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ggml-ci
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Georgi Gerganov 2024-12-23 19:10:27 +02:00
parent 7035c79fb5
commit 0ccae21e6b
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7 changed files with 333 additions and 339 deletions
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62
src/llama-batch.cpp
View File

@ -304,3 +304,65 @@ llama_batch_allocr::llama_batch_allocr(struct llama_batch in_batch, llama_pos p0
batch.logits = logits.data();
}
}
//
// interface implementation
//
struct llama_batch llama_batch_get_one(
llama_token * tokens,
int32_t n_tokens) {
return {
/*n_tokens =*/ n_tokens,
/*tokens =*/ tokens,
/*embd =*/ nullptr,
/*pos =*/ nullptr,
/*n_seq_id =*/ nullptr,
/*seq_id =*/ nullptr,
/*logits =*/ nullptr,
};
}
struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_t n_seq_max) {
llama_batch batch = {
/*n_tokens =*/ 0,
/*tokens =*/ nullptr,
/*embd =*/ nullptr,
/*pos =*/ nullptr,
/*n_seq_id =*/ nullptr,
/*seq_id =*/ nullptr,
/*logits =*/ nullptr,
};
if (embd) {
batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd);
} else {
batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc);
}
batch.pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens_alloc);
batch.n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens_alloc);
batch.seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * (n_tokens_alloc + 1));
for (int i = 0; i < n_tokens_alloc; ++i) {
batch.seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
}
batch.seq_id[n_tokens_alloc] = nullptr;
batch.logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens_alloc);
return batch;
}
void llama_batch_free(struct llama_batch batch) {
if (batch.token) free(batch.token);
if (batch.embd) free(batch.embd);
if (batch.pos) free(batch.pos);
if (batch.n_seq_id) free(batch.n_seq_id);
if (batch.seq_id) {
for (int i = 0; batch.seq_id[i] != nullptr; ++i) {
free(batch.seq_id[i]);
}
free(batch.seq_id);
}
if (batch.logits) free(batch.logits);
}

1
src/llama-batch.h
View File

@ -86,4 +86,3 @@ struct llama_batch_allocr {
// optionally fulfill the batch returned by llama_batch_get_one
llama_batch_allocr(struct llama_batch in_batch, llama_pos p0);
};

51
src/llama-impl.cpp
View File

@ -2,7 +2,9 @@
#include "llama.h"
#include <climits>
#include <cstdarg>
#include <vector>
struct llama_logger_state {
ggml_log_callback log_callback = llama_log_callback_default;
@ -19,23 +21,6 @@ time_meas::~time_meas() {
}
}
void replace_all(std::string & s, const std::string & search, const std::string & replace) {
if (search.empty()) {
return;
}
std::string builder;
builder.reserve(s.length());
size_t pos = 0;
size_t last_pos = 0;
while ((pos = s.find(search, last_pos)) != std::string::npos) {
builder.append(s, last_pos, pos - last_pos);
builder.append(replace);
last_pos = pos + search.length();
}
builder.append(s, last_pos, std::string::npos);
s = std::move(builder);
}
void llama_log_set(ggml_log_callback log_callback, void * user_data) {
ggml_log_set(log_callback, user_data);
g_logger_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
@ -72,3 +57,35 @@ void llama_log_callback_default(ggml_log_level level, const char * text, void *
fputs(text, stderr);
fflush(stderr);
}
void replace_all(std::string & s, const std::string & search, const std::string & replace) {
if (search.empty()) {
return;
}
std::string builder;
builder.reserve(s.length());
size_t pos = 0;
size_t last_pos = 0;
while ((pos = s.find(search, last_pos)) != std::string::npos) {
builder.append(s, last_pos, pos - last_pos);
builder.append(replace);
last_pos = pos + search.length();
}
builder.append(s, last_pos, std::string::npos);
s = std::move(builder);
}
std::string format(const char * fmt, ...) {
va_list ap;
va_list ap2;
va_start(ap, fmt);
va_copy(ap2, ap);
int size = vsnprintf(NULL, 0, fmt, ap);
GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
std::vector<char> buf(size + 1);
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
GGML_ASSERT(size2 == size);
va_end(ap2);
va_end(ap);
return std::string(buf.data(), size);
}

