vbatts/llama.cpp
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now has a model

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This commit is contained in:
mike dupont 2023-11-23 16:32:46 -05:00
parent 8a8859ced4
commit e34fffc77b
6 changed files with 272 additions and 616 deletions
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9
examples/main/main.cpp
View file

@ -184,8 +184,9 @@ int main(int argc, char ** argv) {
g_model = &model; g_model = &model;
g_ctx = &ctx; g_ctx = &ctx;
print_fields(g_model); print_fields(*model);
print_fields(g_ctx); print_fields(*ctx);
print_fields(*ctx_guidance);
// load the model and apply lora adapter, if any // load the model and apply lora adapter, if any
LOG("%s: load the model and apply lora adapter, if any\n", __func__); LOG("%s: load the model and apply lora adapter, if any\n", __func__);
@ -488,7 +489,7 @@ int main(int argc, char ** argv) {
std::vector<llama_token> embd_guidance; std::vector<llama_token> embd_guidance;
struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams); struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
print_fields(ctx_sampling); print_fields(*ctx_sampling);
while ((n_remain != 0 && !is_antiprompt) || params.interactive) { while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
// predict // predict
@ -525,7 +526,7 @@ int main(int argc, char ** argv) {
LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
n_past, n_left, n_ctx, params.n_keep, n_discard); n_past, n_left, n_ctx, params.n_keep, n_discard);
print_fields(ctx); print_fields(*ctx);
llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1);
llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard); llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);

44
ggml-alloc.cpp
View file

@ -8,9 +8,9 @@
#include <stdio.h> #include <stdio.h>
#include <stdlib.h> #include <stdlib.h>
#include <string.h> #include <string.h>
#include "ggml-internal.hpp"
#define MAX(a, b) ((a) > (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b))
#define MAX_FREE_BLOCKS 256
//#define GGML_ALLOCATOR_DEBUG //#define GGML_ALLOCATOR_DEBUG
@ -24,28 +24,7 @@ static size_t aligned_offset(const void * buffer, size_t offset, size_t alignmen
return offset + align; return offset + align;
} }
struct free_block {
void * addr;
size_t size;
};
struct ggml_tallocr {
struct ggml_backend_buffer * buffer;
bool buffer_owned;
void * base;
size_t alignment;
int n_free_blocks;
struct free_block free_blocks[MAX_FREE_BLOCKS];
size_t max_size;
bool measure;
#ifdef GGML_ALLOCATOR_DEBUG
struct ggml_tensor * allocated_tensors[1024];
#endif
};
#ifdef GGML_ALLOCATOR_DEBUG #ifdef GGML_ALLOCATOR_DEBUG
static void add_allocated_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { static void add_allocated_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) {
@ -333,21 +312,6 @@ size_t ggml_tallocr_max_size(ggml_tallocr_t alloc) {
// graph allocator // graph allocator
struct hash_node {
int n_children;
int n_views;
};
struct ggml_gallocr {
ggml_tallocr_t talloc;
struct ggml_hash_set hash_set;
struct hash_node * hash_values;
size_t hash_values_size;
ggml_tallocr_t * hash_allocs;
int * parse_seq;
int parse_seq_len;
};
ggml_gallocr_t ggml_gallocr_new(void) { ggml_gallocr_t ggml_gallocr_new(void) {
ggml_gallocr_t galloc = (ggml_gallocr_t)malloc(sizeof(struct ggml_gallocr)); ggml_gallocr_t galloc = (ggml_gallocr_t)malloc(sizeof(struct ggml_gallocr));
@ -700,10 +664,6 @@ void ggml_gallocr_alloc_graph_n(ggml_gallocr_t galloc, struct ggml_cgraph * grap
// legacy API wrapper // legacy API wrapper
struct ggml_allocr {
ggml_tallocr_t talloc;
ggml_gallocr_t galloc;
};
static ggml_allocr_t ggml_allocr_new_impl(ggml_tallocr_t talloc) { static ggml_allocr_t ggml_allocr_new_impl(ggml_tallocr_t talloc) {
ggml_allocr_t alloc = (ggml_allocr_t)malloc(sizeof(struct ggml_allocr)); ggml_allocr_t alloc = (ggml_allocr_t)malloc(sizeof(struct ggml_allocr));

75
ggml-internal.hpp
View file

@ -38,56 +38,55 @@ struct ggml_context_container {
} }
}; };
typedef int ggml_lock_t;
typedef pthread_t ggml_thread_t;
typedef int ggml_lock_t;
typedef pthread_t ggml_thread_t;
typedef volatile LONG atomic_int;
