# Adapted from llamacpp conversion script # Check https://github.com/ggerganov for more informations import argparse import concurrent.futures import copy import enum import faulthandler import functools import io import itertools import json import math import mmap import pickle import re import signal import struct import sys import zipfile from abc import ABCMeta, abstractmethod from dataclasses import dataclass from pathlib import Path from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Literal, Optional, Sequence, Tuple, TypeVar, Union) import numpy as np from sentencepiece import SentencePieceProcessor # type: ignore if TYPE_CHECKING: from typing_extensions import TypeAlias if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'): faulthandler.register(signal.SIGUSR1) NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]' @dataclass(frozen=True) class UnquantizedDataType: name: str DT_F16 = UnquantizedDataType('F16') DT_F32 = UnquantizedDataType('F32') DT_I32 = UnquantizedDataType('I32') DT_BF16 = UnquantizedDataType('BF16') @dataclass(frozen=True) class QuantizedDataType: groupsize: int have_addends: bool have_g_idx: bool DT_Q4_0 = QuantizedDataType(groupsize=32, have_addends=False, have_g_idx=False) DT_Q4_1 = QuantizedDataType(groupsize=32, have_addends=True, have_g_idx=False) DataType = Union[UnquantizedDataType, QuantizedDataType] DATA_TYPE_TO_FTYPE: Dict[DataType, int] = { DT_F32: 0, DT_F16: 1, DT_Q4_0: 2, DT_Q4_1: 3, } FTYPE_TO_DATA_TYPE: Dict[int, DataType] = \ {ftype: dtype for (dtype, ftype) in DATA_TYPE_TO_FTYPE.items()} DATA_TYPE_TO_NUMPY: Dict[DataType, 'np.dtype[Any]'] = { DT_F16: np.dtype(np.float16), DT_F32: np.dtype(np.float32), DT_I32: np.dtype(np.int32), } NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = \ {dtype: data_type for (data_type, dtype) in DATA_TYPE_TO_NUMPY.items()} class GGMLFileType(enum.Enum): AllF32 = 0 MostlyF16 = 1 # except 1d tensors MostlyQ4_0 = 2 # except 1d tensors MostlyQ4_1 = 3 # except 1d tensors PerLayerIsQ4_1 = 4 # but tok_embeddings.weight and output.weight are F16 def type_for_tensor(self, name: str, tensor: 'LazyTensor') -> DataType: if len(tensor.shape) == 1: # 1D tensors are always F32. return DT_F32 elif self == GGMLFileType.AllF32: return DT_F32 elif self == GGMLFileType.MostlyF16: return DT_F16 elif self == GGMLFileType.MostlyQ4_0: return DT_Q4_0 elif self == GGMLFileType.MostlyQ4_1: return DT_Q4_1 elif self == GGMLFileType.PerLayerIsQ4_1: if name in ('output.weight', 'tok_embeddings.weight'): return DT_F16 else: return DT_Q4_1 else: raise ValueError(self) def make_tensors_list() -> List[str]: ret = [ 'tok_embeddings.weight', 'norm.weight', 'output.weight', ] for i in range(80): # maximum number of layer ret += [ f'layers.{i}.attention.wq.weight', f'layers.{i}.attention.wk.weight', f'layers.{i}.attention.wv.weight', f'layers.{i}.attention.wo.weight', f'layers.{i}.attention_norm.weight', f'layers.{i}.feed_forward.w1.weight', f'layers.{i}.feed_forward.w2.weight', f'layers.{i}.feed_forward.w3.weight', f'layers.{i}.atttention_norm.weight', f'layers.{i}.ffn_norm.weight', ] return ret TENSORS_LIST = make_tensors_list() TENSORS_SET = set(TENSORS_LIST) @dataclass class Params: n_vocab: int n_embd: int n_mult: int n_head: int n_layer: int file_type: GGMLFileType @staticmethod def guessed(model: 'LazyModel', file_type: GGMLFileType) -> 'Params': n_vocab, n_embd = model["tok_embeddings.weight"].shape return Params( n_vocab=n_vocab, n_embd=n_embd, n_mult=256, n_head=n_embd // 128, n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model), file_type=file_type, ) class SentencePieceVocab: def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None: 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() expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) actual_ids = sorted(added_tokens.values()) if expected_ids != actual_ids: raise Exception(f"Expected added token IDs to be sequential and start at {len(added_tokens)}; got {actual_ids}") items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) self.added_tokens_list = [text for (text, idx) in items] self.vocab_size_base: int = vocab_size self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list) self.fname_tokenizer = fname_tokenizer self.fname_added_tokens = fname_added_tokens def sentencepiece_tokens(self) -> Iterable[Tuple[bytes, float]]: tokenizer = self.sentencepiece_tokenizer 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: score = -1000.0 yield text.encode("utf-8"), score def all_tokens(self) -> Iterable[Tuple[bytes, float]]: yield from self.sentencepiece_tokens() yield from self.added_tokens() def __repr__(self) -> str: return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>" class GGMLVocab: def __init__(self, tokens: List[Tuple[bytes, float]]): self.tokens = tokens self.vocab_size = len(tokens) def all_tokens(self) -> Iterable[Tuple[bytes, float]]: return self.tokens def __repr__(self) -> str: return f"<GGMLVocab with {self.vocab_size} tokens>" Vocab = Union[SentencePieceVocab, GGMLVocab] def permute(weights: NDArray, n_head: int) -> NDArray: return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) .swapaxes(1, 2) .reshape(weights.shape)) def dequantize_q4(qvalues_pack32: NDArray, scales: NDArray, addends: Optional[NDArray], g_idx: Optional[NDArray]) -> NDArray: # First reinterpret each row from a list of int32s containing 8 values each # to a list of uint8s containing 2 values each. qvalues_pack8 = qvalues_pack32.view(np.uint8) # Then split out the two values per int8 (which requires an actual # conversion because numpy doesn't natively support int4s). qvalues = np.zeros([qvalues_pack8.shape[0], qvalues_pack8.shape[1] * 2], dtype=np.uint8) qvalues[:, 0::2] = qvalues_pack8 & 0xf qvalues[:, 1::2] = qvalues_pack8 >> 4 assert addends is None or addends.shape == scales.shape assert qvalues.shape[0] == scales.shape[0] assert qvalues.shape[1] % scales.shape[1] == 0 if g_idx is None: repeat_count = qvalues.shape[1] // scales.shape[1] scales = scales[:, :, np.newaxis] if addends is not None: addends = addends[:, :, np.newaxis] # Reshape so that the below computation broadcasts over scales and addends: qvalues.shape = (qvalues.shape[0], scales.shape[1], int(repeat_count)) else: # In this case the scale and addend is selected for each column by g_idx: assert addends is not None scales = scales[:, g_idx] addends = addends[:, g_idx] if addends is None: # Q4_0 qvalues = qvalues.view(np.int8) qvalues -= 8 # And do the actual 'value = scale * qvalue + addend' computation. values = scales * qvalues if addends is not None: values += addends if g_idx is None: values.shape = (values.shape[0], values.shape[1] * values.shape[2]) return values class Tensor(metaclass=ABCMeta): data_type: DataType @abstractmethod def astype(self, data_type: DataType) -> 'Tensor': ... @abstractmethod def permute(self, n_head: int) -> 'Tensor': ... @abstractmethod def to_ggml(self) -> 'GGMLCompatibleTensor': ... class UnquantizedTensor(Tensor): def __init__(self, ndarray: NDArray) -> None: assert isinstance(ndarray, np.ndarray) self.ndarray = ndarray self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype] def astype(self, data_type: DataType) -> Tensor: dtype = DATA_TYPE_TO_NUMPY[data_type] return UnquantizedTensor(self.ndarray.astype(dtype)) def to_ggml(self) -> 'UnquantizedTensor': return self def permute(self, n_head: int) -> 'UnquantizedTensor': return UnquantizedTensor(permute(self.ndarray, n_head)) 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 class GGMLQuantizedTensor(Tensor): data_type: QuantizedDataType def __init__(self, ndarray: NDArray, shape: List[int], data_type: DataType) -> None: rows, columns = shape assert data_type in (DT_Q4_1, DT_Q4_0) # for now assert isinstance(data_type, QuantizedDataType) # redundant, but mypy complains without this assert columns % data_type.groupsize == 0 words_in_block = 6 if data_type == DT_Q4_1 else 5 self.ndarray = ndarray.view(dtype=np.uint32).reshape((rows, columns // data_type.groupsize, words_in_block)) self.shape = shape[:] self.data_type = data_type def astype(self, data_type: DataType) -> Tensor: if data_type == self.data_type: return self scales = self.ndarray[:, :, 0].view(np.float32) if