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convert-llama-ggml-to-gguf: Try to handle files older than GGJTv3 (#3023)
* convert-llama-ggmlv3-to-gguf: Try to handle files older than GGJTv3 * Better error messages for files that cannot be converted * Add file type to GGUF output * Rename to convert-llama-ggml-to-gguf.py * Include original file type information in description * Improve some informational output
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Kerfuffle
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@ -5,6 +5,7 @@ import argparse
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import math
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import struct
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import sys
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from enum import IntEnum
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from pathlib import Path
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import numpy as np
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@ -34,10 +35,35 @@ GGML_QUANT_SIZES = {
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gguf.GGMLQuantizationType.Q8_K : (256, 4 + QK_K + QK_K // 8),
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}
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class GGMLFormat(IntEnum):
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GGML = 0
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GGMF = 1
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GGJT = 2
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class GGMLFType(IntEnum):
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ALL_F32 = 0
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MOSTLY_F16 = 1
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MOSTLY_Q4_0 = 2
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MOSTLY_Q4_1 = 3
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MOSTLY_Q4_1_SOME_F16 = 4
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MOSTLY_Q8_0 = 7
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MOSTLY_Q5_0 = 8
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MOSTLY_Q5_1 = 9
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MOSTLY_Q2_K = 10
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MOSTLY_Q3_K_S = 11
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MOSTLY_Q3_K_M = 12
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MOSTLY_Q3_K_L = 13
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MOSTLY_Q4_K_S = 14
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MOSTLY_Q4_K_M = 15
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MOSTLY_Q5_K_S = 16
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MOSTLY_Q5_K_M = 17
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MOSTLY_Q6_K = 18
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class Hyperparameters:
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def __init__(self):
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self.n_vocab = self.n_embd = self.n_mult = self.n_head = self.n_layer = self.n_rot = self.ftype = 0
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self.n_ff = 0
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self.n_vocab = self.n_embd = self.n_mult = self.n_head = 0
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self.n_layer = self.n_rot = self.n_ff = 0
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self.ftype = GGMLFType.ALL_F32
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def set_n_ff(self, model):
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ff_tensor_idx = model.tensor_map.get(b'layers.0.feed_forward.w1.weight')
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@ -53,16 +79,21 @@ class Hyperparameters:
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self.n_head,
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self.n_layer,
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self.n_rot,
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self.ftype,
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ftype,
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) = struct.unpack('<7I', data[offset:offset + (4 * 7)])
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try:
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self.ftype = GGMLFType(ftype)
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except ValueError:
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raise ValueError(f'Invalid ftype {ftype}')
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return 4 * 7
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def __str__(self):
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return f'<Hyperparameters: n_vocab={self.n_vocab}, n_embd={self.n_embd}, n_mult={self.n_mult}, n_head={self.n_head}, n_layer={self.n_layer}, n_rot={self.n_rot}, n_ff={self.n_ff}, ftype={self.ftype}>'
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return f'<Hyperparameters: n_vocab={self.n_vocab}, n_embd={self.n_embd}, n_mult={self.n_mult}, n_head={self.n_head}, n_layer={self.n_layer}, n_rot={self.n_rot}, n_ff={self.n_ff}, ftype={self.ftype.name}>'
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class Vocab:
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def __init__(self):
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def __init__(self, load_scores = True):
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self.items = []
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self.load_scores = load_scores
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def load(self, data, offset, n_vocab):
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orig_offset = offset
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@ -70,20 +101,24 @@ class Vocab:
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itemlen = struct.unpack('<I', data[offset:offset + 4])[0]
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assert itemlen < 4096, 'Absurd vocab item length'
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offset += 4
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vocab = bytes(data[offset:offset + itemlen])
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item_text = bytes(data[offset:offset + itemlen])
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offset += itemlen
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score = struct.unpack('<f', data[offset:offset + 4])[0]
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offset += 4
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self.items.append((vocab, score))
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if self.load_scores:
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item_score = struct.unpack('<f', data[offset:offset + 4])[0]
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offset += 4
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else:
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item_score = 0.0
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self.items.append((item_text, item_score))
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return offset - orig_offset
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class Tensor:
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def __init__(self):
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def __init__(self, use_padding = True):
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self.name = None
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self.dims: tuple[int, ...] = ()
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self.dtype = None
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self.start_offset = 0
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self.len_bytes = np.int64(0)
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self.use_padding = use_padding
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def load(self, data, offset):
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orig_offset = offset
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@ -99,7 +134,7 @@ class Tensor:
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offset += 4 * n_dims
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self.name = bytes(data[offset:offset + name_len])
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offset += name_len
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pad = ((offset + 31) & ~31) - offset
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pad = ((offset + 31) & ~31) - offset if self.use_padding else 0
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offset += pad
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n_elems = np.prod(self.dims)
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n_bytes = np.int64(np.int64(n_elems) * np.int64(tysize)) // np.int64(blksize)
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@ -109,7 +144,7 @@ class Tensor:
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# print(n_dims, name_len, dtype, self.dims, self.name, pad)
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return offset - orig_offset
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class GGMLV3Model:
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class GGMLModel:
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def __init__(self):
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self.hyperparameters = None
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self.vocab = None
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@ -117,20 +152,52 @@ class GGMLV3Model:
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self.tensors = []
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def validate_header(self, data, offset):
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if bytes(data[offset:offset + 4]) != b'tjgg' or struct.unpack('<I', data[offset + 4:offset + 8])[0] != 3:
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raise ValueError('Only GGJTv3 supported')
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return 8
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magic = bytes(data[offset:offset + 4])
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if magic == b'GGUF':
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raise ValueError('File is already in GGUF format.')