10
src/llama-impl.h
View File

@ -1,6 +1,6 @@
#pragma once
#include "ggml.h"
#include "ggml.h" // for ggml_log_level
#include <string>
@ -22,10 +22,6 @@ LLAMA_ATTRIBUTE_FORMAT(2, 3)
void llama_log_internal (ggml_log_level level, const char * format, ...);
void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data);
// TODO: rename to llama_format ?
LLAMA_ATTRIBUTE_FORMAT(1, 2)
std::string format(const char * fmt, ...);
#define LLAMA_LOG(...) llama_log_internal(GGML_LOG_LEVEL_NONE , __VA_ARGS__)
#define LLAMA_LOG_INFO(...) llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
#define LLAMA_LOG_WARN(...) llama_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
@ -47,3 +43,7 @@ struct time_meas {
};
void replace_all(std::string & s, const std::string & search, const std::string & replace);
// TODO: rename to llama_format ?
LLAMA_ATTRIBUTE_FORMAT(1, 2)
std::string format(const char * fmt, ...);

222
src/llama-model.cpp
View File

@ -189,3 +189,225 @@ struct ggml_tensor * llama_model_get_tensor(const struct llama_model & model, co
return it->second;
}
size_t llama_model_max_nodes(const llama_model & model) {
return std::max<size_t>(8192, model.tensors_by_name.size()*5);
}
//
// interface implementation
//
struct llama_model_params llama_model_default_params() {
struct llama_model_params result = {
/*.devices =*/ nullptr,
/*.n_gpu_layers =*/ 0,
/*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER,
/*.main_gpu =*/ 0,
/*.tensor_split =*/ nullptr,
/*.rpc_servers =*/ nullptr,
/*.progress_callback =*/ nullptr,
/*.progress_callback_user_data =*/ nullptr,
/*.kv_overrides =*/ nullptr,
/*.vocab_only =*/ false,
/*.use_mmap =*/ true,
/*.use_mlock =*/ false,
/*.check_tensors =*/ false,
};
#ifdef GGML_USE_METAL
// note: we usually have plenty of VRAM, so by default offload all layers to the GPU
result.n_gpu_layers = 999;
#endif
return result;
}
void llama_free_model(struct llama_model * model) {
delete model;
}
enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
return model->vocab.type;
}
int32_t llama_n_vocab(const struct llama_model * model) {
return model->hparams.n_vocab;
}
int32_t llama_n_ctx_train(const struct llama_model * model) {
return model->hparams.n_ctx_train;
}
int32_t llama_n_embd(const struct llama_model * model) {
return model->hparams.n_embd;
}
int32_t llama_n_layer(const struct llama_model * model) {
return model->hparams.n_layer;
}
int32_t llama_n_head(const struct llama_model * model) {
return model->hparams.n_head();
}
enum llama_rope_type llama_rope_type(const struct llama_model * model) {
switch (model->arch) {
// these models do not use RoPE
case LLM_ARCH_GPT2:
case LLM_ARCH_GPTJ:
case LLM_ARCH_MPT:
case LLM_ARCH_REFACT:
case LLM_ARCH_BLOOM:
case LLM_ARCH_MAMBA:
case LLM_ARCH_JINA_BERT_V2:
case LLM_ARCH_T5:
case LLM_ARCH_T5ENCODER:
case LLM_ARCH_JAIS:
case LLM_ARCH_RWKV6:
case LLM_ARCH_WAVTOKENIZER_DEC:
return LLAMA_ROPE_TYPE_NONE;
// use what we call a normal RoPE, operating on pairs of consecutive head values
case LLM_ARCH_LLAMA:
case LLM_ARCH_DECI:
case LLM_ARCH_BAICHUAN:
case LLM_ARCH_STARCODER:
case LLM_ARCH_PLAMO:
case LLM_ARCH_ORION:
case LLM_ARCH_INTERNLM2:
case LLM_ARCH_MINICPM:
case LLM_ARCH_XVERSE:
case LLM_ARCH_COMMAND_R:
case LLM_ARCH_OLMO:
case LLM_ARCH_ARCTIC:
case LLM_ARCH_DEEPSEEK:
case LLM_ARCH_DEEPSEEK2:
