convert.py: add python logging instead of print()
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brian khuu
parent
952d03dbea
commit
573dcecda1
1 changed files with 29 additions and 18 deletions
47
convert.py
47
convert.py
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@ -1,6 +1,7 @@
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#!/usr/bin/env python3
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from __future__ import annotations
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import logging
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import argparse
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import concurrent.futures
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import enum
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@ -643,7 +644,7 @@ class LlamaHfVocab(Vocab):
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def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:
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# print( "permute debug " + str(weights.shape[0]) + " x " + str(weights.shape[1]) + " nhead " + str(n_head) + " nheadkv " + str(n_kv_head) )
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# logging.info( "permute debug " + str(weights.shape[0]) + " x " + str(weights.shape[1]) + " nhead " + str(n_head) + " nheadkv " + str(n_kv_head) )
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if n_head_kv is not None and n_head != n_head_kv:
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n_head = n_head_kv
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return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
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@ -1033,12 +1034,12 @@ def check_vocab_size(params: Params, vocab: BaseVocab, pad_vocab: bool = False)
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# Check for a vocab size mismatch
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if params.n_vocab == vocab.vocab_size:
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print("Ignoring added_tokens.json since model matches vocab size without it.")
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logging.warning("Ignoring added_tokens.json since model matches vocab size without it.")
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return
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if pad_vocab and params.n_vocab > vocab.vocab_size:
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pad_count = params.n_vocab - vocab.vocab_size
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print(
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logging.debug(
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f"Padding vocab with {pad_count} token(s) - <dummy00001> through <dummy{pad_count:05}>"
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)
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for i in range(1, pad_count + 1):
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@ -1166,7 +1167,7 @@ class OutputFile:
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elapsed = time.time() - start
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size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
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padi = len(str(len(model)))
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print(
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logging.info(
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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}"
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)
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self.gguf.write_tensor_data(ndarray)
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@ -1281,12 +1282,12 @@ def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) ->
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# HF models permut or pack some of the tensors, so we need to undo that
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for i in itertools.count():
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if f"model.layers.{i}.self_attn.q_proj.weight" in model:
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print(f"Permuting layer {i}")
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logging.debug(f"Permuting layer {i}")
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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)
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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)
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# tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
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elif f"model.layers.{i}.self_attn.W_pack.weight" in model:
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print(f"Unpacking and permuting layer {i}")
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logging.debug(f"Unpacking and permuting layer {i}")
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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)
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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)
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tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = part_lazy (model[f"model.layers.{i}.self_attn.W_pack.weight"], 2)
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@ -1299,15 +1300,15 @@ def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) ->
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tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (".weight", ".bias")) or (None, None)
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if name_new is None:
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if skip_unknown:
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print(f"Unexpected tensor name: {name} - skipping")
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logging.warning(f"Unexpected tensor name: {name} - skipping")
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continue
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raise ValueError(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)")
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if tensor_type in should_skip:
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print(f"skipping tensor {name_new}")
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logging.debug(f"skipping tensor {name_new}")
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continue
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print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}")
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logging.debug(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}")
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out[name_new] = lazy_tensor
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return out
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@ -1372,7 +1373,7 @@ def load_some_model(path: Path) -> ModelPlus:
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paths = find_multifile_paths(path)
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models_plus: list[ModelPlus] = []
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for path in paths:
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print(f"Loading model file {path}")
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logging.info(f"Loading model file {path}")
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models_plus.append(lazy_load_file(path))
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model_plus = merge_multifile_models(models_plus)
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@ -1413,7 +1414,7 @@ class VocabFactory:
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else:
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raise FileNotFoundError(f"Could not find a tokenizer matching any of {vocab_types}")
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print(f"Loaded vocab file {vocab.fname_tokenizer!r}, type {vocab.name!r}")
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logging.info(f"Loaded vocab file {vocab.fname_tokenizer!r}, type {vocab.name!r}")
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return vocab
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def load_vocab(self, vocab_types: list[str] | None, model_parent_path: Path) -> tuple[BaseVocab, gguf.SpecialVocab]:
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@ -1473,8 +1474,18 @@ def main(args_in: list[str] | None = None) -> None:
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parser.add_argument("--big-endian", action="store_true", help="model is executed on big endian machine")
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parser.add_argument("--pad-vocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides")
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parser.add_argument("--skip-unknown", action="store_true", help="skip unknown tensor names instead of failing")
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parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
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args = parser.parse_args(args_in)
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if args.dump_single or args.dump:
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# Avoid printing anything besides the dump output
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logging.basicConfig(level=logging.CRITICAL)
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elif args.verbose:
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logging.basicConfig(level=logging.DEBUG)
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else:
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logging.basicConfig(level=logging.INFO)
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if args.no_vocab and args.vocab_only:
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raise ValueError("--vocab-only does not make sense with --no-vocab")
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@ -1491,6 +1502,7 @@ def main(args_in: list[str] | None = None) -> None:
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if args.dump:
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do_dump_model(model_plus)
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return
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endianess = gguf.GGUFEndian.LITTLE
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if args.big_endian:
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endianess = gguf.GGUFEndian.BIG
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@ -1513,7 +1525,7 @@ def main(args_in: list[str] | None = None) -> None:
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"q8_0": GGMLFileType.MostlyQ8_0,
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}[args.outtype]
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print(f"params = {params}")
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logging.info(f"params = {params}")
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model_parent_path = model_plus.paths[0].parent
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vocab_path = Path(args.vocab_dir or args.model or model_parent_path)
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@ -1528,15 +1540,14 @@ def main(args_in: list[str] | None = None) -> None:
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outfile = args.outfile
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OutputFile.write_vocab_only(outfile, params, vocab, special_vocab,
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endianess=endianess, pad_vocab=args.pad_vocab)
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print(f"Wrote {outfile}")
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logging.info(f"Wrote {outfile}")
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return
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if model_plus.vocab is not None and args.vocab_dir is None and not args.no_vocab:
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vocab = model_plus.vocab
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print(f"Vocab info: {vocab}")
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print(f"Special vocab info: {special_vocab}")
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logging.info(f"Vocab info: {vocab}")
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logging.info(f"Special vocab info: {special_vocab}")
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model = model_plus.model
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model = convert_model_names(model, params, args.skip_unknown)
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ftype = pick_output_type(model, args.outtype)
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@ -1544,11 +1555,11 @@ def main(args_in: list[str] | None = None) -> None:
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outfile = args.outfile or default_outfile(model_plus.paths, ftype)
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params.ftype = ftype
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print(f"Writing {outfile}, format {ftype}")
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logging.info(f"Writing {outfile}, format {ftype}")
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OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab,
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concurrency=args.concurrency, endianess=endianess, pad_vocab=args.pad_vocab)
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print(f"Wrote {outfile}")
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logging.info(f"Wrote {outfile}")
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if __name__ == '__main__':
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