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convert-hf-to-gguf.py
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convert-hf-to-gguf.py
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#!/usr/bin/env python3
from __future__ import annotations
import logging
import argparse
import contextlib
import json
import os
import re
import sys
from enum import IntEnum
from pathlib import Path
from hashlib import sha256
from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Sequence, TypeVar, cast
import math
import numpy as np
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 convert import LlamaHfVocab
logger = logging.getLogger("hf-to-gguf")
###### MODEL DEFINITIONS ######
class SentencePieceTokenTypes(IntEnum):
NORMAL = 1
UNKNOWN = 2
CONTROL = 3
USER_DEFINED = 4
UNUSED = 5
BYTE = 6
AnyModel = TypeVar("AnyModel", bound="type[Model]")
class Model:
_model_classes: dict[str, type[Model]] = {}
dir_model: Path
ftype: int
is_big_endian: bool
endianess: gguf.GGUFEndian
use_temp_file: bool
lazy: bool
part_names: list[str]
is_safetensors: bool
hparams: dict[str, Any]
block_count: int
tensor_map: gguf.TensorNameMap
tensor_names: set[str] | None
fname_out: Path
gguf_writer: gguf.GGUFWriter
# subclasses should define this!
model_arch: gguf.MODEL_ARCH
def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool):
if type(self) is Model:
raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
self.dir_model = dir_model
self.ftype = ftype
self.is_big_endian = is_big_endian
self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
self.use_temp_file = use_temp_file
self.lazy = not eager
self.part_names = Model.get_model_part_names(self.dir_model, ".safetensors")
self.is_safetensors = len(self.part_names) > 0
if not self.is_safetensors:
self.part_names = Model.get_model_part_names(self.dir_model, ".bin")
self.hparams = Model.load_hparams(self.dir_model)
self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
self.tensor_names = None
if self.ftype == gguf.LlamaFileType.GUESSED:
# NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie.
_, first_tensor = next(self.get_tensors())
if first_tensor.dtype == torch.float16:
logger.info(f"choosing --outtype f16 from first tensor type ({first_tensor.dtype})")
self.ftype = gguf.LlamaFileType.MOSTLY_F16
else:
logger.info(f"choosing --outtype bf16 from first tensor type ({first_tensor.dtype})")
self.ftype = gguf.LlamaFileType.MOSTLY_BF16
ftype_up: str = self.ftype.name.partition("_")[2].upper()
ftype_lw: str = ftype_up.lower()
# allow templating the file name with the output ftype, useful with the "auto" ftype
self.fname_out = fname_out.parent / fname_out.name.format(ftype_lw, outtype=ftype_lw, ftype=ftype_lw, OUTTYPE=ftype_up, FTYPE=ftype_up)
self.gguf_writer = gguf.GGUFWriter(self.fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file)
@classmethod
def __init_subclass__(cls):
# can't use an abstract property, because overriding it without type errors
# would require using decorated functions instead of simply defining the property
if "model_arch" not in cls.__dict__:
raise TypeError(f"Missing property 'model_arch' for {cls.__name__!r}")
def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any:
key = next((k for k in keys if k in self.hparams), None)
if key is not None:
return self.hparams[key]
if optional:
return None
raise KeyError(f"could not find any of: {keys}")
def set_vocab(self):
self._set_vocab_gpt2()
def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
tensor_names_from_parts: set[str] = set()
if len(self.part_names) > 1:
self.tensor_names = set()
index_name = "model.safetensors" if self.is_safetensors else "pytorch_model.bin"
index_name += ".index.json"
logger.info(f"gguf: loading model weight map from '{index_name}'")
with open(self.dir_model / index_name, "r", encoding="utf-8") as f:
index: dict[str, Any] = json.load(f)
weight_map = index.get("weight_map")
if weight_map is None or not isinstance(weight_map, dict):
raise ValueError(f"Can't load 'weight_map' from {index_name!r}")
self.tensor_names.update(weight_map.keys())
else:
self.tensor_names = tensor_names_from_parts
for part_name in self.part_names:
logger.info(f"gguf: loading model part '{part_name}'")