case LLM_ARCH_CHATGLM:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
case LLM_ARCH_CHAMELEON:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2
case LLM_ARCH_FALCON:
case LLM_ARCH_GROK:
case LLM_ARCH_DBRX:
case LLM_ARCH_BERT:
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_STABLELM:
case LLM_ARCH_BITNET:
case LLM_ARCH_QWEN:
case LLM_ARCH_QWEN2:
case LLM_ARCH_QWEN2MOE:
case LLM_ARCH_OLMO2:
case LLM_ARCH_OLMOE:
case LLM_ARCH_PHI2:
case LLM_ARCH_PHI3:
case LLM_ARCH_GEMMA:
case LLM_ARCH_GEMMA2:
case LLM_ARCH_STARCODER2:
case LLM_ARCH_OPENELM:
case LLM_ARCH_GPTNEOX:
case LLM_ARCH_CODESHELL:
case LLM_ARCH_NEMOTRON:
case LLM_ARCH_EXAONE:
case LLM_ARCH_MINICPM3:
return LLAMA_ROPE_TYPE_NEOX;
case LLM_ARCH_QWEN2VL:
return LLAMA_ROPE_TYPE_MROPE;
// all model arches should be listed explicitly here
case LLM_ARCH_UNKNOWN:
GGML_ABORT("unknown architecture");
}
return LLAMA_ROPE_TYPE_NONE;
}
float llama_rope_freq_scale_train(const struct llama_model * model) {
return model->hparams.rope_freq_scale_train;
}
int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) {
const auto & it = model->gguf_kv.find(key);
if (it == model->gguf_kv.end()) {
if (buf_size > 0) {
buf[0] = '\0';
}
return -1;
}
return snprintf(buf, buf_size, "%s", it->second.c_str());
}
int32_t llama_model_meta_count(const struct llama_model * model) {
return (int)model->gguf_kv.size();
}
int32_t llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
if (i < 0 || i >= (int)model->gguf_kv.size()) {
if (buf_size > 0) {
buf[0] = '\0';
}
return -1;
}
auto it = model->gguf_kv.begin();
std::advance(it, i);
return snprintf(buf, buf_size, "%s", it->first.c_str());
}
int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size) {
if (i < 0 || i >= (int)model->gguf_kv.size()) {
if (buf_size > 0) {
buf[0] = '\0';
}
return -1;
}
auto it = model->gguf_kv.begin();
std::advance(it, i);
return snprintf(buf, buf_size, "%s", it->second.c_str());
}
int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
return snprintf(buf, buf_size, "%s %s %s",
llama_model_arch_name (*model).c_str(),
llama_model_type_name (*model).c_str(),
llama_model_ftype_name(*model).c_str());
}
uint64_t llama_model_size(const struct llama_model * model) {
return model->n_bytes;
}
uint64_t llama_model_n_params(const struct llama_model * model) {
return model->n_elements;
}
bool llama_model_has_encoder(const struct llama_model * model) {
switch (model->arch) {
case LLM_ARCH_T5: return true;
case LLM_ARCH_T5ENCODER: return true;
default: return false;
}
}
bool llama_model_has_decoder(const struct llama_model * model) {
switch (model->arch) {
case LLM_ARCH_T5ENCODER: return false;
default: return true;
}
}
llama_token llama_model_decoder_start_token(const struct llama_model * model) {
return model->hparams.dec_start_token_id;
}
bool llama_model_is_recurrent(const struct llama_model * model) {
switch (model->arch) {
case LLM_ARCH_MAMBA: return true;
case LLM_ARCH_RWKV6: return true;
default: return false;
}
}

1
src/llama-model.h
View File

@ -377,3 +377,4 @@ ggml_backend_buffer_type_t llama_model_select_buft(const llama_model & model, in
// used by llama_adapter_lora
struct ggml_tensor * llama_model_get_tensor(const struct llama_model & model, const char * name);
size_t llama_model_max_nodes(const llama_model & model);

