#!/usr/bin/env python3 # -*- coding: utf-8 -*- from __future__ import annotations import ast 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, Literal, Sequence, TypeVar, cast from itertools import chain 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 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: gguf.LlamaFileType fname_out: Path 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 gguf_writer: gguf.GGUFWriter model_name: str | None metadata_override: Path | None dir_model_card: Path # 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 = False, use_temp_file: bool = False, eager: bool = False, metadata_override: Path | None = None, model_name: str | None = None, split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False, hparams: dict[str, Any] | None = None): 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.fname_out = fname_out 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, "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, "pytorch_model", ".bin") self.hparams = Model.load_hparams(self.dir_model) if hparams is None else hparams self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"]) self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) self.tensor_names = None self.metadata_override = metadata_override self.model_name = model_name self.dir_model_card = dir_model # overridden in convert_lora_to_gguf.py # Apply heuristics to figure out typical tensor encoding based on first layer tensor encoding type 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 # Configure GGUF Writer self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file, split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard) @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() index_name = "model.safetensors" if self.is_safetensors else "pytorch_model.bin" index_name += ".index.json" index_file = self.dir_model / index_name if index_file.is_file(): self.tensor_names = set() logger.info(f"gguf: loading model weight map from '{index_name}'") with open(index_file, "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 weight_map = {} 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(): if self.is_safetensors: if self.lazy: data = model_part.get_slice(name) data = LazyTorchTensor.from_safetensors_slice(data) else: data = model_part.get_tensor(name) else: data = model_part[name] if self.lazy: data = LazyTorchTensor.from_eager(data) yield name, data # verify tensor name presence and identify potentially missing files if len(tensor_names_from_parts.symmetric_difference(self.tensor_names)) > 0: missing = sorted(self.tensor_names.difference(tensor_names_from_parts)) extra = sorted(tensor_names_from_parts.difference(self.tensor_names)) missing_files = sorted(set(weight_map[n] for n in missing if n in weight_map)) if len(extra) == 0 and len(missing_files) > 0: raise ValueError(f"Missing or incomplete model files: {missing_files}") else: raise ValueError("Mismatch between weight map and model parts for tensor names:\n" f"Missing tensors: {missing}\n" f"Extra tensors: {extra}") 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_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}") if (head_dim := self.hparams.get("head_dim")) is not None: self.gguf_writer.add_key_length(head_dim) self.gguf_writer.add_value_length(head_dim) 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 tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool: del name, new_name, bid, n_dims # unused return False # some models need extra generated tensors (like rope_freqs) def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: return () def prepare_tensors(self): max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,") for name, data_torch in chain(self.generate_extra_tensors(), 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_torch in (self.modify_tensors(data_torch, name, bid)): data = data_torch.squeeze().numpy() # if data ends up empty, it means data_torch was a scalar tensor -> restore if len(data.shape) == 0: data = data_torch.numpy() n_dims = len(data.shape) data_qtype: gguf.GGMLQuantizationType | bool = self.tensor_force_quant(name, new_name, bid, n_dims) # Most of the codebase that takes in 1D tensors or norms only handles F32 tensors if n_dims <= 1 or new_name.endswith("_norm.weight"): data_qtype = gguf.GGMLQuantizationType.F32 # Conditions should closely match those in llama_model_quantize_internal in llama.cpp # Some tensor types are always in float32 if data_qtype is False and ( 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, gguf.MODEL_TENSOR.SSM_CONV1D, gguf.MODEL_TENSOR.TIME_MIX_FIRST, gguf.MODEL_TENSOR.TIME_MIX_W1, gguf.MODEL_TENSOR.TIME_MIX_W2, gguf.MODEL_TENSOR.TIME_MIX_DECAY_W1, gguf.MODEL_TENSOR.TIME_MIX_DECAY_W2, ) ) or not new_name.endswith(".weight") ): data_qtype = gguf.GGMLQuantizationType.F32 if data_qtype is False and any( self.match_model_tensor_name(new_name, key, bid) for key in ( gguf.MODEL_TENSOR.TOKEN_EMBD, gguf.MODEL_TENSOR.OUTPUT, ) ): if self.ftype in ( gguf.LlamaFileType.MOSTLY_TQ1_0, gguf.LlamaFileType.MOSTLY_TQ2_0, ): # TODO: use Q4_K and Q6_K data_qtype = gguf.GGMLQuantizationType.F16 # No override (data_qtype is False), or wants to be quantized (data_qtype is True) if isinstance(data_qtype, bool): if self.ftype == gguf.LlamaFileType.ALL_F32: data_qtype = gguf.GGMLQuantizationType.F32 elif self.ftype == gguf.LlamaFileType.MOSTLY_F16: data_qtype = gguf.GGMLQuantizationType.F16 elif self.ftype == gguf.LlamaFileType.MOSTLY_BF16: data_qtype = gguf.GGMLQuantizationType.BF16 elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0: data_qtype = gguf.GGMLQuantizationType.Q8_0 elif self.ftype == gguf.LlamaFileType.MOSTLY_TQ1_0: data_qtype = gguf.GGMLQuantizationType.TQ1_0 elif self.ftype == gguf.LlamaFileType.MOSTLY_TQ2_0: data_qtype = gguf.GGMLQuantizationType.TQ2_0 else: raise ValueError(f"Unknown file type: {self.ftype.name}") try: data = gguf.quants.quantize(data, data_qtype) except gguf.QuantError as e: logger.warning("%s, %s", e, "falling back to F16") data_qtype = gguf.GGMLQuantizationType.F16 data = gguf.quants.quantize(data, data_qtype) shape = gguf.quant_shape_from_byte_shape(data.shape, data_qtype) if data.dtype == np.uint8 else data.shape # reverse shape to make it similar to the internal ggml dimension order shape_str = f"{{{', '.join(str(n) for n in reversed(shape))}}}" # 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 set_type(self): self.gguf_writer.add_type(gguf.GGUFType.MODEL) def prepare_metadata(self, vocab_only: bool): total_params, shared_params, expert_params, expert_count = self.gguf_writer.get_total_parameter_count() self.metadata = gguf.Metadata.load(self.metadata_override, self.dir_model_card, self.model_name, total_params) # Fallback to model directory name if metadata name is still missing if self.metadata.name is None: self.metadata.name = self.dir_model.name # Generate parameter weight class (useful for leader boards) if not yet determined if self.metadata.size_label is None and total_params > 0: self.metadata.size_label = gguf.size_label(total_params, shared_params, expert_params, expert_count) # Extract the encoding scheme from the file type name. e.g. 'gguf.LlamaFileType.MOSTLY_Q8_0' --> 'Q8_0' output_type: str = self.ftype.name.partition("_")[2] # Filename Output if self.fname_out.is_dir(): # Generate default filename based on model specification and available metadata if not vocab_only: fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, self.metadata.size_label, output_type, model_type="LoRA" if total_params < 0 else None) else: fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, size_label=None, output_type=None, model_type="vocab") # Use the default filename self.fname_out = self.fname_out / f"{fname_default}.gguf" else: # Output path is a custom defined templated filename # Note: `not is_dir()` is used because `.is_file()` will not detect # file template strings as it doesn't actually exist as a file # Process templated file name with the output ftype, useful with the "auto" ftype self.fname_out = self.fname_out.parent / gguf.fill_templated_filename(self.fname_out.name, output_type) self.set_type() logger.info("Set meta model") self.metadata.set_gguf_meta_model(self.gguf_writer) logger.info("Set model parameters") self.set_gguf_parameters() logger.info("Set model tokenizer") self.set_vocab() logger.info("Set model quantization version") self.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION) def write(self): self.prepare_tensors() self.prepare_metadata(vocab_only=False) self.gguf_writer.write_header_to_file(path=self.fname_out) 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): if len(self.gguf_writer.tensors) != 1: raise ValueError('Splitting the vocabulary is not supported') self.prepare_metadata(vocab_only=True) self.gguf_writer.write_header_to_file(path=self.fname_out) self.gguf_writer.write_kv_data_to_file() self.gguf_writer.close() @staticmethod def get_model_part_names(dir_model: Path, prefix: str, suffix: str) -> list[str]: part_names: list[str] = [] for filename in os.listdir(dir_model): if filename.startswith(prefix) and 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 def does_token_look_special(self, token: str | bytes) -> bool: if isinstance(token, (bytes, bytearray)): token_text = token.decode(encoding="utf-8") elif isinstance(token, memoryview): token_text = token.tobytes().decode(encoding="utf-8") else: token_text = token # Some models mark some added tokens which ought to be control tokens as not special. # (e.g. command-r, command-r-plus, deepseek-coder, gemma{,-2}) seems_special = token_text in ( "", # deepseek-coder "", "<2mass>", "[@BOS@]", # gemma{,-2} ) seems_special = seems_special or (token_text.startswith("<|") and token_text.endswith("|>")) seems_special = seems_special or (token_text.startswith("<｜") and token_text.endswith("｜>")) # deepseek-coder # TODO: should these be marked as UNUSED instead? (maybe not) seems_special = seems_special or (token_text.startswith("")) # gemma{,-2} return seems_special # 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.UNUSED) else: token: str = reverse_vocab[i] if token in added_vocab: if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token): toktypes.append(gguf.TokenType.CONTROL) else: token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces toktypes.append(gguf.TokenType.USER_DEFINED) else: toktypes.append(gguf.TokenType.NORMAL) tokens.append(token) 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 == "8e62295832751ca1e8f92f2226f403dea30dc5165e448b5bfa05af5340c64ec7": # ref: https://huggingface.co/BAAI/bge-large-zh-v1.5 res = "bert-bge-large" 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 == "c7699093ba4255a91e702aa38a596aa81669f3525dae06c2953267dde580f448": # ref: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en res = "jina-v1-en" 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 chkhsh == "c136ed14d01c2745d4f60a9596ae66800e2b61fa45643e72436041855ad4089d": # ref: https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct res = "smaug-bpe" if chkhsh == "c7ea5862a53e4272c035c8238367063e2b270d51faa48c0f09e9d5b54746c360": # ref: https://huggingface.co/LumiOpen/Poro-34B-chat res = "poro-chat" if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a": # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code res = "jina-v2-code" if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b": # ref: https://huggingface.co/THUDM/glm-4-9b-chat res = "chatglm-bpe" if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee": # ref: https://huggingface.co/LumiOpen/Viking-7B res = "viking" if chkhsh == "b53802fb28e26d645c3a310b34bfe07da813026ec7c7716883404d5e0f8b1901": # ref: https://huggingface.co/core42/jais-13b res = "jais" if chkhsh == "7b3e7548e4308f52a76e8229e4e6cc831195d0d1df43aed21ac6c93da05fec5f": # ref: https://huggingface.co/WisdomShell/CodeShell-7B res = "codeshell" if chkhsh == "63b97e4253352e6f357cc59ea5b583e3a680eaeaf2632188c2b952de2588485e": # ref: https://huggingface.co/mistralai/Mistral-Nemo-Base-2407 res = "tekken" if chkhsh == "855059429035d75a914d1eda9f10a876752e281a054a7a3d421ef0533e5b6249": # ref: https://huggingface.co/HuggingFaceTB/SmolLM-135M res = "smollm" if chkhsh == "3c30d3ad1d6b64202cd222813e7736c2db6e1bd6d67197090fc1211fbc612ae7": # ref: https://huggingface.co/bigscience/bloom res = "bloom" if chkhsh == "bc01ce58980e1db43859146dc51b1758b3b88729b217a74792e9f8d43e479d21": # ref: https://huggingface.co/TurkuNLP/gpt3-finnish-small res = "gpt3-finnish" if chkhsh == "4e2b24cc4770243d65a2c9ec19770a72f08cffc161adbb73fcbb6b7dd45a0aae": # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct res = "exaone" if chkhsh == "fcace8b9cac38ce847670c970cd5892031a753a1ef381abd1d9af00f713da085": # ref: https://huggingface.co/microsoft/phi-2 res = "phi-2" if chkhsh == "60824e3c0d9401f89943cbb2fff727f0e2d4c545ba4df2d6e4f09a6db0f5b450": # ref: https://huggingface.co/facebook/chameleon-7b res = "chameleon" 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.UNUSED) 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, add_to_gguf=True): tokens, scores, toktypes = self._create_vocab_sentencepiece() 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 _create_vocab_sentencepiece(self): from sentencepiece import SentencePieceProcessor tokenizer_path = self.dir_model / 'tokenizer.model' 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.UNUSED] * 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 tokenizer_config_file = self.dir_model / 'tokenizer_config.json' if tokenizer_config_file.is_file(): with open(tokenizer_config_file, "r", encoding="utf-8") as f: tokenizer_config_json = json.load(f) added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {}) for token_id, token_data in added_tokens_decoder.items(): token_id = int(token_id) token: str = token_data["content"] if toktypes[token_id] != SentencePieceTokenTypes.UNUSED: if tokens[token_id] != token.encode("utf-8"): logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token!r}') if token_data.get("special") or self.does_token_look_special(token): toktypes[token_id] = SentencePieceTokenTypes.CONTROL else: token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED scores[token_id] = -1000.0 tokens[token_id] = token.encode("utf-8") 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) return tokens, scores, toktypes def _set_vocab_llama_hf(self): vocab = gguf.