mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 13:58:46 +01:00
226cea270e
- Renamed 'default_outfile' to 'default_output_file' for clarity. - Refactored argument parser setup into 'get_argument_parser' function. - Introduced descriptive comments for each argument in the parser. - Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing. - Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'. - Enhanced error handling to prevent overwriting input data in 'default_output_file'. - Made 'argv' in 'main' an optional parameter for flexibility. - Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency. These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
1649 lines
60 KiB
Python
Executable File
1649 lines
60 KiB
Python
Executable File
#!/usr/bin/env python3
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from __future__ import annotations
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import argparse
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import concurrent.futures
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import enum
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import faulthandler
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import functools
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import itertools
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import json
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import math
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import mmap
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import os
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import pickle
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import re
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import signal
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import struct
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import sys
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import time
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import warnings
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import zipfile
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from abc import ABCMeta, abstractmethod
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from argparse import ArgumentParser
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from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
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from dataclasses import dataclass
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from pathlib import Path
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from typing import (
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IO,
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TYPE_CHECKING,
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Any,
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Callable,
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Iterable,
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Literal,
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Optional,
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Tuple,
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TypeVar,
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)
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import numpy as np
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from sentencepiece import SentencePieceProcessor
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try:
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from transformers import AutoTokenizer
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except ModuleNotFoundError as e:
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warnings.warn(f"Could not import AutoTokenizer from transformers: {e}")
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# If NO_LOCAL_GGUF is not set, try to import gguf from the local gguf-py directory
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if "NO_LOCAL_GGUF" not in os.environ:
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# Use absolute path to the gguf-py directory
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gguf_py_dir = str(Path(__file__).resolve().parent / "gguf-py")
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print(gguf_py_dir) # NOTE: Remove this once path is verified after changes are completed
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if gguf_py_dir not in sys.path:
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sys.path.insert(1, gguf_py_dir)
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# Import gguf module
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try:
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import gguf
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except ModuleNotFoundError as e:
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print(f"Could not import gguf: {e}")
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sys.exit(1)
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if TYPE_CHECKING: # NOTE: This isn't necessary.
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from typing import TypeAlias # This can technically be omitted.
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if hasattr(faulthandler, "register") and hasattr(signal, "SIGUSR1"):
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faulthandler.register(signal.SIGUSR1)
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# NOTE: n-dimensional arrays should be directly referenced
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NDArray: TypeAlias = "np.ndarray[Any, Any]"
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# Why is this here? LLAMA and GPT are technically the only compatible ARCHs.
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ARCH = gguf.MODEL_ARCH.LLAMA
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DEFAULT_CONCURRENCY = 8
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#
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# data types
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#
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# TODO: Clean up and refactor data types
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@dataclass(frozen=True)
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class DataType:
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name: str
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dtype: np.dtype[Any]
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valid_conversions: list[str]
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def elements_to_bytes(self, n_elements: int) -> int:
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return n_elements * self.dtype.itemsize
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@dataclass(frozen=True)
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class UnquantizedDataType(DataType):
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pass
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DT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
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DT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
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DT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])
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DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])
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@dataclass(frozen=True)
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class QuantizedDataType(DataType):
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block_size: int
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quantized_dtype: np.dtype[Any]
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ggml_type: gguf.GGMLQuantizationType
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def quantize(self, arr: NDArray) -> NDArray:
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raise NotImplementedError(f'Quantization for {self.name} not implemented')
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def elements_to_bytes(self, n_elements: int) -> int:
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assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}'
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return self.quantized_dtype.itemsize * (n_elements // self.block_size)
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@dataclass(frozen=True)
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class Q8_0QuantizedDataType(QuantizedDataType):
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# Mini Q8_0 quantization in Python!
