {
|
|
// printf("\n");
|
|
// printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
|
|
// printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
|
|
// printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
|
|
// printf("nb10 = %5d, nb11 = %5d, nb12 = %5d, nb13 = %5d\n", nb10, nb11, nb12, nb13);
|
|
|
|
// printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
|
|
//}
|
|
}
|
|
|
|
static void ggml_compute_forward_mul_mat_f16_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
int64_t t0 = ggml_perf_time_us();
|
|
UNUSED(t0);
|
|
|
|
const int ne00 = src0->ne[0];
|
|
const int ne01 = src0->ne[1];
|
|
const int ne02 = src0->ne[2];
|
|
const int ne03 = src0->ne[3];
|
|
|
|
const int ne10 = src1->ne[0];
|
|
const int ne11 = src1->ne[1];
|
|
const int ne12 = src1->ne[2];
|
|
const int ne13 = src1->ne[3];
|
|
|
|
const int ne0 = dst->ne[0];
|
|
const int ne1 = dst->ne[1];
|
|
const int ne2 = dst->ne[2];
|
|
const int ne3 = dst->ne[3];
|
|
//const int ne = ne0*ne1*ne2*ne3;
|
|
|
|
const int nb00 = src0->nb[0];
|
|
const int nb01 = src0->nb[1];
|
|
const int nb02 = src0->nb[2];
|
|
const int nb03 = src0->nb[3];
|
|
|
|
const int nb10 = src1->nb[0];
|
|
const int nb11 = src1->nb[1];
|
|
const int nb12 = src1->nb[2];
|
|
const int nb13 = src1->nb[3];
|
|
|
|
const int nb0 = dst->nb[0];
|
|
const int nb1 = dst->nb[1];
|
|
const int nb2 = dst->nb[2];
|
|
const int nb3 = dst->nb[3];
|
|
|
|
const int ith = params->ith;
|
|
const int nth = params->nth;
|
|
|
|
GGML_ASSERT(ne02 == ne12);
|
|
GGML_ASSERT(ne03 == ne13);
|
|
GGML_ASSERT(ne2 == ne12);
|
|
GGML_ASSERT(ne3 == ne13);
|
|
|
|
// TODO: we don't support permuted src0
|
|
GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
|
|
|
|
// dst cannot be transposed or permuted
|
|
GGML_ASSERT(nb0 == sizeof(float));
|
|
GGML_ASSERT(nb0 <= nb1);
|
|
GGML_ASSERT(nb1 <= nb2);
|
|
GGML_ASSERT(nb2 <= nb3);
|
|
|
|
GGML_ASSERT(ne0 == ne01);
|
|
GGML_ASSERT(ne1 == ne11);
|
|
GGML_ASSERT(ne2 == ne02);
|
|
GGML_ASSERT(ne3 == ne03);
|
|
|
|
// nb01 >= nb00 - src0 is not transposed
|
|
// compute by src0 rows
|
|
|
|
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
|
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
|
|
GGML_ASSERT(nb10 == sizeof(float));
|
|
|
|
if (params->ith != 0) {
|
|
return;
|
|
}
|
|
|
|
if (params->type == GGML_TASK_INIT) {
|
|
return;
|
|
}
|
|
|
|
if (params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
float * const wdata = params->wdata;
|
|
|
|
for (int i03 = 0; i03 < ne03; i03++) {
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
{
|
|
size_t id = 0;
|
|
for (int i01 = 0; i01 < ne01; ++i01) {
|
|
for (int i00 = 0; i00 < ne00; ++i00) {
|
|
wdata[id++] = GGML_FP16_TO_FP32(*(ggml_fp16_t *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00));
|
|
}
|
|
}
|
|
}
|
|
|
|
const float * x = wdata;
|
|
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
|
|
|
|
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
|
|
|
|
// zT = y * xT
|
|
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
|
|
ne11, ne01, ne10,
|
|
1.0f, y, ne10,
|
|
x, ne10,
|
|
0.0f, d, ne01);
|
|
}
|
|
}
|
|
|
|
/*printf("CBLAS F16 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);*/
|
|
|
|
return;
|
|
}
|
|
#endif
|
|
|
|
if (params->type == GGML_TASK_INIT) {
|
|
ggml_fp16_t * const wdata = params->wdata;
|
|
|
|
size_t id = 0;
|
|
for (int i13 = 0; i13 < ne13; ++i13) {
|
|
for (int i12 = 0; i12 < ne12; ++i12) {
|
|
for (int i11 = 0; i11 < ne11; ++i11) {
|
|
for (int i10 = 0; i10 < ne10; ++i10) {
|
|
wdata[id++] = GGML_FP32_TO_FP16(*(float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
GGML_ASSERT(id*sizeof(ggml_fp16_t) <= params->wsize);
|
|
|
|
return;
|
|
}
|
|
|
|
if (params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
// fp16 -> half the size, so divide by 2
|
|
// TODO: do not support transposed src1
|
|
assert(nb10/2 == sizeof(ggml_fp16_t));
|
|
|
|
// parallelize by src0 rows using ggml_vec_dot_f16
|
|
|
|
// total rows in src0
|
|
const int nr = ne01*ne02*ne03;
|
|
|
|
// rows per thread
|
|
const int dr = (nr + nth - 1)/nth;
|
|
|
|
// row range for this thread
|
|
const int ir0 = dr*ith;
|
|
const int ir1 = MIN(ir0 + dr, nr);
|
|
|
|
ggml_fp16_t * wdata = params->wdata;
|
|
|
|
for (int ir = ir0; ir < ir1; ++ir) {
|
|
// src0 indices
|
|
const int i03 = ir/(ne02*ne01);
|
|
const int i02 = (ir - i03*ne02*ne01)/ne01;
|
|
const int i01 = (ir - i03*ne02*ne01 - i02*ne01);
|
|
|
|
const int i13 = i03;
|
|
const int i12 = i02;
|
|
|
|
const int i0 = i01;
|
|
const int i2 = i02;
|
|
const int i3 = i03;
|
|
|
|
ggml_fp16_t * src0_row = (ggml_fp16_t *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03));
|
|
ggml_fp16_t * src1_col = wdata + ( 0 + i12*ne11 + i13*ne12*ne11)*ne00;
|
|
|
|
float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3));
|
|
|
|
for (int ic = 0; ic < ne11; ++ic) {
|
|
ggml_vec_dot_f16(ne00, &dst_col[ic*ne0], src0_row, src1_col + ic*ne00);
|
|
}
|
|
}
|
|
|
|
//int64_t t1 = ggml_time_us();
|
|
//static int64_t acc = 0;
|
|
//acc += t1 - t0;
|
|
//if (t1 - t0 > 10) {
|
|
// printf("\n");
|
|
// printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
|
|
// printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
|
|
// printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
|
|
|
|
// printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
|
|
//}
|
|
}
|
|
|
|
typedef void (*dequantize_row_q_t)(const void * restrict x, float * restrict y, int k);
|
|
typedef void (*quantize_row_q_t)(const float * restrict x, void * restrict y, int k);
|
|
typedef void (*vec_dot_q_t)(const int n, float * restrict s, const void * restrict x, const void * restrict y);
|
|
|
|
typedef struct {
|
|
dequantize_row_q_t dequantize_row_q;
|
|
quantize_row_q_t quantize_row_q;
|
|
vec_dot_q_t vec_dot_q;
|
|
} quantize_fns_t;
|
|
|
|
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,
|
|
.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,
|
|
.vec_dot_q = ggml_vec_dot_q4_1,
|
|
},
|
|
};
|
|
|
|
static void ggml_compute_forward_mul_mat_q_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
int64_t t0 = ggml_perf_time_us();
|
|
UNUSED(t0);
|
|
|
|
const int ne00 = src0->ne[0];
|
|
const int ne01 = src0->ne[1];
|
|
const int ne02 = src0->ne[2];
|
|
const int ne03 = src0->ne[3];
|
|
|
|
const int ne10 = src1->ne[0];
|
|
const int ne11 = src1->ne[1];
|
|
const int ne12 = src1->ne[2];
|
|
const int ne13 = src1->ne[3];
|
|
|
|
const int ne0 = dst->ne[0];
|
|
const int ne1 = dst->ne[1];
|
|
const int ne2 = dst->ne[2];
|
|
const int ne3 = dst->ne[3];
|
|
|
|
const int 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;
|
|
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;
|
|
|
|
// 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 == sizeof(float));
|
|
GGML_ASSERT(nb0 <= nb1);
|
|
GGML_ASSERT(nb1 <= nb2);
|
|
GGML_ASSERT(nb2 <= nb3);
|
|
|
|
GGML_ASSERT(ne0 == ne01);
|
|
GGML_ASSERT(ne1 == ne11);
|
|
GGML_ASSERT(ne2 == ne02);
|
|
GGML_ASSERT(ne3 == ne03);
|
|
|
|
// nb01 >= nb00 - src0 is not transposed
|
|
// compute by src0 rows
|
|
|
|
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
|
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
|
|
if (params->ith != 0) {
|
|
return;
|
|
}
|
|
|
|
if (params->type == GGML_TASK_INIT) {
|
|
return;
|
|
}
|
|
|
|
if (params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
float * const wdata = params->wdata;
|
|
dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
|
|
|
|
for (int i03 = 0; i03 < ne03; i03++) {
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
{
|
|
size_t id = 0;
|
|
for (int i01 = 0; i01 < ne01; ++i01) {
|
|
dequantize_row_q((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00);
|
|
id += ne00;
|
|
}
|
|
}
|
|
|
|
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
|
|
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
|
|
ne11, ne01, ne10,
|
|
1.0f, y, ne10,
|
|
x, ne10,
|
|
0.0f, d, ne01);
|
|
}
|
|
}
|
|
|
|
//printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
|
|
|
|
return;
|
|
}
|
|
#endif
|
|
|
|
if (params->type == GGML_TASK_INIT) {
|
|
char * wdata = params->wdata;
|
|
const size_t row_size = ne10*GGML_TYPE_SIZE[type]/GGML_BLCK_SIZE[type];
|
|
|
|
for (int i13 = 0; i13 < ne13; ++i13) {
|
|
for (int i12 = 0; i12 < ne12; ++i12) {
|
|
for (int i11 = 0; i11 < ne11; ++i11) {
|
|
quantize_row_q((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
|
|
wdata += row_size;
|
|
}
|
|
}
|
|
}
|
|
|
|
return;
|
|
}
|
|
|
|
if (params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
// parallelize by src0 rows using ggml_vec_dot_q
|
|
|
|
// 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);
|
|
|
|
void * wdata = params->wdata;
|
|
const size_t row_size = ne00*GGML_TYPE_SIZE[type]/GGML_BLCK_SIZE[type];
|
|
|
|
for (int ir = ir0; ir < ir1; ++ir) {
|
|
// src0 indices
|
|
const int i03 = ir/(ne02*ne01);
|
|
const int i02 = (ir - i03*ne02*ne01)/ne01;
|
|
const int i01 = (ir - i03*ne02*ne01 - i02*ne01);
|
|
|
|
const int i13 = i03;
|
|
const int i12 = i02;
|
|
|
|
const int i0 = i01;
|
|
const int i2 = i02;
|
|
const int i3 = i03;
|
|
|
|
void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03));
|
|
char * src1_col = ((char *) wdata + ( (0 + i12*ne11 + i13*ne12*ne11)*row_size));
|
|
|
|
float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3));
|
|
|
|
assert(ne00 % 32 == 0);
|
|
|
|
for (int ic = 0; ic < ne11; ++ic) {
|
|
vec_dot_q(ne00, &dst_col[ic*ne0], src0_row, (void *) (src1_col + ic*row_size));
|
|
}
|
|
}
|
|
|
|
//int64_t t1 = ggml_time_us();
|
|
//static int64_t acc = 0;
|
|
//acc += t1 - t0;
|
|
//if (t1 - t0 > 10) {
|
|
// printf("\n");
|
|
// printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
|
|
// printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
|
|
// printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
|
|
|
|
// printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
|
|
//}
|
|
}
|
|
|
|
static void ggml_compute_forward_mul_mat(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_Q4_0:
|
|
case GGML_TYPE_Q4_1:
|
|
{
|
|
ggml_compute_forward_mul_mat_q_f32(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_F16:
|
|
{
|
|
ggml_compute_forward_mul_mat_f16_f32(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_mul_mat_f32(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_I8:
|
|
case GGML_TYPE_I16:
|
|
case GGML_TYPE_I32:
|
|
case GGML_TYPE_COUNT:
|
|
{
|
|
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
|
|
|
|
static void ggml_compute_forward_scale_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
GGML_ASSERT(ggml_is_contiguous(src0));
|
|
GGML_ASSERT(ggml_is_contiguous(dst));
|
|
GGML_ASSERT(ggml_are_same_shape(src0, dst));
|
|
GGML_ASSERT(ggml_is_scalar(src1));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
// scale factor
|
|
const float v = *(float *) src1->data;
|
|
|
|
const int ith = params->ith;
|
|
const int nth = params->nth;
|
|
|
|
const int nc = src0->ne[0];
|
|
const int nr = ggml_nrows(src0);
|
|
|
|
// rows per thread
|
|
const int dr = (nr + nth - 1)/nth;
|
|
|
|
// row range for this thread
|
|
const int ir0 = dr*ith;
|
|
const int ir1 = MIN(ir0 + dr, nr);
|
|
|
|
for (int i1 = ir0; i1 < ir1; i1++) {
|
|
ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*(dst->nb[1])), v);
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_scale(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_scale_f32(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_cpy
|
|
|
|
static void ggml_compute_forward_cpy(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
ggml_compute_forward_dup(params, src0, dst);
|
|
}
|
|
|
|
// ggml_compute_forward_reshape
|
|
|
|
static void ggml_compute_forward_reshape(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
// NOP
|
|
UNUSED(params);
|
|
UNUSED(src0);
|
|
UNUSED(dst);
|
|
}
|
|
|
|
// ggml_compute_forward_view
|
|
|
|
static void ggml_compute_forward_view(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0) {
|
|
// NOP
|
|
UNUSED(params);
|
|
UNUSED(src0);
|
|
}
|
|
|
|
// ggml_compute_forward_permute
|
|
|
|
static void ggml_compute_forward_permute(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0) {
|
|
// NOP
|
|
UNUSED(params);
|
|
UNUSED(src0);
|
|
}
|
|
|
|
// ggml_compute_forward_transpose
|
|
|
|
static void ggml_compute_forward_transpose(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0) {
|
|
// NOP
|
|
UNUSED(params);
|
|
UNUSED(src0);
|
|
}
|
|
|
|
// ggml_compute_forward_get_rows
|
|
|
|
static void ggml_compute_forward_get_rows_q(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int nc = src0->ne[0];
|
|
const int nr = ggml_nelements(src1);
|
|
const enum ggml_type type = src0->type;
|
|
dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
|
|
|
|
assert( dst->ne[0] == nc);
|
|
assert( dst->ne[1] == nr);
|
|
assert(src0->nb[0] == GGML_TYPE_SIZE[type]);
|
|
|
|
for (int i = 0; i < nr; ++i) {
|
|
const int r = ((int32_t *) src1->data)[i];
|
|
|
|
dequantize_row_q(
|
|
(const void *) ((char *) src0->data + r*src0->nb[1]),
|
|
(float *) ((char *) dst->data + i*dst->nb[1]), nc);
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_get_rows_f16(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int nc = src0->ne[0];
