#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from __future__ import annotations
from dataclasses import dataclass
import logging
import argparse
import os
import sys
import json
from math import prod
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Iterable, Iterator, Sequence, SupportsIndex, cast
from transformers import AutoConfig, AutoTokenizer
import torch
if TYPE_CHECKING:
from torch import Tensor
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
from gguf.constants import GGUFValueType
# reuse model definitions from the conversion/ package
from conversion import LazyTorchTensor, ModelBase, get_model_class, ModelType, get_model_architecture
logger = logging.getLogger("lora-to-gguf")
@dataclass
class PartialLoraTensor:
A: Tensor | None = None
B: Tensor | None = None
# magic to support tensor shape modifications and splitting
class LoraTorchTensor:
_lora_A: Tensor # (n_rank, row_size)
_lora_B: Tensor # (col_size, n_rank)
_rank: int
def __init__(self, A: Tensor, B: Tensor):
assert len(A.shape) == len(B.shape)
assert A.shape[-2] == B.shape[-1]
if A.dtype != B.dtype:
A = A.to(torch.float32)
B = B.to(torch.float32)
self._lora_A = A
self._lora_B = B
self._rank = B.shape[-1]
def get_lora_A_B(self) -> tuple[Tensor, Tensor]:
return (self._lora_A, self._lora_B)
def __getitem__(
self,
indices: (
SupportsIndex
| slice
| tuple[SupportsIndex | slice | Tensor, ...] # TODO: add ellipsis in the type signature
),
) -> LoraTorchTensor:
shape = self.shape
if isinstance(indices, SupportsIndex):
if len(shape) > 2:
return LoraTorchTensor(self._lora_A[indices], self._lora_B[indices])
else:
raise NotImplementedError # can't return a vector
elif isinstance(indices, slice):
if len(shape) > 2:
return LoraTorchTensor(self._lora_A[indices], self._lora_B[indices])
else:
return LoraTorchTensor(self._lora_A, self._lora_B[indices])
elif isinstance(indices, tuple):
assert len(indices) > 0
if indices[-1] is Ellipsis:
return self[indices[:-1]]
# expand ellipsis
indices = tuple(
u
for v in (
(
(slice(None, None) for _ in range(len(indices) - 1))
if i is Ellipsis
else (i,)
)
for i in indices
)
for u in v
)
if len(indices) < len(shape):
indices = (*indices, *(slice(None, None) for _ in range(len(indices), len(shape))))
# TODO: make sure this is correct
indices_A = (
*(
(
j.__index__() % self._lora_A.shape[i]
if isinstance(j, SupportsIndex)
else slice(None, None)
)
for i, j in enumerate(indices[:-2])
),
slice(None, None),
indices[-1],
)
indices_B = indices[:-1]
return LoraTorchTensor(self._lora_A[indices_A], self._lora_B[indices_B])
else:
raise NotImplementedError # unknown indice type
@property
def dtype(self) -> torch.dtype:
assert self._lora_A.dtype == self._lora_B.dtype
return self._lora_A.dtype
@property
def shape(self) -> tuple[int, ...]:
assert len(self._lora_A.shape) == len(self._lora_B.shape)
return (*self._lora_B.shape[:-1], self._lora_A.shape[-1])
def size(self, dim=None):
assert dim is None
return self.shape
def contiguous(self) -> LoraTorchTensor:
return LoraTorchTensor(
self._lora_A.contiguous(),
self._lora_B.contiguous(),
)
def reshape(self, *shape: int | tuple[int, ...]) -> LoraTorchTensor:
if isinstance(shape[0], tuple):
new_shape: tuple[int, ...] = shape[0]
else:
new_shape = cast(tuple[int, ...], shape)
orig_shape = self.shape
if len(new_shape) < 2:
raise NotImplementedError # can't become a vector
# expand -1 in the shape
if any(dim == -1 for dim in new_shape):
n_elems = prod(orig_shape)
n_new_elems = prod(dim if dim != -1 else 1 for dim in new_shape)
assert n_elems % n_new_elems == 0
new_shape = (*(dim if dim != -1 else n_elems // n_new_elems for dim in new_shape),)
