#!/usr/bin/env python3 from __future__ import annotations import argparse import concurrent.futures import enum import faulthandler import functools import itertools import json import math import mmap import os import pickle import re import signal import struct import sys import textwrap import time import zipfile from abc import ABC, abstractmethod from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor from dataclasses import dataclass from pathlib import Path from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable import numpy as np from sentencepiece import SentencePieceProcessor if 'NO_LOCAL_GGUF' not in os.environ: sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf if TYPE_CHECKING: from typing_extensions import Self, TypeAlias if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'): faulthandler.register(signal.SIGUSR1) NDArray: TypeAlias = 'np.ndarray[Any, Any]' ARCH = gguf.MODEL_ARCH.LLAMA DEFAULT_CONCURRENCY = 8 ADDED_TOKENS_FILE = 'added_tokens.json' FAST_TOKENIZER_FILE = 'tokenizer.json' # # data types # @dataclass(frozen=True) class DataType: name: str dtype: np.dtype[Any] valid_conversions: list[str] def elements_to_bytes(self, n_elements: int) -> int: return n_elements * self.dtype.itemsize @dataclass(frozen=True) class UnquantizedDataType(DataType): pass DT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0']) DT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0']) DT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = []) DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0']) @dataclass(frozen=True) class QuantizedDataType(DataType): block_size: int quantized_dtype: np.dtype[Any] ggml_type: gguf.GGMLQuantizationType def quantize(self, arr: NDArray) -> NDArray: raise NotImplementedError(f'Quantization for {self.name} not implemented') def elements_to_bytes(self, n_elements: int) -> int: assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}' return self.quantized_dtype.itemsize * (n_elements // self.block_size) @dataclass(frozen=True) class Q8_0QuantizedDataType(QuantizedDataType): # Mini Q8_0 quantization in Python! def quantize(self, arr: NDArray) -> NDArray: assert arr.size % self.block_size == 0 and arr.size != 0, f'Bad array size {arr.size}' assert arr.dtype == np.float32, f'Bad array type {arr.dtype}' n_blocks = arr.size // self.block_size blocks = arr.reshape((n_blocks, self.block_size)) # Much faster implementation of block quantization contributed by @Cebtenzzre def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[tuple[Any, Any]]: d = abs(blocks).max(axis = 1) / np.float32(127) with np.errstate(divide = 'ignore'): qs = (blocks / d[:, None]).round() qs[d == 0] = 0 yield from zip(d, qs) return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype) DT_Q8_0 = Q8_0QuantizedDataType('Q8_0', dtype = np.dtype(np.float32), valid_conversions = [], ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32, quantized_dtype = np.dtype([('d', ' DataType: dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self) if dt is None: raise ValueError(self) # Convert all 1D tensors to F32. Most of the codebase that takes in 1D tensors only handles F32 tensors, and most of the outputs tensors are F32. # Also The 1d tensors aren't much of a performance/size issue. So instead of having to have separate F32 and F16 implementations of both, just convert everything to F32 for now. return dt if len(tensor.shape) > 1 else DT_F32 GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = { GGMLFileType.AllF32 : DT_F32, GGMLFileType.MostlyF16 : DT_F16, GGMLFileType.MostlyQ8_0: DT_Q8_0, } # # hparams loading # @dataclass class Params: n_vocab: int n_embd: int n_layer: int n_ctx: int n_ff: int n_head: int n_head_kv: int n_experts: int | None = None n_experts_used: int | None = None f_norm_eps: float | None = None rope_scaling_type: gguf.RopeScalingType | None = None f_rope_freq_base: float | None = None f_rope_scale: float | None = None n_orig_ctx: int | None = None rope_finetuned: bool | None = None ftype: GGMLFileType | None = None # path to the directory containing the model files path_model: Path | None = None @staticmethod def guessed(model: LazyModel) -> Params: # try transformer naming first n_vocab, n_embd = model["model.embed_tokens.weight"].shape if "model.embed_tokens.weight" in model else model["tok_embeddings.weight"].shape # try transformer naming first if "model.layers.0.self_attn.q_proj.weight" in model: n_layer = next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model) elif "model.layers.0.self_attn.W_pack.weight" in model: # next: try baichuan naming n_layer = next(i for i in itertools.count() if f"model.layers.{i}.self_attn.W_pack.weight" not in model) else: n_layer = next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model) if n_layer < 1: msg = """\ failed to guess 'n_layer'. This model is unknown or unsupported. Suggestion: provide 'config.json' of the model in the same directory containing model files.""" raise KeyError(textwrap.dedent(msg)) n_head = n_embd // 128 # guessed n_mult = 256 # guessed # TODO: verify this n_ff = int(2 * (4 * n_embd) / 3) n_ff = n_mult * ((n_ff + n_mult - 1) // n_mult) return Params( n_vocab = n_vocab, n_embd = n_embd, n_layer = n_layer, n_ctx = -1, n_ff = n_ff, n_head = n_head, n_head_kv = n_head, f_norm_eps = 1e-5, ) @staticmethod def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params: with open(config_path) as f: config = json.load(f) rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None rope_scaling = config.get("rope_scaling") if rope_scaling is not None and (typ := rope_scaling.get("type")): rope_factor = rope_scaling.get("factor") f_rope_scale = rope_factor if typ == "linear": rope_scaling_type = gguf.RopeScalingType.LINEAR elif typ == "yarn": rope_scaling_type = gguf.RopeScalingType.YARN n_orig_ctx = rope_scaling['original_max_position_embeddings'] rope_finetuned = rope_scaling['finetuned'] else: raise NotImplementedError(f'Unknown rope scaling type: {typ}') if "max_sequence_length" in config: n_ctx = config["max_sequence_length"] elif "max_position_embeddings" in config: n_ctx = config["max_position_embeddings"] else: msg = """\ failed to guess 'n_ctx'. This model is unknown or unsupported. Suggestion: provide 'config.json' of the model in the same directory containing model files.""" raise KeyError(textwrap.dedent(msg)) n_experts = None n_experts_used = None if "num_local_experts" in config: n_experts = config["num_local_experts"] n_experts_used = config["num_experts_per_tok"] return Params( n_vocab = config["vocab_size"], n_embd = config["hidden_size"], n_layer = config["num_hidden_layers"], n_ctx = n_ctx, n_ff = config["intermediate_size"], n_head = (n_head := config["num_attention_heads"]), n_head_kv = config.get("num_key_value_heads", n_head), n_experts = n_experts, n_experts_used = n_experts_used, f_norm_eps = config["rms_norm_eps"], f_rope_freq_base = config.get("rope_theta"), rope_scaling_type = rope_scaling_type, f_rope_scale = f_rope_scale, n_orig_ctx = n_orig_ctx, rope_finetuned = rope_finetuned, ) # 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: with open(config_path) as f: config = json.load(f) n_experts = None n_experts_used = None f_rope_freq_base = None # hack to determine LLaMA v1 vs v2 vs CodeLlama if config.get("moe"): # Mixtral n_ctx = 32768 elif config.get("rope_theta") == 1000000: # CodeLlama n_ctx = 16384 elif config["norm_eps"] == 1e-05: # LLaMA v2 n_ctx = 4096 else: # LLaMA v1 n_ctx = 2048 if "layers.0.feed_forward.w1.weight" in model: n_ff = model["layers.0.feed_forward.w1.weight"].shape[0] if config.get("moe"): n_ff = model["layers.0.feed_forward.experts.0.w1.weight"].shape[0] n_experts = config["moe"]["num_experts"] n_experts_used = config["moe"]["num_experts_per_tok"] f_rope_freq_base = 1e6 return Params( n_vocab = model["tok_embeddings.weight"].shape[0], n_embd = config["dim"], n_layer = config["n_layers"], n_ctx = n_ctx, n_ff = n_ff, n_head = (n_head := config["n_heads"]), n_head_kv = config.get("n_kv_heads", n_head), n_experts = n_experts, n_experts_used = n_experts_used, f_norm_eps = config["norm_eps"], f_rope_freq_base = config.get("rope_theta", f_rope_freq_base), ) @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" 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) elif model_plus.format != 'none': params = Params.guessed(model_plus.model) else: raise ValueError('Cannot guess params when model format is none') params.path_model = model_plus.paths[0].parent return params # # vocab # @runtime_checkable class BaseVocab(Protocol): tokenizer_model: ClassVar[str] name: ClassVar[str] class NoVocab(BaseVocab): tokenizer_model = "no_vocab" name = "no_vocab" def __repr__(self) -> str: return "" @runtime_checkable class Vocab(BaseVocab, Protocol): vocab_size: int added_tokens_dict: dict[str, int] added_tokens_list: list[str] fname_tokenizer: Path def __init__(self, base_path: Path): ... def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ... class BpeVocab(Vocab): tokenizer_model = "gpt2" name = "bpe" def __init__(self, base_path: Path): added_tokens: dict[str, int] = {} if (fname_tokenizer := base_path / 'vocab.json').exists(): # "slow" tokenizer with open(fname_tokenizer, encoding="utf-8") as f: self.vocab = json.load(f) try: # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab. with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f: added_tokens = json.load(f) except FileNotFoundError: pass else: # "fast" tokenizer fname_tokenizer = base_path / FAST_TOKENIZER_FILE # if this fails, FileNotFoundError propagates to caller with open(fname_tokenizer, encoding="utf-8") as f: tokenizer_json = json.load(f) tokenizer_model: dict[str, Any] = tokenizer_json['model'] if ( tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False) or tokenizer_json['decoder']['type'] != 'ByteLevel' ): raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer') self.vocab = tokenizer_model["vocab"] if (added := tokenizer_json.get('added_tokens')) is not None: # Added tokens here can be duplicates of the main vocabulary. added_tokens = {item['content']: item['id'] for item in added if item['content'] not in self.vocab} vocab_size = len(self.vocab) expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) actual_ids = sorted(added_tokens.values()) if expected_ids != actual_ids: expected_end_id = vocab_size + len(actual_ids) - 1 raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range " f"{vocab_size} - {expected_end_id}; got {actual_ids}") items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) self.added_tokens_dict = added_tokens self.added_tokens_list = [text for (text, idx) in items] self.vocab_size_base = vocab_size self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) self.fname_tokenizer = fname_tokenizer def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()} for i, _ in enumerate(self.vocab): yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: for text in self.added_tokens_list: score = -1000.0 yield text.encode("utf-8"), score, gguf.TokenType.CONTROL def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: yield from self.bpe_tokens() yield from self.added_tokens() def __repr__(self) -> str: return f"" class