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) def _set_vocab_builtin(self, model_name: Literal["gpt-neox", "llama-spm"], vocab_size: int): tokenizer_path = Path(sys.path[0]) / "models" / f"ggml-vocab-{model_name}.gguf" logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'") vocab_reader = gguf.GGUFReader(tokenizer_path, "r") default_pre = "mpt" if model_name == "gpt-neox" else "default" field = vocab_reader.get_field(gguf.Keys.Tokenizer.MODEL) assert field # tokenizer model self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8")) field = vocab_reader.get_field(gguf.Keys.Tokenizer.PRE) self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else default_pre) field = vocab_reader.get_field(gguf.Keys.Tokenizer.LIST) assert field # token list self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size]) if model_name == "llama-spm": field = vocab_reader.get_field(gguf.Keys.Tokenizer.SCORES) assert field # token scores self.gguf_writer.add_token_scores([field.parts[i].tolist()[0] for i in field.data][:vocab_size]) field = vocab_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE) assert field # token types self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size]) if model_name != "llama-spm": field = vocab_reader.get_field(gguf.Keys.Tokenizer.MERGES) assert field # token merges self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data]) if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)) is not None: self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0]) if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)) is not None: self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0]) if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)) is not None: self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0]) if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.PAD_ID)) is not None: self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0]) if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_BOS)) is not None: self.gguf_writer.add_add_bos_token(field.parts[-1].tolist()[0]) if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_EOS)) is not None: self.gguf_writer.add_add_eos_token(field.parts[-1].tolist()[0]) @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_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", "BloomModel") class BloomModel(Model): model_arch = gguf.MODEL_ARCH.BLOOM def set_gguf_parameters(self): 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_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) 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_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) 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_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)) # Since we are checking the maximum index, we need to ensure it's strictly less than vocab_size, # because vocab_size is the count of items, and indexes start at 0. max_vocab_index = max(tokenizer.get_vocab().values()) if max_vocab_index >= vocab_size: raise ValueError("Vocabulary size exceeds expected maximum 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) 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_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]]: del bid # unused head_count = self.hparams["num_attention_heads"] head_count_kv = self.hparams.get("num_key_value_heads", head_count) # HF models permute some of the tensors, so we need to undo that if name.endswith("q_proj.weight"): data_torch = self._reverse_hf_permute(data_torch, head_count, head_count) if name.endswith("k_proj.weight"): data_torch = self._reverse_hf_permute(data_torch, head_count, head_count_kv) return [(self.map_tensor_name(name), data_torch)] 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) ) @Model.register("FalconForCausalLM", "RWForCausalLM") class FalconModel(Model): model_arch = gguf.MODEL_ARCH.FALCON def set_gguf_parameters(self): block_count = self.hparams.get("num_hidden_layers") if block_count is None: block_count = self.hparams["n_layer"] # old name n_head = self.hparams.get("num_attention_heads") if n_head is None: n_head = self.hparams["n_head"] # old name n_head_kv = self.hparams.get("num_kv_heads") if n_head_kv is None: n_head_kv = self.hparams.get("n_head_kv", 1) # old name self.gguf_writer.add_context_length(2048) # not in config.json self.gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) self.gguf_writer.add_feed_forward_length(4 * self.hparams["hidden_size"]) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_head_count(n_head) self.gguf_writer.add_head_count_kv(n_head_kv) 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 # QKV tensor transform # The original query_key_value tensor contains n_head_kv "kv groups", # each consisting of n_head/n_head_kv query weights followed by one key # and one value weight (shared by all query heads in the kv group). # This layout makes it a big pain to work with in GGML. # So we rearrange them here,, so that we have n_head query weights # followed by n_head_kv key weights followed by n_head_kv value weights, # in contiguous fashion. # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py if "query_key_value" in name: n_head = self.find_hparam(["num_attention_heads", "n_head"]) n_head_kv = self.find_hparam(["num_kv_heads", "n_head_kv"], optional=True) or 1 head_dim = self.hparams["hidden_size"] // n_head qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head) q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head) k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head) v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head) data_torch = torch.cat((q, k, v)).reshape_as(data_torch) return [(self.map_tensor_name(name), data_torch)] @Model.register("GPTBigCodeForCausalLM") class StarCoderModel(Model): model_arch = gguf.MODEL_ARCH.STARCODER def set_gguf_parameters(self): block_count = self.hparams["n_layer"] self.gguf_writer.add_context_length(self.hparams["n_positions"]) self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_head_count(self.hparams["n_head"]) self.gguf_writer.add_head_count_kv(1) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_file_type(self.ftype) @Model.register("GPTRefactForCausalLM") class RefactModel(Model): model_arch = gguf.MODEL_ARCH.REFACT def set_vocab(self): super().set_vocab() # TODO: how to determine special FIM tokens automatically? special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False, special_token_types = ['prefix', 'suffix', 'middle', 'eot']) special_vocab._set_special_token("prefix", 1) special_vocab._set_special_token("suffix", 3) special_vocab._set_special_token("middle", 2) special_vocab.chat_template = None # do not add it twice special_vocab.add_to_gguf(self.gguf_writer) def set_gguf_parameters(self): hidden_dim = self.hparams["n_embd"] inner_dim = 4 * hidden_dim hidden_dim = int(2 * inner_dim / 3) multiple_of = 256 ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) block_count = self.hparams["n_layer"] # refact uses Alibi. So this is from config.json which might be used by training. self.gguf_writer.add_context_length(self.hparams["n_positions"]) self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) self.gguf_writer.add_feed_forward_length(ff_dim) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_head_count(self.hparams["n_head"]) self.gguf_writer.add_head_count_kv(1) self.gguf_writer.add_layer_norm_rms_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]]: hidden_dim = self.hparams["n_embd"] inner_dim = 4 * hidden_dim hidden_dim = int(2 * inner_dim / 3) multiple_of = 256 ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) n_head = self.hparams["n_head"] n_head_kv = 1 head_dim = self.hparams["n_embd"] // n_head tensors: list[tuple[str, Tensor]] = [] if bid is not None: if name == f"transformer.h.{bid}.attn.kv.weight": tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), data_torch[:n_head_kv * head_dim])) tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), data_torch[n_head_kv * head_dim:])) elif name == f"transformer.h.{bid}.attn.q.weight": tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), data_torch)) elif name == f"transformer.h.{bid}.mlp.gate_up_proj.weight": tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim])) tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:])) if len(tensors) == 0: tensors.append((self.map_tensor_name(name), data_torch)) return tensors @Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM") class StableLMModel(Model): model_arch = gguf.MODEL_ARCH.STABLELM def set_vocab(self): if (self.dir_model / "tokenizer.json").is_file(): self._set_vocab_gpt2() else: # StableLM 2 1.6B used to have a vocab in a similar format to Qwen's vocab self._set_vocab_qwen() def set_gguf_parameters(self): hparams = self.hparams block_count = hparams["num_hidden_layers"] self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) self.gguf_writer.add_embedding_length(hparams["hidden_size"]) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"]) self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"]))) self.gguf_writer.add_head_count(hparams["num_attention_heads"]) self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"]) self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True) self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"])) self.gguf_writer.add_file_type(self.ftype) _q_norms: list[dict[str, Tensor]] | None = None _k_norms: list[dict[str, Tensor]] | None = None def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: n_head = self.hparams["num_attention_heads"] n_kv_head = self.hparams["num_key_value_heads"] if name.find("q_layernorm.norms") != -1: assert bid is not None if self._q_norms is None: self._q_norms = [{} for _ in range(self.block_count)] self._q_norms[bid][name] = data_torch if len(self._q_norms[bid]) >= n_head: return self._stack_qk_norm(bid, n_head, self._q_norms[bid], "q_layernorm") else: return [] if name.find("k_layernorm.norms") != -1: assert bid is not None if self._k_norms is None: self._k_norms = [{} for _ in range(self.block_count)] self._k_norms[bid][name] = data_torch if len(self._k_norms[bid]) >= n_kv_head: return self._stack_qk_norm(bid, n_kv_head, self._k_norms[bid], "k_layernorm") else: return [] return [(self.map_tensor_name(name), data_torch)] def _stack_qk_norm(self, bid: int, n_head: int, norms: dict[str, Tensor], layer_name: str = "q_layernorm"): datas: list[Tensor] = [] # extract the norms in order for xid in range(n_head): ename = f"model.layers.{bid}.self_attn.{layer_name}.norms.{xid}.weight" datas.append(norms[ename]) del norms[ename] data_torch = torch.stack(datas, dim=0) merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight" new_name = self.map_tensor_name(merged_name) return [(new_name, data_torch)] def prepare_tensors(self): super().prepare_tensors() if self._q_norms is not None or self._k_norms is not None: # flatten two `list[dict[str, Tensor]]` into a single `list[str]` norms = ( [k for d in self._q_norms for k in d.keys()] if self._q_norms is not None else [] ) + ( [k for d in self._k_norms for k in d.keys()] if self._k_norms is not None else [] ) if len(norms) > 0: raise ValueError(f"Unprocessed norms: {norms}") @Model.register("LLaMAForCausalLM", "LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM") class LlamaModel(Model): model_arch = gguf.MODEL_ARCH.LLAMA def set_vocab(self): try: self._set_vocab_sentencepiece() except FileNotFoundError: try: self._set_vocab_llama_hf() except (FileNotFoundError, TypeError): # Llama 3 self._set_vocab_gpt2() # Apply to CodeLlama only (and ignore for Llama 3 with a vocab size of 128256) if self.hparams.get("vocab_size", 32000) == 32016: special_vocab = gguf.SpecialVocab( self.dir_model, load_merges=False, special_token_types = ['prefix', 'suffix', 'middle', 'eot'] ) special_vocab._set_special_token("prefix", 32007) special_vocab._set_special_token("suffix", 32008) special_vocab._set_special_token("middle", 32009) special_vocab._set_special_token("eot", 32010) special_vocab.add_to_gguf(self.gguf_writer) tokenizer_config_file = self.dir_model / 'tokenizer_config.json' if tokenizer_config_file.is_file(): with open(tokenizer_config_file, "r", encoding="utf-8") as f: tokenizer_config_json = json.load(f) if "add_prefix_space" in tokenizer_config_json: self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"]) # Apply to granite small models only if self.hparams.get("vocab_size", 32000) == 49152: self.gguf_writer.add_add_bos_token(False) def set_gguf_parameters(self): super().set_gguf_parameters() hparams = self.hparams self.gguf_writer.add_vocab_size(hparams["vocab_size"]) if "head_dim" in hparams: rope_dim = hparams["head_dim"] else: rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] self.gguf_writer.add_rope_dimension_count(rope_dim) 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"]) @staticmethod def permute(weights: Tensor, n_head: int, n_head_kv: int | None): if n_head_kv is not None and n_head != n_head_kv: n_head = n_head_kv return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) .swapaxes(1, 2) .reshape(weights.shape)) _experts: list[dict[str, Tensor]] | None = None def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: n_head = self.hparams["num_attention_heads"] n_kv_head = self.hparams.get("num_key_value_heads") if name.endswith(("q_proj.weight", "q_proj.bias")): data_torch = LlamaModel.permute(data_torch, n_head, n_head) if name.endswith(("k_proj.weight", "k_proj.bias")): data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) # process the experts separately if name.find("block_sparse_moe.experts") != -1: n_experts = self.hparams["num_local_experts"] assert bid is not None if self._experts is None: self._experts = [{} for _ in range(self.block_count)] self._experts[bid][name] = data_torch if len(self._experts[bid]) >= n_experts * 3: tensors: list[tuple[str, Tensor]] = [] # merge the experts into a single 3d tensor for wid in ["w1", "w2", "w3"]: datas: list[Tensor] = [] for xid in range(n_experts): ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight" datas.append(self._experts[bid][ename]) del self._experts[bid][ename] data_torch = torch.stack(datas, dim=0) merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight" new_name = self.map_tensor_name(merged_name) tensors.append((new_name, data_torch)) return tensors else: return [] return [(self.map_tensor_name(name), data_torch)] def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): if rope_scaling.get("rope_type", '').lower() == "llama3": base = self.hparams.get("rope_theta", 10000.0) dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) factor = rope_scaling.get("factor", 8.0) low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) old_context_len = self.hparams.get("original_max_position_embeddings", 8192) low_freq_wavelen = old_context_len / low_freq_factor high_freq_wavelen = old_context_len / high_freq_factor assert low_freq_wavelen != high_freq_wavelen rope_factors = [] for freq in freqs: wavelen = 2 * math.pi / freq if wavelen < high_freq_wavelen: rope_factors.append(1) elif wavelen > low_freq_wavelen: rope_factors.append(factor) else: smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor) rope_factors.append(1 / ((1 - smooth) / factor + smooth)) yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32)) def prepare_tensors(self): super().prepare_tensors() if self._experts is not None: # flatten `list[dict[str, Tensor]]` into `list[str]` experts = [k for d in self._experts for k in d.keys()] if len(experts) > 0: raise ValueError(f"Unprocessed experts: {experts}") @Model.register("BitnetForCausalLM") class BitnetModel(Model): model_arch = gguf.MODEL_ARCH.BITNET def set_vocab(self): self._set_vocab_sentencepiece() def set_gguf_parameters(self): super().set_gguf_parameters() self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) self.gguf_writer.add_rope_scaling_factor(1.0) def weight_quant(self, weight: Tensor) -> Tensor: dtype = weight.dtype weight = weight.float() scale = weight.abs().mean().clamp(min=1e-5) iscale = 1 / scale # TODO: multiply by the scale directly instead of inverting it twice # (this is also unnecessarily doubly inverted upstream) # ref: https://huggingface.co/1bitLLM/bitnet_b1_58-3B/blob/af89e318d78a70802061246bf037199d2fb97020/utils_quant.py#L10 result = (weight * iscale).round().clamp(-1, 1) / iscale return result.type(dtype) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: new_name = self.map_tensor_name(name) if any(self.match_model_tensor_name(new_name, key, bid) for key in [ gguf.MODEL_TENSOR.ATTN_Q, gguf.MODEL_TENSOR.ATTN_K, gguf.MODEL_TENSOR.ATTN_V, gguf.MODEL_TENSOR.ATTN_OUT, gguf.MODEL_TENSOR.FFN_UP, gguf.MODEL_TENSOR.FFN_DOWN, gguf.MODEL_TENSOR.FFN_GATE, ]): # transform weight into 1/0/-1 (in fp32) data_torch = self.weight_quant(data_torch) yield (new_name, data_torch) @Model.register("GrokForCausalLM") class GrokModel(Model): model_arch = gguf.MODEL_ARCH.GROK def set_vocab(self): self._set_vocab_sentencepiece() def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def set_gguf_parameters(self): super().set_gguf_parameters() _experts: list[dict[str, Tensor]] | None = None def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: # process the experts separately if name.find(".moe.") != -1: n_experts = self.hparams["num_local_experts"] assert bid is not None if self._experts is None: self._experts = [{} for _ in range(self.block_count)] self._experts[bid][name] = data_torch if len(self._experts[bid]) >= n_experts * 3: tensors: list[tuple[str, Tensor]] = [] # merge the experts into a single 3d tensor for wid in ["linear", "linear_1", "linear_v"]: datas: list[Tensor] = [] for xid in range(n_experts): ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight" datas.append(self._experts[bid][ename]) del self._experts[bid][ename] data_torch = torch.stack(datas, dim=0) merged_name = f"transformer.decoder_layer.{bid}.moe.{wid}.weight" new_name = self.map_tensor_name(merged_name) tensors.append((new_name, data_torch)) return tensors else: return [] return [(self.map_tensor_name(name), data_torch)] @Model.register("DbrxForCausalLM") class DbrxModel(Model): model_arch = gguf.MODEL_ARCH.DBRX def set_gguf_parameters(self): ffn_config = self.hparams["ffn_config"] attn_config = self.hparams["attn_config"] self.gguf_writer.add_block_count(self.hparams["n_layers"]) 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_feed_forward_length(ffn_config["ffn_hidden_size"]) self.gguf_writer.add_head_count(self.hparams["n_heads"]) self.gguf_writer.add_head_count_kv(attn_config["kv_n_heads"]) self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"]) self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"]) self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"]) self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"]) self.gguf_writer.add_layer_norm_eps(1e-5) 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 n_expert = self.hparams["ffn_config"]["moe_num_experts"] n_ff = self.hparams["ffn_config"]["ffn_hidden_size"] n_embd = self.hparams["d_model"] # Specific behavior for experts tensors: suffix .weight, view as 3D and transpose # original implementation expects (n_expert, n_ff, n_embd) for all experts weights # But llama.cpp moe graph works differently # AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions # so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor exp_tensor_names = {"ffn.experts.mlp.w1": None, # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert} "ffn.experts.mlp.w2": (0, 2, 1), # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff, n_embd, n_expert} "ffn.experts.mlp.v1": None} # LLM_TENSOR_FFN_UP_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert} experts = False for exp_tensor_name in exp_tensor_names.keys(): if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1: experts = True data_torch = data_torch.view(n_expert, n_ff, n_embd) if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None: data_torch = data_torch.permute(*permute_tensor) break # map tensor names # In MoE models the ffn tensors are typically most of the model weights, # and need to be quantizable. Quantize expects tensor names to be suffixed by .weight. # Every other model has the weight names ending in .weight, # let's assume that is the convention which is not the case for dbrx: # https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15 new_name = self.map_tensor_name(name if not experts else name + ".weight", try_suffixes=(".weight",)) return [(new_name, data_torch)] def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool: del name, new_name, bid # unused return n_dims > 1 @Model.register("MiniCPMForCausalLM") class MiniCPMModel(Model): model_arch = gguf.MODEL_ARCH.MINICPM def set_gguf_parameters(self): block_count = self.hparams["num_hidden_layers"] 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(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) self.gguf_writer.add_file_type(self.ftype) def set_vocab(self): self._set_vocab_llama_hf() 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 modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused n_head = self.hparams["num_attention_heads"] n_kv_head = self.hparams.get("num_key_value_heads") # HF models permute some of the tensors, so we need to undo that if name.endswith(("q_proj.weight")): data_torch = self._reverse_hf_permute(data_torch, n_head, n_head) if name.endswith(("k_proj.weight")): data_torch = self._reverse_hf_permute(data_torch, n_head, n_kv_head) return [(self.map_tensor_name(name), data_torch)] @Model.register("MiniCPM3ForCausalLM") class MiniCPM3Model(Model): model_arch = gguf.MODEL_ARCH.MINICPM3 def set_gguf_parameters(self): hparams = self.hparams self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) self.gguf_writer.add_embedding_length(hparams["hidden_size"]) self.gguf_writer.add_block_count(self.block_count) self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) self.gguf_writer.add_head_count(hparams["num_attention_heads"]) self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"]) self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) self.gguf_writer.add_vocab_size(hparams["vocab_size"]) if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None: self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"]) self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"]) self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"]) self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"]) def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: rope_scaling = self.find_hparam(['rope_scaling'], True) if rope_scaling is not None: rope_dims = self.hparams["qk_rope_head_dim"] long_factors = rope_scaling.get('long_factor', None) short_factors = rope_scaling.get('short_factor', None) if long_factors is None or short_factors is None: raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor') if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2: raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}') yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32)) yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32)) def set_vocab(self): self._set_vocab_sentencepiece() 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) ) @Model.register("QWenLMHeadModel") class QwenModel(Model): model_arch = gguf.MODEL_ARCH.QWEN @staticmethod def token_bytes_to_string(b): from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode byte_encoder = bytes_to_unicode() return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')]) @staticmethod def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]: parts = [bytes([b]) for b in token] while True: min_idx = None min_rank = None for i, pair in enumerate(zip(parts[:-1], parts[1:])): rank = mergeable_ranks.get(pair[0] + pair[1]) if rank is not None and (min_rank is None or rank < min_rank): min_idx = i min_rank = rank if min_rank is None or (max_rank is not None and min_rank >= max_rank): break assert min_idx is not None parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:] return parts def set_vocab(self): self._set_vocab_qwen() def set_gguf_parameters(self): self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"]) self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_file_type(self.ftype) @Model.register("Qwen2ForCausalLM") class Qwen2Model(Model): model_arch = gguf.MODEL_ARCH.QWEN2 def set_vocab(self): try: self._set_vocab_sentencepiece() except FileNotFoundError: self._set_vocab_gpt2() @Model.register("Qwen2MoeForCausalLM") class Qwen2MoeModel(Model): model_arch = gguf.MODEL_ARCH.QWEN2MOE def set_gguf_parameters(self): super().set_gguf_parameters() if (n_experts := self.hparams.get("num_experts")) is not None: self.gguf_writer.add_expert_count(n_experts) if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None: self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size) logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}") if (shared_expert_intermediate_size := self.hparams.get('shared_expert_intermediate_size')) is not None: self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size) logger.info(f"gguf: expert shared feed forward length = {shared_expert_intermediate_size}") _experts: list[dict[str, Tensor]] | None = None def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: # process the experts separately if name.find("experts") != -1: n_experts = self.hparams["num_experts"] assert bid is not None if self._experts is None: self._experts = [{} for _ in range(self.block_count)] self._experts[bid][name] = data_torch if len(self._experts[bid]) >= n_experts * 3: tensors: list[tuple[str, Tensor]] = [] # merge the experts into a single 3d tensor for w_name in ["down_proj", "gate_proj", "up_proj"]: datas: list[Tensor] = [] for xid in range(n_experts): ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" datas.append(self._experts[bid][ename]) del self._experts[bid][ename] data_torch = torch.stack(datas, dim=0) merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" new_name = self.map_tensor_name(merged_name) tensors.append((new_name, data_torch)) return tensors else: return [] return [(self.map_tensor_name(name), data_torch)] def prepare_tensors(self): super().prepare_tensors() if self._experts is not None: # flatten `list[dict[str, Tensor]]` into `list[str]` experts = [k for d in self._experts for k in d.keys()] if len(experts) > 0: raise ValueError(f"Unprocessed experts: {experts}") @Model.register("GPT2LMHeadModel") class GPT2Model(Model): model_arch = gguf.MODEL_ARCH.GPT2 def set_gguf_parameters(self): self.gguf_writer.add_block_count(self.hparams["n_layer"]) self.gguf_writer.add_context_length(self.hparams["n_ctx"]) self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) self.gguf_writer.add_head_count(self.hparams["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 tensors: list[tuple[str, Tensor]] = [] # we don't need these if name.endswith((".attn.bias", ".attn.masked_bias")): return tensors if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")): data_torch = data_torch.transpose(1, 0) new_name = self.map_tensor_name(name) tensors.append((new_name, data_torch)) # note: GPT2 output is tied to (same as) wte in original model if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD): tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch)) return tensors @Model.register("PhiForCausalLM") class Phi2Model(Model): model_arch = gguf.MODEL_ARCH.PHI2 def set_gguf_parameters(self): block_count = self.find_hparam(["num_hidden_layers", "n_layer"]) rot_pct = self.find_hparam(["partial_rotary_factor"]) n_embd = self.find_hparam(["hidden_size", "n_embd"]) n_head = self.find_hparam(["num_attention_heads", "n_head"]) self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"])) self.gguf_writer.add_embedding_length(n_embd) self.gguf_writer.add_feed_forward_length(4 * n_embd) self.gguf_writer.add_block_count(block_count) 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.find_hparam(["layer_norm_epsilon", "layer_norm_eps"])) self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head) self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_add_bos_token(False) @Model.register("Phi3ForCausalLM") class Phi3MiniModel(Model): model_arch = gguf.MODEL_ARCH.PHI3 def set_vocab(self): from sentencepiece import SentencePieceProcessor tokenizer_path = self.dir_model / 'tokenizer.model' if not tokenizer_path.is_file(): raise ValueError(f'Error: Missing {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.UNUSED] * 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.debug(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 tokenizer_config_file = self.dir_model / 'tokenizer_config.json' if tokenizer_config_file.is_file(): with open(tokenizer_config_file, "r", encoding="utf-8") as f: tokenizer_config_json = json.load(f) added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {}) for token_id, foken_data in added_tokens_decoder.items(): token_id = int(token_id) token = foken_data["content"].encode("utf-8") if toktypes[token_id] != SentencePieceTokenTypes.UNUSED: if tokens[token_id] != token: logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}') tokens[token_id] = token scores[token_id] = -1000.0 toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED if foken_data.get("special"): toktypes[token_id] = SentencePieceTokenTypes.CONTROL tokenizer_file = self.dir_model / 'tokenizer.json' if tokenizer_file.is_file(): with open(tokenizer_file, "r", encoding="utf-8") as f: tokenizer_json = json.load(f) added_tokens = tokenizer_json.get("added_tokens", []) for foken_data in added_tokens: token_id = int(foken_data["id"]) token = foken_data["content"].encode("utf-8") if toktypes[token_id] != SentencePieceTokenTypes.UNUSED: if tokens[token_id] != token: logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}') tokens[token_id] = token scores[token_id] = -1000.0 toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED if foken_data.get("special"): toktypes[token_id] = SentencePieceTokenTypes.CONTROL 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_gguf_parameters(self): block_count = self.find_hparam(["num_hidden_layers", "n_layer"]) n_embd = self.find_hparam(["hidden_size", "n_embd"]) n_head = self.find_hparam(["num_attention_heads", "n_head"]) n_head_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"]) rms_eps = self.find_hparam(["rms_norm_eps"]) max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"]) orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"]) rope_dims = n_embd // n_head self.gguf_writer.add_context_length(max_pos_embds) self.gguf_writer.add_rope_scaling_orig_ctx_len(orig_max_pos_embds) self.gguf_writer.add_embedding_length(n_embd) self.gguf_writer.add_feed_forward_length(self.find_hparam(["intermediate_size"])) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_head_count(n_head) self.gguf_writer.add_head_count_kv(n_head_kv) self.gguf_writer.add_layer_norm_rms_eps(rms_eps) self.gguf_writer.add_rope_dimension_count(rope_dims) self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"])) self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_sliding_window(self.find_hparam(["sliding_window"])) def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: n_embd = self.find_hparam(["hidden_size", "n_embd"]) n_head = self.find_hparam(["num_attention_heads", "n_head"]) max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"]) orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"]) rope_dims = n_embd // n_head # write rope scaling for long context (128k) model rope_scaling = self.find_hparam(['rope_scaling'], True) if rope_scaling is None: return scale = max_pos_embds / orig_max_pos_embds rope_scaling_type = rope_scaling.get('type', '').lower() if len(rope_scaling_type) == 0: raise KeyError('Missing the required key rope_scaling.type') if rope_scaling_type == 'su' or rope_scaling_type == 'longrope': attn_factor = math.sqrt(1 + math.log(scale) / math.log(orig_max_pos_embds)) if scale > 1.0 else 1.0 elif rope_scaling_type == 'yarn': attn_factor = 0.1 * math.log(scale) + 1.0 if scale > 1.0 else 1.0 else: raise NotImplementedError(f'The rope scaling type {rope_scaling_type} is not supported yet') self.gguf_writer.add_rope_scaling_attn_factors(attn_factor) long_factors = rope_scaling.get('long_factor', None) short_factors = rope_scaling.get('short_factor', None) if long_factors is None or short_factors is None: raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor') if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2: raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}') yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32)) yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32)) @Model.register("PlamoForCausalLM") class PlamoModel(Model): model_arch = gguf.MODEL_ARCH.PLAMO def set_vocab(self): self._set_vocab_sentencepiece() def set_gguf_parameters(self): hparams = self.hparams block_count = hparams["num_hidden_layers"] self.gguf_writer.add_context_length(4096) # not in config.json self.gguf_writer.add_embedding_length(hparams["hidden_size"]) self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_head_count(hparams["num_attention_heads"]) self.gguf_writer.add_head_count_kv(5) # hparams["num_key_value_heads"]) is wrong self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) self.gguf_writer.add_file_type(self.ftype) def shuffle_attn_q_weight(self, data_torch): assert data_torch.size() == (5120, 5120) data_torch = data_torch.reshape(8, 5, 128, 5120) data_torch = torch.permute(data_torch, (1, 0, 2, 3)) data_torch = torch.reshape(data_torch, (5120, 5120)) return data_torch def shuffle_attn_output_weight(self, data_torch): assert data_torch.size() == (5120, 5120) data_torch = data_torch.reshape(5120, 8, 5, 128) data_torch = torch.permute(data_torch, (0, 2, 1, 3)) data_torch = torch.reshape(data_torch, (5120, 5120)) return data_torch def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused new_name = self.map_tensor_name(name) # shuffle for broadcasting of gqa in ggml_mul_mat if new_name.endswith("attn_q.weight"): data_torch = self.shuffle_attn_q_weight(data_torch) elif new_name.endswith("attn_output.weight"): data_torch = self.shuffle_attn_output_weight(data_torch) return [(new_name, data_torch)] @Model.register("CodeShellForCausalLM") class CodeShellModel(Model): model_arch = gguf.MODEL_ARCH.CODESHELL def set_gguf_parameters(self): block_count = self.hparams["n_layer"] self.gguf_writer.add_context_length(self.hparams["n_positions"]) self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_head_count(self.hparams["n_head"]) self.gguf_writer.add_head_count_kv(self.hparams["num_query_groups"]) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_rope_freq_base(10000.0) self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) self.gguf_writer.add_rope_scaling_factor(1.0) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused new_name = self.map_tensor_name(name) tensors: list[tuple[str, Tensor]] = [(new_name, data_torch)] if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD): assert self.tensor_names is not None if all(s not in self.tensor_names for s in ("lm_head.weight", "output.weight")): # copy tok_embd.weight to output.weight tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch)) return tensors @Model.register("InternLM2ForCausalLM") class InternLM2Model(Model): model_arch = gguf.MODEL_ARCH.INTERNLM2 def set_vocab(self): # (TODO): Is there a better way? # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character # \x00 specially and convert it into an emoji character to prevent it from being mistakenly # recognized as an empty string in C++. from sentencepiece import SentencePieceProcessor from sentencepiece import sentencepiece_model_pb2 as model tokenizer_path = self.dir_model / 'tokenizer.model' tokens: list[bytes] = [] scores: list[float] = [] toktypes: list[int] = [] if not tokenizer_path.is_file(): logger.error(f'Error: Missing {tokenizer_path}') sys.exit(1) sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix tokenizer = SentencePieceProcessor() tokenizer.LoadFromFile(str(tokenizer_path)) vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) for token_id in range(vocab_size): piece = tokenizer.IdToPiece(token_id) text = piece.encode("utf-8") score = tokenizer.GetScore(token_id) if text == b"\x00": # (TODO): fixme # Hack here and replace the \x00 characters. logger.warning(f"InternLM2 convert token '{text}' to '🐉'!") text = "🐉".encode("utf-8") 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 # take care of ununsed raw token if piece.startswith('[UNUSED'): toktype = SentencePieceTokenTypes.UNUSED tokens.append(text) scores.append(score) toktypes.append(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: tokens.append(key.encode("utf-8")) scores.append(-1000.0) toktypes.append(SentencePieceTokenTypes.USER_DEFINED) chat_eos_token = '<|im_end|>' chat_eos_token_id = None tokenizer_config_file = self.dir_model / 'tokenizer_config.json' if tokenizer_config_file.is_file(): with open(tokenizer_config_file, "r", encoding="utf-8") as f: tokenizer_config_json = json.load(f) added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {}) for token_id, foken_data in added_tokens_decoder.items(): token_id = int(token_id) token = foken_data["content"] if token == chat_eos_token: chat_eos_token_id = token_id token = token.encode("utf-8") if toktypes[token_id] != SentencePieceTokenTypes.UNUSED: if tokens[token_id] != token: logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}') tokens[token_id] = token scores[token_id] = -1000.0 toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED if foken_data.get("special"): toktypes[token_id] = SentencePieceTokenTypes.CONTROL tokenizer_file = self.dir_model / 'tokenizer.json' if tokenizer_file.is_file(): with open(tokenizer_file, "r", encoding="utf-8") as f: tokenizer_json = json.load(f) added_tokens = tokenizer_json.get("added_tokens", []) for foken_data in added_tokens: token_id = int(foken_data["id"]) token = foken_data["content"] if token == chat_eos_token: chat_eos_token_id = token_id token = token.encode("utf-8") if toktypes[token_id] != SentencePieceTokenTypes.UNUSED: if tokens[token_id] != token: logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}') tokens[token_id] = token scores[token_id] = -1000.0 toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED if foken_data.get("special"): toktypes[token_id] = SentencePieceTokenTypes.CONTROL 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) self.gguf_writer.add_add_space_prefix(add_prefix) special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) old_eos = special_vocab.special_token_ids["eos"] if chat_eos_token_id is not None: # For the chat model, we replace the eos with '<|im_end|>'. # TODO: this is a hack, should be fixed # https://github.com/ggerganov/llama.cpp/pull/6745#issuecomment-2067687048 special_vocab.special_token_ids["eos"] = chat_eos_token_id logger.warning(f"Replace eos:{old_eos} with a special token:{chat_eos_token_id}" " in chat mode so that the conversation can end normally.") special_vocab.add_to_gguf(self.gguf_writer) def set_gguf_parameters(self): self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"]) self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) 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]]: num_heads = self.hparams["num_attention_heads"] num_kv_heads = self.hparams["num_key_value_heads"] n_embd = self.hparams["hidden_size"] q_per_kv = num_heads // num_kv_heads head_dim = n_embd // num_heads num_groups = num_heads // q_per_kv if bid is not None and f"model.layers.