|
|
const int nr = ggml_nelements(src1);
|
|
|
|
assert( dst->ne[0] == nc);
|
|
assert( dst->ne[1] == nr);
|
|
assert(src0->nb[0] == sizeof(ggml_fp16_t));
|
|
|
|
for (int i = 0; i < nr; ++i) {
|
|
const int r = ((int32_t *) src1->data)[i];
|
|
|
|
for (int j = 0; j < nc; ++j) {
|
|
ggml_fp16_t v = ((ggml_fp16_t *) ((char *) src0->data + r*src0->nb[1]))[j];
|
|
((float *) ((char *) dst->data + i*dst->nb[1]))[j] = GGML_FP16_TO_FP32(v);
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_get_rows_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int nc = src0->ne[0];
|
|
const int nr = ggml_nelements(src1);
|
|
|
|
assert( dst->ne[0] == nc);
|
|
assert( dst->ne[1] == nr);
|
|
assert(src0->nb[0] == sizeof(float));
|
|
|
|
for (int i = 0; i < nr; ++i) {
|
|
const int r = ((int32_t *) src1->data)[i];
|
|
|
|
ggml_vec_cpy_f32(nc,
|
|
(float *) ((char *) dst->data + i*dst->nb[1]),
|
|
(float *) ((char *) src0->data + r*src0->nb[1]));
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_get_rows(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_Q4_0:
|
|
case GGML_TYPE_Q4_1:
|
|
{
|
|
ggml_compute_forward_get_rows_q(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_F16:
|
|
{
|
|
ggml_compute_forward_get_rows_f16(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_get_rows_f32(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_I8:
|
|
case GGML_TYPE_I16:
|
|
case GGML_TYPE_I32:
|
|
case GGML_TYPE_COUNT:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
|
|
//static bool first = true;
|
|
//printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]);
|
|
//if (first) {
|
|
// first = false;
|
|
//} else {
|
|
// for (int k = 0; k < dst->ne[1]; ++k) {
|
|
// for (int j = 0; j < dst->ne[0]/16; ++j) {
|
|
// for (int i = 0; i < 16; ++i) {
|
|
// printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]);
|
|
// }
|
|
// printf("\n");
|
|
// }
|
|
// printf("\n");
|
|
// }
|
|
// printf("\n");
|
|
// exit(0);
|
|
//}
|
|
}
|
|
|
|
// ggml_compute_forward_diag_mask_inf
|
|
|
|
static void ggml_compute_forward_diag_mask_inf_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
assert(src1->type == GGML_TYPE_I32);
|
|
assert(ggml_nelements(src1) == 1);
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int n_past = ((int32_t *) src1->data)[0];
|
|
|
|
// TODO: handle transposed/permuted matrices
|
|
|
|
const int n = ggml_nrows(src0);
|
|
const int nc = src0->ne[0];
|
|
const int nr = src0->ne[1];
|
|
const int nz = n/nr;
|
|
|
|
assert( dst->nb[0] == sizeof(float));
|
|
assert(src0->nb[0] == sizeof(float));
|
|
|
|
for (int k = 0; k < nz; k++) {
|
|
for (int j = 0; j < nr; j++) {
|
|
for (int i = n_past; i < nc; i++) {
|
|
if (i > n_past + j) {
|
|
*(float *)((char *) dst->data + k*dst->nb[2] + j*dst->nb[1] + i*dst->nb[0]) = -INFINITY;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_diag_mask_inf(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_diag_mask_inf_f32(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_soft_max
|
|
|
|
static void ggml_compute_forward_soft_max_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
GGML_ASSERT(ggml_is_contiguous(src0));
|
|
GGML_ASSERT(ggml_is_contiguous(dst));
|
|
GGML_ASSERT(ggml_are_same_shape(src0, dst));
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
// TODO: handle transposed/permuted matrices
|
|
|
|
const int ith = params->ith;
|
|
const int nth = params->nth;
|
|
|
|
const int nc = src0->ne[0];
|
|
const int nr = ggml_nrows(src0);
|
|
|
|
// rows per thread
|
|
const int dr = (nr + nth - 1)/nth;
|
|
|
|
// row range for this thread
|
|
const int ir0 = dr*ith;
|
|
const int ir1 = MIN(ir0 + dr, nr);
|
|
|
|
for (int i1 = ir0; i1 < ir1; i1++) {
|
|
float *p = (float *)((char *) dst->data + i1*dst->nb[1]);
|
|
|
|
#ifndef NDEBUG
|
|
for (int i = 0; i < nc; ++i) {
|
|
//printf("p[%d] = %f\n", i, p[i]);
|
|
assert(!isnan(p[i]));
|
|
}
|
|
#endif
|
|
|
|
float max = -INFINITY;
|
|
ggml_vec_max_f32(nc, &max, p);
|
|
|
|
ggml_float sum = 0.0;
|
|
|
|
uint16_t scvt;
|
|
for (int i = 0; i < nc; i++) {
|
|
if (p[i] == -INFINITY) {
|
|
p[i] = 0.0f;
|
|
} else {
|
|
//const float val = (p[i] == -INFINITY) ? 0.0 : exp(p[i] - max);
|
|
ggml_fp16_t s = GGML_FP32_TO_FP16(p[i] - max);
|
|
memcpy(&scvt, &s, sizeof(scvt));
|
|
const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]);
|
|
sum += (ggml_float)val;
|
|
p[i] = val;
|
|
}
|
|
}
|
|
|
|
assert(sum > 0.0);
|
|
|
|
sum = 1.0/sum;
|
|
ggml_vec_scale_f32(nc, p, sum);
|
|
|
|
#ifndef NDEBUG
|
|
for (int i = 0; i < nc; ++i) {
|
|
assert(!isnan(p[i]));
|
|
assert(!isinf(p[i]));
|
|
}
|
|
#endif
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_soft_max(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_soft_max_f32(params, src0, dst);
|
|
} break;
|
|
case GGML_TYPE_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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_rope
|
|
|
|
static void ggml_compute_forward_rope_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
assert(params->ith == 0);
|
|
assert(src1->type == GGML_TYPE_I32);
|
|
assert(ggml_nelements(src1) == 3);
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int n_past = ((int32_t *) src1->data)[0];
|
|
const int n_dims = ((int32_t *) src1->data)[1];
|
|
const int mode = ((int32_t *) src1->data)[2];
|
|
|
|
//const int ne0 = src0->ne[0];
|
|
const int ne1 = src0->ne[1];
|
|
const int ne2 = src0->ne[2];
|
|
const int ne3 = src0->ne[3];
|
|
|
|
const int nb0 = src0->nb[0];
|
|
const int nb1 = src0->nb[1];
|
|
const int nb2 = src0->nb[2];
|
|
const int nb3 = src0->nb[3];
|
|
|
|
//printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
|
|
//printf("n_past = %d, ne2 = %d\n", n_past, ne2);
|
|
|
|
assert(nb0 == sizeof(float));
|
|
|
|
// TODO: optimize
|
|
for (int i3 = 0; i3 < ne3; i3++) {
|
|
for (int i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) {
|
|
const int p = (mode == 0 ? n_past + i2 : i2);
|
|
for (int i1 = 0; i1 < ne1; i1++) {
|
|
for (int i0 = 0; i0 < n_dims; i0 += 2) {
|
|
const float theta = powf(10000.0, ((float)-i0)/n_dims);
|
|
|
|
const float cos_theta = cosf(p*theta);
|
|
const float sin_theta = sinf(p*theta);
|
|
|
|
const float * const src = (float *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
|
|
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
|
|
|
|
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;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_rope_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) == 3);
|
|
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
const int n_past = ((int32_t *) src1->data)[0];
|
|
const int n_dims = ((int32_t *) src1->data)[1];
|
|
const int mode = ((int32_t *) src1->data)[2];
|
|
|
|
//const int ne0 = src0->ne[0];
|
|
const int ne1 = src0->ne[1];
|
|
const int ne2 = src0->ne[2];
|
|
const int ne3 = src0->ne[3];
|
|
|
|
const int nb0 = src0->nb[0];
|
|
const int nb1 = src0->nb[1];
|
|
const int nb2 = src0->nb[2];
|
|
const int nb3 = src0->nb[3];
|
|
|
|
//printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
|
|
//printf("n_past = %d, ne2 = %d\n", n_past, ne2);
|
|
|
|
assert(nb0 == sizeof(ggml_fp16_t));
|
|
|
|
for (int i3 = 0; i3 < ne3; i3++) {
|
|
for (int i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) {
|
|
const int p = (mode == 0 ? n_past + i2 : i2);
|
|
for (int i1 = 0; i1 < ne1; i1++) {
|
|
for (int i0 = 0; i0 < n_dims; i0 += 2) {
|
|
const float theta = powf(10000.0, ((float)-i0)/n_dims);
|
|
|
|
const float cos_theta = cosf(p*theta);
|
|
const float sin_theta = sinf(p*theta);
|
|
|
|
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]);
|
|
|
|
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);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_rope(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F16:
|
|
{
|
|
ggml_compute_forward_rope_f16(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_F32:
|
|
{
|
|
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:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_conv_1d_1s
|
|
|
|
static void ggml_compute_forward_conv_1d_1s_f16_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F16);
|
|
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
int64_t t0 = ggml_perf_time_us();
|
|
UNUSED(t0);
|
|
|
|
const int ne00 = src0->ne[0];
|
|
const int ne01 = src0->ne[1];
|
|
const int ne02 = src0->ne[2];
|
|
//const int ne03 = src0->ne[3];
|
|
|
|
const int ne10 = src1->ne[0];
|
|
const int ne11 = src1->ne[1];
|
|
//const int ne12 = src1->ne[2];
|
|
//const int ne13 = src1->ne[3];
|
|
|
|
//const int ne0 = dst->ne[0];
|
|
//const int ne1 = dst->ne[1];
|
|
//const int ne2 = dst->ne[2];
|
|
//const int ne3 = dst->ne[3];
|
|
//const int ne = ne0*ne1*ne2*ne3;
|
|
|
|
const int nb00 = src0->nb[0];
|
|
const int nb01 = src0->nb[1];
|
|
const int nb02 = src0->nb[2];
|
|
//const int nb03 = src0->nb[3];
|
|
|
|
const int nb10 = src1->nb[0];
|
|
const int nb11 = src1->nb[1];
|
|
//const int nb12 = src1->nb[2];
|
|
//const int nb13 = src1->nb[3];
|
|
|
|
//const int nb0 = dst->nb[0];
|
|
const int nb1 = dst->nb[1];
|
|
//const int nb2 = dst->nb[2];
|
|
//const int nb3 = dst->nb[3];
|
|
|
|
const int ith = params->ith;
|
|
const int nth = params->nth;
|
|
|
|
const int nk = ne00;
|
|
const int nh = nk/2;
|
|
|
|
const int ew0 = ggml_up32(ne01);
|
|
|
|
GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes
|
|
GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
|
|
GGML_ASSERT(nb10 == sizeof(float));
|
|
|
|
if (params->type == GGML_TASK_INIT) {
|
|
// TODO: fix this memset (wsize is overestimated)
|
|
memset(params->wdata, 0, params->wsize);
|
|
|
|
// prepare kernel data (src0)
|
|
{
|
|
ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
|
|
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
for (int i01 = 0; i01 < ne01; i01++) {
|
|
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01);
|
|
ggml_fp16_t * dst_data = wdata + i02*ew0*ne00;
|
|
for (int i00 = 0; i00 < ne00; i00++) {
|
|
dst_data[i00*ew0 + i01] = src[i00];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// prepare source data (src1)
|
|
{
|
|
ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + ne02*ew0*ne00;
|
|
|
|
for (int i11 = 0; i11 < ne11; i11++) {
|
|
const float * const src = (float *)((char *) src1->data + i11*nb11);
|
|
ggml_fp16_t * dst_data = wdata;
|
|
for (int i10 = 0; i10 < ne10; i10++) {
|
|
dst_data[(i10 + nh)*ew0 + i11] = GGML_FP32_TO_FP16(src[i10]);
|
|
}
|
|
}
|
|
}
|
|
|
|
return;
|
|
}
|
|
|
|
if (params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
// total rows in dst
|
|
const int nr = ne02;
|
|
|
|
// rows per thread
|
|
const int dr = (nr + nth - 1)/nth;
|
|
|
|
// row range for this thread
|
|
const int ir0 = dr*ith;
|
|
const int ir1 = MIN(ir0 + dr, nr);
|
|
|
|
for (int i1 = ir0; i1 < ir1; i1++) {
|
|
float * dst_data = (float *)((char *) dst->data + i1*nb1);
|
|
for (int i0 = 0; i0 < ne10; ++i0) {
|
|
dst_data[i0] = 0;
|
|
for (int k = -nh; k <= nh; k++) {
|
|
float v = 0.0f;
|
|
ggml_vec_dot_f16(ew0, &v,
|
|
(ggml_fp16_t *) params->wdata + i1*ew0*ne00 + (nh + k)*ew0,
|
|
(ggml_fp16_t *) params->wdata + ne02*ew0*ne00 + (i0 + nh + k)*ew0);
|
|
|
|
dst_data[i0] += v;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_conv_1d_1s_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
int64_t t0 = ggml_perf_time_us();
|
|
UNUSED(t0);
|
|
|
|
const int ne00 = src0->ne[0];
|
|
const int ne01 = src0->ne[1];
|
|
const int ne02 = src0->ne[2];
|
|
//const int ne03 = src0->ne[3];
|
|
|
|
const int ne10 = src1->ne[0];
|
|
const int ne11 = src1->ne[1];
|
|
//const int ne12 = src1->ne[2];
|
|
//const int ne13 = src1->ne[3];
|
|
|
|
//const int ne0 = dst->ne[0];
|
|
//const int ne1 = dst->ne[1];
|
|
//const int ne2 = dst->ne[2];
|
|
//const int ne3 = dst->ne[3];
|
|
//const int ne = ne0*ne1*ne2*ne3;
|
|
|
|
const int nb00 = src0->nb[0];
|
|
const int nb01 = src0->nb[1];
|
|
const int nb02 = src0->nb[2];
|
|
//const int nb03 = src0->nb[3];
|
|
|
|
const int nb10 = src1->nb[0];
|
|
const int nb11 = src1->nb[1];
|
|
//const int nb12 = src1->nb[2];
|
|
//const int nb13 = src1->nb[3];
|
|
|
|
//const int nb0 = dst->nb[0];
|
|
const int nb1 = dst->nb[1];
|
|
//const int nb2 = dst->nb[2];
|
|
//const int nb3 = dst->nb[3];
|
|
|
|
const int ith = params->ith;
|
|
const int nth = params->nth;
|
|
|
|
const int nk = ne00;
|
|
const int nh = nk/2;
|
|
|
|
const int ew0 = ggml_up32(ne01);
|
|
|
|
GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes
|
|
GGML_ASSERT(nb00 == sizeof(float));
|
|
GGML_ASSERT(nb10 == sizeof(float));
|
|
|
|
if (params->type == GGML_TASK_INIT) {
|
|
// TODO: fix this memset (wsize is overestimated)
|
|
memset(params->wdata, 0, params->wsize);
|
|
|
|
// prepare kernel data (src0)
|
|
{
|
|
float * const wdata = (float *) params->wdata + 0;
|
|
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
for (int i01 = 0; i01 < ne01; i01++) {
|
|
const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01);
|
|
float * dst_data = wdata + i02*ew0*ne00;
|
|
for (int i00 = 0; i00 < ne00; i00++) {
|
|
dst_data[i00*ew0 + i01] = src[i00];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// prepare source data (src1)