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if magic == b'lmgg':
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self.file_format = GGMLFormat.GGML
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self.format_version = 1
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return 4
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version = struct.unpack('<I', data[offset + 4:offset + 8])[0]
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if magic == b'fmgg':
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if version != 1:
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raise ValueError(f'Cannot handle unexpected GGMF file version {version}')
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self.file_format = GGMLFormat.GGMF
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self.format_version = version
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return 8
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if magic == b'tjgg':
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if version < 1 or version > 3:
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raise ValueError(f'Cannot handle unexpected GGJT file version {version}')
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self.file_format = GGMLFormat.GGJT
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self.format_version = version
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return 8
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raise ValueError(f"Unexpected file magic {magic!r}! This doesn't look like a GGML format file.")
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def validate_conversion(self, ftype):
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err = ''
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if (self.file_format < GGMLFormat.GGJT or self.format_version < 2):
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if ftype not in (GGMLFType.ALL_F32, GGMLFType.MOSTLY_F16):
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err = 'Quantizations changed in GGJTv2. Can only convert unquantized GGML files older than GGJTv2.'
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elif (self.file_format == GGMLFormat.GGJT and self.format_version == 2):
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if ftype in ( GGMLFType.MOSTLY_Q4_0, GGMLFType.MOSTLY_Q4_1,
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GGMLFType.MOSTLY_Q4_1_SOME_F16, GGMLFType.MOSTLY_Q8_0):
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err = 'Q4 and Q8 quantizations changed in GGJTv3.'
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if len(err) > 0:
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raise ValueError(f'{err} Sorry, your {self.file_format.name}v{self.format_version} file of type {ftype.name} is not eligible for conversion.')
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def load(self, data, offset):
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offset += self.validate_header(data, offset)
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hp = Hyperparameters()
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offset += hp.load(data, offset)
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vocab = Vocab()
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print(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}')
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self.validate_conversion(hp.ftype)
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vocab = Vocab(load_scores = self.file_format > GGMLFormat.GGML)
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offset += vocab.load(data, offset, hp.n_vocab)
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tensors: list[Tensor] = []
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tensor_map = {}
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while offset < len(data):
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tensor = Tensor()
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tensor = Tensor(use_padding = self.file_format > GGMLFormat.GGMF)
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offset += tensor.load(data, offset)
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tensor_map[tensor.name] = len(tensors)
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tensors.append(tensor)
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@ -168,7 +235,10 @@ class GGMLToGGUF:
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def save(self):
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print('* Preparing to save GGUF file')
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gguf_writer = gguf.GGUFWriter(self.cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False)
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gguf_writer = gguf.GGUFWriter(
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self.cfg.output,
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gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA],
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use_temp_file = False )
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self.add_params(gguf_writer)
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self.add_vocab(gguf_writer)
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if self.special_vocab is not None:
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@ -185,7 +255,10 @@ class GGMLToGGUF:
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def add_params(self, gguf_writer):
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hp = self.model.hyperparameters
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cfg = self.cfg
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desc = cfg.desc if cfg.desc is not None else 'converted from legacy GGJTv3 format'
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if cfg.desc is not None:
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desc = cfg.desc
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else:
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desc = f'converted from legacy {self.model.file_format.name}v{self.model.format_version} {hp.ftype.name} format'
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try:
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# Filenames aren't necessarily valid UTF8.
