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llama : support models without vocabulary (#5798)

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* additional methods to read model and ctx parameters

* vocab size as a part of a model metadata

* models without vocabulary, convert.py part

* models without vocabulary, llama.cpp part

* PR clean up

* converter scrypt fixes

* llama_vocab_type update (renamed the new key)

* pr review fixes

* revert function renaming

* one more NoVocab assert
This commit is contained in:
Michael Podvitskiy 2024-03-14 17:21:56 +01:00 committed by GitHub
parent 044ec4b2a5
commit 69ff61397d
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5 changed files with 142 additions and 88 deletions
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126
convert.py
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@ -332,6 +332,9 @@ class Params:
#
class BpeVocab:
tokenizer_model = "gpt2"
name = "bpe"
def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read())
if isinstance(self.bpe_tokenizer.get('model'), dict):
@ -390,6 +393,9 @@ class BpeVocab:
class SentencePieceVocab:
tokenizer_model = "llama"
name = "spm"
def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
added_tokens: dict[str, int]
@ -453,6 +459,9 @@ class SentencePieceVocab:
class HfVocab:
tokenizer_model = "llama"
name = "hfft"
def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None = None) -> None:
try:
from transformers import AutoTokenizer
@ -553,7 +562,15 @@ class HfVocab:
return f"<HfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab"
class NoVocab:
tokenizer_model = "no_vocab"
name = "no_vocab"
def __repr__(self) -> str:
return "<NoVocab for a model without integrated vocabulary>"
Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab | NoVocab"
#
@ -935,8 +952,10 @@ def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> N
# Handle special case where the model's vocab size is not set
if params.n_vocab == -1:
raise ValueError(
f"The model's vocab size is set to -1 in params.json. Please update it manually. Maybe {vocab.vocab_size}?"
f"The model's vocab size is set to -1 in params.json. Please update it manually.{f' Maybe {vocab.vocab_size}?' if hasattr(vocab, 'vocab_size') else ''}"
)
if isinstance(vocab, NoVocab):
return # model has no vocab
# Check for a vocab size mismatch
if params.n_vocab == vocab.vocab_size:
@ -977,6 +996,7 @@ class OutputFile:
name = str(params.path_model.parent).split('/')[-1]
self.gguf.add_name (name)
self.gguf.add_vocab_size (params.n_vocab)
self.gguf.add_context_length (params.n_ctx)
self.gguf.add_embedding_length (params.n_embd)
self.gguf.add_block_count (params.n_layer)
@ -1013,21 +1033,9 @@ class OutputFile:
if params.ftype is not None:
self.gguf.add_file_type(params.ftype)
def handle_tokenizer_model(self, vocab: Vocab) -> str:
# Map the vocab types to the supported tokenizer models
tokenizer_model = {
SentencePieceVocab: "llama",
HfVocab: "llama",
BpeVocab: "gpt2",
}.get(type(vocab))
# Block if vocab type is not predefined
if tokenizer_model is None:
raise ValueError("Unknown vocab type: Not supported")
return tokenizer_model
def extract_vocabulary_from_model(self, vocab: Vocab) -> tuple[list[bytes], list[float], list[gguf.TokenType]]:
assert not isinstance(vocab, NoVocab)
tokens = []
scores = []
toktypes = []
@ -1043,11 +1051,8 @@ class OutputFile:
return tokens, scores, toktypes
def add_meta_vocab(self, vocab: Vocab) -> None:
# Handle the tokenizer model
tokenizer_model = self.handle_tokenizer_model(vocab)
# Ensure that tokenizer_model is added to the GGUF model
self.gguf.add_tokenizer_model(tokenizer_model)
self.gguf.add_tokenizer_model(vocab.tokenizer_model)
# Extract model vocabulary for model conversion
tokens, scores, toktypes = self.extract_vocabulary_from_model(vocab)
@ -1074,6 +1079,26 @@ class OutputFile:
def write_tensor_info(self) -> None:
self.gguf.write_ti_data_to_file()
def write_tensor_data(self, ftype: GGMLFileType, model: LazyModel, concurrency: int) -> None:
ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency=concurrency)
if ftype == GGMLFileType.MostlyQ8_0:
ndarrays = bounded_parallel_map(
OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency,
use_processpool_executor=True,
)
else:
ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
start = time.time()
for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
elapsed = time.time() - start
size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
padi = len(str(len(model)))
print(
f"[{i + 1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}"
)
