mirrors/ollama-for-amd
1
0
Fork 0
mirror of https://github.com/likelovewant/ollama-for-amd.git synced 2025-12-22 14:53:56 +00:00
Code Issues Packages Projects Releases Wiki Activity

feat: llama.cpp bump (17f7f4) for SSM performance improvements (#13408)

Browse Source 
* feat: Bump llama.cpp to the latest master (17f7f4b)

This brings in significant improvements to prefill performance for all
models using the SSM_CONV and SSM_SCAN ops (granite4, jamba, falcon-h,
nemotron-h, Qwen3 Next) on Apple Metal.

See https://github.com/ggml-org/llama.cpp/pull/17876

Branch: LlamaCPPMetalSSMImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Update patches 1-4

Branch: LlamaCPPMetalSSMImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix: Update patches 5-12

Branch: LlamaCPPMetalSSMImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Update patches 13-18

Branch: LlamaCPPMetalSSMImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Update patch 20

Branch: LlamaCPPMetalSSMImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Update patches 21-31

Branch: LlamaCPPMetalSSMImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Sync vendored code

The two files I'm not sure about here are the swap from gemma3-iswa.cpp to
gemma3.cpp (I chose to include this because I think it's required), and the
inclusion of `ggml-zendnn.h` which I chose to omit.

Branch: LlamaCPPMetalSSMImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

---------

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
This commit is contained in:
Gabe Goodhart
2025-12-10 13:59:27 -07:00
committed by GitHub
parent c34fc64688
commit b95693056c
115 changed files with 5176 additions and 2585 deletions
Download Patch File Download Diff File Expand all files Collapse all files

77
llama/llama.cpp/common/common.cpp vendored
View File

@@ -694,7 +694,7 @@ bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_over
// Validate if a filename is safe to use
// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function
bool fs_validate_filename(const std::string & filename) {
bool fs_validate_filename(const std::string & filename, bool allow_subdirs) {
if (!filename.length()) {
// Empty filename invalid
return false;
@@ -754,10 +754,14 @@ bool fs_validate_filename(const std::string & filename) {
|| (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs
|| c == 0xFFFD // Replacement Character (UTF-8)
|| c == 0xFEFF // Byte Order Mark (BOM)
|| c == '/' || c == '\\' || c == ':' || c == '*' // Illegal characters
|| c == ':' || c == '*' // Illegal characters
|| c == '?' || c == '"' || c == '<' || c == '>' || c == '|') {
return false;
}
if (!allow_subdirs && (c == '/' || c == '\\')) {
// Subdirectories not allowed, reject path separators
return false;
}
}
// Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename
@@ -782,11 +786,29 @@ bool fs_validate_filename(const std::string & filename) {
#include <iostream>
#ifdef _WIN32
static std::wstring utf8_to_wstring(const std::string & str) {
if (str.empty()) {
return std::wstring();
}
int size = MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), NULL, 0);
if (size <= 0) {
return std::wstring();
}
std::wstring wstr(size, 0);
MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), &wstr[0], size);
return wstr;
}
#endif
// returns true if successful, false otherwise
bool fs_create_directory_with_parents(const std::string & path) {
#ifdef _WIN32
std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
std::wstring wpath = converter.from_bytes(path);
std::wstring wpath = utf8_to_wstring(path);
// if the path already exists, check whether it's a directory
const DWORD attributes = GetFileAttributesW(wpath.c_str());
@@ -859,6 +881,11 @@ bool fs_create_directory_with_parents(const std::string & path) {
#endif // _WIN32
}
bool fs_is_directory(const std::string & path) {
std::filesystem::path dir(path);
return std::filesystem::exists(dir) && std::filesystem::is_directory(dir);
}
std::string fs_get_cache_directory() {
std::string cache_directory = "";
auto ensure_trailing_slash = [](std::string p) {
@@ -893,6 +920,8 @@ std::string fs_get_cache_directory() {
cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
#elif defined(_WIN32)
cache_directory = std::getenv("LOCALAPPDATA");
#elif defined(__EMSCRIPTEN__)
GGML_ABORT("not implemented on this platform");
#else
# error Unknown architecture
#endif
@@ -912,7 +941,7 @@ std::string fs_get_cache_file(const std::string & filename) {
return cache_directory + filename;
}
std::vector<common_file_info> fs_list_files(const std::string & path) {
std::vector<common_file_info> fs_list(const std::string & path, bool include_directories) {
std::vector<common_file_info> files;
if (path.empty()) return files;
@@ -927,14 +956,22 @@ std::vector<common_file_info> fs_list_files(const std::string & path) {
const auto & p = entry.path();
if (std::filesystem::is_regular_file(p)) {
common_file_info info;
info.path = p.string();
info.name = p.filename().string();
info.path = p.string();
info.name = p.filename().string();
info.is_dir = false;
try {
info.size = static_cast<size_t>(std::filesystem::file_size(p));
} catch (const std::filesystem::filesystem_error &) {
info.size = 0;
}
files.push_back(std::move(info));
} else if (include_directories && std::filesystem::is_directory(p)) {
common_file_info info;
info.path = p.string();
info.name = p.filename().string();
info.size = 0; // Directories have no size
info.is_dir = true;
files.push_back(std::move(info));
}
} catch (const std::filesystem::filesystem_error &) {
// skip entries we cannot inspect
@@ -945,6 +982,32 @@ std::vector<common_file_info> fs_list_files(const std::string & path) {
return files;
}
//
// TTY utils
//
bool tty_can_use_colors() {
// Check NO_COLOR environment variable (https://no-color.org/)
if (const char * no_color = std::getenv("NO_COLOR")) {
if (no_color[0] != '\0') {
return false;
}
}
// Check TERM environment variable
if (const char * term = std::getenv("TERM")) {
if (std::strcmp(term, "dumb") == 0) {
return false;
}
}
// Check if stdout and stderr are connected to a terminal
// We check both because log messages can go to either
bool stdout_is_tty = isatty(fileno(stdout));
bool stderr_is_tty = isatty(fileno(stderr));
return stdout_is_tty || stderr_is_tty;
}
//
// Model utils

28
llama/llama.cpp/common/common.h vendored
View File

@@ -12,6 +12,10 @@
#include <vector>
#include <map>
#if defined(_WIN32) && !defined(_WIN32_WINNT)
#define _WIN32_WINNT 0x0A00
#endif
#ifdef _WIN32
#define DIRECTORY_SEPARATOR '\\'
#else
@@ -26,8 +30,6 @@
fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); \
} while(0)
#define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf"
struct common_time_meas {
common_time_meas(int64_t & t_acc, bool disable = false);
~common_time_meas();
@@ -223,6 +225,7 @@ struct common_params_model {
std::string hf_repo = ""; // HF repo // NOLINT
std::string hf_file = ""; // HF file // NOLINT
std::string docker_repo = ""; // Docker repo // NOLINT
std::string name = ""; // in format <user>/<model>[:<tag>] (tag is optional) // NOLINT
};
struct common_params_speculative {
@@ -369,7 +372,7 @@ struct common_params {
std::vector<common_control_vector_load_info> control_vectors; // control vector with user defined scale
int32_t verbosity = 0;
int32_t verbosity = 3; // LOG_LEVEL_INFO
int32_t control_vector_layer_start = -1; // layer range for control vector
int32_t control_vector_layer_end = -1; // layer range for control vector
bool offline = false;
@@ -478,9 +481,15 @@ struct common_params {
bool endpoint_props = false; // only control POST requests, not GET
bool endpoint_metrics = false;
// router server configs
std::string models_dir = ""; // directory containing models for the router server
int models_max = 4; // maximum number of models to load simultaneously
bool models_autoload = true; // automatically load models when requested via the router server
bool log_json = false;
std::string slot_save_path;
std::string media_path; // path to directory for loading media files
float slot_prompt_similarity = 0.1f;
@@ -631,8 +640,9 @@ std::string string_from(const struct llama_context * ctx, const struct llama_bat
// Filesystem utils
//
bool fs_validate_filename(const std::string & filename);
bool fs_validate_filename(const std::string & filename, bool allow_subdirs = false);
bool fs_create_directory_with_parents(const std::string & path);
bool fs_is_directory(const std::string & path);
std::string fs_get_cache_directory();
std::string fs_get_cache_file(const std::string & filename);
@@ -641,8 +651,16 @@ struct common_file_info {
std::string path;
std::string name;
size_t size = 0; // in bytes
bool is_dir = false;
};
std::vector<common_file_info> fs_list_files(const std::string & path);
std::vector<common_file_info> fs_list(const std::string & path, bool include_directories);
//
// TTY utils
//
// Auto-detect if colors can be enabled based on terminal and environment
bool tty_can_use_colors();
//
// Model utils

