diff options
Diffstat (limited to 'convert.py')
| -rw-r--r-- | convert.py | 193 |
1 files changed, 128 insertions, 65 deletions
@@ -1,3 +1,4 @@ +#!/usr/bin/env python import argparse import concurrent.futures import copy @@ -132,7 +133,7 @@ TENSORS_SET = set(TENSORS_LIST) def find_n_mult(n_ff: int, n_embd: int) -> int: # hardcoded magic range - for n_mult in range(256, 1, -1): + for n_mult in range(8192, 1, -1): calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult if calc_ff == n_ff: return n_mult @@ -140,11 +141,12 @@ def find_n_mult(n_ff: int, n_embd: int) -> int: @dataclass class Params: - n_vocab: int - n_embd: int - n_mult: int - n_head: int - n_layer: int + n_vocab: int + n_embd: int + n_mult: int + n_head: int + n_layer: int + n_kv_head: Optional[int] # This parameter is only used for Llama 2 @staticmethod def guessed(model: 'LazyModel') -> 'Params': @@ -166,11 +168,12 @@ class Params: n_head=n_embd // 128 # guessed return Params( - n_vocab=n_vocab, - n_embd=n_embd, - n_mult=256, - n_head=n_head, - n_layer=n_layer, + n_vocab = n_vocab, + n_embd = n_embd, + n_mult = 256, + n_head = n_head, + n_layer = n_layer, + n_kv_head = None, ) @staticmethod @@ -178,28 +181,56 @@ class Params: config = json.load(open(config_path)) n_vocab = config["vocab_size"]; - n_embd = config["hidden_size"]; - n_head = config["num_attention_heads"]; + n_embd = config["hidden_size"]; + n_head = config["num_attention_heads"]; n_layer = config["num_hidden_layers"]; - n_ff = config["intermediate_size"]; + n_ff = config["intermediate_size"]; + n_kv_head = config.get("num_key_value_heads") n_mult = find_n_mult(n_ff, n_embd); return Params( - n_vocab=n_vocab, - n_embd=n_embd, - n_mult=n_mult, - n_head=n_head, - n_layer=n_layer, + n_vocab = n_vocab, + n_embd = n_embd, + n_mult = n_mult, + n_head = n_head, + n_layer = n_layer, + n_kv_head = n_kv_head, + ) + + # LLaMA v2 70B params.json + # {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1 + @staticmethod + def loadOriginalParamsJson(model: 'LazyModel', config_path: 'Path') -> 'Params': + config = json.load(open(config_path)) + + n_vocab = config["vocab_size"]; + n_embd = config["dim"]; + n_head = config["n_heads"]; + n_layer = config["n_layers"]; + n_mult = config["multiple_of"]; + + if n_vocab == -1: + n_vocab = model["tok_embeddings.weight"].shape[0] + + return Params( + n_vocab = n_vocab, + n_embd = n_embd, + n_mult = n_mult, + n_head = n_head, + n_layer = n_layer, + n_kv_head = None, ) @staticmethod def load(model_plus: 'ModelPlus') -> 'Params': + hf_config_path = model_plus.paths[0].parent / "config.json" orig_config_path = model_plus.paths[0].parent / "params.json" - hf_transformer_config_path = model_plus.paths[0].parent / "config.json" - if hf_transformer_config_path.exists(): - params = Params.loadHFTransformerJson(model_plus.model, hf_transformer_config_path) + if hf_config_path.exists(): + params = Params.loadHFTransformerJson(model_plus.model, hf_config_path) + elif orig_config_path.exists(): + params = Params.loadOriginalParamsJson(model_plus.model, orig_config_path) else: params = Params.guessed(model_plus.model) @@ -208,14 +239,21 @@ class Params: class SentencePieceVocab: - def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None: - self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer)) + def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path], vocabtype: Optional[str]) -> None: + self.vocabtype = vocabtype + if self.vocabtype == "bpe": + self.sentencepiece_tokenizer = json.loads(open(str(fname_tokenizer)).read()) + else: + self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer)) added_tokens: Dict[str, int] if fname_added_tokens is not None: added_tokens = json.load(open(fname_added_tokens)) else: added_tokens = {} - vocab_size: int = self.sentencepiece_tokenizer.vocab_size() + if self.vocabtype == "bpe": + vocab_size: int = len(self.sentencepiece_tokenizer) + else: + vocab_size: int = self.sentencepiece_tokenizer.vocab_size() expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) actual_ids = sorted(added_tokens.values()) if expected_ids != actual_ids: @@ -229,22 +267,32 @@ class SentencePieceVocab: def sentencepiece_tokens(self) -> Iterable[Tuple[bytes, float]]: tokenizer = self.sentencepiece_tokenizer - for i in range(tokenizer.vocab_size()): + if self.vocabtype == "bpe": + from transformers.models.gpt2 import tokenization_gpt2 + byte_encoder = tokenization_gpt2.bytes_to_unicode() + byte_decoder = {v: k for k, v in byte_encoder.items()} + for i, item in enumerate(tokenizer): text: bytes - if tokenizer.is_unknown(i): - text = " \u2047 ".encode("utf-8") - elif tokenizer.is_control(i): - text = b"" - elif tokenizer.is_byte(i): - piece = tokenizer.id_to_piece(i) - if len(piece) != 6: - raise Exception(f"Invalid token: {piece}") - byte_value = int(piece[3:-1], 16) - text = struct.pack("B", byte_value) - else: - text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8") - score: float = tokenizer.get_score(i) + text = b''.join([x.to_bytes(1, byteorder='big') for x in [byte_decoder[y] for y in item]]) + score: float = -i yield text, score + else: + for i in range(tokenizer.vocab_size()): + text: bytes + if tokenizer.is_unknown(i): + text = " \u2047 ".encode("utf-8") + elif tokenizer.is_control(i): + text = b"" + elif tokenizer.is_byte(i): + piece = tokenizer.id_to_piece(i) + if len(piece) != 6: + raise Exception(f"Invalid token: {piece}") + byte_value = int(piece[3:-1], 16) + text = struct.pack("B", byte_value) + else: + text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8") + score: float = tokenizer.get_score(i) + yield text, score def added_tokens(self) -> Iterable[Tuple[bytes, float]]: for text in self.added_tokens_list: @@ -274,10 +322,12 @@ class GGMLVocab: Vocab = Union[SentencePieceVocab, GGMLVocab] -def permute(weights: NDArray, n_head: int) -> NDArray: +def permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray: + if n_kv_head is not None and n_head != n_kv_head: + n_head //= n_kv_head return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) - .swapaxes(1, 2) - .reshape(weights.shape)) + .swapaxes(1, 2) + .reshape(weights.shape)) def dequantize_q4(qvalues_pack32: NDArray, scales: NDArray, addends: Optional[NDArray], g_idx: Optional[NDArray]) -> NDArray: @@ -325,7 +375,7 @@ class Tensor(metaclass=ABCMeta): @abstractmethod def astype(self, data_type: DataType) -> 'Tensor': ... @abstractmethod - def permute(self, n_head: int) -> 'Tensor': ... + def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'Tensor': ... @abstractmethod def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ... @abstractmethod @@ -363,8 +413,8 @@ class UnquantizedTensor(Tensor): r = self.ndarray.shape[0] // 3 return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...]) - def permute(self, n_head: int) -> 'UnquantizedTensor': - return UnquantizedTensor(permute(self.ndarray, n_head)) + def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'UnquantizedTensor': + return UnquantizedTensor(permute(self.ndarray, n_head, n_kv_head)) def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, convert: bool = False) -> NDArray: @@ -412,26 +462,34 @@ class GGMLQuantizedTensor(Tensor): def to_ggml(self) -> 'GGMLQuantizedTensor': return self - def permute(self, n_head: int) -> 'GGMLQuantizedTensor': - return GGMLQuantizedTensor(permute(self.ndarray, n_head), self.shape, self.data_type) + def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'GGMLQuantizedTensor': + return GGMLQuantizedTensor(permute(self.ndarray, n_head, n_kv_head), self.shape, self.data_type) + def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': + r = self.ndarray.shape[0] // 3 + return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head)) + + def part(self, n_part: int) -> 'UnquantizedTensor': + r = self.ndarray.shape[0] // 3 + return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...]) GGMLCompatibleTensor = Union[UnquantizedTensor, GGMLQuantizedTensor] class DeferredPermutedTensor(Tensor): - def __init__(self, base: Tensor, n_head: int) -> None: + def __init__(self, base: Tensor, n_head: int, n_kv_head: Optional[int] = None) -> None: self.base = base self.n_head = n_head + self.n_kv_head = n_kv_head self.data_type = self.base.data_type def astype(self, data_type: DataType) -> Tensor: - return self.base.astype(data_type).permute(self.n_head) + return self.base.astype(data_type).permute(self.n_head, self.n_kv_head) def to_ggml(self) -> GGMLCompatibleTensor: - return self.base.to_ggml().permute(self.n_head) + return self.base.to_ggml().permute(self.n_head, self.n_kv_head) - def permute(self, n_head: int) -> Tensor: + def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> Tensor: raise Exception("shouldn't permute twice") @@ -523,8 +581,8 @@ class GPTQForLLaMaQuantizedTensor(Tensor): ret.data_type = QuantizedDataType(groupsize=new_groupsize, have_addends=True, have_g_idx=False) return ret - def permute(self, n_head: int) -> Tensor: - return DeferredPermutedTensor(self, n_head) + def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> Tensor: + return DeferredPermutedTensor(self, n_head, n_kv_head) def to_ggml(self) -> GGMLQuantizedTensor: # The output format looks like this: @@ -655,10 +713,10 @@ def merge_multifile_models(models_plus: List[ModelPlus]) -> ModelPlus: return ModelPlus(model, paths, format, vocab) -def permute_lazy(lazy_tensor: LazyTensor, n_head: int) -> LazyTensor: +def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_kv_head: Optional[int] = None) -> LazyTensor: def load() -> Tensor: - return lazy_tensor.load().permute(n_head) - return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description) + return lazy_tensor.load().permute(n_head, n_kv_head) + return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_kv_head}) ' + lazy_tensor.description) def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor: def load() -> Tensor: @@ -683,7 +741,7 @@ def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel: for i in itertools.count(): if f"model.layers.{i}.self_attn.q_proj.weight" in model: out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head) - out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head) + out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_kv_head) out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] elif f"model.layers.{i}.self_attn.W_pack.weight" in model: out[f"layers.{i}.attention.wq.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head) @@ -1035,8 +1093,7 @@ class OutputFile: @staticmethod def write_vocab_only(fname_out: Path, vocab: Vocab) -> None: of = OutputFile(fname_out) - params = Params(n_vocab=vocab.vocab_size, n_embd=0, n_mult=0, - n_head=1, n_layer=0) + params = Params(n_vocab=vocab.vocab_size, n_embd=0, n_mult=0, n_head=1, n_layer=0) of = OutputFile(fname_out) of.write_file_header(params, file_type=GGMLFileType.AllF32) of.write_vocab(vocab) @@ -1171,14 +1228,18 @@ def filter_and_sort_tensors(model: LazyModel) -> LazyModel: return {name: model[name] for name in TENSORS_LIST if name in model} -def load_vocab(path: Path) -> SentencePieceVocab: +def load_vocab(path: Path, vocabtype: Optional[str]) -> SentencePieceVocab: + print(f"vocabtype: {vocabtype}") # Be extra-friendly and accept either a file or a directory. Also, if it's # a directory, it might be the model directory, and tokenizer.model might # be in the parent of that. if path.is_dir(): - path2 = path / "tokenizer.model" + vocab_file = "tokenizer.model" + if vocabtype == 'bpe': + vocab_file = "vocab.json" + path2 = path / vocab_file # Use `.parent` instead of /.. to handle the symlink case better. - path3 = path.parent / "tokenizer.model" + path3 = path.parent / vocab_file if path2.exists(): path = path2 elif path3.exists(): @@ -1189,7 +1250,8 @@ def load_vocab(path: Path) -> SentencePieceVocab: "if it's in another directory, pass the directory as --vocab-dir") added_tokens_path = path.parent / "added_tokens.json" print(f"Loading vocab file {path}") - return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None) + return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None, + vocabtype) def default_outfile(model_paths: List[Path], file_type: GGMLFileType) -> Path: @@ -1227,6 +1289,7 @@ def main(args_in: Optional[List[str]] = None) -> None: parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") + parser.add_argument("--vocabtype", default='spm', choices=["spm", "bpe"], help="vocab format (default: spm)") args = parser.parse_args(args_in) vocab: Vocab @@ -1234,7 +1297,7 @@ def main(args_in: Optional[List[str]] = None) -> None: model_plus = lazy_load_file(args.model) do_dump_model(model_plus) elif args.vocab_only: - vocab = load_vocab(args.vocab_dir or args.model) + vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype) assert args.outfile, "need --outfile if using --vocab-only" outfile = args.outfile OutputFile.write_vocab_only(outfile, vocab) @@ -1248,7 +1311,7 @@ def main(args_in: Optional[List[str]] = None) -> None: vocab = model_plus.vocab else: vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent - vocab = load_vocab(vocab_dir) + vocab = load_vocab(vocab_dir, args.vocabtype) params = Params.load(model_plus) model = model_plus.model model = do_necessary_conversions(model, params) |