self.data_type.have_addends: addends = self.ndarray[:, :, 1].view(np.float32) else: addends = None qweights = self.ndarray[:, :, -4:].reshape([self.shape[0], self.shape[1] // 8]) dq = dequantize_q4(qweights, scales, addends, g_idx=None) return UnquantizedTensor(dq).astype(data_type) 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) GGMLCompatibleTensor = Union[UnquantizedTensor, GGMLQuantizedTensor] class DeferredPermutedTensor(Tensor): def __init__(self, base: Tensor, n_head: int) -> None: self.base = base self.n_head = n_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) def to_ggml(self) -> GGMLCompatibleTensor: return self.base.to_ggml().permute(self.n_head) def permute(self, n_head: int) -> Tensor: raise Exception("shouldn't permute twice") class GPTQForLLaMaQuantizedTensor(Tensor): def __init__(self, model: 'LazyModel', namebase: str) -> None: qweight = load_unquantized(model[f"{namebase}.qweight"], np.int32) scales = load_unquantized(model[f"{namebase}.scales"], np.float32, convert=True) bias = model.get(f"{namebase}.bias") if bias is not None: # Q4_1 does not support bias; good thing the bias is always all zeros. assert not np.any(load_unquantized(bias)) if f"{namebase}.zeros" in model: zeros = load_unquantized(model[f"{namebase}.zeros"], np.float32) else: qzeros = load_unquantized(model[f"{namebase}.qzeros"], np.int32) assert qzeros.dtype == np.int32 zeros = dequantize_q4(qzeros, scales, scales, g_idx=None) assert zeros.dtype == np.float32 assert zeros.shape == scales.shape # Output is transposed compared to the input, and addends have their sign flipped. # Scales and zeros similarly must be transposed but only for newer # versions of GPTQ-for-LLaMa; the older versions can be identified by # having shape (n_embd, 1). qweight = qweight.T if scales.shape[1] != 1: scales = scales.T zeros = zeros.T # Output also has signs flipped for the addends. self.qweight = qweight self.scales = scales self.addends = -zeros self.g_idx: Optional[NDArray] if f"{namebase}.g_idx" in model: self.g_idx = load_unquantized(model[f"{namebase}.g_idx"], np.int32) assert self.g_idx.shape == (qweight.shape[1] * 8,) else: self.g_idx = None self.shape = [self.qweight.shape[0], self.qweight.shape[1] * 8] self.data_type = QuantizedDataType(groupsize=self.groupsize(), have_addends=True, have_g_idx=(self.g_idx is not None)) def inspect(self, row: int, col: int) -> None: '''For debugging.''' qweight = (self.qweight[row, col // 8] >> (4 * (col & 7))) & 0xf if self.g_idx is not None: group = self.g_idx[col] else: group = int(col // self.groupsize()) scale = self.scales[row, group] addend = self.addends[row, group] with np.printoptions(precision=None, suppress=True): print(f'scale:{scale} addend:{addend} qweight:{qweight}') print('possible values:', np.arange(16) * scale + addend) print('actual value:', qweight * scale + addend) def astype(self, data_type: DataType) -> Tensor: if isinstance(data_type, QuantizedDataType): assert self.g_idx is None and data_type.have_addends is True and data_type.have_g_idx is False return self.regroup(data_type.groupsize) dequantized = dequantize_q4(np.ascontiguousarray(self.qweight), self.scales, self.addends, self.g_idx) return UnquantizedTensor(dequantized).astype(data_type) def groupsize(self) -> int: assert self.addends.shape == self.scales.shape assert self.shape[1] % self.scales.shape[1] == 0 return self.shape[1] // self.scales.shape[1] def regroup(self, new_groupsize: int = 32) -> 'GPTQForLLaMaQuantizedTensor': # Old versions of GPTQ-for-LLaMa shared scales and addends between all the # columns in a row. Newer versions share them between every set of N # columns in a row, where N is the `groupsize` parameter, usually 128. The # output format shares them between every set of 32 columns. To handle # this, duplicate scales and addends for every smaller group. # (In the above, 'row' and 'column' are in the sense of the output.) assert self.g_idx is None old_groupsize = self.groupsize() assert old_groupsize >= new_groupsize and old_groupsize % new_groupsize == 0, old_groupsize ret = copy.copy(self) ret.addends = self.addends.repeat(old_groupsize // new_groupsize, axis=1) ret.scales = self.scales.repeat(old_groupsize // new_groupsize, axis=1) 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 to_ggml(self) -> GGMLQuantizedTensor: # The output format looks like this: # For each row: # For each group of 32 columns: # - addend (float32, 4 bytes) # - scale (float32, 4 bytes) # - weights (int4 * 32, 16 bytes) if self.groupsize() != 32: raise Exception("should have been regrouped before converting to ggml") # Since the output format is mixed between integers and floats, we have # to hackily view the floats as int32s just so numpy will let us # concatenate them. addends_view = self.addends.view(dtype=np.int32)[:, :, np.newaxis] scales_view = self.scales.view(dtype=np.int32)[:, :, np.newaxis] # Split into groups of 4 columns (i.e. 32 columns of quantized data): grouped = self.qweight.reshape([self.qweight.shape[0], self.qweight.shape[1] // 4, 4]) # And concatenate: grouped = np.concatenate([scales_view, addends_view, grouped], axis=2, casting='no') return GGMLQuantizedTensor(grouped, self.shape, DT_Q4_1) @dataclass class LazyTensor: _load: Callable[[], Tensor] shape: List[int] data_type: DataType description: str def load(self) -> Tensor: ret = self._load() assert ret.data_type == self.data_type, (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: return if isinstance(data_type, QuantizedDataType): if not isinstance(self.data_type, QuantizedDataType): raise Exception(f"Can't turn an unquantized tensor into a quantized type ({data_type})") if self.data_type.have_g_idx: sys.stderr.write("Error: Input uses the newer GPTQ-for-LLaMa format (using g_idx), which is not yet natively supported by GGML. For now you can still convert this model by passing `--outtype f16` to dequantize, but that will result in a much larger output file for no quality benefit.\n") sys.exit(1) assert not data_type.have_g_idx and self.data_type.have_addends and data_type.have_addends LazyModel = Dict[str, LazyTensor] @dataclass class ModelPlus: model: LazyModel paths: List[Path] # Where this was read from. format: Literal['ggml', 'torch', 'safetensors'] vocab: Optional[Vocab] # 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: List[LazyTensor] = [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: NDArray = 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 indivdual 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) def permute_lazy(lazy_tensor: LazyTensor, n_head: int) -> 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) def convert_transformers_to_orig(model: LazyModel) -> LazyModel: out: LazyModel = {} out["tok_embeddings.weight"] = model["model.embed_tokens.weight"] out["norm.weight"] = model["model.norm.weight"] out["output.weight"] = model["lm_head.weight"] n_head = model["model.layers.0.self_attn.q_proj.weight"].shape[1] // 128 for i in itertools.count(): if f"model.layers.{i}.self_attn.q_proj.weight" not in model: break out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], n_head) out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], n_head) out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] out[f"layers.{i}.attention.wo.weight"] = model[f"model.layers.{i}.self_attn.o_proj.weight"] out[f"layers.{i}.feed_forward.w1.weight"] = model[f"model.layers.{i}.mlp.gate_proj.weight"] out[f"layers.{i}.feed_forward.w2.weight"] = model[f"model.layers.{i}.mlp.down_proj.weight"] out[f"layers.{i}.feed_forward.w3.weight"] = model[f"model.layers.{i}.mlp.up_proj.weight"] out[f"layers.{i}.attention_norm.weight"] = model[f"model.layers.{i}.input_layernorm.weight"] out[f"layers.{i}.ffn_norm.weight"] = model[f"model.layers.