Files
llama.cpp/src/llama-vocab.h
Gabe Goodhart ca71fb9b36 model : Granite docling + Idefics3 preprocessing (SmolVLM) (#16206)
* feat: Add granite-docling conversion using trillion pretokenizer

Branch: gabe-l-hart/GraniteDocling

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

* feat: Add granite-docling vocab pre enum

Branch: gabe-l-hart/GraniteDocling

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

* fix: Use granite-docling pre

Branch: gabe-l-hart/GraniteDocling

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

* feat: Add clip_is_idefics3

Branch: gabe-l-hart/GraniteDocling

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

* feat: Allow multi-token boundary sequences for image templating

Branch: gabe-l-hart/GraniteDocling

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

* feat: Add tiling support for idefices3 in clip.cpp

This should likely be moved into llava_uhd::get_slice_instructions, but for
now this avoids disrupting the logic there.

Branch: gabe-l-hart/GraniteDocling

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

* feat: Partial support for full templating for idefics3 in mtmd

There are still errors encoding some of the image chunks, but the token
sequence now matches transformers _almost_ perfectly, except for the double
newline before the global image which shows up as two consecutive newline
tokens instead of a single double-newline token. I think this is happening
because the blocks are tokenized separately then concatenated.

Branch: gabe-l-hart/GraniteDocling

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

* feat: Fully working image preprocessing for idefics3 w/ resize and slicing

Branch: gabe-l-hart/GraniteDocling

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

* feat: Parse the preprocessor config's longest side and add it to the mmproj hparams

Branch: GraniteDocling

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

* fix: Use the longest side instead of size * scale_factor

For Granite Docling, these come out to the same value, but that was just a
conicidence.

