llama.cpp/convert.py

#!/usr/bin/env python3
from __future__ import annotations

import logging
import argparse
import concurrent.futures
import enum
import faulthandler
import functools
import itertools
import json
import math
import mmap
import os
import pickle
import re
import signal
import struct
import sys
import textwrap
import time
import zipfile
from abc import ABC, abstractmethod
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
from dataclasses import dataclass
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable, Optional

import numpy as np
from sentencepiece import SentencePieceProcessor

if 'NO_LOCAL_GGUF' not in os.environ:
    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf

if TYPE_CHECKING:
    from typing_extensions import Self, TypeAlias

logger = logging.getLogger("convert")

if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'):
    faulthandler.register(signal.SIGUSR1)

NDArray: TypeAlias = 'np.ndarray[Any, Any]'

ARCH = gguf.MODEL_ARCH.LLAMA

DEFAULT_CONCURRENCY = 8

ADDED_TOKENS_FILE = 'added_tokens.json'
FAST_TOKENIZER_FILE = 'tokenizer.json'

#
# data types
#


@dataclass(frozen=True)
class DataType:
    name: str
    dtype: np.dtype[Any]
    valid_conversions: list[str]

    def elements_to_bytes(self, n_elements: int) -> int:
        return n_elements * self.dtype.itemsize


@dataclass(frozen=True)
class UnquantizedDataType(DataType):
    pass


DT_F16  = UnquantizedDataType('F16',  dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
DT_F32  = UnquantizedDataType('F32',  dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
DT_I32  = UnquantizedDataType('I32',  dtype = np.dtype(np.int16),   valid_conversions = [])
DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16),  valid_conversions = ['F32', 'F16', 'Q8_0'])


@dataclass(frozen=True)
class QuantizedDataType(DataType):
    block_size: int
    quantized_dtype: np.dtype[Any]
    ggml_type: gguf.GGMLQuantizationType

    def quantize(self, arr: NDArray) -> NDArray:
        raise NotImplementedError(f'Quantization for {self.name} not implemented')

    def elements_to_bytes(self, n_elements: int) -> int:
        assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}'
        return self.quantized_dtype.itemsize * (n_elements // self.block_size)


@dataclass(frozen=True)
class Q8_0QuantizedDataType(QuantizedDataType):
    # Mini Q8_0 quantization in Python!
    def quantize(self, arr: NDArray) -> NDArray:
        assert arr.size % self.block_size == 0 and arr.size != 0, f'Bad array size {arr.size}'
        assert arr.dtype == np.float32, f'Bad array type {arr.dtype}'
        n_blocks = arr.size // self.block_size
        blocks = arr.reshape((n_blocks, self.block_size))
        # Much faster implementation of block quantization contributed by @Cebtenzzre

        def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[tuple[Any, Any]]:
            d = abs(blocks).max(axis = 1) / np.float32(127)
            with np.errstate(divide = 'ignore'):
                qs = (blocks / d[:, None]).round()
            qs[d == 0] = 0
            yield from zip(d, qs)
        return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype)


DT_Q8_0 = Q8_0QuantizedDataType('Q8_0',
                                dtype = np.dtype(np.float32), valid_conversions = [],
                                ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32,
                                quantized_dtype = np.dtype([('d', '<f2'), ('qs', 'i1', (32,))]))

# Quantized types skipped here because they may also map to np.float32
NUMPY_TYPE_TO_DATA_TYPE: dict[np.dtype[Any], DataType] = {}
for dt in (DT_BF16, DT_F16, DT_F32, DT_I32):
    if dt.dtype in NUMPY_TYPE_TO_DATA_TYPE:
        raise ValueError(f'Invalid duplicate data type {dt}')
    NUMPY_TYPE_TO_DATA_TYPE[dt.dtype] = dt

SAFETENSORS_DATA_TYPES: dict[str, DataType] = {
    'BF16': DT_BF16,
    'F16': DT_F16,
    'F32': DT_F32,
    'I32': DT_I32,
}

# TODO: match this with `llama_ftype`
# TODO: rename to LLAMAFileType
# TODO: move to `gguf.py`


class GGMLFileType(enum.IntEnum):
    AllF32     = 0
    MostlyF16  = 1  # except 1d tensors
    MostlyQ8_0 = 7  # except 1d tensors

    def type_for_tensor(self, name: str, tensor: LazyTensor) -> DataType:
        dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self)
        if dt is None:
            raise ValueError(self)
        # Convert all 1D tensors to F32.  Most of the codebase that takes in 1D tensors only handles F32 tensors, and most of the outputs tensors are F32.
        #  Also The 1d tensors aren't much of a performance/size issue.  So instead of having to have separate F32 and F16 implementations of both, just convert everything to F32 for now.
        return dt if len(tensor.shape) > 1 else DT_F32


GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {
    GGMLFileType.AllF32    : DT_F32,
    GGMLFileType.MostlyF16 : DT_F16,
    GGMLFileType.MostlyQ8_0: DT_Q8_0,
}

#
# hparams loading
#


@dataclass
class Params:
    n_vocab:        int
    n_embd:         int
    n_layer:        int
    n_ctx:          int
    n_ff:           int
    n_head:         int
    n_head_kv:      int
    n_experts:      int | None = None
    n_experts_used: int | None = None
    f_norm_eps:     float | None = None

    rope_scaling_type: gguf.RopeScalingType | None = None
    f_rope_freq_base: float | None = None
    f_rope_scale: float | None = None
    n_orig_ctx: int | None = None
    rope_finetuned: bool | None = None

    ftype: GGMLFileType | None = None

    # path to the directory containing the model files
    path_model: Path | None = None

    @staticmethod
    def guessed(model: LazyModel) -> Params:
        # try transformer naming first
        n_vocab, n_embd = model["model.embed_tokens.weight"].shape if "model.embed_tokens.weight" in model else model["tok_embeddings.weight"].shape

        # try transformer naming first
        if "model.layers.0.self_attn.q_proj.weight" in model:
            n_layer = next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model)
        elif "model.layers.0.self_attn.W_pack.weight" in model:   # next: try baichuan naming
            n_layer = next(i for i in itertools.count() if f"model.layers.{i}.self_attn.W_pack.weight" not in model)
        else:
            n_layer = next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model)

        if n_layer < 1:
            msg = """\
                failed to guess 'n_layer'. This model is unknown or unsupported.
                Suggestion: provide 'config.json' of the model in the same directory containing model files."""
            raise KeyError(textwrap.dedent(msg))

        n_head = n_embd // 128 # guessed
        n_mult = 256           # guessed

        # TODO: verify this
        n_ff = int(2 * (4 * n_embd) / 3)
        n_ff = n_mult * ((n_ff + n_mult - 1) // n_mult)

        return Params(
            n_vocab    = n_vocab,
            n_embd     = n_embd,
            n_layer    = n_layer,
            n_ctx      = -1,
            n_ff       = n_ff,
            n_head     = n_head,
            n_head_kv  = n_head,
            f_norm_eps = 1e-5,
        )