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def quantize(self, arr: NDArray) -> NDArray:
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assert arr.size % self.block_size == 0 and arr.size != 0, f'Bad array size {arr.size}'
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assert arr.dtype == np.float32, f'Bad array type {arr.dtype}'
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n_blocks = arr.size // self.block_size
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blocks = arr.reshape((n_blocks, self.block_size))
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# Much faster implementation of block quantization contributed by @Cebtenzzre
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def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[tuple[Any, Any]]:
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d = abs(blocks).max(axis = 1) / np.float32(127)
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with np.errstate(divide = 'ignore'):
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qs = (blocks / d[:, None]).round()
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qs[d == 0] = 0
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yield from zip(d, qs)
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return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype)
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DT_Q8_0 = Q8_0QuantizedDataType('Q8_0',
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dtype = np.dtype(np.float32), valid_conversions = [],
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ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32,
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quantized_dtype = np.dtype([('d', '<f2'), ('qs', 'i1', (32,))]))
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# Quantized types skipped here because they may also map to np.float32
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NUMPY_TYPE_TO_DATA_TYPE: dict[np.dtype[Any], DataType] = {}
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for dt in (DT_BF16, DT_F16, DT_F32, DT_I32):
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if dt.dtype in NUMPY_TYPE_TO_DATA_TYPE:
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raise ValueError(f'Invalid duplicate data type {dt}')
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NUMPY_TYPE_TO_DATA_TYPE[dt.dtype] = dt
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SAFETENSORS_DATA_TYPES: dict[str, DataType] = {
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'BF16': DT_BF16,
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'F16': DT_F16,
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'F32': DT_F32,
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'I32': DT_I32,
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}
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# TODO: match this with `llama_ftype`
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# TODO: rename to LLAMAFileType
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# TODO: move to `gguf.py`
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class GGMLFileType(enum.IntEnum):
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AllF32 = 0
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MostlyF16 = 1 # except 1d tensors
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MostlyQ8_0 = 7 # except 1d tensors
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def type_for_tensor(self, name: str, tensor: LazyTensor) -> DataType:
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dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self)
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if dt is None:
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raise ValueError(self)
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# 1D tensors are always F32.
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return dt if len(tensor.shape) > 1 else DT_F32
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GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {
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GGMLFileType.AllF32 : DT_F32,
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GGMLFileType.MostlyF16 : DT_F16,
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GGMLFileType.MostlyQ8_0: DT_Q8_0,
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}
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#
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# hparams loading
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#
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@dataclass
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class Params:
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n_vocab: int
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n_embd: int
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n_layer: int
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n_ctx: int
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n_ff: int
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n_head: int
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n_head_kv: int
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f_norm_eps: Optional[float] = None
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n_experts: Optional[int] = None
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n_experts_used: Optional[int] = None
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rope_scaling_type: Optional[gguf.RopeScalingType] = None
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f_rope_freq_base: Optional[float] = None
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f_rope_scale: Optional[float] = None
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n_orig_ctx: Optional[int] = None
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rope_finetuned: Optional[bool] = None
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ftype: Optional[GGMLFileType] = None
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# path to the directory containing the model files
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path_model: Optional[Path] = None
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@staticmethod
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def guessed(model: LazyModel) -> "Params":
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# try transformer naming first
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n_vocab, n_embd = (
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model["model.embed_tokens.weight"].shape
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if "model.embed_tokens.weight" in model
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else model["tok_embeddings.weight"].shape
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)
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# try transformer naming first
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if "model.layers.0.self_attn.q_proj.weight" in model:
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n_layer = next(
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i
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for i in itertools.count()
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if f"model.layers.{i}.self_attn.q_proj.weight" not in model
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)
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elif (
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"model.layers.0.self_attn.W_pack.weight" in model
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): # next: try baichuan naming
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n_layer = next(
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i
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for i in itertools.count()
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if f"model.layers.{i}.self_attn.W_pack.weight" not in model
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)
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else:
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n_layer = next(
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i
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for i in itertools.count()
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if f"layers.{i}.attention.wq.weight" not in model
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)
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if n_layer < 1:
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raise Exception(
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"failed to guess 'n_layer'. This model is unknown or unsupported.\n"
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"Suggestion: provide 'config.json' of the model in the same directory containing model files."
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)
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n_head = n_embd // 128 # guessed
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n_mult = 256 # guessed
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# TODO: verify this
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n_ff = int(2 * (4 * n_embd) / 3)
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n_ff = n_mult * ((n_ff + n_mult - 1) // n_mult)
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return Params(
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n_vocab=n_vocab,
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n_embd=n_embd,
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n_layer=n_layer,
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n_ctx=-1,
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n_ff=n_ff,
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n_head=n_head,
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n_head_kv=n_head,
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f_norm_eps=1e-5,
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)
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@staticmethod
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def load_transformers_config(model: LazyModel, config_path: Path) -> "Params":
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config = json.load(open(config_path))
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rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None
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rope_scaling = config.get("rope_scaling")
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if rope_scaling is not None and (typ := rope_scaling.get("type")):
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rope_factor = rope_scaling.get("factor")
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f_rope_scale = rope_factor
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if typ == "linear":
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rope_scaling_type = gguf.RopeScalingType.LINEAR
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elif typ == "yarn":
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rope_scaling_type = gguf.RopeScalingType.YARN
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n_orig_ctx = rope_scaling["original_max_position_embeddings"]
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rope_finetuned = rope_scaling["finetuned"]
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else:
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raise NotImplementedError(f"Unknown rope scaling type: {typ}")
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if "max_sequence_length" in config:
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n_ctx = config["max_sequence_length"]
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elif "max_position_embeddings" in config:
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n_ctx = config["max_position_embeddings"]
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else:
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raise Exception(
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"failed to guess 'n_ctx'. This model is unknown or unsupported.\n"
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"Suggestion: provide 'config.json' of the model in the same directory containing model files."
