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# Convert GPT-J-6B h5 transformer model to ggml format
#
# Load the model using GPTJForCausalLM.
# Iterate over all variables and write them to a binary file.
#
# For each variable, write the following:
# - Number of dimensions (int)
# - Name length (int)
# - Dimensions (int[n_dims])
# - Name (char[name_length])
# - Data (float[n_dims])
#
# By default, the bigger matrices are converted to 16-bit floats.
# This can be disabled by adding the "use-f32" CLI argument.
#
# At the start of the ggml file we write the model parameters
# and vocabulary.
#
import sys
import struct
import json
import torch
import numpy as np
from transformers import GPTJForCausalLM
# 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 signficant 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: 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-model.bin"
with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
encoder = json.load(f)
with open(dir_model + "/added_tokens.json", "r", encoding="utf-8") as f:
encoder_added = json.load(f)
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)
# 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-model-" + ftype_str[ftype] + ".bin"
model = GPTJForCausalLM.from_pretrained(dir_model, low_cpu_mem_usage=True)
#print (model)
list_vars = model.state_dict()
#print (list_vars)
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", len(encoder) + len(encoder_added)))
for key in encoder:
text = bytearray([byte_decoder[c] for c in key])
fout.write(struct.pack("i", len(text)))
fout.write(text)
for key in encoder_added:
text = bytearray([byte_decoder[c] for c in key])
fout.write(struct.pack("i", len(text)))
fout.write(text)
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
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
# for efficiency - transpose these matrices:
# (note - with latest ggml this is no longer more efficient, so disabling it)
# "transformer.h.*.mlp.fc_in.weight"
# "transformer.h.*.attn.out_proj.weight"
# "transformer.h.*.attn.q_proj.weight"
# "transformer.h.*.attn.k_proj.weight"
# "transformer.h.*.attn.v_proj.weight"
#if name.endswith(".mlp.fc_in.weight") or \
# name.endswith(".attn.out_proj.weight") or \
# name.endswith(".attn.q_proj.weight") or \
# name.endswith(".attn.k_proj.weight") or \
# name.endswith(".attn.v_proj.weight"):
# print(" Transposing")
# data = data.transpose()
# 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("") |