typedef atomic_int atomic_bool;
typedef HANDLE pthread_t;
typedef DWORD thread_ret_t;
typedef void * thread_ret_t;
typedef double ggml_float; typedef double ggml_float;
typedef void * thread_ret_t;
#define ggml_lock_init(x) UNUSED(x) #define MAX_FREE_BLOCKS 256
#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 struct free_block {
void * addr;
size_t size;
};
struct ggml_tallocr {
struct ggml_backend_buffer * buffer;
bool buffer_owned;
void * base;
size_t alignment;
int n_free_blocks;
struct free_block free_blocks[MAX_FREE_BLOCKS];
#define ggml_thread_create pthread_create size_t max_size;
#define ggml_thread_join pthread_join
bool measure;
#ifdef GGML_ALLOCATOR_DEBUG
//typedef pthread_spinlock_t ggml_lock_t; struct ggml_tensor * allocated_tensors[1024];
//#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
#define ggml_lock_init(x) UNUSED(x)
#define ggml_lock_destroy(x) UNUSED(x)
#if defined(__x86_64__) || (defined(_MSC_VER) && defined(_M_AMD64))
#define ggml_lock_lock(x) _mm_pause()
#else
#define ggml_lock_lock(x) UNUSED(x)
#endif #endif
#define ggml_lock_unlock(x) UNUSED(x) };
#define GGML_LOCK_INITIALIZER 0
struct hash_node {
int n_children;
int n_views;
};
#define ggml_thread_create pthread_create typedef struct ggml_tallocr * ggml_tallocr_t;
#define ggml_thread_join pthread_join typedef struct ggml_gallocr * ggml_gallocr_t;
struct ggml_gallocr {
ggml_tallocr_t talloc;
struct ggml_hash_set hash_set;
struct hash_node * hash_values;
size_t hash_values_size;
ggml_tallocr_t * hash_allocs;
int * parse_seq;
int parse_seq_len;
};
struct ggml_allocr {
ggml_tallocr_t talloc;
ggml_gallocr_t galloc;
};

45
ggml.cpp
View file

@ -48,6 +48,8 @@
#include <windows.h> #include <windows.h>
typedef volatile LONG atomic_int;
typedef atomic_int atomic_bool;
static void atomic_store(atomic_int * ptr, LONG val) { static void atomic_store(atomic_int * ptr, LONG val) {
InterlockedExchange(ptr, val); InterlockedExchange(ptr, val);
@ -15723,6 +15725,49 @@ void ggml_graph_clear(struct ggml_cgraph * cgraph) {
// //
//#define GGML_LOCK_INITIALIZER OS_UNFAIR_LOCK_INIT //#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)
#if defined(__x86_64__) || (defined(_MSC_VER) && defined(_M_AMD64))
#define ggml_lock_lock(x) _mm_pause()
#else
#define ggml_lock_lock(x) UNUSED(x)
#endif
#define ggml_lock_unlock(x) UNUSED(x)
#define GGML_LOCK_INITIALIZER 0
typedef pthread_t ggml_thread_t;
#define ggml_thread_create pthread_create
#define ggml_thread_join pthread_join
#endif
// Android's libc implementation "bionic" does not support setting affinity // Android's libc implementation "bionic" does not support setting affinity
#if defined(__linux__) && !defined(__BIONIC__) #if defined(__linux__) && !defined(__BIONIC__)

614
llama.cpp
View file

@ -77,6 +77,8 @@
#include <thread> #include <thread>
#include <unordered_map> #include <unordered_map>
#include "llama-internal.hpp"
#if defined(_MSC_VER) #if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data #pragma warning(disable: 4244 4267) // possible loss of data
#endif #endif
@ -176,25 +178,11 @@ static std::string format(const char * fmt, ...) {
return std::string(buf.data(), size); return std::string(buf.data(), size);
} }
// //
// gguf constants (sync with gguf.py) // gguf constants (sync with gguf.py)
// //
enum llm_arch {
LLM_ARCH_LLAMA,
LLM_ARCH_FALCON,
LLM_ARCH_BAICHUAN,
LLM_ARCH_GPT2,
LLM_ARCH_GPTJ,
LLM_ARCH_GPTNEOX,
LLM_ARCH_MPT,
LLM_ARCH_STARCODER,
LLM_ARCH_PERSIMMON,
LLM_ARCH_REFACT,
LLM_ARCH_BLOOM,
LLM_ARCH_STABLELM,
LLM_ARCH_UNKNOWN,
};
static std::map<llm_arch, std::string> LLM_ARCH_NAMES = { static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
{ LLM_ARCH_LLAMA, "llama" }, { LLM_ARCH_LLAMA, "llama" },
@ -211,55 +199,6 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
{ LLM_ARCH_STABLELM, "stablelm" }, { LLM_ARCH_STABLELM, "stablelm" },
}; };
enum llm_kv {
LLM_KV_GENERAL_ARCHITECTURE,
LLM_KV_GENERAL_QUANTIZATION_VERSION,
LLM_KV_GENERAL_ALIGNMENT,
LLM_KV_GENERAL_NAME,
LLM_KV_GENERAL_AUTHOR,
LLM_KV_GENERAL_URL,
LLM_KV_GENERAL_DESCRIPTION,
LLM_KV_GENERAL_LICENSE,
LLM_KV_GENERAL_SOURCE_URL,
LLM_KV_GENERAL_SOURCE_HF_REPO,
LLM_KV_CONTEXT_LENGTH,
LLM_KV_EMBEDDING_LENGTH,