ctx: ContextManager[Any]
if self.is_safetensors:
from safetensors import safe_open
ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu"))
else:
ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
with ctx as model_part:
tensor_names_from_parts.update(model_part.keys())
for name in model_part.keys():
data = model_part.get_tensor(name) if self.is_safetensors else model_part[name]
if self.lazy:
data = LazyTorchTensor.from_eager(data)
yield name, data
# only verify tensor name presence; it doesn't matter if they are not in the right files
if len(sym_diff := tensor_names_from_parts.symmetric_difference(self.tensor_names)) > 0:
raise ValueError(f"Mismatch between weight map and model parts for tensor names: {sym_diff}")
def format_tensor_name(self, key: gguf.MODEL_TENSOR, bid: int | None = None, suffix: str = ".weight") -> str:
if key not in gguf.MODEL_TENSORS[self.model_arch]:
raise ValueError(f"Missing {key!r} for MODEL_TENSORS of {self.model_arch!r}")
name: str = gguf.TENSOR_NAMES[key]
if "{bid}" in name:
assert bid is not None
name = name.format(bid=bid)
return name + suffix
def match_model_tensor_name(self, name: str, key: gguf.MODEL_TENSOR, bid: int | None, suffix: str = ".weight") -> bool:
if key not in gguf.MODEL_TENSORS[self.model_arch]:
return False
key_name: str = gguf.TENSOR_NAMES[key]
if "{bid}" in key_name:
if bid is None:
return False
key_name = key_name.format(bid=bid)
else:
if bid is not None:
return False
return name == (key_name + suffix)
def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
new_name = self.tensor_map.get_name(key=name, try_suffixes=try_suffixes)
if new_name is None:
raise ValueError(f"Can not map tensor {name!r}")
return new_name
def set_gguf_parameters(self):
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_block_count(self.block_count)
if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None:
self.gguf_writer.add_context_length(n_ctx)
logger.info(f"gguf: context length = {n_ctx}")
n_embd = self.find_hparam(["hidden_size", "n_embd"])
self.gguf_writer.add_embedding_length(n_embd)
logger.info(f"gguf: embedding length = {n_embd}")
if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None:
self.gguf_writer.add_feed_forward_length(n_ff)
logger.info(f"gguf: feed forward length = {n_ff}")
n_head = self.find_hparam(["num_attention_heads", "n_head"])
self.gguf_writer.add_head_count(n_head)
logger.info(f"gguf: head count = {n_head}")
if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
self.gguf_writer.add_head_count_kv(n_head_kv)
logger.info(f"gguf: key-value head count = {n_head_kv}")
if (rope_theta := self.hparams.get("rope_theta")) is not None:
self.gguf_writer.add_rope_freq_base(rope_theta)
logger.info(f"gguf: rope theta = {rope_theta}")
if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")
if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
self.gguf_writer.add_layer_norm_eps(f_norm_eps)
logger.info(f"gguf: layer norm epsilon = {f_norm_eps}")
if (n_experts := self.hparams.get("num_local_experts")) is not None:
self.gguf_writer.add_expert_count(n_experts)
logger.info(f"gguf: expert count = {n_experts}")
if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
self.gguf_writer.add_expert_used_count(n_experts_used)
logger.info(f"gguf: experts used count = {n_experts_used}")
self.gguf_writer.add_file_type(self.ftype)
logger.info(f"gguf: file type = {self.ftype}")
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
return [(self.map_tensor_name(name), data_torch)]
def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
del name, new_name, bid, n_dims # unused
return False
def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
del name, new_name, bid, n_dims # unused
return False
def write_tensors(self):
max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
for name, data_torch in self.get_tensors():
# we don't need these
if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
continue
old_dtype = data_torch.dtype
# convert any unsupported data types to float32
if data_torch.dtype not in (torch.float16, torch.float32):
data_torch = data_torch.to(torch.float32)
# use the first number-like part of the tensor name as the block id
bid = None
