Remove old script

convert-persimmon-st-to-gguf.py

@@ -1,135 +0,0 @@
-from convert import lazy_load_safetensors_file
-import sys
-import torch
-from safetensors import safe_open
-from pathlib import Path
-from pprint import pprint
-from sentencepiece import SentencePieceProcessor
-import argparse
-import gguf
-import json
-import struct
-
-def file_is_safetensors(path: Path) -> bool:
-    fp = open(path, 'rb')
-    first8 = fp.read(8)
-    fp.seek(0)
-    if first8[:2] == b'PK':
-        # A zip file, i.e. PyTorch format
-        return False
-    return struct.unpack('<Q', first8)[0] < 16 * 1024 * 1024
-
-def get_tokenizer_info(dir_model: Path):
-    tokenizer_path = dir_model / 'adept_vocab.model'
-    print('gguf: getting sentencepiece tokenizer from', tokenizer_path)
-    tokenizer = SentencePieceProcessor(str(tokenizer_path))  
-    tokens: list[bytes] = []
-    scores: list[float] = []
-    toktypes: list[int] = []
-
-    for i in range(tokenizer.vocab_size()):
-        text: bytes
-        score: float
-
-        piece = tokenizer.id_to_piece(i)
-        text = piece.encode("utf-8")
-        score = tokenizer.get_score(i)
-
-        toktype = 1  # defualt to normal token type
-        if tokenizer.is_unknown(i):
-            toktype = 2
-        if tokenizer.is_control(i):
-            toktype = 3
-
-        # toktype = 4 is user-defined = tokens from added_tokens.json
-
-        if tokenizer.is_unused(i):
-            toktype = 5
-        if tokenizer.is_byte(i):
-            toktype = 6
-
-        tokens.append(text)
-        scores.append(score)
-        toktypes.append(toktype)
-        pass
-    return tokens, scores, toktypes
-
-
-def get_args():
-    parser = argparse.ArgumentParser(description="Convert a Persimmon model from Adept (e.g. Persimmon 8b chat) to a GGML compatible file")
-    parser.add_argument("--outfile",     type=Path,              help="path to write to; default: based on input")
-    parser.add_argument("model",         type=Path,              help="directory containing model file, or model file itself (*.safetensors)")
-    args = parser.parse_args()
-    return args
-
-
-def main() -> None:
-    args = get_args()
-    assert file_is_safetensors(args.model), 'Error: model file is not a SafeTensors file'
-    dir_model = args.model.parent
-    with open(dir_model / 'config.json', 'r') as f:
-        hparams = json.load(f)
-    arch = gguf.MODEL_ARCH.PERSIMMON
-    gguf_writer = gguf.GGUFWriter(args.outfile, gguf.MODEL_ARCH_NAMES[arch])
-    
-    block_count = hparams['num_layers']
-    head_count = hparams['num_attention_heads']
-    head_count_kv = head_count
-    ctx_length = hparams['seq_length']
-    hidden_size = hparams['hidden_size']
-
-    gguf_writer.add_name('persimmon-8b-chat')
-    gguf_writer.add_context_length(ctx_length)
-    gguf_writer.add_embedding_length(hidden_size)
-    gguf_writer.add_block_count(block_count)
-    gguf_writer.add_feed_forward_length(hparams['ffn_hidden_size'])
-    gguf_writer.add_rope_dimension_count(hidden_size // head_count)
-    gguf_writer.add_head_count(head_count)
-    gguf_writer.add_head_count_kv(head_count_kv)
-    gguf_writer.add_rope_freq_base(hparams['rotary_emb_base'])
-    gguf_writer.add_layer_norm_eps(hparams['layernorm_epsilon'])
-    tokens, scores, toktypes = get_tokenizer_info(dir_model)
-    gguf_writer.add_tokenizer_model('llama')
-    gguf_writer.add_token_list(tokens)
-    gguf_writer.add_token_scores(scores)
-    gguf_writer.add_token_types(toktypes)
-    gguf_writer.add_bos_token_id(71013)
-    gguf_writer.add_eos_token_id(71013)
-
-    tensor_map = gguf.get_tensor_name_map(arch, block_count)
-    print(tensor_map)
-    tensors = {}
-    with safe_open(args.model, framework="pt") as f:
-        for k in f.keys():
-            tensors[k] = f.get_tensor(k)
-    for name in tensors.keys():
-        data = tensors[name]
-        if name.endswith(".self_attention.rotary_emb.inv_freq"):
-            continue
-        old_dtype = data.dtype
-        # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?)
-        data = data.to(torch.float32).squeeze().numpy()
-        new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
-        if new_name is None:
-            print("Can not map tensor '" + name + "'")
-            sys.exit()
-        n_dims = len(data.shape)
-        print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
-
-        gguf_writer.add_tensor(new_name, data)
-    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()
-
-    print(f"gguf: model successfully exported to '{args.outfile}'")
-    print("")
-
-
-
-if __name__ == '__main__':
-    main()