|
|
{
|
|
float * const wdata = (float *) params->wdata + ne02*ew0*ne00;
|
|
|
|
for (int i11 = 0; i11 < ne11; i11++) {
|
|
const float * const src = (float *)((char *) src1->data + i11*nb11);
|
|
float * dst_data = wdata;
|
|
for (int i10 = 0; i10 < ne10; i10++) {
|
|
dst_data[(i10 + nh)*ew0 + i11] = src[i10];
|
|
}
|
|
}
|
|
}
|
|
|
|
return;
|
|
}
|
|
|
|
if (params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
// total rows in dst
|
|
const int nr = ne02;
|
|
|
|
// rows per thread
|
|
const int dr = (nr + nth - 1)/nth;
|
|
|
|
// row range for this thread
|
|
const int ir0 = dr*ith;
|
|
const int ir1 = MIN(ir0 + dr, nr);
|
|
|
|
for (int i1 = ir0; i1 < ir1; i1++) {
|
|
float * dst_data = (float *)((char *) dst->data + i1*nb1);
|
|
for (int i0 = 0; i0 < ne10; ++i0) {
|
|
dst_data[i0] = 0;
|
|
for (int k = -nh; k <= nh; k++) {
|
|
float v = 0.0f;
|
|
ggml_vec_dot_f32(ew0, &v,
|
|
(float *) params->wdata + i1*ew0*ne00 + (nh + k)*ew0,
|
|
(float *) params->wdata + ne02*ew0*ne00 + (i0 + nh + k)*ew0);
|
|
|
|
dst_data[i0] += v;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_conv_1d_1s(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F16:
|
|
{
|
|
ggml_compute_forward_conv_1d_1s_f16_f32(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_conv_1d_1s_f32(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_Q4_0:
|
|
case GGML_TYPE_Q4_1:
|
|
case GGML_TYPE_I8:
|
|
case GGML_TYPE_I16:
|
|
case GGML_TYPE_I32:
|
|
case GGML_TYPE_COUNT:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_conv_1d_2s
|
|
|
|
static void ggml_compute_forward_conv_1d_2s_f16_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F16);
|
|
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
int64_t t0 = ggml_perf_time_us();
|
|
UNUSED(t0);
|
|
|
|
const int ne00 = src0->ne[0];
|
|
const int ne01 = src0->ne[1];
|
|
const int ne02 = src0->ne[2];
|
|
//const int ne03 = src0->ne[3];
|
|
|
|
const int ne10 = src1->ne[0];
|
|
const int ne11 = src1->ne[1];
|
|
//const int ne12 = src1->ne[2];
|
|
//const int ne13 = src1->ne[3];
|
|
|
|
//const int ne0 = dst->ne[0];
|
|
//const int ne1 = dst->ne[1];
|
|
//const int ne2 = dst->ne[2];
|
|
//const int ne3 = dst->ne[3];
|
|
//const int ne = ne0*ne1*ne2*ne3;
|
|
|
|
const int nb00 = src0->nb[0];
|
|
const int nb01 = src0->nb[1];
|
|
const int nb02 = src0->nb[2];
|
|
//const int nb03 = src0->nb[3];
|
|
|
|
const int nb10 = src1->nb[0];
|
|
const int nb11 = src1->nb[1];
|
|
//const int nb12 = src1->nb[2];
|
|
//const int nb13 = src1->nb[3];
|
|
|
|
//const int nb0 = dst->nb[0];
|
|
const int nb1 = dst->nb[1];
|
|
//const int nb2 = dst->nb[2];
|
|
//const int nb3 = dst->nb[3];
|
|
|
|
const int ith = params->ith;
|
|
const int nth = params->nth;
|
|
|
|
const int nk = ne00;
|
|
const int nh = nk/2;
|
|
|
|
const int ew0 = ggml_up32(ne01);
|
|
|
|
GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes
|
|
GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
|
|
GGML_ASSERT(nb10 == sizeof(float));
|
|
|
|
if (params->type == GGML_TASK_INIT) {
|
|
// TODO: fix this memset (wsize is overestimated)
|
|
memset(params->wdata, 0, params->wsize);
|
|
|
|
// prepare kernel data (src0)
|
|
{
|
|
ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
|
|
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
for (int i01 = 0; i01 < ne01; i01++) {
|
|
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01);
|
|
ggml_fp16_t * dst_data = wdata + i02*ew0*ne00;
|
|
for (int i00 = 0; i00 < ne00; i00++) {
|
|
dst_data[i00*ew0 + i01] = src[i00];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// prepare source data (src1)
|
|
{
|
|
ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + ne02*ew0*ne00;
|
|
|
|
for (int i11 = 0; i11 < ne11; i11++) {
|
|
const float * const src = (float *)((char *) src1->data + i11*nb11);
|
|
ggml_fp16_t * dst_data = wdata;
|
|
for (int i10 = 0; i10 < ne10; i10++) {
|
|
dst_data[(i10 + nh)*ew0 + i11] = GGML_FP32_TO_FP16(src[i10]);
|
|
}
|
|
}
|
|
}
|
|
|
|
return;
|
|
}
|
|
|
|
if (params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
// total rows in dst
|
|
const int nr = ne02;
|
|
|
|
// rows per thread
|
|
const int dr = (nr + nth - 1)/nth;
|
|
|
|
// row range for this thread
|
|
const int ir0 = dr*ith;
|
|
const int ir1 = MIN(ir0 + dr, nr);
|
|
|
|
for (int i1 = ir0; i1 < ir1; i1++) {
|
|
float * dst_data = (float *)((char *) dst->data + i1*nb1);
|
|
for (int i0 = 0; i0 < ne10; i0 += 2) {
|
|
dst_data[i0/2] = 0;
|
|
for (int k = -nh; k <= nh; k++) {
|
|
float v = 0.0f;
|
|
ggml_vec_dot_f16(ew0, &v,
|
|
(ggml_fp16_t *) params->wdata + i1*ew0*ne00 + (nh + k)*ew0,
|
|
(ggml_fp16_t *) params->wdata + ne02*ew0*ne00 + (i0 + nh + k)*ew0);
|
|
|
|
dst_data[i0/2] += v;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_conv_1d_2s_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
int64_t t0 = ggml_perf_time_us();
|
|
UNUSED(t0);
|
|
|
|
const int ne00 = src0->ne[0];
|
|
const int ne01 = src0->ne[1];
|
|
const int ne02 = src0->ne[2];
|
|
//const int ne03 = src0->ne[3];
|
|
|
|
const int ne10 = src1->ne[0];
|
|
const int ne11 = src1->ne[1];
|
|
//const int ne12 = src1->ne[2];
|
|
//const int ne13 = src1->ne[3];
|
|
|
|
//const int ne0 = dst->ne[0];
|
|
//const int ne1 = dst->ne[1];
|
|
//const int ne2 = dst->ne[2];
|
|
//const int ne3 = dst->ne[3];
|
|
//const int ne = ne0*ne1*ne2*ne3;
|
|
|
|
const int nb00 = src0->nb[0];
|
|
const int nb01 = src0->nb[1];
|
|
const int nb02 = src0->nb[2];
|
|
//const int nb03 = src0->nb[3];
|
|
|
|
const int nb10 = src1->nb[0];
|
|
const int nb11 = src1->nb[1];
|
|
//const int nb12 = src1->nb[2];
|
|
//const int nb13 = src1->nb[3];
|
|
|
|
//const int nb0 = dst->nb[0];
|
|
const int nb1 = dst->nb[1];
|
|
//const int nb2 = dst->nb[2];
|
|
//const int nb3 = dst->nb[3];
|
|
|
|
const int ith = params->ith;
|
|
const int nth = params->nth;
|
|
|
|
const int nk = ne00;
|
|
const int nh = nk/2;
|
|
|
|
const int ew0 = ggml_up32(ne01);
|
|
|
|
GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes
|
|
GGML_ASSERT(nb00 == sizeof(float));
|
|
GGML_ASSERT(nb10 == sizeof(float));
|
|
|
|
if (params->type == GGML_TASK_INIT) {
|
|
// TODO: fix this memset (wsize is overestimated)
|
|
memset(params->wdata, 0, params->wsize);
|
|
|
|
// prepare kernel data (src0)
|
|
{
|
|
float * const wdata = (float *) params->wdata + 0;
|
|
|
|
for (int i02 = 0; i02 < ne02; i02++) {
|
|
for (int i01 = 0; i01 < ne01; i01++) {
|
|
const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01);
|
|
float * dst_data = wdata + i02*ew0*ne00;
|
|
for (int i00 = 0; i00 < ne00; i00++) {
|
|
dst_data[i00*ew0 + i01] = src[i00];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// prepare source data (src1)
|
|
{
|
|
float * const wdata = (float *) params->wdata + ne02*ew0*ne00;
|
|
|
|
for (int i11 = 0; i11 < ne11; i11++) {
|
|
const float * const src = (float *)((char *) src1->data + i11*nb11);
|
|
float * dst_data = wdata;
|
|
for (int i10 = 0; i10 < ne10; i10++) {
|
|
dst_data[(i10 + nh)*ew0 + i11] = src[i10];
|
|
}
|
|
}
|
|
}
|
|
|
|
return;
|
|
}
|
|
|
|
if (params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
// total rows in dst
|
|
const int nr = ne02;
|
|
|
|
// rows per thread
|
|
const int dr = (nr + nth - 1)/nth;
|
|
|
|
// row range for this thread
|
|
const int ir0 = dr*ith;
|
|
const int ir1 = MIN(ir0 + dr, nr);
|
|
|
|
for (int i1 = ir0; i1 < ir1; i1++) {
|
|
float * dst_data = (float *)((char *) dst->data + i1*nb1);
|
|
for (int i0 = 0; i0 < ne10; i0 += 2) {
|
|
dst_data[i0/2] = 0;
|
|
for (int k = -nh; k <= nh; k++) {
|
|
float v = 0.0f;
|
|
ggml_vec_dot_f32(ew0, &v,
|
|
(float *) params->wdata + i1*ew0*ne00 + (nh + k)*ew0,
|
|
(float *) params->wdata + ne02*ew0*ne00 + (i0 + nh + k)*ew0);
|
|
|
|
dst_data[i0/2] += v;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_conv_1d_2s(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * src0,
|
|
const struct ggml_tensor * src1,
|
|
struct ggml_tensor * dst) {
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F16:
|
|
{
|
|
ggml_compute_forward_conv_1d_2s_f16_f32(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_conv_1d_2s_f32(params, src0, src1, dst);
|
|
} break;
|
|
case GGML_TYPE_Q4_0:
|
|
case GGML_TYPE_Q4_1:
|
|
case GGML_TYPE_I8:
|
|
case GGML_TYPE_I16:
|
|
case GGML_TYPE_I32:
|
|
case GGML_TYPE_COUNT:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_flash_attn
|
|
|
|
static void ggml_compute_forward_flash_attn_f32(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * q,
|
|
const struct ggml_tensor * k,
|
|
const struct ggml_tensor * v,
|
|
const bool masked,
|
|
struct ggml_tensor * dst) {
|
|
int64_t t0 = ggml_perf_time_us();
|
|
UNUSED(t0);
|
|
|
|
const int neq0 = q->ne[0];
|
|
const int neq1 = q->ne[1];
|
|
const int neq2 = q->ne[2];
|
|
const int neq3 = q->ne[3];
|
|
|
|
const int nek0 = k->ne[0];
|
|
const int nek1 = k->ne[1];
|
|
//const int nek2 = k->ne[2];
|
|
//const int nek3 = k->ne[3];
|
|
|
|
//const int nev0 = v->ne[0];
|
|
const int nev1 = v->ne[1];
|
|
//const int nev2 = v->ne[2];
|
|
//const int nev3 = v->ne[3];
|
|
|
|
const int ne0 = dst->ne[0];
|
|
const int ne1 = dst->ne[1];
|
|
//const int ne2 = dst->ne[2];
|
|
//const int ne3 = dst->ne[3];
|
|
|
|
const int nbk0 = k->nb[0];
|
|
const int nbk1 = k->nb[1];
|
|
const int nbk2 = k->nb[2];
|
|
const int nbk3 = k->nb[3];
|
|
|
|
const int nbq0 = q->nb[0];
|
|
const int nbq1 = q->nb[1];
|
|
const int nbq2 = q->nb[2];
|
|
const int nbq3 = q->nb[3];
|
|
|
|
const int nbv0 = v->nb[0];
|
|
const int nbv1 = v->nb[1];
|
|
const int nbv2 = v->nb[2];
|
|
const int nbv3 = v->nb[3];
|
|
|
|
const int nb0 = dst->nb[0];
|
|
const int nb1 = dst->nb[1];
|
|
const int nb2 = dst->nb[2];
|
|
const int nb3 = dst->nb[3];
|
|
|
|
const int ith = params->ith;
|
|
const int nth = params->nth;
|
|
|
|
const int D = neq0;
|
|
const int N = neq1;
|
|
const int P = nek1 - N;
|
|
const int M = P + N;
|
|
|
|
const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL);
|
|
|
|
GGML_ASSERT(ne0 == D);
|
|
GGML_ASSERT(ne1 == N);
|
|
GGML_ASSERT(P >= 0);
|
|
|
|
GGML_ASSERT(nbq0 == sizeof(float));
|
|
GGML_ASSERT(nbk0 == sizeof(float));
|
|
GGML_ASSERT(nbv0 == sizeof(float));
|
|
|
|
GGML_ASSERT(neq0 == D);
|
|
GGML_ASSERT(nek0 == D);
|
|
GGML_ASSERT(nev1 == D);
|
|
|
|
GGML_ASSERT(neq1 == N);
|
|
GGML_ASSERT(nek1 == N + P);
|
|
GGML_ASSERT(nev1 == D);
|
|
|
|
// dst cannot be transposed or permuted
|
|
GGML_ASSERT(nb0 == sizeof(float));
|
|
GGML_ASSERT(nb0 <= nb1);
|
|
GGML_ASSERT(nb1 <= nb2);
|
|
GGML_ASSERT(nb2 <= nb3);
|
|
|
|
if (params->type == GGML_TASK_INIT) {
|
|
return;
|
|
}
|
|
|
|
if (params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
// parallelize by q rows using ggml_vec_dot_f32
|
|
|
|
// total rows in q
|
|
const int nr = neq1*neq2*neq3;
|
|
|
|
// rows per thread
|
|
const int dr = (nr + nth - 1)/nth;
|
|
|
|
// row range for this thread
|
|
const int ir0 = dr*ith;
|
|
const int ir1 = MIN(ir0 + dr, nr);
|
|
|
|
const float scale = 1.0f/sqrtf(D);
|
|
|
|
//printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale);
|
|
|
|
for (int ir = ir0; ir < ir1; ++ir) {
|
|
// q indices
|
|
const int iq3 = ir/(neq2*neq1);
|
|
const int iq2 = (ir - iq3*neq2*neq1)/neq1;
|
|
const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1);
|
|
|
|
float * S = (float *) params->wdata + ith*(Mup + CACHE_LINE_SIZE_F32);
|
|
|
|
for (int i = M; i < Mup; ++i) {
|
|
S[i] = -INFINITY;
|
|
}
|
|
|
|
for (int ic = 0; ic < nek1; ++ic) {
|
|
// k indices
|
|
const int ik3 = iq3;
|
|
const int ik2 = iq2;
|
|
const int ik1 = ic;
|
|
|
|
// S indices
|
|
const int i1 = ik1;
|
|
|
|
ggml_vec_dot_f32(neq0,
|
|
S + i1,
|
|
(float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)),
|
|
(float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)));
|
|
}
|
|
|
|
// scale
|
|
ggml_vec_scale_f32(nek1, S, scale);
|
|
|
|
if (masked) {
|
|
for (int i = P; i < M; i++) {
|
|
if (i > P + iq1) {
|
|
S[i] = -INFINITY;
|
|
}
|
|
}
|
|
}
|
|
|
|
// softmax
|
|
{
|
|
float max = -INFINITY;
|
|
ggml_vec_max_f32(M, &max, S);
|
|
|
|
ggml_float sum = 0.0;
|
|
{
|
|
#ifdef GGML_SOFT_MAX_ACCELERATE
|
|
max = -max;
|
|
vDSP_vsadd(S, 1, &max, S, 1, Mup);
|
|
vvexpf(S, S, &Mup);
|
|
ggml_vec_sum_f32(Mup, &sum, S);
|
|
#else
|
|
uint16_t scvt[GGML_SOFT_MAX_UNROLL];
|
|
ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 };
|
|
|
|
for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) {
|
|
float * SS = S + i;
|
|
|
|
for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) {
|
|
if (SS[j] == -INFINITY) {
|
|
SS[j] = 0.0f;
|
|
} else {
|
|
ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max);
|
|
memcpy(&scvt[j], &s, sizeof(uint16_t));
|
|
const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]);
|
|
sump[j] += (ggml_float)val;
|
|
SS[j] = val;
|
|
}
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) {
|
|
sum += sump[i];
|
|
}
|
|
#endif
|
|
}
|
|
|
|
assert(sum > 0.0);
|
|
|
|
sum = 1.0/sum;
|
|
ggml_vec_scale_f32(M, S, sum);
|
|
|
|
#ifndef NDEBUG
|
|
for (int i = 0; i < M; ++i) {
|
|
assert(!isnan(S[i]));
|
|
assert(!isinf(S[i]));
|
|
}
|
|
#endif
|
|
}
|
|
|
|
for (int ic = 0; ic < nev1; ++ic) {
|
|
// dst indices
|
|
const int i1 = iq1;
|
|