LLM_KV_BLOCK_COUNT,
LLM_KV_FEED_FORWARD_LENGTH,
LLM_KV_USE_PARALLEL_RESIDUAL,
LLM_KV_TENSOR_DATA_LAYOUT,
LLM_KV_ATTENTION_HEAD_COUNT,
LLM_KV_ATTENTION_HEAD_COUNT_KV,
LLM_KV_ATTENTION_MAX_ALIBI_BIAS,
LLM_KV_ATTENTION_CLAMP_KQV,
LLM_KV_ATTENTION_LAYERNORM_EPS,
LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
LLM_KV_ROPE_DIMENSION_COUNT,
LLM_KV_ROPE_FREQ_BASE,
LLM_KV_ROPE_SCALE_LINEAR,
LLM_KV_ROPE_SCALING_TYPE,
LLM_KV_ROPE_SCALING_FACTOR,
LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,
LLM_KV_ROPE_SCALING_FINETUNED,
LLM_KV_TOKENIZER_MODEL,
LLM_KV_TOKENIZER_LIST,
LLM_KV_TOKENIZER_TOKEN_TYPE,
LLM_KV_TOKENIZER_SCORES,
LLM_KV_TOKENIZER_MERGES,
LLM_KV_TOKENIZER_BOS_ID,
LLM_KV_TOKENIZER_EOS_ID,
LLM_KV_TOKENIZER_UNK_ID,
LLM_KV_TOKENIZER_SEP_ID,
LLM_KV_TOKENIZER_PAD_ID,
LLM_KV_TOKENIZER_ADD_BOS,
LLM_KV_TOKENIZER_ADD_EOS,
LLM_KV_TOKENIZER_HF_JSON,
LLM_KV_TOKENIZER_RWKV,
};
static std::map<llm_kv, std::string> LLM_KV_NAMES = { static std::map<llm_kv, std::string> LLM_KV_NAMES = {
{ LLM_KV_GENERAL_ARCHITECTURE, "general.architecture" }, { LLM_KV_GENERAL_ARCHITECTURE, "general.architecture" },
@ -311,38 +250,6 @@ static std::map<llm_kv, std::string> LLM_KV_NAMES = {
{ LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" }, { LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" },
}; };
struct LLM_KV {
LLM_KV(llm_arch arch) : arch(arch) {}
llm_arch arch;
std::string operator()(llm_kv kv) const {
return ::format(LLM_KV_NAMES[kv].c_str(), LLM_ARCH_NAMES[arch].c_str());
}
};
enum llm_tensor {
LLM_TENSOR_TOKEN_EMBD,
LLM_TENSOR_TOKEN_EMBD_NORM,
LLM_TENSOR_POS_EMBD,
LLM_TENSOR_OUTPUT,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_ROPE_FREQS,
LLM_TENSOR_ATTN_Q,
LLM_TENSOR_ATTN_K,
LLM_TENSOR_ATTN_V,
LLM_TENSOR_ATTN_QKV,
LLM_TENSOR_ATTN_OUT,
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_NORM_2,
LLM_TENSOR_ATTN_ROT_EMBD,
LLM_TENSOR_FFN_GATE,
LLM_TENSOR_FFN_DOWN,
LLM_TENSOR_FFN_UP,
LLM_TENSOR_FFN_NORM,
LLM_TENSOR_ATTN_Q_NORM,
LLM_TENSOR_ATTN_K_NORM,
};
static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = { static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
{ {
@ -547,27 +454,6 @@ static llm_arch llm_arch_from_string(const std::string & name) {
// std::string name = tn(LLM_TENSOR_TOKEN_EMBD, "bias"); -> "token_embd.bias" // std::string name = tn(LLM_TENSOR_TOKEN_EMBD, "bias"); -> "token_embd.bias"
// std::string name = tn(LLM_TENSOR_ATTN_NORM, "weight", 3); -> "blk.3.attn_norm.weight" // std::string name = tn(LLM_TENSOR_ATTN_NORM, "weight", 3); -> "blk.3.attn_norm.weight"
// //
struct LLM_TN {
LLM_TN(llm_arch arch) : arch(arch) {}
llm_arch arch;
std::string operator()(llm_tensor tensor) const {
return LLM_TENSOR_NAMES[arch].at(tensor);
}
std::string operator()(llm_tensor tensor, const std::string & suffix) const {
return LLM_TENSOR_NAMES[arch].at(tensor) + "." + suffix;
}
std::string operator()(llm_tensor tensor, int bid) const {
return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid);
}
std::string operator()(llm_tensor tensor, const std::string & suffix, int bid) const {
return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid) + "." + suffix;
}
};
// //
// gguf helpers // gguf helpers
@ -723,15 +609,9 @@ static std::string llama_format_win_err(DWORD err) {
} }
#endif #endif
struct llama_buffer { //struct llama_buffer {
void * data = NULL;
size_t size = 0;
// fallback to malloc / free void llama_buffer::resize(size_t n) {
// useful in cases where CUDA can try to allocate PINNED memory
bool fallback = false;
void resize(size_t n) {
llama_host_free(data); llama_host_free(data);
data = llama_host_malloc(n); data = llama_host_malloc(n);
@ -746,7 +626,7 @@ struct llama_buffer {
size = n; size = n;
} }
~llama_buffer() { llama_buffer::~llama_buffer() {
if (data) { if (data) {
if (fallback) { // NOLINT if (fallback) { // NOLINT
free(data); free(data);
@ -757,7 +637,7 @@ struct llama_buffer {
data = NULL; data = NULL;
} }
};
struct llama_file { struct llama_file {
// use FILE * so we don't have to re-open the file to mmap // use FILE * so we don't have to re-open the file to mmap
@ -835,16 +715,11 @@ struct llama_file {
} }
}; };
struct llama_mmap { //