SentencePieceVocab(Vocab): tokenizer_model = "llama" name = "spm" def __init__(self, base_path: Path): added_tokens: dict[str, int] = {} if (fname_tokenizer := base_path / 'tokenizer.model').exists(): # normal location try: with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f: added_tokens = json.load(f) except FileNotFoundError: pass elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists(): # not found in alternate location either raise FileNotFoundError('Cannot find tokenizer.model') self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer)) vocab_size = self.sentencepiece_tokenizer.vocab_size() new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size} expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens))) actual_new_ids = sorted(new_tokens.keys()) if expected_new_ids != actual_new_ids: raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}") # Token pieces that were added to the base vocabulary. self.added_tokens_dict = added_tokens self.added_tokens_list = [new_tokens[id] for id in actual_new_ids] self.vocab_size_base = vocab_size self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) self.fname_tokenizer = fname_tokenizer def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: tokenizer = self.sentencepiece_tokenizer for i in range(tokenizer.vocab_size()): piece = tokenizer.id_to_piece(i) text = piece.encode("utf-8") score: float = tokenizer.get_score(i) toktype = gguf.TokenType.NORMAL if tokenizer.is_unknown(i): toktype = gguf.TokenType.UNKNOWN if tokenizer.is_control(i): toktype = gguf.TokenType.CONTROL # NOTE: I think added_tokens are user defined. # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED if tokenizer.is_unused(i): toktype = gguf.TokenType.UNUSED if tokenizer.is_byte(i): toktype = gguf.TokenType.BYTE yield text, score, toktype def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: for text in self.added_tokens_list: score = -1000.0 yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: yield from self.sentencepiece_tokens() yield from self.added_tokens() def __repr__(self) -> str: return f"" class LlamaHfVocab(Vocab): tokenizer_model = "llama" name = "hfft" def __init__(self, base_path: Path): fname_tokenizer = base_path / FAST_TOKENIZER_FILE # if this fails, FileNotFoundError propagates to caller with open(fname_tokenizer, encoding='utf-8') as f: tokenizer_json = json.load(f) # pre-check so we know if we need transformers tokenizer_model: dict[str, Any] = tokenizer_json['model'] if ( tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False) or tokenizer_json['decoder']['type'] != 'Sequence' ): raise FileNotFoundError('Cannot find Llama BPE tokenizer') try: from transformers import AutoTokenizer except ImportError as e: raise ImportError( "To use LlamaHfVocab, please install the `transformers` package. " "You can install it with `pip install transformers`." ) from e # Allow the tokenizer to default to slow or fast versions. # Explicitly set tokenizer to use local paths. self.tokenizer = AutoTokenizer.from_pretrained( base_path, cache_dir=base_path, local_files_only=True, ) assert self.tokenizer.is_fast # assume tokenizer.json is used # Initialize lists and dictionaries for added tokens self.added_tokens_list = [] self.added_tokens_dict = dict() self.added_tokens_ids = set() # Process added tokens for tok, tokidx in sorted( self.tokenizer.get_added_vocab().items(), key=lambda x: x[1] ): # Only consider added tokens that are not in the base vocabulary if tokidx >= self.tokenizer.vocab_size: self.added_tokens_list.append(tok) self.added_tokens_dict[tok] = tokidx self.added_tokens_ids.add(tokidx) # Store special tokens and their IDs self.specials = { tok: self.tokenizer.get_vocab()[tok] for tok in self.tokenizer.all_special_tokens } self.special_ids = set(self.tokenizer.all_special_ids) # Set vocabulary sizes self.vocab_size_base = self.tokenizer.vocab_size self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) self.fname_tokenizer = fname_tokenizer def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: reverse_vocab = { id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items() } for token_id in range(self.vocab_size_base): # Skip processing added tokens here if token_id in self.added_tokens_ids: continue # Convert token text to bytes token_text = reverse_vocab[token_id].encode("utf-8") # Yield token text, score, and type yield token_text, self.get_token_score(token_id), self.get_token_type( token_id, token_text, self.special_ids # Reuse already stored special IDs ) def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType: # Special case for byte tokens if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text): return gguf.TokenType.BYTE # Determine token type based on whether it's a special token return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL def get_token_score(self, token_id: int) -> float: # Placeholder for actual logic to determine the token's score # This needs to be implemented based on specific requirements return -1000.0 # Default score def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: for text in self.added_tokens_list: if text in self.specials: toktype = self.get_token_type(self.specials[text], b'', self.special_ids) score = self.get_token_score(self.specials[text]) else: toktype = gguf.TokenType.USER_DEFINED score = -1000.0 yield text.encode("utf-8"), score, toktype def has_newline_token(self): return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: yield from self.hf_tokens() yield from self.added_tokens() def __repr__(self) -> str: return f"" # # data loading # TODO: reuse (probably move to