325
src/llama.cpp
View File

@ -7,7 +7,7 @@
#include "llama-sampling.h"
#include "llama-kv-cache.h"
#include "unicode.h"
#include "unicode.h" // TODO: remove
#include "ggml.h"
#include "ggml-alloc.h"
@ -40,7 +40,6 @@
#include <memory>
#include <mutex>
#include <numeric>
#include <set>
#include <sstream>
#include <thread>
#include <type_traits>
@ -54,41 +53,6 @@
// helpers
//
std::string format(const char * fmt, ...) {
va_list ap;
va_list ap2;
va_start(ap, fmt);
va_copy(ap2, ap);
int size = vsnprintf(NULL, 0, fmt, ap);
GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
std::vector<char> buf(size + 1);
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
GGML_ASSERT(size2 == size);
va_end(ap2);
va_end(ap);
return std::string(buf.data(), size);
}
static bool is_float_close(float a, float b, float abs_tol) {
// Check for non-negative tolerance
if (abs_tol < 0.0) {
throw std::invalid_argument("Tolerance must be non-negative");
}
// Exact equality check
if (a == b) {
return true;
}
// Check for infinities
if (std::isinf(a) || std::isinf(b)) {
return false;
}
// Regular comparison using the provided absolute tolerance
return std::fabs(b - a) <= abs_tol;
}
static void zeros(std::ofstream & file, size_t n) {
char zero = 0;
for (size_t i = 0; i < n; ++i) {
@ -416,10 +380,6 @@ namespace GGUFMeta {
using llama_buf_map = std::unordered_map<uint32_t, ggml_backend_buffer_t>;
static size_t llama_model_max_nodes(const llama_model & model) {
return std::max<size_t>(8192, model.tensors_by_name.size()*5);
}
struct llama_model_loader {
int n_kv = 0;
int n_tensors = 0;
@ -2468,8 +2428,6 @@ static void llm_load_vocab(
for (uint32_t i = 0; i < n_vocab; i++) {
std::string word = gguf_get_arr_str(ctx, token_idx, i);
//GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
if (word.empty()) {
LLAMA_LOG_WARN("%s: empty token at index %u\n", __func__, i);
word = "[EMPTY_" + std::to_string(i) + "]";
@ -15674,31 +15632,6 @@ int32_t llama_control_vector_apply(
// interface implementation
//
struct llama_model_params llama_model_default_params() {
struct llama_model_params result = {
/*.devices =*/ nullptr,
/*.n_gpu_layers =*/ 0,
/*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER,
/*.main_gpu =*/ 0,
/*.tensor_split =*/ nullptr,
/*.rpc_servers =*/ nullptr,
/*.progress_callback =*/ nullptr,
/*.progress_callback_user_data =*/ nullptr,
/*.kv_overrides =*/ nullptr,
/*.vocab_only =*/ false,
/*.use_mmap =*/ true,
/*.use_mlock =*/ false,
/*.check_tensors =*/ false,
};
#ifdef GGML_USE_METAL
// note: we usually have plenty of VRAM, so by default offload all layers to the GPU
result.n_gpu_layers = 999;
#endif
return result;
}
struct llama_context_params llama_context_default_params() {
struct llama_context_params result = {
/*.n_ctx =*/ 512,
@ -15825,7 +15758,7 @@ int64_t llama_time_us(void) {
struct llama_model * llama_load_model_from_file(
const char * path_model,
struct llama_model_params params) {
struct llama_model_params params) {
ggml_time_init();
llama_model * model = new llama_model;
@ -15943,10 +15876,6 @@ struct llama_model * llama_load_model_from_file(
return model;
}
void llama_free_model(struct llama_model * model) {
delete model;
}
struct llama_context * llama_new_context_with_model(
struct llama_model * model,
struct llama_context_params params) {
@ -16318,30 +16247,6 @@ uint32_t llama_n_seq_max(const struct llama_context * ctx) {
return ctx->kv_self.size;
}
enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