    @staticmethod
    def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params:
        with open(config_path) as f:
            config = json.load(f)

        rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None
        rope_scaling = config.get("rope_scaling")

        if rope_scaling is not None and (typ := rope_scaling.get("type")):
            rope_factor = rope_scaling.get("factor")
            f_rope_scale = rope_factor
            if typ == "linear":
                rope_scaling_type = gguf.RopeScalingType.LINEAR
            elif typ == "yarn":
                rope_scaling_type = gguf.RopeScalingType.YARN
                n_orig_ctx = rope_scaling['original_max_position_embeddings']
                rope_finetuned = rope_scaling['finetuned']
            else:
                raise NotImplementedError(f'Unknown rope scaling type: {typ}')

        if "max_sequence_length" in config:
            n_ctx = config["max_sequence_length"]
        elif "max_position_embeddings" in config:
            n_ctx = config["max_position_embeddings"]
        else:
            msg = """\
                failed to guess 'n_ctx'. This model is unknown or unsupported.
                Suggestion: provide 'config.json' of the model in the same directory containing model files."""
            raise KeyError(textwrap.dedent(msg))

        n_experts      = None
        n_experts_used = None

        if "num_local_experts" in config:
            n_experts = config["num_local_experts"]
            n_experts_used = config["num_experts_per_tok"]

        return Params(
            n_vocab           = config["vocab_size"],
            n_embd            = config["hidden_size"],
            n_layer           = config["num_hidden_layers"],
            n_ctx             = n_ctx,
            n_ff              = config["intermediate_size"],
            n_head            = (n_head := config["num_attention_heads"]),
            n_head_kv         = config.get("num_key_value_heads", n_head),
            n_experts         = n_experts,
            n_experts_used    = n_experts_used,
            f_norm_eps        = config["rms_norm_eps"],
            f_rope_freq_base  = config.get("rope_theta"),
            rope_scaling_type = rope_scaling_type,
            f_rope_scale      = f_rope_scale,
            n_orig_ctx        = n_orig_ctx,
            rope_finetuned    = rope_finetuned,
        )

    # LLaMA v2 70B params.json
    # {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1}
    @staticmethod
    def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params:
        with open(config_path) as f:
            config = json.load(f)

        n_experts      = None
        n_experts_used = None
        f_rope_freq_base = None
        n_ff = None

        # hack to determine LLaMA v1 vs v2 vs CodeLlama
        if config.get("moe"):
            # Mixtral
            n_ctx = 32768
        elif config.get("rope_theta") == 1000000:
            # CodeLlama
            n_ctx = 16384
        elif config["norm_eps"] == 1e-05:
            # LLaMA v2
            n_ctx = 4096
        else:
            # LLaMA v1
            n_ctx = 2048

        if "layers.0.feed_forward.w1.weight" in model:
            n_ff = model["layers.0.feed_forward.w1.weight"].shape[0]

        if config.get("moe"):
            n_ff = model["layers.0.feed_forward.experts.0.w1.weight"].shape[0]
            n_experts      = config["moe"]["num_experts"]
            n_experts_used = config["moe"]["num_experts_per_tok"]
            f_rope_freq_base = 1e6

        assert n_ff is not None

        return Params(
            n_vocab          = model["tok_embeddings.weight"].shape[0],
            n_embd           = config["dim"],
            n_layer          = config["n_layers"],
            n_ctx            = n_ctx,
            n_ff             = n_ff,
            n_head           = (n_head := config["n_heads"]),
            n_head_kv        = config.get("n_kv_heads", n_head),
            n_experts        = n_experts,
            n_experts_used   = n_experts_used,
            f_norm_eps       = config["norm_eps"],
            f_rope_freq_base = config.get("rope_theta", f_rope_freq_base),
        )

    @staticmethod
    def load(model_plus: ModelPlus) -> Params:
        hf_config_path   = model_plus.paths[0].parent / "config.json"
        orig_config_path = model_plus.paths[0].parent / "params.json"

        if hf_config_path.exists():
            params = Params.loadHFTransformerJson(model_plus.model, hf_config_path)
        elif orig_config_path.exists():
            params = Params.loadOriginalParamsJson(model_plus.model, orig_config_path)
        elif model_plus.format != 'none':
            params = Params.guessed(model_plus.model)
        else:
            raise ValueError('Cannot guess params when model format is none')

        params.path_model = model_plus.paths[0].parent

        return params


@dataclass
class Metadata:
    name: Optional[str] = None
    author: Optional[str] = None
    version: Optional[str] = None
    url: Optional[str] = None
    description: Optional[str] = None
    licence: Optional[str] = None
    source_url: Optional[str] = None
    source_hf_repo: Optional[str] = None

    @staticmethod
    def load(metadata_path: Path) -> Metadata:
        if metadata_path is None or not metadata_path.exists():
            return Metadata()

        with open(metadata_path, 'r') as file:
            data = json.load(file)

        # Create a new Metadata instance
        metadata = Metadata()

        # Assigning values to Metadata attributes if they exist in the JSON file
        # This is based on LLM_KV_NAMES mapping in llama.cpp
        metadata.name = data.get("general.name")
        metadata.author = data.get("general.author")
        metadata.version = data.get("general.version")
        metadata.url = data.get("general.url")
        metadata.description = data.get("general.description")
        metadata.license = data.get("general.license")
        metadata.source_url = data.get("general.source.url")
        metadata.source_hf_repo = data.get("general.source.huggingface.repository")

        return metadata


#
# vocab
#


@runtime_checkable
class BaseVocab(Protocol):
    tokenizer_model: ClassVar[str]
    name: ClassVar[str]


class NoVocab(BaseVocab):
    tokenizer_model = "no_vocab"
    name = "no_vocab"

    def __repr__(self) -> str:
        return "<NoVocab for a model without integrated vocabulary>"


@runtime_checkable
class Vocab(BaseVocab, Protocol):
    vocab_size: int
    added_tokens_dict: dict[str, int]
    added_tokens_list: list[str]
    fname_tokenizer: Path

    def __init__(self, base_path: Path): ...
    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ...


class BpeVocab(Vocab):
    tokenizer_model = "gpt2"
    name = "bpe"

    def __init__(self, base_path: Path):
        added_tokens: dict[str, int] = {}

        if (fname_tokenizer := base_path / 'vocab.json').exists():
            # "slow" tokenizer
            with open(fname_tokenizer, encoding="utf-8") as f:
                self.vocab = json.load(f)

            try:
                # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
                with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
                    added_tokens = json.load(f)
            except FileNotFoundError:
                pass
        else:
            # "fast" tokenizer
            fname_tokenizer = base_path / FAST_TOKENIZER_FILE

            # if this fails, FileNotFoundError propagates to caller
            with open(fname_tokenizer, encoding="utf-8") as f:
                tokenizer_json = json.load(f)

            tokenizer_model: dict[str, Any] = tokenizer_json['model']
            if (
                tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False)
                or tokenizer_json['decoder']['type'] != 'ByteLevel'
            ):
                raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer')

            self.vocab = tokenizer_model["vocab"]

            if (added := tokenizer_json.get('added_tokens')) is not None:
                # Added tokens here can be duplicates of the main vocabulary.
                added_tokens = {item['content']: item['id']
                                for item in added
                                if item['content'] not in self.vocab}

        vocab_size   = len(self.vocab)
        expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
        actual_ids   = sorted(added_tokens.values())
        if expected_ids != actual_ids:
            expected_end_id = vocab_size + len(actual_ids) - 1
            raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range "
                             f"{vocab_size} - {expected_end_id}; got {actual_ids}")

        items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
        self.added_tokens_dict    = added_tokens
        self.added_tokens_list    = [text for (text, idx) in items]
        self.vocab_size_base      = vocab_size
        self.vocab_size           = self.vocab_size_base + len(self.added_tokens_list)
        self.fname_tokenizer      = fname_tokenizer

    def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
        reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()}

        for i, _ in enumerate(self.vocab):
            yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL

    def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
        for text in self.added_tokens_list:
            score = -1000.0
            yield text.encode("utf-8"), score, gguf.TokenType.CONTROL

