The new CodeGen2 Release

[czkoko]

Salesforce releases a new model codegen2, CodeGen2 is capable of infilling, and supports more programming languages. https://huggingface.co/Salesforce/codegen2-1B I tested codegen2-1B, and the code quality has improved, but it seems that the conversion script for codegen is no longer suitable to codegen2. Codegen2 > gpt-j > ggml conversion is successful, no error prompt appears, but the output is abnormal code.

For example, the return:

<mask_52>...(<mask_1>hasportvt(porthandleport
,formhasvarformvarform(ODUCT(
((form((vchas<mask_52>,polyportvtpoly#(<mask_1>formoform(
in<mask_1>polyhandle(form,(...haswisetextpm,wisewise
(portformoform<mask_1>var(text(form/

The following is the conversion script Codegen2 > gpt-j

#!/usr/bin/env python3

import torch
from transformers import GPTJForCausalLM, GPTJConfig
# Note: these need the git version of Transformers as of 7/22/2022
from transformers import AutoTokenizer, AutoModelForCausalLM

print("Creating tokenizer")
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen2-1B")

print('Loading CodeGen model')
cg_model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen2-1B", trust_remote_code=True)
cg_config = cg_model.config

# Create empty GPTJ model
print('Creating empty GPTJ model')
config = GPTJConfig(
    vocab_size=cg_config.vocab_size,
    n_positions=cg_config.n_positions,
    n_embd=cg_config.n_embd,
    n_layer=cg_config.n_layer,
    n_head=cg_config.n_head,
    rotary_dim=cg_config.rotary_dim,
    n_inner=cg_config.n_inner,
    activation_function=cg_config.activation_function,
    resid_pdrop=cg_config.resid_pdrop,
    embd_pdrop=cg_config.embd_pdrop,
    attn_pdrop=cg_config.attn_pdrop,
    layer_norm_epsilon=cg_config.layer_norm_epsilon,
    initializer_range=cg_config.initializer_range,
    scale_attn_weights=cg_config.scale_attn_weights,
    use_cache=cg_config.use_cache,
    bos_token_id=cg_config.bos_token_id,
    eos_token_id=cg_config.eos_token_id,
    torch_dtype=cg_config.torch_dtype,
)

# Fix tokenizer type
config.tokenizer_class = 'CodeGenTokenizer'

gptj_model = GPTJForCausalLM(config)
embed_dim = config.n_embd

# Sample input for validating the conversion went OK
inputs = tokenizer.encode('fun download(url string', return_tensors='pt')

def replace(model, weights, name):
    model.state_dict()[name].copy_(weights.detach())

def replace_by_name(dest_model, src_model, old_name, new_name):
    assert old_name in src_model.state_dict()
    assert new_name in dest_model.state_dict()
    replace(dest_model, src_model.state_dict()[old_name], new_name)

print('Converting...')
# Copy weights from CodeGen model
with torch.no_grad():
    cg_model.eval()
    gptj_model.eval()

    for name, param in cg_model.named_parameters():
        # Handle the qkv weights separately because we need to split them
        if 'qkv_proj' in name:
            qkv_proj = param.detach().clone()
            mp_num = 4 # number of cores on their TPU I guess?
            local_dim = embed_dim // mp_num
            # GPT-J and CodeGen slice up the qkv projection slightly differently.
            # After a great deal of pain, I figured out that this permutation on
            # the weights of the qkv_proj fixes it.
            base_permutation = [0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11]
            permutation = torch.cat([torch.arange(i*local_dim, (i+1)*local_dim) for i in base_permutation])
            # NB: we permute the *rows* here because the computation is xA.T
            new_qkv_proj = qkv_proj[permutation,:]
            # NB: the name QKV is misleading here; they are actually stored in
            #     the order QVK
            query, value, key = torch.split(new_qkv_proj, embed_dim, dim=0)
            replace(gptj_model, query, name.replace('qkv_proj', 'q_proj'))
            replace(gptj_model, key, name.replace('qkv_proj', 'k_proj'))
            replace(gptj_model, value, name.replace('qkv_proj', 'v_proj'))
        else:
            replace_by_name(gptj_model, cg_model, name, name)

    print('Conversion complete, running inference')
    cg_out = cg_model.generate(inputs, min_length=32, max_length=32, do_sample=False, pad_token_id=50256)
    gptj_out = gptj_model.generate(inputs, min_length=32, max_length=32, do_sample=False, pad_token_id=50256)
    print(cg_out[0])
    print(gptj_out[0])
    cg_dec, gptj_dec = tokenizer.batch_decode(torch.stack([cg_out,gptj_out]).squeeze())
    print("====== CodeGen ======")
    print(cg_dec)
    print("======  GPT-J  ======")
    print(gptj_dec)
    assert cg_dec == gptj_dec
    print("Saving model...")
    gptj_model.save_pretrained("gpt-j")
    tokenizer.save_pretrained("gpt-j")

[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