gguf.py?) # def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray: # print( "permute debug " + str(weights.shape[0]) + " x " + str(weights.shape[1]) + " nhead " + str(n_head) + " nheadkv " + str(n_kv_head) ) if n_head_kv is not None and n_head != n_head_kv: n_head = n_head_kv return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) .swapaxes(1, 2) .reshape(weights.shape)) class Tensor(ABC): ndarray: NDArray data_type: DataType @abstractmethod def astype(self, data_type: DataType) -> Self: ... @abstractmethod def permute(self, n_head: int, n_head_kv: int) -> Self: ... @abstractmethod def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> Self: ... @abstractmethod def part(self, n_part: int) -> Self: ... @abstractmethod def to_ggml(self) -> GGMLCompatibleTensor: ... def bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray: assert bf16_arr.dtype == np.uint16, f"Input array should be of dtype uint16, but got {bf16_arr.dtype}" fp32_arr = bf16_arr.astype(np.uint32) << 16 return fp32_arr.view(np.float32) class UnquantizedTensor(Tensor): def __init__(self, ndarray: NDArray): assert isinstance(ndarray, np.ndarray) self.ndarray = ndarray self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype] def astype(self, data_type: DataType) -> UnquantizedTensor: dtype = data_type.dtype if self.data_type == DT_BF16: self.ndarray = bf16_to_fp32(self.ndarray) return UnquantizedTensor(self.ndarray.astype(dtype)) def to_ggml(self) -> Self: return self def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: r = self.ndarray.shape[0] // 3 return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head, n_head_kv)) def part(self, n_part: int) -> UnquantizedTensor: r = self.ndarray.shape[0] // 3 return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...]) def permute(self, n_head: int, n_head_kv: int) -> UnquantizedTensor: return UnquantizedTensor(permute(self.ndarray, n_head, n_head_kv)) def load_unquantized(lazy_tensor: LazyTensor, expected_dtype: Any = None, convert: bool = False) -> NDArray: tensor = lazy_tensor.load() assert isinstance(tensor, UnquantizedTensor) # double-check: actual_shape = list(tensor.ndarray.shape) assert actual_shape == lazy_tensor.shape, (actual_shape, lazy_tensor.shape) if expected_dtype is not None and expected_dtype != tensor.ndarray.dtype: if convert: tensor.ndarray = tensor.ndarray.astype(expected_dtype) else: raise ValueError(f'expected this tensor to have dtype {expected_dtype}, got {tensor.ndarray.dtype}') return tensor.ndarray GGMLCompatibleTensor = UnquantizedTensor @dataclass class LazyTensor: _load: Callable[[], Tensor] shape: list[int] data_type: DataType description: str def load(self) -> Tensor: ret = self._load() # Should be okay if it maps to the same numpy type? assert ret.data_type == self.data_type or (self.data_type.dtype == ret.data_type.dtype), \ (self.data_type, ret.data_type, self.description) return ret def astype(self, data_type: DataType) -> LazyTensor: self.validate_conversion_to(data_type) def load() -> Tensor: return self.load().astype(data_type) return LazyTensor(load, self.shape, data_type, f'convert({data_type}) {self.description}') def validate_conversion_to(self, data_type: DataType) -> None: if data_type != self.data_type and data_type.name not in self.data_type.valid_conversions: raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.') LazyModel: TypeAlias = 'dict[str, LazyTensor]' @dataclass class ModelPlus: model: LazyModel paths: list[Path] # Where this was read from. format: Literal['ggml', 'torch', 'safetensors', 'none'] vocab: BaseVocab | None # For GGML models (which have vocab built in), the vocab. def merge_sharded(models: list[LazyModel]) -> LazyModel: # Original LLaMA models have each file contain one part of each tensor. # Use a dict instead of a set to preserve order. names = {name: None for model in models for name in model} def convert(name: str) -> LazyTensor: lazy_tensors = [model[name] for model in models] if len(lazy_tensors) == 1: # only one file; don't go through this procedure since there might # be quantized tensors return lazy_tensors[0] if len(lazy_tensors[0].shape) == 1: # the tensor is just duplicated in every file return lazy_tensors[0] if name.startswith('tok_embeddings.') or \ name.endswith('.attention.wo.weight') or \ name.endswith('.feed_forward.w2.weight'): # split by columns axis = 1 else: # split by rows axis = 0 concatenated_shape = list(lazy_tensors[0].shape) concatenated_shape[axis] = sum(tensor.shape[axis] for tensor in lazy_tensors) def load() -> UnquantizedTensor: ndarrays = [load_unquantized(tensor) for tensor in lazy_tensors] concatenated = np.concatenate(ndarrays, axis=axis) return UnquantizedTensor(concatenated) description = 'concatenated[[' + '] | ['.join(lt.description for lt in lazy_tensors) + ']]' return LazyTensor(load, concatenated_shape, lazy_tensors[0].data_type, description) return {name: convert(name) for name in names} def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus: formats = set(mp.format for mp in models_plus) assert len(formats) == 1, "different formats?" format = formats.pop() paths = [path for mp in models_plus for path in mp.paths] # Use the first non-None vocab, if any. try: vocab = next(mp.vocab for mp in models_plus if mp.vocab is not None) except StopIteration: vocab = None if any("model.embed_tokens.weight" in mp.model for mp in models_plus): # Transformers models put different tensors in different files, but # don't split individual tensors between files. model: LazyModel = {} for mp in models_plus: model.update(mp.model) else: model = merge_sharded([mp.model for