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-rw-r--r--convert-llama-ggmlv3-to-gguf.py334
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diff --git a/convert-llama-ggmlv3-to-gguf.py b/convert-llama-ggmlv3-to-gguf.py
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+++ b/convert-llama-ggmlv3-to-gguf.py
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+import sys, struct, math, argparse
+from pathlib import Path
+
+import numpy as np
+
+import gguf
+
+# Note: Does not support GGML_QKK_64
+QK_K = 256
+# Items here are (block size, type size)
+GGML_QUANT_SIZES = {
+ gguf.GGMLQuantizationType.F32 : (1, 4),
+ gguf.GGMLQuantizationType.F16 : (1, 2),
+ gguf.GGMLQuantizationType.Q4_0 : (32, 2 + 16),
+ gguf.GGMLQuantizationType.Q4_1 : (32, 2 + 2 + 16),
+ gguf.GGMLQuantizationType.Q5_0 : (32, 2 + 4 + 16),
+ gguf.GGMLQuantizationType.Q5_1 : (32, 2 + 2 + 4 + 16),
+ gguf.GGMLQuantizationType.Q8_0 : (32, 2 + 32),
+ gguf.GGMLQuantizationType.Q8_1 : (32, 4 + 4 + 32),
+ gguf.GGMLQuantizationType.Q2_K : (256, 2 + 2 + QK_K // 16 + QK_K // 4),
+ gguf.GGMLQuantizationType.Q3_K : (256, 2 + QK_K // 4 + QK_K // 8 + 12),
+ gguf.GGMLQuantizationType.Q4_K : (256, 2 + 2 + QK_K // 2 + 12),
+ gguf.GGMLQuantizationType.Q5_K : (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12),
+ gguf.GGMLQuantizationType.Q6_K : (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16),
+ gguf.GGMLQuantizationType.Q8_K : (256, 4 + QK_K + QK_K // 8),
+}
+
+class Hyperparameters:
+ def __init__(self):
+ self.n_vocab = self.n_embd = self.n_mult = self.n_head = self.n_layer = self.n_rot = self.ftype = 0
+ self.n_ff = 0
+
+ def set_n_ff(self, model):
+ ff_tensor_idx = model.tensor_map.get(b'layers.0.feed_forward.w1.weight')
+ assert ff_tensor_idx is not None, 'Missing layer 0 FF tensor'
+ ff_tensor = model.tensors[ff_tensor_idx]
+ self.n_ff = ff_tensor.dims[1]
+
+ def load(self, data, offset):
+ (
+ self.n_vocab,
+ self.n_embd,
+ self.n_mult,
+ self.n_head,
+ self.n_layer,
+ self.n_rot,
+ self.ftype,
+ ) = struct.unpack('<7I', data[offset:offset + (4 * 7)])
+ return 4 * 7
+
+ def __str__(self):
+ 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}>'
+
+class Vocab:
+ def __init__(self):
+ self.items = []
+
+ def load(self, data, offset, n_vocab):
+ orig_offset = offset
+ for _ in range(n_vocab):
+ itemlen = struct.unpack('<I', data[offset:offset + 4])[0]
+ assert itemlen < 4096, 'Absurd vocab item length'
+ offset += 4
+ vocab = bytes(data[offset:offset + itemlen])
+ offset += itemlen
+ score = struct.unpack('<f', data[offset:offset + 4])[0]
+ offset += 4
+ self.items.append((vocab, score))
+ return offset - orig_offset
+
+class Tensor:
+ def __init__(self):
+ self.name = None
+ self.dims = ()
+ self.dtype = None
+ self.start_offset = 0
+ self.len_bytes = 0
+
+ def load(self, data, offset):
+ orig_offset = offset
+ (n_dims, name_len, dtype) = struct.unpack('<3I', data[offset:offset + 12])
+ assert n_dims >= 0 and n_dims <= 4, f'Invalid tensor dimensions {n_dims}'
+ assert name_len < 4096, 'Absurd tensor name length'