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)
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n_experts = None
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n_experts_used = None
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if "num_local_experts" in config:
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n_experts = config["num_local_experts"]
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n_experts_used = config["num_experts_per_tok"]
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return Params(
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n_vocab=config["vocab_size"],
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n_embd=config["hidden_size"],
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n_layer=config["num_hidden_layers"],
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n_ctx=n_ctx,
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n_ff=config["intermediate_size"],
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n_head=(n_head := config["num_attention_heads"]),
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n_head_kv=config.get("num_key_value_heads", n_head),
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n_experts=n_experts,
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n_experts_used=n_experts_used,
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f_norm_eps=config["rms_norm_eps"],
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f_rope_freq_base=config.get("rope_theta"),
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rope_scaling_type=rope_scaling_type,
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f_rope_scale=f_rope_scale,
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n_orig_ctx=n_orig_ctx,
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rope_finetuned=rope_finetuned,
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)
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# LLaMA v2 70B params.json
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# {"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}
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@staticmethod
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def load_torch_params(model: LazyModel, config_path: Path) -> "Params":
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config = json.load(open(config_path))
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n_experts = None
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n_experts_used = None
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f_rope_freq_base = None
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# hack to determine LLaMA v1 vs v2 vs CodeLlama
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if config.get("moe"):
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# Mixtral
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n_ctx = 32768
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elif config.get("rope_theta") == 1000000:
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# CodeLlama
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n_ctx = 16384
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elif config["norm_eps"] == 1e-05:
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# LLaMA v2
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n_ctx = 4096
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else:
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# LLaMA v1
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n_ctx = 2048
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if "layers.0.feed_forward.w1.weight" in model:
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n_ff = model["layers.0.feed_forward.w1.weight"].shape[0]
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if config.get("moe"):
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n_ff = model["layers.0.feed_forward.experts.0.w1.weight"].shape[0]
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n_experts = config["moe"]["num_experts"]
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n_experts_used = config["moe"]["num_experts_per_tok"]
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f_rope_freq_base = 1e6
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return Params(
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n_vocab=config.get("vocab_size", model["tok_embeddings.weight"].shape[0]),
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n_embd=config["dim"],
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n_layer=config["n_layers"],
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n_ctx=n_ctx,
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n_ff=n_ff,
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n_head=(n_head := config["n_heads"]),
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n_head_kv=config.get("n_kv_heads", n_head),
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n_experts=n_experts,
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n_experts_used=n_experts_used,
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f_norm_eps=config["norm_eps"],
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f_rope_freq_base=config.get("rope_theta", f_rope_freq_base),
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)
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@staticmethod
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def load(model_plus: ModelPlus) -> "Params":
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hf_config_path = model_plus.paths[0].parent / "config.json"
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orig_config_path = model_plus.paths[0].parent / "params.json"
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if hf_config_path.exists():
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params = Params.load_transformers_config(model_plus.model, hf_config_path)
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elif orig_config_path.exists():
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params = Params.load_torch_params(model_plus.model, orig_config_path)
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elif model_plus.format != "none":
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params = Params.guessed(model_plus.model)
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else:
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raise ValueError("Cannot guess params when model format is none")
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params.path_model = model_plus.paths[0].parent
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return params
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class BpeVocab: # GPT
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def __init__(
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self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]
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) -> None:
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self.bpe_tokenizer = json.loads(
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open(str(fname_tokenizer), encoding="utf-8").read()
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)
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added_tokens: dict[str, int]
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if fname_added_tokens is not None:
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# FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
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added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
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else:
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# Fall back to trying to find the added tokens in tokenizer.json
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tokenizer_json_file = fname_tokenizer.parent / "tokenizer.json"
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if not tokenizer_json_file.is_file():
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added_tokens = {}
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else:
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tokenizer_json = json.load(open(tokenizer_json_file, encoding="utf-8"))
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added_tokens = dict(
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(item["content"], item["id"])
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for item in tokenizer_json.get("added_tokens", [])
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# Added tokens here can be duplicates of the main vocabulary.