if new_shape[-1] != orig_shape[-1]:
raise NotImplementedError # can't reshape the row size trivially
shape_A = (*(1 for _ in new_shape[:-2]), self._rank, orig_shape[-1])
shape_B = (*new_shape[:-1], self._rank)
return LoraTorchTensor(
self._lora_A.reshape(shape_A),
self._lora_B.reshape(shape_B),
)
def reshape_as(self, other: Tensor) -> LoraTorchTensor:
return self.reshape(*other.shape)
def view(self, *size: int) -> LoraTorchTensor:
return self.reshape(*size)
def permute(self, *dims: int) -> LoraTorchTensor:
shape = self.shape
dims = tuple(dim - len(shape) if dim >= 0 else dim for dim in dims)
if dims[-1] == -1:
# TODO: support higher dimensional A shapes bigger than 1
assert all(dim == 1 for dim in self._lora_A.shape[:-2])
return LoraTorchTensor(self._lora_A, self._lora_B.permute(*dims))
if len(shape) == 2 and dims[-1] == -2 and dims[-2] == -1:
return LoraTorchTensor(self._lora_B.permute(*dims), self._lora_A.permute(*dims))
else:
# TODO: compose the above two
raise NotImplementedError
def transpose(self, dim0: int, dim1: int) -> LoraTorchTensor:
shape = self.shape
dims = [i for i in range(len(shape))]
dims[dim0], dims[dim1] = dims[dim1], dims[dim0]
return self.permute(*dims)
def swapaxes(self, axis0: int, axis1: int) -> LoraTorchTensor:
return self.transpose(axis0, axis1)
def split(self, split_size: int | Sequence[int], dim: int = 0) -> tuple[LoraTorchTensor, ...]:
shape = self.shape
ndim = len(shape)
if dim < 0:
dim += ndim
if dim == ndim - 1:
A_chunks = self._lora_A.split(split_size, dim=-1)
return tuple(LoraTorchTensor(a, self._lora_B) for a in A_chunks)
elif dim == ndim - 2:
B_chunks = self._lora_B.split(split_size, dim=-2)
return tuple(LoraTorchTensor(self._lora_A, b) for b in B_chunks)
else:
B_chunks = self._lora_B.split(split_size, dim=dim)
if self._lora_A.shape[dim] == 1:
return tuple(LoraTorchTensor(self._lora_A, b) for b in B_chunks)
A_chunks = self._lora_A.split(split_size, dim=dim)
return tuple(LoraTorchTensor(a, b) for a, b in zip(A_chunks, B_chunks))
def to(self, *args, **kwargs):
return LoraTorchTensor(self._lora_A.to(*args, **kwargs), self._lora_B.to(*args, **kwargs))
def __mul__(self, other) -> LoraTorchTensor:
# Only output-side multiplication for now
# W = B @ A, so M_out * W == (M_out * B) @ A
if not isinstance(other, (int, float)) and other.shape and other.shape[-1] != 1:
raise NotImplementedError
return LoraTorchTensor(self._lora_A, self._lora_B * other)
def __rmul__(self, other) -> LoraTorchTensor:
return self * other
@classmethod
def __torch_function__(cls, func: Callable, types, args=(), kwargs=None):
del types # unused
if kwargs is None:
kwargs = {}
if func is torch.permute:
assert len(args)
return type(args[0]).permute(*args, **kwargs)
elif func is torch.reshape:
assert len(args)
return type(args[0]).reshape(*args, **kwargs)
elif func is torch.stack:
assert len(args)
assert isinstance(args[0], Sequence)
dim = kwargs.get("dim", 0)
assert dim == 0
return LoraTorchTensor(
torch.stack([a._lora_A for a in args[0]], dim),
torch.stack([b._lora_B for b in args[0]], dim),
)
elif func is torch.cat:
assert len(args)
assert isinstance(args[0], Sequence)
dim = kwargs.get("dim", 0)
assert dim == 0
if len(args[0][0].shape) > 2:
return LoraTorchTensor(
torch.cat([a._lora_A for a in args[0]], dim),
torch.cat([b._lora_B for b in args[0]], dim),
)
elif all(torch.equal(args[0][0]._lora_A, t._lora_A) for t in args[0][1:]):
return LoraTorchTensor(
args[0][0]._lora_A,
torch.cat([b._lora_B for b in args[0]], dim),
)
else:
raise NotImplementedError
elif func is torch.split:
assert len(args) and len(args) >= 2
tensor, split_size = args[0], args[1]
dim = args[2] if len(args) > 2 else kwargs.get("dim", 0)
