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0f83e727af
llama.cpp/convert.py
Georgi Gerganov 0f83e727af
py : fix whitespace
2024-01-17 18:37:36 +02:00

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#!/usr/bin/env python3
from __future__ import annotations
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 time
import warnings
import zipfile
from abc import ABCMeta, abstractmethod
from argparse import ArgumentParser
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
from dataclasses import dataclass
from pathlib import Path
from typing import (
IO,
TYPE_CHECKING,
Any,
Callable,
Iterable,
Literal,
Optional,
Tuple,
TypeVar,
)
import numpy as np
from sentencepiece import SentencePieceProcessor
try:
from transformers import AutoTokenizer
except ModuleNotFoundError as e:
warnings.warn(f"Could not import AutoTokenizer from transformers: {e}")
# If NO_LOCAL_GGUF is not set, try to import gguf from the local gguf-py directory
if "NO_LOCAL_GGUF" not in os.environ:
# Use absolute path to the gguf-py directory
gguf_py_dir = str(Path(__file__).resolve().parent / "gguf-py")
print(gguf_py_dir) # NOTE: Remove this once path is verified after changes are completed
if gguf_py_dir not in sys.path:
sys.path.insert(1, gguf_py_dir)
# Import gguf module
try:
import gguf
except ModuleNotFoundError as e:
print(f"Could not import gguf: {e}")
sys.exit(1)
if TYPE_CHECKING: # NOTE: This isn't necessary.
from typing import TypeAlias # This can technically be omitted.
if hasattr(faulthandler, "register") and hasattr(signal, "SIGUSR1"):
faulthandler.register(signal.SIGUSR1)
# NOTE: n-dimensional arrays should be directly referenced
NDArray: TypeAlias = "np.ndarray[Any, Any]"
# Why is this here? LLAMA and GPT are technically the only compatible ARCHs.
ARCH = gguf.MODEL_ARCH.LLAMA
DEFAULT_CONCURRENCY = 8
#
# data types
#
# TODO: Clean up and refactor 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)
# 1D tensors are always F32.
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
f_norm_eps: Optional[float] = None
n_experts: Optional[int] = None
n_experts_used: Optional[int] = None
rope_scaling_type: Optional[gguf.RopeScalingType] = None
f_rope_freq_base: Optional[float] = None
f_rope_scale: Optional[float] = None
n_orig_ctx: Optional[int] = None
rope_finetuned: Optional[bool] = None
ftype: Optional[GGMLFileType] = None
# path to the directory containing the model files
path_model: Optional[Path] = 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:
raise Exception(
"failed to guess 'n_layer'. This model is unknown or unsupported.\n"
"Suggestion: provide 'config.json' of the model in the same directory containing model files."
)
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 load_transformers_config(model: LazyModel, config_path: Path) -> "Params":
config = json.load(open(config_path))
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:
raise Exception(
"failed to guess 'n_ctx'. This model is unknown or unsupported.\n"
"Suggestion: provide 'config.json' of the model in the same directory containing model files."
)
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 load_torch_params(model: LazyModel, config_path: Path) -> "Params":
config = json.load(open(config_path))
n_experts = None
n_experts_used = None
f_rope_freq_base = 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
return Params(
n_vocab=config.get("vocab_size", 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.load_transformers_config(model_plus.model, hf_config_path)
elif orig_config_path.exists():
params = Params.load_torch_params(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
class BpeVocab: # GPT
def __init__(
self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]
) -> None:
self.bpe_tokenizer = json.loads(
open(str(fname_tokenizer), encoding="utf-8").read()
)
self.vocab = self.bpe_tokenizer["model"]["vocab"]
added_tokens: dict[str, int]
if fname_added_tokens is not None:
# FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
else:
# Fall back to trying to find the added tokens in tokenizer.json
tokenizer_json_file = fname_tokenizer.parent / "tokenizer.json"
if not tokenizer_json_file.is_file():
added_tokens = {}
else:
tokenizer_json = json.load(open(tokenizer_json_file, encoding="utf-8"))
added_tokens = dict(
(item["content"], item["id"])
for item in tokenizer_json.get("added_tokens", [])
# Added tokens here can be duplicates of the main vocabulary.
if item["content"] not in self.bpe_tokenizer
)
vocab_size: int = 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 Exception(
f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range {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: int = vocab_size
self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
self.fname_added_tokens = fname_added_tokens
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: # LlaMa
def __init__(
self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]
) -> None:
self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
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_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
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:
# Handle special case where the model's vocab size is not set
if params.n_vocab == -1:
raise ValueError(
f"The model's vocab size is set to -1 in params.json. Please update it manually. Maybe {vocab.vocab_size}?"
)
# Check for a vocab size mismatch
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, 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 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)
assert len(tokens) == vocab.vocab_size
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
print(f"Found vocab files: {self.files}")
def _select_file(self, vocabtype: Optional[str]) -> Path:
if vocabtype in ["spm", "bpe"]:
for file_key in self.files.keys():
if self.files[file_key]:
return self.files[file_key]
raise FileNotFoundError(f"{vocabtype} vocab 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 --outfile.\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(
"--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 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(
"--outfile",
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__).resolve().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.big_endian:
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}")
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, special_vocab = vocab_factory.load_vocab(args.vocab_type, model_parent_path)
if args.vocab_only:
if not args.outfile:
raise ValueError("need --outfile if using --vocab-only")
outfile = args.outfile
OutputFile.write_vocab_only(
outfile,
params,
vocab,
special_vocab,
endianess=endianess,
pad_vocab=args.pad_vocab,
)
print(f"Wrote {outfile}")
return
if model_plus.vocab is not None and args.vocab_dir is None:
vocab = model_plus.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_output_file(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.pad_vocab,
)
print(f"Wrote {outfile}")
if __name__ == "__main__":
main(sys.argv[1:]) # Exclude the first element (script name) from sys.argv
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