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if item["content"] not in self.bpe_tokenizer
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)
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vocab_size: int = len(self.bpe_tokenizer)
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expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
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actual_ids = sorted(added_tokens.values())
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if expected_ids != actual_ids:
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expected_end_id = vocab_size + len(actual_ids) - 1
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raise Exception(
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f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range {vocab_size} - {expected_end_id}; got {actual_ids}"
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)
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items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
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self.added_tokens_list = [text for (text, idx) in items]
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self.vocab_size_base: int = vocab_size
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self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list)
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self.fname_tokenizer = fname_tokenizer
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self.fname_added_tokens = fname_added_tokens
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def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
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tokenizer = self.bpe_tokenizer
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reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.items()}
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for i, _ in enumerate(tokenizer):
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yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL
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def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
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for text in self.added_tokens_list:
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score = -1000.0
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yield text.encode("utf-8"), score, gguf.TokenType.CONTROL
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def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
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yield from self.bpe_tokens()
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yield from self.added_tokens()
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def __repr__(self) -> str:
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return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
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class SentencePieceVocab: # LlaMa
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def __init__(
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self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]
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) -> None:
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self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
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|
added_tokens: dict[str, int]
|
|
if fname_added_tokens is not None:
|
|
added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
|
|
else:
|
|
added_tokens = {}
|
|
|
|
vocab_size: int = 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_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
|
|
self.fname_added_tokens = fname_added_tokens
|
|
|
|
def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
|
|
tokenizer = self.sentencepiece_tokenizer
|
|
for i in range(tokenizer.vocab_size()):
|
|
piece = tokenizer.id_to_piece(i)
|
|
text: bytes = piece.encode("utf-8")
|
|
score: float = tokenizer.get_score(i)
|
|
|
|
toktype = gguf.TokenType.NORMAL
|
|
if tokenizer.is_unknown(i):
|
|
toktype = gguf.TokenType.UNKNOWN
|
|
if tokenizer.is_control(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.is_unused(i):
|
|
toktype = gguf.TokenType.UNUSED
|
|
if tokenizer.is_byte(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 HfVocab:
|
|
def __init__(
|
|
self,
|
|
fname_tokenizer: Path,
|
|
fname_added_tokens: Optional[Path] = None,
|
|
) -> None:
|
|
print("fname_tokenizer:", fname_tokenizer)
|
|
# Allow the tokenizer to default to slow or fast versions.
|
|
# Explicitly set tokenizer to use local paths.
|
|
self.tokenizer = AutoTokenizer.from_pretrained(
|
|
fname_tokenizer,
|
|
cache_dir=fname_tokenizer,
|
|
local_files_only=True,
|
|
)
|
|
|
|
# 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
|
|
self.fname_added_tokens = fname_added_tokens
|
|
|
|
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, self.special_ids # Reuse already stored special IDs
|
|
)
|
|
|
|
def get_token_type(self, token_id: int, special_ids: set) -> gguf.TokenType:
|
|
# 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], 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"<HfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
|
|
|
|
|
|
Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab"
|
|
|
|
|
|
#
|
|
# data loading
|
|
# TODO: reuse (probably move to gguf.py?)
|
|
#
|
|
|
|
|
|
def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:
|
|
# print( "permute debug " + str(weights.shape[0]) + " x " + str(weights.shape[1]) + " nhead " + str(n_head) + " nheadkv " + str(n_kv_head) )
|
|
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(metaclass=ABCMeta):
|
|
data_type: DataType
|
|
|
|
@abstractmethod
|
|
def astype(self, data_type: DataType) -> Tensor: ...
|
|
@abstractmethod
|
|
def permute(self, n_head: int, n_head_kv: int) -> Tensor: ...
|
|
@abstractmethod
|
|
def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: ...
|
|
@abstractmethod
|
|
def part(self, n_part: int) -> UnquantizedTensor: ...