+ quant = GGML_QUANT_SIZES.get(dtype)
+ assert quant is not None, 'Unknown tensor type'
+ (blksize, tysize) = quant
+ offset += 12
+ self.dtype= dtype
+ self.dims = struct.unpack(f'<{n_dims}I', data[offset:offset + (4 * n_dims)])
+ offset += 4 * n_dims
+ self.name = bytes(data[offset:offset + name_len])
+ offset += name_len
+ pad = ((offset + 31) & ~31) - offset
+ offset += pad
+ n_elems = np.prod(self.dims)
+ n_bytes = (n_elems * tysize) // blksize
+ self.start_offset = offset
+ self.len_bytes = n_bytes
+ offset += n_bytes
+ # print(n_dims, name_len, dtype, self.dims, self.name, pad)
+ return offset - orig_offset
+
+class GGMLV3Model:
+ def __init__(self):
+ self.hyperparameters = None
+ self.vocab = None
+ self.tensor_map = {}
+ self.tensors = []
+
+ def validate_header(self, data, offset):
+ if bytes(data[offset:offset + 4]) != b'tjgg' or struct.unpack('<I', data[offset + 4:offset + 8])[0] != 3:
+ raise ValueError('Only GGJTv3 supported')
+ return 8
+
+ def load(self, data, offset):
+ offset += self.validate_header(data, offset)
+ hp = Hyperparameters()
+ offset += hp.load(data, offset)
+ vocab = Vocab()
+ offset += vocab.load(data, offset, hp.n_vocab)
+ tensors = []
+ tensor_map = {}
+ while offset < len(data):
+ tensor = Tensor()
+ offset += tensor.load(data, offset)
+ tensor_map[tensor.name] = len(tensors)
+ tensors.append(tensor)
+ self.hyperparameters = hp
+ self.vocab = vocab
+ self.tensors = tensors
+ self.tensor_map = tensor_map
+ hp.set_n_ff(self)
+ return offset
+
+class GGMLToGGUF:
+ def __init__(self, ggml_model, data, cfg, params_override = None, vocab_override = None):
+ hp = ggml_model.hyperparameters
+ self.model = ggml_model
+ self.data = data
+ self.cfg = cfg
+ self.params_override = params_override
+ self.vocab_override = vocab_override
+ if params_override is not None:
+ n_kv_head = params_override.n_head_kv
+ else:
+ if cfg.gqa == 1:
+ n_kv_head = hp.n_head
+ else:
+ gqa = float(cfg.gqa)
+ n_kv_head = None
+ for x in range(1, 256):
+ if float(hp.n_head) / float(x) == gqa:
+ n_kv_head = x
+ assert n_kv_head is not None, "Couldn't determine n_kv_head from GQA param"
+ print(f'- Guessed n_kv_head = {n_kv_head} based on GQA {cfg.gqa}')
+ self.n_kv_head = n_kv_head
+ self.name_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.LLAMA, ggml_model.hyperparameters.n_layer)
+
+ def save(self):
+ print('* Preparing to save GGUF file')
+ gguf_writer = gguf.GGUFWriter(self.cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False)
+ self.add_params(gguf_writer)
+ self.add_vocab(gguf_writer)
+ self.add_tensors(gguf_writer)
+ print(" gguf: write header")
+ gguf_writer.write_header_to_file()
+ print(" gguf: write metadata")
+ gguf_writer.write_kv_data_to_file()
+ print(" gguf: write tensors")
+ gguf_writer.write_tensors_to_file()
+ gguf_writer.close()
+
+ def add_params(self, gguf_writer):
+ hp = self.model.hyperparameters
+ cfg = self.cfg
+ desc = cfg.desc if cfg.desc is not None else 'converted from legacy GGJTv3 format'
+ try:
+ # Filenames aren't necessarily valid UTF8.
