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Use some tricks to eliminate the necessity for a new format
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@Igoorx
Igoorx committed Jun 22, 2023
1 parent aa90c83 commit bfccc62
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Showing 3 changed files with 35 additions and 43 deletions.
23 changes: 9 additions & 14 deletions convert.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -133,7 +133,7 @@ def make_tensors_list() -> List[str]:
@dataclass
class Params:
n_vocab: int
n_vocab_sp: int
n_vocab_base: int
n_embd: int
n_mult: int
n_head: int
Expand All @@ -146,7 +146,7 @@ def guessed(model: 'LazyModel', vocab: 'Vocab', file_type: GGMLFileType) -> 'Par

return Params(
n_vocab=n_vocab,
n_vocab_sp=vocab.vocab_special_size,
n_vocab_base=vocab.vocab_size_base,
n_embd=n_embd,
n_mult=256,
n_head=n_embd // 128,
Expand Down Expand Up @@ -190,7 +190,7 @@ def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path], fn
else:
tokenizer_config = {}
for key, value in tokenizer_config.items():
if not isinstance(value, dict) or not isinstance(value, str):
if not isinstance(value, dict) and not isinstance(value, str):
continue
token_id = TOKEN_NAME_TO_ID.get(key, -1)
if token_id == -1:
Expand All @@ -203,15 +203,13 @@ def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path], fn
else:
special_tokens = {}
for key, value in special_tokens.items():
if not isinstance(value, dict) or not isinstance(value, str):
if not isinstance(value, dict) and not isinstance(value, str):
continue
token_id = TOKEN_NAME_TO_ID.get(key, -1)
if token_id == -1 or token_id in self.special_tokens_map:
continue
self.special_tokens_map[token_id] = value["content"] if isinstance(value, dict) else value

self.vocab_special_size: int = len(self.added_tokens_list) + len(self.special_tokens_map)

def sentencepiece_tokens(self) -> Iterable[Tuple[bytes, float]]:
tokenizer = self.sentencepiece_tokenizer
special_tokens = [tokenizer.bos_id(), tokenizer.eos_id(), tokenizer.pad_id()]
Expand Down Expand Up @@ -258,7 +256,7 @@ def __init__(self, tokens: List[Tuple[bytes, float]]):
self.tokens = tokens
self.special_tokens = []
self.vocab_size = len(tokens)
self.vocab_special_size = 0
self.vocab_size_base = 0

def all_tokens(self) -> Iterable[Tuple[bytes, float]]:
return self.tokens
Expand Down Expand Up @@ -976,17 +974,16 @@ def __init__(self, fname_out: Path) -> None:
def write_file_header(self, params: Params) -> None:
self.fout.write(b"ggjt"[::-1]) # magic
values = [
4, # file version
1, # file version
params.n_vocab,
params.n_vocab_sp,
params.n_embd,
params.n_mult,
params.n_head,
params.n_layer,
params.n_embd // params.n_head, # rot (obsolete)
params.n_vocab_base | 0xF0000000, # reuse obsolete rot value to store vocab_base
params.file_type.value,
]
self.fout.write(struct.pack("i" * len(values), *values))
self.fout.write(struct.pack("I" * len(values), *values))

def write_tensor_header(self, name: str, shape: Sequence[int], data_type: DataType) -> None:
sname = name.encode('utf-8')
Expand All @@ -1000,13 +997,11 @@ def write_vocab(self, vocab: Vocab) -> None:
self.fout.write(struct.pack("i", len(text)))
self.fout.write(text)
self.fout.write(struct.pack("f", score))
for token_id in vocab.all_special_tokens():
self.fout.write(struct.pack("i", token_id))