for part in name.split("."):
if part.isdecimal():
bid = int(part)
break
for new_name, data in ((n, d.squeeze().numpy()) for n, d in self.modify_tensors(data_torch, name, bid)):
data: np.ndarray = data # type hint
n_dims = len(data.shape)
data_dtype = data.dtype
data_qtype: gguf.GGMLQuantizationType | None = None
# when both are True, f32 should win
extra_f32 = self.extra_f32_tensors(name, new_name, bid, n_dims)
extra_f16 = self.extra_f16_tensors(name, new_name, bid, n_dims)
# Most of the codebase that takes in 1D tensors or norms only handles F32 tensors
# Conditions should closely match those in llama_model_quantize_internal in llama.cpp
extra_f32 = any(cond for cond in (
extra_f32,
n_dims == 1,
new_name.endswith("_norm.weight"),
))
# Some tensor types are always in float32
extra_f32 = extra_f32 or any(self.match_model_tensor_name(new_name, key, bid) for key in (
gguf.MODEL_TENSOR.FFN_GATE_INP,
gguf.MODEL_TENSOR.POS_EMBD,
gguf.MODEL_TENSOR.TOKEN_TYPES,
))
# if f16 desired, convert any float32 2-dim weight tensors to float16
extra_f16 = any(cond for cond in (
extra_f16,
(name.endswith(".weight") and n_dims >= 2),
))
if self.ftype != gguf.LlamaFileType.ALL_F32 and extra_f16 and not extra_f32:
if self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
data = gguf.quantize_bf16(data)
assert data.dtype == np.int16
data_qtype = gguf.GGMLQuantizationType.BF16
elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0 and gguf.can_quantize_to_q8_0(data):
data = gguf.quantize_q8_0(data)
assert data.dtype == np.uint8
data_qtype = gguf.GGMLQuantizationType.Q8_0
else: # default to float16 for quantized tensors
if data_dtype != np.float16:
data = data.astype(np.float16)
data_qtype = gguf.GGMLQuantizationType.F16
if data_qtype is None: # by default, convert to float32
if data_dtype != np.float32:
data = data.astype(np.float32)
data_qtype = gguf.GGMLQuantizationType.F32
block_size, type_size = gguf.GGML_QUANT_SIZES[data_qtype]
# reverse shape to make it similar to the internal ggml dimension order
shape_str = f"""{{{', '.join(str(n) for n in reversed(
(*data.shape[:-1], data.shape[-1] * data.dtype.itemsize // type_size * block_size))
)}}}"""
# n_dims is implicit in the shape
logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype)
def write(self):
self.write_tensors()
self.gguf_writer.write_header_to_file()
self.gguf_writer.write_kv_data_to_file()
self.gguf_writer.write_tensors_to_file(progress=True)
self.gguf_writer.close()
def write_vocab(self):
self.gguf_writer.write_header_to_file()
self.gguf_writer.write_kv_data_to_file()
self.gguf_writer.close()
@staticmethod
def get_model_part_names(dir_model: Path, suffix: str) -> list[str]:
part_names: list[str] = []
for filename in os.listdir(dir_model):
if filename.endswith(suffix):
part_names.append(filename)
part_names.sort()
return part_names
@staticmethod
def load_hparams(dir_model: Path):
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
return json.load(f)
@classmethod
def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]:
assert names
def func(modelcls: AnyModel) -> AnyModel:
for name in names:
cls._model_classes[name] = modelcls
return modelcls
return func
@classmethod
def from_model_architecture(cls, arch: str) -> type[Model]:
try:
return cls._model_classes[arch]
except KeyError:
raise NotImplementedError(f'Architecture {arch!r} not supported!') from None
# used for GPT-2 BPE and WordPiece vocabs
def get_vocab_base(self) -> tuple[list[str], list[int], str]:
tokens: list[str] = []
toktypes: list[int] = []
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab))
assert max(tokenizer.vocab.values()) < vocab_size
tokpre = self.get_vocab_base_pre(tokenizer)
reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
added_vocab = tokenizer.get_added_vocab()
for i in range(vocab_size):
if i not in reverse_vocab:
tokens.append(f"[PAD{i}]")
toktypes.append(gguf.TokenType.USER_DEFINED)
elif reverse_vocab[i] in added_vocab:
tokens.append(reverse_vocab[i])
if tokenizer.added_tokens_decoder[i].special:
toktypes.append(gguf.TokenType.CONTROL)
else:
toktypes.append(gguf.TokenType.USER_DEFINED)
else:
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.NORMAL)