    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
        yield from self.bpe_tokens()
        yield from self.added_tokens()

    def __repr__(self) -> str:
        return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"


class SentencePieceVocab(Vocab):
    tokenizer_model = "llama"
    name = "spm"

    def __init__(self, base_path: Path):
        added_tokens: dict[str, int] = {}
        if (fname_tokenizer := base_path / 'tokenizer.model').exists():
            # normal location
            try:
                with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
                    added_tokens = json.load(f)
            except FileNotFoundError:
                pass
        elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists():
            # not found in alternate location either
            raise FileNotFoundError('Cannot find tokenizer.model')

        self.sentencepiece_tokenizer = SentencePieceProcessor()
        self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer))
        vocab_size = self.sentencepiece_tokenizer.vocab_size()

        new_tokens       = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}
        expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))
        actual_new_ids   = sorted(new_tokens.keys())

        if expected_new_ids != actual_new_ids:
            raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}")

        # Token pieces that were added to the base vocabulary.
        self.added_tokens_dict  = added_tokens
        self.added_tokens_list  = [new_tokens[id] for id in actual_new_ids]
        self.vocab_size_base    = vocab_size
        self.vocab_size         = self.vocab_size_base + len(self.added_tokens_list)
        self.fname_tokenizer    = fname_tokenizer

    def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
        tokenizer = self.sentencepiece_tokenizer
        for i in range(tokenizer.vocab_size()):
            piece = tokenizer.IdToPiece(i)
            text         = piece.encode("utf-8")
            score: float = tokenizer.GetScore(i)

            toktype = gguf.TokenType.NORMAL
            if tokenizer.IsUnknown(i):
                toktype = gguf.TokenType.UNKNOWN
            if tokenizer.IsControl(i):
                toktype = gguf.TokenType.CONTROL

            # NOTE: I think added_tokens are user defined.
            # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
            # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED

            if tokenizer.IsUnused(i):
                toktype = gguf.TokenType.UNUSED
            if tokenizer.IsByte(i):
                toktype = gguf.TokenType.BYTE

            yield text, score, toktype

    def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
        for text in self.added_tokens_list:
            score = -1000.0
            yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED

    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
        yield from self.sentencepiece_tokens()
        yield from self.added_tokens()

    def __repr__(self) -> str:
        return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"


class LlamaHfVocab(Vocab):
    tokenizer_model = "llama"
    name = "hfft"

    def __init__(self, base_path: Path):
        fname_tokenizer = base_path / FAST_TOKENIZER_FILE
        # if this fails, FileNotFoundError propagates to caller
        with open(fname_tokenizer, encoding='utf-8') as f:
            tokenizer_json = json.load(f)

        # pre-check so we know if we need transformers
        tokenizer_model: dict[str, Any] = tokenizer_json['model']
        is_llama3 = (
            tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False)
            and not tokenizer_model.get('byte_fallback', True)
        )
        if is_llama3:
            raise TypeError('Llama 3 must be converted with BpeVocab')

        if not is_llama3 and (
            tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
            or tokenizer_json['decoder']['type'] != 'Sequence'
        ):
            raise FileNotFoundError('Cannot find Llama BPE tokenizer')

        try:
            from transformers import AutoTokenizer
        except ImportError as e:
            raise ImportError(
                "To use LlamaHfVocab, please install the `transformers` package. "
                "You can install it with `pip install transformers`."
            ) from e

        # Allow the tokenizer to default to slow or fast versions.
        # Explicitly set tokenizer to use local paths.
        self.tokenizer = AutoTokenizer.from_pretrained(
            base_path,
            cache_dir=base_path,
            local_files_only=True,
        )
        assert self.tokenizer.is_fast  # assume tokenizer.json is used

        # Initialize lists and dictionaries for added tokens
        self.added_tokens_list = []
        self.added_tokens_dict = dict()
        self.added_tokens_ids  = set()

        # Process added tokens
        for tok, tokidx in sorted(
            self.tokenizer.get_added_vocab().items(), key=lambda x: x[1]
        ):
            # Only consider added tokens that are not in the base vocabulary
            if tokidx >= self.tokenizer.vocab_size:
                self.added_tokens_list.append(tok)
                self.added_tokens_dict[tok] = tokidx
                self.added_tokens_ids.add(tokidx)

        # Store special tokens and their IDs
        self.specials = {
            tok: self.tokenizer.get_vocab()[tok]
            for tok in self.tokenizer.all_special_tokens
        }
        self.special_ids = set(self.tokenizer.all_special_ids)

        # Set vocabulary sizes
        self.vocab_size_base = self.tokenizer.vocab_size
        self.vocab_size      = self.vocab_size_base + len(self.added_tokens_list)

        self.fname_tokenizer = fname_tokenizer

    def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
        reverse_vocab = {
            id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items()
        }

        for token_id in range(self.vocab_size_base):
            # Skip processing added tokens here
            if token_id in self.added_tokens_ids:
                continue

            # Convert token text to bytes
            token_text = reverse_vocab[token_id].encode("utf-8")

            # Yield token text, score, and type
            yield token_text, self.get_token_score(token_id), self.get_token_type(
                token_id, token_text, self.special_ids  # Reuse already stored special IDs
            )

    def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType:
        # Special case for byte tokens
        if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
            return gguf.TokenType.BYTE

        # Determine token type based on whether it's a special token
        return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL

    def get_token_score(self, token_id: int) -> float:
        # Placeholder for actual logic to determine the token's score
        # This needs to be implemented based on specific requirements
        return -1000.0  # Default score

    def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
        for text in self.added_tokens_list:
            if text in self.specials:
                toktype = self.get_token_type(self.specials[text], b'', self.special_ids)
                score = self.get_token_score(self.specials[text])
            else:
                toktype = gguf.TokenType.USER_DEFINED
                score = -1000.0

            yield text.encode("utf-8"), score, toktype

    def has_newline_token(self):
        return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab

    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
        yield from self.hf_tokens()
        yield from self.added_tokens()

    def __repr__(self) -> str:
        return f"<LlamaHfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"


#
# data loading
# TODO: reuse (probably move to gguf.py?)
#


def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:
    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))


class Tensor(ABC):
    ndarray: NDArray
    data_type: DataType

    @abstractmethod
    def astype(self, data_type: DataType) -> Self: ...
    @abstractmethod
    def permute(self, n_head: int, n_head_kv: int) -> Self: ...
    @abstractmethod
    def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> Self: ...
    @abstractmethod
    def part(self, n_part: int) -> Self: ...
    @abstractmethod
    def to_ggml(self) -> GGMLCompatibleTensor: ...


def bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray:
    assert bf16_arr.dtype == np.uint16, f"Input array should be of dtype uint16, but got {bf16_arr.dtype}"
    fp32_arr = bf16_arr.astype(np.uint32) << 16
    return fp32_arr.view(np.float32)


class UnquantizedTensor(Tensor):
    def __init__(self, ndarray: NDArray):
        assert isinstance(ndarray, np.ndarray)
        self.ndarray = ndarray
        self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype]

    def astype(self, data_type: DataType) -> UnquantizedTensor:
        dtype = data_type.dtype
        if self.data_type == DT_BF16:
            self.ndarray = bf16_to_fp32(self.ndarray)
        return UnquantizedTensor(self.ndarray.astype(dtype))

    def to_ggml(self) -> Self:
        return self

    def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor:
        r = self.ndarray.shape[0] // 3
        return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head, n_head_kv))

    def part(self, n_part: int) -> UnquantizedTensor:
        r = self.ndarray.shape[0] // 3
        return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])

    def permute(self, n_head: int, n_head_kv: int) -> UnquantizedTensor:
        return UnquantizedTensor(permute(self.ndarray, n_head, n_head_kv))


def load_unquantized(lazy_tensor: LazyTensor, expected_dtype: Any = None, convert: bool = False) -> NDArray:
    tensor = lazy_tensor.load()
    assert isinstance(tensor, UnquantizedTensor)

    # double-check:
    actual_shape = list(tensor.ndarray.shape)
    assert actual_shape == lazy_tensor.shape, (actual_shape, lazy_tensor.shape)
    if expected_dtype is not None and expected_dtype != tensor.ndarray.dtype:
        if convert:
            tensor.ndarray = tensor.ndarray.astype(expected_dtype)
        else:
            raise ValueError(f'expected this tensor to have dtype {expected_dtype}, got {tensor.ndarray.dtype}')

    return tensor.ndarray


GGMLCompatibleTensor = UnquantizedTensor


@dataclass
class LazyTensor:
    _load: Callable[[], Tensor]
    shape: list[int]
    data_type: DataType
    description: str

    def load(self) -> Tensor:
        ret = self._load()
        # Should be okay if it maps to the same numpy type?
        assert ret.data_type == self.data_type or (self.data_type.dtype == ret.data_type.dtype), \
            (self.data_type, ret.data_type, self.description)
        return ret

    def astype(self, data_type: DataType) -> LazyTensor:
        self.validate_conversion_to(data_type)

        def load() -> Tensor:
            return self.load().astype(data_type)
        return LazyTensor(load, self.shape, data_type, f'convert({data_type}) {self.description}')

    def validate_conversion_to(self, data_type: DataType) -> None:
        if data_type != self.data_type and data_type.name not in self.data_type.valid_conversions:
            raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.')


LazyModel: TypeAlias = 'dict[str, LazyTensor]'


@dataclass
class ModelPlus:
    model: LazyModel
    paths: list[Path]  # Where this was read from.
    format: Literal['ggml', 'torch', 'safetensors', 'none']
    vocab: BaseVocab | None  # For GGML models (which have vocab built in), the vocab.


def merge_sharded(models: list[LazyModel]) -> LazyModel:
    # Original LLaMA models have each file contain one part of each tensor.
    # Use a dict instead of a set to preserve order.
    names = {name: None for model in models for name in model}

    def convert(name: str) -> LazyTensor:
        lazy_tensors = [model[name] for model in models]
        if len(lazy_tensors) == 1:
            # only one file; don't go through this procedure since there might
            # be quantized tensors
            return lazy_tensors[0]
        if len(lazy_tensors[0].shape) == 1:
            # the tensor is just duplicated in every file
            return lazy_tensors[0]
        if name.startswith('tok_embeddings.') or \
           name.endswith('.attention.wo.weight') or \
           name.endswith('.feed_forward.w2.weight'):
            # split by columns
            axis = 1
        else:
            # split by rows
            axis = 0
        concatenated_shape = list(lazy_tensors[0].shape)
        concatenated_shape[axis] = sum(tensor.shape[axis] for tensor in lazy_tensors)

        def load() -> UnquantizedTensor:
            ndarrays = [load_unquantized(tensor) for tensor in lazy_tensors]
            concatenated = np.concatenate(ndarrays, axis=axis)
            return UnquantizedTensor(concatenated)
        description = 'concatenated[[' + '] | ['.join(lt.description for lt in lazy_tensors) + ']]'
        return LazyTensor(load, concatenated_shape, lazy_tensors[0].data_type, description)
    return {name: convert(name) for name in names}


def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus:
    formats = set(mp.format for mp in models_plus)
    assert len(formats) == 1, "different formats?"
    format = formats.pop()
    paths = [path for mp in models_plus for path in mp.paths]
    # Use the first non-None vocab, if any.
    try:
        vocab = next(mp.vocab for mp in models_plus if mp.vocab is not None)
    except StopIteration:
        vocab = None

    if any("model.embed_tokens.weight" in mp.model for mp in models_plus):
        # Transformers models put different tensors in different files, but
        # don't split individual tensors between files.
        model: LazyModel = {}
        for mp in models_plus:
            model.update(mp.model)
    else:
        model = merge_sharded([mp.model for mp in models_plus])

    return ModelPlus(model, paths, format, vocab)  # pytype: disable=wrong-arg-types


def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTensor:
    def load() -> Tensor:
        return lazy_tensor.load().permute(n_head, n_head_kv)
    return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)


def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int, n_head_kv: int) -> LazyTensor:
    def load() -> Tensor:
        return lazy_tensor.load().permute_part(n_part, n_head, n_head_kv)
    s = lazy_tensor.shape.copy()
    s[0] = s[0] // 3
    return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)


def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor:
    def load() -> Tensor:
        return lazy_tensor.load().part(n_part)
    s = lazy_tensor.shape.copy()
    s[0] = s[0] // 3
    return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description)


def pack_experts_lazy(lazy_tensors: list[LazyTensor]) -> LazyTensor:
    def load() -> Tensor:
        tensors = [lazy_tensor.load() for lazy_tensor in lazy_tensors]
        return UnquantizedTensor(np.array([tensor.ndarray for tensor in tensors]))
    s = lazy_tensors[0].shape.copy()
    s.insert(0, len(lazy_tensors))
    return LazyTensor(load, s, lazy_tensors[0].data_type, 'pack_experts ' + ' | '.join(lt.description for lt in lazy_tensors))


# Functionality that simulates `torch.load` but where individual tensors are
# only loaded into memory on demand, not all at once.
# PyTorch can't do this natively as of time of writing:
# - https://github.com/pytorch/pytorch/issues/64327
# This allows us to de-shard without multiplying RAM usage, and also
# conveniently drops the PyTorch dependency (though we still need numpy).


@dataclass
class LazyStorageKind:
    data_type: DataType


@dataclass
class LazyStorage:
    load: Callable[[int, int], NDArray]
    kind: LazyStorageKind
    description: str


class LazyUnpickler(pickle.Unpickler):
    def __init__(self, fp: IO[bytes], data_base_path: str, zip_file: zipfile.ZipFile):
        super().__init__(fp)
        self.data_base_path = data_base_path
        self.zip_file = zip_file

    def persistent_load(self, pid: Any) -> Any:
        assert pid[0] == 'storage'
        assert isinstance(pid[1], LazyStorageKind)
        data_type = pid[1].data_type
        filename_stem = pid[2]
        filename = f'{self.data_base_path}/{filename_stem}'
        info = self.zip_file.getinfo(filename)

        def load(offset: int, elm_count: int) -> NDArray:
            dtype = data_type.dtype
            with self.zip_file.open(info) as fp:
                fp.seek(offset * dtype.itemsize)
                size = elm_count * dtype.itemsize
                data = fp.read(size)
            assert len(data) == size
            return np.frombuffer(data, dtype)
        description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}'
        return LazyStorage(load=load, kind=pid[1], description=description)