@staticmethod
def write_vocab_only(fname_out: Path, vocab: Vocab) -> None:
of = OutputFile(fname_out)
params = Params(n_vocab=vocab.vocab_size, n_vocab_sp=vocab.vocab_special_size, n_embd=0, n_mult=0,
params = Params(n_vocab=vocab.vocab_size, n_vocab_base=vocab.vocab_size_base, n_embd=0, n_mult=0,
n_head=1, n_layer=0, file_type=GGMLFileType.AllF32)
of = OutputFile(fname_out)
of.write_file_header(params)
Expand Down
53 changes: 25 additions & 28 deletions llama.cpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -128,13 +128,12 @@ static const std::map<e_model, size_t> & MEM_REQ_EVAL()
// default hparams (LLaMA 7B)
struct llama_hparams {
uint32_t n_vocab = 32000;
uint32_t n_vocab_sp = 0;
uint32_t n_vocab_base = 32000;
uint32_t n_ctx = 512; // this is provided as user input?
uint32_t n_embd = 4096;
uint32_t n_mult = 256;
uint32_t n_head = 32;
uint32_t n_layer = 32;
uint32_t n_rot = 64;
enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;

bool operator!=(const llama_hparams & other) const {
Expand Down Expand Up @@ -460,7 +459,6 @@ enum llama_file_version {
LLAMA_FILE_VERSION_GGJT_V1, // added padding
LLAMA_FILE_VERSION_GGJT_V2, // changed quantization format
LLAMA_FILE_VERSION_GGJT_V3, // changed Q4 and Q8 quantization format
LLAMA_FILE_VERSION_GGJT_V4, // improved support for added/special tokens
};

struct llama_file_loader {
Expand All @@ -476,6 +474,7 @@ struct llama_file_loader {
read_hparams();
read_vocab();
read_tensor_metadata(file_idx, tensors_map);
set_vocab_sp();
}
void read_magic() {
uint32_t magic = file.read_u32();
Expand All @@ -498,7 +497,6 @@ struct llama_file_loader {
case 1: file_version = LLAMA_FILE_VERSION_GGJT_V1; return;
case 2: file_version = LLAMA_FILE_VERSION_GGJT_V2; return;
case 3: file_version = LLAMA_FILE_VERSION_GGJT_V3; return;
case 4: file_version = LLAMA_FILE_VERSION_GGJT_V4; return;
}
}

Expand All @@ -507,12 +505,12 @@ struct llama_file_loader {
}
void read_hparams() {
hparams.n_vocab = file.read_u32();
hparams.n_vocab_sp = file_version >= LLAMA_FILE_VERSION_GGJT_V4 ? file.read_u32() : 0;
hparams.n_embd = file.read_u32();
hparams.n_mult = file.read_u32();
hparams.n_head = file.read_u32();
hparams.n_layer = file.read_u32();
hparams.n_rot = file.read_u32();
hparams.n_vocab_base = file.read_u32();
hparams.n_vocab_base = (hparams.n_vocab_base & 0xF0000000) == 0 ? hparams.n_vocab : (hparams.n_vocab_base & ~0xF0000000); // this bitwise operation is necessary for compatibility with older models
hparams.ftype = (enum llama_ftype) file.read_u32();
}
void read_vocab() {
Expand All @@ -533,20 +531,6 @@ struct llama_file_loader {
tok_score.tok = std::move(word);
tok_score.score = score;
}

vocab.special_token_to_id.reserve(hparams.n_vocab_sp);

for (uint32_t i = 0; i < hparams.n_vocab_sp; i++) {
llama_vocab::id token_id = file.read_u32();
const auto & word = vocab.id_to_token[token_id].tok;

vocab.special_token_trie.add(word);
vocab.special_token_to_id[word] = token_id;

if (vocab.max_special_token_length < word.size()) {
vocab.max_special_token_length = word.size();
}
}
}
void read_tensor_metadata(size_t file_idx, llama_load_tensors_map & tensors_map) {
while (file.tell() < file.size) {
Expand Down Expand Up @@ -601,6 +585,24 @@ struct llama_file_loader {
tensors_map.tensors.at(idx).shards.push_back(shard);
}
}
void set_vocab_sp() {
uint32_t vocab_sp = 3 + hparams.n_vocab - hparams.n_vocab_base;
vocab.special_token_to_id.reserve(vocab_sp);
for (uint32_t i = 0; i < vocab_sp; i++) {
llama_vocab::id token_id = i > 2 ? hparams.n_vocab_base + i : i;
const auto & word = vocab.id_to_token[token_id].tok;
if (word.empty()) {
continue;
}

