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llama.cpp/ggml/src/ggml-impl.h
Johannes Gäßler 02e4eaf22f
ggml-opt: fix data corruption (ggml/1022)
2024-11-21 09:22:02 +02:00

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#pragma once
// GGML internal header
#include "ggml.h"
#include <assert.h>
#include <math.h>
#include <stdlib.h> // load `stdlib.h` before other headers to work around MinGW bug: https://sourceforge.net/p/mingw-w64/bugs/192/
#include <stdbool.h>
#include <stdint.h>
#include <string.h>
#ifdef __ARM_FEATURE_SVE
#include <arm_sve.h>
#endif // __ARM_FEATURE_SVE
#if defined(__ARM_NEON)
// if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
//
// $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/
//
#include <arm_neon.h>
#endif
#if defined(__F16C__)
#include <immintrin.h>
#endif
#ifdef __cplusplus
extern "C" {
#endif
#undef MIN
#undef MAX
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define MAX(a, b) ((a) > (b) ? (a) : (b))
// required for mmap as gguf only guarantees 32-byte alignment
#define TENSOR_ALIGNMENT 32
// static_assert should be a #define, but if it's not,
// fall back to the _Static_assert C11 keyword.
// if C99 - static_assert is noop
// ref: https://stackoverflow.com/a/53923785/4039976
#ifndef __cplusplus
#ifndef static_assert
#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
#define static_assert(cond, msg) _Static_assert(cond, msg)
#else
#define static_assert(cond, msg) struct global_scope_noop_trick
#endif
#endif
#endif
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);
}
//
// logging
//
GGML_ATTRIBUTE_FORMAT(2, 3)
void ggml_log_internal (enum ggml_log_level level, const char * format, ...);
void ggml_log_callback_default(enum ggml_log_level level, const char * text, void * user_data);
#define GGML_LOG(...) ggml_log_internal(GGML_LOG_LEVEL_NONE , __VA_ARGS__)
#define GGML_LOG_INFO(...) ggml_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
#define GGML_LOG_WARN(...) ggml_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
#define GGML_LOG_ERROR(...) ggml_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
#define GGML_LOG_DEBUG(...) ggml_log_internal(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
#define GGML_LOG_CONT(...) ggml_log_internal(GGML_LOG_LEVEL_CONT , __VA_ARGS__)
#define GGML_DEBUG 0
#if (GGML_DEBUG >= 1)
#define GGML_PRINT_DEBUG(...) GGML_LOG_DEBUG(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG(...)
#endif
#if (GGML_DEBUG >= 5)
#define GGML_PRINT_DEBUG_5(...) GGML_LOG_DEBUG(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_5(...)
#endif
#if (GGML_DEBUG >= 10)
#define GGML_PRINT_DEBUG_10(...) GGML_LOG_DEBUG(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_10(...)
