llama.cpp/convert-llama-7b-pth-to-gguf.py

#!/usr/bin/env python3
# 7b pth llama --> gguf conversion
# Only models with a single datafile are supported, like 7B
# HF files required in the model dir: config.json tokenizer_config.json tokenizer.json tokenizer.model

import gguf
import os
import sys
import struct
import json
import numpy as np
import torch
import argparse

from typing import Any, List, TypeAlias
from pathlib import Path
from sentencepiece import SentencePieceProcessor

#NDArray = np.ndarray[Any, Any]
# compatible with python < 3.9
NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'


def count_model_parts(dir_model: Path) -> int:
    num_parts = 0
    for filename in os.listdir(dir_model):
        if filename.startswith("consolidated."):
            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 PyTorch 7B LLaMA 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],   help="output format - use 0 for float32, 1 for float16", default = 1)
    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] != "LlamaForCausalLM":
    print("Model architecture not supported: " + hparams["architectures"][0])
    sys.exit()

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

if num_parts > 1:
    print("gguf: Only models with a single datafile are supported.")

    sys.exit()

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


print("gguf: get model metadata")

block_count = hparams["num_hidden_layers"]
head_count = hparams["num_attention_heads"]

if "num_key_value_heads" in hparams:
    head_count_kv = hparams["num_key_value_heads"]
else:
    head_count_kv = head_count

if "_name_or_path" in hparams:
    hf_repo = hparams["_name_or_path"]
else:
    hf_repo = ""

if "max_sequence_length" in hparams:
    ctx_length = hparams["max_sequence_length"]
elif "max_position_embeddings" in hparams:
    ctx_length = hparams["max_position_embeddings"]
else:
    print("gguf: can not find ctx length parameter.")

    sys.exit()


gguf_writer.add_name(dir_model.name)
gguf_writer.add_source_hf_repo(hf_repo)
gguf_writer.add_tensor_data_layout("Meta AI original pth")
gguf_writer.add_context_length(ctx_length)
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(hparams["hidden_size"] // hparams["num_attention_heads"])
gguf_writer.add_head_count(head_count)
gguf_writer.add_head_count_kv(head_count_kv)
gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])

if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]:
    if "type" in hparams["rope_scaling"]:
        if hparams["rope_scaling"]["type"] == "linear":
            gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"])


# TOKENIZATION

print("gguf: get tokenizer metadata")

tokens: List[bytes] = []
scores: List[float] = []
toktypes: List[int] = []

tokenizer_model_file = dir_model / 'tokenizer.model'
if not tokenizer_model_file.is_file():
    print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr)
    sys.exit(1)

# vocab type sentencepiece
print("gguf: get sentencepiece tokenizer vocab and scores")

tokenizer = SentencePieceProcessor(str(tokenizer_model_file))

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)

added_tokens_file = dir_model / 'added_tokens.json'
if added_tokens_file.is_file():
    with open(added_tokens_file, "r", encoding="utf-8") as f:
        addtokens_json = json.load(f)

        print("gguf: get added tokens")

        for key in addtokens_json:
            tokens.append( key.encode("utf-8") )
            scores.append(-1000.0)
            toktypes.append(4) # user-defined token type

gguf_writer.add_tokenizer_model("llama")
gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
gguf_writer.add_token_types(toktypes)

special_vocab = gguf.SpecialVocab(dir_model)
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")

part_names = (f"consolidated.{n:02}.pth" for n in range(0, num_parts))

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 == "rope.freqs":
            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("")