const int i2 = iq2;
|
|
const int i3 = iq3;
|
|
|
|
ggml_vec_dot_f32(nek1,
|
|
(float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)),
|
|
(float *) ((char *) v->data + ( ic*nbv1 + i2*nbv2 + i3*nbv3)),
|
|
S);
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_flash_attn_f16(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * q,
|
|
const struct ggml_tensor * k,
|
|
const struct ggml_tensor * v,
|
|
const bool masked,
|
|
struct ggml_tensor * dst) {
|
|
int64_t t0 = ggml_perf_time_us();
|
|
UNUSED(t0);
|
|
|
|
const int neq0 = q->ne[0];
|
|
const int neq1 = q->ne[1];
|
|
const int neq2 = q->ne[2];
|
|
const int neq3 = q->ne[3];
|
|
|
|
const int nek0 = k->ne[0];
|
|
const int nek1 = k->ne[1];
|
|
//const int nek2 = k->ne[2];
|
|
//const int nek3 = k->ne[3];
|
|
|
|
//const int nev0 = v->ne[0];
|
|
const int nev1 = v->ne[1];
|
|
//const int nev2 = v->ne[2];
|
|
//const int nev3 = v->ne[3];
|
|
|
|
const int ne0 = dst->ne[0];
|
|
const int ne1 = dst->ne[1];
|
|
//const int ne2 = dst->ne[2];
|
|
//const int ne3 = dst->ne[3];
|
|
|
|
const int nbk0 = k->nb[0];
|
|
const int nbk1 = k->nb[1];
|
|
const int nbk2 = k->nb[2];
|
|
const int nbk3 = k->nb[3];
|
|
|
|
const int nbq0 = q->nb[0];
|
|
const int nbq1 = q->nb[1];
|
|
const int nbq2 = q->nb[2];
|
|
const int nbq3 = q->nb[3];
|
|
|
|
const int nbv0 = v->nb[0];
|
|
const int nbv1 = v->nb[1];
|
|
const int nbv2 = v->nb[2];
|
|
const int nbv3 = v->nb[3];
|
|
|
|
const int nb0 = dst->nb[0];
|
|
const int nb1 = dst->nb[1];
|
|
const int nb2 = dst->nb[2];
|
|
const int nb3 = dst->nb[3];
|
|
|
|
const int ith = params->ith;
|
|
const int nth = params->nth;
|
|
|
|
const int D = neq0;
|
|
const int N = neq1;
|
|
const int P = nek1 - N;
|
|
const int M = P + N;
|
|
|
|
const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL);
|
|
|
|
GGML_ASSERT(ne0 == D);
|
|
GGML_ASSERT(ne1 == N);
|
|
GGML_ASSERT(P >= 0);
|
|
|
|
GGML_ASSERT(nbq0 == sizeof(ggml_fp16_t));
|
|
GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t));
|
|
GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t));
|
|
|
|
GGML_ASSERT(neq0 == D);
|
|
GGML_ASSERT(nek0 == D);
|
|
GGML_ASSERT(nev1 == D);
|
|
|
|
GGML_ASSERT(neq1 == N);
|
|
GGML_ASSERT(nek1 == N + P);
|
|
GGML_ASSERT(nev1 == D);
|
|
|
|
// dst cannot be transposed or permuted
|
|
GGML_ASSERT(nb0 == sizeof(float));
|
|
GGML_ASSERT(nb0 <= nb1);
|
|
GGML_ASSERT(nb1 <= nb2);
|
|
GGML_ASSERT(nb2 <= nb3);
|
|
|
|
if (params->type == GGML_TASK_INIT) {
|
|
return;
|
|
}
|
|
|
|
if (params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
// parallelize by q rows using ggml_vec_dot_f32
|
|
|
|
// total rows in q
|
|
const int nr = neq1*neq2*neq3;
|
|
|
|
// rows per thread
|
|
const int dr = (nr + nth - 1)/nth;
|
|
|
|
// row range for this thread
|
|
const int ir0 = dr*ith;
|
|
const int ir1 = MIN(ir0 + dr, nr);
|
|
|
|
const float scale = 1.0f/sqrtf(D);
|
|
|
|
//printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale);
|
|
|
|
for (int ir = ir0; ir < ir1; ++ir) {
|
|
// q indices
|
|
const int iq3 = ir/(neq2*neq1);
|
|
const int iq2 = (ir - iq3*neq2*neq1)/neq1;
|
|
const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1);
|
|
|
|
float * S = (float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32);
|
|
|
|
for (int i = M; i < Mup; ++i) {
|
|
S[i] = -INFINITY;
|
|
}
|
|
|
|
if (GGML_VEC_DOT_UNROLL > 2 || nek1 % GGML_VEC_DOT_UNROLL != 0) {
|
|
for (int ic = 0; ic < nek1; ++ic) {
|
|
// k indices
|
|
const int ik3 = iq3;
|
|
const int ik2 = iq2;
|
|
const int ik1 = ic;
|
|
|
|
// S indices
|
|
const int i1 = ik1;
|
|
|
|
ggml_vec_dot_f16(neq0,
|
|
S + i1,
|
|
(ggml_fp16_t *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)),
|
|
(ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)));
|
|
}
|
|
} else {
|
|
for (int ic = 0; ic < nek1; ic += GGML_VEC_DOT_UNROLL) {
|
|
// k indices
|
|
const int ik3 = iq3;
|
|
const int ik2 = iq2;
|
|
const int ik1 = ic;
|
|
|
|
// S indices
|
|
const int i1 = ik1;
|
|
|
|
ggml_vec_dot_f16_unroll(neq0, nbk1,
|
|
S + i1,
|
|
((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)),
|
|
(ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)));
|
|
}
|
|
}
|
|
|
|
// scale
|
|
ggml_vec_scale_f32(nek1, S, scale);
|
|
|
|
if (masked) {
|
|
for (int i = P; i < M; i++) {
|
|
if (i > P + iq1) {
|
|
S[i] = -INFINITY;
|
|
}
|
|
}
|
|
}
|
|
|
|
// softmax
|
|
{
|
|
float max = -INFINITY;
|
|
ggml_vec_max_f32(M, &max, S);
|
|
|
|
ggml_float sum = 0.0;
|
|
{
|
|
#ifdef GGML_SOFT_MAX_ACCELERATE
|
|
max = -max;
|
|
vDSP_vsadd(S, 1, &max, S, 1, Mup);
|
|
vvexpf(S, S, &Mup);
|
|
ggml_vec_sum_f32(Mup, &sum, S);
|
|
#else
|
|
uint16_t scvt[GGML_SOFT_MAX_UNROLL];
|
|
ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 };
|
|
|
|
for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) {
|
|
float * SS = S + i;
|
|
|
|
for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) {
|
|
if (SS[j] == -INFINITY) {
|
|
SS[j] = 0.0f;
|
|
} else {
|
|
ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max);
|
|
memcpy(&scvt[j], &s, sizeof(uint16_t));
|
|
const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]);
|
|
sump[j] += (ggml_float)val;
|
|
SS[j] = val;
|
|
}
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) {
|
|
sum += sump[i];
|
|
}
|
|
#endif
|
|
}
|
|
|
|
assert(sum > 0.0);
|
|
|
|
sum = 1.0/sum;
|
|
ggml_vec_scale_f32(M, S, sum);
|
|
|
|
#ifndef NDEBUG
|
|
for (int i = 0; i < M; ++i) {
|
|
assert(!isnan(S[i]));
|
|
assert(!isinf(S[i]));
|
|
}
|
|
#endif
|
|
}
|
|
|
|
ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32) + Mup);
|
|
|
|
for (int i = 0; i < M; i++) {
|
|
S16[i] = GGML_FP32_TO_FP16(S[i]);
|
|
}
|
|
|
|
if (GGML_VEC_DOT_UNROLL == 1 || (nev1 % GGML_VEC_DOT_UNROLL != 0)) {
|
|
for (int ic = 0; ic < nev1; ++ic) {
|
|
// dst indices
|
|
const int i1 = iq1;
|
|
const int i2 = iq2;
|
|
const int i3 = iq3;
|
|
|
|
ggml_vec_dot_f16(nek1,
|
|
(float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)),
|
|
(ggml_fp16_t *) ((char *) v->data + ( ic*nbv1 + i2*nbv2 + i3*nbv3)),
|
|
S16);
|
|
}
|
|
} else {
|
|
for (int ic = 0; ic < nev1; ic += GGML_VEC_DOT_UNROLL) {
|
|
// dst indices
|
|
const int i1 = iq1;
|
|
const int i2 = iq2;
|
|
const int i3 = iq3;
|
|
|
|
ggml_vec_dot_f16_unroll(nek1, nbv1,
|
|
(float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)),
|
|
((char *) v->data + ( ic*nbv1 + i2*nbv2 + i3*nbv3)),
|
|
S16);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_flash_attn(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * q,
|
|
const struct ggml_tensor * k,
|
|
const struct ggml_tensor * v,
|
|
const bool masked,
|
|
struct ggml_tensor * dst) {
|
|
switch (q->type) {
|
|
case GGML_TYPE_F16:
|
|
{
|
|
ggml_compute_forward_flash_attn_f16(params, q, k, v, masked, dst);
|
|
} break;
|
|
case GGML_TYPE_F32:
|
|
{
|
|
ggml_compute_forward_flash_attn_f32(params, q, k, v, masked, dst);
|
|
} break;
|
|
case GGML_TYPE_Q4_0:
|
|
case GGML_TYPE_Q4_1:
|
|
case GGML_TYPE_I8:
|
|
case GGML_TYPE_I16:
|
|
case GGML_TYPE_I32:
|
|
case GGML_TYPE_COUNT:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
// ggml_compute_forward_flash_ff
|
|
|
|
static void ggml_compute_forward_flash_ff_f16(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * a, // F16
|
|
const struct ggml_tensor * b0, // F16 fc_w
|
|
const struct ggml_tensor * b1, // F32 fc_b
|
|
const struct ggml_tensor * c0, // F16 proj_w
|
|
const struct ggml_tensor * c1, // F32 proj_b
|
|
struct ggml_tensor * dst) {
|
|
int64_t t0 = ggml_perf_time_us();
|
|
UNUSED(t0);
|
|
|
|
const int nea0 = a->ne[0];
|
|
const int nea1 = a->ne[1];
|
|
const int nea2 = a->ne[2];
|
|
const int nea3 = a->ne[3];
|
|
|
|
const int neb00 = b0->ne[0];
|
|
const int neb01 = b0->ne[1];
|
|
//const int neb02 = b0->ne[2];
|
|
//const int neb03 = b0->ne[3];
|
|
|
|
const int neb10 = b1->ne[0];
|
|
const int neb11 = b1->ne[1];
|
|
//const int neb12 = b1->ne[2];
|
|
//const int neb13 = b1->ne[3];
|
|
|
|
const int nec00 = c0->ne[0];
|
|
const int nec01 = c0->ne[1];
|
|
//const int nec02 = c0->ne[2];
|
|
//const int nec03 = c0->ne[3];
|
|
|
|
const int nec10 = c1->ne[0];
|
|
const int nec11 = c1->ne[1];
|
|
//const int nec12 = c1->ne[2];
|
|
//const int nec13 = c1->ne[3];
|
|
|
|
const int ne0 = dst->ne[0];
|
|
const int ne1 = dst->ne[1];
|
|
const int ne2 = dst->ne[2];
|
|
//const int ne3 = dst->ne[3];
|
|
|
|
const int nba0 = a->nb[0];
|
|
const int nba1 = a->nb[1];
|
|
const int nba2 = a->nb[2];
|
|
const int nba3 = a->nb[3];
|
|
|
|
const int nbb00 = b0->nb[0];
|
|
const int nbb01 = b0->nb[1];
|
|
const int nbb02 = b0->nb[2];
|
|
const int nbb03 = b0->nb[3];
|
|
|
|
const int nbb10 = b1->nb[0];
|
|
//const int nbb11 = b1->nb[1];
|
|
//const int nbb12 = b1->nb[2];
|
|
//const int nbb13 = b1->nb[3];
|
|
|
|
const int nbc00 = c0->nb[0];
|
|
const int nbc01 = c0->nb[1];
|
|
const int nbc02 = c0->nb[2];
|
|
const int nbc03 = c0->nb[3];
|
|
|
|
const int nbc10 = c1->nb[0];
|
|
//const int nbc11 = c1->nb[1];
|
|
//const int nbc12 = c1->nb[2];
|
|
//const int nbc13 = c1->nb[3];
|
|
|
|
const int nb0 = dst->nb[0];
|
|
const int nb1 = dst->nb[1];
|
|
const int nb2 = dst->nb[2];
|
|
const int nb3 = dst->nb[3];
|
|
|
|
const int ith = params->ith;
|
|
const int nth = params->nth;
|
|
|
|
const int D = nea0;
|
|
//const int N = nea1;
|
|
const int M = neb01;
|
|
|
|
GGML_ASSERT(ne0 == nea0);
|
|
GGML_ASSERT(ne1 == nea1);
|
|
GGML_ASSERT(ne2 == nea2);
|
|
|
|
GGML_ASSERT(nba0 == sizeof(ggml_fp16_t));
|
|
GGML_ASSERT(nbb00 == sizeof(ggml_fp16_t));
|
|
GGML_ASSERT(nbb10 == sizeof(float));
|
|
GGML_ASSERT(nbc00 == sizeof(ggml_fp16_t));
|
|
GGML_ASSERT(nbc10 == sizeof(float));
|
|
|
|
GGML_ASSERT(neb00 == D);
|
|
GGML_ASSERT(neb01 == M);
|
|
GGML_ASSERT(neb10 == M);
|
|
GGML_ASSERT(neb11 == 1);
|
|
|
|
GGML_ASSERT(nec00 == M);
|
|
GGML_ASSERT(nec01 == D);
|
|
GGML_ASSERT(nec10 == D);
|
|
GGML_ASSERT(nec11 == 1);
|
|
|
|
// dst cannot be transposed or permuted
|
|
GGML_ASSERT(nb0 == sizeof(float));
|
|
GGML_ASSERT(nb0 <= nb1);
|
|
GGML_ASSERT(nb1 <= nb2);
|
|
GGML_ASSERT(nb2 <= nb3);
|
|
|
|
if (params->type == GGML_TASK_INIT) {
|
|
return;
|
|
}
|
|
|
|
if (params->type == GGML_TASK_FINALIZE) {
|
|
return;
|
|
}
|
|
|
|
// parallelize by a rows using ggml_vec_dot_f32
|
|
|
|
// total rows in a
|
|
const int nr = nea1*nea2*nea3;
|
|
|
|
// rows per thread
|
|
const int dr = (nr + nth - 1)/nth;
|
|
|
|
// row range for this thread
|
|
const int ir0 = dr*ith;
|
|
const int ir1 = MIN(ir0 + dr, nr);
|
|
|
|
for (int ir = ir0; ir < ir1; ++ir) {
|
|
// a indices
|
|
const int ia3 = ir/(nea2*nea1);
|
|
const int ia2 = (ir - ia3*nea2*nea1)/nea1;
|
|
const int ia1 = (ir - ia3*nea2*nea1 - ia2*nea1);
|
|
|
|
float * S = (float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32);
|
|
|
|
for (int ic = 0; ic < neb01; ++ic) {
|
|
// b0 indices
|
|
const int ib03 = ia3;
|
|
const int ib02 = ia2;
|
|
const int ib01 = ic;
|
|
|
|
// S indices
|
|
const int i1 = ib01;
|
|
|
|
ggml_vec_dot_f16(nea0,
|
|
S + i1,
|
|
(ggml_fp16_t *) ((char *) b0->data + (ib01*nbb01 + ib02*nbb02 + ib03*nbb03)),
|
|
(ggml_fp16_t *) ((char *) a->data + ( ia1*nba1 + ia2*nba2 + ia3*nba3)));
|
|
}
|
|
|
|
ggml_vec_add_f32(neb01, S, S, (float *) b1->data);
|
|
//ggml_vec_gelu_f32(neb01, S, S);
|
|
|
|
ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32) + M);
|
|
|
|
for (int i = 0; i < M; i++) {
|
|
S16[i] = GGML_FP32_TO_FP16(S[i]);
|
|
}
|
|
|
|
ggml_vec_gelu_f16(neb01, S16, S16);
|
|
|
|
{
|
|
// dst indices
|
|
const int i1 = ia1;
|
|
const int i2 = ia2;
|
|
const int i3 = ia3;
|
|
|
|
for (int ic = 0; ic < nec01; ++ic) {
|
|
|
|
ggml_vec_dot_f16(neb01,
|
|
(float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)),
|
|
(ggml_fp16_t *) ((char *) c0->data + ( ic*nbc01 + i2*nbc02 + i3*nbc03)),
|
|
S16);
|
|
}
|
|
|
|
ggml_vec_add_f32(nec01,
|
|
(float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)),
|
|
(float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)),
|
|
(float *) c1->data);
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_compute_forward_flash_ff(
|
|
const struct ggml_compute_params * params,
|
|
const struct ggml_tensor * a,
|
|
const struct ggml_tensor * b0,
|
|
const struct ggml_tensor * b1,
|
|
const struct ggml_tensor * c0,
|
|
const struct ggml_tensor * c1,
|
|
struct ggml_tensor * dst) {
|
|
switch (b0->type) {
|
|
case GGML_TYPE_F16:
|
|
{
|
|
ggml_compute_forward_flash_ff_f16(params, a, b0, b1, c0, c1, dst);
|
|
} break;
|
|
case GGML_TYPE_F32:
|
|
{
|
|
GGML_ASSERT(false); // TODO
|
|
} break;
|
|
case GGML_TYPE_Q4_0:
|
|
case GGML_TYPE_Q4_1:
|
|
case GGML_TYPE_I8:
|
|
case GGML_TYPE_I16:
|
|
case GGML_TYPE_I32:
|
|
case GGML_TYPE_COUNT:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
/////////////////////////////////
|
|
|
|
static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
|
|
GGML_ASSERT(params);
|
|
|
|
switch (tensor->op) {
|
|
case GGML_OP_DUP:
|
|
{
|
|
ggml_compute_forward_dup(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_ADD:
|
|
{
|
|
ggml_compute_forward_add(params, tensor->src0, tensor->src1, tensor);
|
|
} break;
|
|
case GGML_OP_SUB:
|
|
{
|
|
ggml_compute_forward_sub(params, tensor->src0, tensor->src1, tensor);
|
|
} break;
|
|
case GGML_OP_MUL:
|
|
{
|
|
ggml_compute_forward_mul(params, tensor->src0, tensor->src1, tensor);
|
|
} break;
|
|
case GGML_OP_DIV:
|
|
{
|
|
ggml_compute_forward_div(params, tensor->src0, tensor->src1, tensor);
|
|
} break;
|
|
case GGML_OP_SQR:
|
|
{
|
|
ggml_compute_forward_sqr(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_SQRT:
|
|
{
|
|
ggml_compute_forward_sqrt(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_SUM:
|
|
{
|
|
ggml_compute_forward_sum(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_MEAN:
|
|
{
|
|
ggml_compute_forward_mean(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_REPEAT:
|
|
{
|
|
ggml_compute_forward_repeat(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_ABS:
|
|
{
|
|
ggml_compute_forward_abs(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_SGN:
|
|
{
|
|
ggml_compute_forward_sgn(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_NEG:
|
|
{
|
|
ggml_compute_forward_neg(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_STEP:
|
|
{
|
|
ggml_compute_forward_step(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_RELU:
|
|
{
|
|
ggml_compute_forward_relu(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_GELU:
|
|
{
|
|
ggml_compute_forward_gelu(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_SILU:
|
|
{
|
|
ggml_compute_forward_silu(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_NORM:
|
|
{
|
|
ggml_compute_forward_norm(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_RMS_NORM:
|
|
{
|
|
ggml_compute_forward_rms_norm(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_MUL_MAT:
|
|
{
|
|
ggml_compute_forward_mul_mat(params, tensor->src0, tensor->src1, tensor);
|
|
} break;
|
|
case GGML_OP_SCALE:
|
|
{
|
|
ggml_compute_forward_scale(params, tensor->src0, tensor->src1, tensor);
|
|
} break;
|
|
case GGML_OP_CPY:
|
|
{
|
|
ggml_compute_forward_cpy(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_RESHAPE:
|
|
{
|
|
ggml_compute_forward_reshape(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_VIEW:
|
|
{
|
|
ggml_compute_forward_view(params, tensor->src0);
|
|
} break;
|
|
case GGML_OP_PERMUTE:
|
|
{
|
|
ggml_compute_forward_permute(params, tensor->src0);
|
|
} break;
|
|
case GGML_OP_TRANSPOSE:
|
|
{
|
|
ggml_compute_forward_transpose(params, tensor->src0);
|
|
} break;
|
|
case GGML_OP_GET_ROWS:
|
|
{
|
|
ggml_compute_forward_get_rows(params, tensor->src0, tensor->src1, tensor);
|
|
} break;
|
|
case GGML_OP_DIAG_MASK_INF:
|
|
{
|
|
ggml_compute_forward_diag_mask_inf(params, tensor->src0, tensor->src1, tensor);
|
|
} break;
|
|
case GGML_OP_SOFT_MAX:
|
|
{
|
|
ggml_compute_forward_soft_max(params, tensor->src0, tensor);
|
|
} break;
|
|
case GGML_OP_ROPE:
|
|
{
|
|
ggml_compute_forward_rope(params, tensor->src0, tensor->src1, tensor);
|
|
} break;
|
|
case GGML_OP_CONV_1D_1S:
|
|
{
|
|
ggml_compute_forward_conv_1d_1s(params, tensor->src0, tensor->src1, tensor);
|
|
} break;
|
|
case GGML_OP_CONV_1D_2S:
|
|
{
|
|
ggml_compute_forward_conv_1d_2s(params, tensor->src0, tensor->src1, tensor);
|
|
} break;
|
|
case GGML_OP_FLASH_ATTN:
|
|
{
|
|
int32_t t = ggml_get_i32_1d(tensor->opt[1], 0);
|
|
GGML_ASSERT(t == 0 || t == 1);
|
|
bool masked = t != 0;
|
|
ggml_compute_forward_flash_attn(params, tensor->src0, tensor->src1, tensor->opt[0], masked, tensor);
|
|
} break;
|
|
case GGML_OP_FLASH_FF:
|
|
{
|
|
ggml_compute_forward_flash_ff(params, tensor->src0, tensor->src1, tensor->opt[0], tensor->opt[1], tensor->opt[2], tensor);
|
|
} break;
|
|
case GGML_OP_NONE:
|
|
{
|
|
// nop
|
|
} break;
|
|
case GGML_OP_COUNT:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, bool inplace) {
|
|
struct ggml_tensor * src0 = tensor->src0;
|
|
struct ggml_tensor * src1 = tensor->src1;
|
|
|
|
switch (tensor->op) {
|
|
case GGML_OP_DUP:
|
|
{
|
|
if (src0->grad) {
|
|
src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace);
|
|
}
|
|
} break;
|
|
case GGML_OP_ADD:
|
|
{
|
|
if (src0->grad) {
|
|
src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace);
|
|
}
|
|
if (src1->grad) {
|
|
src1->grad = ggml_add_impl(ctx, src1->grad, tensor->grad, inplace);
|
|
}
|
|
} break;
|
|
case GGML_OP_SUB:
|
|
{
|
|
if (src0->grad) {
|
|
src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace);
|
|
}
|
|
if (src1->grad) {
|
|
src1->grad = ggml_sub_impl(ctx, src1->grad, tensor->grad, inplace);
|
|
}
|
|
} break;
|
|
case GGML_OP_MUL:
|
|
{
|
|
if (src0->grad) {
|
|
src0->grad =
|
|
ggml_add_impl(ctx,
|
|
src0->grad,
|
|
ggml_mul(ctx, src1, tensor->grad),
|
|
inplace);
|
|
}
|
|
if (src1->grad) {
|
|
src1->grad =
|
|
ggml_add_impl(ctx,
|
|
src1->grad,
|
|
ggml_mul(ctx, src0, tensor->grad),
|
|
inplace);
|
|
}
|
|
} break;
|
|
case GGML_OP_DIV:
|
|
{
|
|
if (src0->grad) {
|
|
src0->grad =
|
|
ggml_add_impl(ctx,
|
|
src0->grad,
|
|
ggml_div(ctx, tensor->grad, src1),
|
|
inplace);
|
|
}
|
|
if (src1->grad) {
|
|
src1->grad =
|
|
ggml_sub_impl(ctx,
|
|
src1->grad,
|
|
ggml_mul(ctx,
|
|
tensor->grad,
|
|
ggml_div(ctx, tensor, src1)),
|
|
inplace);
|
|
}
|
|
} break;
|
|
case GGML_OP_SQR:
|
|
{
|
|
if (src0->grad) {
|
|
src0->grad =
|
|
ggml_add_impl(ctx,
|
|
src0->grad,
|
|
ggml_mul(ctx,
|
|
ggml_mul(ctx, src0, tensor->grad),
|
|
ggml_repeat(ctx, ggml_new_f32(ctx, 2.0f), src0)),
|
|
inplace);
|
|
}
|
|
} break;
|
|
case GGML_OP_SQRT:
|
|
{
|
|
if (src0->grad) {
|
|
src0->grad =
|
|
ggml_add_impl(ctx,
|
|
src0->grad,
|
|
ggml_div(ctx,
|
|
ggml_repeat(ctx, ggml_new_f32(ctx, 0.5f), tensor),
|
|
tensor),
|
|
inplace);
|
|
}
|
|
} break;
|
|
case GGML_OP_SUM:
|
|
{
|
|
if (src0->grad) {
|
|
src0->grad =
|
|
ggml_add_impl(ctx,
|
|
src0->grad,
|
|
ggml_repeat(ctx, tensor->grad, src0->grad),
|
|
inplace);
|
|
}
|
|
} break;
|
|
case GGML_OP_MEAN:
|
|
{
|
|
GGML_ASSERT(false); // TODO: implement
|
|
} break;
|
|
case GGML_OP_REPEAT:
|
|
{
|
|
if (src0->grad) {
|
|
src0->grad =
|
|
ggml_add_impl(ctx,
|
|
src0->grad,
|
|
ggml_sum(ctx, tensor->grad),
|
|
inplace);
|
|
}
|
|
} break;
|
|
case GGML_OP_ABS:
|
|
{
|
|
if (src0->grad) {
|
|
src0->grad =
|
|
ggml_add_impl(ctx,
|
|
src0->grad,
|
|
ggml_mul(ctx,
|
|
ggml_sgn(ctx, src0),
|
|
tensor->grad),
|
|
inplace);
|
|
}
|
|
} break;
|
|
case GGML_OP_SGN:
|
|
{
|
|
if (src0->grad) {
|
|
// noop
|
|
}
|
|
} break;
|
|
case GGML_OP_NEG:
|
|
{
|
|
if (src0->grad) {
|
|
src0->grad = ggml_sub_impl(ctx, src0->grad, tensor->grad, inplace);
|
|
}
|
|
} break;
|
|
case GGML_OP_STEP:
|
|
{
|
|
if (src0->grad) {
|
|
// noop
|
|
}
|
|
} break;
|
|
case GGML_OP_RELU:
|
|
{
|
|
if (src0->grad) {
|
|
src0->grad = ggml_sub_impl(ctx,
|
|
src0->grad,
|
|
ggml_mul(ctx,
|
|
ggml_step(ctx, src0),
|
|
tensor->grad),
|
|
inplace);
|
|
}
|
|
} break;
|
|
case GGML_OP_GELU:
|
|
{
|
|
GGML_ASSERT(false); // TODO: not implemented
|
|
} break;
|
|
case GGML_OP_SILU:
|
|
{
|
|
GGML_ASSERT(false); // TODO: not implemented
|
|
} break;
|
|
case GGML_OP_NORM:
|
|
{
|
|
GGML_ASSERT(false); // TODO: not implemented
|
|
} break;
|
|
case GGML_OP_RMS_NORM:
|
|
{
|
|
GGML_ASSERT(false); // TODO: not implemented
|
|
} break;
|
|
case GGML_OP_MUL_MAT:
|
|
{
|
|
if (src0->grad) {
|
|
// TODO: this requires outer product - ggml_out_prod(ctx, src1, tensor->grad);
|
|
GGML_ASSERT(false);
|
|
}
|
|
if (src1->grad) {
|
|
src1->grad =
|
|
ggml_add_impl(ctx,
|
|
src1->grad,
|
|
// TODO: fix transpose, the node will break the graph connections
|
|
ggml_mul_mat(ctx, ggml_transpose(ctx, src0), tensor->grad),
|
|
inplace);
|
|
}
|
|
} break;
|
|
case GGML_OP_SCALE:
|
|
{
|
|
GGML_ASSERT(false); // TODO: not implemented
|
|
} break;
|
|
case GGML_OP_CPY:
|
|
{
|
|
GGML_ASSERT(false); // TODO: not implemented
|
|
} break;
|
|
case GGML_OP_RESHAPE:
|
|
{
|
|
GGML_ASSERT(false); // TODO: not implemented
|
|
} break;
|
|
case GGML_OP_VIEW:
|
|
{
|
|
GGML_ASSERT(false); // not supported
|
|
} break;
|
|
case GGML_OP_PERMUTE:
|
|
{
|
|
GGML_ASSERT(false); // TODO: not implemented
|
|
} break;
|
|
case GGML_OP_TRANSPOSE:
|
|
{
|
|
GGML_ASSERT(false); // TODO: not implemented
|
|
} break;
|
|
case GGML_OP_GET_ROWS:
|
|
{
|
|
GGML_ASSERT(false); // TODO: not implemented
|
|
} break;
|
|
case GGML_OP_DIAG_MASK_INF:
|
|
{
|
|
GGML_ASSERT(false); // TODO: not implemented
|
|
} break;
|
|
case GGML_OP_SOFT_MAX:
|
|
{
|
|
GGML_ASSERT(false); // TODO: not implemented
|
|
} break;
|
|
case GGML_OP_ROPE:
|
|
{
|
|
GGML_ASSERT(false); // TODO: not implemented
|
|
} break;
|
|
case GGML_OP_CONV_1D_1S:
|
|
{
|
|
GGML_ASSERT(false); // TODO: not implemented
|
|
} break;
|
|
case GGML_OP_CONV_1D_2S:
|
|
{
|
|
GGML_ASSERT(false); // TODO: not implemented
|
|
} break;
|
|
case GGML_OP_FLASH_ATTN:
|
|
{
|
|
GGML_ASSERT(false); // not supported
|
|
} break;
|
|
case GGML_OP_FLASH_FF:
|
|
{
|
|
GGML_ASSERT(false); // not supported
|
|
} break;
|
|
case GGML_OP_NONE:
|
|
{
|
|
// nop
|
|
} break;
|
|
case GGML_OP_COUNT:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * node) {
|
|
if (node->grad == NULL) {
|
|
// this usually happens when we generate intermediate nodes from constants in the backward pass
|
|
// it can also happen during forward pass, if the user performs computations with constants
|
|
if (node->op != GGML_OP_NONE) {
|
|
//GGML_PRINT_DEBUG("%s: warning: node %p has no grad, but op %d\n", __func__, (void *) node, node->op);
|
|
}
|
|
}
|
|
|
|
// check if already visited
|
|
for (int i = 0; i < cgraph->n_nodes; i++) {
|
|
if (cgraph->nodes[i] == node) {
|
|
return;
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < cgraph->n_leafs; i++) {
|
|
if (cgraph->leafs[i] == node) {
|
|
return;
|
|
}
|
|
}
|
|
|
|
if (node->src0) {
|
|
ggml_visit_parents(cgraph, node->src0);
|
|
}
|
|
|
|
if (node->src1) {
|
|
ggml_visit_parents(cgraph, node->src1);
|
|
}
|
|
|
|
for (int i = 0; i < GGML_MAX_OPT; ++i) {
|
|
if (node->opt[i]) {
|
|
ggml_visit_parents(cgraph, node->opt[i]);
|
|
}
|
|
}
|
|
|
|
if (node->op == GGML_OP_NONE && node->grad == NULL) {
|
|
// reached a leaf node, not part of the gradient graph (e.g. a constant)
|
|
GGML_ASSERT(cgraph->n_leafs < GGML_MAX_NODES);
|
|
|
|
cgraph->leafs[cgraph->n_leafs] = node;
|
|
cgraph->n_leafs++;
|
|
} else {
|
|
GGML_ASSERT(cgraph->n_nodes < GGML_MAX_NODES);
|
|
|
|
cgraph->nodes[cgraph->n_nodes] = node;
|
|
cgraph->grads[cgraph->n_nodes] = node->grad;
|
|
cgraph->n_nodes++;
|
|
}
|
|
}
|
|
|
|
static void ggml_build_forward_impl(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor, bool expand) {
|
|
if (!expand) {
|
|
cgraph->n_nodes = 0;
|
|
cgraph->n_leafs = 0;
|
|
}
|
|
|
|
const int n0 = cgraph->n_nodes;
|
|
UNUSED(n0);
|
|
|
|
ggml_visit_parents(cgraph, tensor);
|
|
|
|
const int n_new = cgraph->n_nodes - n0;
|
|
GGML_PRINT_DEBUG("%s: visited %d new nodes\n", __func__, n_new);
|
|
|
|
if (n_new > 0) {
|
|
// the last added node should always be starting point
|
|
GGML_ASSERT(cgraph->nodes[cgraph->n_nodes - 1] == tensor);
|
|
}
|
|
}
|
|
|
|
void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor) {
|
|
ggml_build_forward_impl(cgraph, tensor, true);
|
|
}
|
|
|
|
struct ggml_cgraph ggml_build_forward(struct ggml_tensor * tensor) {
|
|
struct ggml_cgraph result = {
|
|
/*.n_nodes =*/ 0,
|
|
/*.n_leafs =*/ 0,
|
|
/*.n_threads =*/ 0,
|
|
/*.work_size =*/ 0,
|
|
/*.work =*/ NULL,
|
|
/*.nodes =*/ { NULL },
|
|
/*.grads =*/ { NULL },
|
|
/*.leafs =*/ { NULL },
|
|
/*.perf_runs =*/ 0,
|
|
/*.perf_cycles =*/ 0,
|
|
/*.perf_time_us =*/ 0,
|
|
};
|
|
|
|
ggml_build_forward_impl(&result, tensor, false);
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep) {
|
|
struct ggml_cgraph result = *gf;
|
|
|
|
GGML_ASSERT(gf->n_nodes > 0);
|
|
|
|
// if we are keeping the gradient graph, we have to detach the gradient nodes from the original graph
|
|
if (keep) {
|
|
for (int i = 0; i < gf->n_nodes; i++) {
|
|
struct ggml_tensor * node = gf->nodes[i];
|
|
|
|
if (node->grad) {
|
|
node->grad = ggml_dup_tensor(ctx, node);
|
|
gf->grads[i] = node->grad;
|
|
}
|
|
}
|
|
}
|
|
|
|
for (int i = gf->n_nodes - 1; i >= 0; i--) {
|
|
struct ggml_tensor * node = gf->nodes[i];
|
|
|
|
// because we detached the grad nodes from the original graph, we can afford inplace operations
|
|
if (node->grad) {
|
|
ggml_compute_backward(ctx, node, keep);
|
|
}
|
|
}
|
|
|
|
for (int i = gf->n_nodes - 1; i >= 0; i--) {
|
|
struct ggml_tensor * node = gf->nodes[i];
|
|
|
|
if (node->is_param) {
|
|
GGML_PRINT_DEBUG("%s: found root node %p\n", __func__, (void *) node);
|
|
ggml_build_forward_impl(&result, node->grad, true);
|
|
}
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
//
|
|
// thread data
|
|
//
|
|
// synchronization is done via busy loops
|
|
// I tried using spin locks, but not sure how to use them correctly - the things I tried were slower than busy loops
|
|
//
|
|
|
|
#ifdef __APPLE__
|
|
|
|
//#include <os/lock.h>
|
|
//
|
|
//typedef os_unfair_lock ggml_lock_t;
|
|
//
|
|
//#define ggml_lock_init(x) UNUSED(x)
|
|
//#define ggml_lock_destroy(x) UNUSED(x)
|
|
//#define ggml_lock_lock os_unfair_lock_lock
|
|
//#define ggml_lock_unlock os_unfair_lock_unlock
|
|
//
|
|
//#define GGML_LOCK_INITIALIZER OS_UNFAIR_LOCK_INIT
|
|
|
|
typedef int ggml_lock_t;
|
|
|
|
#define ggml_lock_init(x) UNUSED(x)
|
|
#define ggml_lock_destroy(x) UNUSED(x)
|
|
#define ggml_lock_lock(x) UNUSED(x)