mp in models_plus]) return ModelPlus(model, paths, format, vocab) # pytype: disable=wrong-arg-types def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTensor: def load() -> Tensor: return lazy_tensor.load().permute(n_head, n_head_kv) return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description) def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int, n_head_kv: int) -> LazyTensor: def load() -> Tensor: return lazy_tensor.load().permute_part(n_part, n_head, n_head_kv) s = lazy_tensor.shape.copy() s[0] = s[0] // 3 return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description) def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor: def load() -> Tensor: return lazy_tensor.load().part(n_part) s = lazy_tensor.shape.copy() s[0] = s[0] // 3 return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description) def pack_experts_lazy(lazy_tensors: list[LazyTensor]) -> LazyTensor: def load() -> Tensor: tensors = [lazy_tensor.load() for lazy_tensor in lazy_tensors] return UnquantizedTensor(np.array([tensor.ndarray for tensor in tensors])) s = lazy_tensors[0].shape.copy() s.insert(0, len(lazy_tensors)) return LazyTensor(load, s, lazy_tensors[0].data_type, 'pack_experts ' + ' | '.join(lt.description for lt in lazy_tensors)) # Functionality that simulates `torch.load` but where individual tensors are # only loaded into memory on demand, not all at once. # PyTorch can't do this natively as of time of writing: # - https://github.com/pytorch/pytorch/issues/64327 # This allows us to de-shard without multiplying RAM usage, and also # conveniently drops the PyTorch dependency (though we still need numpy). @dataclass class LazyStorageKind: data_type: DataType @dataclass class LazyStorage: load: Callable[[int, int], NDArray] kind: LazyStorageKind description: str class LazyUnpickler(pickle.Unpickler): def __init__(self, fp: IO[bytes], data_base_path: str, zip_file: zipfile.ZipFile): super().__init__(fp) self.data_base_path = data_base_path self.zip_file = zip_file def persistent_load(self, pid: Any) -> Any: assert pid[0] == 'storage' assert isinstance(pid[1], LazyStorageKind) data_type = pid[1].data_type filename_stem = pid[2] filename = f'{self.data_base_path}/{filename_stem}' info = self.zip_file.getinfo(filename) def load(offset: int, elm_count: int) -> NDArray: dtype = data_type.dtype with self.zip_file.open(info) as fp: fp.seek(offset * dtype.itemsize) size = elm_count * dtype.itemsize data = fp.read(size) assert len(data) == size return np.frombuffer(data, dtype) description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}' return LazyStorage(load=load, kind=pid[1], description=description) @staticmethod def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any, requires_grad: Any, backward_hooks: Any, metadata: Any = None) -> LazyTensor: assert isinstance(storage, LazyStorage) def load() -> UnquantizedTensor: elm_count = stride[0] * size[0] return UnquantizedTensor(storage.load(storage_offset, elm_count).reshape(size)) description = f'pickled storage_offset={storage_offset} in {storage.description}' return LazyTensor(load, list(size), storage.kind.data_type, description) @staticmethod def rebuild_from_type_v2(func, new_type, args, state): return func(*args) CLASSES = { # getattr used here as a workaround for mypy not being smart enough to determine # the staticmethods have a __func__ attribute. ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'), ('torch._utils', '_rebuild_tensor_v2'): getattr(lazy_rebuild_tensor_v2, '__func__'), ('torch', 'BFloat16Storage'): LazyStorageKind(DT_BF16), ('torch', 'HalfStorage'): LazyStorageKind(DT_F16), ('torch', 'FloatStorage'): LazyStorageKind(DT_F32), ('torch', 'IntStorage'): LazyStorageKind(DT_I32), ('torch', 'Tensor'): LazyTensor, } def find_class(self, module: str, name: str) -> Any: if not module.startswith('torch'): return super().find_class(module, name) return self.CLASSES[(module, name)] def lazy_load_torch_file(outer_fp: IO[bytes], path: Path) -> ModelPlus: zf = zipfile.ZipFile(outer_fp) pickle_paths = [name for name in zf.namelist() if name.endswith('.pkl')] assert len(pickle_paths) == 1, pickle_paths pickle_fp = zf.open(pickle_paths[0], 'r') unpickler = LazyUnpickler(pickle_fp, data_base_path=pickle_paths[0][:-4], zip_file=zf) model = unpickler.load() if 'model' in model: model = model['model'] as_dict = dict(model.items()) return ModelPlus(model=as_dict, paths=[path], format='torch', vocab=None) def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus: header_size, = struct.unpack(' LazyTensor: data_type = SAFETENSORS_DATA_TYPES[info['dtype']] numpy_dtype = data_type.dtype shape: list[int] = info['shape'] begin, end = info['data_offsets'] assert 0 <= begin <= end <= len(byte_buf) assert end - begin == math.prod(shape) * numpy_dtype.itemsize buf = byte_buf[begin:end] def load() -> UnquantizedTensor: return UnquantizedTensor(np.frombuffer(buf, dtype=numpy_dtype).reshape(shape)) description = f'safetensors begin={begin} end={end} type={data_type} path={path}' return LazyTensor(load, shape, data_type, description) model = {name: convert(info) for (name, info) in header.items() if name != '__metadata__'} return ModelPlus(model=model, paths=[path], format='safetensors', vocab=None) def must_read(fp: IO[bytes], length: int) -> bytes: ret = fp.read(length) if len(ret) < length: raise EOFError("unexpectedly reached end of file") return ret @functools.lru_cache(maxsize=None) def lazy_load_file(path: Path) -> ModelPlus: fp = open(path, 'rb') first8 = fp.read(8) fp.seek(0) if first8[:2] == b'PK': # A zip file, i.e. PyTorch format return