llama.cpp/convert-refact-hf-to-gguf.py

#!/usr/bin/env python3
# HF refact--> gguf conversion

from __future__ import annotations

import argparse
import json
import os
import sys
from pathlib import Path

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 bytes_to_unicode():
    # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
    """
    Returns list of utf-8 byte and a corresponding list of unicode strings.
    The reversible bpe codes work on unicode strings.
    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
    This is a significant percentage of your normal, say, 32K bpe vocab.
    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
    And avoids mapping to whitespace/control characters the bpe code barfs on.
    """
    bs = (
        list(range(ord("!"), ord("~") + 1))
        + list(range(ord("¡"), ord("¬") + 1))
        + list(range(ord("®"), ord("ÿ") + 1))
    )
    cs = bs[:]
    n = 0
    for b in range(2**8):
        if b not in bs:
            bs.append(b)
            cs.append(2**8 + n)
            n += 1
    return dict(zip(bs, (chr(n) for n in cs)))


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 Refact 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] != "GPTRefactForCausalLM":
    print("Model architecture not supported: " + hparams["architectures"][0])

    sys.exit(1)

# get number of model parts
num_parts = count_model_parts(dir_model)

ARCH = gguf.MODEL_ARCH.REFACT
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])

print("gguf: get model metadata")

# Get refact feed forward dimension
hidden_dim = hparams["n_embd"]
inner_dim = 4 * hidden_dim
hidden_dim = int(2 * inner_dim / 3)
multiple_of = 256
ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)

block_count = hparams["n_layer"]

gguf_writer.add_name("Refact")
# refact uses Alibi. So this is from config.json which might be used by training.
gguf_writer.add_context_length(hparams["n_positions"])
gguf_writer.add_embedding_length(hparams["n_embd"])

gguf_writer.add_feed_forward_length(ff_dim)
gguf_writer.add_block_count(block_count)
gguf_writer.add_head_count(hparams["n_head"])
gguf_writer.add_head_count_kv(1)
gguf_writer.add_layer_norm_rms_eps(hparams["layer_norm_epsilon"])
gguf_writer.add_file_type(ftype)

# TOKENIZATION

print("gguf: get tokenizer metadata")

tokens: list[bytearray] = []
scores: list[float] = []
toktypes: list[int] = []

tokenizer_json_file = dir_model / "tokenizer.json"
if not tokenizer_json_file.is_file():
    print(f"Error: Missing {tokenizer_json_file}", file=sys.stderr)
    sys.exit(1)

# gpt2 tokenizer
gguf_writer.add_tokenizer_model("gpt2")

with open(tokenizer_json_file, "r", encoding="utf-8") as f:
    tokenizer_json = json.load(f)

print("gguf: get gpt2 tokenizer vocab")

# 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["vocab_size"]
    if "vocab_size" in hparams
    else len(tokenizer_json["model"]["vocab"])
)

tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)

reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
byte_encoder = bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()}

for i in range(vocab_size):
    if i in reverse_vocab:
        text = reverse_vocab[i]
        try:
            text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
        except KeyError:
            text = bytearray()
            for c in reverse_vocab[i]:
                if ord(c) < 256:  # single byte character
                    text.append(byte_decoder[ord(c)])
                else:  # multibyte special token character
                    text.extend(c.encode("utf-8"))
    else:
        print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
        pad_token = f"[PAD{i}]".encode("utf8")
        text = bytearray(pad_token)

    tokens.append(text)
    scores.append(0.0)  # dymmy
    toktypes.append(gguf.TokenType.NORMAL)  # dummy

gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
gguf_writer.add_token_types(toktypes)

special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
special_vocab.add_to_gguf(gguf_writer)

# TENSORS

tensor_map = gguf.get_tensor_name_map(ARCH, block_count)

# params for qkv transform
n_head = hparams["n_head"]
n_head_kv = 1

head_dim = hparams["n_embd"] // n_head

# 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(dir_model / part_name, map_location="cpu")

    for i in range(block_count):
        if f"transformer.h.{i}.attn.kv.weight" in model_part:
            data = model_part[f"transformer.h.{i}.attn.kv.weight"]
            model_part[f"model.layers.{i}.self_attn.k_proj.weight"] = data[
                : n_head_kv * head_dim
            ]
            model_part[f"model.layers.{i}.self_attn.v_proj.weight"] = data[
                n_head_kv * head_dim :
            ]
            del model_part[f"transformer.h.{i}.attn.kv.weight"]
        if f"transformer.h.{i}.attn.q.weight" in model_part:
            model_part[f"model.layers.{i}.self_attn.q_proj.weight"] = model_part[
                f"transformer.h.{i}.attn.q.weight"
            ]
            del model_part[f"transformer.h.{i}.attn.q.weight"]
        if f"transformer.h.{i}.mlp.gate_up_proj.weight" in model_part:
            data = model_part[f"transformer.h.{i}.mlp.gate_up_proj.weight"]
            model_part[f"model.layers.{i}.mlp.gate_proj.weight"] = data[:ff_dim]
            model_part[f"model.layers.{i}.mlp.up_proj.weight"] = data[ff_dim:]
            del model_part[f"transformer.h.{i}.mlp.gate_up_proj.weight"]

    for name in model_part.keys():
        data = model_part[name]

        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",))
        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("")