    @staticmethod
    def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any,
                               requires_grad: Any, backward_hooks: Any, metadata: Any = None) -> LazyTensor:
        assert isinstance(storage, LazyStorage)

        def load() -> UnquantizedTensor:
            elm_count = stride[0] * size[0]
            return UnquantizedTensor(storage.load(storage_offset, elm_count).reshape(size))
        description = f'pickled storage_offset={storage_offset} in {storage.description}'
        return LazyTensor(load, list(size), storage.kind.data_type, description)

    @staticmethod
    def rebuild_from_type_v2(func, new_type, args, state):
        return func(*args)

    CLASSES: dict[tuple[str, str], type[LazyTensor] | LazyStorageKind] = {
        # getattr used here as a workaround for mypy not being smart enough to determine
        # the staticmethods have a __func__ attribute.
        ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'),
        ('torch._utils', '_rebuild_tensor_v2'): getattr(lazy_rebuild_tensor_v2, '__func__'),
        ('torch', 'BFloat16Storage'): LazyStorageKind(DT_BF16),
        ('torch', 'HalfStorage'): LazyStorageKind(DT_F16),
        ('torch', 'FloatStorage'): LazyStorageKind(DT_F32),
        ('torch', 'IntStorage'): LazyStorageKind(DT_I32),
        ('torch', 'Tensor'): LazyTensor,
    }

    def find_class(self, module: str, name: str) -> Any:
        if not module.startswith('torch'):
            return super().find_class(module, name)
        return self.CLASSES[(module, name)]


def lazy_load_torch_file(outer_fp: IO[bytes], path: Path) -> ModelPlus:
    zf = zipfile.ZipFile(outer_fp)
    pickle_paths = [name for name in zf.namelist() if name.endswith('.pkl')]
    assert len(pickle_paths) == 1, pickle_paths
    pickle_fp = zf.open(pickle_paths[0], 'r')
    unpickler = LazyUnpickler(pickle_fp,
                              data_base_path=pickle_paths[0][:-4],
                              zip_file=zf)
    model = unpickler.load()
    if 'model' in model: model = model['model']
    as_dict = dict(model.items())
    return ModelPlus(model=as_dict, paths=[path], format='torch', vocab=None)


def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus:
    header_size, = struct.unpack('<Q', fp.read(8))
    header: dict[str, dict[str, Any]] = json.loads(fp.read(header_size))
    # Use mmap for the actual data to avoid race conditions with the file offset.
    mapped = memoryview(mmap.mmap(fp.fileno(), 0, access=mmap.ACCESS_READ))
    byte_buf = mapped[8 + header_size:]

    def convert(info: dict[str, Any]) -> LazyTensor:
        data_type = SAFETENSORS_DATA_TYPES[info['dtype']]
        numpy_dtype = data_type.dtype
        shape: list[int] = info['shape']
        begin, end = info['data_offsets']
        assert 0 <= begin <= end <= len(byte_buf)
        assert end - begin == math.prod(shape) * numpy_dtype.itemsize
        buf = byte_buf[begin:end]

        def load() -> UnquantizedTensor:
            return UnquantizedTensor(np.frombuffer(buf, dtype=numpy_dtype).reshape(shape))
        description = f'safetensors begin={begin} end={end} type={data_type} path={path}'
        return LazyTensor(load, shape, data_type, description)
    model = {name: convert(info) for (name, info) in header.items() if name != '__metadata__'}
    return ModelPlus(model=model, paths=[path], format='safetensors', vocab=None)


def must_read(fp: IO[bytes], length: int) -> bytes:
    ret = fp.read(length)
    if len(ret) < length:
        raise EOFError("unexpectedly reached end of file")
    return ret


@functools.lru_cache(maxsize=None)
def lazy_load_file(path: Path) -> ModelPlus:
    fp = open(path, 'rb')
    first8 = fp.read(8)
    fp.seek(0)
    if first8[:2] == b'PK':
        # A zip file, i.e. PyTorch format
        return lazy_load_torch_file(fp, path)
    elif struct.unpack('<Q', first8)[0] < 16 * 1024 * 1024:
        # Probably safetensors
        return lazy_load_safetensors_file(fp, path)
    else:
        raise ValueError(f"unknown format: {path}")


In = TypeVar('In')
Out = TypeVar('Out')


def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: int | None = None, use_processpool_executor: bool = False) -> Iterable[Out]:
    '''Parallel map, but with backpressure.  If the caller doesn't call `next`
    fast enough, this will stop calling `func` at some point rather than
    letting results pile up in memory.  Specifically, there is a max of one
    output value buffered per thread.'''
    if concurrency < 2:
        yield from map(func, iterable)
        # Not reached.
    iterable = iter(iterable)
    executor_class: type[ThreadPoolExecutor] | type[ProcessPoolExecutor]
    if use_processpool_executor:
        executor_class = ProcessPoolExecutor
    else:
        executor_class = ThreadPoolExecutor
    with executor_class(max_workers=max_workers) as executor:
        futures: list[concurrent.futures.Future[Out]] = []
        done = False
        for _ in range(concurrency):
            try:
                futures.append(executor.submit(func, next(iterable)))
            except StopIteration:
                done = True
                break

        while futures:
            result = futures.pop(0).result()
            while not done and len(futures) < concurrency:
                try:
                    futures.append(executor.submit(func, next(iterable)))
                except StopIteration:
                    done = True
                    break
            yield result


def check_vocab_size(params: Params, vocab: BaseVocab, pad_vocab: bool = False) -> None:
    # Handle special case where the model's vocab size is not set
    if params.n_vocab == -1:
        raise ValueError(
            "The model's vocab size is set to -1 in params.json. Please update it manually."
            + (f" Maybe {vocab.vocab_size}?" if isinstance(vocab, Vocab) else ""),
        )
    if not isinstance(vocab, Vocab):
        return  # model has no vocab

    # Check for a vocab size mismatch
    if params.n_vocab == vocab.vocab_size:
        logger.warning("Ignoring added_tokens.json since model matches vocab size without it.")
        return

    if pad_vocab and params.n_vocab > vocab.vocab_size:
        pad_count = params.n_vocab - vocab.vocab_size
        logger.debug(
            f"Padding vocab with {pad_count} token(s) - <dummy00001> through <dummy{pad_count:05}>"
        )
        for i in range(1, pad_count + 1):
            vocab.added_tokens_dict[f"<dummy{i:05}>"] = -1
            vocab.added_tokens_list.append(f"<dummy{i:05}>")
        vocab.vocab_size = params.n_vocab
        return

    msg = f"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer} has {vocab.vocab_size})."
    if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20:
        msg += f"  Most likely you are missing added_tokens.json (should be in {vocab.fname_tokenizer.parent})."
    if vocab.vocab_size < params.n_vocab:
        msg += " Add the --pad-vocab option and try again."