return tokens, toktypes, tokpre
# NOTE: this function is generated by convert-hf-to-gguf-update.py
# do not modify it manually!
# ref: https://github.com/ggerganov/llama.cpp/pull/6920
# Marker: Start get_vocab_base_pre
def get_vocab_base_pre(self, tokenizer) -> str:
# encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that
# is specific for the BPE pre-tokenizer used by the model
# we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can
# use in llama.cpp to implement the same pre-tokenizer
chktxt = '\n \n\n \n\n\n \t \t\t \t\n \n \n \n \n🚀 (normal) 😶\u200d🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天~ ------======= нещо на Български \'\'\'\'\'\'```````""""......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
chktok = tokenizer.encode(chktxt)
chkhsh = sha256(str(chktok).encode()).hexdigest()
logger.debug(f"chktok: {chktok}")
logger.debug(f"chkhsh: {chkhsh}")
res = None
# NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script
# or pull the latest version of the model from Huggingface
# don't edit the hashes manually!
if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
# ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
res = "llama-bpe"
if chkhsh == "049ecf7629871e3041641907f3de7c733e4dbfdc736f57d882ba0b0845599754":
# ref: https://huggingface.co/deepseek-ai/deepseek-llm-7b-base
res = "deepseek-llm"
if chkhsh == "347715f544604f9118bb75ed199f68779f423cabb20db6de6f31b908d04d7821":
# ref: https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base
res = "deepseek-coder"
if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed":
# ref: https://huggingface.co/tiiuae/falcon-7b
res = "falcon"
if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
# ref: https://huggingface.co/BAAI/bge-small-en-v1.5
res = "bert-bge"
if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166":
# ref: https://huggingface.co/mosaicml/mpt-7b
res = "mpt"
if chkhsh == "35d91631860c815f952d711435f48d356ebac988362536bed955d43bfa436e34":
# ref: https://huggingface.co/bigcode/starcoder2-3b
res = "starcoder"
if chkhsh == "3ce83efda5659b07b1ad37ca97ca5797ea4285d9b9ab0dc679e4a720c9da7454":
# ref: https://huggingface.co/openai-community/gpt2
res = "gpt-2"
if chkhsh == "32d85c31273f8019248f2559fed492d929ea28b17e51d81d3bb36fff23ca72b3":
# ref: https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b
res = "stablelm2"
if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff":
# ref: https://huggingface.co/smallcloudai/Refact-1_6-base
res = "refact"
if chkhsh == "9c2227e4dd922002fb81bde4fc02b0483ca4f12911410dee2255e4987644e3f8":
# ref: https://huggingface.co/CohereForAI/c4ai-command-r-v01
res = "command-r"
if chkhsh == "e636dc30a262dcc0d8c323492e32ae2b70728f4df7dfe9737d9f920a282b8aea":
# ref: https://huggingface.co/Qwen/Qwen1.5-7B
res = "qwen2"
if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166":
# ref: https://huggingface.co/allenai/OLMo-1.7-7B-hf
res = "olmo"
if chkhsh == "a8594e3edff7c29c003940395316294b2c623e09894deebbc65f33f1515df79e":
# ref: https://huggingface.co/databricks/dbrx-base
res = "dbrx"
if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-en
res = "jina-v2-en"
if chkhsh == "171aeeedd6fb548d418a7461d053f11b6f1f1fc9b387bd66640d28a4b9f5c643":
# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-es
res = "jina-v2-es"
if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6":
# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de
res = "jina-v2-de"
if res is None:
logger.warning("\n")
logger.warning("**************************************************************************************")
logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!")
logger.warning("** There are 2 possible reasons for this:")
logger.warning("** - the model has not been added to convert-hf-to-gguf-update.py yet")
logger.warning("** - the pre-tokenization config has changed upstream")
logger.warning("** Check your model files and convert-hf-to-gguf-update.py and update them accordingly.")