|
|
@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) -> None:
|
|
assert isinstance(ndarray, np.ndarray)
|
|
self.ndarray = ndarray
|
|
self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype]
|
|
|
|
def astype(self, data_type: DataType) -> Tensor:
|
|
dtype = data_type.dtype
|
|
if self.data_type == DT_BF16:
|
|
self.ndarray = bf16_to_fp32(self.ndarray)
|
|
return UnquantizedTensor(self.ndarray.astype(dtype))
|
|
|
|
def to_ggml(self) -> UnquantizedTensor:
|
|
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: Vocab | 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: list[LazyTensor] = [model[name] for model in models]
|
|
if len(lazy_tensors) == 1:
|
|
# only one file; don't go through this procedure since there might
|
|
# be quantized tensors
|
|
return lazy_tensors[0]
|
|
if len(lazy_tensors[0].shape) == 1:
|
|
# the tensor is just duplicated in every file
|
|
return lazy_tensors[0]
|
|
if name.startswith('tok_embeddings.') or \
|
|
name.endswith('.attention.wo.weight') or \
|
|
name.endswith('.feed_forward.w2.weight'):
|
|
# split by columns
|
|
axis = 1
|
|
else:
|
|
# split by rows
|
|
axis = 0
|
|
concatenated_shape = list(lazy_tensors[0].shape)
|
|
concatenated_shape[axis] = sum(tensor.shape[axis] for tensor in lazy_tensors)
|
|
|
|
def load() -> UnquantizedTensor:
|
|
ndarrays = [load_unquantized(tensor) for tensor in lazy_tensors]
|
|
concatenated: NDArray = np.concatenate(ndarrays, axis=axis)
|
|
return UnquantizedTensor(concatenated)
|
|
description = 'concatenated[[' + '] | ['.join(lt.description for lt in lazy_tensors) + ']]'
|
|
return LazyTensor(load, concatenated_shape, lazy_tensors[0].data_type, description)
|
|
return {name: convert(name) for name in names}
|
|
|
|
|
|
def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus:
|
|
formats = set(mp.format for mp in models_plus)
|
|
assert len(formats) == 1, "different formats?"
|
|
format = formats.pop()
|
|
paths = [path for mp in models_plus for path in mp.paths]
|
|
# Use the first non-None vocab, if any.
|
|
try:
|
|
vocab = next(mp.vocab for mp in models_plus if mp.vocab is not None)
|
|
except StopIteration:
|
|
vocab = None
|
|
|
|
if any("model.embed_tokens.weight" in mp.model for mp in models_plus):
|
|
# Transformers models put different tensors in different files, but
|
|
# don't split 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)
|
|
|
|
|
|
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)
|
|
|
|
|
|
# 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
|
|
fp = self.zip_file.open(info)
|
|
fp.seek(offset * dtype.itemsize)
|
|
size = elm_count * dtype.itemsize
|
|
data = fp.read(size)
|
|
assert len(data) == size
|
|
return np.frombuffer(data, dtype)
|
|
description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}'
|
|
return LazyStorage(load=load, kind=pid[1], description=description)
|
|
|
|
@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], Any] = {
|
|
# 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 Exception("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: Vocab, pad_vocab: bool = False) -> None:
|
|
if params.n_vocab != vocab.vocab_size:
|
|
if params.n_vocab == vocab.vocab_size:
|
|
print(
|
|
"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
|
|
print(
|
|
f"Padding vocab with {pad_count} token(s) - <dummy00001> through <dummy{pad_count:05}>"
|
|
)
|
|
for i in range(1, (params.n_vocab - vocab.vocab_size) + 1):
|
|
vocab.added_tokens_dict[f"<dummy{i:05}>"] = -1
|
|
vocab.vocab_size = params.n_vocab
|
|
return
|
|
msg = f"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer}"
|
|
msg += f" 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."
|
|
|
|
# Check if params.n_vocab is -1 and issue a warning
|
|
if params.n_vocab == -1:
|
|
warnings.warn(
|
|
"WARNING: The model's vocab size is set to -1 in params.json. Please update it manually."