self.gguf.write_tensor_data(ndarray)
def close(self) -> None:
self.gguf.close()
@ -1082,7 +1107,7 @@ class OutputFile:
fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab,
endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False,
) -> None:
check_vocab_size(params, vocab, pad_vocab = pad_vocab)
check_vocab_size(params, vocab, pad_vocab=pad_vocab)
of = OutputFile(fname_out, endianess=endianess)
@ -1120,8 +1145,11 @@ class OutputFile:
# meta data
of.add_meta_arch(params)
of.add_meta_vocab(vocab)
of.add_meta_special_vocab(svocab)
if isinstance(vocab, NoVocab):
of.gguf.add_tokenizer_model(vocab.tokenizer_model)
else:
of.add_meta_vocab(vocab)
of.add_meta_special_vocab(svocab)
# tensor info
for name, lazy_tensor in model.items():
@ -1131,24 +1159,7 @@ class OutputFile:
of.write_tensor_info()
# tensor data
ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency)
if ftype == GGMLFileType.MostlyQ8_0:
ndarrays = bounded_parallel_map(
OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency,
use_processpool_executor=True,
)
else:
ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
start = time.time()
for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
elapsed = time.time() - start
size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
padi = len(str(len(model)))
print(
f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}"
)
of.gguf.write_tensor_data(ndarray)
of.write_tensor_data(ftype, model, concurrency)
of.close()
@ -1309,8 +1320,8 @@ class VocabFactory:
return vtype, path
raise FileNotFoundError(f"Could not find any of {[self._FILES[vt] for vt in vocab_types]}")
def _create_special_vocab(self, vocab: Vocab, vocabtype: str, model_parent_path: Path) -> gguf.SpecialVocab:
load_merges = vocabtype == "bpe"
def _create_special_vocab(self, vocab: Vocab, model_parent_path: Path) -> gguf.SpecialVocab:
load_merges = vocab.name == "bpe"
n_vocab = vocab.vocab_size if hasattr(vocab, "vocab_size") else None
return gguf.SpecialVocab(
model_parent_path,
@ -1319,30 +1330,34 @@ class VocabFactory:
n_vocab=n_vocab,
)
def load_vocab(self, vocab_types: list[str], model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]:
def _create_vocab_by_path(self, vocab_types: list[str]) -> Vocab:
vocab_type, path = self._select_file(vocab_types)
print(f"Loading vocab file {path!r}, type {vocab_type!r}")
added_tokens_path = path.parent / "added_tokens.json"
vocab: Vocab
if vocab_type == "bpe":
vocab = BpeVocab(
return BpeVocab(
path, added_tokens_path if added_tokens_path.exists() else None
)
elif vocab_type == "spm":
vocab = SentencePieceVocab(
if vocab_type == "spm":
return SentencePieceVocab(
path, added_tokens_path if added_tokens_path.exists() else None
)
elif vocab_type == "hfft":
vocab = HfVocab(
if vocab_type == "hfft":
return HfVocab(
path.parent, added_tokens_path if added_tokens_path.exists() else None
)
raise ValueError(vocab_type)
def load_vocab(self, vocab_types: list[str], model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]:
vocab: Vocab
if len(vocab_types) == 1 and "no_vocab" in vocab_types:
vocab = NoVocab()
else:
raise ValueError(vocab_type)
vocab = self._create_vocab_by_path(vocab_types)
# FIXME: Respect --vocab-dir?
special_vocab = self._create_special_vocab(
vocab,
vocab_type,
model_parent_path,
)
return vocab, special_vocab
@ -1380,6 +1395,7 @@ def main(args_in: list[str] | None = None) -> None:
parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model")
parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file")
parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
parser.add_argument("--no-vocab", action="store_true", help="store model without the vocab")
parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file")
parser.add_argument("--vocab-type", help="vocab types to try in order, choose from 'spm', 'bpe', 'hfft' (default: spm,hfft)", default="spm,hfft")
@ -1392,6 +1408,10 @@ def main(args_in: list[str] | None = None) -> None:
parser.add_argument("--skip-unknown", action="store_true", help="skip unknown tensor names instead of failing")
args = parser.parse_args(args_in)
if args.no_vocab:
if args.vocab_only:
raise ValueError("no need to specify --vocab-only if using --no-vocab")
args.vocab_type = "no_vocab"
if args.dump_single:
model_plus = lazy_load_file(args.model)
@ -1442,7 +1462,7 @@ def main(args_in: list[str] | None = None) -> None:
print(f"Wrote {outfile}")
return
if model_plus.vocab is not None and args.vocab_dir is None:
if model_plus.vocab is not None and args.vocab_dir is None and not args.no_vocab:
vocab = model_plus.vocab
print(f"Vocab info: {vocab}")