logger.warning("** ref: https://github.com/ggerganov/llama.cpp/pull/6920")
logger.warning("**")
logger.warning(f"** chkhsh: {chkhsh}")
logger.warning("**************************************************************************************")
logger.warning("\n")
raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
logger.debug(f"tokenizer.ggml.pre: {repr(res)}")
logger.debug(f"chkhsh: {chkhsh}")
return res
# Marker: End get_vocab_base_pre
def _set_vocab_gpt2(self) -> None:
tokens, toktypes, tokpre = self.get_vocab_base()
self.gguf_writer.add_tokenizer_model("gpt2")
self.gguf_writer.add_tokenizer_pre(tokpre)
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
special_vocab.add_to_gguf(self.gguf_writer)
def _set_vocab_qwen(self):
dir_model = self.dir_model
hparams = self.hparams
tokens: list[str] = []
toktypes: list[int] = []
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
vocab_size = hparams["vocab_size"]
assert max(tokenizer.get_vocab().values()) < vocab_size
tokpre = self.get_vocab_base_pre(tokenizer)
merges = []
vocab = {}
mergeable_ranks = tokenizer.mergeable_ranks
for token, rank in mergeable_ranks.items():
vocab[QwenModel.token_bytes_to_string(token)] = rank
if len(token) == 1:
continue
merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
assert len(merged) == 2
merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
# for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
added_vocab = tokenizer.special_tokens
reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()}
for i in range(vocab_size):
if i not in reverse_vocab:
tokens.append(f"[PAD{i}]")
toktypes.append(gguf.TokenType.USER_DEFINED)
elif reverse_vocab[i] in added_vocab:
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.CONTROL)
else:
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.NORMAL)
self.gguf_writer.add_tokenizer_model("gpt2")
self.gguf_writer.add_tokenizer_pre(tokpre)
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
special_vocab.merges = merges
# only add special tokens when they were not already loaded from config.json
if len(special_vocab.special_token_ids) == 0:
special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])
special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])
# this one is usually not in config.json anyway
special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])
special_vocab.add_to_gguf(self.gguf_writer)
def _set_vocab_sentencepiece(self):
from sentencepiece import SentencePieceProcessor
tokenizer_path = self.dir_model / 'tokenizer.model'
tokens: list[bytes] = []
scores: list[float] = []
toktypes: list[int] = []
if not tokenizer_path.is_file():
raise FileNotFoundError(f"File not found: {tokenizer_path}")
tokenizer = SentencePieceProcessor()
tokenizer.LoadFromFile(str(tokenizer_path))
vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
scores: list[float] = [-10000.0] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
for token_id in range(tokenizer.vocab_size()):
piece = tokenizer.IdToPiece(token_id)
text = piece.encode("utf-8")
score = tokenizer.GetScore(token_id)
toktype = SentencePieceTokenTypes.NORMAL
if tokenizer.IsUnknown(token_id):
toktype = SentencePieceTokenTypes.UNKNOWN
elif tokenizer.IsControl(token_id):
toktype = SentencePieceTokenTypes.CONTROL
elif tokenizer.IsUnused(token_id):
toktype = SentencePieceTokenTypes.UNUSED
elif tokenizer.IsByte(token_id):
toktype = SentencePieceTokenTypes.BYTE
tokens[token_id] = text
scores[token_id] = score
toktypes[token_id] = toktype
added_tokens_file = self.dir_model / 'added_tokens.json'
if added_tokens_file.is_file():
with open(added_tokens_file, "r", encoding="utf-8") as f:
added_tokens_json = json.load(f)
for key in added_tokens_json:
token_id = added_tokens_json[key]
if (token_id >= vocab_size):
logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
continue
tokens[token_id] = key.encode("utf-8")
scores[token_id] = -1000.0
toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
if vocab_size > len(tokens):
pad_count = vocab_size - len(tokens)
logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
for i in range(1, pad_count + 1):
tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
scores.append(-1000.0)
toktypes.append(SentencePieceTokenTypes.UNUSED)
self.gguf_writer.add_tokenizer_model("llama")
self.gguf_writer.add_tokenizer_pre("default")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
def _set_vocab_llama_hf(self):
vocab = LlamaHfVocab(self.dir_model)
tokens = []
scores = []
toktypes = []
for text, score, toktype in vocab.all_tokens():
tokens.append(text)
scores.append(score)
toktypes.append(toktype)
assert len(tokens) == vocab.vocab_size
self.gguf_writer.add_tokenizer_model("llama")
self.gguf_writer.add_tokenizer_pre("default")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
@Model.register("GPTNeoXForCausalLM")
class GPTNeoXModel(Model):
model_arch = gguf.MODEL_ARCH.GPTNEOX
def set_gguf_parameters(self):
block_count = self.hparams["num_hidden_layers"]
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_dimension_count(