|
|
)
|
|
|
|
raise Exception(msg)
|
|
|
|
|
|
class OutputFile:
|
|
def __init__(
|
|
self, fname_out: Path, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE
|
|
) -> None:
|
|
self.gguf = gguf.GGUFWriter(
|
|
fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess
|
|
)
|
|
|
|
def add_meta_arch(self, params: Params) -> None:
|
|
name = "LLaMA"
|
|
|
|
# TODO: better logic to determine model name
|
|
if params.n_ctx == 4096:
|
|
name = "LLaMA v2"
|
|
elif params.path_model is not None:
|
|
name = str(params.path_model.parent).split("/")[-1]
|
|
|
|
self.gguf.add_name(name)
|
|
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.f_norm_eps is None:
|
|
raise ValueError("f_norm_eps is None")
|
|
|
|
self.gguf.add_layer_norm_rms_eps(params.f_norm_eps)
|
|
|
|
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_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 handle_tokenizer_model(self, vocab: Vocab) -> str:
|
|
# Map the vocab types to the supported tokenizer models
|
|
tokenizer_model = {
|
|
SentencePieceVocab: "llama",
|
|
HfVocab: "llama",
|
|
BpeVocab: "gpt2",
|
|
}.get(type(vocab))
|
|
|
|
# Block if vocab type is not predefined
|
|
if tokenizer_model is None:
|
|
raise ValueError("Unknown vocab type: Not supported")
|
|
|
|
return tokenizer_model
|
|
|
|
def extract_vocabulary_from_model(self, vocab: Vocab) -> Tuple[list, list, list]:
|
|
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)
|
|
|
|
return tokens, scores, toktypes
|
|
|
|
def add_meta_vocab(self, vocab: Vocab) -> None:
|
|
# Handle the tokenizer model
|
|
tokenizer_model = self.handle_tokenizer_model(vocab)
|
|
|
|
# Ensure that tokenizer_model is added to the GGUF model
|
|
self.gguf.add_tokenizer_model(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 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,
|
|
) -> None:
|
|
check_vocab_size(params, vocab, pad_vocab=pad_vocab)
|
|
|
|
of = OutputFile(fname_out, endianess=endianess)
|
|
|
|
# meta data
|
|
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: Vocab,
|
|
svocab: gguf.SpecialVocab,
|
|
concurrency: int = DEFAULT_CONCURRENCY,
|
|
endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
|
|
pad_vocab: bool = False,
|
|
) -> None:
|
|
check_vocab_size(params, vocab, pad_vocab=pad_vocab)
|
|
|
|
of = OutputFile(fname_out, endianess=endianess)
|
|
|
|
# meta data
|
|
of.add_meta_arch(params)
|
|
of.add_meta_vocab(vocab)
|
|
of.add_meta_special_vocab(svocab)
|
|
|
|
# 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
|
|
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)))
|
|
print(
|
|
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}"
|
|
)
|
|
of.gguf.write_tensor_data(ndarray)
|
|
|
|
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 == DT_F32):
|
|
return GGMLFileType.AllF32
|
|
if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)):
|
|
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 Exception(f"Unexpected combination of types: {name_to_type}")
|
|
|
|
|
|
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) -> LazyModel:
|
|
tmap = gguf.TensorNameMap(ARCH, params.n_layer)
|
|
should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
|
|
|
|
tmp = model
|
|
|
|
# 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:
|
|
print(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:
|
|
print(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:
|
|
raise Exception(f"Unexpected tensor name: {name}")
|
|
|
|
if tensor_type in should_skip:
|
|
print(f"skipping tensor {name_new}")
|
|
continue
|
|
|
|
print(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: list[tuple[str, str]] = [
|
|
# - x.00.pth, x.01.pth, etc.
|
|
(r'\.[0-9]{2}\.pth$', f'.{n:02}.pth'),
|
|
# - x-00001-of-00002.bin, x-00002-of-00002.bin, etc.
|
|
(r'-[0-9]{5}-of-(.*)$', fr'-{n:05}-of-\1'),
|
|
# x.bin, x.bin.1, etc.
|
|
(r'(\.[0-9]+)?$', r'\1' if n == 0 else fr'\1.{n}')
|
|
]
|
|
for regex, replacement in patterns:
|
|
if re.search(regex, path.name):
|
|
new_path = path.with_name(re.sub(regex, replacement, path.name))
|
|
if new_path.exists():
|
|
return new_path
|
|
return None
|
|
|
|
|
|
def find_multifile_paths(path: Path) -> list[Path]:
|
|
'''Given any path belonging to a multi-file model (e.g. foo.bin.1), return
|
|
the whole list of paths in the model.
|
|
'''
|
|
ret: list[Path] = []
|
|
for i in itertools.count():
|
|
nth_path = nth_multifile_path(path, i)
|
|
if nth_path is None:
|
|
break
|
|
ret.append(nth_path)
|
|
if not ret:
|
|
# No matches. This should only happen if the file was named, e.g.,
|
|
# foo.0, and there was no file named foo. Oh well, try to process it
|
|
# as a single file.
|
|
return [path]
|
|
return ret
|
|
|
|
|
|
def load_some_model(path: Path) -> ModelPlus:
|
|
'''Load a model of any supported format.'''