void * addr;
size_t size;
llama_mmap(const llama_mmap &) = delete;
#ifdef _POSIX_MAPPED_FILES #ifdef _POSIX_MAPPED_FILES
static constexpr bool SUPPORTED = true; llama_mmap::llama_mmap(struct llama_file * file, size_t prefetch , bool numa ) {
llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) {
size = file->size; size = file->size;
int fd = fileno(file->fp); int fd = fileno(file->fp);
int flags = MAP_SHARED; int flags = MAP_SHARED;
@ -875,13 +750,12 @@ struct llama_mmap {
} }
} }
~llama_mmap() { llama_mmap::~llama_mmap() {
munmap(addr, size); munmap(addr, size);
} }
#elif defined(_WIN32) #elif defined(_WIN32)
static constexpr bool SUPPORTED = true;
llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) { llama_mmap::llama_mmap(struct llama_file * file, size_t prefetch = 1, bool numa = false) {
(void) numa; (void) numa;
size = file->size; size = file->size;
@ -903,7 +777,7 @@ struct llama_mmap {
throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str())); throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
} }
if (prefetch) { if (prefetch == 1) {
// PrefetchVirtualMemory is only present on Windows 8 and above, so we dynamically load it // PrefetchVirtualMemory is only present on Windows 8 and above, so we dynamically load it
BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG); BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG);
HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll"); HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll");
@ -924,7 +798,7 @@ struct llama_mmap {
} }
} }
~llama_mmap() { llama_mmap::~llama_mmap() {
if (!UnmapViewOfFile(addr)) { if (!UnmapViewOfFile(addr)) {
fprintf(stderr, "warning: UnmapViewOfFile failed: %s\n", fprintf(stderr, "warning: UnmapViewOfFile failed: %s\n",
llama_format_win_err(GetLastError()).c_str()); llama_format_win_err(GetLastError()).c_str());
@ -941,31 +815,27 @@ struct llama_mmap {
throw std::runtime_error(std::string("mmap not supported")); throw std::runtime_error(std::string("mmap not supported"));
} }
#endif #endif
};
// Represents some region of memory being locked using mlock or VirtualLock; // Represents some region of memory being locked using mlock or VirtualLock;
// will automatically unlock on destruction. // will automatically unlock on destruction.
struct llama_mlock { // llama_mlock
void * addr = NULL;
size_t size = 0;
bool failed_already = false;
llama_mlock() {} llama_mlock::llama_mlock() {}
llama_mlock(const llama_mlock &) = delete;
~llama_mlock() { llama_mlock::~llama_mlock() {
if (size) { if (size) {
raw_unlock(addr, size); raw_unlock(addr, size);
} }
} }
void init(void * ptr) { void llama_mlock::init(void * ptr) {
GGML_ASSERT(addr == NULL && size == 0); // NOLINT GGML_ASSERT(addr == NULL && size == 0); // NOLINT
addr = ptr; addr = ptr;
} }
void grow_to(size_t target_size) { void llama_mlock::grow_to(size_t target_size) {
GGML_ASSERT(addr); GGML_ASSERT(addr);
if (failed_already) { if (failed_already) {
return; return;
@ -982,9 +852,8 @@ struct llama_mlock {
} }
#ifdef _POSIX_MEMLOCK_RANGE #ifdef _POSIX_MEMLOCK_RANGE
static constexpr bool SUPPORTED = true;
static size_t lock_granularity() { size_t llama_mlock::lock_granularity() {
return (size_t) sysconf(_SC_PAGESIZE); return (size_t) sysconf(_SC_PAGESIZE);
} }
@ -997,7 +866,7 @@ struct llama_mlock {
"Try increasing RLIMIT_MLOCK ('ulimit -l' as root).\n" "Try increasing RLIMIT_MLOCK ('ulimit -l' as root).\n"
#endif #endif
bool raw_lock(const void * addr, size_t size) const { bool llama_mlock::raw_lock(const void * addr, size_t size) const {
if (!mlock(addr, size)) { if (!mlock(addr, size)) {
return true; return true;
} }
@ -1021,21 +890,21 @@ struct llama_mlock {
#undef MLOCK_SUGGESTION #undef MLOCK_SUGGESTION
static void raw_unlock(void * addr, size_t size) { void llama_mlock::raw_unlock(void * addr, size_t size) {
if (munlock(addr, size)) { if (munlock(addr, size)) {
fprintf(stderr, "warning: failed to munlock buffer: %s\n", std::strerror(errno)); fprintf(stderr, "warning: failed to munlock buffer: %s\n", std::strerror(errno));
} }
} }
#elif defined(_WIN32) #elif defined(_WIN32)
static constexpr bool SUPPORTED = true;