int(self.hparams["rotary_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])),
)
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
tensors: list[tuple[str, Tensor]] = []
if re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.weight", name):
# Map bloom-style qkv_linear to gpt-style qkv_linear
# bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa
# gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa
qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed))
data_torch = torch.cat(
(
qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
),
dim=0,
)
logger.info("re-format attention.linear_qkv.weight")
elif re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.bias", name):
qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head))
data_torch = torch.cat(
(
qkv_bias[:, 0, :].reshape((n_embed,)),
qkv_bias[:, 1, :].reshape((n_embed,)),
qkv_bias[:, 2, :].reshape((n_embed,)),
),
dim=0,
)
logger.info("re-format attention.linear_qkv.bias")
tensors.append((self.map_tensor_name(name), data_torch))
return tensors
@Model.register("BloomForCausalLM")
class BloomModel(Model):
model_arch = gguf.MODEL_ARCH.BLOOM
def set_gguf_parameters(self):
self.gguf_writer.add_name("Bloom")
n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
self.gguf_writer.add_embedding_length(n_embed)
self.gguf_writer.add_feed_forward_length(4 * n_embed)
self.gguf_writer.add_block_count(self.hparams["n_layer"])
self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_head_count_kv(n_head)
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_file_type(self.ftype)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
name = re.sub(r'transformer\.', '', name)
tensors: list[tuple[str, Tensor]] = []
if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name):
# Map bloom-style qkv_linear to gpt-style qkv_linear
# bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa
# gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa
qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed))
data_torch = torch.cat(
(
qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
),
dim=0,
)
logger.info("re-format attention.linear_qkv.weight")
elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name):
qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head))
data_torch = torch.cat(
(
qkv_bias[:, 0, :].reshape((n_embed,)),
qkv_bias[:, 1, :].reshape((n_embed,)),
qkv_bias[:, 2, :].reshape((n_embed,)),
),
dim=0,
)
logger.info("re-format attention.linear_qkv.bias")
tensors.append((self.map_tensor_name(name), data_torch))
if name == "word_embeddings.weight":
assert self.tensor_names is not None
# TODO: tie them at runtime, don't duplicate in the model file
if all(s not in self.tensor_names for s in ("lm_head.weight", "output.weight")):
tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch))
return tensors
@Model.register("MPTForCausalLM")
class MPTModel(Model):
model_arch = gguf.MODEL_ARCH.MPT
def set_vocab(self):
try:
self._set_vocab_gpt2()
except Exception:
# Fallback for SEA-LION model
self._set_vocab_sentencepiece()
self.gguf_writer.add_add_bos_token(False)
self.gguf_writer.add_pad_token_id(3)
self.gguf_writer.add_eos_token_id(1)
self.gguf_writer.add_unk_token_id(0)
def set_gguf_parameters(self):
block_count = self.hparams["n_layers"]
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
self.gguf_writer.add_embedding_length(self.hparams["d_model"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_feed_forward_length(4 * self.hparams["d_model"])
self.gguf_writer.add_head_count(self.hparams["n_heads"])
if kv_n_heads := self.hparams["attn_config"].get("kv_n_heads"):
self.gguf_writer.add_head_count_kv(kv_n_heads)
self.gguf_writer.add_layer_norm_eps(1e-5)
if self.hparams["attn_config"]["clip_qkv"] is not None:
self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"])
if self.hparams["attn_config"]["alibi"]:
self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
else:
self.gguf_writer.add_max_alibi_bias(0.0)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
if "scales" in name:
new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias", ".scales"))
new_name = new_name.replace("scales", "act.scales")
else:
new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias"))
return [(new_name, data_torch)]
@Model.register("OrionForCausalLM")
class OrionModel(Model):
model_arch = gguf.MODEL_ARCH.ORION
def set_vocab(self):
self._set_vocab_sentencepiece()
def set_gguf_parameters(self):
block_count = self.hparams["num_hidden_layers"]
head_count = self.hparams["num_attention_heads"]
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
hf_repo = self.hparams.get("_name_or_path", "")
ctx_length = 0
if "max_sequence_length" in self.hparams:
ctx_length = self.hparams["max_sequence_length"]
elif "max_position_embeddings" in self.hparams:
ctx_length = self.hparams["max_position_embeddings"]
elif "model_max_length" in self.hparams:
ctx_length = self.hparams["model_max_length"]
else:
raise ValueError("gguf: can not find ctx length parameter.")