    raise ValueError(msg)


class OutputFile:
    def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE):
        self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)

    def add_meta_model(self, params: Params, metadata: Metadata) -> None:
        # Metadata About The Model And Its Provenence
        name = "LLaMA"
        if metadata is not None and metadata.name is not None:
            name = metadata.name
        elif params.path_model is not None:
            name = params.path_model.name
        elif params.n_ctx == 4096:
            # Heuristic detection of LLaMA v2 model
            name = "LLaMA v2"

        self.gguf.add_name(name)

        if metadata is not None:
            if metadata.author is not None:
                self.gguf.add_author(metadata.author)
            if metadata.version is not None:
                self.gguf.add_version(metadata.version)
            if metadata.url is not None:
                self.gguf.add_url(metadata.url)
            if metadata.description is not None:
                self.gguf.add_description(metadata.description)
            if metadata.licence is not None:
                self.gguf.add_licence(metadata.licence)
            if metadata.source_url is not None:
                self.gguf.add_source_url(metadata.source_url)
            if metadata.source_hf_repo is not None:
                self.gguf.add_source_hf_repo(metadata.source_hf_repo)

    def add_meta_arch(self, params: Params) -> None:
        # Metadata About The Neural Architecture Itself
        self.gguf.add_vocab_size(params.n_vocab)
        self.gguf.add_context_length(params.n_ctx)
        self.gguf.add_embedding_length(params.n_embd)
        self.gguf.add_block_count(params.n_layer)
        self.gguf.add_feed_forward_length(params.n_ff)
        self.gguf.add_rope_dimension_count(params.n_embd // params.n_head)
        self.gguf.add_head_count          (params.n_head)
        self.gguf.add_head_count_kv       (params.n_head_kv)

        if params.n_experts:
            self.gguf.add_expert_count(params.n_experts)

        if params.n_experts_used:
            self.gguf.add_expert_used_count(params.n_experts_used)

        if params.f_norm_eps:
            self.gguf.add_layer_norm_rms_eps(params.f_norm_eps)
        else:
            raise ValueError('f_norm_eps is None')

        if params.f_rope_freq_base is not None:
            self.gguf.add_rope_freq_base(params.f_rope_freq_base)

        if params.rope_scaling_type:
            assert params.f_rope_scale is not None
            self.gguf.add_rope_scaling_type(params.rope_scaling_type)
            self.gguf.add_rope_scaling_factor(params.f_rope_scale)

        if params.n_orig_ctx is not None:
            self.gguf.add_rope_scaling_orig_ctx_len(params.n_orig_ctx)

        if params.rope_finetuned is not None:
            self.gguf.add_rope_scaling_finetuned(params.rope_finetuned)

        if params.ftype is not None:
            self.gguf.add_file_type(params.ftype)

    def extract_vocabulary_from_model(self, vocab: Vocab) -> tuple[list[bytes], list[float], list[gguf.TokenType]]:
        tokens = []
        scores = []
        toktypes = []

        # NOTE: `all_tokens` returns the base vocabulary and added tokens
        for text, score, toktype in vocab.all_tokens():
            tokens.append(text)
            scores.append(score)
            toktypes.append(toktype)

        assert len(tokens) == vocab.vocab_size

        return tokens, scores, toktypes

    def add_meta_vocab(self, vocab: Vocab) -> None:
        # Ensure that tokenizer_model is added to the GGUF model
        self.gguf.add_tokenizer_model(vocab.tokenizer_model)

        # Extract model vocabulary for model conversion
        tokens, scores, toktypes = self.extract_vocabulary_from_model(vocab)

        # Add extracted token information for model conversion
        self.gguf.add_token_list(tokens)
        self.gguf.add_token_scores(scores)
        self.gguf.add_token_types(toktypes)

    def add_meta_special_vocab(self, svocab: gguf.SpecialVocab) -> None:
        svocab.add_to_gguf(self.gguf)

    def add_tensor_info(self, name: str, tensor: LazyTensor) -> None:
        n_elements = int(np.prod(tensor.shape))
        raw_dtype = getattr(tensor.data_type, 'ggml_type', None)
        data_type = getattr(tensor.data_type, 'quantized_type', None) or tensor.data_type.dtype
        data_nbytes = tensor.data_type.elements_to_bytes(n_elements)
        self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes, raw_dtype=raw_dtype)

    def write_meta(self) -> None:
        self.gguf.write_header_to_file()
        self.gguf.write_kv_data_to_file()

    def write_tensor_info(self) -> None:
        self.gguf.write_ti_data_to_file()

    def write_tensor_data(self, ftype: GGMLFileType, model: LazyModel, concurrency: int) -> None:
        ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency=concurrency)
        if ftype == GGMLFileType.MostlyQ8_0:
            ndarrays = bounded_parallel_map(
                OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency,
                use_processpool_executor=True,
            )
        else:
            ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)

        start = time.time()
        for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
            elapsed = time.time() - start
            size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
            padi = len(str(len(model)))
            logger.info(
                f"[{i + 1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}"
            )
            self.gguf.write_tensor_data(ndarray)

    def close(self) -> None:
        self.gguf.close()

    @staticmethod
    def write_vocab_only(
        fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab,
        endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, metadata: Metadata = None,
    ) -> None:
        check_vocab_size(params, vocab, pad_vocab=pad_vocab)

        of = OutputFile(fname_out, endianess=endianess)

        # meta data
        of.add_meta_model(params, metadata)
        of.add_meta_arch(params)
        of.add_meta_vocab(vocab)
        of.add_meta_special_vocab(svocab)

        of.write_meta()

        of.close()

    @staticmethod
    def do_item(item: tuple[str, LazyTensor]) -> tuple[DataType, NDArray]:
        name, lazy_tensor = item
        tensor = lazy_tensor.load().to_ggml()
        return (lazy_tensor.data_type, tensor.ndarray)

    @staticmethod
    def maybe_do_quantize(item: tuple[DataType, NDArray]) -> NDArray:
        dt, arr = item
        if not isinstance(dt, QuantizedDataType):
            return arr
        return dt.quantize(arr)

    @staticmethod
    def write_all(
        fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: BaseVocab, svocab: gguf.SpecialVocab,
        concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
        pad_vocab: bool = False,
        metadata: Metadata = None,
    ) -> None:
        check_vocab_size(params, vocab, pad_vocab=pad_vocab)

        of = OutputFile(fname_out, endianess=endianess)

        # meta data
        of.add_meta_model(params, metadata)
        of.add_meta_arch(params)
        if isinstance(vocab, Vocab):
            of.add_meta_vocab(vocab)
            of.add_meta_special_vocab(svocab)
        else:  # NoVocab
            of.gguf.add_tokenizer_model(vocab.tokenizer_model)

        # tensor info
        for name, lazy_tensor in model.items():
            of.add_tensor_info(name, lazy_tensor)

        of.write_meta()
        of.write_tensor_info()

