# Convert Hugging Face fine-tuned bloom-like models to ggml format
#
# Usage:
#
#   python3 models/convert-h5-to-ggml.py 
#
# This script is similar to "convert-pt-to-ggml.py"
#

from __future__ import annotations

import json
import os
import struct
import sys
from pathlib import Path

import gguf
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BloomForCausalLM


# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
def bytes_to_unicode():
    """
    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
    cs = [chr(n) for n in cs]
    return dict(zip(bs, cs))


if not 3 <= len(sys.argv) < 5:
    print("Usage: python {} model-name dir-output [ftype]".format(Path(__file__).name))
    print("  model-name: name of the model to convert. Example: 'bigscience/bloomz-560m'")
    print("  dir-output: directory where the output file will be written")
    print("  ftype == 0 -> float32")
    print("  ftype == 1 -> float16")
    sys.exit(1)

model_name = sys.argv[1]
dir_out = Path(sys.argv[2])

# make sure the output directory exists
dir_out.mkdir(exist_ok=True)

# possible data types
#   ftype == 0 -> float32
#   ftype == 1 -> float16
#
# map from ftype to string
ftype_str = ["f32", "f16"]
ftype = 1
if len(sys.argv) > 3:
    ftype = int(sys.argv[3])
    if ftype < 0 or ftype > 1:
        print("Invalid ftype: " + str(ftype))
        sys.exit(1)

fname_out = dir_out / f"ggml-model-{Path(model_name).name}-{ftype_str[ftype]}.gguf"


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

print("gguf: get model metadata")

config = AutoConfig.from_pretrained(model_name)
print("Loading model:", model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, config=config, torch_dtype=torch.float16 if ftype == 1 else torch.float32, low_cpu_mem_usage=True,
)
config = model.config
print("Model loaded:", model_name)

block_count = config.n_layers
gguf_writer.add_name("MPT")
gguf_writer.add_context_length(config.max_seq_len)
gguf_writer.add_embedding_length(config.d_model)
gguf_writer.add_block_count(block_count)
gguf_writer.add_head_count(config.n_heads)
gguf_writer.add_max_alibi_bias(config.attn_config.alibi_bias_max)
gguf_writer.add_layer_norm_eps(config.layer_norm_epsilon)
gguf_writer.add_file_type(ftype)

clip_qkv = config.attn_config.clip_qkv
if clip_qkv is not None:
    gguf_writer.add_clamp_kqv(clip_qkv)

print("gguf: get gpt2 tokenizer vocab")

tokenizer = AutoTokenizer.from_pretrained(model_name)

special_ids = tokenizer.all_special_ids

tokens: list[bytearray] = []
toktypes: list[gguf.TokenType] = []

# TODO(cebtenzzre): this is probably wrong, but I don't know what else to put here
dot_token = tokenizer.encode('.')[0]

# 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
for i in range(config.vocab_size):
    text = tokenizer.decode([dot_token, i]).encode('utf-8')
    text = text[1:]  # remove the first byte (it's always '.')
    tokens.append(text)

    # TODO(cebtenzzre): is there a better way to do this?
    toktypes.append(gguf.TokenType.CONTROL if i in special_ids else gguf.TokenType.NORMAL)

gguf_writer.add_tokenizer_model("gpt2")
gguf_writer.add_token_list(tokens)
gguf_writer.add_token_types(toktypes)

print("gguf: get tensor metadata")

tensor_map = gguf.get_tensor_name_map(ARCH, block_count)

list_vars = model.state_dict()
for name in list_vars.keys():
    data = list_vars[name].squeeze().numpy()
    print("Processing variable:", name, "with shape:", data.shape)

    n_dims = len(data.shape)

    # ftype == 0 -> float32, ftype == 1 -> float16
    ftype_cur = 0
    # Keep token embeddings in fp32
    if ftype == 1 and name[-7:] == ".weight" and n_dims == 2 and ".wte" not in name:
        print("  Converting to float16")
        data = data.astype(np.float16)
        ftype_cur = 1
    elif ftype == 1 or data.dtype != np.float32:
        print("  Converting to float32")
        data = data.astype(np.float32)
        ftype_cur = 0

    # 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()

    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()

gguf_writer.close()

print(f"gguf: model successfully exported to '{fname_out}'")
print()