2
gguf-py/gguf/constants.py
View File

@ -32,6 +32,7 @@ class Keys:
FILE_TYPE = "general.file_type"
class LLM:
VOCAB_SIZE = "{arch}.vocab_size"
CONTEXT_LENGTH = "{arch}.context_length"
EMBEDDING_LENGTH = "{arch}.embedding_length"
BLOCK_COUNT = "{arch}.block_count"
@ -752,6 +753,7 @@ KEY_GENERAL_SOURCE_HF_REPO = Keys.General.SOURCE_HF_REPO
KEY_GENERAL_FILE_TYPE = Keys.General.FILE_TYPE
# LLM
KEY_VOCAB_SIZE = Keys.LLM.VOCAB_SIZE
KEY_CONTEXT_LENGTH = Keys.LLM.CONTEXT_LENGTH
KEY_EMBEDDING_LENGTH = Keys.LLM.EMBEDDING_LENGTH
KEY_BLOCK_COUNT = Keys.LLM.BLOCK_COUNT

3
gguf-py/gguf/gguf_writer.py
View File

@ -321,6 +321,9 @@ class GGUFWriter:
self.data_alignment = alignment
self.add_uint32(Keys.General.ALIGNMENT, alignment)
def add_vocab_size(self, size: int) -> None:
self.add_uint32(Keys.LLM.VOCAB_SIZE.format(arch=self.arch), size)
def add_context_length(self, length: int) -> None:
self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length)

