The new CodeGen2 Release about turbopilot

[ravenscroftj]

hey there - yes I definitely plan to allow the use of the new codegen model and possibly others like Santacoder.

The new models have a slightly different model architecture to the original codegen models which means that there will need to be some modification to get them to work - the best solution may be to directly implement the new archtecture in GGML rather than converting via GPT-j - I need to do some exploration to work out the best path.

[czkoko]

After some exploration, I have completed the following conversion script, and can directly convert the original codegen2 model to ggml, There is no need to convert to GPTJ first.

The codegen2-1B successful operation, and the output of codegen2-7B seems to be abnormal.

import sys
import struct
import json
import torch
import numpy as np
from accelerate import init_empty_weights

from transformers import AutoModelForCausalLM, AutoTokenizer

def bytes_to_unicode():
    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 len(sys.argv) < 2:
    print("Usage: codegen2-to-ggml.py codegen2-1B(dir)\n")
    sys.exit(1)

# output in the same directory as the model
dir_model = sys.argv[1]

with open(dir_model + "/vocab.json", "r", encoding="utf8") as f:
    encoder = json.load(f)

with open(dir_model + "/added_tokens.json", "r") as f:
    encoder_added = json.load(f)

with open(dir_model + "/config.json", "r") as f:
    hparams = json.load(f)

ftype = 0
fname_out = sys.argv[1] + "/ggml-model-f32.bin"

model = AutoModelForCausalLM.from_pretrained(dir_model, trust_remote_code=True, low_cpu_mem_usage=True)
tokenizer = AutoTokenizer.from_pretrained(dir_model)

list_vars = model.state_dict()

fout = open(fname_out, "wb")

fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
fout.write(struct.pack("i", hparams['vocab_size']))
fout.write(struct.pack("i", hparams["n_positions"]))
fout.write(struct.pack("i", hparams["n_embd"]))
fout.write(struct.pack("i", hparams["n_head"]))
fout.write(struct.pack("i", hparams["n_layer"]))
fout.write(struct.pack("i", hparams["rotary_dim"]))
fout.write(struct.pack("i", ftype))

byte_encoder = bytes_to_unicode()
byte_decoder = {v:k for k, v in byte_encoder.items()}

fout.write(struct.pack("i", hparams['vocab_size']))

for word,idx in sorted(tokenizer.vocab.items(), key=lambda x: x[1]) :
    text = bytearray([byte_decoder[c] for c in word if c in byte_decoder])

    if(len(text)) < 1:
        text = bytearray(word.encode('utf8'))

    fout.write(struct.pack("i", len(text)))
    fout.write(text)

empty_vocab = hparams['vocab_size'] - tokenizer.vocab_size

for i in range( hparams['vocab_size'] - len(encoder) - len(encoder_added)):
    text = "<|endoftext|>".encode("utf8")
    fout.write(struct.pack("i", len(text)))
    fout.write(text)

new_list_vars = {}

for name in list_vars.keys():
    if name.endswith("attn.qkv_proj.weight"):
        data = list_vars[name]
        n_dims = len(data.shape)
        assert n_dims == 2
        n_embd = hparams["n_embd"]
        q_unshaped, v_unshaped, k_unshaped = torch.split(data.reshape(8, -1, n_embd), n_embd//8, dim=1)
        q_shaped, v_shaped, k_shaped = (q_unshaped.reshape(-1, n_embd), v_unshaped.reshape(-1, n_embd), k_unshaped.reshape(-1, n_embd))
        new_list_vars[name.replace(".qkv_proj.", ".q_proj.")] = q_shaped
        new_list_vars[name.replace(".qkv_proj.", ".v_proj.")] = v_shaped
        new_list_vars[name.replace(".qkv_proj.", ".k_proj.")] = k_shaped
    else:
        new_list_vars[name] = list_vars[name]
        
list_vars = new_list_vars


for name in list_vars.keys():
    data = list_vars[name].squeeze().numpy()

    if name.endswith("attn.masked_bias") or name.endswith(".attn.bias") or name.endswith("attn.causal_mask"):
        continue

    n_dims = len(data.shape);
    ftype_cur = 0;

    if data.dtype != np.float32:
        print("  Converting to float32")
        data = data.astype(np.float32)
        ftype_cur = 0

    str = name.encode('utf-8')

    fout.write(struct.pack("iii", n_dims, len(str), ftype_cur))
    for i in range(n_dims):
        fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
    fout.write(str);
    data.tofile(fout)

fout.close()

print("Done. Output file: " + fname_out)
print("")

[ravenscroftj]

hey there @czkoko - this is great progress thank you for contributing. I'd love to add this script to the repo which will allow us to support the 1b codegen2 model - along with merging recent ggml libraries that support starcoder this will allow Turbopilot to support some really interesting new use cases and additional libraries