return tensor.split(split_size, dim=dim)
else:
raise NotImplementedError
def get_base_tensor_name(lora_tensor_name: str) -> str:
base_name = lora_tensor_name.replace("base_model.model.", "")
base_name = base_name.replace(".lora_A.weight", ".weight")
base_name = base_name.replace(".lora_B.weight", ".weight")
# models produced by mergekit-extract-lora have token embeddings in the adapter
base_name = base_name.replace(".lora_embedding_A", ".weight")
base_name = base_name.replace(".lora_embedding_B", ".weight")
return base_name
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Convert a Hugging Face PEFT LoRA adapter to a GGUF file")
parser.add_argument(
"--outfile", type=Path,
help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
)
parser.add_argument(
"--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f32",
help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
)
parser.add_argument(
"--bigendian", action="store_true",
help="model is executed on big endian machine",
)
parser.add_argument(
"--no-lazy", action="store_true",
help="use more RAM by computing all outputs before writing (use in case lazy evaluation is broken)",
)
parser.add_argument(
"--verbose", action="store_true",
help="increase output verbosity",
)
parser.add_argument(
"--dry-run", action="store_true",
help="only print out what will be done, without writing any new files",
)
parser.add_argument(
"--base", type=Path,
help="directory containing Hugging Face model config files (config.json, tokenizer.json) for the base model that the adapter is based on - only config is needed, actual model weights are not required. If base model is unspecified, it will be loaded from Hugging Face hub based on the adapter config",
)
parser.add_argument(
"--base-model-id", type=str,
help="the model ID of the base model, if it is not available locally or in the adapter config. If specified, it will ignore --base and load the base model config from the Hugging Face hub (Example: 'meta-llama/Llama-3.2-1B-Instruct')",
)
parser.add_argument(
"--trust-remote-code", default=False, action="store_true",
help="trust remote code in the model",
)
parser.add_argument(
"lora_path", type=Path,
help="directory containing Hugging Face PEFT LoRA config (adapter_model.json) and weights (adapter_model.safetensors or adapter_model.bin)",
)
return parser.parse_args()
def load_hparams_from_hf(hf_model_id: str, trust_remote_code: bool) -> tuple[dict[str, Any], Path | None]:
from huggingface_hub import try_to_load_from_cache
# normally, adapter does not come with base model config, we need to load it from AutoConfig
config = AutoConfig.from_pretrained(hf_model_id, trust_remote_code=trust_remote_code)
cache_dir = try_to_load_from_cache(hf_model_id, "config.json")
cache_dir = Path(cache_dir).parent if isinstance(cache_dir, str) else None
return config.to_dict(), cache_dir
if __name__ == '__main__':
args = parse_args()
logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
ftype_map: dict[str, gguf.LlamaFileType] = {
"f32": gguf.LlamaFileType.ALL_F32,
"f16": gguf.LlamaFileType.MOSTLY_F16,
"bf16": gguf.LlamaFileType.MOSTLY_BF16,
"q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
"auto": gguf.LlamaFileType.GUESSED,
}
ftype = ftype_map[args.outtype]
dir_base_model: Path | None = args.base
dir_lora: Path = args.lora_path
base_model_id: str | None = args.base_model_id
lora_config = dir_lora / "adapter_config.json"
input_model = dir_lora / "adapter_model.safetensors"
if args.outfile is not None:
fname_out = args.outfile
else:
# output in the same directory as the model by default
fname_out = dir_lora
if os.path.exists(input_model):
