GGUF : GPT2 Support
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245
convert-gpt2-hf-to-gguf.py
Executable file
245
convert-gpt2-hf-to-gguf.py
Executable file
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#!/usr/bin/env python3
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# HF gpt2 --> gguf conversion
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from __future__ import annotations
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import argparse
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import json
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import os
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import sys
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from pathlib import Path
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from typing import Any, Dict
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import numpy as np
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import torch
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from transformers import AutoTokenizer # type: ignore[import]
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import logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s')
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logger = logging.getLogger(__name__)
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if 'NO_LOCAL_GGUF' not in os.environ:
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sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
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import gguf
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def bytes_to_unicode() -> Dict[int, str]:
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# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
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"""
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Returns list of utf-8 byte and a corresponding list of unicode strings.
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The reversible bpe codes work on unicode strings.
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This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
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When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
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This is a significant percentage of your normal, say, 32K bpe vocab.
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To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
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And avoids mapping to whitespace/control characters the bpe code barfs on.
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"""
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bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
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cs = bs[:]
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n = 0
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for b in range(2**8):
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if b not in bs:
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bs.append(b)
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cs.append(2**8+n)
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n += 1
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return dict(zip(bs, (chr(n) for n in cs)))
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def count_model_parts(dir_model: Path) -> int:
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num_parts = 0
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for filename in os.listdir(dir_model):
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if filename.startswith("pytorch_model-"):
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num_parts += 1
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if num_parts > 0:
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logger.info("gguf: found " + str(num_parts) + " model parts")
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return num_parts
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Convert a gpt2 model to a GGML compatible file")
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parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
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parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
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parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)")
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parser.add_argument("ftype", type=int, help="output format - use 0 for float32, 1 for float16", choices=[0, 1], default = 1)
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return parser.parse_args()
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args = parse_args()
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dir_model = args.model
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ftype = args.ftype
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arch_name = 'GPT2LMHeadModel'
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if not dir_model.is_dir():
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raise FileNotFoundError(f'{args.model} is not a directory')
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# possible tensor data types
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# ftype == 0 -> float32
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# ftype == 1 -> float16
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# map from ftype to string
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ftype_str = ["f32", "f16"]
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if args.outfile is not None:
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fname_out = args.outfile
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else:
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# output in the same directory as the model by default
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fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
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logger.info(f"gguf: loading model {dir_model.name}")
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with open(dir_model / "config.json", "r", encoding="utf-8") as f:
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hparams = json.load(f)
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if hparams["architectures"][0] != arch_name:
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raise ValueError("Only GPT2LMHeadModel is supported")
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# get number of model parts
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num_parts = count_model_parts(dir_model)
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ARCH = gguf.MODEL_ARCH.GPT2
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gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
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logger.info("gguf: get model metadata")
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block_count = hparams["n_layer"]
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gguf_writer.add_name("gpt2")
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gguf_writer.add_context_length(hparams["n_positions"])
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gguf_writer.add_embedding_length(hparams["n_embd"])
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gguf_writer.add_feed_forward_length(4 * hparams["n_embd"])
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gguf_writer.add_block_count(block_count)
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gguf_writer.add_head_count(hparams["n_head"])
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gguf_writer.add_head_count_kv(1)
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gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
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gguf_writer.add_file_type(ftype)
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# TOKENIZATION
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print("gguf: get tokenizer metadata")
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tokens: list[bytearray] = []
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tokenizer_json_file = dir_model / 'tokenizer.json'
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if not tokenizer_json_file.is_file():
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print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr)
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sys.exit(1)
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# gpt2 tokenizer
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gguf_writer.add_tokenizer_model("gpt2")
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with open(tokenizer_json_file, "r", encoding="utf-8") as f:
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tokenizer_json = json.load(f)
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print("gguf: get gpt2 tokenizer vocab")
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vocab_size = len(tokenizer_json["model"]["vocab"])
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# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
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byte_encoder = bytes_to_unicode()
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byte_decoder = {v: k for k, v in byte_encoder.items()}
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for i in range(vocab_size):
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if i in reverse_vocab:
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try:
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text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
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except KeyError:
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text = bytearray()
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for c in reverse_vocab[i]:
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if ord(c) < 256: # single byte character
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text.append(byte_decoder[ord(c)])
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else: # multibyte special token character
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text.extend(c.encode('utf-8'))
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else:
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print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
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pad_token = f"[PAD{i}]".encode("utf8")
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text = bytearray(pad_token)
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tokens.append(text)
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gguf_writer.add_token_list(tokens)
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special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
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special_vocab.add_to_gguf(gguf_writer)
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# TENSORS
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tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
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# params for qkv transform
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n_head = hparams["n_head"]