#endif
// tensor params
static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) {
GGML_ASSERT(tensor != NULL); // silence -Warray-bounds warnings
assert(params_size <= GGML_MAX_OP_PARAMS);
memcpy(tensor->op_params, params, params_size);
}
static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_t i) {
assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t));
return ((const int32_t *)(tensor->op_params))[i];
}
static float ggml_get_op_params_f32(const struct ggml_tensor * tensor, uint32_t i) {
assert(i < GGML_MAX_OP_PARAMS / sizeof(float));
return ((const float *)(tensor->op_params))[i];
}
static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) {
assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t));
((int32_t *)(tensor->op_params))[i] = value;
}
static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, float value) {
assert(i < GGML_MAX_OP_PARAMS / sizeof(float));
((float *)(tensor->op_params))[i] = value;
}
struct ggml_map_custom1_op_params {
ggml_custom1_op_t fun;
int n_tasks;
void * userdata;
};
struct ggml_map_custom2_op_params {
ggml_custom2_op_t fun;
int n_tasks;
void * userdata;
};
struct ggml_map_custom3_op_params {
ggml_custom3_op_t fun;
int n_tasks;
void * userdata;
};
// bitset
typedef uint32_t ggml_bitset_t;
static_assert(sizeof(ggml_bitset_t) == 4, "bitset_t constants must be updated");
#define BITSET_SHR 5 // log2(sizeof(ggml_bitset_t)*8)
#define BITSET_MASK (sizeof(ggml_bitset_t)*8 - 1)
static size_t ggml_bitset_size(size_t n) {
return (n + BITSET_MASK) >> BITSET_SHR;
}
static inline bool ggml_bitset_get(const ggml_bitset_t * bitset, size_t i) {
return !!(bitset[i >> BITSET_SHR] & (1u << (i & BITSET_MASK)));
}
static inline void ggml_bitset_set(ggml_bitset_t * bitset, size_t i) {
bitset[i >> BITSET_SHR] |= (1u << (i & BITSET_MASK));
}
static inline void ggml_bitset_clear(ggml_bitset_t * bitset, size_t i) {
bitset[i >> BITSET_SHR] &= ~(1u << (i & BITSET_MASK));
}
// hash set
#define GGML_HASHSET_FULL ((size_t)-1)
#define GGML_HASHSET_ALREADY_EXISTS ((size_t)-2)
struct ggml_hash_set {
size_t size;
ggml_bitset_t * used; // whether or not the keys are in use i.e. set
struct ggml_tensor ** keys; // actual tensors in the set, keys[i] is only defined if ggml_bitset_get(used, i)
};
struct ggml_hash_set ggml_hash_set_new(size_t size);
void ggml_hash_set_free(struct ggml_hash_set * hash_set);
// returns the minimum size for a hash set that can hold min_sz elements
size_t ggml_hash_size(size_t min_sz);
// remove all elements from the hash set
void ggml_hash_set_reset(struct ggml_hash_set * hash_set);
// returns true if key is in the hash set
static bool ggml_hash_contains(const struct ggml_hash_set * hash_set, struct ggml_tensor * key);
// returns GGML_HASHSET_FULL if table is full, otherwise the current index of the key or where it should be inserted
static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, const struct ggml_tensor * key);
// returns GGML_HASHSET_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
static size_t ggml_hash_insert(struct ggml_hash_set * hash_set, struct ggml_tensor * key);
// return index, asserts if table is full
static size_t ggml_hash_find_or_insert(struct ggml_hash_set * hash_set, struct ggml_tensor * key);
// hash function for ggml_tensor
static inline size_t ggml_hash(const struct ggml_tensor * p) {
// the last 4 bits are always zero due to alignment
return (size_t)(uintptr_t)p >> 4;
}
static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, const struct ggml_tensor * key) {
size_t h = ggml_hash(key) % hash_set->size;
// linear probing
size_t i = h;
while (ggml_bitset_get(hash_set->used, i) && hash_set->keys[i] != key) {
i = (i + 1) % hash_set->size;
if (i == h) {
// visited all hash table entries -> not found
return GGML_HASHSET_FULL;
}
}
return i;
}
static bool ggml_hash_contains(const struct ggml_hash_set * hash_set, struct ggml_tensor * key) {
size_t i = ggml_hash_find(hash_set, key);
return i != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, i);
}
static size_t ggml_hash_insert(struct ggml_hash_set * hash_set, struct ggml_tensor * key) {