+ name = cfg.name if cfg.name is not None else cfg.input.name
+ except UnicodeDecodeError:
+ name = None
+ print('* Adding model parameters and KV items')
+ if name is not None:
+ gguf_writer.add_name(name)
+ gguf_writer.add_description(desc)
+ if self.params_override is not None:
+ po = self.params_override
+ assert po.n_embd == hp.n_embd, 'Model hyperparams mismatch'
+ assert po.n_layer == hp.n_layer, 'Model hyperparams mismatch'
+ assert po.n_head == hp.n_head, 'Model hyperparams mismatch'
+ gguf_writer.add_context_length (po.n_ctx)
+ gguf_writer.add_embedding_length (po.n_embd)
+ gguf_writer.add_block_count (po.n_layer)
+ gguf_writer.add_feed_forward_length (po.n_ff)
+ gguf_writer.add_rope_dimension_count(po.n_embd // po.n_head)
+ gguf_writer.add_head_count (po.n_head)
+ gguf_writer.add_head_count_kv (po.n_head_kv)
+ gguf_writer.add_layer_norm_rms_eps (po.f_norm_eps)
+ return
+ gguf_writer.add_context_length(cfg.context_length)
+ gguf_writer.add_embedding_length(hp.n_embd)
+ gguf_writer.add_block_count(hp.n_layer)
+ gguf_writer.add_feed_forward_length(hp.n_ff)
+ gguf_writer.add_rope_dimension_count(hp.n_embd // hp.n_head)
+ gguf_writer.add_head_count(hp.n_head)
+ gguf_writer.add_head_count_kv(self.n_kv_head)
+ gguf_writer.add_layer_norm_rms_eps(float(cfg.eps))
+
+ def add_vocab(self, gguf_writer):
+ hp = self.model.hyperparameters
+ gguf_writer.add_tokenizer_model('llama')
+ tokens = []
+ scores = []
+ toktypes = []
+ if self.vocab_override is not None:
+ vo = self.vocab_override
+ print('* Adding vocab item(s)')
+ for (idx, vitem) in enumerate(vo.all_tokens()):
+ if len(vitem) == 3:
+ tokens.append(vitem[0])
+ scores.append(vitem[1])
+ toktypes.append(vitem[2])
+ else:
+ # Maybe try to guess the token type here?
+ tokens.append(vitem[0])
+ scores.append(vitem[1])
+ 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}'
+ gguf_writer.add_token_list(tokens)
+ gguf_writer.add_token_scores(scores)
+ if len(toktypes) > 0:
+ gguf_writer.add_token_types(toktypes)
+ return
+ print(f'* Adding {hp.n_vocab} vocab item(s)')
+ for (tokid, (vbytes, vscore)) in enumerate(self.model.vocab.items):
+ tt = 1 # Normal
+ if len(vbytes) == 0:
+ tt = 3 # Control
+ elif tokid >= 3 and tokid <= 258 and len(vbytes) == 1:
+ hv = hex(vbytes[0])[2:].upper()
+ vbytes = bytes(f'<0x{hv}>', encoding = 'UTF-8')
+ tt = 6 # Byte
+ else:
+ vbytes = vbytes.replace(b' ', b'\xe2\x96\x81')
+ toktypes.append(tt)
+ tokens.append(vbytes)
+ scores.append(vscore)
+ gguf_writer.add_token_list(tokens)
+ gguf_writer.add_token_scores(scores)
+ gguf_writer.add_token_types(toktypes)
+
+ def add_tensors(self, gguf_writer):
+ nm = self.name_map
+ data = self.data
+ print(f'* Adding {len(self.model.tensors)} tensor(s)')
+ for tensor in self.model.tensors:
+ name = str(tensor.name, 'UTF-8')
+ if name.endswith('.weight'):
+ name = name[:-7]
+ suffix = '.weight'
+ elif name.endswith('.bias'):
+ name = name[:-5]
+ suffix = '.bias'
+ mapped_name = nm.get(name)
+ assert mapped_name is not None, f'Bad name {name}'
+ mapped_name += suffix
+ tempdims = list(tensor.dims[:])
+ if len(tempdims) > 1:
+ temp = tempdims[1]
+ tempdims[1] = tempdims[0]
+ tempdims[0] = temp
+ # print(f'+ {tensor.name} | {mapped_name} {tensor.dims} :: {tempdims}')
+ gguf_writer.add_tensor(mapped_name, data[tensor.start_offset:tensor.start_offset + tensor.len_bytes], raw_shape = tempdims, raw_dtype = tensor.dtype)
+
+def handle_metadata(cfg, hp):
+ import convert
+ assert cfg.model_metadata_dir.is_dir(), 'Metadata dir is not a directory'
+ hf_config_path = cfg.model_metadata_dir / "config.json"
+ orig_config_path = cfg.model_metadata_dir / "params.json"
+ # We pass a fake model here. "original" mode will check the shapes of some
+ # tensors if information is missing in the .json file: other than that, the
+ # model data isn't used so this should be safe (at least for now).
