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Diffstat (limited to 'convert-gptneox-hf-to-gguf.py')
| -rwxr-xr-x | convert-gptneox-hf-to-gguf.py | 221 |
1 files changed, 0 insertions, 221 deletions
diff --git a/convert-gptneox-hf-to-gguf.py b/convert-gptneox-hf-to-gguf.py deleted file mode 100755 index 02d1fdf1..00000000 --- a/convert-gptneox-hf-to-gguf.py +++ /dev/null @@ -1,221 +0,0 @@ -#!/usr/bin/env python3 -# HF gptneox--> gguf conversion - -from __future__ import annotations - -import argparse -import json -import os -import struct -import sys -from pathlib import Path -from typing import Any - -import numpy as np -import torch -from transformers import AutoTokenizer # type: ignore[import] - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) -import gguf - - -def count_model_parts(dir_model: Path) -> int: - num_parts = 0 - for filename in os.listdir(dir_model): - if filename.startswith("pytorch_model-"): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - return num_parts - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert a GPT-NeoX model to a GGML compatible file") - parser.add_argument( - "--vocab-only", action="store_true", - help="extract only the vocab", - ) - 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 (*.bin)", - ) - parser.add_argument( - "ftype", type=int, choices=[0, 1], default=1, nargs='?', - help="output format - use 0 for float32, 1 for float16", - ) - return parser.parse_args() - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file = sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - -print("gguf: loading model "+dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] != "GPTNeoXForCausalLM": - print("Model architecture not supported: " + hparams["architectures"][0]) - - sys.exit() - -# get number of model parts -num_parts = count_model_parts(dir_model) - -ARCH=gguf.MODEL_ARCH.GPTNEOX -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - -print("gguf: get model metadata") - -block_count = hparams["num_hidden_layers"] - -gguf_writer.add_name(dir_model.name) -gguf_writer.add_context_length(hparams["max_position_embeddings"]) -gguf_writer.add_embedding_length(hparams["hidden_size"]) -gguf_writer.add_block_count(block_count) -gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) -gguf_writer.add_rope_dimension_count(int(hparams["rotary_pct"]*(hparams["hidden_size"]//hparams["num_attention_heads"]))) -gguf_writer.add_head_count(hparams["num_attention_heads"]) -gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True) -gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"]) - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytearray] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -# gpt2 tokenizer -gguf_writer.add_tokenizer_model("gpt2") - -print("gguf: get gpt2 tokenizer vocab") - -# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py -tokenizer = AutoTokenizer.from_pretrained(dir_model) - -# The number of tokens in tokenizer.json can differ from the expected vocab size. -# This causes downstream issues with mismatched tensor sizes when running the inference -vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) -assert max(tokenizer.vocab.values()) < vocab_size - -added_vocab = tokenizer.get_added_vocab() -reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} - -for i in range(vocab_size): - if i not in reverse_vocab: - tokens.append(f"[PAD{i}]") - toktypes.append(gguf.TokenType.USER_DEFINED) - elif reverse_vocab[i] in added_vocab: - tokens.append(reverse_vocab[i]) - if tokenizer.added_tokens_decoder[i].special: - toktypes.append(gguf.TokenType.CONTROL) - else: - toktypes.append(gguf.TokenType.USER_DEFINED) - else: - tokens.append(reverse_vocab[i]) - toktypes.append(gguf.TokenType.NORMAL) - -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_types(toktypes) - -special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens)) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH,block_count) - -# tensor info -print("gguf: get tensor metadata") - -if num_parts == 0: - part_names = iter(("pytorch_model.bin",)) -else: - part_names = ( - f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) - ) - -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") - - for name in model_part.keys(): - data = model_part[name] - - # we don't need these - if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"): - continue - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - data = data.squeeze().numpy() - - # map tensor names - 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) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - 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() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") |