static size_t lock_granularity() { size_t llama_mlock::lock_granularity() {
SYSTEM_INFO si; SYSTEM_INFO si;
GetSystemInfo(&si); GetSystemInfo(&si);
return (size_t) si.dwPageSize; return (size_t) si.dwPageSize;
} }
bool raw_lock(void * ptr, size_t len) const { bool llama_mlock::raw_lock(void * ptr, size_t len) const {
for (int tries = 1; ; tries++) { for (int tries = 1; ; tries++) {
if (VirtualLock(ptr, len)) { if (VirtualLock(ptr, len)) {
return true; return true;
@ -1070,27 +939,26 @@ struct llama_mlock {
} }
} }
static void raw_unlock(void * ptr, size_t len) { static void llama_mlock::raw_unlock(void * ptr, size_t len) {
if (!VirtualUnlock(ptr, len)) { if (!VirtualUnlock(ptr, len)) {
fprintf(stderr, "warning: failed to VirtualUnlock buffer: %s\n", fprintf(stderr, "warning: failed to VirtualUnlock buffer: %s\n",
llama_format_win_err(GetLastError()).c_str()); llama_format_win_err(GetLastError()).c_str());
} }
} }
#else #else
static constexpr bool SUPPORTED = false;
static size_t llama_mlock::lock_granularity() {
static size_t lock_granularity() {
return (size_t) 65536; return (size_t) 65536;
} }
bool raw_lock(const void * addr, size_t len) const { bool llama_mlock::raw_lock(const void * addr, size_t len) const {
fprintf(stderr, "warning: mlock not supported on this system\n"); fprintf(stderr, "warning: mlock not supported on this system\n");
return false; return false;
} }
static void raw_unlock(const void * addr, size_t len) {} static void llama_mlock::raw_unlock(const void * addr, size_t len) {}
#endif #endif
};
typedef void (*offload_func_t)(struct ggml_tensor * tensor); typedef void (*offload_func_t)(struct ggml_tensor * tensor);
@ -1125,364 +993,12 @@ struct llama_state {
static llama_state g_state; static llama_state g_state;
// available llama models
enum e_model {
MODEL_UNKNOWN,
MODEL_1B,
MODEL_3B,
MODEL_7B,
MODEL_8B,
MODEL_13B,
MODEL_15B,
MODEL_30B,
MODEL_34B,
MODEL_40B,
MODEL_65B,
MODEL_70B,
};
static const size_t kiB = 1024; static const size_t kiB = 1024;
static const size_t MiB = 1024*kiB; static const size_t MiB = 1024*kiB;
static const size_t GiB = 1024*MiB; static const size_t GiB = 1024*MiB;
struct llama_hparams {
bool vocab_only;
uint32_t n_vocab;
uint32_t n_ctx_train; // context size the model was trained on
uint32_t n_embd;
uint32_t n_head;
uint32_t n_head_kv;
uint32_t n_layer;
uint32_t n_rot;
uint32_t n_ff;
float f_norm_eps;
float f_norm_rms_eps;
float rope_freq_base_train;
float rope_freq_scale_train;
uint32_t n_yarn_orig_ctx;
int8_t rope_scaling_type_train : 3;
bool rope_finetuned : 1;
float f_clamp_kqv;
float f_max_alibi_bias;
bool operator!=(const llama_hparams & other) const {
if (this->vocab_only != other.vocab_only) return true;
if (this->n_vocab != other.n_vocab) return true;
if (this->n_ctx_train != other.n_ctx_train) return true;
if (this->n_embd != other.n_embd) return true;
if (this->n_head != other.n_head) return true;
if (this->n_head_kv != other.n_head_kv) return true;
if (this->n_layer != other.n_layer) return true;
if (this->n_rot != other.n_rot) return true;
if (this->n_ff != other.n_ff) return true;
if (this->rope_finetuned != other.rope_finetuned) return true;
if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true;
const float EPSILON = 1e-9;
if (!is_float_close(this->f_norm_eps, other.f_norm_eps, EPSILON)) return true;
if (!is_float_close(this->f_norm_rms_eps, other.f_norm_rms_eps, EPSILON)) return true;
if (!is_float_close(this->rope_freq_base_train, other.rope_freq_base_train, EPSILON)) return true;
if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true;
return false;
}
uint32_t n_gqa() const {
return n_head/n_head_kv;
}
uint32_t n_embd_head() const {
return n_embd/n_head;
}
uint32_t n_embd_gqa() const {
return n_embd/n_gqa();
}
};
struct llama_cparams {
uint32_t n_ctx; // context size used during inference
uint32_t n_batch;
uint32_t n_threads; // number of threads to use for generation
uint32_t n_threads_batch; // number of threads to use for batch processing
float rope_freq_base;
float rope_freq_scale;
uint32_t n_yarn_orig_ctx;
// These hyperparameters are not exposed in GGUF, because all
// existing YaRN models use the same values for them.