lazy_load_torch_file(fp, path) elif struct.unpack(' Iterable[Out]: '''Parallel map, but with backpressure. If the caller doesn't call `next` fast enough, this will stop calling `func` at some point rather than letting results pile up in memory. Specifically, there is a max of one output value buffered per thread.''' if concurrency < 2: yield from map(func, iterable) # Not reached. iterable = iter(iterable) executor_class: type[ThreadPoolExecutor] | type[ProcessPoolExecutor] if use_processpool_executor: executor_class = ProcessPoolExecutor else: executor_class = ThreadPoolExecutor with executor_class(max_workers=max_workers) as executor: futures: list[concurrent.futures.Future[Out]] = [] done = False for _ in range(concurrency): try: futures.append(executor.submit(func, next(iterable))) except StopIteration: done = True break while futures: result = futures.pop(0).result() while not done and len(futures) < concurrency: try: futures.append(executor.submit(func, next(iterable))) except StopIteration: done = True break yield result def check_vocab_size(params: Params, vocab: BaseVocab, pad_vocab: bool = False) -> None: # Handle special case where the model's vocab size is not set if params.n_vocab == -1: raise ValueError( "The model's vocab size is set to -1 in params.json. Please update it manually." + (f" Maybe {vocab.vocab_size}?" if isinstance(vocab, Vocab) else ""), ) if not isinstance(vocab, Vocab): return # model has no vocab # Check for a vocab size mismatch if params.n_vocab == vocab.vocab_size: print("Ignoring added_tokens.json since model matches vocab size without it.") return if pad_vocab and params.n_vocab > vocab.vocab_size: pad_count = params.n_vocab - vocab.vocab_size print( f"Padding vocab with {pad_count} token(s) - through " ) for i in range(1, pad_count + 1): vocab.added_tokens_dict[f""] = -1 vocab.added_tokens_list.append(f"") vocab.vocab_size = params.n_vocab return msg = f"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer} has {vocab.vocab_size})." if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20: msg += f" Most likely you are missing added_tokens.json (should be in {vocab.fname_tokenizer.parent})." if vocab.vocab_size < params.n_vocab: msg += " Add the --pad-vocab option and try again." raise ValueError(msg) class OutputFile: def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE): self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess) def add_meta_arch(self, params: Params) -> None: name = "LLaMA" # TODO: better logic to determine model name if params.n_ctx == 4096: name = "LLaMA v2" elif params.path_model is not None: 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) self.gguf.add_feed_forward_length (params.n_ff) self.gguf.add_rope_dimension_count(params.n_embd // params.n_head) self.gguf.add_head_count (params.n_head) self.gguf.add_head_count_kv (params.n_head_kv) if params.n_experts: self.gguf.add_expert_count(params.n_experts) if params.n_experts_used: self.gguf.add_expert_used_count(params.n_experts_used) if params.f_norm_eps: self.gguf.add_layer_norm_rms_eps(params.f_norm_eps) else: raise ValueError('f_norm_eps is None') if params.f_rope_freq_base is not None: self.gguf.add_rope_freq_base(params.f_rope_freq_base) if params.rope_scaling_type: assert params.f_rope_scale is not None self.gguf.add_rope_scaling_type(params.rope_scaling_type) self.gguf.add_rope_scaling_factor(params.f_rope_scale) if params.n_orig_ctx is not None: self.gguf.add_rope_scaling_orig_ctx_len(params.n_orig_ctx) if params.rope_finetuned is not None: self.gguf.add_rope_scaling_finetuned(params.rope_finetuned) if params.ftype is not None: self.gguf.add_file_type(params.ftype) def extract_vocabulary_from_model(self, vocab: Vocab) -> tuple[list[bytes], list[float], list[gguf.TokenType]]: tokens = [] scores = [] toktypes = [] # NOTE: `all_tokens` returns the base vocabulary and added tokens for text, score, toktype in vocab.all_tokens(): tokens.append(text) scores.append(score) toktypes.append(toktype) assert len(tokens) == vocab.vocab_size return tokens, scores, toktypes def add_meta_vocab(self, vocab: Vocab) -> None: # Ensure that tokenizer_model is added to the GGUF model self.gguf.add_tokenizer_model(vocab.tokenizer_model) # Extract model vocabulary for model conversion tokens, scores, toktypes = self.extract_vocabulary_from_model(vocab) # Add extracted token information for model conversion self.gguf.add_token_list(tokens) self.gguf.add_token_scores(scores) self.gguf.add_token_types(toktypes) def add_meta_special_vocab(self, svocab: gguf.SpecialVocab) -> None: svocab.add_to_gguf(self.gguf) def add_tensor_info(self, name: str, tensor: LazyTensor) -> None: n_elements = int(np.prod(tensor.shape)) raw_dtype = getattr(tensor.data_type, 'ggml_type', None) data_type = getattr(tensor.data_type, 'quantized_type', None) or tensor.data_type.dtype data_nbytes = tensor.data_type.elements_to_bytes(n_elements) self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes, raw_dtype=raw_dtype) def write_meta(self) -> None: self.gguf.write_header_to_file() self.gguf.write_kv_data_to_file() 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() @staticmethod def write_vocab_only( 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) of = OutputFile(fname_out, endianess=endianess) # meta data of.add_meta_arch(params) of.add_meta_vocab(vocab) of.add_meta_special_vocab(svocab) of.write_meta() of.close() @staticmethod def do_item(item: tuple[str, LazyTensor]) -> tuple[DataType, NDArray]: name, lazy_tensor = item tensor = lazy_tensor.load().to_ggml() return (lazy_tensor.data_type, tensor.ndarray) @staticmethod