# lazy import load_file only if lora is in safetensors format.
from safetensors.torch import load_file
lora_model = load_file(input_model, device="cpu")
else:
input_model = os.path.join(dir_lora, "adapter_model.bin")
lora_model = torch.load(input_model, map_location="cpu", weights_only=True)
# load LoRA config
with open(lora_config, "r") as f:
lparams: dict[str, Any] = json.load(f)
# load base model
if base_model_id is not None:
logger.info(f"Loading base model from Hugging Face: {base_model_id}")
hparams, dir_base_model = load_hparams_from_hf(base_model_id, args.trust_remote_code)
elif dir_base_model is None:
if "base_model_name_or_path" in lparams:
model_id = lparams["base_model_name_or_path"]
logger.info(f"Loading base model from Hugging Face: {model_id}")
try:
hparams, dir_base_model = load_hparams_from_hf(model_id, args.trust_remote_code)
except OSError as e:
logger.error(f"Failed to load base model config: {e}")
logger.error("Please try downloading the base model and add its path to --base")
sys.exit(1)
else:
logger.error("'base_model_name_or_path' is not found in adapter_config.json")
logger.error("Base model config is required. Please download the base model and add its path to --base")
sys.exit(1)
else:
logger.info(f"Loading base model: {dir_base_model.name}")
hparams = ModelBase.load_hparams(dir_base_model, False)
with torch.inference_mode():
model_arch = get_model_architecture(hparams, ModelType.TEXT)
try:
model_class = get_model_class(model_arch)
logger.info("Using model architecture: %s", model_arch)
except NotImplementedError:
logger.error(f"Model {model_arch} is not supported")
sys.exit(1)
class LoraModel(model_class): # ty: ignore[unsupported-base]
model_arch = model_class.model_arch
lora_alpha: float
def __init__(self, *args, dir_lora_model: Path, lora_alpha: float, **kwargs):
super().__init__(*args, **kwargs)
self.dir_model_card = dir_lora_model
self.lora_alpha = float(lora_alpha)
def set_vocab(self):
pass
def set_type(self):
self.gguf_writer.add_type(gguf.GGUFType.ADAPTER)
self.gguf_writer.add_string(gguf.Keys.Adapter.TYPE, "lora")
def set_gguf_parameters(self):
logger.debug("GGUF KV: %s = %d", gguf.Keys.Adapter.LORA_ALPHA, self.lora_alpha)
self.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, self.lora_alpha)
alora_invocation_tokens = lparams.get("alora_invocation_tokens")
invocation_string = lparams.get("invocation_string")
if invocation_string and not alora_invocation_tokens:
logger.debug("Tokenizing invocation_string -> alora_invocation_tokens")
base_model_path_or_id = hparams.get("_name_or_path")
try:
tokenizer = AutoTokenizer.from_pretrained(base_model_path_or_id)
except ValueError:
logger.error("Unable to load tokenizer from %s", base_model_path_or_id)