92
llama.cpp
View File

@ -258,6 +258,7 @@ enum llm_kv {
LLM_KV_GENERAL_SOURCE_URL,
LLM_KV_GENERAL_SOURCE_HF_REPO,
LLM_KV_VOCAB_SIZE,
LLM_KV_CONTEXT_LENGTH,
LLM_KV_EMBEDDING_LENGTH,
LLM_KV_BLOCK_COUNT,
@ -321,6 +322,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_GENERAL_SOURCE_URL, "general.source.url" },
{ LLM_KV_GENERAL_SOURCE_HF_REPO, "general.source.huggingface.repository" },
{ LLM_KV_VOCAB_SIZE, "%s.vocab_size" },
{ LLM_KV_CONTEXT_LENGTH, "%s.context_length" },
{ LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" },
{ LLM_KV_BLOCK_COUNT, "%s.block_count" },
@ -3242,10 +3244,11 @@ static const char * llama_model_type_name(e_model type) {
static const char * llama_model_vocab_type_name(enum llama_vocab_type type){
switch (type) {
case LLAMA_VOCAB_TYPE_SPM: return "SPM";
case LLAMA_VOCAB_TYPE_BPE: return "BPE";
case LLAMA_VOCAB_TYPE_WPM: return "WPM";
default: return "unknown";
case LLAMA_VOCAB_TYPE_NONE: return "no vocab";
case LLAMA_VOCAB_TYPE_SPM: return "SPM";
case LLAMA_VOCAB_TYPE_BPE: return "BPE";
case LLAMA_VOCAB_TYPE_WPM: return "WPM";
default: return "unknown";
}
}
@ -3277,14 +3280,14 @@ static void llm_load_hparams(
ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
// get hparams kv
ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
ml.get_key (LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train);
ml.get_key (LLM_KV_EMBEDDING_LENGTH, hparams.n_embd);
ml.get_key (LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff);
ml.get_key (LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head);
ml.get_key (LLM_KV_BLOCK_COUNT, hparams.n_layer);
ml.get_key (LLM_KV_EXPERT_COUNT, hparams.n_expert, false);
ml.get_key (LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train);
ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd);
ml.get_key(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff);
ml.get_key(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head);
ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer);
ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false);
ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
@ -3645,30 +3648,25 @@ static void llm_load_vocab(
const auto kv = LLM_KV(model.arch);
const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
if (token_idx == -1) {
throw std::runtime_error("cannot find tokenizer vocab in model file\n");
}
const float * scores = nullptr;
const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str());
if (score_idx != -1) {
scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
}
const int * toktypes = nullptr;
const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
if (toktype_idx != -1) {
toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
}
// determine vocab type
{
std::string tokenizer_name;
ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_name);
if (tokenizer_name == "llama") {
if (tokenizer_name == "no_vocab") {
vocab.type = LLAMA_VOCAB_TYPE_NONE;
// default special tokens
vocab.special_bos_id = -1;
vocab.special_eos_id = -1;
vocab.special_unk_id = -1;
vocab.special_sep_id = -1;
vocab.special_pad_id = -1;
vocab.linefeed_id = -1;
return;
} else if (tokenizer_name == "llama") {
vocab.type = LLAMA_VOCAB_TYPE_SPM;
// default special tokens
@ -3734,6 +3732,23 @@ static void llm_load_vocab(
}
}
const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
if (token_idx == -1) {
throw std::runtime_error("cannot find tokenizer vocab in model file\n");
}
const float * scores = nullptr;
const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str());
if (score_idx != -1) {
scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
}
const int * toktypes = nullptr;
const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
if (toktype_idx != -1) {
toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
}
const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
vocab.id_to_token.resize(n_vocab);
@ -5023,7 +5038,8 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
llm_load_print_meta(ml, model);
if (model.hparams.n_vocab != model.vocab.id_to_token.size()) {
if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE &&
model.hparams.n_vocab != model.vocab.id_to_token.size()) {
throw std::runtime_error("vocab size mismatch");
}
@ -9361,26 +9377,32 @@ static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) {
}
static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL;
}
static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNKNOWN;
}
static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL;
}
static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_BYTE;
}
static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id) {
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_USER_DEFINED;
}
static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) {
GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
GGML_ASSERT(llama_is_byte_token(vocab, id));
const auto& token_data = vocab.id_to_token.at(id);
switch (llama_vocab_get_type(vocab)) {
@ -9401,6 +9423,7 @@ static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) {
}
static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
static const char * hex = "0123456789ABCDEF";
switch (llama_vocab_get_type(vocab)) {
case LLAMA_VOCAB_TYPE_SPM: {
@ -10232,6 +10255,8 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
}
}
} break;
case LLAMA_VOCAB_TYPE_NONE:
GGML_ASSERT(false);
}
return output;
@ -13138,7 +13163,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
}
int32_t llama_n_vocab(const struct llama_model * model) {
return model->vocab.id_to_token.size();
return model->hparams.n_vocab;
}
int32_t llama_n_ctx_train(const struct llama_model * model) {
@ -13962,14 +13987,17 @@ float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id
}
const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
return model->vocab.id_to_token[token].text.c_str();
}
float llama_token_get_score(const struct llama_model * model, llama_token token) {
GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
return model->vocab.id_to_token[token].score;
}
llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token) {
GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
return model->vocab.id_to_token[token].type;
}

7
llama.h
View File

@ -59,9 +59,10 @@ extern "C" {
typedef int32_t llama_seq_id;
enum llama_vocab_type {
LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece
LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding
LLAMA_VOCAB_TYPE_WPM = 2, // WordPiece
LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab
LLAMA_VOCAB_TYPE_SPM = 1, // SentencePiece
LLAMA_VOCAB_TYPE_BPE = 2, // Byte Pair Encoding
LLAMA_VOCAB_TYPE_WPM = 3, // WordPiece
};
// note: these values should be synchronized with ggml_rope