return model->vocab.type;
}
int32_t llama_n_vocab(const struct llama_model * model) {
return model->hparams.n_vocab;
}
int32_t llama_n_ctx_train(const struct llama_model * model) {
return model->hparams.n_ctx_train;
}
int32_t llama_n_embd(const struct llama_model * model) {
return model->hparams.n_embd;
}
int32_t llama_n_layer(const struct llama_model * model) {
return model->hparams.n_layer;
}
int32_t llama_n_head(const struct llama_model * model) {
return model->hparams.n_head();
}
const struct llama_model * llama_get_model(const struct llama_context * ctx) {
return &ctx->model;
}
@ -16350,166 +16255,6 @@ enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx) {
return ctx->cparams.pooling_type;
}
enum llama_rope_type llama_rope_type(const struct llama_model * model) {
switch (model->arch) {
// these models do not use RoPE
case LLM_ARCH_GPT2:
case LLM_ARCH_GPTJ:
case LLM_ARCH_MPT:
case LLM_ARCH_REFACT:
case LLM_ARCH_BLOOM:
case LLM_ARCH_MAMBA:
case LLM_ARCH_JINA_BERT_V2:
case LLM_ARCH_T5:
case LLM_ARCH_T5ENCODER:
case LLM_ARCH_JAIS:
case LLM_ARCH_RWKV6:
case LLM_ARCH_WAVTOKENIZER_DEC:
return LLAMA_ROPE_TYPE_NONE;
// use what we call a normal RoPE, operating on pairs of consecutive head values
case LLM_ARCH_LLAMA:
case LLM_ARCH_DECI:
case LLM_ARCH_BAICHUAN:
case LLM_ARCH_STARCODER:
case LLM_ARCH_PLAMO:
case LLM_ARCH_ORION:
case LLM_ARCH_INTERNLM2:
case LLM_ARCH_MINICPM:
case LLM_ARCH_XVERSE:
case LLM_ARCH_COMMAND_R:
case LLM_ARCH_OLMO:
case LLM_ARCH_ARCTIC:
case LLM_ARCH_DEEPSEEK:
case LLM_ARCH_DEEPSEEK2:
case LLM_ARCH_CHATGLM:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
case LLM_ARCH_CHAMELEON:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2
case LLM_ARCH_FALCON:
case LLM_ARCH_GROK:
case LLM_ARCH_DBRX:
case LLM_ARCH_BERT:
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_STABLELM:
case LLM_ARCH_BITNET:
case LLM_ARCH_QWEN:
case LLM_ARCH_QWEN2:
case LLM_ARCH_QWEN2MOE:
case LLM_ARCH_OLMO2:
case LLM_ARCH_OLMOE:
case LLM_ARCH_PHI2:
case LLM_ARCH_PHI3:
case LLM_ARCH_GEMMA:
case LLM_ARCH_GEMMA2:
case LLM_ARCH_STARCODER2:
case LLM_ARCH_OPENELM:
case LLM_ARCH_GPTNEOX:
case LLM_ARCH_CODESHELL:
case LLM_ARCH_NEMOTRON:
case LLM_ARCH_EXAONE:
case LLM_ARCH_MINICPM3:
return LLAMA_ROPE_TYPE_NEOX;
case LLM_ARCH_QWEN2VL:
return LLAMA_ROPE_TYPE_MROPE;
// all model arches should be listed explicitly here
case LLM_ARCH_UNKNOWN:
GGML_ABORT("unknown architecture");
}
return LLAMA_ROPE_TYPE_NONE;
}
float llama_rope_freq_scale_train(const struct llama_model * model) {
return model->hparams.rope_freq_scale_train;
}
int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) {
const auto & it = model->gguf_kv.find(key);
if (it == model->gguf_kv.end()) {
if (buf_size > 0) {
buf[0] = '\0';
}
return -1;
}
return snprintf(buf, buf_size, "%s", it->second.c_str());
}
int32_t llama_model_meta_count(const struct llama_model * model) {
return (int)model->gguf_kv.size();
}
int32_t llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
if (i < 0 || i >= (int)model->gguf_kv.size()) {
if (buf_size > 0) {
buf[0] = '\0';
}
return -1;
}
auto it = model->gguf_kv.begin();
std::advance(it, i);
return snprintf(buf, buf_size, "%s", it->first.c_str());
}
int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size) {
if (i < 0 || i >= (int)model->gguf_kv.size()) {
if (buf_size > 0) {
buf[0] = '\0';
}
return -1;
}
auto it = model->gguf_kv.begin();
std::advance(it, i);