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name = cfg.name if cfg.name is not None else cfg.input.name
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@ -195,6 +268,7 @@ class GGMLToGGUF:
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if name is not None:
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gguf_writer.add_name(name)
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gguf_writer.add_description(desc)
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gguf_writer.add_file_type(int(hp.ftype))
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if self.params_override is not None:
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po = self.params_override
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assert po.n_embd == hp.n_embd, 'Model hyperparams mismatch'
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@ -231,7 +305,8 @@ class GGMLToGGUF:
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tokens.append(vbytes)
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scores.append(score)
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toktypes.append(ttype)
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assert len(tokens) == hp.n_vocab, f'Override vocab has a different number of items than hyperparameters - override = {len(tokens)} but n_vocab={hp.n_vocab}'
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assert len(tokens) == hp.n_vocab, \
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f'Override vocab has a different number of items than hyperparameters - override = {len(tokens)} but n_vocab={hp.n_vocab}'
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gguf_writer.add_token_list(tokens)
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gguf_writer.add_token_scores(scores)
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if len(toktypes) > 0:
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@ -283,7 +358,11 @@ class GGMLToGGUF:
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tempdims[1] = tempdims[0]
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tempdims[0] = temp
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# print(f'+ {tensor.name} | {mapped_name} {tensor.dims} :: {tempdims}')
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gguf_writer.add_tensor(mapped_name, data[tensor.start_offset:tensor.start_offset + tensor.len_bytes], raw_shape = tempdims, raw_dtype = tensor.dtype)
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gguf_writer.add_tensor(
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mapped_name,
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data[tensor.start_offset:tensor.start_offset + tensor.len_bytes],
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raw_shape = tempdims,
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raw_dtype = tensor.dtype )
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def handle_metadata(cfg, hp):
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import convert
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@ -305,32 +384,46 @@ def handle_metadata(cfg, hp):
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params = convert.Params.loadOriginalParamsJson(fakemodel, orig_config_path)
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else:
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raise ValueError('Unable to load metadata')
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vocab = convert.load_vocab(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir, cfg.vocabtype)
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vocab = convert.load_vocab(
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cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir,
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cfg.vocabtype )
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# FIXME: Respect cfg.vocab_dir?
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svocab = gguf.SpecialVocab(cfg.model_metadata_dir)
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convert.check_vocab_size(params, vocab)
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return (params, vocab, svocab)
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def handle_args():
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parser = argparse.ArgumentParser(description = 'Convert GGMLv3 models to GGUF')
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parser.add_argument('--input', '-i', type = Path, required = True, help = 'Input GGMLv3 filename')
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parser.add_argument('--output', '-o', type = Path, required = True, help ='Output GGUF filename')
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parser.add_argument('--name', help = 'Set model name')
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parser.add_argument('--desc', help = 'Set model description')
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parser.add_argument('--gqa', type = int, default = 1, help = 'grouped-query attention factor (use 8 for LLaMA2 70B)')
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parser.add_argument('--eps', default = '5.0e-06', help = 'RMS norm eps: Use 1e-6 for LLaMA1 and OpenLLaMA, use 1e-5 for LLaMA2')
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parser.add_argument('--context-length', '-c', type=int, default = 2048, help = 'Default max context length: LLaMA1 is typically 2048, LLaMA2 is typically 4096')
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parser.add_argument('--model-metadata-dir', '-m', type = Path, help ='Load HuggingFace/.pth vocab and metadata from the specified directory')
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parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir")
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parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)", default="spm")
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parser = argparse.ArgumentParser(description = 'Convert GGML models to GGUF')
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parser.add_argument('--input', '-i', type = Path, required = True,
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help = 'Input GGMLv3 filename')
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parser.add_argument('--output', '-o', type = Path, required = True,
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help ='Output GGUF filename')
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parser.add_argument('--name',
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help = 'Set model name')
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parser.add_argument('--desc',
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help = 'Set model description')
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parser.add_argument('--gqa', type = int, default = 1,
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help = 'grouped-query attention factor (use 8 for LLaMA2 70B)')
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parser.add_argument('--eps', default = '5.0e-06',
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help = 'RMS norm eps: Use 1e-6 for LLaMA1 and OpenLLaMA, use 1e-5 for LLaMA2')
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parser.add_argument('--context-length', '-c', type=int, default = 2048,
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help = 'Default max context length: LLaMA1 is typically 2048, LLaMA2 is typically 4096')
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parser.add_argument('--model-metadata-dir', '-m', type = Path,
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help ='Load HuggingFace/.pth vocab and metadata from the specified directory')
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parser.add_argument("--vocab-dir", type=Path,
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help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir")
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parser.add_argument("--vocabtype", choices=["spm", "bpe"], default="spm",
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help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)")
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return parser.parse_args()
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def main():
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cfg = handle_args()
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print(f'* Using config: {cfg}')
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print('\n=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===\n')
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if cfg.model_metadata_dir is None and (cfg.gqa == 1 or cfg.eps == '5.0e-06'):
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print('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".')
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data = np.memmap(cfg.input, mode = 'r')
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model = GGMLV3Model()
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model = GGMLModel()
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print('* Scanning GGML input file')
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offset = model.load(data, 0)
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print(f'* GGML model hyperparameters: {model.hyperparameters}')
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@ -345,7 +438,12 @@ def main():
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print(f'* Special vocab: {special_vocab}')
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else:
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print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
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converter = GGMLToGGUF(model, data, cfg, params_override = params_override, vocab_override = vocab_override, special_vocab = special_vocab)
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if model.file_format == GGMLFormat.GGML:
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print('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!')
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converter = GGMLToGGUF(model, data, cfg,
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params_override = params_override,
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vocab_override = vocab_override,
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special_vocab = special_vocab )
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converter.save()
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print(f'* Successful completion. Output saved to: {cfg.output}')
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