{bid}.attention.wqkv" in name: qkv = data_torch qkv = qkv.reshape((num_groups, q_per_kv + 2, head_dim, n_embd)) q, k, v = qkv[:, : q_per_kv], qkv[:, -2], qkv[:, -1] # The model weights of q and k equire additional reshape. q = LlamaModel.permute(q.reshape((-1, q.shape[-1])), num_heads, num_heads) k = LlamaModel.permute(k.reshape((-1, k.shape[-1])), num_heads, num_kv_heads) v = v.reshape((-1, v.shape[-1])) return [ (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), q), (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), k), (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), v), ] else: return [(self.map_tensor_name(name), data_torch)] @Model.register("BertModel", "CamembertModel") class BertModel(Model): model_arch = gguf.MODEL_ARCH.BERT def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.vocab_size = None def set_gguf_parameters(self): super().set_gguf_parameters() self.gguf_writer.add_causal_attention(False) # get pooling path pooling_path = None module_path = self.dir_model / "modules.json" if module_path.is_file(): with open(module_path, encoding="utf-8") as f: modules = json.load(f) for mod in modules: if mod["type"] == "sentence_transformers.models.Pooling": pooling_path = mod["path"] break # get pooling type if pooling_path is not None: with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f: pooling = json.load(f) if pooling["pooling_mode_mean_tokens"]: pooling_type = gguf.PoolingType.MEAN elif pooling["pooling_mode_cls_token"]: pooling_type = gguf.PoolingType.CLS else: raise NotImplementedError("Only MEAN and CLS pooling types supported") self.gguf_writer.add_pooling_type(pooling_type) def set_vocab(self): tokens, toktypes, tokpre = self.get_vocab_base() self.vocab_size = len(tokens) # we need this to validate the size of the token_type embeddings # though currently we are passing all zeros to the token_type embeddings self.gguf_writer.add_token_type_count(2) # "Sequence A" or "Sequence B" # convert to phantom space vocab def phantom(tok): if tok.startswith("[") and tok.endswith("]"): return tok if tok.startswith("##"): return tok[2:] return "\u2581" + tok tokens = list(map(phantom, tokens)) # add vocab to gguf self.gguf_writer.add_tokenizer_model("bert") self.gguf_writer.add_tokenizer_pre(tokpre) self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_types(toktypes) # handle special tokens special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) special_vocab.add_to_gguf(self.gguf_writer) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused # we are only using BERT for embeddings so we don't need the pooling layer if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"): return [] # we don't need these return [(self.map_tensor_name(name), data_torch)] @Model.register("NomicBertModel") class NomicBertModel(BertModel): model_arch = gguf.MODEL_ARCH.NOMIC_BERT def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # the HF config claims n_ctx=8192, but it uses RoPE scaling self.hparams["n_ctx"] = 2048 # SwigLU activation assert self.hparams["activation_function"] == "swiglu" # this doesn't do anything in the HF version assert self.hparams["causal"] is False # no bias tensors assert self.hparams["qkv_proj_bias"] is False assert self.hparams["mlp_fc1_bias"] is False assert self.hparams["mlp_fc2_bias"] is False # norm at end of layer assert self.hparams["prenorm"] is False # standard RoPE assert self.hparams["rotary_emb_fraction"] == 1.0 assert self.hparams["rotary_emb_interleaved"] is False assert self.hparams["rotary_emb_scale_base"] is None def set_gguf_parameters(self): super().set_gguf_parameters() self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) @Model.register("XLMRobertaModel", "XLMRobertaForSequenceClassification") class XLMRobertaModel(BertModel): model_arch = gguf.MODEL_ARCH.BERT def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # we need the pad_token_id to know how to chop down position_embd matrix if (pad_token_id := self.hparams.get("pad_token_id")) is not None: self._position_offset = 1 + pad_token_id if "max_position_embeddings" in self.hparams: self.hparams["max_position_embeddings"] -= self._position_offset else: self._position_offset = None def set_vocab(self): # to avoid TypeError: Descriptors cannot be created directly # exception when importing sentencepiece_model_pb2 os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" from sentencepiece import SentencePieceProcessor from sentencepiece import sentencepiece_model_pb2 as model tokenizer_path = self.dir_model / 'sentencepiece.bpe.model' if not tokenizer_path.is_file(): raise FileNotFoundError(f"File not found: {tokenizer_path}") sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap 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.UNUSED] * 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 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) # realign tokens (see HF tokenizer code) tokens = [b'', b'', b'', b''] + tokens[3:-1] scores = [0.0, 0.0, 0.0, 0.0] + scores[3:-1] toktypes = [ SentencePieceTokenTypes.CONTROL, SentencePieceTokenTypes.CONTROL, SentencePieceTokenTypes.CONTROL, SentencePieceTokenTypes.UNKNOWN, ] + toktypes[3:-1] self.gguf_writer.add_tokenizer_model("t5") 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) self.gguf_writer.add_add_space_prefix(add_prefix) self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1)) self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces) if precompiled_charsmap: self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap) special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) special_vocab.add_to_gguf(self.gguf_writer) self.gguf_writer.add_add_bos_token(True) self.gguf_writer.add_add_eos_token(True) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: # if name starts with "roberta.", remove the prefix # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main if name.startswith("roberta."): name = name[8:] # position embeddings start at pad_token_id + 1, so just chop down the weight tensor if name == "embeddings.position_embeddings.weight": if self._position_offset is not None: data_torch = data_torch[self._position_offset:,:] return super().modify_tensors(data_torch, name, bid) @Model.register("GemmaForCausalLM") class GemmaModel(Model): model_arch = gguf.MODEL_ARCH.GEMMA def set_vocab(self): self._set_vocab_sentencepiece() # TODO: these special tokens should be exported only for the CodeGemma family special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False, special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot']) special_vocab._set_special_token("prefix", 67) special_vocab._set_special_token("suffix", 69) special_vocab._set_special_token("middle", 68) special_vocab._set_special_token("fsep", 70) special_vocab._set_special_token("eot", 107) special_vocab.chat_template = None # do not add it twice special_vocab.add_to_gguf(self.gguf_writer) self.gguf_writer.add_add_space_prefix(False) def set_gguf_parameters(self): hparams = self.hparams block_count = hparams["num_hidden_layers"] self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) self.gguf_writer.add_embedding_length(hparams["hidden_size"]) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) self.gguf_writer.add_head_count(hparams["num_attention_heads"]) self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"]) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) self.gguf_writer.add_key_length(hparams["head_dim"]) self.gguf_writer.add_value_length(hparams["head_dim"]) 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 # lm_head is not used in llama.cpp, while autoawq will include this tensor in model # To prevent errors, skip loading lm_head.weight. if name == "lm_head.weight": logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.") return [] # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 if name.endswith("norm.weight"): data_torch = data_torch + 1 return [(self.map_tensor_name(name), data_torch)] @Model.register("Gemma2ForCausalLM") class Gemma2Model(Model): model_arch = gguf.MODEL_ARCH.GEMMA2 def set_vocab(self): self._set_vocab_sentencepiece() self.gguf_writer.add_add_space_prefix(False) def set_gguf_parameters(self): hparams = self.hparams block_count = hparams["num_hidden_layers"] self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) self.gguf_writer.add_embedding_length(hparams["hidden_size"]) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) self.gguf_writer.add_head_count(hparams["num_attention_heads"]) self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"]) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) self.gguf_writer.add_key_length(hparams["head_dim"]) self.gguf_writer.add_value_length(hparams["head_dim"]) self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_attn_logit_softcapping( self.hparams["attn_logit_softcapping"] ) self.gguf_writer.add_final_logit_softcapping( self.hparams["final_logit_softcapping"] ) self.gguf_writer.add_sliding_window(self.hparams["sliding_window"]) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused # lm_head is not used in llama.cpp, while autoawq will include this tensor in model # To prevent errors, skip loading lm_head.weight. if name == "lm_head.weight": logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.") return [] # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 if name.endswith("norm.weight"): data_torch = data_torch + 1 return [(self.map_tensor_name(name), data_torch)] @Model.register("Starcoder2ForCausalLM") class StarCoder2Model(Model): model_arch = gguf.MODEL_ARCH.STARCODER2 @Model.register("Rwkv6ForCausalLM") class Rwkv6Model(Model): model_arch = gguf.MODEL_ARCH.RWKV6 def set_vocab(self): assert (self.dir_model / "rwkv_vocab_v20230424.txt").is_file() vocab_size = self.hparams.get("vocab_size", 65536) tokens: list[bytes] = [''.encode("utf-8")] toktypes: list[int] = [gguf.TokenType.CONTROL] with open(self.dir_model / "rwkv_vocab_v20230424.txt", "r", encoding="utf-8") as f: lines = f.readlines() for line in lines: parts = line.split(' ') assert len(parts) >= 3 token, token_len = ast.literal_eval(' '.join(parts[1:-1])), int(parts[-1]) token = token.encode("utf-8") if isinstance(token, str) else token assert isinstance(token, bytes) assert len(token) == token_len token_text: str = repr(token)[2:-1] # "b'\xff'" -> "\xff" tokens.append(token_text.encode("utf-8")) toktypes.append(gguf.TokenType.NORMAL) remainder = vocab_size - len(tokens) assert remainder >= 0 for i in range(len(tokens), vocab_size): tokens.append(f"[PAD{i}]".encode("utf-8")) toktypes.append(gguf.TokenType.UNUSED) self.gguf_writer.add_tokenizer_model("rwkv") self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_types(toktypes) special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False) special_vocab.chat_template = "rwkv-world" # hack: Add '\n\n' as the EOT token to make it chat normally special_vocab._set_special_token("eot", 261) special_vocab.add_to_gguf(self.gguf_writer) def set_gguf_parameters(self): block_count = self.hparams["num_hidden_layers"] head_size = self.hparams["head_size"] hidden_size = self.hparams["hidden_size"] layer_norm_eps = self.hparams["layer_norm_epsilon"] rescale_every_n_layers = self.hparams["rescale_every"] intermediate_size = self.hparams["intermediate_size"] if self.hparams["intermediate_size"] is not None else int((hidden_size * 3.5) // 32 * 32) time_mix_extra_dim = 64 if hidden_size == 4096 else 32 time_decay_extra_dim = 128 if hidden_size == 4096 else 64 # RWKV isn't context limited self.gguf_writer.add_context_length(1048576) self.gguf_writer.add_embedding_length(hidden_size) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_layer_norm_eps(layer_norm_eps) self.gguf_writer.add_rescale_every_n_layers(rescale_every_n_layers) self.gguf_writer.add_wkv_head_size(head_size) self.gguf_writer.add_time_mix_extra_dim(time_mix_extra_dim) self.gguf_writer.add_time_decay_extra_dim(time_decay_extra_dim) self.gguf_writer.add_feed_forward_length(intermediate_size) self.gguf_writer.add_file_type(self.ftype) # required by llama.cpp, unused self.gguf_writer.add_head_count(0) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: new_name = self.map_tensor_name(name) if not (new_name.endswith(".weight") or new_name.endswith(".bias")): new_name += ".weight" if new_name.endswith("time_mix_w1.weight") or new_name.endswith("time_mix_decay_w1.weight") or new_name.endswith("time_mix_decay_w2.weight"): data_torch = data_torch.transpose(0, 1) if new_name.endswith("time_mix_w2.weight"): data_torch = data_torch.permute(0, 2, 1) rescale_every_n_layers = self.hparams["rescale_every"] if rescale_every_n_layers > 0: if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"): data_torch = data_torch.div_(2 ** int(bid // rescale_every_n_layers)) yield (new_name, data_torch) @Model.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM") class MambaModel(Model): model_arch = gguf.MODEL_ARCH.MAMBA def set_vocab(self): vocab_size = self.hparams["vocab_size"] # Round vocab size to next multiple of 8 pad_vocab = self.hparams.get("pad_vocab_size_multiple", 8) # pad using ceiling division # ref: https://stackoverflow.com/a/17511341/22827863 vocab_size = -(vocab_size // -pad_vocab) * pad_vocab self.hparams["vocab_size"] = vocab_size if (self.dir_model / "tokenizer.json").is_file(): self._set_vocab_gpt2() elif (self.dir_model / "tokenizer.model").is_file(): self._set_vocab_sentencepiece() else: # Use the GPT-NeoX tokenizer when no tokenizer files are present self._set_vocab_builtin("gpt-neox", vocab_size) def set_gguf_parameters(self): d_model = self.find_hparam(["hidden_size", "d_model"]) d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4 d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16 # ceiling division # ref: https://stackoverflow.com/a/17511341/22827863 # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58 dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16) rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5 use_dt_b_c_norm = False # For falconmamba we do apply RMS norm on B / DT and C layers if self.find_hparam(["model_type"], optional=True) in ("falcon_mamba",): use_dt_b_c_norm = True # Fail early for models which don't have a block expansion factor of 2 assert d_inner == 2 * d_model self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default self.gguf_writer.add_embedding_length(d_model) self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading self.gguf_writer.add_block_count(self.block_count) self.gguf_writer.add_ssm_conv_kernel(d_conv) self.gguf_writer.add_ssm_inner_size(d_inner) self.gguf_writer.add_ssm_state_size(d_state) self.gguf_writer.add_ssm_time_step_rank(dt_rank) self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps) self.gguf_writer.add_ssm_dt_b_c_rms(use_dt_b_c_norm) # For classic Mamba we don't apply rms norm on B / DT layers self.gguf_writer.add_file_type(self.ftype) _tok_embd = None def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT) tok_embd_name = self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD) new_name = self.map_tensor_name(name) if name.endswith(".A_log"): logger.debug("A_log --> A ==> " + new_name) data_torch = -torch.exp(data_torch) # assuming token_embd.weight is seen before output.weight if self._tok_embd is not None and new_name == output_name: if torch.equal(self._tok_embd, data_torch): logger.debug(f"{output_name} is equivalent to {tok_embd_name}, omitting") return [] elif new_name == tok_embd_name: self._tok_embd = data_torch return [(new_name, data_torch)] @Model.register("CohereForCausalLM") class CommandR2Model(Model): model_arch = gguf.MODEL_ARCH.COMMAND_R def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # max_position_embeddings = 8192 in config.json but model was actually # trained on 128k context length # aya-23 models don't have model_max_length specified self.hparams["max_position_embeddings"] = self.find_hparam(["model_max_length", "max_position_embeddings"]) def set_gguf_parameters(self): super().set_gguf_parameters() self.gguf_writer.add_logit_scale(self.hparams["logit_scale"]) self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) @Model.register("OlmoForCausalLM") @Model.register("OLMoForCausalLM") class OlmoModel(Model): model_arch = gguf.MODEL_ARCH.OLMO def set_gguf_parameters(self): super().set_gguf_parameters() self.gguf_writer.add_layer_norm_eps(1e-5) clip_qkv = self.hparams.get("clip_qkv") if clip_qkv is not None: self.gguf_writer.add_clamp_kqv(clip_qkv) # Same as super class, but permuting q_proj, k_proj # Copied from: LlamaModel def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused n_head = self.hparams["num_attention_heads"] n_kv_head = self.hparams.get("num_key_value_heads") if name.endswith("q_proj.weight"): data_torch = LlamaModel.permute(data_torch, n_head, n_head) if name.endswith("k_proj.weight"): data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) return [(self.map_tensor_name(name), data_torch)] @Model.register("Olmo2ForCausalLM") class Olmo2Model(Model): model_arch = gguf.MODEL_ARCH.OLMO2 @Model.register("OlmoeForCausalLM") class OlmoeModel(Model): model_arch = gguf.MODEL_ARCH.OLMOE def set_gguf_parameters(self): super().set_gguf_parameters() self.gguf_writer.add_layer_norm_rms_eps(1e-5) if (n_experts := self.hparams.get("num_experts")) is not None: self.gguf_writer.add_expert_count(n_experts) _experts: list[dict[str, Tensor]] | None = None # Copied from: Qwen2MoeModel def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: # process the experts separately if name.find("experts") != -1: n_experts = self.hparams["num_experts"] assert bid is not None if self._experts is None: self._experts = [{} for _ in range(self.block_count)] self._experts[bid][name] = data_torch if len(self._experts[bid]) >= n_experts * 3: tensors: list[tuple[str, Tensor]] = [] # merge the experts into a single 3d tensor for w_name in ["down_proj", "gate_proj", "up_proj"]: datas: list[Tensor] = [] for xid in range(n_experts): ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" datas.append(self._experts[bid][ename]) del self._experts[bid][ename] data_torch = torch.stack(datas, dim=0) merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" new_name = self.map_tensor_name(merged_name) tensors.append((new_name, data_torch)) return tensors else: return [] return [(self.map_tensor_name(name), data_torch)] # Copied from: Qwen2MoeModel def prepare_tensors(self): super().prepare_tensors() if self._experts is not None: # flatten `list[dict[str, Tensor]]` into `list[str]` experts = [k for d in self._experts for k in d.keys()] if len(experts) > 0: raise ValueError(f"Unprocessed experts: {experts}") @Model.register("JinaBertModel", "JinaBertForMaskedLM") class JinaBertV2Model(BertModel): model_arch = gguf.MODEL_ARCH.JINA_BERT_V2 def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.intermediate_size = self.hparams["intermediate_size"] def get_tensors(self): for name, data in super().get_tensors(): if 'gated_layer' in name: d1 = data[:self.intermediate_size, :] name1 = name.replace('gated_layers', 'gated_layers_w') name1 = name1.replace('up_gated_layer', 'gated_layers_v') d2 = data[self.intermediate_size:, :] name2 = name.replace('gated_layers', 'gated_layers_v') name2 = name2.replace('up_gated_layer', 'gated_layers_w') yield name1, d1 yield name2, d2 continue yield name, data def set_vocab(self): tokenizer_class = 'BertTokenizer' with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f: tokenizer_class = json.load(f)['tokenizer_class'] if tokenizer_class == 'BertTokenizer': super().set_vocab() elif tokenizer_class == 'RobertaTokenizer': self._set_vocab_gpt2() self.gguf_writer.add_token_type_count(2) else: raise NotImplementedError(f'Tokenizer {tokenizer_class} is not supported for JinaBertModel') self.gguf_writer.add_add_bos_token(True) self.gguf_writer.add_add_eos_token(True) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: # if name starts with "bert.", remove the prefix # e.g. https://huggingface.co/jinaai/jina-reranker-v1-tiny-en if name.startswith("bert."): name = name[5:] return super().modify_tensors(data_torch, name, bid) @Model.register("OpenELMForCausalLM") class OpenELMModel(Model): model_arch = gguf.MODEL_ARCH.OPENELM @staticmethod def _make_divisible(v: float | int, divisor: int) -> int: # ref: https://huggingface.co/apple/OpenELM-270M-Instruct/blob/eb111ff2e6724348e5b905984063d4064d4bc579/configuration_openelm.py#L34-L38 new_v = max(divisor, int(v + divisor / 2) // divisor * divisor) # Make sure that round down does not go down by more than 10%. if new_v < 0.9 * v: new_v += divisor return new_v def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) ffn_multipliers: list[float] = self.hparams["ffn_multipliers"] ffn_dim_divisor: int = self.hparams["ffn_dim_divisor"] self._n_embd: int = self.hparams["model_dim"] self._num_kv_heads: list[int] = self.hparams["num_kv_heads"] self._num_query_heads: list[int] = self.hparams["num_query_heads"] self._ffn_dims: list[int] = [ OpenELMModel._make_divisible(multiplier * self._n_embd, ffn_dim_divisor) for multiplier in ffn_multipliers ] assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int) assert isinstance(self._num_query_heads, list) and isinstance(self._num_query_heads[0], int) # Uses the tokenizer from meta-llama/Llama-2-7b-hf def set_vocab(self): try: self._set_vocab_sentencepiece() except FileNotFoundError: self._set_vocab_builtin("llama-spm", self.hparams["vocab_size"]) def set_gguf_parameters(self): n_embd = self._n_embd head_dim = self.hparams["head_dim"] rot_pct = 1.0 assert self.block_count == len(self._num_kv_heads) assert self.block_count == len(self._num_query_heads) assert self.block_count == len(self._ffn_dims) self.gguf_writer.add_block_count(self.block_count) self.gguf_writer.add_context_length(self.hparams["max_context_length"]) self.gguf_writer.add_embedding_length(n_embd) self.gguf_writer.add_feed_forward_length(self._ffn_dims) self.gguf_writer.add_head_count(self._num_query_heads) self.gguf_writer.add_head_count_kv(self._num_kv_heads) self.gguf_writer.add_rope_freq_base(self.hparams["rope_freq_constant"]) # https://huggingface.co/apple/OpenELM-270M-Instruct/blob/c401df2/modeling_openelm.py#L30 self.gguf_writer.add_layer_norm_rms_eps(1e-6) self.gguf_writer.add_rope_dimension_count(int(rot_pct * head_dim)) self.gguf_writer.add_key_length(head_dim) self.gguf_writer.add_value_length(head_dim) self.gguf_writer.add_file_type(self.ftype) def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any: if "n_layers" in keys: return self.hparams["num_transformer_layers"] return super().find_hparam(keys, optional) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: # split ff if bid is not None and name == f"transformer.layers.{bid}.ffn.proj_1.weight": ff_dim = self._ffn_dims[bid] yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim]) yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:]) return yield (self.map_tensor_name(name), data_torch) @Model.register("ArcticForCausalLM") class ArcticModel(Model): model_arch = gguf.MODEL_ARCH.ARCTIC def set_vocab(self): # The reason for using a custom implementation here is that the # snowflake-arctic-instruct model redefined tokens 31998 and 31999 from # tokenizer.model and used them as BOS and EOS instead of adding new tokens. from sentencepiece import SentencePieceProcessor tokenizer_path = self.dir_model / 'tokenizer.model' if not tokenizer_path.is_file(): logger.error(f'Error: Missing {tokenizer_path}') sys.exit(1) # Read the whole vocabulary from the tokenizer.model file 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.UNUSED] * 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 # Use the added_tokens_decoder field from tokeniser_config.json as the source # of information about added/redefined tokens and modify them accordingly. tokenizer_config_file = self.dir_model / 'tokenizer_config.json' if tokenizer_config_file.is_file(): with open(tokenizer_config_file, "r", encoding="utf-8") as f: tokenizer_config_json = json.load(f) if "added_tokens_decoder" in tokenizer_config_json: added_tokens_decoder = tokenizer_config_json["added_tokens_decoder"] for token_id, token_json in added_tokens_decoder.items(): token_id = int(token_id) if token_id >= vocab_size: logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') continue token_content = token_json["content"] token_type = SentencePieceTokenTypes.USER_DEFINED token_score = -10000.0 # Map unk_token to UNKNOWN, other special tokens to CONTROL # Set the score to 0.0 as in the original tokenizer.model if ("special" in token_json) and token_json["special"]: if token_content == tokenizer_config_json["unk_token"]: token_type = SentencePieceTokenTypes.UNKNOWN else: token_type = SentencePieceTokenTypes.CONTROL token_score = 0.0 logger.info(f"Setting added token {token_id} to '{token_content}' (type: {token_type}, score: {token_score:.2f})") tokens[token_id] = token_content.encode("utf-8") toktypes[token_id] = token_type scores[token_id] = token_score 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_gguf_parameters(self): super().set_gguf_parameters() hparams = self.hparams self.gguf_writer.add_vocab_size(hparams["vocab_size"]) self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"]) _experts: list[dict[str, Tensor]] | None = None def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: n_head = self.hparams["num_attention_heads"] n_kv_head = self.hparams.get("num_key_value_heads") if name.endswith("q_proj.weight"): data_torch = LlamaModel.permute(data_torch, n_head, n_head) if name.endswith("k_proj.weight"): data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) # process the experts separately if name.find("block_sparse_moe.experts") != -1: n_experts = self.hparams["num_local_experts"] assert bid is not None if self._experts is None: self._experts = [{} for _ in range(self.block_count)] self._experts[bid][name] = data_torch if len(self._experts[bid]) >= n_experts * 3: tensors: list[tuple[str, Tensor]] = [] # merge the experts into a single 3d tensor for wid in ["w1", "w2", "w3"]: datas: list[Tensor] = [] for xid in range(n_experts): ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight" datas.append(self._experts[bid][ename]) del self._experts[bid][ename] data_torch = torch.stack(datas, dim=0) merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight" new_name = self.map_tensor_name(merged_name) tensors.append((new_name, data_torch)) return tensors else: return [] return [(self.map_tensor_name(name), data_torch)] def prepare_tensors(self): super().prepare_tensors() if self._experts is not None: # flatten `list[dict[str, Tensor]]` into `list[str]` experts = [k for d in self._experts for k in d.keys()] if len(experts) > 0: raise ValueError(f"Unprocessed experts: {experts}") @Model.register("DeepseekV2ForCausalLM") class DeepseekV2Model(Model): model_arch = gguf.MODEL_ARCH.DEEPSEEK2 def set_vocab(self): self._set_vocab_gpt2() def set_gguf_parameters(self): super().set_gguf_parameters() hparams = self.hparams self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"]) self.gguf_writer.add_vocab_size(hparams["vocab_size"]) if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None: self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"]) self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"]) self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"]) self.gguf_writer.add_value_length(hparams["v_head_dim"]) self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) self.gguf_writer.add_expert_count(hparams["n_routed_experts"]) self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"]) self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"]) self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"]) if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: if self.hparams["rope_scaling"].get("type") == "yarn": self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"]) self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * hparams["rope_scaling"]["mscale_all_dim"]) _experts: list[dict[str, Tensor]] | None = None def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: # process the experts separately if name.find("mlp.experts") != -1: n_experts = self.hparams["n_routed_experts"] assert bid is not None if self._experts is None: self._experts = [{} for _ in range(self.block_count)] self._experts[bid][name] = data_torch if len(self._experts[bid]) >= n_experts * 3: tensors: list[tuple[str, Tensor]] = [] # merge the experts into a single 3d tensor for w_name in ["down_proj", "gate_proj", "up_proj"]: datas: list[Tensor] = [] for xid in range(n_experts): ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" datas.append(self._experts[bid][ename]) del self._experts[bid][ename] data_torch = torch.stack(datas, dim=0) merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" new_name = self.map_tensor_name(merged_name) tensors.append((new_name, data_torch)) return tensors else: return [] return [(self.map_tensor_name(name), data_torch)] def prepare_tensors(self): super().prepare_tensors() if self._experts is not None: # flatten `list[dict[str, Tensor]]` into `list[str]` experts = [k for d in self._experts for k in d.keys()] if len(experts) > 0: raise ValueError(f"Unprocessed experts: {experts}") @Model.register("T5WithLMHeadModel") @Model.register("T5ForConditionalGeneration") @Model.register("MT5ForConditionalGeneration") @Model.register("UMT5ForConditionalGeneration") class T5Model(Model): model_arch = gguf.MODEL_ARCH.T5 def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.shared_token_embeddings_found = False def set_vocab(self): # to avoid TypeError: Descriptors cannot be created directly # exception when importing sentencepiece_model_pb2 os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" from sentencepiece import SentencePieceProcessor from sentencepiece import sentencepiece_model_pb2 as model tokenizer_path = self.dir_model / 'tokenizer.model' # many older models use spiece.model tokenizer model filename if not tokenizer_path.is_file(): tokenizer_path = self.dir_model / 'spiece.model' if not tokenizer_path.is_file(): raise FileNotFoundError(f"File not found: {tokenizer_path}") sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) # some models like Pile-T5 family use BPE tokenizer instead of Unigram if sentencepiece_model.trainer_spec.model_type == 2: # BPE # assure the tokenizer model file name is correct assert tokenizer_path.name == 'tokenizer.model' return self._set_vocab_sentencepiece() else: assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap 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.UNUSED] * 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("t5") 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) self.gguf_writer.add_add_space_prefix(add_prefix) self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces) if precompiled_charsmap: self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap) special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) special_vocab.add_to_gguf(self.gguf_writer) self.gguf_writer.add_add_bos_token(False) self.gguf_writer.add_add_eos_token(True) def set_gguf_parameters(self): if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None: logger.warning("Couldn't find context length in config.json, assuming default value of 512") n_ctx = 512 self.gguf_writer.add_context_length(n_ctx) self.gguf_writer.add_embedding_length(self.hparams["d_model"]) self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"]) self.gguf_writer.add_block_count(self.hparams["num_layers"]) self.gguf_writer.add_head_count(self.hparams["num_heads"]) self.gguf_writer.add_key_length(self.hparams["d_kv"]) self.gguf_writer.add_value_length(self.hparams["d_kv"]) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"]) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_decoder_start_token_id(self.hparams["decoder_start_token_id"]) 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 # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight", # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder # and decoder and ignore the remaining ones. if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]: if not self.shared_token_embeddings_found: name = "shared.weight" self.shared_token_embeddings_found = True else: logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.") return [] return [(self.map_tensor_name(name), data_torch)] @Model.register("T5EncoderModel") class T5EncoderModel(Model): model_arch = gguf.MODEL_ARCH.T5ENCODER def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.shared_token_embeddings_found = False def set_vocab(self): # to avoid TypeError: Descriptors cannot be created directly # exception when importing sentencepiece_model_pb2 os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" from sentencepiece import SentencePieceProcessor from sentencepiece import sentencepiece_model_pb2 as model tokenizer_path = self.dir_model / 'tokenizer.model' # many older models use spiece.model tokenizer model filename if not tokenizer_path.is_file(): tokenizer_path = self.dir_model / 'spiece.model' if not tokenizer_path.is_file(): raise FileNotFoundError(f"File not found: {tokenizer_path}") sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) # some models like Pile-T5 family use BPE tokenizer instead of Unigram if sentencepiece_model.trainer_spec.model_type == 2: # BPE # assure the tokenizer model file name is correct assert tokenizer_path.name == 'tokenizer.model' return self._set_vocab_sentencepiece() else: assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap 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.UNUSED] * 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("t5") 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) self.gguf_writer.add_add_space_prefix(add_prefix) self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces) if precompiled_charsmap: self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap) special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) special_vocab.add_to_gguf(self.gguf_writer) self.gguf_writer.add_add_bos_token(False) self.gguf_writer.add_add_eos_token(True) def set_gguf_parameters(self): if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None: logger.warning("Couldn't find context length in config.json, assuming default value of 512") n_ctx = 512 self.gguf_writer.add_context_length(n_ctx) self.gguf_writer.add_embedding_length(self.hparams["d_model"]) self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"]) self.gguf_writer.add_block_count(self.hparams["num_layers"]) self.gguf_writer.add_head_count(self.hparams["num_heads"]) self.gguf_writer.add_key_length(self.hparams["d_kv"]) self.gguf_writer.add_value_length(self.hparams["d_kv"]) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"]) self.gguf_writer.add_layer_norm_rms_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 # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight", # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder # and decoder and ignore the remaining ones. if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]: if not self.shared_token_embeddings_found: name = "shared.weight" self.shared_token_embeddings_found = True else: logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.") return [] return [(self.map_tensor_name(name), data_torch)] @Model.register("JAISLMHeadModel") class JaisModel(Model): model_arch = gguf.MODEL_ARCH.JAIS def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # SwigLU activation assert self.hparams["activation_function"] == "swiglu" # ALiBi position embedding assert self.hparams["position_embedding_type"] == "alibi" # Embeddings scale self.embeddings_scale = 1.0 if 'mup_embeddings_scale' in self.hparams: self.embeddings_scale = self.hparams['mup_embeddings_scale'] elif 'embeddings_scale' in self.hparams: self.embeddings_scale = self.hparams['embeddings_scale'] else: assert False self.width_scale = 1.0 if 'mup_output_alpha' in self.hparams: assert 'mup_width_scale' in self.hparams self.width_scale = self.hparams['mup_output_alpha'] * self.hparams['mup_width_scale'] elif 'width_scale' in self.hparams: self.width_scale = self.hparams['width_scale'] else: assert False self.max_alibi_bias = 8.0 def set_vocab(self): self._set_vocab_gpt2() def set_gguf_parameters(self): self.gguf_writer.add_block_count(self.hparams["n_layer"]) self.gguf_writer.add_context_length(self.hparams["n_positions"]) self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) self.gguf_writer.add_feed_forward_length(self.hparams["n_inner"]) self.gguf_writer.add_head_count(self.hparams["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 tensors: list[tuple[str, Tensor]] = [] # we don't need these if name.endswith((".attn.bias")): return tensors if name.endswith(("relative_pe.slopes")): # Calculate max ALiBi bias (this is the inverse of the ALiBi calculation) # Some other models has max_alibi_bias spelled out explicitly in the hyperparams, # but Jais's PyTorch model simply precalculates the slope values and places them # in relative_pes.slopes n_head_closest_log2 = 2 ** math.floor(math.log2(self.hparams["n_head"])) first_val = float(data_torch[0].item()) self.max_alibi_bias = -round(math.log2(first_val) * n_head_closest_log2) return tensors if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_fc2.weight")): data_torch = data_torch.transpose(1, 0) new_name = self.map_tensor_name(name) if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD): tensors.append((new_name, data_torch * self.embeddings_scale)) elif new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT): tensors.append((new_name, data_torch * self.width_scale)) else: tensors.append((new_name, data_torch)) return tensors def prepare_tensors(self): super().prepare_tensors() self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias) @Model.register("ChatGLMModel", "ChatGLMForConditionalGeneration") class ChatGLMModel(Model): model_arch = gguf.MODEL_ARCH.CHATGLM def set_vocab_chatglm3(self): dir_model = self.dir_model hparams = self.hparams tokens: list[bytes] = [] toktypes: list[int] = [] scores: list[float] = [] from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True) vocab_size = hparams.get("padded_vocab_size", len(tokenizer.get_vocab())) assert max(tokenizer.get_vocab().values()) < vocab_size role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"] special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens for token_id in range(vocab_size): piece = tokenizer._convert_id_to_token(token_id) if token_id == 0: piece = "" elif token_id == 1: piece = "" elif token_id == 2: piece = "" text = piece.encode("utf-8") score = 0.0 # Referencing the tokenizer Python implementation(https://huggingface.co/THUDM/chatglm3-6b/blob/main/tokenization_chatglm.py), # it is only valid if it is less than tokenizer.tokenizer.sp_model.vocab_size() if len(piece) != 0 and token_id < tokenizer.tokenizer.sp_model.vocab_size(): score = tokenizer.tokenizer.sp_model.get_score(token_id) if token_id >= tokenizer.tokenizer.sp_model.vocab_size(): if piece in special_tokens: toktype = SentencePieceTokenTypes.CONTROL elif len(piece) == 0: text = f"[PAD{token_id}]".encode("utf-8") toktype = SentencePieceTokenTypes.UNUSED else: toktype = SentencePieceTokenTypes.USER_DEFINED tokens.append(text) scores.append(score) toktypes.append(toktype) continue toktype = SentencePieceTokenTypes.NORMAL if tokenizer.tokenizer.sp_model.is_unknown(token_id): toktype = SentencePieceTokenTypes.UNKNOWN elif tokenizer.tokenizer.sp_model.is_control(token_id): toktype = SentencePieceTokenTypes.CONTROL elif tokenizer.tokenizer.sp_model.is_unused(token_id): toktype = SentencePieceTokenTypes.UNUSED elif tokenizer.tokenizer.sp_model.is_byte(token_id): toktype = SentencePieceTokenTypes.BYTE tokens.append(text) scores.append(score) toktypes.append(toktype) self.gguf_writer.add_tokenizer_model("llama") # glm3 needs prefix and suffix formatted as: # prompt = "[gMASK]sop<|user|>\n" + prompt + "<|assistant|>" self.gguf_writer.add_tokenizer_pre("chatglm-spm") 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) @staticmethod def token_bytes_to_string(b): from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode byte_encoder = bytes_to_unicode() return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')]) @staticmethod def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]: parts = [bytes([b]) for b in token] while True: min_idx = None min_rank = None for i, pair in enumerate(zip(parts[:-1], parts[1:])): rank = mergeable_ranks.get(pair[0] + pair[1]) if rank is not None and (min_rank is None or rank < min_rank): min_idx = i min_rank = rank if min_rank is None or (max_rank is not None and min_rank >= max_rank): break assert min_idx is not None parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:] return parts def set_vocab(self): if "THUDM/chatglm3-6b" in self.hparams.get("_name_or_path", ""): self.set_vocab_chatglm3() return 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["padded_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[ChatGLMModel.token_bytes_to_string(token)] = rank if len(token) == 1: continue merged = ChatGLMModel.bpe(mergeable_ranks, token, max_rank=rank) assert len(merged) >= 2 and len(merged) <= 7 merges.append(' '.join(map(ChatGLMModel.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.get_added_vocab() 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.UNUSED) 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) 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 special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"]) special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"]) # this one is usually not in config.json anyway special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) special_vocab.add_to_gguf(self.gguf_writer) def set_gguf_parameters(self): 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")) n_head_kv = self.hparams.get("multi_query_group_num", n_head) 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(self.hparams.get("ffn_hidden_size", 4 * n_embed)) self.gguf_writer.add_block_count(self.hparams["num_layers"]) self.gguf_writer.add_head_count(n_head) self.gguf_writer.add_head_count_kv(n_head_kv) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layernorm_epsilon"]) self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_rope_dimension_count(64) self.gguf_writer.add_add_bos_token(False) rope_freq = 10000 if "rope_ratio" in self.hparams: rope_freq = rope_freq * self.hparams["rope_ratio"] self.gguf_writer.add_rope_freq_base(rope_freq) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused if name.endswith(".rotary_pos_emb.inv_freq"): return [] name = name.removeprefix("transformer.") return [(self.map_tensor_name(name), data_torch)] @Model.register("NemotronForCausalLM") class NemotronModel(Model): model_arch = gguf.MODEL_ARCH.NEMOTRON def set_vocab(self): self._set_vocab_sentencepiece() self.gguf_writer.add_pad_token_id(0) self.gguf_writer.add_unk_token_id(1) def set_gguf_parameters(self): super().set_gguf_parameters() hparams = self.hparams self.gguf_writer.add_vocab_size(hparams["vocab_size"]) f_norm_eps = self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon", "norm_eps"]) self.gguf_writer.add_layer_norm_eps(f_norm_eps) # * Partial RoPE rot_pct = self.find_hparam(["partial_rotary_factor", "rope_pct", "rope_percent"]) n_embd = self.find_hparam(["hidden_size", "n_embd"]) n_head = self.find_hparam(["num_attention_heads", "n_head"]) self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head) # * RopeScaling for Nemotron if "rope_scaling" not in self.hparams or self.hparams["rope_scaling"] is None: self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) else: self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) self.gguf_writer.add_rope_scaling_factor(self.hparams["factor"]) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: # * Adding +1 to LayerNorm's weights here to implement layernorm1p w/o changing anything on the GGML engine side # model.layers.