def maybe_do_quantize(item: tuple[DataType, NDArray]) -> NDArray: dt, arr = item if not isinstance(dt, QuantizedDataType): return arr return dt.quantize(arr) @staticmethod def write_all( fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: BaseVocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, ) -> None: check_vocab_size(params, vocab, pad_vocab=pad_vocab) of = OutputFile(fname_out, endianess=endianess) # meta data of.add_meta_arch(params) if isinstance(vocab, Vocab): of.add_meta_vocab(vocab) of.add_meta_special_vocab(svocab) else: # NoVocab of.gguf.add_tokenizer_model(vocab.tokenizer_model) # tensor info for name, lazy_tensor in model.items(): of.add_tensor_info(name, lazy_tensor) of.write_meta() of.write_tensor_info() # tensor data of.write_tensor_data(ftype, model, concurrency) of.close() def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType: wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) + ".weight"].data_type if output_type_str == "f32" or (output_type_str is None and wq_type in (DT_F32, DT_BF16)): return GGMLFileType.AllF32 if output_type_str == "f16" or (output_type_str is None and wq_type == DT_F16): return GGMLFileType.MostlyF16 if output_type_str == "q8_0": return GGMLFileType.MostlyQ8_0 name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()} raise ValueError(f"Unexpected combination of types: {name_to_type}") def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel: return {name: tensor.astype(output_type.type_for_tensor(name, tensor)) for (name, tensor) in model.items()} def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) -> LazyModel: tmap = gguf.TensorNameMap(ARCH, params.n_layer) should_skip = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, [])) tmp = model # merge experts into one tensor if params.n_experts and params.n_experts > 0: for i_l in range(params.n_layer): for w in range(1, 4): experts = [] for e in range(params.n_experts): if f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight" in model: experts.append(model[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"]) del tmp[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"] elif f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight" in model: experts.append(model[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"]) del tmp[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"] else: raise ValueError(f"Expert tensor not found: layers.{i_l}.feed_forward.experts.{e}.w{w}.weight") tmp[f"layers.{i_l}.feed_forward.experts.w{w}.weight"] = pack_experts_lazy(experts) # HF models permut or pack some of the tensors, so we need to undo that for i in itertools.count(): if f"model.layers.{i}.self_attn.q_proj.weight" in model: print(f"Permuting layer {i}") tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head, params.n_head) tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_head_kv) # tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] elif f"model.layers.{i}.self_attn.W_pack.weight" in model: print(f"Unpacking and permuting layer {i}") tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head, params.n_head) tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head, params.n_head_kv) tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = part_lazy (model[f"model.layers.{i}.self_attn.W_pack.weight"], 2) del tmp[f"model.layers.{i}.self_attn.W_pack.weight"] else: break out: LazyModel = {} for name, lazy_tensor in model.items(): tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (".weight", ".bias")) or (None, None) if name_new is None: if skip_unknown: print(f"Unexpected tensor name: {name} - skipping") continue raise ValueError(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)") if tensor_type in should_skip: print(f"skipping tensor {name_new}") continue print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}") out[name_new] = lazy_tensor return out def nth_multifile_path(path: Path, n: int) -> Path | None: '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return the nth path in the model. ''' # Support the following patterns: patterns = [ # - x.00.pth, x.01.pth, etc. (r'\.[0-9]{2}\.pth$', f'.{n:02}.pth'), # - x-00001-of-00002.bin, x-00002-of-00002.bin, etc. (r'-[0-9]{5}-of-(.*)$', fr'-{n:05}-of-\1'), # x.bin, x.bin.1, etc. (r'(\.[0-9]+)?$', r'\1' if n == 0 else fr'\1.{n}') ] for regex, replacement in patterns: if re.search(regex, path.name): new_path = path.with_name(re.sub(regex, replacement, path.name)) if new_path.exists(): return new_path return None def find_multifile_paths(path: Path) -> list[Path]: '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return the whole list of paths in the model. ''' ret: list[Path] = [] for i in itertools.count(): nth_path = nth_multifile_path(path, i) if nth_path is None: break ret.append(nth_path) if not ret: # No matches. This should only happen if the file was named, e.g., # foo.0, and there was no file named foo. Oh well, try to process it # as a single file. return [path] return ret def load_some_model(path: Path) -> ModelPlus: '''Load a model of any supported format.''' # Be extra-friendly and accept either a file or a directory: if path.is_dir(): # Check if it's a set of safetensors files first globs = ["model-00001-of-*.safetensors", "model.safetensors", "consolidated.safetensors"] files = [file for glob in globs for file in path.glob(glob)] if not files: # Try the PyTorch patterns too, with lower priority globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin"] files = [file for glob in globs for file in path.glob(glob)] if not files: raise FileNotFoundError(f"Can't find