|
|
# Be extra-friendly and accept either a file or a directory:
|
|
if path.is_dir():
|
|
# Check if it's a set of safetensors files first
|
|
globs = ["model-00001-of-*.safetensors", "model.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 Exception(f"Can't find model in directory {path}")
|
|
if len(files) > 1:
|
|
raise Exception(f"Found multiple models in {path}, not sure which to pick: {files}")
|
|
path = files[0]
|
|
|
|
paths = find_multifile_paths(path)
|
|
models_plus: list[ModelPlus] = []
|
|
for path in paths:
|
|
print(f"Loading model file {path}")
|
|
models_plus.append(lazy_load_file(path))
|
|
|
|
model_plus = merge_multifile_models(models_plus)
|
|
return model_plus
|
|
|
|
|
|
class VocabFactory:
|
|
def __init__(self, path: Path):
|
|
self.path = path
|
|
self.files = {
|
|
"tokenizer.model": None,
|
|
"vocab.json": None,
|
|
"tokenizer.json": None,
|
|
}
|
|
self._detect_files()
|
|
|
|
def _detect_files(self):
|
|
for file in self.files.keys():
|
|
file_path = self.path / file
|
|
parent_file_path = self.path.parent / file
|
|
if file_path.exists():
|
|
self.files[file] = file_path
|
|
elif parent_file_path.exists():
|
|
self.files[file] = parent_file_path
|
|
|
|
def _select_file(self, vocabtype: Optional[str]) -> Path:
|
|
if vocabtype in ["spm", "bpe"]:
|
|
# For SentencePiece and BPE, return specific files as before
|
|
file_key = "tokenizer.model" if vocabtype == "spm" else "vocab.json"
|
|
if self.files[file_key]:
|
|
return self.files[file_key]
|
|
else:
|
|
raise FileNotFoundError(f"{vocabtype} {file_key} not found.")
|
|
elif vocabtype == "hfft":
|
|
# For Hugging Face Fast Tokenizer, return the directory path instead of a specific file
|
|
return self.path
|
|
else:
|
|
raise ValueError(f"Unsupported vocabulary type {vocabtype}")
|
|
|
|
def _create_special_vocab(
|
|
self,
|
|
vocab: Vocab,
|
|
vocabtype: str,
|
|
model_parent_path: Path,
|
|
) -> gguf.SpecialVocab:
|
|
load_merges = vocabtype == "bpe"
|
|
n_vocab = vocab.vocab_size if hasattr(vocab, "vocab_size") 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 load_vocab(
|
|
self, vocabtype: str, model_parent_path: Path
|
|
) -> Tuple[Vocab, gguf.SpecialVocab]:
|
|
path = self._select_file(vocabtype)
|
|
print(f"Loading vocab file '{path}', type '{vocabtype}'")
|
|
|
|
added_tokens_path = path.parent / "added_tokens.json"
|
|
if vocabtype == "bpe":
|
|
vocab = BpeVocab(
|
|
path, added_tokens_path if added_tokens_path.exists() else None
|
|
)
|
|
elif vocabtype == "spm":
|
|
vocab = SentencePieceVocab(
|
|
path, added_tokens_path if added_tokens_path.exists() else None
|
|
)
|
|
elif vocabtype == "hfft":
|
|
vocab = HfVocab(
|
|
path, added_tokens_path if added_tokens_path.exists() else None
|
|
)
|
|
else:
|
|
raise ValueError(f"Unsupported vocabulary type {vocabtype}")
|
|
special_vocab = self._create_special_vocab(
|
|
vocab,
|
|
vocabtype,
|
|
model_parent_path,
|
|
)
|
|
return vocab, special_vocab
|
|
|
|
|
|
def default_output_file(model_paths: list[Path], file_type: GGMLFileType) -> Path:
|
|
namestr = {
|
|
GGMLFileType.AllF32: "f32",
|
|
GGMLFileType.MostlyF16: "f16",
|
|
GGMLFileType.MostlyQ8_0: "q8_0",
|
|
}[file_type]
|
|
ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf"
|
|
if ret in model_paths:
|
|
sys.stderr.write(
|
|
f"Error: Default output path ({ret}) would overwrite the input. "
|
|
"Please explicitly specify a path using --out-file.\n"
|
|
)
|
|
sys.exit(1)
|
|
return ret
|
|
|
|
|
|
def do_dump_model(model_plus: ModelPlus) -> None:
|
|
print(f"model_plus.paths = {model_plus.paths!r}")
|
|
print(f"model_plus.format = {model_plus.format!r}")
|
|
print(f"model_plus.vocab = {model_plus.vocab!r}")
|
|
for name, lazy_tensor in model_plus.model.items():
|
|
print(
|
|
f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}"
|
|
)
|
|
|
|
|
|
def get_argument_parser() -> ArgumentParser:
|
|
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(
|
|
"model",
|
|
type=Path,
|
|
help="Directory containing the model file or the model file itself (*.pth, *.pt, *.bin)",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--awq-path",
|
|
type=Path,
|
|
help="Path to the Activation-aware Weight Quantization cache file",
|
|
default=None,
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--dump",
|
|