return snprintf(buf, buf_size, "%s", it->second.c_str());
}
int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
return snprintf(buf, buf_size, "%s %s %s",
llama_model_arch_name (*model).c_str(),
llama_model_type_name (*model).c_str(),
llama_model_ftype_name(*model).c_str());
}
uint64_t llama_model_size(const struct llama_model * model) {
return model->n_bytes;
}
uint64_t llama_model_n_params(const struct llama_model * model) {
return model->n_elements;
}
bool llama_model_has_encoder(const struct llama_model * model) {
switch (model->arch) {
case LLM_ARCH_T5: return true;
case LLM_ARCH_T5ENCODER: return true;
default: return false;
}
}
bool llama_model_has_decoder(const struct llama_model * model) {
switch (model->arch) {
case LLM_ARCH_T5ENCODER: return false;
default: return true;
}
}
llama_token llama_model_decoder_start_token(const struct llama_model * model) {
return model->hparams.dec_start_token_id;
}
bool llama_model_is_recurrent(const struct llama_model * model) {
switch (model->arch) {
case LLM_ARCH_MAMBA: return true;
case LLM_ARCH_RWKV6: return true;
default: return false;
}
}
uint32_t llama_model_quantize(
const char * fname_inp,
const char * fname_out,
@ -16523,7 +16268,11 @@ uint32_t llama_model_quantize(
}
}
///
//
// kv cache
//
// TODO: tmp bridges below until `struct llama_kv_cache` is exposed through the public API
struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max) {
return llama_kv_cache_view_init(ctx->kv_self, n_seq_max);
@ -16628,64 +16377,6 @@ void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
ctx->cparams.causal_attn = causal_attn;
}
struct llama_batch llama_batch_get_one(
llama_token * tokens,
int32_t n_tokens) {
return {
/*n_tokens =*/ n_tokens,
/*tokens =*/ tokens,
/*embd =*/ nullptr,
/*pos =*/ nullptr,
/*n_seq_id =*/ nullptr,
/*seq_id =*/ nullptr,
/*logits =*/ nullptr,
};
}
struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_t n_seq_max) {
llama_batch batch = {
/*n_tokens =*/ 0,
/*tokens =*/ nullptr,
/*embd =*/ nullptr,
/*pos =*/ nullptr,
/*n_seq_id =*/ nullptr,
/*seq_id =*/ nullptr,
/*logits =*/ nullptr,
};
if (embd) {
batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd);
} else {
batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc);
}
batch.pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens_alloc);
batch.n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens_alloc);
batch.seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * (n_tokens_alloc + 1));
for (int i = 0; i < n_tokens_alloc; ++i) {
batch.seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
}
batch.seq_id[n_tokens_alloc] = nullptr;
batch.logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens_alloc);
return batch;
}
void llama_batch_free(struct llama_batch batch) {
if (batch.token) free(batch.token);
if (batch.embd) free(batch.embd);
if (batch.pos) free(batch.pos);
if (batch.n_seq_id) free(batch.n_seq_id);
if (batch.seq_id) {
for (int i = 0; batch.seq_id[i] != nullptr; ++i) {
free(batch.seq_id[i]);
}
free(batch.seq_id);
}
if (batch.logits) free(batch.logits);
}
int32_t llama_encode(
struct llama_context * ctx,
struct llama_batch batch) {
@ -16852,6 +16543,8 @@ float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id
// vocab
//
// TODO: tmp bridges below until `struct llama_vocab` is exposed through the public API
const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
return llama_token_get_text_impl(model->vocab, token);
}