2
llama/llama.cpp/common/json-schema-to-grammar.cpp vendored
View File

@@ -974,7 +974,7 @@ public:
void check_errors() {
if (!_errors.empty()) {
throw std::runtime_error("JSON schema conversion failed:\n" + string_join(_errors, "\n"));
throw std::invalid_argument("JSON schema conversion failed:\n" + string_join(_errors, "\n"));
}
if (!_warnings.empty()) {
fprintf(stderr, "WARNING: JSON schema conversion was incomplete: %s\n", string_join(_warnings, "; ").c_str());

45
llama/llama.cpp/common/log.cpp vendored
View File

@@ -1,3 +1,4 @@
#include "common.h"
#include "log.h"
#include <chrono>
@@ -26,30 +27,6 @@ void common_log_set_verbosity_thold(int verbosity) {
common_log_verbosity_thold = verbosity;
}
// Auto-detect if colors should be enabled based on terminal and environment
static bool common_log_should_use_colors_auto() {
// Check NO_COLOR environment variable (https://no-color.org/)
if (const char * no_color = std::getenv("NO_COLOR")) {
if (no_color[0] != '\0') {
return false;
}
}
// Check TERM environment variable
if (const char * term = std::getenv("TERM")) {
if (std::strcmp(term, "dumb") == 0) {
return false;
}
}
// Check if stdout and stderr are connected to a terminal
// We check both because log messages can go to either
bool stdout_is_tty = isatty(fileno(stdout));
bool stderr_is_tty = isatty(fileno(stderr));
return stdout_is_tty || stderr_is_tty;
}
static int64_t t_us() {
return std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::system_clock::now().time_since_epoch()).count();
}
@@ -391,7 +368,7 @@ struct common_log * common_log_main() {
static std::once_flag init_flag;
std::call_once(init_flag, [&]() {
// Set default to auto-detect colors
log.set_colors(common_log_should_use_colors_auto());
log.set_colors(tty_can_use_colors());
});
return &log;
@@ -422,7 +399,7 @@ void common_log_set_file(struct common_log * log, const char * file) {
void common_log_set_colors(struct common_log * log, log_colors colors) {
if (colors == LOG_COLORS_AUTO) {
log->set_colors(common_log_should_use_colors_auto());
log->set_colors(tty_can_use_colors());
return;
}
@@ -443,8 +420,22 @@ void common_log_set_timestamps(struct common_log * log, bool timestamps) {
log->set_timestamps(timestamps);
}
static int common_get_verbosity(enum ggml_log_level level) {
switch (level) {
case GGML_LOG_LEVEL_DEBUG: return LOG_LEVEL_DEBUG;
case GGML_LOG_LEVEL_INFO: return LOG_LEVEL_INFO;
case GGML_LOG_LEVEL_WARN: return LOG_LEVEL_WARN;
case GGML_LOG_LEVEL_ERROR: return LOG_LEVEL_ERROR;
case GGML_LOG_LEVEL_CONT: return LOG_LEVEL_INFO; // same as INFO
case GGML_LOG_LEVEL_NONE:
default:
return LOG_LEVEL_OUTPUT;
}
}
void common_log_default_callback(enum ggml_log_level level, const char * text, void * /*user_data*/) {
if (LOG_DEFAULT_LLAMA <= common_log_verbosity_thold) {
auto verbosity = common_get_verbosity(level);
if (verbosity <= common_log_verbosity_thold) {
common_log_add(common_log_main(), level, "%s", text);
}
}

31
llama/llama.cpp/common/log.h vendored
View File

@@ -21,8 +21,14 @@
# define LOG_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
#endif
#define LOG_DEFAULT_DEBUG 1
#define LOG_DEFAULT_LLAMA 0
#define LOG_LEVEL_DEBUG 4
#define LOG_LEVEL_INFO 3
#define LOG_LEVEL_WARN 2
#define LOG_LEVEL_ERROR 1
#define LOG_LEVEL_OUTPUT 0 // output data from tools
#define LOG_DEFAULT_DEBUG LOG_LEVEL_DEBUG
#define LOG_DEFAULT_LLAMA LOG_LEVEL_INFO
enum log_colors {
LOG_COLORS_AUTO = -1,
@@ -67,10 +73,11 @@ void common_log_add(struct common_log * log, enum ggml_log_level level, const ch
// 0.00.090.578 I llm_load_tensors: offloading 32 repeating layers to GPU
// 0.00.090.579 I llm_load_tensors: offloading non-repeating layers to GPU
//
// I - info (stdout, V = 0)
// W - warning (stderr, V = 0)
// E - error (stderr, V = 0)
// D - debug (stderr, V = LOG_DEFAULT_DEBUG)
// I - info (stdout, V = LOG_DEFAULT_INFO)
// W - warning (stderr, V = LOG_DEFAULT_WARN)
// E - error (stderr, V = LOG_DEFAULT_ERROR)
// O - output (stdout, V = LOG_DEFAULT_OUTPUT)
//
void common_log_set_file (struct common_log * log, const char * file); // not thread-safe
@@ -95,14 +102,14 @@ void common_log_set_timestamps(struct common_log * log, bool timestamps); // w
} \
} while (0)
#define LOG(...) LOG_TMPL(GGML_LOG_LEVEL_NONE, 0, __VA_ARGS__)
#define LOGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_NONE, verbosity, __VA_ARGS__)
#define LOG(...) LOG_TMPL(GGML_LOG_LEVEL_NONE, LOG_LEVEL_OUTPUT, __VA_ARGS__)
#define LOGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_NONE, verbosity, __VA_ARGS__)
#define LOG_INF(...) LOG_TMPL(GGML_LOG_LEVEL_INFO, 0, __VA_ARGS__)
#define LOG_WRN(...) LOG_TMPL(GGML_LOG_LEVEL_WARN, 0, __VA_ARGS__)
#define LOG_ERR(...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, 0, __VA_ARGS__)
#define LOG_DBG(...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, LOG_DEFAULT_DEBUG, __VA_ARGS__)
#define LOG_CNT(...) LOG_TMPL(GGML_LOG_LEVEL_CONT, 0, __VA_ARGS__)
#define LOG_DBG(...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, LOG_LEVEL_DEBUG, __VA_ARGS__)
#define LOG_INF(...) LOG_TMPL(GGML_LOG_LEVEL_INFO, LOG_LEVEL_INFO, __VA_ARGS__)
#define LOG_WRN(...) LOG_TMPL(GGML_LOG_LEVEL_WARN, LOG_LEVEL_WARN, __VA_ARGS__)
#define LOG_ERR(...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, LOG_LEVEL_ERROR, __VA_ARGS__)
#define LOG_CNT(...) LOG_TMPL(GGML_LOG_LEVEL_CONT, LOG_LEVEL_INFO, __VA_ARGS__) // same as INFO
#define LOG_INFV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_INFO, verbosity, __VA_ARGS__)
#define LOG_WRNV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_WARN, verbosity, __VA_ARGS__)

31
llama/llama.cpp/src/llama-arch.cpp vendored
View File

@@ -112,6 +112,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_COGVLM, "cogvlm" },
{ LLM_ARCH_RND1, "rnd1" },
{ LLM_ARCH_PANGU_EMBED, "pangu-embedded" },
{ LLM_ARCH_MISTRAL3, "mistral3" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@@ -205,6 +206,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_ATTENTION_SCALE, "%s.attention.scale" },
{ LLM_KV_ATTENTION_OUTPUT_SCALE, "%s.attention.output_scale" },
{ LLM_KV_ATTENTION_TEMPERATURE_LENGTH, "%s.attention.temperature_length" },
{ LLM_KV_ATTENTION_TEMPERATURE_SCALE, "%s.attention.temperature_scale" },
{ LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION, "%s.attention.block_skip_connection" },
{ LLM_KV_ATTENTION_KEY_LENGTH_MLA, "%s.attention.key_length_mla" },
{ LLM_KV_ATTENTION_VALUE_LENGTH_MLA, "%s.attention.value_length_mla" },
@@ -855,7 +857,7 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" },
{ LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
{ LLM_TENSOR_SSM_A, "blk.%d.ssm_a" },
{ LLM_TENSOR_SSM_A_NOSCAN, "blk.%d.ssm_a" },
{ LLM_TENSOR_SSM_CONV1D, "blk.%d.ssm_conv1d" },
{ LLM_TENSOR_SSM_DT, "blk.%d.ssm_dt" },
{ LLM_TENSOR_SSM_BETA_ALPHA, "blk.%d.ssm_ba" },
@@ -2532,6 +2534,32 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
},
},
{
LLM_ARCH_MISTRAL3,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" },
{ LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
{ LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
},
},
{
LLM_ARCH_UNKNOWN,
{
@@ -2631,6 +2659,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_FFN_ACT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_DIV}},
{LLM_TENSOR_SSM_CONV1D, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_CONV}},
{LLM_TENSOR_SSM_A, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_SCAN}},
{LLM_TENSOR_SSM_A_NOSCAN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, // a version of SSM_A used for MUL instead of SSM_SCAN
{LLM_TENSOR_SSM_DT_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_SSM_B_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_SSM_C_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},