size_t h = ggml_hash(key) % hash_set->size;
// linear probing
size_t i = h;
do {
if (!ggml_bitset_get(hash_set->used, i)) {
ggml_bitset_set(hash_set->used, i);
hash_set->keys[i] = key;
return i;
}
if (hash_set->keys[i] == key) {
return GGML_HASHSET_ALREADY_EXISTS;
}
i = (i + 1) % hash_set->size;
} while (i != h);
// visited all hash table entries -> not found
GGML_ABORT("fatal error");
}
static size_t ggml_hash_find_or_insert(struct ggml_hash_set * hash_set, struct ggml_tensor * key) {
size_t h = ggml_hash(key) % hash_set->size;
// linear probing
size_t i = h;
do {
if (!ggml_bitset_get(hash_set->used, i)) {
ggml_bitset_set(hash_set->used, i);
hash_set->keys[i] = key;
return i;
}
if (hash_set->keys[i] == key) {
return i;
}
i = (i + 1) % hash_set->size;
} while (i != h);
// visited all hash table entries -> not found
GGML_ABORT("fatal error");
}
// computation graph
enum ggml_cgraph_eval_order {
GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT = 0,
GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT,
GGML_CGRAPH_EVAL_ORDER_COUNT
};
struct ggml_cgraph {
int size; // maximum number of nodes/leafs/grads/grad_accs
int n_nodes; // number of nodes currently in use
int n_leafs; // number of leafs currently in use
struct ggml_tensor ** nodes; // tensors with data that can change if the graph is evaluated
struct ggml_tensor ** grads; // the outputs of these tensors are the gradients of the nodes
struct ggml_tensor ** grad_accs; // accumulators for node gradients
struct ggml_tensor ** leafs; // tensors with constant data
struct ggml_hash_set visited_hash_set;
enum ggml_cgraph_eval_order order;
};
// returns a slice of cgraph with nodes [i0, i1)
// the slice does not have leafs or gradients
// if you need the gradients, get them from the original graph
struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph, int i0, int i1);
// Memory allocation
void * ggml_aligned_malloc(size_t size);
void ggml_aligned_free(void * ptr, size_t size);
// FP16 to FP32 conversion
#if defined(__ARM_NEON)
#ifdef _MSC_VER
typedef uint16_t ggml_fp16_internal_t;
#else
typedef __fp16 ggml_fp16_internal_t;
#endif
#endif
#if defined(__ARM_NEON) && !defined(_MSC_VER)
#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
#define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
ggml_fp16_internal_t tmp;
memcpy(&tmp, &h, sizeof(ggml_fp16_t));
return (float)tmp;
}
static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
ggml_fp16_t res;
ggml_fp16_internal_t tmp = f;
memcpy(&res, &tmp, sizeof(ggml_fp16_t));
return res;
}
#elif defined(__F16C__)
#ifdef _MSC_VER
#define GGML_COMPUTE_FP16_TO_FP32(x) _mm_cvtss_f32(_mm_cvtph_ps(_mm_cvtsi32_si128(x)))
#define GGML_COMPUTE_FP32_TO_FP16(x) _mm_extract_epi16(_mm_cvtps_ph(_mm_set_ss(x), 0), 0)
#else
#define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x)
#define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0)
#endif
#elif defined(__POWER9_VECTOR__)
#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
/* the inline asm below is about 12% faster than the lookup method */
#define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x)
#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
register float f;
register double d;
__asm__(
"mtfprd %0,%2\n"
"xscvhpdp %0,%0\n"
"frsp %1,%0\n" :
/* temp */ "=d"(d),
/* out */ "=f"(f):
/* in */ "r"(h));
return f;
}
static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
register double d;
register ggml_fp16_t r;
__asm__( /* xscvdphp can work on double or single precision */
"xscvdphp %0,%2\n"
"mffprd %1,%0\n" :
/* temp */ "=d"(d),
/* out */ "=r"(r):
/* in */ "f"(f));
return r;
}
#else
// FP16 <-> FP32
// ref: https://github.com/Maratyszcza/FP16
static inline float fp32_from_bits(uint32_t w) {
union {
uint32_t as_bits;
float as_value;
} fp32;
fp32.as_bits = w;
return fp32.as_value;
}
static inline uint32_t fp32_to_bits(float f) {
union {
float as_value;
uint32_t as_bits;
} fp32;
fp32.as_value = f;
return fp32.as_bits;
}
static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
const uint32_t w = (uint32_t) h << 16;
const uint32_t sign = w & UINT32_C(0x80000000);