vocab.special_token_trie.add(word);
vocab.special_token_to_id[word] = token_id;

if (vocab.max_special_token_length < word.size()) {
vocab.max_special_token_length = word.size();
}
}
}
};

struct llama_file_saver {
Expand All @@ -620,12 +622,11 @@ struct llama_file_saver {
void write_hparams(enum llama_ftype new_ftype) {
const llama_hparams & hparams = any_file_loader->hparams;
file.write_u32(hparams.n_vocab);
file.write_u32(hparams.n_vocab_sp);
file.write_u32(hparams.n_embd);
file.write_u32(hparams.n_mult);
file.write_u32(hparams.n_head);
file.write_u32(hparams.n_layer);
file.write_u32(hparams.n_rot);
file.write_u32(hparams.n_vocab_base | 0xF0000000); // this bitwise operation is necessary for compatibility with older models
file.write_u32(new_ftype);
}
void write_vocab() {
Expand All @@ -639,9 +640,6 @@ struct llama_file_saver {
file.write_raw(token_score.tok.data(), token_score.tok.size());
file.write_raw(&token_score.score, sizeof(token_score.score));
}
for (const auto & pair : any_file_loader->vocab.special_token_to_id) {
file.write_u32(pair.second);
}
}
void write_tensor(llama_load_tensor & tensor, enum ggml_type new_type, const void * new_data, size_t new_size) {
switch (new_type) {
Expand Down Expand Up @@ -1015,8 +1013,7 @@ static const char *llama_file_version_name(llama_file_version version) {
case LLAMA_FILE_VERSION_GGMF_V1: return "ggmf v1 (old version with no mmap support)";
case LLAMA_FILE_VERSION_GGJT_V1: return "ggjt v1 (pre #1405)";
case LLAMA_FILE_VERSION_GGJT_V2: return "ggjt v2 (pre #1508)";
case LLAMA_FILE_VERSION_GGJT_V3: return "ggjt v3 (pre #1931)";
case LLAMA_FILE_VERSION_GGJT_V4: return "ggjt v4 (latest)";
case LLAMA_FILE_VERSION_GGJT_V3: return "ggjt v3 (latest)";
}

return "unknown";
Expand Down Expand Up @@ -1113,7 +1110,7 @@ static void llama_model_load_internal(
fprintf(stderr, "%s: n_mult = %u\n", __func__, hparams.n_mult);
fprintf(stderr, "%s: n_head = %u\n", __func__, hparams.n_head);
fprintf(stderr, "%s: n_layer = %u\n", __func__, hparams.n_layer);
fprintf(stderr, "%s: n_rot = %u\n", __func__, hparams.n_rot);
fprintf(stderr, "%s: n_rot = %u\n", __func__, hparams.n_embd/hparams.n_head);
fprintf(stderr, "%s: ftype = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype));
fprintf(stderr, "%s: n_ff = %u\n", __func__, n_ff);
fprintf(stderr, "%s: n_parts = %zu\n", __func__, ml->file_loaders.size());
Expand Down
2 changes: 1 addition & 1 deletion llama.h
View file
Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,7 @@
#define LLAMA_FILE_MAGIC_GGML 0x67676d6cu // 'ggml'
#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'

#define LLAMA_FILE_VERSION 4
#define LLAMA_FILE_VERSION 3
#define LLAMA_FILE_MAGIC LLAMA_FILE_MAGIC_GGJT
#define LLAMA_FILE_MAGIC_UNVERSIONED LLAMA_FILE_MAGIC_GGML
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
Expand Down

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