3
llama/llama.cpp/src/llama-arch.h vendored
View File

@@ -116,6 +116,7 @@ enum llm_arch {
LLM_ARCH_COGVLM,
LLM_ARCH_RND1,
LLM_ARCH_PANGU_EMBED,
LLM_ARCH_MISTRAL3,
LLM_ARCH_UNKNOWN,
};
@@ -209,6 +210,7 @@ enum llm_kv {
LLM_KV_ATTENTION_SCALE,
LLM_KV_ATTENTION_OUTPUT_SCALE,
LLM_KV_ATTENTION_TEMPERATURE_LENGTH,
LLM_KV_ATTENTION_TEMPERATURE_SCALE,
LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION,
LLM_KV_ATTENTION_KEY_LENGTH_MLA,
LLM_KV_ATTENTION_VALUE_LENGTH_MLA,
@@ -379,6 +381,7 @@ enum llm_tensor {
LLM_TENSOR_SSM_DT,
LLM_TENSOR_SSM_DT_NORM,
LLM_TENSOR_SSM_A,
LLM_TENSOR_SSM_A_NOSCAN, // qwen3next special case with MUL instead of SSM_SCAN
LLM_TENSOR_SSM_B_NORM,
LLM_TENSOR_SSM_C_NORM,
LLM_TENSOR_SSM_D,

12
llama/llama.cpp/src/llama-context.cpp vendored
View File

@@ -248,7 +248,10 @@ llama_context::llama_context(
LLAMA_LOG_DEBUG("%s: backend_ptrs.size() = %zu\n", __func__, backend_ptrs.size());
const size_t max_nodes = this->graph_max_nodes();
const uint32_t n_seqs = cparams.n_seq_max;
const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
const size_t max_nodes = this->graph_max_nodes(n_tokens);
LLAMA_LOG_DEBUG("%s: max_nodes = %zu\n", __func__, max_nodes);
@@ -300,9 +303,6 @@ llama_context::llama_context(
cross.v_embd.clear();
const uint32_t n_seqs = cparams.n_seq_max;
const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
// avoid reserving graphs with zero outputs - assume one output per sequence
n_outputs = n_seqs;
@@ -1385,9 +1385,9 @@ void llama_context::output_reorder() {
// graph
//
uint32_t llama_context::graph_max_nodes() const {
uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
if (model.arch == LLM_ARCH_QWEN3NEXT) {
return std::max<uint32_t>(8192u, 32u*model.n_tensors());
return std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
}
return std::max<uint32_t>(1024u, 8u*model.n_tensors());
}

2
llama/llama.cpp/src/llama-context.h vendored
View File

@@ -197,7 +197,7 @@ private:
//
public:
uint32_t graph_max_nodes() const;
uint32_t graph_max_nodes(uint32_t n_tokens) const;
// can reuse the llm_graph_result instance of the context (for example to update a memory module)
llm_graph_result * get_gf_res_reserve() const;