float yarn_ext_factor;
float yarn_attn_factor;
float yarn_beta_fast;
float yarn_beta_slow;
bool mul_mat_q;
};
struct llama_layer {
// normalization
struct ggml_tensor * attn_norm;
struct ggml_tensor * attn_norm_b;
struct ggml_tensor * attn_norm_2;
struct ggml_tensor * attn_norm_2_b;
struct ggml_tensor * attn_q_norm;
struct ggml_tensor * attn_q_norm_b;
struct ggml_tensor * attn_k_norm;
struct ggml_tensor * attn_k_norm_b;
// attention
struct ggml_tensor * wq;
struct ggml_tensor * wk;
struct ggml_tensor * wv;
struct ggml_tensor * wo;
struct ggml_tensor * wqkv;
// attention bias
struct ggml_tensor * bo;
struct ggml_tensor * bqkv;
// normalization
struct ggml_tensor * ffn_norm;
struct ggml_tensor * ffn_norm_b;
// ff
struct ggml_tensor * ffn_gate; // w1
struct ggml_tensor * ffn_down; // w2
struct ggml_tensor * ffn_up; // w3
// ff bias
struct ggml_tensor * ffn_down_b; // b2
struct ggml_tensor * ffn_up_b; // b3
};
struct llama_kv_cell {
llama_pos pos = -1;
llama_pos delta = 0;
std::set<llama_seq_id> seq_id;
bool has_seq_id(const llama_seq_id & id) const {
return seq_id.find(id) != seq_id.end();
}
};
// ring-buffer of cached KV data
struct llama_kv_cache {
bool has_shift = false;
// Note: The value of head isn't only used to optimize searching
// for a free KV slot. llama_decode_internal also uses it, so it
// cannot be freely changed after a slot has been allocated.
uint32_t head = 0;
uint32_t size = 0;
// computed before each graph build
uint32_t n = 0;
std::vector<llama_kv_cell> cells;
struct ggml_tensor * k = NULL;
struct ggml_tensor * v = NULL;
struct ggml_context * ctx = NULL;
llama_buffer buf;
~llama_kv_cache() {
if (ctx) {
ggml_free(ctx);
}
#ifdef GGML_USE_CUBLAS
if (ggml_cublas_loaded()) {
ggml_cuda_free_data(k);
ggml_cuda_free_data(v);
}
#endif
}
};
struct llama_vocab {
using id = int32_t;
using token = std::string;
using ttype = llama_token_type;
struct token_data {
token text;
float score;
ttype type;
};
enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM;
std::unordered_map<token, id> token_to_id;
std::vector<token_data> id_to_token;
std::unordered_map<token, id> special_tokens_cache;
std::map<std::pair<std::string, std::string>, int> bpe_ranks;
// default LLaMA special tokens
id special_bos_id = 1;
id special_eos_id = 2;
id special_unk_id = 0;
id special_sep_id = -1;
id special_pad_id = -1;
int special_add_bos = -1; // -1 unknown, 1 add, 0 don't add.
int special_add_eos = -1; // -1 unknown, 1 add, 0 don't add.
id linefeed_id = 13;
id special_prefix_id = 32007;
id special_middle_id = 32009;
id special_suffix_id = 32008;
id special_eot_id = 32010;
int find_bpe_rank(std::string token_left, std::string token_right) const {
GGML_ASSERT(token_left.find(" ") == std::string::npos);
GGML_ASSERT(token_left.find("\n") == std::string::npos);
GGML_ASSERT(token_right.find(" ") == std::string::npos);
GGML_ASSERT(token_right.find("\n") == std::string::npos);
auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
if (it == bpe_ranks.end()) {
return -1;
}
return it->second;
}
};
struct llama_model {
e_model type = MODEL_UNKNOWN;
llm_arch arch = LLM_ARCH_UNKNOWN;
llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
std::string name = "n/a";
llama_hparams hparams = {};
llama_vocab vocab;
struct ggml_tensor * tok_embd;
struct ggml_tensor * pos_embd;
struct ggml_tensor * tok_norm;
struct ggml_tensor * tok_norm_b;
struct ggml_tensor * output_norm;
struct ggml_tensor * output_norm_b;
struct ggml_tensor * output;
std::vector<llama_layer> layers;
int n_gpu_layers;
// gguf metadata
std::unordered_map<std::string, std::string> gguf_kv;
// context
struct ggml_context * ctx = NULL;
// the model memory buffer
llama_buffer buf;
// model memory mapped file
std::unique_ptr<llama_mmap> mapping;
// objects representing data potentially being locked in memory
llama_mlock mlock_buf;
llama_mlock mlock_mmap;
// for quantize-stats only
std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;
int64_t t_load_us = 0;
int64_t t_start_us = 0;
~llama_model() {
