--- /dev/null
+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
+import numpy as np
+from sentencepiece import SentencePieceProcessor # type: ignore
+from typing import (IO, Any, Callable, Iterable, Literal, Optional, Sequence,
+ TypeVar, Union, List, Dict, Tuple, TYPE_CHECKING)
+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[fp.tell():]
+
+ 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[bytes], 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.
+ mapped = memoryview(mmap.mmap(fp.fileno(), 0, access=mmap.ACCESS_READ))
+
+ 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(map(str, lazy_tensor.shape))
+ print(f"[{i+1}/{len(model)}] Writing tensor {name}, size {size}...")
+ 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_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"], 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()
overview / pkg / ggml / sources / llama.cpp / commitdiff