model in directory {path}") if len(files) > 1: raise ValueError(f"Found multiple models in {path}, not sure which to pick: {files}") path = files[0] paths = find_multifile_paths(path) models_plus: list[ModelPlus] = [] for path in paths: print(f"Loading model file {path}") models_plus.append(lazy_load_file(path)) model_plus = merge_multifile_models(models_plus) return model_plus class VocabFactory: _VOCAB_CLASSES: list[type[Vocab]] = [SentencePieceVocab, BpeVocab, LlamaHfVocab] def __init__(self, path: Path): self.path = path def _create_special_vocab(self, vocab: BaseVocab, model_parent_path: Path) -> gguf.SpecialVocab: load_merges = vocab.name == "bpe" n_vocab = vocab.vocab_size if isinstance(vocab, Vocab) else None return gguf.SpecialVocab( model_parent_path, load_merges=load_merges, special_token_types=None, # Predetermined or passed as a parameter n_vocab=n_vocab, ) def _create_vocab_by_path(self, vocab_types: list[str]) -> Vocab: vocab_classes: dict[str, type[Vocab]] = {cls.name: cls for cls in self._VOCAB_CLASSES} selected_vocabs: dict[str, type[Vocab]] = {} for vtype in vocab_types: try: selected_vocabs[vtype] = vocab_classes[vtype] except KeyError: raise ValueError(f"Unsupported vocabulary type {vtype}") from None for vtype, cls in selected_vocabs.items(): try: vocab = cls(self.path) break except FileNotFoundError: pass # ignore unavailable tokenizers else: raise FileNotFoundError(f"Could not find a tokenizer matching any of {vocab_types}") print(f"Loaded vocab file {vocab.fname_tokenizer!r}, type {vocab.name!r}") return vocab def load_vocab(self, vocab_types: list[str] | None, model_parent_path: Path) -> tuple[BaseVocab, gguf.SpecialVocab]: vocab: BaseVocab if vocab_types is None: vocab = NoVocab() else: vocab = self._create_vocab_by_path(vocab_types) # FIXME: Respect --vocab-dir? special_vocab = self._create_special_vocab( vocab, model_parent_path, ) return vocab, special_vocab def default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path: namestr = { GGMLFileType.AllF32: "f32", GGMLFileType.MostlyF16: "f16", GGMLFileType.MostlyQ8_0:"q8_0", }[file_type] ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf" if ret in model_paths: sys.stderr.write( f"Error: Default output path ({ret}) would overwrite the input. " "Please explicitly specify a path using --outfile.\n") sys.exit(1) return ret def do_dump_model(model_plus: ModelPlus) -> None: print(f"model_plus.paths = {model_plus.paths!r}") print(f"model_plus.format = {model_plus.format!r}") print(f"model_plus.vocab = {model_plus.vocab!r}") for name, lazy_tensor in model_plus.model.items(): print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}") def main(args_in: list[str] | None = None) -> None: output_choices = ["f32", "f16"] if np.uint32(1) == np.uint32(1).newbyteorder("<"): # We currently only support Q8_0 output on little endian systems. output_choices.append("q8_0") parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file") 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") 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("--ctx", type=int, help="model training context (default: based on input)") parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default=DEFAULT_CONCURRENCY) parser.add_argument("--big-endian", action="store_true", help="model is executed on big endian machine") parser.add_argument("--pad-vocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides") 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 and args.vocab_only: raise ValueError("--vocab-only does not make sense with --no-vocab") if args.dump_single: model_plus = lazy_load_file(args.model) do_dump_model(model_plus) return if not args.vocab_only: model_plus = load_some_model(args.model) else: model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None) if args.dump: do_dump_model(model_plus) return endianess = gguf.GGUFEndian.LITTLE if args.big_endian: endianess = gguf.GGUFEndian.BIG params = Params.load(model_plus) if params.n_ctx == -1: if args.ctx is None: msg = """\ The model doesn't have a context size, and you didn't specify one with --ctx Please specify one with --ctx: - LLaMA v1: --ctx 2048 - LLaMA v2: --ctx 4096""" parser.error(textwrap.dedent(msg)) params.n_ctx = args.ctx if args.outtype: params.ftype = { "f32": GGMLFileType.AllF32, "f16": GGMLFileType.MostlyF16, "q8_0": GGMLFileType.MostlyQ8_0, }[args.outtype] print(f"params = {params}") model_parent_path = model_plus.paths[0].parent vocab_path = Path(args.vocab_dir or args.model or model_parent_path) vocab_factory = VocabFactory(vocab_path) vocab_types = None if args.no_vocab else args.vocab_type.split(",") vocab, special_vocab = vocab_factory.load_vocab(vocab_types, model_parent_path) if args.vocab_only: assert isinstance(vocab, Vocab) if not args.outfile: raise ValueError("need --outfile if using --vocab-only") outfile = args.outfile OutputFile.write_vocab_only(outfile, params, vocab, special_vocab, endianess=endianess, pad_vocab=args.pad_vocab) print(f"Wrote {outfile}") return 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}") print(f"Special vocab info: {special_vocab}") model = model_plus.model model = convert_model_names(model, params, args.skip_unknown) ftype = pick_output_type(model, args.outtype) model = convert_to_output_type(model, ftype) outfile = args.outfile or default_outfile(model_plus.paths, ftype) params.ftype = ftype print(f"Writing {outfile}, format {ftype}") OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency=args.concurrency, endianess=endianess, pad_vocab=args.pad_vocab) print(f"Wrote {outfile}") if __name__ == '__main__': main()