action="store_true",
|
|
help="Display the model content without converting it",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--dump-single",
|
|
action="store_true",
|
|
help="Display the content of a single model file without conversion",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--vocab-only",
|
|
action="store_true",
|
|
help="Extract and output only the vocabulary",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--out-type",
|
|
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 the tokenizer.model, if separate from the model file",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--vocab-type",
|
|
choices=["spm", "bpe", "hfft"], # hfft: Hugging Face Fast Tokenizer
|
|
default="spm",
|
|
help="The vocabulary format used to define the tokenizer model (default: spm)",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--pad-vocab",
|
|
action="store_true",
|
|
help="Add padding tokens when the model's vocabulary size exceeds the tokenizer metadata",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--out-file",
|
|
type=Path,
|
|
help="Specify the path for the output file (default is based on input)",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--ctx", type=int, help="Model training context (default is 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="Indicate that the model is executed on a big-endian machine",
|
|
)
|
|
|
|
return parser
|
|
|
|
|
|
def main(argv: Optional[list[str]] = None) -> None:
|
|
parser = get_argument_parser()
|
|
args = parser.parse_args(argv)
|
|
|
|
if args.awq_path:
|
|
sys.path.insert(1, str(Path(__file__).parent / 'awq-py'))
|
|
from awq.apply_awq import add_scale_weights
|
|
tmp_model_path = args.model / "weighted_model"
|
|
if tmp_model_path.is_dir():
|
|
print(f"{tmp_model_path} exists as a weighted model.")
|
|
else:
|
|
tmp_model_path.mkdir(parents=True, exist_ok=True)
|
|
print("Saving new weighted model ...")
|
|
add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path))
|
|
print(f"Saved weighted model at {tmp_model_path}.")
|
|
args.model = tmp_model_path
|
|
|
|
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)
|
|
|
|
if args.dump:
|
|
do_dump_model(model_plus)
|
|
return
|
|
endianess = gguf.GGUFEndian.LITTLE
|
|
if args.bigendian:
|
|
endianess = gguf.GGUFEndian.BIG
|
|
|
|
params = Params.load(model_plus)
|
|
if params.n_ctx == -1:
|
|
if args.ctx is None:
|
|
raise Exception("The model doesn't have a context size, and you didn't specify one with --ctx\n"
|
|
"Please specify one with --ctx:\n"
|
|
" - LLaMA v1: --ctx 2048\n"
|
|
" - LLaMA v2: --ctx 4096\n")
|
|
params.n_ctx = args.ctx
|
|
|
|
if args.outtype:
|
|
params.ftype = {
|
|
"f32": GGMLFileType.AllF32,
|
|
"f16": GGMLFileType.MostlyF16,
|
|
"q8_0": GGMLFileType.MostlyQ8_0,
|
|
}[args.outtype]
|
|
|
|
print(f"params = {params}")
|
|
|
|
vocab: Vocab
|
|
if args.vocab_only:
|
|
if not args.outfile:
|
|
raise ValueError("need --outfile if using --vocab-only")
|
|
# FIXME: Try to respect vocab_dir somehow?
|
|
vocab = VocabLoader(params, args.vocab_dir or args.model)
|
|
special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,
|
|
load_merges = True,
|
|
n_vocab = vocab.vocab_size)
|
|
outfile = args.outfile
|
|
OutputFile.write_vocab_only(outfile, params, vocab, special_vocab,
|
|
endianess = endianess, pad_vocab = args.padvocab)
|
|
print(f"Wrote {outfile}")
|
|
return
|
|
|
|
if model_plus.vocab is not None and args.vocab_dir is None:
|
|
vocab = model_plus.vocab
|
|
else:
|
|
vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent
|
|
vocab = VocabLoader(params, vocab_dir)
|
|
|
|
# FIXME: Try to respect vocab_dir somehow?
|
|
print(f"Vocab info: {vocab}")
|
|
special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,
|
|
load_merges = True,
|
|
n_vocab = vocab.vocab_size)
|
|
|
|
print(f"Special vocab info: {special_vocab}")
|
|
model = model_plus.model
|
|
model = convert_model_names(model, params)
|
|
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.ftype = ftype
|
|
print(f"Writing {outfile}, format {ftype}")
|
|
|
|
OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab,
|
|
concurrency = args.concurrency, endianess = endianess, pad_vocab = args.padvocab)
|
|
print(f"Wrote {outfile}")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|