265
llama/llama.cpp/src/llama-grammar.cpp vendored
View File

@@ -181,6 +181,52 @@ static std::pair<uint32_t, const char *> parse_char(const char * src) {
throw std::runtime_error("unexpected end of input");
}
static std::pair<uint32_t, const char *> parse_token(const llama_vocab * vocab, const char * src) {
const char * pos = src;
if (*pos != '<') {
throw std::runtime_error(std::string("expecting '<' at ") + pos);
}
pos++;
// Parse <[id]>
if (*pos == '[') {
pos++;
const char * int_end = parse_int(pos);
uint32_t token_id = std::stoul(std::string(pos, int_end - pos));
pos = int_end;
if (*pos != ']') {
throw std::runtime_error(std::string("expecting ']' at ") + pos);
}
pos++;
if (*pos != '>') {
throw std::runtime_error(std::string("expecting '>' at ") + pos);
}
pos++;
return std::make_pair(token_id, pos);
}
if (vocab == nullptr) {
throw std::runtime_error(std::string("no vocab to parse token at ") + src);
}
// Parse <token> and tokenize to obtain the token id
while (*pos != 0 && *pos != '>') {
pos++;
}
if (*pos != '>') {
throw std::runtime_error(std::string("expecting '>' at ") + pos);
}
pos++;
llama_token tokens[2];
int32_t n_tokens = vocab->tokenize(src, static_cast<int32_t>(pos - src), tokens, 2, false, true);
if (n_tokens != 1) {
// must tokenize to exactly 1 token
throw std::runtime_error("invalid token '" + std::string(src, pos - src) + "'");
}
return std::make_pair(tokens[0], pos);
}
static void print_grammar_char(FILE * file, uint32_t c) {
if (0x20 <= c && c <= 0x7f) {
fprintf(file, "%c", static_cast<char>(c));
@@ -212,6 +258,8 @@ static void print_rule_binary(FILE * file, const llama_grammar_rule & rule) {
case LLAMA_GRETYPE_CHAR_RNG_UPPER: fprintf(file, "CHAR_RNG_UPPER"); break;
case LLAMA_GRETYPE_CHAR_ALT: fprintf(file, "CHAR_ALT"); break;
case LLAMA_GRETYPE_CHAR_ANY: fprintf(file, "CHAR_ANY"); break;
case LLAMA_GRETYPE_TOKEN: fprintf(file, "TOKEN"); break;
case LLAMA_GRETYPE_TOKEN_NOT: fprintf(file, "TOKEN_NOT"); break;
}
switch (elem.type) {
case LLAMA_GRETYPE_END:
@@ -228,6 +276,17 @@ static void print_rule_binary(FILE * file, const llama_grammar_rule & rule) {
print_grammar_char(file, elem.value);
fprintf(file, "\") ");
break;
case LLAMA_GRETYPE_TOKEN:
fprintf(file, "<[");
fprintf(file, "%u", elem.value);
fprintf(file, "]> ");
break;
case LLAMA_GRETYPE_TOKEN_NOT:
fprintf(file, "!");
fprintf(file, "<[");
fprintf(file, "%u", elem.value);
fprintf(file, "]> ");
break;
}
}
fprintf(file, "\n");
@@ -284,6 +343,17 @@ static void print_rule(
case LLAMA_GRETYPE_CHAR_ANY:
fprintf(file, ".");
break;
case LLAMA_GRETYPE_TOKEN:
fprintf(file, "<[");
fprintf(file, "%u", elem.value);
fprintf(file, "]> ");
break;
case LLAMA_GRETYPE_TOKEN_NOT:
fprintf(file, "!");
fprintf(file, "<[");
fprintf(file, "%u", elem.value);
fprintf(file, "]> ");
break;
}
if (is_char_element(elem)) {
switch (rule[i + 1].type) {
@@ -444,6 +514,17 @@ const char * llama_grammar_parser::parse_sequence(
}
}
pos = parse_space(pos + 1, is_nested);
} else if (*pos == '<' || *pos == '!') { // token
auto type = LLAMA_GRETYPE_TOKEN;
if (*pos == '!') { // token inverse
type = LLAMA_GRETYPE_TOKEN_NOT;
pos++;
}
auto token_pair = parse_token(vocab, pos);
const char * token_end = token_pair.second;
last_sym_start = rule.size();
rule.push_back({type, token_pair.first});
pos = parse_space(token_end, is_nested);
} else if (is_word_char(*pos)) { // rule reference
const char * name_end = parse_name(pos);
uint32_t ref_rule_id = get_symbol_id(pos, name_end - pos);
@@ -691,6 +772,21 @@ static bool llama_grammar_match_partial_char(
return !is_positive_char;
}
// returns true iff token matches the rule at pos (regular or inverse)
// asserts that pos is pointing to a token element
static bool llama_grammar_match_token(
const llama_grammar_element * pos,
const llama_token token) {
GGML_ASSERT(pos->type == LLAMA_GRETYPE_TOKEN || pos->type == LLAMA_GRETYPE_TOKEN_NOT);
if (pos->type == LLAMA_GRETYPE_TOKEN) {
return pos->value == static_cast<uint32_t>(token);
}
if (pos->type == LLAMA_GRETYPE_TOKEN_NOT) {
return pos->value != static_cast<uint32_t>(token);
}
return false;
}
// transforms a grammar pushdown stack into N possible stacks, all ending
// at a character range (terminal element)
static void llama_grammar_advance_stack(
@@ -738,6 +834,8 @@ static void llama_grammar_advance_stack(
case LLAMA_GRETYPE_CHAR:
case LLAMA_GRETYPE_CHAR_NOT:
case LLAMA_GRETYPE_CHAR_ANY:
case LLAMA_GRETYPE_TOKEN:
case LLAMA_GRETYPE_TOKEN_NOT:
if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
// only add the stack if it's not a duplicate of one we already have
new_stacks.emplace_back(stack);
@@ -831,26 +929,38 @@ llama_grammar_stacks & llama_grammar_get_stacks(struct llama_grammar * grammar)
return grammar->stacks;
}
static void llama_grammar_accept_chr(
struct llama_grammar & grammar,
const llama_grammar_stack & stack,
uint32_t chr,
llama_grammar_stacks & new_stacks) {
if (stack.empty()) {
return;
}
const llama_grammar_element * pos = stack.back();
// ignore if this turns into a token
if (pos->type == LLAMA_GRETYPE_TOKEN || pos->type == LLAMA_GRETYPE_TOKEN_NOT) {
return;
}
auto match = llama_grammar_match_char(pos, chr);
if (match.first) {
llama_grammar_stack new_stack(stack.begin(), stack.end() - 1);
if (!llama_grammar_is_end_of_sequence(match.second)) {
new_stack.push_back(match.second);
}
llama_grammar_advance_stack(grammar.rules, new_stack, new_stacks);
}
}
void llama_grammar_accept(struct llama_grammar * grammar, uint32_t chr) {
llama_grammar_stacks stacks_new;
stacks_new.reserve(grammar->stacks.size());
for (const auto & stack : grammar->stacks) {
if (stack.empty()) {
continue;
}
auto match = llama_grammar_match_char(stack.back(), chr);
if (match.first) {
const llama_grammar_element * pos = match.second;
// update top of stack to next element, if any
llama_grammar_stack new_stack(stack.begin(), stack.end() - 1);
if (!llama_grammar_is_end_of_sequence(pos)) {
new_stack.push_back(pos);
}
llama_grammar_advance_stack(grammar->rules, new_stack, stacks_new);
}
llama_grammar_accept_chr(*grammar, stack, chr, stacks_new);
}
grammar->stacks = std::move(stacks_new);
@@ -875,6 +985,22 @@ llama_grammar_candidates llama_grammar_reject_candidates_for_stack(
const llama_grammar_element * stack_pos = stack.back();
// if the top of the stack is a token rule, then we only need to check the token id
if (stack_pos->type == LLAMA_GRETYPE_TOKEN || stack_pos->type == LLAMA_GRETYPE_TOKEN_NOT) {
for (const auto & tok : candidates) {
if (*tok.code_points == 0) {
// reached the end of a token consumed by char rules, reject iff it ended
// in a partial response
if (tok.partial_utf8.n_remain != 0) {
rejects.push_back(tok);
}
} else if (!llama_grammar_match_token(stack_pos, tok.id)) {
rejects.push_back(tok);
}
}
return rejects;
}
llama_grammar_candidates next_candidates;
next_candidates.reserve(candidates.size());
@@ -887,7 +1013,7 @@ llama_grammar_candidates llama_grammar_reject_candidates_for_stack(
rejects.push_back(tok);
}
} else if (llama_grammar_match_char(stack_pos, *tok.code_points).first) {
next_candidates.push_back({ tok.index, tok.code_points + 1, tok.partial_utf8 });
next_candidates.push_back({ tok.index, tok.code_points + 1, tok.partial_utf8, tok.id });
} else {
rejects.push_back(tok);
}
@@ -905,7 +1031,7 @@ llama_grammar_candidates llama_grammar_reject_candidates_for_stack(
auto next_rejects = llama_grammar_reject_candidates(rules, next_stacks, next_candidates);
for (const auto & tok : next_rejects) {
rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8 });
rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8, tok.id });
}
return rejects;
@@ -974,12 +1100,13 @@ struct llama_grammar * llama_grammar_init_impl(
ollama_vocab,
std::move(vec_rules),
std::move(stacks),
/* .partial_utf8 = */ {},
/* .lazy =*/ false,
/* .awaiting_trigger = */ false,
/* .trigger_buffer = */ "",
/* .trigger_tokens = */ {},
/* .trigger_patterns = */ {},
/* .partial_utf8 = */ {},
/* .lazy = */ false,
/* .awaiting_trigger = */ false,
/* .trigger_buffer = */ "",
/* .trigger_buffer_positions = */ {},
/* .trigger_tokens = */ {},
/* .trigger_patterns = */ {},
};
}
@@ -993,7 +1120,7 @@ struct llama_grammar * llama_grammar_init_impl(
size_t num_trigger_patterns,
const llama_token * trigger_tokens,
size_t num_trigger_tokens) {
llama_grammar_parser parser;
llama_grammar_parser parser(vocab);
// if there is a grammar, parse it
// rules will be empty (default) if there are parse errors
@@ -1081,10 +1208,11 @@ struct llama_grammar * llama_grammar_init_impl(
ollama_vocab,
std::move(vec_rules),
std::move(stacks),
/* .partial_utf8 = */ {},
/* .lazy = */ lazy,
/* .awaiting_trigger = */ lazy,
/* .trigger_buffer = */ "",
/* .partial_utf8 = */ {},
/* .lazy = */ lazy,
/* .awaiting_trigger = */ lazy,
/* .trigger_buffer = */ "",
/* .trigger_buffer_positions = */ {},
std::move(vec_trigger_tokens),
std::move(vec_trigger_patterns),
};
@@ -1108,6 +1236,7 @@ struct llama_grammar * llama_grammar_clone_impl(const struct llama_grammar & gra
grammar.lazy,
grammar.awaiting_trigger,
grammar.trigger_buffer,
grammar.trigger_buffer_positions,
grammar.trigger_tokens,
grammar.trigger_patterns,
};
@@ -1164,7 +1293,7 @@ void llama_grammar_apply_impl(const struct llama_grammar & grammar, llama_token_
cur_p->data[i].logit = -INFINITY;
} else {
candidates_decoded.push_back(decode_utf8(piece, grammar.partial_utf8));
candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second, id });
}
}
@@ -1184,10 +1313,12 @@ void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token
if (std::find(grammar.trigger_tokens.begin(), grammar.trigger_tokens.end(), token) != grammar.trigger_tokens.end()) {
grammar.awaiting_trigger = false;
grammar.trigger_buffer.clear();
llama_grammar_accept_str(grammar, piece);
llama_grammar_accept_token(grammar, token, piece);
LLAMA_LOG_DEBUG("Grammar triggered on token %u (`%s`)", token, piece.c_str());
return;
} else {
auto position = std::make_pair(grammar.trigger_buffer.size(), grammar.trigger_buffer.size() + piece.size());
grammar.trigger_buffer_positions.push_back(std::make_pair(token, position));
grammar.trigger_buffer += piece;
std::smatch match;
@@ -1205,10 +1336,23 @@ void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token
if (start == std::string::npos) {
start = match.position(0);
}
// replay tokens that overlap with [start, end)
for (const auto & [tok, tok_pos] : grammar.trigger_buffer_positions) {
auto [tok_start, tok_end] = tok_pos;
if (tok_end <= start) {
continue;
}
size_t piece_start = (tok_start < start) ? start : tok_start; // allow for partial token pieces
size_t piece_len = tok_end - piece_start;
auto tok_piece = grammar.trigger_buffer.substr(piece_start, piece_len);
llama_grammar_accept_token(grammar, tok, tok_piece);
}
auto constrained_str = grammar.trigger_buffer.substr(start);
// std::string constrained_str(match[1].first, grammar.trigger_buffer.end());
grammar.trigger_buffer.clear();
llama_grammar_accept_str(grammar, constrained_str);
grammar.trigger_buffer_positions.clear();
LLAMA_LOG_DEBUG("Grammar triggered on regex: '%s'\n", constrained_str.c_str());
return;
}
@@ -1228,7 +1372,7 @@ void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token
GGML_ABORT("grammar error: end of grammar token received but grammar stack is not empty");
}
llama_grammar_accept_str(grammar, piece);
llama_grammar_accept_token(grammar, token, piece);
}
void llama_grammar_accept_str(struct llama_grammar & grammar, const std::string & piece) {
@@ -1246,6 +1390,61 @@ void llama_grammar_accept_str(struct llama_grammar & grammar, const std::string
}
}
void llama_grammar_accept_token(struct llama_grammar & grammar, llama_token token, const std::string & piece) {
// Note terminating 0 in decoded string
const auto decoded = decode_utf8(piece, grammar.partial_utf8);
const auto & code_points = decoded.first;
llama_grammar_stacks stacks_new;
stacks_new.reserve(grammar.stacks.size());
for (const auto & stack : grammar.stacks) {
if (stack.empty()) {
continue;
}
const llama_grammar_element * pos = stack.back();
if (pos->type == LLAMA_GRETYPE_TOKEN || pos->type == LLAMA_GRETYPE_TOKEN_NOT) {
if (llama_grammar_match_token(pos, token)) {
llama_grammar_stack new_stack(stack.begin(), stack.end() - 1);
if (!llama_grammar_is_end_of_sequence(pos + 1)) {
new_stack.push_back(pos + 1);
}
llama_grammar_advance_stack(grammar.rules, new_stack, stacks_new);
}
} else {
llama_grammar_stacks current_stacks = {stack};
for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
llama_grammar_stacks next_stacks;
for (const auto & cur_stack : current_stacks) {
llama_grammar_accept_chr(grammar, cur_stack, *it, next_stacks);
}
current_stacks = std::move(next_stacks);
if (current_stacks.empty()) {
break;
}
}
for (auto & surviving_stack : current_stacks) {
if (std::find(stacks_new.begin(), stacks_new.end(), surviving_stack) == stacks_new.end()) {
stacks_new.emplace_back(surviving_stack);
}
}
}
}
grammar.stacks = std::move(stacks_new);
grammar.partial_utf8 = decoded.second;
if (grammar.stacks.empty()) {
throw std::runtime_error("Unexpected empty grammar stack after accepting piece: " + piece + " (" + std::to_string(token) + ")");
}
}
const std::string & ollama_vocab::token_to_piece(const uint32_t token) const {
try {