|
|
#define ggml_lock_unlock(x) UNUSED(x)
|
|
|
|
#define GGML_LOCK_INITIALIZER 0
|
|
|
|
typedef pthread_t ggml_thread_t;
|
|
|
|
#define ggml_thread_create pthread_create
|
|
#define ggml_thread_join pthread_join
|
|
|
|
#else
|
|
|
|
//typedef pthread_spinlock_t ggml_lock_t;
|
|
|
|
//#define ggml_lock_init(x) pthread_spin_init(x, PTHREAD_PROCESS_PRIVATE)
|
|
//#define ggml_lock_destroy pthread_spin_destroy
|
|
//#define ggml_lock_lock pthread_spin_lock
|
|
//#define ggml_lock_unlock pthread_spin_unlock
|
|
|
|
typedef int ggml_lock_t;
|
|
|
|
#define ggml_lock_init(x) UNUSED(x)
|
|
#define ggml_lock_destroy(x) UNUSED(x)
|
|
#define ggml_lock_lock(x) UNUSED(x)
|
|
#define ggml_lock_unlock(x) UNUSED(x)
|
|
|
|
#define GGML_LOCK_INITIALIZER 0
|
|
|
|
typedef pthread_t ggml_thread_t;
|
|
|
|
#define ggml_thread_create pthread_create
|
|
#define ggml_thread_join pthread_join
|
|
|
|
#endif
|
|
|
|
struct ggml_compute_state_shared {
|
|
ggml_lock_t spin;
|
|
|
|
int n_threads;
|
|
|
|
// synchronization primitives
|
|
atomic_int n_ready;
|
|
atomic_bool has_work;
|
|
atomic_bool stop; // stop all threads
|
|
};
|
|
|
|
struct ggml_compute_state {
|
|
ggml_thread_t thrd;
|
|
|
|
struct ggml_compute_params params;
|
|
struct ggml_tensor * node;
|
|
|
|
struct ggml_compute_state_shared * shared;
|
|
};
|
|
|
|
static thread_ret_t ggml_graph_compute_thread(void * data) {
|
|
struct ggml_compute_state * state = (struct ggml_compute_state *) data;
|
|
|
|
const int n_threads = state->shared->n_threads;
|
|
|
|
while (true) {
|
|
if (atomic_fetch_add(&state->shared->n_ready, 1) == n_threads - 1) {
|
|
atomic_store(&state->shared->has_work, false);
|
|
} else {
|
|
while (atomic_load(&state->shared->has_work)) {
|
|
if (atomic_load(&state->shared->stop)) {
|
|
return 0;
|
|
}
|
|
ggml_lock_lock (&state->shared->spin);
|
|
ggml_lock_unlock(&state->shared->spin);
|
|
}
|
|
}
|
|
|
|
atomic_fetch_sub(&state->shared->n_ready, 1);
|
|
|
|
// wait for work
|
|
while (!atomic_load(&state->shared->has_work)) {
|
|
if (atomic_load(&state->shared->stop)) {
|
|
return 0;
|
|
}
|
|
ggml_lock_lock (&state->shared->spin);
|
|
ggml_lock_unlock(&state->shared->spin);
|
|
}
|
|
|
|
// check if we should stop
|
|
if (atomic_load(&state->shared->stop)) {
|
|
break;
|
|
}
|
|
|
|
if (state->node) {
|
|
if (state->params.ith < state->params.nth) {
|
|
ggml_compute_forward(&state->params, state->node);
|
|
}
|
|
|
|
state->node = NULL;
|
|
} else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) {
|
|
const int n_threads = cgraph->n_threads;
|
|
|
|
struct ggml_compute_state_shared state_shared = {
|
|
/*.spin =*/ GGML_LOCK_INITIALIZER,
|
|
/*.n_threads =*/ n_threads,
|
|
/*.n_ready =*/ 0,
|
|
/*.has_work =*/ false,
|
|
/*.stop =*/ false,
|
|
};
|
|
struct ggml_compute_state * workers = n_threads > 1 ? alloca(sizeof(struct ggml_compute_state)*(n_threads - 1)) : NULL;
|
|
|
|
// create thread pool
|
|
if (n_threads > 1) {
|
|
ggml_lock_init(&state_shared.spin);
|
|
|
|
atomic_store(&state_shared.has_work, true);
|
|
|
|
for (int j = 0; j < n_threads - 1; j++) {
|
|
workers[j] = (struct ggml_compute_state) {
|
|
.thrd = 0,
|
|
.params = {
|
|
.type = GGML_TASK_COMPUTE,
|
|
.ith = j + 1,
|
|
.nth = n_threads,
|
|
.wsize = cgraph->work ? ggml_nbytes(cgraph->work) : 0,
|
|
.wdata = cgraph->work ? cgraph->work->data : NULL,
|
|
},
|
|
.node = NULL,
|
|
.shared = &state_shared,
|
|
};
|
|
|
|
int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]);
|
|
GGML_ASSERT(rc == 0);
|
|
UNUSED(rc);
|
|
}
|
|
}
|
|
|
|
// initialize tasks + work buffer
|
|
{
|
|
size_t work_size = 0;
|
|
|
|
// thread scheduling for the different operations
|
|
for (int i = 0; i < cgraph->n_nodes; i++) {
|
|
struct ggml_tensor * node = cgraph->nodes[i];
|
|
|
|
switch (node->op) {
|
|
case GGML_OP_DUP:
|
|
{
|
|
node->n_tasks = 1;
|
|
} break;
|
|
case GGML_OP_ADD:
|
|
{
|
|
node->n_tasks = n_threads;
|
|
} break;
|
|
case GGML_OP_SUB:
|
|
case GGML_OP_MUL:
|
|
case GGML_OP_DIV:
|
|
case GGML_OP_SQR:
|
|
case GGML_OP_SQRT:
|
|
case GGML_OP_SUM:
|
|
case GGML_OP_MEAN:
|
|
case GGML_OP_REPEAT:
|
|
case GGML_OP_ABS:
|
|
case GGML_OP_SGN:
|
|
case GGML_OP_NEG:
|
|
case GGML_OP_STEP:
|
|
case GGML_OP_RELU:
|
|
{
|
|
node->n_tasks = 1;
|
|
} break;
|
|
case GGML_OP_GELU:
|
|
{
|
|
node->n_tasks = n_threads;
|
|
} break;
|
|
case GGML_OP_SILU:
|
|
{
|
|
node->n_tasks = n_threads;
|
|
} break;
|
|
case GGML_OP_NORM:
|
|
case GGML_OP_RMS_NORM:
|
|
{
|
|
node->n_tasks = n_threads;
|
|
} break;
|
|
case GGML_OP_MUL_MAT:
|
|
{
|
|
node->n_tasks = n_threads;
|
|
|
|
// TODO: use different scheduling for different matrix sizes
|
|
//const int nr0 = ggml_nrows(node->src0);
|
|
//const int nr1 = ggml_nrows(node->src1);
|
|
|
|
//node->n_tasks = MIN(n_threads, MAX(1, nr0/128));
|
|
//printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks = %d\n", nr0, nr1, nr0*nr1, node->n_tasks);
|
|
|
|
size_t cur = 0;
|
|
|
|
if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) {
|
|
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
|
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
|
|
node->n_tasks = 1; // TODO: this actually is doing nothing
|
|
// the threads are still spinning
|
|
cur = 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 {
|
|
cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
|
|
}
|
|
#else
|
|
cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
|
|
#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 (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];
|
|
}
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
work_size = MAX(work_size, cur);
|
|
} break;
|
|
case GGML_OP_SCALE:
|
|
{
|
|
node->n_tasks = n_threads;
|
|
} break;
|
|
case GGML_OP_CPY:
|
|
case GGML_OP_RESHAPE:
|
|
case GGML_OP_VIEW:
|
|
case GGML_OP_PERMUTE:
|
|
case GGML_OP_TRANSPOSE:
|
|
case GGML_OP_GET_ROWS:
|
|
case GGML_OP_DIAG_MASK_INF:
|
|
{
|
|
node->n_tasks = 1;
|
|
} break;
|
|
case GGML_OP_SOFT_MAX:
|
|
{
|
|
node->n_tasks = n_threads;
|
|
} break;
|
|
case GGML_OP_ROPE:
|
|
{
|
|
node->n_tasks = 1;
|
|
} break;
|
|
case GGML_OP_CONV_1D_1S:
|
|
case GGML_OP_CONV_1D_2S:
|
|
{
|
|
node->n_tasks = n_threads;
|
|
|
|
GGML_ASSERT(node->src0->ne[3] == 1);
|
|
GGML_ASSERT(node->src1->ne[2] == 1);
|
|
GGML_ASSERT(node->src1->ne[3] == 1);
|
|
|
|
size_t cur = 0;
|
|
const int nk = node->src0->ne[0];
|
|
|
|
if (node->src0->type == GGML_TYPE_F16 &&
|
|
node->src1->type == GGML_TYPE_F32) {
|
|
cur = sizeof(ggml_fp16_t)*(
|
|
nk*ggml_up32(node->src0->ne[1])*node->src0->ne[2] +
|
|
( 2*(nk/2) + node->src1->ne[0])*node->src1->ne[1]
|
|
);
|
|
} else if (node->src0->type == GGML_TYPE_F32 &&
|
|
node->src1->type == GGML_TYPE_F32) {
|
|
cur = sizeof(float)*(
|
|
nk*ggml_up32(node->src0->ne[1])*node->src0->ne[2] +
|
|
( 2*(nk/2) + node->src1->ne[0])*node->src1->ne[1]
|
|
);
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
work_size = MAX(work_size, cur);
|
|
} break;
|
|
case GGML_OP_FLASH_ATTN:
|
|
{
|
|
node->n_tasks = n_threads;
|
|
|
|
size_t cur = 0;
|
|
|
|
const int ne11 = ggml_up(node->src1->ne[1], GGML_SOFT_MAX_UNROLL);
|
|
|
|
if (node->src1->type == GGML_TYPE_F32) {
|
|
cur = sizeof(float)*ne11*node->n_tasks; // TODO: this can become (n_tasks-1)
|
|
cur += sizeof(float)*ne11*node->n_tasks; // this is overestimated by x2
|
|
}
|
|
|
|
if (node->src1->type == GGML_TYPE_F16) {
|
|
cur = sizeof(float)*ne11*node->n_tasks; // TODO: this can become (n_tasks-1)
|
|
cur += sizeof(float)*ne11*node->n_tasks; // this is overestimated by x2
|
|
}
|
|
|
|
work_size = MAX(work_size, cur);
|
|
} break;
|
|
case GGML_OP_FLASH_FF:
|
|
{
|
|
node->n_tasks = n_threads;
|
|
|
|
size_t cur = 0;
|
|
|
|
if (node->src1->type == GGML_TYPE_F32) {
|
|
cur = sizeof(float)*node->src1->ne[1]*node->n_tasks; // TODO: this can become (n_tasks-1)
|
|
cur += sizeof(float)*node->src1->ne[1]*node->n_tasks; // this is overestimated by x2
|
|
}
|
|
|
|
if (node->src1->type == GGML_TYPE_F16) {
|
|
cur = sizeof(float)*node->src1->ne[1]*node->n_tasks; // TODO: this can become (n_tasks-1)
|
|
cur += sizeof(float)*node->src1->ne[1]*node->n_tasks; // this is overestimated by x2
|
|
}
|
|
|
|
work_size = MAX(work_size, cur);
|
|
} break;
|
|
case GGML_OP_NONE:
|
|
{
|
|
node->n_tasks = 1;
|
|
} break;
|
|
case GGML_OP_COUNT:
|
|
{
|
|
GGML_ASSERT(false);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
if (cgraph->work != NULL && work_size > cgraph->work_size) {
|
|
GGML_ASSERT(false); // TODO: better handling
|
|
}
|
|
|
|
if (work_size > 0 && cgraph->work == NULL) {
|
|
cgraph->work_size = work_size + CACHE_LINE_SIZE*(n_threads - 1);
|
|
|
|
GGML_PRINT_DEBUG("%s: allocating work buffer for graph (%zu bytes)\n", __func__, cgraph->work_size);
|
|
cgraph->work = ggml_new_tensor_1d(ctx, GGML_TYPE_I8, cgraph->work_size);
|
|
}
|
|
}
|
|
|
|
const int64_t perf_start_cycles = ggml_perf_cycles();
|
|
const int64_t perf_start_time_us = ggml_perf_time_us();
|
|
|
|
for (int i = 0; i < cgraph->n_nodes; i++) {
|
|
GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, i, cgraph->n_nodes);
|
|
|
|
struct ggml_tensor * node = cgraph->nodes[i];
|
|
|
|
// TODO: this could be used to avoid unnecessary computations, but it needs to be improved
|
|
//if (node->grad == NULL && node->perf_runs > 0) {
|
|
// continue;
|
|
//}
|
|
|
|
const int64_t perf_node_start_cycles = ggml_perf_cycles();
|
|
const int64_t perf_node_start_time_us = ggml_perf_time_us();
|
|
|
|
// INIT
|
|
struct ggml_compute_params params = {
|
|
/*.type =*/ GGML_TASK_INIT,
|
|
/*.ith =*/ 0,
|
|
/*.nth =*/ node->n_tasks,
|
|
/*.wsize =*/ cgraph->work ? ggml_nbytes(cgraph->work) : 0,
|
|
/*.wdata =*/ cgraph->work ? cgraph->work->data : NULL,
|
|
};
|
|
|
|
ggml_compute_forward(¶ms, node);
|
|
|
|
// COMPUTE
|
|
if (node->n_tasks > 1) {
|
|
if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) {
|
|
atomic_store(&state_shared.has_work, false);
|
|
}
|
|
|
|
while (atomic_load(&state_shared.has_work)) {
|
|
ggml_lock_lock (&state_shared.spin);
|
|
ggml_lock_unlock(&state_shared.spin);
|
|
}
|
|
|
|
// launch thread pool
|
|
for (int j = 0; j < n_threads - 1; j++) {
|
|
workers[j].params = (struct ggml_compute_params) {
|
|
.type = GGML_TASK_COMPUTE,
|
|
.ith = j + 1,
|
|
.nth = node->n_tasks,
|
|
.wsize = cgraph->work ? ggml_nbytes(cgraph->work) : 0,
|
|
.wdata = cgraph->work ? cgraph->work->data : NULL,
|
|
};
|
|
workers[j].node = node;
|
|
}
|
|
|
|
atomic_fetch_sub(&state_shared.n_ready, 1);
|
|
|
|
while (atomic_load(&state_shared.n_ready) > 0) {
|
|
ggml_lock_lock (&state_shared.spin);
|
|
ggml_lock_unlock(&state_shared.spin);
|
|
}
|
|
|
|
atomic_store(&state_shared.has_work, true);
|
|
}
|
|
|
|
params.type = GGML_TASK_COMPUTE;
|
|
ggml_compute_forward(¶ms, node);
|
|
|
|
// wait for thread pool
|
|
if (node->n_tasks > 1) {
|
|
if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) {
|
|
atomic_store(&state_shared.has_work, false);
|
|
}
|
|
|
|
while (atomic_load(&state_shared.has_work)) {
|
|
ggml_lock_lock (&state_shared.spin);
|
|
ggml_lock_unlock(&state_shared.spin);
|
|
}
|
|
|
|
atomic_fetch_sub(&state_shared.n_ready, 1);
|
|
|
|
while (atomic_load(&state_shared.n_ready) != 0) {
|
|
ggml_lock_lock (&state_shared.spin);
|
|
ggml_lock_unlock(&state_shared.spin);
|
|
}
|
|
}
|
|
|
|
// FINALIZE
|
|
if (node->n_tasks > 1) {
|
|
if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) {
|
|
atomic_store(&state_shared.has_work, false);
|
|
}
|
|
|
|
while (atomic_load(&state_shared.has_work)) {
|
|
ggml_lock_lock (&state_shared.spin);
|
|
ggml_lock_unlock(&state_shared.spin);
|
|
}
|
|
|
|
// launch thread pool
|
|
for (int j = 0; j < n_threads - 1; j++) {
|
|
workers[j].params = (struct ggml_compute_params) {
|
|
.type = GGML_TASK_FINALIZE,
|
|
.ith = j + 1,
|
|
.nth = node->n_tasks,
|
|
.wsize = cgraph->work ? ggml_nbytes(cgraph->work) : 0,
|
|
.wdata = cgraph->work ? cgraph->work->data : NULL,
|
|
};
|
|
workers[j].node = node;
|
|
}
|
|
|
|
atomic_fetch_sub(&state_shared.n_ready, 1);
|
|
|
|
while (atomic_load(&state_shared.n_ready) > 0) {
|
|
ggml_lock_lock (&state_shared.spin);
|
|
ggml_lock_unlock(&state_shared.spin);
|
|
}
|
|
|
|
atomic_store(&state_shared.has_work, true);
|
|
}
|
|
|
|
params.type = GGML_TASK_FINALIZE;
|
|
ggml_compute_forward(¶ms, node);
|
|
|
|
// wait for thread pool
|
|
if (node->n_tasks > 1) {
|
|
if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) {
|
|
atomic_store(&state_shared.has_work, false);
|
|
}
|
|
|
|
while (atomic_load(&state_shared.has_work)) {
|
|
ggml_lock_lock (&state_shared.spin);
|
|
ggml_lock_unlock(&state_shared.spin);
|
|
}
|
|
|
|
atomic_fetch_sub(&state_shared.n_ready, 1);
|
|
|
|
while (atomic_load(&state_shared.n_ready) != 0) {
|
|
ggml_lock_lock (&state_shared.spin);
|
|
ggml_lock_unlock(&state_shared.spin);
|
|
}
|
|
}
|
|
|
|
// performance stats (node)
|
|
{
|
|
int64_t perf_cycles_cur = ggml_perf_cycles() - perf_node_start_cycles;
|
|
int64_t perf_time_us_cur = ggml_perf_time_us() - perf_node_start_time_us;
|
|
|
|
node->perf_runs++;
|
|
node->perf_cycles += perf_cycles_cur;
|
|
node->perf_time_us += perf_time_us_cur;
|
|
}
|
|
}
|
|
|
|
// join thread pool
|
|
if (n_threads > 1) {
|
|