const uint32_t two_w = w + w;
const uint32_t exp_offset = UINT32_C(0xE0) << 23;
#if (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)) && (!defined(__cplusplus) || __cplusplus >= 201703L)
const float exp_scale = 0x1.0p-112f;
#else
const float exp_scale = fp32_from_bits(UINT32_C(0x7800000));
#endif
const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale;
const uint32_t magic_mask = UINT32_C(126) << 23;
const float magic_bias = 0.5f;
const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias;
const uint32_t denormalized_cutoff = UINT32_C(1) << 27;
const uint32_t result = sign |
(two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value));
return fp32_from_bits(result);
}
static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
#if (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)) && (!defined(__cplusplus) || __cplusplus >= 201703L)
const float scale_to_inf = 0x1.0p+112f;
const float scale_to_zero = 0x1.0p-110f;
#else
const float scale_to_inf = fp32_from_bits(UINT32_C(0x77800000));
const float scale_to_zero = fp32_from_bits(UINT32_C(0x08800000));
#endif
float base = (fabsf(f) * scale_to_inf) * scale_to_zero;
const uint32_t w = fp32_to_bits(f);
const uint32_t shl1_w = w + w;
const uint32_t sign = w & UINT32_C(0x80000000);
uint32_t bias = shl1_w & UINT32_C(0xFF000000);
if (bias < UINT32_C(0x71000000)) {
bias = UINT32_C(0x71000000);
}
base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base;
const uint32_t bits = fp32_to_bits(base);
const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00);
const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF);
const uint32_t nonsign = exp_bits + mantissa_bits;
return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign);
}
#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
#endif // defined(__ARM_NEON) && (!defined(__MSC_VER)
// precomputed f32 table for f16 (256 KB)
// defined in ggml.c, initialized in ggml_init()
GGML_API float ggml_table_f32_f16[1 << 16];
// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32,
// so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON.
// This is also true for POWER9.
#if !defined(GGML_FP16_TO_FP32)
inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
uint16_t s;
memcpy(&s, &f, sizeof(uint16_t));
return ggml_table_f32_f16[s];
}
#define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x)
#endif
#if !defined(GGML_FP32_TO_FP16)
#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
#endif
/**
* Converts brain16 to float32.
*
* The bfloat16 floating point format has the following structure:
*
* ┌sign
* │
* │ ┌exponent
* │ │
* │ │ ┌mantissa
* │ │ │
* │┌──┴───┐┌─┴───┐
* 0b0000000000000000 brain16
*
* Since bf16 has the same number of exponent bits as a 32bit float,
* encoding and decoding numbers becomes relatively straightforward.
*
* ┌sign
* │
* │ ┌exponent
* │ │
* │ │ ┌mantissa
* │ │ │
* │┌──┴───┐┌─┴───────────────────┐
* 0b00000000000000000000000000000000 IEEE binary32
*
* For comparison, the standard fp16 format has fewer exponent bits.
*
* ┌sign
* │
* │ ┌exponent
* │ │
* │ │ ┌mantissa
* │ │ │
* │┌─┴─┐┌─┴──────┐
* 0b0000000000000000 IEEE binary16
*
* @see IEEE 754-2008
*/
static inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) {
union {
float f;
uint32_t i;
} u;
u.i = (uint32_t)h.bits << 16;
return u.f;
}
/**
* Converts float32 to brain16.
*
* This is binary identical with Google Brain float conversion.
* Floats shall round to nearest even, and NANs shall be quiet.
* Subnormals aren't flushed to zero, except perhaps when used.
* This code should vectorize nicely if using modern compilers.
*/
static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) {
ggml_bf16_t h;
union {
float f;
uint32_t i;
} u;
u.f = s;
if ((u.i & 0x7fffffff) > 0x7f800000) { /* nan */
h.bits = (u.i >> 16) | 64; /* force to quiet */
return h;
}
h.bits = (u.i + (0x7fff + ((u.i >> 16) & 1))) >> 16;
return h;
}
#define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x)
#define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x)
#ifdef __cplusplus
}
#endif
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