mirror of
https://github.com/nomic-ai/gpt4all
synced 2024-11-08 07:10:32 +00:00
feat: mpt convert from hf to ggml
This commit is contained in:
Zach Nussbaum
Adam Treat
parent
2f6ecbe798
commit
03bde18e49
176
convert_hf_to_ggml.py
Normal file
176
convert_hf_to_ggml.py
Normal file
@ -0,0 +1,176 @@
|
||||
# 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"
|
||||
#
|
||||
|
||||
import io
|
||||
import os
|
||||
import sys
|
||||
import struct
|
||||
import json
|
||||
import code
|
||||
import torch
|
||||
import numpy as np
|
||||
|
||||
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 len(sys.argv) < 3:
|
||||
print("Usage: python convert-hf-to-ggml.py model_name dir-output [use-f32]")
|
||||
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(" use-f32: if present, use float32 instead of float16")
|
||||
sys.exit(1)
|
||||
|
||||
model_name = sys.argv[1]
|
||||
dir_out = sys.argv[2]
|
||||
|
||||
# make sure the output directory exists
|
||||
os.makedirs(dir_out, 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 = 0
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
|
||||
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
|
||||
hparams = config.to_dict()
|
||||
print("Loading model: ", model_name)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, config=config, torch_dtype=torch.float16 if ftype == 1 else torch.float32, low_cpu_mem_usage=True)
|
||||
print("Model loaded: ", model_name)
|
||||
|
||||
|
||||
fname_out = dir_out + f"/ggml-model-{model_name.split('/')[-1]}-{ftype_str[ftype]}.bin"
|
||||
fout = open(fname_out, "wb")
|
||||
|
||||
hparams["multiple_of"] = 1
|
||||
fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
|
||||
fout.write(struct.pack("i", hparams["vocab_size"]))
|
||||
# fout.write(struct.pack("i", hparams["seq_length"]))
|
||||
fout.write(struct.pack("i", hparams["d_model"]))
|
||||
fout.write(struct.pack("i", hparams["multiple_of"]))
|
||||
fout.write(struct.pack("i", hparams["n_heads"]))
|
||||
fout.write(struct.pack("i", hparams["n_layers"]))
|
||||
fout.write(struct.pack("i", ftype))
|
||||
|
||||
# # Is this correct??
|
||||
# dot_token = tokenizer.encode(".")[0]
|
||||
# for i in range(hparams["vocab_size"]):
|
||||
# text = tokenizer.decode([i]).encode('utf-8')
|
||||
# fout.write(struct.pack("i", len(text)))
|
||||
# fout.write(text)
|
||||
|
||||
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)
|
||||
|
||||
# we don't need these
|
||||
if name.endswith("attn.masked_bias") or name.endswith(".attn.bias"):
|
||||
print(" Skipping variable: " + name)
|
||||
continue
|
||||
|
||||
if "Wqkv.weight" in name:
|
||||
# chunk qkv
|
||||
query, key, value = np.split(data, 3, axis=0)
|
||||
|
||||
new_name = name.split("Wqkv.weight")[0]
|
||||
|
||||
for (data, name) in [(query, new_name + "q_proj_w"), (key, new_name + "k_proj_w"), (value, new_name + "v_proj_w")]:
|
||||
print(f"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.write(struct.pack("i", len(data)))
|
||||
|
||||
else:
|
||||
|
||||
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("")
|
||||
Loading…
Reference in New Issue
Block a user