raise
# NOTE: There's an off-by-one with the older aLoRAs where
# the invocation string includes the "<|start_of_turn|>"
# token, but the adapters themselves were trained to
# activate _after_ that first token, so we drop it here.
alora_invocation_tokens = tokenizer(invocation_string)["input_ids"][1:] # ty: ignore[call-non-callable]
if alora_invocation_tokens:
logger.debug("GGUF KV: %s = %s", gguf.Keys.Adapter.ALORA_INVOCATION_TOKENS, alora_invocation_tokens)
self.gguf_writer.add_key_value(
gguf.Keys.Adapter.ALORA_INVOCATION_TOKENS,
alora_invocation_tokens,
GGUFValueType.ARRAY,
GGUFValueType.UINT32,
)
def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
# Never add extra tensors (e.g. rope_freqs) for LoRA adapters
return ()
def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
tensor_map: dict[str, PartialLoraTensor] = {}
for name, tensor in lora_model.items():
if self.lazy:
tensor = LazyTorchTensor.from_eager(tensor)
base_name = get_base_tensor_name(name)
# filter base name, ignore tensor transformations for now
data_gen = lambda g=tensor: g # noqa: E731
if (titem := self.filter_tensors((base_name, data_gen))) is None:
continue
base_name, _ = titem
# note: mergekit-extract-lora also adds token embeddings to the adapter
is_lora_a = ".lora_A.weight" in name or ".lora_embedding_A" in name
is_lora_b = ".lora_B.weight" in name or ".lora_embedding_B" in name
if not is_lora_a and not is_lora_b:
if ".base_layer.weight" in name:
continue
# mergekit-extract-lora add these layernorm to the adapter, we need to keep them
if "_layernorm" in name or ".norm" in name:
yield (base_name, tensor)
continue
logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor")
if ".embed_tokens.weight" in name or ".lm_head.weight" in name:
logger.error("Embeddings is present in the adapter. This can be due to new tokens added during fine tuning")
logger.error("Please refer to https://github.com/ggml-org/llama.cpp/pull/9948")
sys.exit(1)
if base_name in tensor_map:
if is_lora_a:
tensor_map[base_name].A = tensor
else:
tensor_map[base_name].B = tensor
else:
if is_lora_a:
tensor_map[base_name] = PartialLoraTensor(A=tensor)
else:
tensor_map[base_name] = PartialLoraTensor(B=tensor)
for name, tensor in tensor_map.items():
assert tensor.A is not None
assert tensor.B is not None
yield (name, cast(torch.Tensor, LoraTorchTensor(tensor.A, tensor.B)))
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
dest = list(super().modify_tensors(data_torch, name, bid))
# some archs may have the same tensor for lm_head and output (tie word embeddings)
# in this case, adapters targeting lm_head will fail when using llama-export-lora
# therefore, we ignore them for now
# see: https://github.com/ggml-org/llama.cpp/issues/9065
if name == "lm_head.weight" and len(dest) == 0:
raise ValueError("lm_head is present in adapter, but is ignored in base model")
for dest_name, dest_data in dest:
# mergekit-extract-lora add these layernorm to the adapter
if "_norm" in dest_name:
assert dest_data.dim() == 1
yield (dest_name, dest_data)
continue
# otherwise, we must get the lora_A and lora_B tensors
assert isinstance(dest_data, LoraTorchTensor)
lora_a, lora_b = dest_data.get_lora_A_B()
# note: mergekit-extract-lora flip and transpose A and B
# here we only need to transpose token_embd.lora_a, see llm_build_inp_embd()
if "token_embd.weight" in dest_name:
lora_a = lora_a.T
yield (dest_name + ".lora_a", lora_a)
yield (dest_name + ".lora_b", lora_b)
alpha: float = lparams["lora_alpha"]
model_instance = LoraModel(
dir_base_model,
ftype,
fname_out,
is_big_endian=args.bigendian,
use_temp_file=False,
eager=args.no_lazy,
dry_run=args.dry_run,
dir_lora_model=dir_lora,
lora_alpha=alpha,
hparams=hparams,
remote_hf_model_id=base_model_id,
)
logger.info("Exporting model...")
model_instance.write()
logger.info(f"Model successfully exported to {model_instance.fname_out}")