self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_source_hf_repo(hf_repo)
self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
self.gguf_writer.add_context_length(ctx_length)
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_head_count(head_count)
self.gguf_writer.add_head_count_kv(head_count_kv)
# note: config provides rms norm but it is actually layer norm
# ref: https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/276a17221ce42beb45f66fac657a41540e71f4f5/modeling_orion.py#L570-L571
self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"])
@Model.register("BaichuanForCausalLM", "BaiChuanForCausalLM")
class BaichuanModel(Model):
model_arch = gguf.MODEL_ARCH.BAICHUAN
def set_vocab(self):
self._set_vocab_sentencepiece()
def set_gguf_parameters(self):
block_count = self.hparams["num_hidden_layers"]
head_count = self.hparams["num_attention_heads"]
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
hf_repo = self.hparams.get("_name_or_path", "")
ctx_length = 0
if "max_sequence_length" in self.hparams:
ctx_length = self.hparams["max_sequence_length"]
elif "max_position_embeddings" in self.hparams:
ctx_length = self.hparams["max_position_embeddings"]
elif "model_max_length" in self.hparams:
ctx_length = self.hparams["model_max_length"]
else:
raise ValueError("gguf: can not find ctx length parameter.")
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_source_hf_repo(hf_repo)
self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
self.gguf_writer.add_context_length(ctx_length)
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_head_count(head_count)
self.gguf_writer.add_head_count_kv(head_count_kv)
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
self.gguf_writer.add_file_type(self.ftype)
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
if self.hparams["rope_scaling"].get("type") == "linear":
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
head_count = self.hparams["num_attention_heads"]
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
tensors: list[tuple[str, Tensor]] = []
if bid is not None and name == f"model.layers.{bid}.self_attn.W_pack.weight":
logger.info(f"Unpacking and permuting layer {bid}")
tensors = [
(self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid),
self._reverse_hf_permute_part(data_torch, 0, head_count, head_count)),
(self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid),
self._reverse_hf_permute_part(data_torch, 1, head_count, head_count_kv)),
(self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid),
self._reverse_hf_part(data_torch, 2)),
]
else:
tensors = [(self.map_tensor_name(name), data_torch)]
return tensors
def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
if n_kv_head is not None and n_head != n_kv_head:
n_head //= n_kv_head
return (
weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape)
)
def _reverse_hf_permute_part(
self, weights: Tensor, n_part: int, n_head: int, n_head_kv: int | None = None,
) -> Tensor:
r = weights.shape[0] // 3
return self._reverse_hf_permute(weights[r * n_part:r * n_part + r, ...], n_head, n_head_kv)
def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor:
r = weights.shape[0] // 3
return weights[r * n_part:r * n_part + r, ...]
@Model.register("XverseForCausalLM")
class XverseModel(Model):
model_arch = gguf.MODEL_ARCH.XVERSE
def set_vocab(self):
assert (self.dir_model / "tokenizer.json").is_file()
dir_model = self.dir_model
hparams = self.hparams
tokens: list[bytes] = []
toktypes: list[int] = []
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(dir_model)
vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
assert max(tokenizer.vocab.values()) < vocab_size
reverse_vocab: dict[int, str] = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
added_vocab = tokenizer.get_added_vocab()
for token_id in range(vocab_size):
token_text = reverse_vocab[token_id].encode('utf-8')
# replace "\x00" to string with length > 0
if token_text == b"\x00":
toktype = gguf.TokenType.BYTE # special
token_text = f"<{token_text}>".encode('utf-8')
elif re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
toktype = gguf.TokenType.BYTE # special
elif reverse_vocab[token_id] in added_vocab:
if tokenizer.added_tokens_decoder[token_id].special:
toktype = gguf.TokenType.CONTROL
else:
toktype = gguf.TokenType.USER_DEFINED
else:
toktype = gguf.TokenType.NORMAL
tokens.append(token_text)
toktypes.append(toktype)
self.gguf_writer.add_tokenizer_model("llama")
self.gguf_writer.add_tokenizer_pre("default")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
def set_gguf_parameters(self):
block_count = self.hparams["num_hidden_layers"]
head_count = self.hparams["num_attention_heads"]
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
hf_repo = self.hparams.get("_name_or_path", "")
ctx_length = 0
if "max_sequence_length" in self.hparams: