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…example * 'master' of github.com:ggerganov/llama.cpp: py : change version of numpy requirement to 1.24.4 (ggerganov#3515) quantize : fail fast on write errors (ggerganov#3521) metal : support default.metallib load & reuse code for swift package (ggerganov#3522) llm : support Adept Persimmon 8B (ggerganov#3410) Fix for ggerganov#3454 (ggerganov#3455) readme : update models, cuda + ppl instructions (ggerganov#3510) server : docs fix default values and add n_probs (ggerganov#3506)
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| *.gcno | ||
| *.gcda | ||
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| .DS_Store | ||
| .build/ | ||
| .cache/ | ||
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| import torch | ||
| import os | ||
| from pprint import pprint | ||
| import sys | ||
| import argparse | ||
| from pathlib import Path | ||
| from sentencepiece import SentencePieceProcessor | ||
| if 'NO_LOCAL_GGUF' not in os.environ: | ||
| sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) | ||
| import gguf | ||
|
|
||
| def _flatten_dict(dct, tensors, prefix=None): | ||
| assert isinstance(dct, dict) | ||
| for key in dct.keys(): | ||
| new_prefix = prefix + '.' + key if prefix is not None else key | ||
| if isinstance(dct[key], torch.Tensor): | ||
| tensors[new_prefix] = dct[key] | ||
| elif isinstance(dct[key], dict): | ||
| _flatten_dict(dct[key], tensors, new_prefix) | ||
| else: | ||
| raise ValueError(type(dct[key])) | ||
| return None | ||
|
|
||
| def _get_sentencepiece_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)) | ||
| print('gguf: adding tokens') | ||
| tokens: list[bytes] = [] | ||
| scores: list[float] = [] | ||
| toktypes: list[int] = [] | ||
|
|
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| for i in range(tokenizer.vocab_size()): | ||
| text: bytes | ||
| score: float | ||
|
|
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| piece = tokenizer.id_to_piece(i) | ||
| text = piece.encode("utf-8") | ||
| score = tokenizer.get_score(i) | ||
|
|
||
| toktype = 1 | ||
| if tokenizer.is_unknown(i): | ||
| toktype = 2 | ||
| if tokenizer.is_control(i): | ||
| toktype = 3 | ||
| 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 main(): | ||
| 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("--ckpt-path", type=Path, help="path to persimmon checkpoint .pt file") | ||
| parser.add_argument("--model-dir", type=Path, help="directory containing model e.g. 8b_chat_model_release") | ||
| parser.add_argument("--adept-inference-dir", type=str, help="path to adept-inference code directory") | ||
| args = parser.parse_args() | ||
| sys.path.append(str(args.adept_inference_dir)) | ||
| persimmon_model = torch.load(args.ckpt_path) | ||
| hparams = persimmon_model['args'] | ||
| pprint(hparams) | ||
| tensors = {} | ||
| _flatten_dict(persimmon_model['model'], tensors, None) | ||
|
|
||
| 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_sentencepiece_tokenizer_info(args.model_dir) | ||
| 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) | ||
| 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() | ||
|
|
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| gguf_writer.close() | ||
|
|
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| print(f"gguf: model successfully exported to '{args.outfile}'") | ||
| print("") | ||
|
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|
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|
|
||
| if __name__ == '__main__': | ||
| main() |
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