llama.cpp/ggml/src/ggml-impl.h

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#pragma once
// GGML internal header
#include "ggml.h"
#include <assert.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 __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)
};
llama : ggml-backend integration (#4766) * llama : ggml-backend integration * ggml-backend : add names to buffers * fix unmap after loading * batched-bench : add tensor_split param * llama : check for null tensor_split * ggml-backend : increase GGML_MAX_BACKENDS * improve graph splitting, partial fix for --no-kv-offload * cuda : add ggml-backend split buffer support * cuda : do not create buffer types for devices that don't exist (fixes usage without CUDA devices available) * ggml : fix null backend dereference (#4807) * ggml : fix null backend dereference * ggml : also check ggml_backend_is_cpu * test-backend-ops : check buffer allocation failures * llama : add cparam (split_mode) and command line argument (--split-mode, -sm) to configure the split mode (none, layer or row) * ggml : fix mul_mat_id work size * llama : rewrite session kv load/set without graphs * minor * llama : only initialize used backends, free backends on context free * llama : abort ctx if cuda backend init fails * llama : rewrite lora with ggml-backend and compute on CPU ggml-ci * llama : only map to a backend buffer the region of the file mapping containing the tensors used in the buffer * opencl : add ggml-backend buffer type * cuda : only use batched_cublas with batched mat muls (fixes fp16 tg perf) * llama : on Metal, by default offload the full model ggml-ci * metal : page align the data ptr (#4854) * Apply suggestions from code review Co-authored-by: Johannes Gäßler <johannesg@5d6.de> * cuda : fix split buffer free * address review comments * llama-bench : add split-mode parameter * fix whitespace * opencl : fix double initialization * server : add --split-mode parameter * use async copy and compute to improve multi-gpu performance ggml-ci * use async memcpys to copy the graph outputs to the CPU * fix opencl * use a host buffer for the cpu compute buffer for faster copies to the gpu --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2024-01-12 20:07:38 +01:00
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);
llama : ggml-backend integration (#4766) * llama : ggml-backend integration * ggml-backend : add names to buffers * fix unmap after loading * batched-bench : add tensor_split param * llama : check for null tensor_split * ggml-backend : increase GGML_MAX_BACKENDS * improve graph splitting, partial fix for --no-kv-offload * cuda : add ggml-backend split buffer support * cuda : do not create buffer types for devices that don't exist (fixes usage without CUDA devices available) * ggml : fix null backend dereference (#4807) * ggml : fix null backend dereference * ggml : also check ggml_backend_is_cpu * test-backend-ops : check buffer allocation failures * llama : add cparam (split_mode) and command line argument (--split-mode, -sm) to configure the split mode (none, layer or row) * ggml : fix mul_mat_id work size * llama : rewrite session kv load/set without graphs * minor * llama : only initialize used backends, free backends on context free * llama : abort ctx if cuda backend init fails * llama : rewrite lora with ggml-backend and compute on CPU ggml-ci * llama : only map to a backend buffer the region of the file mapping containing the tensors used in the buffer * opencl : add ggml-backend buffer type * cuda : only use batched_cublas with batched mat muls (fixes fp16 tg perf) * llama : on Metal, by default offload the full model ggml-ci * metal : page align the data ptr (#4854) * Apply suggestions from code review Co-authored-by: Johannes Gäßler <johannesg@5d6.de> * cuda : fix split buffer free * address review comments * llama-bench : add split-mode parameter * fix whitespace * opencl : fix double initialization * server : add --split-mode parameter * use async copy and compute to improve multi-gpu performance ggml-ci * use async memcpys to copy the graph outputs to the CPU * fix opencl * use a host buffer for the cpu compute buffer for faster copies to the gpu --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2024-01-12 20:07:38 +01:00
// 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, 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, 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;
int n_nodes;
int n_leafs;
struct ggml_tensor ** nodes;
struct ggml_tensor ** grads;
struct ggml_tensor ** leafs;
struct ggml_hash_set visited_hash_set;
enum ggml_cgraph_eval_order order;
};
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);
// TODO: move to threading file
void ggml_critical_section_start(void);
void ggml_critical_section_end(void);
#ifdef __cplusplus
}
#endif