if (ctx) {
ggml_free(ctx);
}
#ifdef GGML_USE_CUBLAS
if (ggml_cublas_loaded()) {
for (size_t i = 0; i < tensors_by_name.size(); ++i) {
ggml_cuda_free_data(tensors_by_name[i].second);
}
ggml_cuda_free_scratch();
}
#endif
#if defined(GGML_USE_CLBLAST)
for (size_t i = 0; i < tensors_by_name.size(); ++i) {
ggml_cl_free_data(tensors_by_name[i].second);
}
#endif
}
};
struct llama_context {
llama_context(const llama_model & model) : model(model), t_start_us(model.t_start_us), t_load_us(model.t_load_us) {}
~llama_context() {
#ifdef GGML_USE_METAL
if (ctx_metal) {
ggml_metal_free(ctx_metal);
}
#endif
if (alloc) {
ggml_allocr_free(alloc);
}
}
llama_cparams cparams;
const llama_model & model;
// key + value cache for the self attention
struct llama_kv_cache kv_self;
std::mt19937 rng;
bool has_evaluated_once = false;
int64_t t_start_us;
int64_t t_load_us;
int64_t t_sample_us = 0;
int64_t t_p_eval_us = 0;
int64_t t_eval_us = 0;
int32_t n_sample = 0; // number of tokens sampled
int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
int32_t n_eval = 0; // number of eval calls
// decode output (2-dimensional array: [n_tokens][n_vocab])
std::vector<float> logits;
bool logits_all = false;
// input embedding (1-dimensional array: [n_embd])
std::vector<float> embedding;
// reusable buffer for `struct ggml_graph_plan.work_data`
std::vector<uint8_t> work_buffer;
// memory buffers used to evaluate the model
llama_buffer buf_compute;
llama_buffer buf_alloc;
ggml_allocr * alloc = NULL;
#ifdef GGML_USE_METAL
ggml_metal_context * ctx_metal = NULL;
#endif
#ifdef GGML_USE_MPI
ggml_mpi_context * ctx_mpi = NULL;
#endif
};
// //
// kv cache helpers // kv cache helpers
@ -1731,11 +1247,6 @@ static void llama_kv_cache_seq_shift(
// model loading and saving // model loading and saving
// //
enum llama_fver {
GGUF_FILE_VERSION_V1 = 1,
GGUF_FILE_VERSION_V2 = 2,
GGUF_FILE_VERSION_V3 = 3,
};
static const char * llama_file_version_name(llama_fver version) { static const char * llama_file_version_name(llama_fver version) {
switch (version) { switch (version) {
@ -6328,6 +5839,32 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
} }
} }
// struct
bool llama_hparams::operator!=(const llama_hparams & other) const {
if (this->vocab_only != other.vocab_only) return true;
if (this->n_vocab != other.n_vocab) return true;
if (this->n_ctx_train != other.n_ctx_train) return true;
if (this->n_embd != other.n_embd) return true;
if (this->n_head != other.n_head) return true;
if (this->n_head_kv != other.n_head_kv) return true;
if (this->n_layer != other.n_layer) return true;
if (this->n_rot != other.n_rot) return true;
if (this->n_ff != other.n_ff) return true;
if (this->rope_finetuned != other.rope_finetuned) return true;
if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true;
const float EPSILON = 1e-9;
if (!is_float_close(this->f_norm_eps, other.f_norm_eps, EPSILON)) return true;
if (!is_float_close(this->f_norm_rms_eps, other.f_norm_rms_eps, EPSILON)) return true;
if (!is_float_close(this->rope_freq_base_train, other.rope_freq_base_train, EPSILON)) return true;
if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true;
return false;
}
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special) { static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special) {
std::vector<llama_vocab::id> output; std::vector<llama_vocab::id> output;
@ -9713,3 +9250,40 @@ static void llama_log_callback_default(ggml_log_level level, const char * text,
fputs(text, stderr); fputs(text, stderr);
fflush(stderr); fflush(stderr);
} }
// LLM_TN
LLM_TN::LLM_TN(llm_arch arch) : arch(arch) {}
std::string LLM_TN::operator()(llm_tensor tensor) const {
return LLM_TENSOR_NAMES[arch].at(tensor);
}
std::string LLM_TN::operator()(llm_tensor tensor, const std::string & suffix) const {
return LLM_TENSOR_NAMES[arch].at(tensor) + "." + suffix;
}
std::string LLM_TN::operator()(llm_tensor tensor, int bid) const {
return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid);
}