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n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1
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head_dim = hparams["n_embd"] // n_head
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# tensor info
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logger.info("gguf: get tensor metadata")
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if num_parts == 0:
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part_names = iter(("pytorch_model.bin",))
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else:
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part_names = (
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f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
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)
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for part_name in part_names:
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if args.vocab_only:
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break
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logger.info("gguf: loading model part '" + part_name + "'")
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model_part = torch.load(dir_model / part_name, map_location="cpu")
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for name in model_part.keys():
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data = model_part[name]
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if name.endswith("attn.masked_bias") or name.endswith(".attn.bias"):
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logger.info(f"Skipping variable: {name}")
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continue
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old_dtype = data.dtype
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# convert any unsupported data types to float32
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if data.dtype != torch.float16 and data.dtype != torch.float32:
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data = data.to(torch.float32)
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data = data.squeeze().numpy()
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# map tensor names
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new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
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if new_name is None:
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raise ValueError(f"Cannot map tensor name: {name}")
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n_dims = len(data.shape)
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data_dtype = data.dtype
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# if f32 desired, convert any float16 to float32
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if ftype == 0 and data_dtype == np.float16:
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data = data.astype(np.float32)
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# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
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if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
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data = data.astype(np.float32)
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data = data.astype(np.float16)
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logger.info(f"{name} => {new_name}, shape = {data.shape}, {old_dtype} --> {data.dtype}")
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gguf_writer.add_tensor(new_name, data)
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del model_part
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logger.info("gguf: write header")
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gguf_writer.write_header_to_file()
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logger.info("gguf: write metadata")
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gguf_writer.write_kv_data_to_file()
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if not args.vocab_only:
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logger.info("gguf: write tensors")
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gguf_writer.write_tensors_to_file()
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gguf_writer.close()
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logger.info(f"gguf: model successfully exported to '{fname_out}'\n")
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@ -186,7 +186,16 @@ MODEL_TENSOR_NAMES: dict[MODEL_ARCH, dict[MODEL_TENSOR, str]] = {
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MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
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},
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MODEL_ARCH.GPT2: {
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# TODO
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MODEL_TENSOR.TOKEN_EMBD: "token_embd",
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MODEL_TENSOR.POS_EMBD: "position_embd",
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MODEL_TENSOR.OUTPUT_NORM: "output_norm",
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MODEL_TENSOR.OUTPUT: "output",
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MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm",
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MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv",
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MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
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MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm",
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MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
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MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
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},
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# TODO
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}
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66
llama.cpp
66
llama.cpp
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@ -2436,6 +2436,72 @@ static void llm_load_tensors(
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}
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}
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} break;
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case LLM_ARCH_GPT2:
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{
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model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
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{
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ggml_backend backend_norm;
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ggml_backend backend_output;
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if (n_gpu_layers > int(n_layer)) {
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// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
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// on Windows however this is detrimental unless everything is on the GPU
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#ifndef _WIN32
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backend_norm = LLAMA_BACKEND_OFFLOAD;
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#else
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backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
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#endif // _WIN32
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backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
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} else {
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backend_norm = GGML_BACKEND_CPU;
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backend_output = GGML_BACKEND_CPU;
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}
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model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
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model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
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if (backend_norm == GGML_BACKEND_GPU) {
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vram_weights += ggml_nbytes(model.output_norm);
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}
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if (backend_output == GGML_BACKEND_GPU_SPLIT) {
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vram_weights += ggml_nbytes(model.output);
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}
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}
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const uint32_t n_ff = hparams.n_ff;
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const int i_gpu_start = n_layer - n_gpu_layers;
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model.layers.resize(n_layer);
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for (uint32_t i = 0; i < n_layer; ++i) {
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const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
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const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
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auto & layer = model.layers[i];
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layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
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layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split);
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layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split);
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layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split);
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layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
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layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
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layer.w1 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
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layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
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layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
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if (backend == GGML_BACKEND_GPU) {
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vram_weights +=
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ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
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ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
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ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
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}
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}
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} break;
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default:
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throw std::runtime_error("unknown architecture");
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}
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