atomic_store(&state_shared.stop, true);
|
|
atomic_store(&state_shared.has_work, true);
|
|
|
|
for (int j = 0; j < n_threads - 1; j++) {
|
|
int rc = ggml_thread_join(workers[j].thrd, NULL);
|
|
GGML_ASSERT(rc == 0);
|
|
UNUSED(rc);
|
|
}
|
|
|
|
ggml_lock_destroy(&state_shared.spin);
|
|
}
|
|
|
|
// performance stats (graph)
|
|
{
|
|
int64_t perf_cycles_cur = ggml_perf_cycles() - perf_start_cycles;
|
|
int64_t perf_time_us_cur = ggml_perf_time_us() - perf_start_time_us;
|
|
|
|
cgraph->perf_runs++;
|
|
cgraph->perf_cycles += perf_cycles_cur;
|
|
cgraph->perf_time_us += perf_time_us_cur;
|
|
|
|
GGML_PRINT_DEBUG("%s: perf (%d) - cpu = %.3f / %.3f ms, wall = %.3f / %.3f ms\n",
|
|
__func__, cgraph->perf_runs,
|
|
(double) perf_cycles_cur / (double) ggml_cycles_per_ms(),
|
|
(double) cgraph->perf_cycles / (double) ggml_cycles_per_ms() / (double) cgraph->perf_runs,
|
|
(double) perf_time_us_cur / 1000.0,
|
|
(double) cgraph->perf_time_us / 1000.0 / cgraph->perf_runs);
|
|
}
|
|
}
|
|
|
|
void ggml_graph_reset(struct ggml_cgraph * cgraph) {
|
|
for (int i = 0; i < cgraph->n_nodes; i++) {
|
|
struct ggml_tensor * grad = cgraph->grads[i];
|
|
|
|
if (grad) {
|
|
ggml_set_zero(grad);
|
|
}
|
|
}
|
|
}
|
|
|
|
void ggml_graph_print(const struct ggml_cgraph * cgraph) {
|
|
int64_t perf_total_per_op_us[GGML_OP_COUNT] = {0};
|
|
|
|
GGML_PRINT("=== GRAPH ===\n");
|
|
|
|
GGML_PRINT_DEBUG("n_threads = %d\n", cgraph->n_threads);
|
|
GGML_PRINT_DEBUG("total work size = %zu bytes\n",cgraph->work_size);
|
|
|
|
GGML_PRINT("n_nodes = %d\n", cgraph->n_nodes);
|
|
for (int i = 0; i < cgraph->n_nodes; i++) {
|
|
struct ggml_tensor * node = cgraph->nodes[i];
|
|
|
|
perf_total_per_op_us[node->op] += node->perf_time_us;
|
|
|
|
GGML_PRINT(" - %3d: [ %6d, %6d, %6d] %16s %s (%3d) cpu = %7.3f / %7.3f ms, wall = %7.3f / %7.3f ms\n",
|
|
i,
|
|
node->ne[0], node->ne[1], node->ne[2],
|
|
GGML_OP_LABEL[node->op], node->is_param ? "x" : node->grad ? "g" : " ", node->perf_runs,
|
|
(double) node->perf_cycles / (double) ggml_cycles_per_ms(),
|
|
(double) node->perf_cycles / (double) ggml_cycles_per_ms() / (double) node->perf_runs,
|
|
(double) node->perf_time_us / 1000.0,
|
|
(double) node->perf_time_us / 1000.0 / node->perf_runs);
|
|
}
|
|
|
|
GGML_PRINT("n_leafs = %d\n", cgraph->n_leafs);
|
|
for (int i = 0; i < cgraph->n_leafs; i++) {
|
|
struct ggml_tensor * node = cgraph->leafs[i];
|
|
|
|
GGML_PRINT(" - %3d: [ %6d, %6d] %8s\n",
|
|
i,
|
|
node->ne[0], node->ne[1],
|
|
GGML_OP_LABEL[node->op]);
|
|
}
|
|
|
|
for (int i = 0; i < GGML_OP_COUNT; i++) {
|
|
GGML_PRINT("perf_total_per_op_us[%16s] = %7.3f ms\n", GGML_OP_LABEL[i], (double) perf_total_per_op_us[i] / 1000.0);
|
|
}
|
|
|
|
GGML_PRINT("========================================\n");
|
|
}
|
|
|
|
// check if node is part of the graph
|
|
static bool ggml_graph_find(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) {
|
|
if (cgraph == NULL) {
|
|
return true;
|
|
}
|
|
|
|
for (int i = 0; i < cgraph->n_nodes; i++) {
|
|
if (cgraph->nodes[i] == node) {
|
|
return true;
|
|
}
|
|
}
|
|
|
|
return false;
|
|
}
|
|
|
|
static struct ggml_tensor * ggml_graph_get_parent(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) {
|
|
for (int i = 0; i < cgraph->n_nodes; i++) {
|
|
struct ggml_tensor * parent = cgraph->nodes[i];
|
|
|
|
if (parent->grad == node) {
|
|
return parent;
|
|
}
|
|
}
|
|
|
|
return NULL;
|
|
}
|
|
|
|
void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename) {
|
|
char color[16];
|
|
|
|
FILE * fp = fopen(filename, "w");
|
|
GGML_ASSERT(fp);
|
|
|
|
fprintf(fp, "digraph G {\n");
|
|
fprintf(fp, " newrank = true;\n");
|
|
fprintf(fp, " rankdir = LR;\n");
|
|
|
|
for (int i = 0; i < gb->n_nodes; i++) {
|
|
struct ggml_tensor * node = gb->nodes[i];
|
|
|
|
if (ggml_graph_get_parent(gb, node) != NULL) {
|
|
continue;
|
|
}
|
|
|
|
if (node->is_param) {
|
|
snprintf(color, sizeof(color), "yellow");
|
|
} else if (node->grad) {
|
|
if (ggml_graph_find(gf, node)) {
|
|
snprintf(color, sizeof(color), "green");
|
|
} else {
|
|
snprintf(color, sizeof(color), "lightblue");
|
|
}
|
|
} else {
|
|
snprintf(color, sizeof(color), "white");
|
|
}
|
|
|
|
fprintf(fp, " \"%p\" [ \
|
|
style = filled; fillcolor = %s; shape = record; \
|
|
label=\"%d [%d, %d] | <x>%s",
|
|
(void *) node, color,
|
|
i, node->ne[0], node->ne[1],
|
|
GGML_OP_SYMBOL[node->op]);
|
|
|
|
if (node->grad) {
|
|
fprintf(fp, " | <g>%s\"; ]\n", GGML_OP_SYMBOL[node->grad->op]);
|
|
} else {
|
|
fprintf(fp, "\"; ]\n");
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < gb->n_leafs; i++) {
|
|
struct ggml_tensor * node = gb->leafs[i];
|
|
|
|
snprintf(color, sizeof(color), "pink");
|
|
|
|
if (ggml_nelements(node) == 1) {
|
|
fprintf(fp, " \"%p\" [ \
|
|
style = filled; fillcolor = %s; shape = record; \
|
|
label=\"<x>%.1e\"; ]\n",
|
|
(void *) node, color, (double)ggml_get_f32_1d(node, 0));
|
|
} else {
|
|
fprintf(fp, " \"%p\" [ \
|
|
style = filled; fillcolor = %s; shape = record; \
|
|
label=\"<x>CONST %d [%d, %d]\"; ]\n",
|
|
(void *) node, color,
|
|
i, node->ne[0], node->ne[1]);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < gb->n_nodes; i++) {
|
|
struct ggml_tensor * node = gb->nodes[i];
|
|
|
|
struct ggml_tensor * parent = ggml_graph_get_parent(gb, node);
|
|
|
|
if (node->src0) {
|
|
struct ggml_tensor * parent0 = ggml_graph_get_parent(gb, node->src0);
|
|
|
|
fprintf(fp, " \"%p\":%s -> \"%p\":%s [ arrowhead = %s; style = %s; label = \"x\"; ]\n",
|
|
parent0 ? (void *) parent0 : (void *) node->src0,
|
|
parent0 ? "g" : "x",
|
|
parent ? (void *) parent : (void *) node,
|
|
parent ? "g" : "x",
|
|
parent ? "empty" : "vee",
|
|
parent ? "dashed" : "solid");
|
|
}
|
|
|
|
if (node->src1) {
|
|
struct ggml_tensor * parent1 = ggml_graph_get_parent(gb, node->src1);
|
|
|
|
fprintf(fp, " \"%p\":%s -> \"%p\":%s [ arrowhead = %s; style = %s; label = \"y\"; ]\n",
|
|
parent1 ? (void *) parent1 : (void *) node->src1,
|
|
parent1 ? "g" : "x",
|
|
parent ? (void *) parent : (void *) node,
|
|
parent ? "g" : "x",
|
|
parent ? "empty" : "vee",
|
|
parent ? "dashed" : "solid");
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < gb->n_leafs; i++) {
|
|
struct ggml_tensor * node = gb->leafs[i];
|
|
|
|
if (node->src0) {
|
|
fprintf(fp, " \"%p\":%s -> \"%p\":%s [ label = \"x\"; ]\n",
|
|
(void *) node->src0, "x",
|
|
(void *) node, "x");
|
|
}
|
|
|
|
if (node->src1) {
|
|
fprintf(fp, " \"%p\":%s -> \"%p\":%s [ label = \"y\"; ]\n",
|
|
(void *) node->src1, "x",
|
|
(void *) node, "x");
|
|
}
|
|
}
|
|
|
|
fprintf(fp, "}\n");
|
|
|
|
fclose(fp);
|
|
|
|
GGML_PRINT("%s: dot -Tpng %s -o %s.png && open %s.png\n", __func__, filename, filename, filename);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
static void ggml_opt_set_params(int np, struct ggml_tensor * const ps[], const float * x) {
|
|
int i = 0;
|
|
for (int p = 0; p < np; ++p) {
|
|
const int ne = ggml_nelements(ps[p]) ;
|
|
// TODO: add function to set tensor from array
|
|
for (int j = 0; j < ne; ++j) {
|
|
ggml_set_f32_1d(ps[p], j, x[i++]);
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_opt_get_params(int np, struct ggml_tensor * const ps[], float * x) {
|
|
int i = 0;
|
|
for (int p = 0; p < np; ++p) {
|
|
const int ne = ggml_nelements(ps[p]) ;
|
|
// TODO: add function to get all elements at once
|
|
for (int j = 0; j < ne; ++j) {
|
|
x[i++] = ggml_get_f32_1d(ps[p], j);
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_opt_get_grad(int np, struct ggml_tensor * const ps[], float * g) {
|
|
int i = 0;
|
|
for (int p = 0; p < np; ++p) {
|
|
const int ne = ggml_nelements(ps[p]) ;
|
|
// TODO: add function to get all elements at once
|
|
for (int j = 0; j < ne; ++j) {
|
|
g[i++] = ggml_get_f32_1d(ps[p]->grad, j);
|
|
}
|
|
}
|
|
}
|
|
|
|
//
|
|
// ADAM
|
|
//
|
|
// ref: https://arxiv.org/pdf/1412.6980.pdf
|
|
//
|
|
|
|
static enum ggml_opt_result ggml_opt_adam(
|
|
struct ggml_context * ctx,
|
|
struct ggml_opt_params params,
|
|
struct ggml_tensor * f,
|
|
struct ggml_cgraph * gf,
|
|
struct ggml_cgraph * gb) {
|
|
GGML_ASSERT(ggml_is_scalar(f));
|
|
|
|
gf->n_threads = params.n_threads;
|
|
gb->n_threads = params.n_threads;
|
|
|
|
// these will store the parameters we want to optimize
|
|
struct ggml_tensor * ps[GGML_MAX_PARAMS];
|
|
|
|
int np = 0;
|
|
int nx = 0;
|
|
for (int i = 0; i < gf->n_nodes; ++i) {
|
|
if (gf->nodes[i]->is_param) {
|
|
GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op);
|
|
|
|
GGML_ASSERT(np < GGML_MAX_PARAMS);
|
|
|
|
ps[np++] = gf->nodes[i];
|
|
nx += ggml_nelements(gf->nodes[i]);
|
|
}
|
|
}
|
|
|
|
// constants
|
|
const float alpha = params.adam.alpha;
|
|
const float beta1 = params.adam.beta1;
|
|
const float beta2 = params.adam.beta2;
|
|
const float eps = params.adam.eps;
|
|
|
|
float * x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // view of the parameters
|
|
float * g1 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // gradient
|
|
float * g2 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // gradient squared
|
|
float * m = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // first moment
|
|
float * v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // second moment
|
|
float * mh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // first moment hat
|
|
float * vh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // second moment hat
|
|
|
|
float * pf = params.past > 0 ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past)->data : NULL; // past function values
|
|
|
|
// initialize
|
|
ggml_vec_set_f32(nx, m, 0.0f);
|
|
ggml_vec_set_f32(nx, v, 0.0f);
|
|
|
|
// update view
|
|
ggml_opt_get_params(np, ps, x);
|
|
|
|
// compute the function value
|
|
ggml_graph_reset (gf);
|
|
ggml_set_f32 (f->grad, 1.0f);
|
|
ggml_graph_compute(ctx, gb);
|
|
|
|
float fx_prev = ggml_get_f32_1d(f, 0);
|
|
if (pf) {
|
|
pf[0] = fx_prev;
|
|
}
|
|
|
|
int n_no_improvement = 0;
|
|
float fx_best = fx_prev;
|
|
|
|
// run the optimizer
|
|
for (int t = 0; t < params.adam.n_iter; ++t) {
|
|
GGML_PRINT_DEBUG ("=== iter %d ===\n", t);
|
|
|
|
GGML_PRINT_DEBUG ("f = %10.6f\n", ggml_get_f32_1d(f, 0));
|
|
GGML_PRINT_DEBUG_5("df/dx0 = %10.6f\n", ggml_get_f32_1d(ps[0]->grad, 0));
|
|
GGML_PRINT_DEBUG_5("df/dx1 = %10.6f\n", ggml_get_f32_1d(ps[1]->grad, 0));
|
|
|
|
for (int i = 0; i < np; ++i) {
|
|
GGML_PRINT_DEBUG("param %d: %10.6f, g = %10.6f\n", i,
|
|
ggml_get_f32_1d(ps[i], 0), ggml_get_f32_1d(ps[i]->grad, 0));
|
|
}
|
|
|
|
const int64_t t_start_wall = ggml_time_us();
|
|
const int64_t t_start_cpu = ggml_cycles();
|
|
UNUSED(t_start_wall);
|
|
UNUSED(t_start_cpu);
|
|
|
|
{
|
|
// update the gradient
|
|
ggml_opt_get_grad(np, ps, g1);
|
|
|
|
// m_t = beta1*m_t-1 + (1 - beta1)*g_t
|
|
ggml_vec_scale_f32(nx, m, beta1);
|
|
ggml_vec_mad_f32 (nx, m, g1, 1.0f - beta1);
|
|
|
|
// g2 = g1^2
|
|
ggml_vec_sqr_f32 (nx, g2, g1);
|
|
|
|
// v_t = beta2*v_t-1 + (1 - beta2)*g_t^2
|
|
ggml_vec_scale_f32(nx, v, beta2);
|
|
ggml_vec_mad_f32 (nx, v, g2, 1.0f - beta2);
|
|
|
|
// m^hat = m_t / (1 - beta1^t)
|
|
// v^hat = v_t / (1 - beta2^t)
|
|
// x_t = x_t-1 - alpha*m^hat/(sqrt(v^hat) + eps)
|
|
ggml_vec_cpy_f32 (nx, mh, m);
|
|
ggml_vec_cpy_f32 (nx, vh, v);
|
|
|
|
ggml_vec_scale_f32(nx, mh, alpha/(1.0f - powf(beta1, t + 1)));
|
|
ggml_vec_scale_f32(nx, vh, 1.0f/(1.0f - powf(beta2, t + 1)));
|
|
|
|
ggml_vec_sqrt_f32 (nx, vh, vh);
|
|
ggml_vec_acc1_f32 (nx, vh, eps);
|
|
|
|
ggml_vec_div_f32 (nx, mh, mh, vh);
|
|
ggml_vec_sub_f32 (nx, x, x, mh);
|
|
|
|
// update the parameters
|
|
ggml_opt_set_params(np, ps, x);
|
|
}
|
|
|
|
ggml_graph_reset (gf);
|
|
ggml_set_f32 (f->grad, 1.0f);
|
|
ggml_graph_compute(ctx, gb);
|
|
|
|
const float fx = ggml_get_f32_1d(f, 0);
|
|
|
|
// check convergence
|
|
if (fabsf(fx - fx_prev)/fx < params.adam.eps_f) {
|
|
GGML_PRINT_DEBUG("converged\n");
|
|
|
|
return GGML_OPT_OK;
|
|
}
|
|
|
|
// delta-based convergence test
|
|
if (pf != NULL) {
|
|
// need at least params.past iterations to start checking for convergence
|
|
if (params.past <= t) {
|
|
const float rate = (pf[t%params.past] - fx)/fx;
|
|
|
|
if (fabsf(rate) < params.delta) {
|
|
return GGML_OPT_OK;
|
|
}
|
|
}
|
|
|
|
pf[t%params.past] = fx;
|
|
}
|
|
|
|
// check for improvement
|
|
if (params.max_no_improvement > 0) {
|
|
if (fx_best > fx) {
|
|
fx_best = fx;
|
|
n_no_improvement = 0;
|
|
} else {
|
|
++n_no_improvement;
|
|
|
|
if (n_no_improvement >= params.max_no_improvement) {
|
|
return GGML_OPT_OK;
|
|
}
|
|
}
|
|
}
|
|
|
|
fx_prev = fx;
|
|
|
|
{
|
|
const int64_t t_end_cpu = ggml_cycles();
|
|
GGML_PRINT_DEBUG("time iter: %5.3f s\n", ((float)(t_end_cpu - t_start_cpu))/CLOCKS_PER_SEC);
|
|
UNUSED(t_end_cpu);
|
|
|
|
const int64_t t_end_wall = ggml_time_us();
|
|
GGML_PRINT_DEBUG("wall time iter: %5.3f s\n", (t_end_wall - t_start_wall)/1e6);
|
|
UNUSED(t_end_wall);
|
|
}
|
|
}
|
|
|
|
return GGML_OPT_DID_NOT_CONVERGE;
|
|
}
|
|
|
|
//
|
|
// L-BFGS
|
|
//
|
|
// the L-BFGS implementation below is based on the following implementation:
|
|
//
|
|
// https://github.com/chokkan/liblbfgs
|
|
//
|
|
|
|
struct ggml_lbfgs_iteration_data {
|
|
float alpha;
|
|
float ys;
|
|
float * s;
|
|
float * y;
|
|
};
