gpt4all/gpt4all-backend/scripts/convert_replit_hf_to_ggml.py
Richard Guo c4706d0c14
Replit Model (#713)
* porting over replit code model to gpt4all

* replaced memory with kv_self struct

* continuing debug

* welp it built but lot of sus things

* working model loading and somewhat working generate.. need to format response?

* revert back to semi working version

* finally got rid of weird formatting

* figured out problem is with python bindings - this is good to go for testing

* addressing PR feedback

* output refactor

* fixed prompt reponse collection

* cleanup

* addressing PR comments

* building replit backend with new ggmlver code

* chatllm replit and clean python files

* cleanup

* updated replit to match new llmodel api

* match llmodel api and change size_t to Token

* resolve PR comments

* replit model commit comment
2023-06-06 17:09:00 -04:00

114 lines
3.1 KiB
Python

from pathlib import Path
import sys
import struct
import json
import numpy as np
from transformers import AutoModelForCausalLM, AutoTokenizer
import sentencepiece.sentencepiece_model_pb2 as model
if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model [use-f32]\n")
print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16")
sys.exit(1)
# output in the same directory as the model
dir_model = sys.argv[1]
fname_out = sys.argv[1] + "/ggml-replit-code-v1-3b.bin"
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)
sp_proto = model.ModelProto()
sp_proto.ParseFromString(open(Path(sys.argv[1]) / "spiece.model", "rb").read())
# possible data types
# ftype == 0 -> float32
# ftype == 1 -> float16
#
# map from ftype to string
ftype_str = ["f32", "f16"]
ftype = 1
if len(sys.argv) > 2:
ftype = int(sys.argv[2])
if ftype < 0 or ftype > 1:
print("Invalid ftype: " + str(ftype))
sys.exit(1)
fname_out = sys.argv[1] + "/ggml-replit-code-v1-3b-" + ftype_str[ftype] + ".bin"
tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
dir_model, low_cpu_mem_usage=True, trust_remote_code=True
)
# print (model)
# print(tokenizer.encode('I believe the meaning of life is'))
list_vars = model.state_dict()
for name in list_vars.keys():
print(name, list_vars[name].shape, list_vars[name].dtype)
fout = open(fname_out, "wb")
print(hparams)
fout.write(struct.pack("i", 0x7265706c)) # magic: repl in hex
fout.write(struct.pack("i", hparams["vocab_size"]))
fout.write(struct.pack("i", hparams["max_seq_len"]))
fout.write(struct.pack("i", hparams["d_model"]))
fout.write(struct.pack("i", hparams["n_heads"]))
fout.write(struct.pack("i", hparams["n_layers"]))
fout.write(struct.pack("i", ftype))
# TODO: temporary hack to not deal with implementing the tokenizer
for piece in sp_proto.pieces:
encoded_piece = piece.piece.encode("utf-8")
fout.write(struct.pack("i", len(encoded_piece)))
fout.write(encoded_piece)
fout.write(struct.pack("f", piece.score))
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
if ftype != 0:
if name[-7:] == ".weight" and n_dims == 2:
print(" Converting to float16")
data = data.astype(np.float16)
ftype_cur = 1
else:
print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
else:
if data.dtype != np.float32:
print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
# header
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
data.tofile(fout)
fout.close()
print("Done. Output file: " + fname_out)
print("")