Branch: GraniteDocling

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

* fix: Allow batch encoding and remove clip_is_idefics3

Branch: GraniteDocling

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

* refactor: Remove unnecessary conditionals for empty token vectors

Branch: GraniteDocling

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

* refactor: Use image_manipulation util

Branch: GraniteDocling

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

* add test model

---------

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
2025-10-05 14:57:47 +02:00

179 lines
6.0 KiB
C++

#pragma once
#include "llama.h"
#include <string>
#include <vector>
#include <memory>
// pre-tokenization types
enum llama_vocab_pre_type {
LLAMA_VOCAB_PRE_TYPE_DEFAULT = 0,
LLAMA_VOCAB_PRE_TYPE_LLAMA3 = 1,
LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM = 2,
LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER = 3,
LLAMA_VOCAB_PRE_TYPE_FALCON = 4,
LLAMA_VOCAB_PRE_TYPE_MPT = 5,
LLAMA_VOCAB_PRE_TYPE_STARCODER = 6,
LLAMA_VOCAB_PRE_TYPE_GPT2 = 7,
LLAMA_VOCAB_PRE_TYPE_REFACT = 8,
LLAMA_VOCAB_PRE_TYPE_COMMAND_R = 9,
LLAMA_VOCAB_PRE_TYPE_STABLELM2 = 10,
LLAMA_VOCAB_PRE_TYPE_QWEN2 = 11,
LLAMA_VOCAB_PRE_TYPE_OLMO = 12,
LLAMA_VOCAB_PRE_TYPE_DBRX = 13,
LLAMA_VOCAB_PRE_TYPE_SMAUG = 14,
LLAMA_VOCAB_PRE_TYPE_PORO = 15,
LLAMA_VOCAB_PRE_TYPE_CHATGLM3 = 16,
LLAMA_VOCAB_PRE_TYPE_CHATGLM4 = 17,
LLAMA_VOCAB_PRE_TYPE_VIKING = 18,
LLAMA_VOCAB_PRE_TYPE_JAIS = 19,
LLAMA_VOCAB_PRE_TYPE_TEKKEN = 20,
LLAMA_VOCAB_PRE_TYPE_SMOLLM = 21,
LLAMA_VOCAB_PRE_TYPE_CODESHELL = 22,
LLAMA_VOCAB_PRE_TYPE_BLOOM = 23,
LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24,
LLAMA_VOCAB_PRE_TYPE_EXAONE = 25,
LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26,
LLAMA_VOCAB_PRE_TYPE_MINERVA = 27,
LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM = 28,
LLAMA_VOCAB_PRE_TYPE_GPT4O = 29,
LLAMA_VOCAB_PRE_TYPE_SUPERBPE = 30,
LLAMA_VOCAB_PRE_TYPE_TRILLION = 31,
LLAMA_VOCAB_PRE_TYPE_BAILINGMOE = 32,
LLAMA_VOCAB_PRE_TYPE_LLAMA4 = 33,
LLAMA_VOCAB_PRE_TYPE_PIXTRAL = 34,
LLAMA_VOCAB_PRE_TYPE_SEED_CODER = 35,
LLAMA_VOCAB_PRE_TYPE_HUNYUAN = 36,
LLAMA_VOCAB_PRE_TYPE_KIMI_K2 = 37,
LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE = 38,
LLAMA_VOCAB_PRE_TYPE_GROK_2 = 39,
LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING = 40,
};
struct LLM_KV;
struct llama_model_loader;
struct llama_vocab {
struct token_data {
std::string text;
float score;
llama_token_attr attr;
};
llama_vocab();
~llama_vocab();
void load(llama_model_loader & ml, const LLM_KV & kv);
std::string get_tokenizer_model() const;
std::string get_tokenizer_pre() const;
enum llama_vocab_type get_type() const;
enum llama_vocab_pre_type get_pre_type() const;
uint32_t n_tokens() const;
uint32_t n_token_types() const;
std::string type_name() const;
bool is_normal (llama_token id) const;
bool is_unknown (llama_token id) const;
bool is_control (llama_token id) const;
bool is_byte (llama_token id) const;
bool is_user_defined(llama_token id) const;
bool is_unused (llama_token id) const;
bool is_eog (llama_token id) const;
uint8_t token_to_byte(llama_token id) const;
llama_token byte_to_token(uint8_t ch) const;
llama_token text_to_token(const std::string & text) const;
const token_data & get_token_data(llama_token id) const;
const char * token_get_text (llama_token id) const;
float token_get_score(llama_token id) const;
llama_token_attr token_get_attr (llama_token id) const;
llama_token token_bos() const;
llama_token token_eos() const;
llama_token token_eot() const;
llama_token token_eom() const;
llama_token token_unk() const;
llama_token token_sep() const;
llama_token token_nl () const;
llama_token token_pad() const;
llama_token token_mask() const;
llama_token token_prefix() const;
llama_token token_middle() const;
llama_token token_suffix() const;
llama_token token_fim_pre() const;
llama_token token_fim_suf() const;
llama_token token_fim_mid() const;
llama_token token_fim_pad() const;
llama_token token_fim_rep() const;
llama_token token_fim_sep() const;
bool get_add_space_prefix () const;
bool get_add_bos () const;
bool get_add_eos () const;
bool get_add_sep () const;
bool get_ignore_merges () const;
bool get_clean_spaces () const;
bool get_remove_extra_whitespaces () const;
bool get_escape_whitespaces () const;
bool get_treat_whitespace_as_suffix() const;
int max_token_len() const;
int find_bpe_rank(const std::string & token_left, const std::string & token_right) const;
std::vector<std::string> get_bpe_merges() const;
std::vector<char> get_precompiled_charsmap() const;
int32_t tokenize(
const char * text,
int32_t text_len,
llama_token * tokens,
int32_t n_tokens_max,
bool add_special,
bool parse_special) const;
std::vector<llama_token> tokenize(
const std::string & raw_text,
bool add_special,
bool parse_special = false) const;
// does not write null-terminator to buf
int32_t token_to_piece(
llama_token token,
char * buf,
int32_t length,
int32_t lstrip,
bool special) const;
// use cached data
const std::string & token_to_piece(llama_token token) const;
int32_t detokenize(
const llama_token * tokens,
int32_t n_tokens,
char * text,
int32_t text_len_max,
bool remove_special,
bool unparse_special) const;
std::string detokenize(
const std::vector<llama_token> & tokens,
bool special) const;
void print_info() const;
private:
struct impl;
std::unique_ptr<impl> pimpl;
};