21
llama/llama.cpp/src/llama-grammar.h vendored
View File

@@ -47,11 +47,17 @@ enum llama_gretype {
// any character (.)
LLAMA_GRETYPE_CHAR_ANY = 7,
// terminal element: token (<[token-id]>)
LLAMA_GRETYPE_TOKEN = 8,
// inverse token (!<[token-id]>)
LLAMA_GRETYPE_TOKEN_NOT = 9,
};
typedef struct llama_grammar_element {
enum llama_gretype type;
uint32_t value; // Unicode code point or rule ID
uint32_t value; // Unicode code point, rule ID, or token ID
} llama_grammar_element;
struct llama_partial_utf8 {
@@ -63,6 +69,7 @@ struct llama_grammar_candidate {
size_t index;
const uint32_t * code_points;
llama_partial_utf8 partial_utf8;
llama_token id;
};
using llama_grammar_rule = std::vector< llama_grammar_element>;
@@ -88,10 +95,13 @@ std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_stack(
const llama_grammar_candidates & candidates);
struct llama_grammar_parser {
const llama_vocab * vocab;
std::map<std::string, uint32_t> symbol_ids;
llama_grammar_rules rules;
llama_grammar_parser(const struct llama_vocab * vocab = nullptr) : vocab(vocab) {}
llama_grammar_stack c_rules() const;
uint32_t get_symbol_id(const char * src, size_t len);
@@ -123,6 +133,9 @@ struct llama_grammar_trigger_pattern {
};
struct llama_grammar {
// maintain a list of llama_tokens and their positions in the trigger_buffer
using token_pos = std::pair<llama_token, std::pair<size_t, size_t>>;
// note: allow null vocab for testing (not great)
const llama_vocab * vocab;
const ollama_vocab * o_vocab;
@@ -139,6 +152,7 @@ struct llama_grammar {
bool lazy = false;
bool awaiting_trigger = false; // Initialized to true for lazy grammars only
std::string trigger_buffer; // Output buffered by lazy grammar. Will be cleared once trigger is found.
std::vector<token_pos> trigger_buffer_positions; // Tokens buffered by lazy grammar. Used to replay when a trigger is found.
std::vector<llama_token> trigger_tokens; // Tokens that trigger a lazy grammar, or tokens to force printing of (even if special).
std::vector<llama_grammar_trigger_pattern>
trigger_patterns; // Regular expressions that trigger a lazy grammar. Must be a full match of the entire generated
@@ -185,3 +199,8 @@ void llama_grammar_accept_impl(
void llama_grammar_accept_str(
struct llama_grammar & grammar,
const std::string & piece);
void llama_grammar_accept_token(
struct llama_grammar & grammar,
llama_token token,
const std::string & piece);

11
llama/llama.cpp/src/llama-graph.cpp vendored
View File

@@ -71,6 +71,9 @@ void llm_graph_input_attn_temp::set_input(const llama_ubatch * ubatch) {
if (ubatch->pos && attn_scale) {
const int64_t n_tokens = ubatch->n_tokens;
GGML_ASSERT(f_attn_temp_scale != 0.0f);
GGML_ASSERT(n_attn_temp_floor_scale != 0);
std::vector<float> attn_scale_data(n_tokens, 0.0f);
for (int i = 0; i < n_tokens; ++i) {
const float pos = ubatch->pos[i];
@@ -810,9 +813,6 @@ ggml_tensor * llm_graph_context::build_ffn(
GGML_ABORT("fatal error");
}
//expand here so that we can fuse ffn gate
ggml_build_forward_expand(gf, cur);
if (gate && type_gate == LLM_FFN_PAR) {
cur = ggml_mul(ctx0, cur, tmp);
cb(cur, "ffn_gate_par", il);
@@ -973,7 +973,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
// mask out the other groups
selection_probs = ggml_get_rows(ctx0, selection_groups, expert_groups); // [n_exp_per_group, n_group_used, n_tokens]
selection_probs = ggml_set_rows(ctx0, ggml_scale_bias(ctx0, selection_groups, 0.0f, -INFINITY), selection_probs, expert_groups); // [n_exp_per_group, n_expert_groups, n_tokens]
selection_probs = ggml_set_rows(ctx0, ggml_fill(ctx0, selection_groups, -INFINITY), selection_probs, expert_groups); // [n_exp_per_group, n_expert_groups, n_tokens]
selection_probs = ggml_reshape_2d(ctx0, selection_probs, n_expert, n_tokens); // [n_expert, n_tokens]
cb(selection_probs, "ffn_moe_probs_masked", il);
}
@@ -1093,9 +1093,6 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
GGML_ABORT("fatal error");
}
//expand here so that we can fuse ffn gate
ggml_build_forward_expand(gf, cur);
experts = build_lora_mm_id(down_exps, cur, selected_experts); // [n_embd, n_expert_used, n_tokens]
cb(experts, "ffn_moe_down", il);

4
llama/llama.cpp/src/llama-hparams.h vendored
View File

@@ -164,8 +164,8 @@ struct llama_hparams {
// llama4 smallthinker
uint32_t n_moe_layer_step = 0;
uint32_t n_no_rope_layer_step = 4;
uint32_t n_attn_temp_floor_scale = 8192;
float f_attn_temp_scale = 0.1;
uint32_t n_attn_temp_floor_scale = 0;
float f_attn_temp_scale = 0.0f;
// gemma3n altup
uint32_t n_altup = 4; // altup_num_inputs

2
llama/llama.cpp/src/llama-impl.h vendored
View File

@@ -37,7 +37,7 @@ void llama_log_callback_default(ggml_log_level level, const char * text, void *
template <typename T>
struct no_init {
T value;
no_init() { /* do nothing */ }
no_init() = default;
};
struct time_meas {

2
llama/llama.cpp/src/llama-mmap.cpp vendored
View File

@@ -485,7 +485,7 @@ struct llama_mlock::impl {
if (suggest && getrlimit(RLIMIT_MEMLOCK, &lock_limit)) {
suggest = false;
}
if (suggest && (lock_limit.rlim_max > lock_limit.rlim_cur + size)) {
if (suggest && ((uint64_t)lock_limit.rlim_max > (uint64_t)lock_limit.rlim_cur + size)) {
suggest = false;
}
#endif

88
llama/llama.cpp/src/llama-model.cpp vendored
View File

@@ -423,8 +423,8 @@ static buft_list_t make_gpu_buft_list(ggml_backend_dev_t dev, llama_split_mode s
}
struct llama_model::impl {
impl() {}
~impl() {}
impl() = default;
~impl() = default;
uint64_t n_elements = 0;
@@ -461,7 +461,7 @@ llama_model::llama_model(const llama_model_params & params) : params(params), pi
pimpl->has_tensor_overrides = params.tensor_buft_overrides && params.tensor_buft_overrides[0].pattern;
}
llama_model::~llama_model() {}
llama_model::~llama_model() = default;
void llama_model::load_stats(llama_model_loader & ml) {
pimpl->n_elements = ml.n_elements;
@@ -663,8 +663,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
hparams.swa_type = LLAMA_SWA_TYPE_NONE;
hparams.n_no_rope_layer_step = hparams.n_layer; // always use rope
} else {
hparams.swa_type = LLAMA_SWA_TYPE_CHUNKED;
hparams.n_swa = 8192;
hparams.swa_type = LLAMA_SWA_TYPE_CHUNKED;
hparams.n_swa = 8192;
hparams.n_attn_temp_floor_scale = 8192;
hparams.f_attn_temp_scale = 0.1f;
hparams.set_swa_pattern(4); // pattern: 3 chunked - 1 full
}
@@ -1262,18 +1264,25 @@ void llama_model::load_hparams(llama_model_loader & ml) {
} break;
case LLM_ARCH_GEMMA3:
{
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
hparams.set_swa_pattern(6);
const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
if (found_swa && hparams.n_swa > 0) {
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
hparams.set_swa_pattern(6);
hparams.rope_freq_base_train_swa = 10000.0f;
hparams.rope_freq_scale_train_swa = 1.0f;
hparams.rope_freq_base_train_swa = 10000.0f;
hparams.rope_freq_scale_train_swa = 1.0f;
} else {
hparams.swa_type = LLAMA_SWA_TYPE_NONE;
}
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
hparams.f_final_logit_softcapping = 0.0f;
ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 18: type = LLM_TYPE_270M; break;
case 26: type = LLM_TYPE_1B; break;
case 32: type = LLM_TYPE_8B; break; // Rnj-1
case 34: type = LLM_TYPE_4B; break;
case 48: type = LLM_TYPE_12B; break;
case 62: type = LLM_TYPE_27B; break;
@@ -1597,8 +1606,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
switch (hparams.n_layer) {
case 28: type = LLM_TYPE_20B; break;
switch (hparams.n_ff_exp) {
case 1408: type = LLM_TYPE_16B; break;
case 1792: type = LLM_TYPE_20B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
@@ -1626,6 +1636,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
}
ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, false);
// (optional) temperature tuning - used by mistral-large
ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false);
ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false);
switch (hparams.n_layer) {
case 27: type = LLM_TYPE_16B; break;
case 60: type = LLM_TYPE_236B; break;
@@ -2262,6 +2276,42 @@ void llama_model::load_hparams(llama_model_loader & ml) {
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_MISTRAL3:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false);
ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast, false);
ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow, false);
ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, false);
// TODO: maybe add n_attn_temp_floor_scale as a separate KV?
if (hparams.f_attn_temp_scale != 0.0f) {
hparams.n_attn_temp_floor_scale = hparams.n_ctx_orig_yarn;
if (hparams.n_attn_temp_floor_scale == 0) {
throw std::runtime_error("invalid n_ctx_orig_yarn for attention temperature scaling");
}
}
// TODO: this seems to be correct with the case of mscale == mscale_all_dims == 1.0f
// but may need further verification with other values
if (hparams.rope_yarn_log_mul != 0.0f) {
float factor = 1.0f / hparams.rope_freq_scale_train;
float mscale = 1.0f;
float mscale_all_dims = hparams.rope_yarn_log_mul;
static auto get_mscale = [](float scale, float mscale) {
return scale <= 1.0f ? 1.0f : (0.1f * mscale * logf(scale) + 1.0f);
};
hparams.yarn_attn_factor = get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dims);
}
switch (hparams.n_layer) {
case 26: type = LLM_TYPE_3B; break;
case 34: type = LLM_TYPE_8B; break;
case 40: type = LLM_TYPE_14B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
default: throw std::runtime_error("unsupported model architecture");
}
@@ -2575,6 +2625,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
case LLM_ARCH_MINICPM:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
case LLM_ARCH_MISTRAL3:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -6530,7 +6581,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), { n_embd, qkvz_dim }, 0);
layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), { hparams.ssm_dt_rank }, 0);
layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), { hparams.ssm_dt_rank }, 0);
layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN, i), { hparams.ssm_dt_rank }, 0);
layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), { head_v_dim }, 0);
layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), { value_dim, n_embd }, 0);
@@ -7304,7 +7355,11 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
} break;
case LLM_ARCH_GEMMA3:
{
llm = std::make_unique<llm_build_gemma3_iswa>(*this, params);
if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
llm = std::make_unique<llm_build_gemma3<true>>(*this, params);
} else {
llm = std::make_unique<llm_build_gemma3<false>>(*this, params);
}
} break;
case LLM_ARCH_GEMMA3N:
{
@@ -7569,6 +7624,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
{
llm = std::make_unique<llm_build_qwen3next>(*this, params);
} break;
case LLM_ARCH_MISTRAL3:
{
llm = std::make_unique<llm_build_mistral3>(*this, params);
} break;
default:
GGML_ABORT("fatal error");
}
@@ -7738,6 +7797,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_ARCEE:
case LLM_ARCH_ERNIE4_5:
case LLM_ARCH_ERNIE4_5_MOE:
case LLM_ARCH_MISTRAL3:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2

29
llama/llama.cpp/src/llama-quant.cpp vendored
View File

@@ -666,7 +666,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
std::map<int, std::string> mapped;
int blk_id = 0;
int pruned_attention_w = 0;
// make a list of weights
std::vector<const llama_model_loader::llama_tensor_weight *> tensors;
@@ -674,11 +673,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
for (const auto & it : ml.weights_map) {
const std::string remapped_name(remap_layer(it.first, prune_list, mapped, blk_id));
if (remapped_name.empty()) {
if (it.first.find("attn_v.weight") != std::string::npos ||
it.first.find("attn_qkv.weight") != std::string::npos ||
it.first.find("attn_kv_b.weight") != std::string::npos) {
pruned_attention_w++;
}
LLAMA_LOG_DEBUG("%s: pruning tensor %s\n", __func__, it.first.c_str());
continue;
}
@@ -703,7 +697,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
});
}
bool is_clip_model = false;
for (const auto * it : tensors) {
const struct ggml_tensor * tensor = it->tensor;
@@ -717,32 +710,10 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
} else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) {
qs.has_output = true;
}
is_clip_model |= name.rfind("mm.", 0) == 0; // check the "mm." prefix
}
qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer;
// sanity checks for models that have attention layers
if (qs.n_attention_wv != 0 && !is_clip_model)
{
const auto & n_head_kv_iter = model.hparams.n_head_kv_arr.begin();
// attention layers have a non-zero number of kv heads
int32_t n_layer_attn = model.hparams.n_layer - std::count(n_head_kv_iter, n_head_kv_iter + model.hparams.n_layer, 0);
if (llama_model_has_encoder(&model)) {
// now n_layer_attn is the number of attention layers in the encoder
// for each decoder block, there are 2 attention layers
n_layer_attn += 2 * model.hparams.dec_n_layer;
}
// note: for linear-attention models (such as Qwen3 Next) this is the number of linear layers
const int32_t n_layer_recr = std::count(model.hparams.recurrent_layer_arr.begin(), model.hparams.recurrent_layer_arr.end(), true);
LLAMA_LOG_INFO("%s: n_layer_attn = %d, n_layer_recr = %d, pruned_attention_w = %d\n", __func__, n_layer_attn, n_layer_recr, pruned_attention_w);
GGML_ASSERT((qs.n_attention_wv == n_layer_attn - pruned_attention_w - n_layer_recr) && "n_attention_wv is unexpected");
}
size_t total_size_org = 0;
size_t total_size_new = 0;

3
llama/llama.cpp/src/llama-vocab.cpp vendored
View File

@@ -3243,8 +3243,7 @@ void llama_vocab::impl::print_info() const {
llama_vocab::llama_vocab() : pimpl(new impl(*this)) {
}
llama_vocab::~llama_vocab() {
}
llama_vocab::~llama_vocab() = default;
void llama_vocab::load(llama_model_loader & ml, const LLM_KV & kv) {
pimpl->load(ml, kv);

18
llama/llama.cpp/src/models/deepseek2.cpp vendored
View File

@@ -30,6 +30,12 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
// {n_embd, n_tokens}
inpL = build_inp_embd(model.tok_embd);
// (optional) temperature tuning - used by mistral-large
ggml_tensor * inp_attn_scale = nullptr;
if (hparams.f_attn_temp_scale != 0.0f) {
inp_attn_scale = build_inp_attn_scale();
}
// inp_pos - contains the positions
ggml_tensor * inp_pos = build_inp_pos();
@@ -128,6 +134,12 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
ggml_tensor * Vcur = kv_cmpr;
cb(Vcur, "Vcur", il);
if (inp_attn_scale) {
// apply llama 4 temperature scaling
Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);
cb(Qcur, "Qcur_attn_temp_scaled", il);
}
// note: MLA with the absorption optimzation converts into MQA (ie: GQA with 1 group)
cur = build_attn(inp_attn,
model.layers[il].wo, NULL,
@@ -160,6 +172,12 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
ggml_tensor * Kcur = ggml_concat(ctx0, ggml_repeat(ctx0, k_pe, q_pe), k_nope, 0);
cb(Kcur, "Kcur", il);
if (inp_attn_scale) {
// apply llama 4 temperature scaling
Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);
cb(Qcur, "Qcur_attn_temp_scaled", il);
}
// note: MLA without the absorption optimization converts into MHA (ie: GQA with full n_head groups)
cur = build_attn(inp_attn,
model.layers[il].wo, NULL,

35
llama/llama.cpp/src/models/gemma3-iswa.cpp → llama/llama.cpp/src/models/gemma3.cpp vendored
View File

@@ -1,6 +1,7 @@
#include "models.h"
llm_build_gemma3_iswa::llm_build_gemma3_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
template <bool iswa>
llm_build_gemma3<iswa>::llm_build_gemma3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_k;
ggml_tensor * cur;
@@ -17,13 +18,28 @@ llm_build_gemma3_iswa::llm_build_gemma3_iswa(const llama_model & model, const ll
ggml_tensor * inp_pos = build_inp_pos();
// TODO: is causal == true correct? might need some changes
auto * inp_attn = build_attn_inp_kv_iswa();
using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;
inp_attn_type * inp_attn = nullptr;
if constexpr (iswa) {
inp_attn = build_attn_inp_kv_iswa();
} else {
inp_attn = build_attn_inp_kv();
}
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
const float freq_base_l = model.get_rope_freq_base (cparams, il);
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
float freq_base_l = 0.0f;
float freq_scale_l = 0.0f;
if constexpr (iswa) {
freq_base_l = model.get_rope_freq_base (cparams, il);
freq_scale_l = model.get_rope_freq_scale(cparams, il);
} else {
freq_base_l = freq_base;
freq_scale_l = freq_scale;
}
// norm
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
@@ -102,7 +118,7 @@ llm_build_gemma3_iswa::llm_build_gemma3_iswa(const llama_model & model, const ll
cur = build_norm(cur,
model.layers[il].ffn_post_norm, NULL,
LLM_NORM_RMS, -1);
cb(cur, "ffn_post_norm", -1);
cb(cur, "ffn_post_norm", il);
cur = ggml_add(ctx0, cur, sa_out);
@@ -124,8 +140,17 @@ llm_build_gemma3_iswa::llm_build_gemma3_iswa(const llama_model & model, const ll
// lm_head
cur = build_lora_mm(model.output, cur);
if (hparams.f_final_logit_softcapping) {
cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);
cur = ggml_tanh(ctx0, cur);
cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping);
}
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
template struct llm_build_gemma3<false>;
template struct llm_build_gemma3<true>;

160
llama/llama.cpp/src/models/mistral3.cpp vendored Normal file
View File

@@ -0,0 +1,160 @@
#include "models.h"
llm_build_mistral3::llm_build_mistral3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_head == hparams.n_rot);
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
// inp_pos - contains the positions
ggml_tensor * inp_pos = build_inp_pos();
// (optional) temperature tuning
ggml_tensor * inp_attn_scale = nullptr;
if (hparams.f_attn_temp_scale != 0.0f) {
inp_attn_scale = build_inp_attn_scale();
}
auto * inp_attn = build_attn_inp_kv();
const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
// norm
cur = build_norm(inpL,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
// self-attention
{
// rope freq factors for llama3; may return nullptr for llama2 and other models
ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
// compute Q and K and RoPE them
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
if (model.layers[il].bq) {
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
cb(Qcur, "Qcur", il);
}
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
if (model.layers[il].bk) {
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
cb(Kcur, "Kcur", il);
}
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
if (model.layers[il].bv) {
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
cb(Vcur, "Vcur", il);
}
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, rope_factors,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, rope_factors,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
if (inp_attn_scale) {
// apply llama 4 temperature scaling
Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);
cb(Qcur, "Qcur_attn_temp_scaled", il);
}
cur = build_attn(inp_attn,
model.layers[il].wo, model.layers[il].bo,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
cb(cur, "attn_out", il);
}
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network (non-MoE)
if (model.layers[il].ffn_gate_inp == nullptr) {
cur = build_norm(ffn_inp,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
cur = build_ffn(cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
} else {
// MoE branch
cur = build_norm(ffn_inp,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
cur = build_moe_ffn(cur,
model.layers[il].ffn_gate_inp,
model.layers[il].ffn_up_exps,
model.layers[il].ffn_gate_exps,
model.layers[il].ffn_down_exps,
nullptr,
n_expert, n_expert_used,
LLM_FFN_SILU, true,
false, 0.0,
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
il);
cb(cur, "ffn_moe_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = build_norm(cur,
model.output_norm, NULL,
LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
// lm_head
cur = build_lora_mm(model.output, cur);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}

9
llama/llama.cpp/src/models/models.h vendored
View File

@@ -179,8 +179,9 @@ struct llm_build_gemma2_iswa : public llm_graph_context {
llm_build_gemma2_iswa(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_gemma3_iswa : public llm_graph_context {
llm_build_gemma3_iswa(const llama_model & model, const llm_graph_params & params);
template <bool iswa>
struct llm_build_gemma3 : public llm_graph_context {
llm_build_gemma3(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_gemma3n_iswa : public llm_graph_context {
@@ -322,6 +323,10 @@ struct llm_build_minimax_m2 : public llm_graph_context {
llm_build_minimax_m2(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_mistral3 : public llm_graph_context {
llm_build_mistral3(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_mpt : public llm_graph_context {
llm_build_mpt(const llama_model & model, const llm_graph_params & params);
};

4
llama/llama.cpp/src/unicode.cpp vendored
View File

@@ -520,7 +520,7 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
// use std::wregex to split the text
static std::vector<size_t> unicode_regex_split_stl(const std::wstring & wtext, const std::wstring & regex_expr, const std::vector<size_t> & offsets) {
std::wregex expr(regex_expr);
std::wregex expr(regex_expr, std::regex_constants::optimize | std::regex_constants::nosubs);
std::vector<size_t> bpe_offsets; // store the offset of each word
bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
size_t start = 0;
@@ -550,7 +550,7 @@ static std::vector<size_t> unicode_regex_split_stl(const std::wstring & wtext, c
// use std::regex to split the text
static std::vector<size_t> unicode_regex_split_stl(const std::string & text, const std::string & regex_expr, const std::vector<size_t> & offsets) {
std::regex expr(regex_expr);
std::regex expr(regex_expr, std::regex_constants::optimize | std::regex_constants::nosubs);
std::vector<size_t> bpe_offsets; // store the offset of each word
bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
size_t start = 0;

70
llama/llama.cpp/tools/mtmd/clip.cpp vendored
View File

@@ -441,6 +441,7 @@ struct clip_ctx {
int max_nodes = 8192;
ggml_backend_sched_ptr sched;
clip_flash_attn_type flash_attn_type = CLIP_FLASH_ATTN_TYPE_AUTO;
bool is_allocated = false;
// for debugging
bool debug_graph = false;
@@ -2033,7 +2034,7 @@ private:
ggml_tensor * pos_embd = model.position_embeddings;
const int height = img.ny / patch_size;
const int width = img.nx / patch_size;
const uint32_t mode = GGML_SCALE_MODE_BILINEAR;
const uint32_t mode = GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS;
const int n_per_side = (int)std::sqrt(pos_embd->ne[1]);
GGML_ASSERT(pos_embd);
@@ -2812,7 +2813,8 @@ struct clip_model_loader {
{
get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
// ref: https://huggingface.co/LiquidAI/LFM2-VL-3B/blob/main/preprocessor_config.json
hparams.set_limit_image_tokens(64, 256);
// config above specifies number of tokens after downsampling, while here it is before, relax lowerbound to 64
hparams.set_limit_image_tokens(64, 1024);
} break;
case PROJECTOR_TYPE_PIXTRAL:
case PROJECTOR_TYPE_LIGHTONOCR:
@@ -3347,12 +3349,30 @@ struct clip_model_loader {
};
static void warmup(clip_ctx & ctx_clip) {
// create a fake batch
const auto & hparams = ctx_clip.model.hparams;
clip_image_f32_batch batch;
clip_image_f32_ptr img(clip_image_f32_init());
if (ctx_clip.model.modality == CLIP_MODALITY_VISION) {
img->nx = hparams.warmup_image_size;
img->ny = hparams.warmup_image_size;
LOG_INF("%s: warmup with image size = %d x %d\n", __func__, img->nx, img->ny);
} else {
img->nx = hparams.warmup_audio_size;
img->ny = hparams.n_mel_bins;
LOG_INF("%s: warmup with audio size = %d\n", __func__, img->nx);
}
batch.entries.push_back(std::move(img));
warmup(ctx_clip, batch);
}
static void warmup(clip_ctx & ctx_clip, const clip_image_f32_batch & batch) {
support_info_graph info;
if (ctx_clip.flash_attn_type == CLIP_FLASH_ATTN_TYPE_AUTO) {
// try to enable flash attention to see if it's supported
ctx_clip.flash_attn_type = CLIP_FLASH_ATTN_TYPE_ENABLED;
info = alloc_compute_meta(ctx_clip);
info = alloc_compute_meta(ctx_clip, batch);
if (!info.fattn && info.fattn_op) {
auto op = info.fattn_op;
LOG_WRN("%s: *****************************************************************\n", __func__);
@@ -3371,15 +3391,17 @@ struct clip_model_loader {
LOG_WRN("%s: please report this on github as an issue\n", __func__);
LOG_WRN("%s: *****************************************************************\n", __func__);
ctx_clip.flash_attn_type = CLIP_FLASH_ATTN_TYPE_DISABLED;
alloc_compute_meta(ctx_clip);
alloc_compute_meta(ctx_clip, batch);
}
} else {
info = alloc_compute_meta(ctx_clip);
info = alloc_compute_meta(ctx_clip, batch);
if (!info.fattn && ctx_clip.flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) {
LOG_WRN("%s: flash attention is not supported by the current backend; falling back to CPU (performance will be degraded)\n", __func__);
}
}
ctx_clip.is_allocated = true; // mark buffers as allocated
LOG_INF("%s: flash attention is %s\n", __func__,
(ctx_clip.flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) ? "enabled" : "disabled");
@@ -3411,24 +3433,9 @@ struct clip_model_loader {
}
}
static support_info_graph alloc_compute_meta(clip_ctx & ctx_clip) {
const auto & hparams = ctx_clip.model.hparams;
static support_info_graph alloc_compute_meta(clip_ctx & ctx_clip, const clip_image_f32_batch & batch) {
ctx_clip.buf_compute_meta.resize(ctx_clip.max_nodes * ggml_tensor_overhead() + ggml_graph_overhead());
// create a fake batch
clip_image_f32_batch batch;
clip_image_f32_ptr img(clip_image_f32_init());
if (ctx_clip.model.modality == CLIP_MODALITY_VISION) {
img->nx = hparams.warmup_image_size;
img->ny = hparams.warmup_image_size;
LOG_INF("%s: warmup with image size = %d x %d\n", __func__, img->nx, img->ny);
} else {
img->nx = hparams.warmup_audio_size;
img->ny = hparams.n_mel_bins;
LOG_INF("%s: warmup with audio size = %d\n", __func__, img->nx);
}
batch.entries.push_back(std::move(img));
ggml_cgraph * gf = clip_image_build_graph(&ctx_clip, batch);
ggml_backend_sched_reserve(ctx_clip.sched.get(), gf);
@@ -3568,14 +3575,18 @@ struct clip_init_result clip_init(const char * fname, struct clip_context_params
ctx_vision = new clip_ctx(ctx_params);
loader.load_hparams(ctx_vision->model, CLIP_MODALITY_VISION);
loader.load_tensors(*ctx_vision);
loader.warmup(*ctx_vision);
if (ctx_params.warmup) {
loader.warmup(*ctx_vision);
}
}
if (loader.has_audio) {
ctx_audio = new clip_ctx(ctx_params);
loader.load_hparams(ctx_audio->model, CLIP_MODALITY_AUDIO);
loader.load_tensors(*ctx_audio);
loader.warmup(*ctx_audio);
if (ctx_params.warmup) {
loader.warmup(*ctx_audio);
}
}
} catch (const std::exception & e) {
@@ -3788,12 +3799,13 @@ struct img_tool {
const int width = inp_size.width;
const int height = inp_size.height;
auto round_by_factor = [f = align_size](float x) { return static_cast<int>(std::round(x / static_cast<float>(f))) * f; };
auto ceil_by_factor = [f = align_size](float x) { return static_cast<int>(std::ceil(x / static_cast<float>(f))) * f; };
auto floor_by_factor = [f = align_size](float x) { return static_cast<int>(std::floor(x / static_cast<float>(f))) * f; };
// always align up first
int h_bar = std::max(align_size, ceil_by_factor(height));
int w_bar = std::max(align_size, ceil_by_factor(width));
int h_bar = std::max(align_size, round_by_factor(height));
int w_bar = std::max(align_size, round_by_factor(width));
if (h_bar * w_bar > max_pixels) {
const auto beta = std::sqrt(static_cast<float>(height * width) / max_pixels);
@@ -4408,7 +4420,8 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
const std::array<uint8_t, 3> pad_color = {122, 116, 104};
clip_image_u8 resized_img;
img_tool::resize(*img, resized_img, target_size, img_tool::RESIZE_ALGO_BILINEAR, true, pad_color);
const bool pad = (ctx->proj_type() != PROJECTOR_TYPE_LFM2);
img_tool::resize(*img, resized_img, target_size, img_tool::RESIZE_ALGO_BILINEAR, pad, pad_color);
clip_image_f32_ptr res(clip_image_f32_init());
normalize_image_u8_to_f32(resized_img, *res, params.image_mean, params.image_std);
res_imgs->entries.push_back(std::move(res));
@@ -4666,6 +4679,11 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
return false; // only support batch size of 1
}
// if buffers are not allocated, we need to do a warmup run to allocate them
if (!ctx->is_allocated) {
clip_model_loader::warmup(*ctx, *imgs_c_ptr);
}
// build the inference graph
ctx->debug_print_tensors.clear();
ggml_backend_sched_reset(ctx->sched.get());

1
llama/llama.cpp/tools/mtmd/clip.h vendored
View File

@@ -34,6 +34,7 @@ struct clip_context_params {
enum clip_flash_attn_type flash_attn_type;
int image_min_tokens;
int image_max_tokens;
bool warmup;
};
struct clip_init_result {

6
llama/llama.cpp/tools/mtmd/mtmd.cpp vendored
View File

@@ -118,6 +118,7 @@ mtmd_context_params mtmd_context_params_default() {
/* image_marker */ MTMD_DEFAULT_IMAGE_MARKER,
/* media_marker */ mtmd_default_marker(),
/* flash_attn_type */ LLAMA_FLASH_ATTN_TYPE_AUTO,
/* warmup */ true,
/* image_min_tokens */ -1,
/* image_max_tokens */ -1,
};
@@ -187,6 +188,7 @@ struct mtmd_context {
/* flash_attn_type */ CLIP_FLASH_ATTN_TYPE_AUTO,
/* image_min_tokens */ ctx_params.image_min_tokens,
/* image_max_tokens */ ctx_params.image_max_tokens,
/* warmup */ ctx_params.warmup,
};
auto res = clip_init(mmproj_fname, ctx_clip_params);
@@ -314,6 +316,10 @@ struct mtmd_context {
img_beg = "<|im_start|>";
img_end = "<|im_end|>";
} else if (proj == PROJECTOR_TYPE_LFM2) {
img_beg = "<|image_start|>";
img_end = "<|image_end|>";
}
}

1
llama/llama.cpp/tools/mtmd/mtmd.h vendored
View File

@@ -85,6 +85,7 @@ struct mtmd_context_params {
const char * image_marker; // deprecated, use media_marker instead
const char * media_marker;
enum llama_flash_attn_type flash_attn_type;
bool warmup; // whether to run a warmup encode pass after initialization
// limit number of image tokens, only for vision models with dynamic resolution
int image_min_tokens; // minimum number of tokens for image input (default: read from metadata)