|
|
|
|
static enum ggml_opt_result linesearch_backtracking(
|
|
struct ggml_context * ctx,
|
|
const struct ggml_opt_params * params,
|
|
int nx,
|
|
float * x,
|
|
float * fx,
|
|
float * g,
|
|
float * d,
|
|
float * step,
|
|
const float * xp,
|
|
struct ggml_tensor * f,
|
|
struct ggml_cgraph * gf,
|
|
struct ggml_cgraph * gb,
|
|
const int np,
|
|
struct ggml_tensor * ps[]) {
|
|
int count = 0;
|
|
|
|
float width = 0.0f;
|
|
float dg = 0.0f;
|
|
float finit = 0.0f;
|
|
float dginit = 0.0f;
|
|
float dgtest = 0.0f;
|
|
|
|
const float dec = 0.5f;
|
|
const float inc = 2.1f;
|
|
|
|
if (*step <= 0.f) {
|
|
return GGML_LINESEARCH_INVALID_PARAMETERS;
|
|
}
|
|
|
|
// compute the initial gradient in the search direction
|
|
ggml_vec_dot_f32(nx, &dginit, g, d);
|
|
|
|
// make sure that d points to a descent direction
|
|
if (0 < dginit) {
|
|
return GGML_LINESEARCH_FAIL;
|
|
}
|
|
|
|
// initialize local variables
|
|
finit = *fx;
|
|
dgtest = params->lbfgs.ftol*dginit;
|
|
|
|
while (true) {
|
|
ggml_vec_cpy_f32(nx, x, xp);
|
|
ggml_vec_mad_f32(nx, x, d, *step);
|
|
|
|
// evaluate the function and gradient values
|
|
{
|
|
ggml_opt_set_params(np, ps, x);
|
|
|
|
ggml_graph_reset (gf);
|
|
ggml_set_f32 (f->grad, 1.0f);
|
|
ggml_graph_compute(ctx, gb);
|
|
|
|
ggml_opt_get_grad(np, ps, g);
|
|
|
|
*fx = ggml_get_f32_1d(f, 0);
|
|
}
|
|
|
|
++count;
|
|
|
|
if (*fx > finit + (*step)*dgtest) {
|
|
width = dec;
|
|
} else {
|
|
// Armijo condition is satisfied
|
|
if (params->lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_ARMIJO) {
|
|
return count;
|
|
}
|
|
|
|
ggml_vec_dot_f32(nx, &dg, g, d);
|
|
|
|
// check the Wolfe condition
|
|
if (dg < params->lbfgs.wolfe * dginit) {
|
|
width = inc;
|
|
} else {
|
|
if(params->lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE) {
|
|
// regular Wolfe conditions
|
|
return count;
|
|
}
|
|
|
|
if(dg > -params->lbfgs.wolfe*dginit) {
|
|
width = dec;
|
|
} else {
|
|
// strong Wolfe condition (GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE)
|
|
return count;
|
|
}
|
|
return count;
|
|
}
|
|
}
|
|
|
|
if (*step < params->lbfgs.min_step) {
|
|
return GGML_LINESEARCH_MINIMUM_STEP;
|
|
}
|
|
if (*step > params->lbfgs.max_step) {
|
|
return GGML_LINESEARCH_MAXIMUM_STEP;
|
|
}
|
|
if (params->lbfgs.max_linesearch <= count) {
|
|
return GGML_LINESEARCH_MAXIMUM_ITERATIONS;
|
|
}
|
|
|
|
(*step) *= width;
|
|
}
|
|
|
|
return GGML_LINESEARCH_FAIL;
|
|
}
|
|
|
|
static enum ggml_opt_result ggml_opt_lbfgs(
|
|
struct ggml_context * ctx,
|
|
struct ggml_opt_params params,
|
|
struct ggml_tensor * f,
|
|
struct ggml_cgraph * gf,
|
|
struct ggml_cgraph * gb) {
|
|
if (params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE ||
|
|
params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) {
|
|
if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1.f <= params.lbfgs.wolfe) {
|
|
return GGML_OPT_INVALID_WOLFE;
|
|
}
|
|
}
|
|
|
|
gf->n_threads = params.n_threads;
|
|
gb->n_threads = params.n_threads;
|
|
|
|
const int m = params.lbfgs.m;
|
|
|
|
// these will store the parameters we want to optimize
|
|
struct ggml_tensor * ps[GGML_MAX_PARAMS];
|
|
|
|
int np = 0;
|
|
int nx = 0;
|
|
for (int i = 0; i < gf->n_nodes; ++i) {
|
|
if (gf->nodes[i]->is_param) {
|
|
GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op);
|
|
|
|
GGML_ASSERT(np < GGML_MAX_PARAMS);
|
|
|
|
ps[np++] = gf->nodes[i];
|
|
nx += ggml_nelements(gf->nodes[i]);
|
|
}
|
|
}
|
|
|
|
float * x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // current parameters
|
|
float * xp = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // previous parameters
|
|
float * g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // current gradient
|
|
float * gp = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // previous gradient
|
|
float * d = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // search direction
|
|
|
|
float * pf = params.past > 0 ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past)->data : NULL; // past function values
|
|
|
|
float fx = 0.0f; // cost function value
|
|
float xnorm = 0.0f; // ||x||
|
|
float gnorm = 0.0f; // ||g||
|
|
float step = 0.0f;
|
|
|
|
// initialize x from the graph nodes
|
|
ggml_opt_get_params(np, ps, x);
|
|
|
|
// the L-BFGS memory
|
|
struct ggml_lbfgs_iteration_data * lm = alloca(sizeof(struct ggml_lbfgs_iteration_data)*m);
|
|
|
|
for (int i = 0; i < m; ++i) {
|
|
lm[i].alpha = 0.0f;
|
|
lm[i].ys = 0.0f;
|
|
lm[i].s = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data;
|
|
lm[i].y = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data;
|
|
}
|
|
|
|
// evaluate the function value and its gradient
|
|
{
|
|
ggml_opt_set_params(np, ps, x);
|
|
|
|
ggml_graph_reset (gf);
|
|
ggml_set_f32 (f->grad, 1.0f);
|
|
ggml_graph_compute(ctx, gb);
|
|
|
|
ggml_opt_get_grad(np, ps, g);
|
|
|
|
fx = ggml_get_f32_1d(f, 0);
|
|
}
|
|
|
|
if (pf) {
|
|
pf[0] = fx;
|
|
}
|
|
|
|
float fx_best = fx;
|
|
|
|
// search direction = -gradient
|
|
ggml_vec_neg_f32(nx, d, g);
|
|
|
|
// ||x||, ||g||
|
|
ggml_vec_norm_f32(nx, &xnorm, x);
|
|
ggml_vec_norm_f32(nx, &gnorm, g);
|
|
|
|
if (xnorm < 1.0f) {
|
|
xnorm = 1.0f;
|
|
}
|
|
|
|
// already optimized
|
|
if (gnorm/xnorm <= params.lbfgs.eps) {
|
|
return GGML_OPT_OK;
|
|
}
|
|
|
|
// initial step
|
|
ggml_vec_norm_inv_f32(nx, &step, d);
|
|
|
|
int j = 0;
|
|
int k = 1;
|
|
int ls = 0;
|
|
int end = 0;
|
|
int bound = 0;
|
|
int n_no_improvement = 0;
|
|
|
|
float ys = 0.0f;
|
|
float yy = 0.0f;
|
|
float beta = 0.0f;
|
|
|
|
while (true) {
|
|
// store the current position and gradient vectors
|
|
ggml_vec_cpy_f32(nx, xp, x);
|
|
ggml_vec_cpy_f32(nx, gp, g);
|
|
|
|
ls = linesearch_backtracking(ctx, ¶ms, nx, x, &fx, g, d, &step, xp, f, gf, gb, np, ps);
|
|
|
|
if (ls < 0) {
|
|
// linesearch failed - go back to the previous point and return
|
|
ggml_vec_cpy_f32(nx, x, xp);
|
|
ggml_vec_cpy_f32(nx, g, gp);
|
|
|
|
return ls;
|
|
}
|
|
|
|
ggml_vec_norm_f32(nx, &xnorm, x);
|
|
ggml_vec_norm_f32(nx, &gnorm, g);
|
|
|
|
GGML_PRINT_DEBUG("f = %10.6f\n", ggml_get_f32_1d(f, 0));
|
|
|
|
if (xnorm < 1.0f) {
|
|
xnorm = 1.0f;
|
|
}
|
|
if (gnorm/xnorm <= params.lbfgs.eps) {
|
|
// converged
|
|
return GGML_OPT_OK;
|
|
}
|
|
|
|
// delta-based convergence test
|
|
if (pf != NULL) {
|
|
// need at least params.past iterations to start checking for convergence
|
|
if (params.past <= k) {
|
|
const float rate = (pf[k%params.past] - fx)/fx;
|
|
|
|
if (fabsf(rate) < params.delta) {
|
|
return GGML_OPT_OK;
|
|
}
|
|
}
|
|
|
|
pf[k%params.past] = fx;
|
|
}
|
|
|
|
// check for improvement
|
|
if (params.max_no_improvement > 0) {
|
|
if (fx < fx_best) {
|
|
fx_best = fx;
|
|
n_no_improvement = 0;
|
|
} else {
|
|
n_no_improvement++;
|
|
|
|
if (n_no_improvement >= params.max_no_improvement) {
|
|
return GGML_OPT_OK;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (params.lbfgs.n_iter != 0 && params.lbfgs.n_iter < k + 1) {
|
|
// reached the maximum number of iterations
|
|
return GGML_OPT_DID_NOT_CONVERGE;
|
|
}
|
|
|
|
// update vectors s and y:
|
|
// s_{k+1} = x_{k+1} - x_{k} = \step * d_{k}.
|
|
// y_{k+1} = g_{k+1} - g_{k}.
|
|
//
|
|
ggml_vec_sub_f32(nx, lm[end].s, x, xp);
|
|
ggml_vec_sub_f32(nx, lm[end].y, g, gp);
|
|
|
|
// compute scalars ys and yy:
|
|
// ys = y^t \cdot s -> 1 / \rho.
|
|
// yy = y^t \cdot y.
|
|
//
|
|
ggml_vec_dot_f32(nx, &ys, lm[end].y, lm[end].s);
|
|
ggml_vec_dot_f32(nx, &yy, lm[end].y, lm[end].y);
|
|
|
|
lm[end].ys = ys;
|
|
|
|
// find new search direction
|
|
// ref: https://en.wikipedia.org/wiki/Limited-memory_BFGS
|
|
|
|
bound = (m <= k) ? m : k;
|
|
k++;
|
|
end = (end + 1)%m;
|
|
|
|
// initialize search direction with -g
|
|
ggml_vec_neg_f32(nx, d, g);
|
|
|
|
j = end;
|
|
for (int i = 0; i < bound; ++i) {
|
|
j = (j + m - 1) % m;
|
|
// \alpha_{j} = \rho_{j} s^{t}_{j} \cdot q_{k+1}
|
|
ggml_vec_dot_f32(nx, &lm[j].alpha, lm[j].s, d);
|
|
lm[j].alpha /= lm[j].ys;
|
|
// q_{i} = q_{i+1} - \alpha_{i} y_{i}
|
|
ggml_vec_mad_f32(nx, d, lm[j].y, -lm[j].alpha);
|
|
}
|
|
|
|
ggml_vec_scale_f32(nx, d, ys/yy);
|
|
|
|
for (int i = 0; i < bound; ++i) {
|
|
// \beta_{j} = \rho_{j} y^t_{j} \cdot \gamma_{i}
|
|
ggml_vec_dot_f32(nx, &beta, lm[j].y, d);
|
|
beta /= lm[j].ys;
|
|
// \gamma_{i+1} = \gamma_{i} + (\alpha_{j} - \beta_{j}) s_{j}
|
|
ggml_vec_mad_f32(nx, d, lm[j].s, lm[j].alpha - beta);
|
|
j = (j + 1)%m;
|
|
}
|
|
|
|
step = 1.0;
|
|
}
|
|
|
|
return GGML_OPT_DID_NOT_CONVERGE;
|
|
}
|
|
|
|
struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) {
|
|
struct ggml_opt_params result;
|
|
|
|
switch (type) {
|
|
case GGML_OPT_ADAM:
|
|
{
|
|
result = (struct ggml_opt_params) {
|
|
.type = GGML_OPT_ADAM,
|
|
.n_threads = 1,
|
|
.past = 0,
|
|
.delta = 1e-5f,
|
|
|
|
.max_no_improvement = 100,
|
|
|
|
.print_forward_graph = true,
|
|
.print_backward_graph = true,
|
|
|
|
.adam = {
|
|
.n_iter = 10000,
|
|
.alpha = 0.001f,
|
|
.beta1 = 0.9f,
|
|
.beta2 = 0.999f,
|
|
.eps = 1e-8f,
|
|
.eps_f = 1e-5f,
|
|
.eps_g = 1e-3f,
|
|
},
|
|
};
|
|
} break;
|
|
case GGML_OPT_LBFGS:
|
|
{
|
|
result = (struct ggml_opt_params) {
|
|
.type = GGML_OPT_LBFGS,
|
|
.n_threads = 1,
|
|
.past = 0,
|
|
.delta = 1e-5f,
|
|
|
|
.max_no_improvement = 0,
|
|
|
|
.print_forward_graph = true,
|
|
.print_backward_graph = true,
|
|
|
|
.lbfgs = {
|
|
.m = 6,
|
|
.n_iter = 100,
|
|
.max_linesearch = 20,
|
|
|
|
.eps = 1e-5f,
|
|
.ftol = 1e-4f,
|
|
.wolfe = 0.9f,
|
|
.min_step = 1e-20f,
|
|
.max_step = 1e+20f,
|
|
|
|
.linesearch = GGML_LINESEARCH_DEFAULT,
|
|
},
|
|
};
|
|
} break;
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
enum ggml_opt_result ggml_opt(
|
|
struct ggml_context * ctx,
|
|
struct ggml_opt_params params,
|
|
struct ggml_tensor * f) {
|
|
bool free_ctx = false;
|
|
if (ctx == NULL) {
|
|
struct ggml_init_params params_ctx = {
|
|
.mem_size = 16*1024*1024,
|
|
.mem_buffer = NULL,
|
|
};
|
|
|
|
ctx = ggml_init(params_ctx);
|
|
if (ctx == NULL) {
|
|
return GGML_OPT_NO_CONTEXT;
|
|
}
|
|
|
|
free_ctx = true;
|
|
}
|
|
|
|
enum ggml_opt_result result = GGML_OPT_OK;
|
|
|
|
// build forward + backward compute graphs
|
|
struct ggml_cgraph gf = ggml_build_forward (f);
|
|
struct ggml_cgraph gb = ggml_build_backward(ctx, &gf, false);
|
|
|
|
switch (params.type) {
|
|
case GGML_OPT_ADAM:
|
|
{
|
|
result = ggml_opt_adam(ctx, params, f, &gf, &gb);
|
|
} break;
|
|
case GGML_OPT_LBFGS:
|
|
{
|
|
result = ggml_opt_lbfgs(ctx, params, f, &gf, &gb);
|
|
} break;
|
|
}
|
|
|
|
if (params.print_forward_graph) {
|
|
ggml_graph_print (&gf);
|
|
ggml_graph_dump_dot(&gf, NULL, "opt-forward.dot");
|
|
}
|
|
|
|
if (params.print_backward_graph) {
|
|
ggml_graph_print (&gb);
|
|
ggml_graph_dump_dot(&gb, &gf, "opt-backward.dot");
|
|
}
|
|
|
|
if (free_ctx) {
|
|
ggml_free(ctx);
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
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;
|
|
|
|
for (int j = 0; j < n; j += k) {
|
|
block_q4_0 * restrict y = (block_q4_0 *)dst + j/QK;
|
|
|
|
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;
|
|
const uint8_t vi1 = y[i].qs[l/2] >> 4;
|
|
|
|
hist[vi0]++;
|
|
hist[vi1]++;
|
|
}
|
|
}
|
|
}
|
|
|
|
return (n/QK*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;
|
|
|
|
for (int j = 0; j < n; j += k) {
|
|
block_q4_1 * restrict y = (block_q4_1 *)dst + j/QK;
|
|
|
|
quantize_row_q4_1(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;
|
|
const uint8_t vi1 = y[i].qs[l/2] >> 4;
|
|
|
|
hist[vi0]++;
|
|
hist[vi1]++;
|
|
}
|
|
}
|
|
}
|
|
|
|
return (n/QK*sizeof(block_q4_1));
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
int ggml_cpu_has_avx(void) {
|
|
#if defined(__AVX__)
|
|
return 1;
|
|
#else
|
|
return 0;
|
|
#endif
|
|
}
|
|
|
|
int ggml_cpu_has_avx2(void) {
|
|
#if defined(__AVX2__)
|
|
return 1;
|
|
#else
|
|
return 0;
|
|
#endif
|
|
}
|
|
|
|
int ggml_cpu_has_avx512(void) {
|
|
#if defined(__AVX512F__)
|
|
return 1;
|
|
#else
|
|
return 0;
|
|
#endif
|
|
}
|
|
|
|
int ggml_cpu_has_fma(void) {
|
|
#if defined(__FMA__)
|
|
return 1;
|
|
#else
|
|
return 0;
|
|
#endif
|
|
}
|
|
|
|
int ggml_cpu_has_neon(void) {
|
|
#if defined(__ARM_NEON)
|
|
return 1;
|
|
#else
|
|
return 0;
|
|
#endif
|
|
}
|
|
|
|
int ggml_cpu_has_arm_fma(void) {
|
|
#if defined(__ARM_FEATURE_FMA)
|
|
return 1;
|
|
#else
|
|
return 0;
|
|
#endif
|
|
}
|
|
|
|
int ggml_cpu_has_f16c(void) {
|
|
#if defined(__F16C__)
|
|
return 1;
|
|
#else
|
|
return 0;
|
|
#endif
|
|
}
|
|
|
|
int ggml_cpu_has_fp16_va(void) {
|
|
#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
|
|
return 1;
|
|
#else
|
|
return 0;
|
|
#endif
|
|
}
|
|
|
|
int ggml_cpu_has_wasm_simd(void) {
|
|
#if defined(__wasm_simd128__)
|
|
return 1;
|
|
#else
|
|
return 0;
|
|
#endif
|
|
}
|
|
|
|
int ggml_cpu_has_blas(void) {
|
|
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
|
return 1;
|
|
#else
|
|
return 0;
|
|
#endif
|
|
}
|
|
|
|
int ggml_cpu_has_sse3(void) {
|
|
#if defined(__SSE3__)
|
|
return 1;
|
|
#else
|
|
return 0;
|
|
#endif
|
|
}
|
|
|
|
int ggml_cpu_has_vsx(void) {
|
|
#if defined(__POWER9_VECTOR__)
|
|
return 1;
|
|
#else
|
|
return 0;
|
|
#endif
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|