{l}.input_layernorm.weight # model.layers.{l}.post_attention_layernorm.weight # model.norm.weight if name.endswith("norm.weight"): data_torch = data_torch + 1 return [(self.map_tensor_name(name), data_torch)] @Model.register("ExaoneForCausalLM") class ExaoneModel(Model): model_arch = gguf.MODEL_ARCH.EXAONE def set_gguf_parameters(self): hparams = self.hparams assert (hparams["activation_function"] == "silu") max_position_embeddings = hparams["max_position_embeddings"] embed_dim = hparams["hidden_size"] num_heads = hparams["num_attention_heads"] num_kv_heads = hparams.get("num_key_value_heads", num_heads) layer_norm_eps = hparams["layer_norm_epsilon"] intermediate_size = hparams["intermediate_size"] if "intermediate_size" in hparams else 4 * embed_dim num_layers = hparams["num_layers"] # ignore for now as EXAONE-3.0-7.8B-Instruct attentino_dropout is 0.0 # attention_dropout_rate = hparams["attention_dropout"] # ignore for now as EXAONE-3.0-7.8B-Instruct embed_dropout is 0.0 # embed_dropout_rate = hparams["embed_dropout"] self.gguf_writer.add_embedding_length(embed_dim) self.gguf_writer.add_head_count(num_heads) self.gguf_writer.add_head_count_kv(num_kv_heads) self.gguf_writer.add_context_length(max_position_embeddings) self.gguf_writer.add_layer_norm_rms_eps(layer_norm_eps) self.gguf_writer.add_feed_forward_length(intermediate_size) self.gguf_writer.add_block_count(num_layers) self.gguf_writer.add_file_type(self.ftype) if (rope_theta := self.hparams.get("rope_theta")) is not None: self.gguf_writer.add_rope_freq_base(rope_theta) rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"], optional=True) rotary_factor = rotary_factor if rotary_factor is not None else 1.0 self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"]))) if hparams.get("rope_scaling") is not None and "factor" in hparams["rope_scaling"]: if hparams["rope_scaling"].get("type") == "linear": self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) self.gguf_writer.add_rope_scaling_factor(hparams["rope_scaling"]["factor"]) def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): if rope_scaling.get("rope_type", '').lower() == "llama3": base = self.hparams.get("rope_theta", 10000.0) dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) factor = rope_scaling.get("factor", 8.0) low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) old_context_len = self.hparams.get("original_max_position_embeddings", 8192) low_freq_wavelen = old_context_len / low_freq_factor high_freq_wavelen = old_context_len / high_freq_factor assert low_freq_wavelen != high_freq_wavelen rope_factors = [] for freq in freqs: wavelen = 2 * math.pi / freq if wavelen < high_freq_wavelen: rope_factors.append(1) elif wavelen > low_freq_wavelen: rope_factors.append(factor) else: smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor) rope_factors.append(1 / ((1 - smooth) / factor + smooth)) yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32)) @Model.register("GraniteForCausalLM") class GraniteModel(LlamaModel): """Conversion for IBM's GraniteForCausalLM""" model_arch = gguf.MODEL_ARCH.GRANITE def set_gguf_parameters(self): """Granite uses standard llama parameters with the following differences: - No head_dim support - New multiplier params: - attention_scale - embedding_scale - residual_scale - logits_scaling """ if head_dim := self.hparams.pop("head_dim", None): logger.warning("Ignoring head_dim (%s) from config for Granite", head_dim) super().set_gguf_parameters() # NOTE: Convert _multiplier params to _scale params for naming # consistency if attention_scale := self.hparams.get("attention_multiplier"): self.gguf_writer.add_attention_scale(attention_scale) logger.info("gguf: (granite) attention_scale = %s", attention_scale) if embedding_scale := self.hparams.get("embedding_multiplier"): self.gguf_writer.add_embedding_scale(embedding_scale) logger.info("gguf: (granite) embedding_scale = %s", embedding_scale) if residual_scale := self.hparams.get("residual_multiplier"): self.gguf_writer.add_residual_scale(residual_scale) logger.info("gguf: (granite) residual_scale = %s", residual_scale) if logits_scale := self.hparams.get("logits_scaling"): self.gguf_writer.add_logit_scale(logits_scale) logger.info("gguf: (granite) logits_scale = %s", logits_scale) @Model.register("GraniteMoeForCausalLM") class GraniteMoeModel(GraniteModel): """Conversion for IBM's GraniteMoeForCausalLM""" model_arch = gguf.MODEL_ARCH.GRANITE_MOE def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: """In modeling_granitemoe, the JetMoe implementation of parallel experts is used. This essentially merges w1 and w3 into a single tensor with 2x the hidden size that is then split during forward. To keep compatibility with existing mixtral support, we pull them apart here. """ if name.endswith("block_sparse_moe.input_linear.weight"): ffn_dim = self.hparams["intermediate_size"] assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * intermediate_size" gate, up = data_torch[..., :ffn_dim, :], data_torch[..., ffn_dim:, :] return [ (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), gate), (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), up), ] return super().modify_tensors(data_torch, name, bid) @Model.register("ChameleonForConditionalGeneration") @Model.register("ChameleonForCausalLM") # obsolete class ChameleonModel(Model): model_arch = gguf.MODEL_ARCH.CHAMELEON def set_gguf_parameters(self): super().set_gguf_parameters() self.gguf_writer.add_swin_norm(self.hparams.get("swin_norm", False)) def set_vocab(self): self._set_vocab_gpt2() def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: # ignore image tokenizer for now # TODO: remove this once image support is implemented for Chameleon if name.startswith("model.vqmodel"): return [] n_head = self.hparams["num_attention_heads"] n_kv_head = self.hparams.get("num_key_value_heads") hidden_dim = self.hparams.get("hidden_size") if name.endswith(("q_proj.weight", "q_proj.bias")): data_torch = LlamaModel.permute(data_torch, n_head, n_head) if name.endswith(("k_proj.weight", "k_proj.bias")): data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) if name.endswith(("q_norm.weight", "q_norm.bias")): data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_head, hidden_dim) if name.endswith(("k_norm.weight", "k_norm.bias")): data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_kv_head, hidden_dim) return [(self.map_tensor_name(name), data_torch)] # see: https://github.com/huggingface/transformers/blob/72fb02c47dbbe1999ae105319f24631cad6e2e00/src/transformers/models/chameleon/convert_chameleon_weights_to_hf.py#L176-L203 @staticmethod def _reverse_hf_permute(data_torch, n_heads, hidden_dim): head_dim = hidden_dim // n_heads data_torch = data_torch[0].view(2, head_dim // 2).t().reshape(1, -1) data_torch = data_torch.repeat_interleave(n_heads, 0) return data_torch ###### CONVERSION LOGIC ###### # tree of lazy tensors class LazyTorchTensor(gguf.LazyBase): _tensor_type = torch.Tensor # to keep the type-checker happy dtype: torch.dtype shape: torch.Size # only used when converting a torch.Tensor to a np.ndarray _dtype_map: dict[torch.dtype, type] = { torch.float16: np.float16, torch.float32: np.float32, } # used for safetensors slices # ref: https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/src/lib.rs#L1046 # TODO: uncomment U64, U32, and U16, ref: https://github.com/pytorch/pytorch/issues/58734 _dtype_str_map: dict[str, torch.dtype] = { "F64": torch.float64, "F32": torch.float32, "BF16": torch.bfloat16, "F16": torch.float16, # "U64": torch.uint64, "I64": torch.int64, # "U32": torch.uint32, "I32": torch.int32, # "U16": torch.uint16, "I16": torch.int16, "U8": torch.uint8, "I8": torch.int8, "BOOL": torch.bool, "F8_E4M3": torch.float8_e4m3fn, "F8_E5M2": torch.float8_e5m2, } def numpy(self) -> gguf.LazyNumpyTensor: dtype = self._dtype_map[self.dtype] return gguf.LazyNumpyTensor( meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape), args=(self,), func=(lambda s: s.numpy()) ) @classmethod def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: tuple[int, ...]) -> Tensor: return torch.empty(size=shape, dtype=dtype, device="meta") @classmethod def from_safetensors_slice(cls, st_slice: Any) -> Tensor: dtype = cls._dtype_str_map[st_slice.get_dtype()] shape: tuple[int, ...] = tuple(st_slice.get_shape()) lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(st_slice,), func=lambda s: s[:]) return cast(torch.Tensor, lazy) @classmethod def __torch_function__(cls, func, types, args=(), kwargs=None): del types # unused if kwargs is None: kwargs = {} if func is torch.Tensor.numpy: return args[0].numpy() return cls._wrap_fn(func)(*args, **kwargs) def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description="Convert a huggingface model to a GGML compatible file") parser.add_argument( "--vocab-only", action="store_true", help="extract only the vocab", ) 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", "tq1_0", "tq2_0", "auto"], default="f16", help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, tq1_0 or tq2_0 for ternary, and 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( "model", type=Path, help="directory containing model file", ) parser.add_argument( "--use-temp-file", action="store_true", help="use the tempfile library while processing (helpful when running out of memory, process killed)", ) 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( "--model-name", type=str, default=None, help="name of the model", ) parser.add_argument( "--verbose", action="store_true", help="increase output verbosity", ) parser.add_argument( "--split-max-tensors", type=int, default=0, help="max tensors in each split", ) parser.add_argument( "--split-max-size", type=str, default="0", help="max size per split N(M|G)", ) parser.add_argument( "--dry-run", action="store_true", help="only print out a split plan and exit, without writing any new files", ) parser.add_argument( "--no-tensor-first-split", action="store_true", help="do not add tensors to the first split (disabled by default)" ) parser.add_argument( "--metadata", type=Path, help="Specify the path for an authorship metadata override file" ) return parser.parse_args() def split_str_to_n_bytes(split_str: str) -> int: if split_str.endswith("K"): n = int(split_str[:-1]) * 1000 elif split_str.endswith("M"): n = int(split_str[:-1]) * 1000 * 1000 elif split_str.endswith("G"): n = int(split_str[:-1]) * 1000 * 1000 * 1000 elif split_str.isnumeric(): n = int(split_str) else: raise ValueError(f"Invalid split size: {split_str}, must be a number, optionally followed by K, M, or G") if n < 0: raise ValueError(f"Invalid split size: {split_str}, must be positive") return n def main() -> None: args = parse_args() if args.verbose: logging.basicConfig(level=logging.DEBUG) else: logging.basicConfig(level=logging.INFO) dir_model = args.model if not dir_model.is_dir(): logger.error(f'Error: {args.model} is not a directory') sys.exit(1) 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, "tq1_0": gguf.LlamaFileType.MOSTLY_TQ1_0, "tq2_0": gguf.LlamaFileType.MOSTLY_TQ2_0, "auto": gguf.LlamaFileType.GUESSED, } is_split = args.split_max_tensors > 0 or args.split_max_size != "0" if args.use_temp_file and is_split: logger.error("Error: Cannot use temp file when splitting") sys.exit(1) if args.outfile is not None: fname_out = args.outfile else: fname_out = dir_model logger.info(f"Loading model: {dir_model.name}") hparams = Model.load_hparams(dir_model) with torch.inference_mode(): output_type = ftype_map[args.outtype] model_architecture = hparams["architectures"][0] try: model_class = Model.from_model_architecture(model_architecture) except NotImplementedError: logger.error(f"Model {model_architecture} is not supported") sys.exit(1) model_instance = model_class(dir_model=dir_model, ftype=output_type, fname_out=fname_out, is_big_endian=args.bigendian, use_temp_file=args.use_temp_file, eager=args.no_lazy, metadata_override=args.metadata, model_name=args.model_name, split_max_tensors=args.split_max_tensors, split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run, small_first_shard=args.no_tensor_first_split) if args.vocab_only: logger.info("Exporting model vocab...") model_instance.write_vocab() logger.info(f"Model vocab successfully exported to {model_instance.fname_out}") else: logger.info("Exporting model...") model_instance.write() out_path = f"{model_instance.fname_out.parent}{os.sep}" if is_split else model_instance.fname_out logger.info(f"Model successfully exported to {out_path}") if __name__ == '__main__': main()