{i}.post_attention_layernorm.weight"] return out def handle_quantization(model: LazyModel) -> LazyModel: '''Convert a model with entries for 'foo.qweight', 'foo.scales', etc. (which resolve to UnquantizedTensors with the raw data) to one with entries for 'foo.weight' (which resolve to QuantizedTensors). ''' def convert(name: str) -> Tuple[str, LazyTensor]: if name.endswith(".qweight"): namebase = name.rsplit('.', 1)[0] orig_name = namebase + ".weight" lazy_tensor = model[name] assert len(lazy_tensor.shape) == 2 real_shape = [lazy_tensor.shape[1], lazy_tensor.shape[0] * 8] # Calculate type. This replicates the logic in # GPTQForLLaMaQuantizedTensor (which is executed when the modelis # actually loaded). lazy_scales = model[f"{namebase}.scales"] scales_width = 1 if lazy_scales.shape[1] == 1 else lazy_scales.shape[0] assert real_shape[1] % scales_width == 0 groupsize = real_shape[1] // scales_width have_g_idx = f"{namebase}.g_idx" in model data_type = QuantizedDataType(groupsize=groupsize, have_addends=True, have_g_idx=have_g_idx) def load() -> Tensor: return GPTQForLLaMaQuantizedTensor(model, namebase) return (orig_name, LazyTensor(load, real_shape, data_type, '[quantized]')) else: return (name, model[name]) return dict(convert(name) for name in model) # 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 = self.data_base_path + '/' + filename_stem info = self.zip_file.getinfo(filename) def load(offset: int, elm_count: int) -> NDArray: dtype = DATA_TYPE_TO_NUMPY.get(data_type) if dtype is None: raise Exception("tensor stored in unsupported format") fp = self.zip_file.open(info) 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) def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any, # pyright: ignore[reportSelfClsParameterName] 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) CLASSES: Dict[Any, Any] = { ('torch._utils', '_rebuild_tensor_v2'): lazy_rebuild_tensor_v2, ('torch', 'BFloat16Storage'): LazyStorageKind(DT_BF16), ('torch', 'HalfStorage'): LazyStorageKind(DT_F16), ('torch', 'FloatStorage'): LazyStorageKind(DT_F32), ('torch', 'IntStorage'): LazyStorageKind(DT_I32), } 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() as_dict = dict(model.items()) return ModelPlus(model=as_dict, paths=[path], format='torch', vocab=None) SAFETENSORS_DATA_TYPES: Dict[str, DataType] = { 'F16': DT_F16, 'F32': DT_F32, 'I32': DT_I32, } def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus: header_size, = struct.unpack('<Q', fp.read(8)) header: Dict[str, Dict[str, Any]] = json.loads(fp.read(header_size)) # Use mmap for the actual data to avoid race conditions with the file offset. mapped = memoryview(mmap.mmap(fp.fileno(), 0, access=mmap.ACCESS_READ)) byte_buf = mapped[8 + header_size:] def convert(info: Dict[str, Any]) -> LazyTensor: data_type = SAFETENSORS_DATA_TYPES[info['dtype']] numpy_dtype = DATA_TYPE_TO_NUMPY[data_type] 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()} 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 Exception("unexpectedly reached end of file") return ret def lazy_load_ggml_file(fp: io.BufferedReader, path: Path) -> ModelPlus: magic = must_read(fp, 4)[::-1] if magic in (b'ggmf', b'ggjt'): version, = struct.unpack("i", must_read(fp, 4)) assert version == 1 else: assert magic == b'ggml' version = None n_vocab, n_embd, n_mult, n_head, n_layer, rot, file_type = struct.unpack('<7i', must_read(fp, 28)) tokens: List[Tuple[bytes, float]] = [] for i in range(n_vocab): if i == 32000: # HACK: GPT4All messed with the format without changing the magic # number. Specifically, they changed the vocab section to contain # `n_vocab - 1` tokens instead of `n_vocab` (i.e. omitting the # extra pad token). Try to detect if we're reading a file like # this. orig_pos = fp.tell() fp.seek(20, io.SEEK_CUR) is_gpt4all = fp.read(21) == b'tok_embeddings.weight' fp.seek(orig_pos) if is_gpt4all: break length, = struct.unpack("i", must_read(fp, 4)) text = must_read(fp, length) if magic != b'ggml': score, = struct.unpack("f", must_read(fp, 4)) tokens.append((text, score)) vocab = GGMLVocab(tokens) if magic != b'ggml' else None model: LazyModel = {} # Use mmap for the actual data to avoid race conditions with the file offset. off = fp.raw.tell() mapped = memoryview(mmap.mmap(fp.fileno(), 0, access=mmap.ACCESS_READ)) fp.raw.seek(off) # needed on Windows def read_tensor() -> None: # this is a function so that variables captured in `load` don't change shape_len, name_len, ftype = struct.unpack("iii", must_read(fp, 12)) assert 0 <= shape_len <= 3 shape: List[int] = list(struct.unpack(f"{shape_len}i", must_read(fp, 4 * shape_len))) shape = shape[::-1] name = must_read(fp, name_len).decode('utf-8') data_type = FTYPE_TO_DATA_TYPE[ftype] if magic == b'ggjt': fp.seek((fp.tell() + 31) & -32) if data_type == DT_Q4_1: # See GPTQForLLaMaQuantizedTensor.ggml_ndarray() size = 24 * (shape[1] // 32) * shape[0] elif data_type == DT_Q4_0: size = 20 * (shape[1] // 32) * shape[0] else: numpy_dtype = DATA_TYPE_TO_NUMPY[data_type] elm_count = math.prod(shape) size = elm_count * numpy_dtype.itemsize offset = fp.tell() buf = mapped[offset:offset+size] fp.seek(size, io.SEEK_CUR) def load() -> Tensor: if isinstance(data_type, QuantizedDataType): ndarray = np.frombuffer(buf, dtype=np.uint32) return GGMLQuantizedTensor(ndarray, shape, data_type) else: return UnquantizedTensor(np.frombuffer(buf, dtype=numpy_dtype).reshape(shape)) description = f'ggml offset={offset} type={data_type} path={path}' model[name] = LazyTensor(load, shape, data_type, description) while fp.read(1) != b'': fp.seek(-1, io.SEEK_CUR) read_tensor() return ModelPlus(model=model, paths=[path], format='ggml', vocab=vocab) @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 first8[2:4] == b'gg': # GGML format return lazy_load_ggml_file(fp, path) elif struct.unpack('<Q', first8)[0] < 16 * 1024 * 1024: # Probably safetensors return lazy_load_safetensors_file(fp, path) else: raise ValueError(f"unknown format: {path}") In = TypeVar('In') Out = TypeVar('Out') def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int) -> 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.''' with concurrent.futures.ThreadPoolExecutor() as executor: futures: List[concurrent.futures.Future[Out]] = [] items_rev = list(iterable)[::-1] for i in range(min(concurrency, len(items_rev))): futures.append(executor.submit(func, items_rev.pop())) while futures: result = futures.pop(0).result() if items_rev: futures.append(executor.submit(func, items_rev.pop())) yield result def check_vocab_size(params: Params, vocab: Vocab) -> None: if params.n_vocab != vocab.vocab_size: # GGMLVocab comes from the same file as the model so shouldn't mismatch: assert isinstance(vocab, SentencePieceVocab) if params.n_vocab == vocab.vocab_size_base: print("Ignoring added_tokens.json since model matches vocab size without it.") vocab.added_tokens_list = [] vocab.vocab_size = vocab.vocab_size_base return msg = f"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer}" if vocab.fname_added_tokens is not None: msg += f" combined with {vocab.fname_added_tokens}" msg += f" has {vocab.vocab_size})." if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20 and vocab.fname_added_tokens is None: msg += f" Most likely you are missing added_tokens.json (should be in {vocab.fname_tokenizer.parent})." raise Exception(msg) class OutputFile: def __init__(self, fname_out: Path) -> None: self.fout = open(fname_out, "wb") def write_file_header(self, params: Params) -> None: self.fout.write(b"ggjt"[::-1]) # magic values = [ 1, # file version params.n_vocab, params.n_embd, params.n_mult, params.n_head, params.n_layer, params.n_embd // params.n_head, # rot (obsolete) params.file_type.value, ] 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') self.fout.write(struct.pack("iii", len(shape), len(sname), DATA_TYPE_TO_FTYPE[data_type])) self.fout.write(struct.pack("i" * len(shape), *shape[::-1])) self.fout.write(sname) self.fout.seek((self.fout.tell() + 31) & -32) def write_vocab(self, vocab: Vocab) -> None: for text, score in vocab.all_tokens(): self.fout.write(struct.pack("i", len(text))) self.fout.write(text) self.fout.write(struct.pack("f", score)) @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, file_type=GGMLFileType.AllF32) of = OutputFile(fname_out) of.write_file_header(params) of.write_vocab(vocab) of.fout.close() @staticmethod def write_all(fname_out: Path, params: Params, model: LazyModel, vocab: Vocab) -> None: check_vocab_size(params, vocab) of = OutputFile(fname_out) of.write_file_header(params) print("Writing vocab...") of.write_vocab(vocab) def do_item(item: Tuple[str, LazyTensor]) -> NDArray: name, lazy_tensor = item return lazy_tensor.load().to_ggml().ndarray ndarrays = bounded_parallel_map(do_item, model.items(), concurrency=8) for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)): 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}") of.write_tensor_header(name, lazy_tensor.shape, lazy_tensor.data_type) ndarray.tofile(of.fout) of.fout.close() def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFileType: wq_type = model["layers.0.attention.wq.weight"].data_type if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32): 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 == "q4_1" or (output_type_str is None and isinstance(wq_type, QuantizedDataType) and wq_type.have_addends): if isinstance(model["output.weight"].data_type, QuantizedDataType): return GGMLFileType.MostlyQ4_1 else: return GGMLFileType.PerLayerIsQ4_1 if output_type_str == "q4_0" or (output_type_str is None and isinstance(wq_type, QuantizedDataType)): return GGMLFileType.MostlyQ4_0 name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()} raise Exception(f"Unexpected combination of types: {name_to_type}") def do_necessary_conversions(model: LazyModel) -> LazyModel: model = handle_quantization(model) if "lm_head.weight" in model: model = convert_transformers_to_orig(model) model = filter_and_sort_tensors(model) return model 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 nth_multifile_path(path: Path, n: int) -> Optional[Path]: '''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: List[Tuple[str, str]] = [ # - 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(): globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt"] files = [file for glob in globs for file in path.glob(glob)] if not files: # Try GGML too, but with lower priority, since if both a non-GGML # model and a GGML model exist in the same directory, we assume the # latter was converted from the former. files = list(path.glob("ggml-model*.bin*")) if not files: raise Exception(f"Can't find model in directory {path}") if len(files) > 1: raise Exception(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 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: # 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" # Use `.parent` instead of /.. to handle the symlink case better. path3 = path.parent / "tokenizer.model" if path2.exists(): path = path2 elif path3.exists(): path = path3 else: raise FileNotFoundError(f"Could not find tokenizer.model in {path} or its parent; 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) def default_outfile(model_paths: List[Path], params: Params) -> Path: namestr = { GGMLFileType.AllF32: "f32", GGMLFileType.MostlyF16: "f16", GGMLFileType.MostlyQ4_0: "q4_0", GGMLFileType.MostlyQ4_1: "q4_1", GGMLFileType.PerLayerIsQ4_1: "q4_1", }[params.file_type] ret = model_paths[0].parent / f"ggml-model-{namestr}.bin" 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: Optional[List[str]] = None) -> None: 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("--outtype", choices=["f32", "f16", "q4_1", "q4_0"], help="output format (default: based on input)") parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") 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)") args = parser.parse_args(args_in) vocab: Vocab if args.dump_single: 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) assert args.outfile, "need --outfile if using --vocab-only" outfile = args.outfile OutputFile.write_vocab_only(outfile, vocab) print(f"Wrote {outfile}") else: model_plus = load_some_model(args.model) if args.dump: do_dump_model(model_plus) return if model_plus.vocab is not None and args.vocab_dir is 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) model = model_plus.model model = do_necessary_conversions(model) output_type = pick_output_type(model, args.outtype) model = convert_to_output_type(model, output_type) params = Params.guessed(model, output_type) outfile = args.outfile or default_outfile(model_plus.paths, params) OutputFile.write_all(outfile, params, model, vocab) print(f"Wrote {outfile}") if __name__ == '__main__': main()