convert : partially revert PR ggerganov#4818 (ggerganov#5041)

convert-hf-to-gguf.py

@@ -10,7 +10,7 @@
 import sys
 from enum import IntEnum
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast, Optional
+from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast
 
 import numpy as np
 import torch
@@ -487,7 +487,8 @@ def write_tensors(self):
             # map tensor names
             if "scales" in name:
                 new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias", ".scales"))
-                new_name = new_name.replace("scales", "act.scales")
+                if new_name is not None:
+                    new_name = new_name.replace("scales", "act.scales")
             else:
                 new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
             if new_name is None:
@@ -904,7 +905,7 @@ def token_bytes_to_string(b):
         return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
 
     @staticmethod
-    def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: Optional[int] = None) -> list[bytes]:
+    def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
         parts = [bytes([b]) for b in token]
         while True:
             min_idx = None
@@ -1285,7 +1286,7 @@ def main() -> None:
 
     if args.awq_path:
         sys.path.insert(1, str(Path(__file__).parent / 'awq-py'))
-        from awq.apply_awq import add_scale_weights
+        from awq.apply_awq import add_scale_weights  # type: ignore[import-not-found]
         tmp_model_path = args.model / "weighted_model"
         dir_model = tmp_model_path
         if tmp_model_path.is_dir():

convert-llama-ggml-to-gguf.py

@@ -2,14 +2,14 @@
 from __future__ import annotations
 
 import argparse
+import os
 import struct
 import sys
 from enum import IntEnum
 from pathlib import Path
 
 import numpy as np
 
-import os
 if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
@@ -371,15 +371,11 @@ def handle_metadata(cfg, hp):
         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)
-    # FIXME: Respect cfg.vocab_dir?
-    svocab = gguf.SpecialVocab(cfg.model_metadata_dir,
-                               load_merges = cfg.vocabtype == 'bpe',
-                               n_vocab = vocab.vocab_size)
+    vocab_path = Path(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir)
+    vocab_factory = convert.VocabFactory(vocab_path)
+    vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype, cfg.model_metadata_dir)
     convert.check_vocab_size(params, vocab)
-    return (params, vocab, svocab)
+    return params, vocab, special_vocab
 
 
 def handle_args():

convert-lora-to-ggml.py

@@ -5,17 +5,16 @@
 import os
 import struct
 import sys
+from pathlib import Path
 from typing import Any, BinaryIO, Sequence
 
 import numpy as np
 import torch
 
-from pathlib import Path
 if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
 import gguf
 
-
 NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}
 
 

convert-persimmon-to-gguf.py

@@ -1,11 +1,13 @@
 #!/usr/bin/env python3
-import torch
+import argparse
 import os
-from pprint import pprint
 import sys
-import argparse
 from pathlib import Path
+from pprint import pprint
+
+import torch
 from sentencepiece import SentencePieceProcessor
+
 if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
@@ -69,7 +71,7 @@ def main():
     persimmon_model = torch.load(args.ckpt_path)
     hparams = persimmon_model['args']
     pprint(hparams)
-    tensors = {}
+    tensors: dict[str, torch.Tensor] = {}
     _flatten_dict(persimmon_model['model'], tensors, None)
 
     arch = gguf.MODEL_ARCH.PERSIMMON