        # tensor data
        of.write_tensor_data(ftype, model, concurrency)

        of.close()


def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType:
    wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) + ".weight"].data_type

    if output_type_str == "f32" or (output_type_str is None and wq_type in (DT_F32, DT_BF16)):
        return GGMLFileType.AllF32
    if output_type_str == "f16" or (output_type_str is None and wq_type == DT_F16):
        return GGMLFileType.MostlyF16
    if output_type_str == "q8_0":
        return GGMLFileType.MostlyQ8_0

    name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()}

    raise ValueError(f"Unexpected combination of types: {name_to_type}")


def model_parameter_count(model: LazyModel) -> int:
    total_model_parameters = 0
    for i, (name, lazy_tensor) in enumerate(model.items()):
        sum_weights_in_tensor = 1
        for dim in lazy_tensor.shape:
            sum_weights_in_tensor *= dim
        total_model_parameters += sum_weights_in_tensor
    return total_model_parameters


def model_parameter_count_rounded_notation(model_params_count: int) -> str:
    if model_params_count > 1e12 :
        # Trillions Of Parameters
        scaled_model_params = model_params_count * 1e-12
        scale_suffix = "T"
    elif model_params_count > 1e9 :
        # Billions Of Parameters
        scaled_model_params = model_params_count * 1e-9
        scale_suffix = "B"
    elif model_params_count > 1e6 :
        # Millions Of Parameters
        scaled_model_params = model_params_count * 1e-6
        scale_suffix = "M"
    else:
        # Thousands Of Parameters
        scaled_model_params = model_params_count * 1e-3
        scale_suffix = "K"

    return f"{round(scaled_model_params)}{scale_suffix}"


def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel:
    return {name: tensor.astype(output_type.type_for_tensor(name, tensor))
            for (name, tensor) in model.items()}


def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) -> LazyModel:
    tmap = gguf.TensorNameMap(ARCH, params.n_layer)
    should_skip = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))

    tmp = model

    # merge experts into one tensor
    if params.n_experts and params.n_experts > 0:
        for i_l in range(params.n_layer):
            for w in range(1, 4):
                experts = []
                for e in range(params.n_experts):
                    if f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight" in model:
                        experts.append(model[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"])
                        del tmp[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"]
                    elif f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight" in model:
                        experts.append(model[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"])
                        del tmp[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"]
                    else:
                        raise ValueError(f"Expert tensor not found: layers.{i_l}.feed_forward.experts.{e}.w{w}.weight")
                tmp[f"layers.{i_l}.feed_forward.experts.w{w}.weight"] = pack_experts_lazy(experts)

    # HF models permut or pack some of the tensors, so we need to undo that
    for i in itertools.count():
        if f"model.layers.{i}.self_attn.q_proj.weight" in model:
            logger.debug(f"Permuting layer {i}")
            tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head, params.n_head)
            tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_head_kv)
            # tmp[f"model.layers.{i}.self_attn.v_proj.weight"] =              model[f"model.layers.{i}.self_attn.v_proj.weight"]
        elif f"model.layers.{i}.self_attn.W_pack.weight" in model:
            logger.debug(f"Unpacking and permuting layer {i}")
            tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head, params.n_head)
            tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head, params.n_head_kv)
            tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = part_lazy        (model[f"model.layers.{i}.self_attn.W_pack.weight"], 2)
            del tmp[f"model.layers.{i}.self_attn.W_pack.weight"]
        else:
            break

    out: LazyModel = {}
    for name, lazy_tensor in model.items():
        tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (".weight", ".bias")) or (None, None)
        if name_new is None:
            if skip_unknown:
                logger.warning(f"Unexpected tensor name: {name} - skipping")
                continue
            raise ValueError(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)")

        if tensor_type in should_skip:
            logger.debug(f"skipping tensor {name_new}")
            continue

        logger.debug(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}")
        out[name_new] = lazy_tensor

    return out


def nth_multifile_path(path: Path, n: int) -> Path | None:
    '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return
    the nth path in the model.
    '''
    # Support the following patterns:
    patterns = [
        # - x.00.pth, x.01.pth, etc.
        (r'\.[0-9]{2}\.pth$', f'.{n:02}.pth'),
        # - x-00001-of-00002.bin, x-00002-of-00002.bin, etc.
        (r'-[0-9]{5}-of-(.*)$', fr'-{n:05}-of-\1'),
        # x.bin, x.bin.1, etc.
        (r'(\.[0-9]+)?$', r'\1' if n == 0 else fr'\1.{n}')
    ]
    for regex, replacement in patterns:
        if re.search(regex, path.name):
            new_path = path.with_name(re.sub(regex, replacement, path.name))
            if new_path.exists():
                return new_path
    return None


def find_multifile_paths(path: Path) -> list[Path]:
    '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return
    the whole list of paths in the model.
    '''
    ret: list[Path] = []
    for i in itertools.count():
        nth_path = nth_multifile_path(path, i)
        if nth_path is None:
            break
        ret.append(nth_path)
    if not ret:
        # No matches.  This should only happen if the file was named, e.g.,
        # foo.0, and there was no file named foo.  Oh well, try to process it
        # as a single file.
        return [path]
    return ret


def load_some_model(path: Path) -> ModelPlus:
    '''Load a model of any supported format.'''
    # Be extra-friendly and accept either a file or a directory:
    if path.is_dir():
        # Check if it's a set of safetensors files first
        globs = ["model-00001-of-*.safetensors", "model.safetensors", "consolidated.safetensors"]
        files = [file for glob in globs for file in path.glob(glob)]
        if not files:
            # Try the PyTorch patterns too, with lower priority
            globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin"]
            files = [file for glob in globs for file in path.glob(glob)]
        if not files:
            raise FileNotFoundError(f"Can't find model in directory {path}")
        if len(files) > 1:
            raise ValueError(f"Found multiple models in {path}, not sure which to pick: {files}")
        path = files[0]

    paths = find_multifile_paths(path)
    models_plus: list[ModelPlus] = []
    for path in paths:
        logger.info(f"Loading model file {path}")
        models_plus.append(lazy_load_file(path))

    model_plus = merge_multifile_models(models_plus)
    return model_plus


class VocabFactory:
    _VOCAB_CLASSES: list[type[Vocab]] = [SentencePieceVocab, BpeVocab, LlamaHfVocab]

    def __init__(self, path: Path):
        self.path = path

    def _create_special_vocab(self, vocab: BaseVocab, model_parent_path: Path) -> gguf.SpecialVocab:
        load_merges = vocab.name == "bpe"
        n_vocab = vocab.vocab_size if isinstance(vocab, Vocab) else None
        return gguf.SpecialVocab(
            model_parent_path,
            load_merges=load_merges,
            special_token_types=None,  # Predetermined or passed as a parameter
            n_vocab=n_vocab,
        )

    def _create_vocab_by_path(self, vocab_types: list[str]) -> Vocab:
        vocab_classes: dict[str, type[Vocab]] = {cls.name: cls for cls in self._VOCAB_CLASSES}
        selected_vocabs: dict[str, type[Vocab]] = {}
        for vtype in vocab_types:
            try:
                selected_vocabs[vtype] = vocab_classes[vtype]
            except KeyError:
                raise ValueError(f"Unsupported vocabulary type {vtype}") from None

        for vtype, cls in selected_vocabs.items():
            try:
                vocab = cls(self.path)
                break
            except FileNotFoundError:
                pass  # ignore unavailable tokenizers
        else:
            raise FileNotFoundError(f"Could not find a tokenizer matching any of {vocab_types}")

        logger.info(f"Loaded vocab file {vocab.fname_tokenizer!r}, type {vocab.name!r}")
        return vocab

    def load_vocab(self, vocab_types: list[str] | None, model_parent_path: Path) -> tuple[BaseVocab, gguf.SpecialVocab]:
        vocab: BaseVocab
        if vocab_types is None:
            vocab = NoVocab()
        else:
            vocab = self._create_vocab_by_path(vocab_types)
        # FIXME: Respect --vocab-dir?
        special_vocab = self._create_special_vocab(
            vocab,
            model_parent_path,
        )
        return vocab, special_vocab


def default_convention_outfile(file_type: GGMLFileType, params: Params, model_params_count: int, metadata: Metadata) -> str:
    quantization = {
        GGMLFileType.AllF32:    "F32",
        GGMLFileType.MostlyF16: "F16",
        GGMLFileType.MostlyQ8_0: "Q8_0",
    }[file_type]

    parameters = model_parameter_count_rounded_notation(model_params_count)

    expert_count = ""
    if params.n_experts is not None:
        expert_count = f"{params.n_experts}x"

    version = ""
    if metadata is not None and metadata.version is not None:
        version = f"-{metadata.version}"

    name = "ggml-model"
    if metadata is not None and metadata.name is not None:
        name = metadata.name
    elif params.path_model is not None:
        name = params.path_model.name

    return f"{name}{version}-{expert_count}{parameters}-{quantization}"


def default_outfile(model_paths: list[Path], file_type: GGMLFileType, params: Params, model_params_count: int, metadata: Metadata) -> Path:
    default_filename = default_convention_outfile(file_type, params, model_params_count, metadata)
    ret = model_paths[0].parent / f"{default_filename}.gguf"
    if ret in model_paths:
        logger.error(
            f"Error: Default output path ({ret}) would overwrite the input. "
            "Please explicitly specify a path using --outfile.")
        sys.exit(1)
    return ret


def do_dump_model(model_plus: ModelPlus) -> None:
    print(f"model_plus.paths = {model_plus.paths!r}") # noqa: NP100
    print(f"model_plus.format = {model_plus.format!r}") # noqa: NP100
    print(f"model_plus.vocab = {model_plus.vocab!r}") # noqa: NP100
    for name, lazy_tensor in model_plus.model.items():
        print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}") # noqa: NP100


def main(args_in: list[str] | None = None) -> None:
    output_choices = ["f32", "f16"]
    if np.uint32(1) == np.uint32(1).newbyteorder("<"):
        # We currently only support Q8_0 output on little endian systems.
        output_choices.append("q8_0")
    parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file")
    parser.add_argument("--dump",         action="store_true",    help="don't convert, just show what's in the model")
    parser.add_argument("--dump-single",  action="store_true",    help="don't convert, just show what's in a single model file")
    parser.add_argument("--vocab-only",   action="store_true",    help="extract only the vocab")
    parser.add_argument("--no-vocab",     action="store_true",    help="store model without the vocab")
    parser.add_argument("--outtype",      choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
    parser.add_argument("--vocab-dir",    type=Path,              help="directory containing tokenizer.model, if separate from model file")
    parser.add_argument("--vocab-type",                           help="vocab types to try in order, choose from 'spm', 'bpe', 'hfft' (default: spm,hfft)", default="spm,hfft")
    parser.add_argument("--outfile",      type=Path,              help="path to write to; default: based on input")
    parser.add_argument("model",          type=Path,              help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
    parser.add_argument("--ctx",          type=int,               help="model training context (default: based on input)")
    parser.add_argument("--concurrency",  type=int,               help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default=DEFAULT_CONCURRENCY)
    parser.add_argument("--big-endian",   action="store_true",    help="model is executed on big endian machine")
    parser.add_argument("--pad-vocab",    action="store_true",    help="add pad tokens when model vocab expects more than tokenizer metadata provides")
    parser.add_argument("--skip-unknown", action="store_true",    help="skip unknown tensor names instead of failing")
    parser.add_argument("--verbose",      action="store_true",    help="increase output verbosity")
    parser.add_argument("--metadata",     type=Path,              help="Specify the path for a metadata file")
    parser.add_argument("--get-outfile",  action="store_true",    help="get calculated default outfile name")

    args = parser.parse_args(args_in)

    if args.verbose:
        logging.basicConfig(level=logging.DEBUG)
    elif args.dump_single or args.dump or args.get_outfile:
        # Avoid printing anything besides the dump output
        logging.basicConfig(level=logging.WARNING)
    else:
        logging.basicConfig(level=logging.INFO)

    metadata = Metadata.load(args.metadata)

    if args.get_outfile:
        model_plus = load_some_model(args.model)
        params = Params.load(model_plus)
        model   = convert_model_names(model_plus.model, params, args.skip_unknown)
        model_params_count = model_parameter_count(model_plus.model)
        ftype   = pick_output_type(model, args.outtype)
        print(f"{default_convention_outfile(ftype, params, model_params_count, metadata)}") # noqa: NP100
        return

    if args.no_vocab and args.vocab_only:
        raise ValueError("--vocab-only does not make sense with --no-vocab")

    if args.dump_single:
        model_plus = lazy_load_file(args.model)
        do_dump_model(model_plus)
        return

    if not args.vocab_only:
        model_plus = load_some_model(args.model)
    else:
        model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None)

    model_params_count = model_parameter_count(model_plus.model)
    logger.info(f"model parameters count : {model_params_count} ({model_parameter_count_rounded_notation(model_params_count)})")

    if args.dump:
        do_dump_model(model_plus)
        return

    endianess = gguf.GGUFEndian.LITTLE
    if args.big_endian:
        endianess = gguf.GGUFEndian.BIG

    params = None
    if args.pad_vocab or not args.vocab_only:
        params = Params.load(model_plus)
        if params.n_ctx == -1:
            if args.ctx is None:
                msg = """\
                    The model doesn't have a context size, and you didn't specify one with --ctx
                    Please specify one with --ctx:
                     - LLaMA v1: --ctx 2048
                     - LLaMA v2: --ctx 4096"""
                parser.error(textwrap.dedent(msg))
            params.n_ctx = args.ctx

        if args.outtype:
            params.ftype = {
                "f32": GGMLFileType.AllF32,
                "f16": GGMLFileType.MostlyF16,
                "q8_0": GGMLFileType.MostlyQ8_0,
            }[args.outtype]

        logger.info(f"params = {params}")

    model_parent_path = model_plus.paths[0].parent
    vocab_path = Path(args.vocab_dir or args.model or model_parent_path)
    vocab_factory = VocabFactory(vocab_path)
    vocab_types = None if args.no_vocab else args.vocab_type.split(",")
    vocab, special_vocab = vocab_factory.load_vocab(vocab_types, model_parent_path)

    if args.vocab_only:
        assert isinstance(vocab, Vocab)
        if not args.outfile:
            raise ValueError("need --outfile if using --vocab-only")
        outfile = args.outfile
        if params is None:
            params = Params(
                n_vocab    = vocab.vocab_size,
                n_embd     = 1,
                n_layer    = 1,
                n_ctx      = 1,
                n_ff       = 1,
                n_head     = 1,
                n_head_kv  = 1,
                f_norm_eps = 1e-5,
            )
        OutputFile.write_vocab_only(outfile, params, vocab, special_vocab,
                                    endianess=endianess, pad_vocab=args.pad_vocab, metadata=metadata)
        logger.info(f"Wrote {outfile}")
        return

    if model_plus.vocab is not None and args.vocab_dir is None and not args.no_vocab:
        vocab = model_plus.vocab

    logger.info(f"Vocab info: {vocab}")
    logger.info(f"Special vocab info: {special_vocab}")
    model   = model_plus.model
    model   = convert_model_names(model, params, args.skip_unknown)
    ftype   = pick_output_type(model, args.outtype)
    model   = convert_to_output_type(model, ftype)
    outfile = args.outfile or default_outfile(model_plus.paths, ftype, params, model_params_count, metadata)

    params.ftype = ftype
    logger.info(f"Writing {outfile}, format {ftype}")

    OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab,
                         concurrency=args.concurrency, endianess=endianess, pad_vocab=args.pad_vocab, metadata=metadata)
    logger.info(f"Wrote {outfile}")


if __name__ == '__main__':
    main()