+ fakemodel = {
+ 'tok_embeddings.weight': convert.LazyTensor.__new__(convert.LazyTensor),
+ 'layers.0.feed_forward.w1.weight': convert.LazyTensor.__new__(convert.LazyTensor),
+ }
+ fakemodel['tok_embeddings.weight'].shape = [hp.n_vocab]
+ fakemodel['layers.0.feed_forward.w1.weight'].shape = [hp.n_ff]
+ if hf_config_path.exists():
+ params = convert.Params.loadHFTransformerJson(fakemodel, hf_config_path)
+ elif orig_config_path.exists():
+ params = convert.Params.loadOriginalParamsJson(fakemodel, orig_config_path)
+ else:
+ raise ValueError('Unable to load metadata')
+ vocab = convert.load_vocab(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir, cfg.vocabtype)
+ convert.check_vocab_size(params, vocab)
+ return (params, vocab)
+
+def handle_args():
+ parser = argparse.ArgumentParser(description = 'Convert GGMLv3 models to GGUF')
+ parser.add_argument('--input', '-i', type = Path, help = 'Input GGMLv3 filename')
+ parser.add_argument('--output', '-o', type = Path, help ='Output GGUF filename')
+ parser.add_argument('--name', help = 'Set model name')
+ parser.add_argument('--desc', help = 'Set model description')
+ parser.add_argument('--gqa', type = int, default = 1, help = 'grouped-query attention factor (use 8 for LLaMA2 70B)')
+ parser.add_argument('--eps', default = '5.0e-06', help = 'RMS norm eps: Use 1e-6 for LLaMA1 and OpenLLaMA, use 1e-5 for LLaMA2')
+ parser.add_argument('--context-length', '-c', type=int, default = 2048, help = 'Default max context length: LLaMA1 is typically 2048, LLaMA2 is typically 4096')
+ parser.add_argument('--model-metadata-dir', '-m', type = Path, help ='Load HuggingFace/.pth vocab and metadata from the specified directory')
+ parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir")
+ parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)", default="spm")
+ return parser.parse_args()
+
+def main():
+ cfg = handle_args()
+ print(f'* Using config: {cfg}')
+ print('\n=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===\n')
+ data = np.memmap(cfg.input, mode = 'r')
+ model = GGMLV3Model()
+ print('* Scanning GGML input file')
+ offset = model.load(data, 0)
+ print(f'* GGML model hyperparameters: {model.hyperparameters}')
+ vocab_override = None
+ params_override = None
+ if cfg.model_metadata_dir is not None:
+ (params_override, vocab_override) = handle_metadata(cfg, model.hyperparameters)
+ print('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.')
+ print(f'* Overriding params: {params_override}')
+ print(f'* Overriding vocab: {vocab_override}')
+ else:
+ print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
+ converter = GGMLToGGUF(model, data, cfg, params_override = params_override, vocab_override = vocab_override)
+ converter.save()
+ print(f'* Successful completion. Output saved to: {cfg.output}')
+
+main()
generated by cgit v1.2.3 (git 2.25.1) at 2025-08-16 03:29:40 +0000