std::string LLM_TN::operator()(llm_tensor tensor, const std::string & suffix, int bid) const {
return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid) + "." + suffix;
}
std::string LLM_KV::operator()(llm_kv kv) const {
return ::format(LLM_KV_NAMES[kv].c_str(), LLM_ARCH_NAMES[arch].c_str());
}
llama_context::~llama_context() {
#ifdef GGML_USE_METAL
if (ctx_metal) {
ggml_metal_free(ctx_metal);
}
#endif
if (alloc) {
ggml_allocr_free(alloc);
}
}

101
print.hpp
View file

@ -3,6 +3,8 @@
#include <iostream> #include <iostream>
//#include <refl.hpp> //#include <refl.hpp>
#include "llama.h" #include "llama.h"
#include "ggml-internal.hpp"
#include "llama-internal.hpp"
REFL_TYPE(ggml_init_params ) REFL_TYPE(ggml_init_params )
REFL_END REFL_END
@ -92,6 +94,12 @@ REFL_END
REFL_TYPE(llama_sampling_context ) REFL_TYPE(llama_sampling_context )
REFL_FIELD( params)
REFL_FIELD( mirostat_mu)
REFL_FIELD( grammar)
REFL_FIELD( parsed_grammar)
REFL_FIELD( prev)
REFL_FIELD( cur)
REFL_END REFL_END
REFL_TYPE(llama_token_data ) REFL_TYPE(llama_token_data )
@ -148,15 +156,24 @@ REFL_TYPE(ggml_something)
REFL_FIELD(type_name) REFL_FIELD(type_name)
REFL_END REFL_END
// REFL_TYPE(ggml_context) REFL_TYPE(ggml_context)
// REFL_FIELD(mem_size) REFL_FIELD(mem_size)
// REFL_FIELD(mem_buffer) REFL_FIELD(mem_buffer)
// REFL_END REFL_FIELD(mem_buffer_owned)
REFL_FIELD( no_alloc)
REFL_FIELD( no_alloc_save)
REFL_FIELD( n_objects)
REFL_FIELD( objects_begin)
REFL_FIELD( objects_end)
REFL_FIELD( scratch)
REFL_FIELD( scratch_save)
//REFL_TYPE(ggml_context_container) REFL_END
// REFL_FIELD(used)
// REFL_FIELD(context) REFL_TYPE(ggml_context_container)
//REFL_END REFL_FIELD(used)
REFL_FIELD(context)
REFL_END
// REFL_TYPE(ggml_numa_node) // REFL_TYPE(ggml_numa_node)
// REFL_FIELD(cpus) // REFL_FIELD(cpus)
@ -340,11 +357,70 @@ REFL_END
// REFL_END // REFL_END
REFL_TYPE(llama_model) REFL_TYPE(llama_model)
// REFL_FIELD(type) REFL_FIELD(type)
// REFL_FIELD(arch) REFL_FIELD(arch)
REFL_FIELD(ftype )
REFL_FIELD( name )
REFL_FIELD( hparams )
REFL_FIELD( vocab)
REFL_FIELD( tok_embd)
REFL_FIELD( pos_embd)
REFL_FIELD( tok_norm)
REFL_FIELD( tok_norm_b)
REFL_FIELD( output_norm)
REFL_FIELD( output_norm_b)
REFL_FIELD( output)
REFL_FIELD( layers)
REFL_FIELD( n_gpu_layers)
REFL_FIELD( gguf_kv)
REFL_FIELD( ctx)
REFL_FIELD( buf)
REFL_FIELD( mapping)
REFL_FIELD( mlock_buf)
REFL_FIELD( mlock_mmap)
REFL_FIELD( tensors_by_name)
REFL_FIELD( t_load_us)
REFL_FIELD( t_start_us)
REFL_END REFL_END
REFL_TYPE(llama_context) REFL_TYPE(llama_context)
REFL_FIELD( cparams)
//REFL_FIELD(model)
REFL_FIELD(kv_self)
REFL_FIELD(rng)
REFL_FIELD(has_evaluated_once )
REFL_FIELD( t_start_us)
REFL_FIELD( t_load_us)
REFL_FIELD( t_sample_us )
REFL_FIELD( t_p_eval_us )
REFL_FIELD( t_eval_us)
REFL_FIELD( n_sample )
REFL_FIELD( n_p_eval )
REFL_FIELD( n_eval )
REFL_FIELD( logits)
REFL_FIELD( logits_all )
REFL_FIELD( embedding)
REFL_FIELD( work_buffer)
REFL_FIELD( buf_compute)
REFL_FIELD( buf_alloc)
REFL_FIELD( alloc )
#ifdef GGML_USE_METAL
REFL_FIELD( ctx_metal )
#endif
#ifdef GGML_USE_MPI
REFL_FIELD( ctx_mpi )
#endif
REFL_END REFL_END
// REFL_TYPE(llama_model_loader) // REFL_TYPE(llama_model_loader)
@ -459,7 +535,7 @@ void print_fields(const T& ) {
// T instance{}; // T instance{};
for_each(refl::reflect<T>().members, [&](auto member) { for_each(refl::reflect<T>().members, [&](auto member) {
std::cout << "MEMBER" << member.name.str() << "\n"; std::cout << "MEMBER:" << member.name.str() << "\n";
}); });
@ -468,7 +544,8 @@ void print_fields(const T& ) {
//if ((refl::descriptor::is_field(member)) && (!member.has_attribute<hidden>()))) { //if ((refl::descriptor::is_field(member)) && (!member.has_attribute<hidden>()))) {
//if ((refl::descriptor::is_field(member))) { //if ((refl::descriptor::is_field(member))) {
// // Print the member name and value // // Print the member name and value
std::cout << member.name << ": " << "\n"; std::cout
<< "Auto:" << member.name << ": " << "\n";
// refl::get(member, obj) // refl::get(member, obj)
//} //}
}); });