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# Convert HF models to ggml format
#
import sys
import struct
import json
import torch
import numpy as np
import re
import os
from transformers import AutoModelForCausalLM
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 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) < 2:
print("Usage: python convert-hf-to-ggml.py hf-model-name [use-f32]")
print("Example: python convert-hf-to-ggml.py bigcode/gpt_bigcode-santacoder")
print("Example: python convert-hf-to-ggml.py bigcode/starcoder")
sys.exit(1)
model_name = sys.argv[1].strip()
fname_out = "models/" + sys.argv[1].strip() + "-ggml.bin"
os.makedirs(os.path.dirname(fname_out), exist_ok=True)
# use 16-bit or 32-bit floats
use_f16 = True
if len(sys.argv) > 2:
use_f16 = False
print("Loading model: ", model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
hparams = config.to_dict()
model = AutoModelForCausalLM.from_pretrained(model_name, config=config, torch_dtype=torch.float16 if use_f16 else torch.float32, low_cpu_mem_usage=True, trust_remote_code=True, offload_state_dict=True)
print("Model loaded: ", model_name)
#print (model)
list_vars = model.state_dict()
#print (list_vars)
encoder = tokenizer.vocab
# Add added_tokens (special tokens) to the encoder
encoder.update(tokenizer.get_added_vocab())
print(hparams)
print("Saving ggml model to: ", fname_out)
fout = open(fname_out, "wb")
fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
vocab_size = hparams["vocab_size"]
fout.write(struct.pack("i", vocab_size))
# fout.write(struct.pack("i", len(encoder)))
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", use_f16))
byte_encoder = bytes_to_unicode()
byte_decoder = {v:k for k, v in byte_encoder.items()}
fout.write(struct.pack("i", vocab_size))
counter = 0
# sort by value
for key in sorted(encoder, key=encoder.get):
text = bytearray([byte_decoder[c] for c in key])
fout.write(struct.pack("i", len(text)))
fout.write(text)
counter += 1
# TODO: Repeat last token until vocab_size
while counter < vocab_size:
fout.write(struct.pack("i", len(text)))
fout.write(text)
counter += 1
# assert counter == config.vocab_size
for name in list_vars.keys():
data = list_vars[name].squeeze().numpy()
print("Processing variable: " + name + " with shape: ", data.shape)
# rename headers to keep compatibility
if name == "transformer.ln_f.weight":
name = "model/ln_f/g"
elif name == "transformer.ln_f.bias":
name = "model/ln_f/b"
elif name == "transformer.wte.weight":
name = "model/wte"
elif name == "transformer.wpe.weight":
name = "model/wpe"
elif name == "lm_head.weight":
name = "model/lm_head"
elif re.match(r"transformer.h\.\d+\.ln_1\.weight", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/ln_1/g"
elif re.match(r"transformer.h\.\d+\.ln_1\.bias", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/ln_1/b"
elif re.match(r"transformer.h\.\d+\.attn\.c_attn\.weight", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/attn/c_attn/w"
elif re.match(r"transformer.h\.\d+\.attn\.c_attn\.bias", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/attn/c_attn/b"
elif re.match(r"transformer.h\.\d+\.attn\.c_proj\.weight", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/attn/c_proj/w"
elif re.match(r"transformer.h.\d+.attn.c_proj.bias", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/attn/c_proj/b"
elif re.match(r"transformer.h.\d+.ln_2.weight", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/ln_2/g"
elif re.match(r"transformer.h.\d+.ln_2.bias", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/ln_2/b"
elif re.match(r"transformer.h.\d+.mlp.c_fc.weight", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/mlp/c_fc/w"
elif re.match(r"transformer.h.\d+.mlp.c_fc.bias", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/mlp/c_fc/b"
elif re.match(r"transformer.h.\d+.mlp.c_proj.weight", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/mlp/c_proj/w"
elif re.match(r"transformer.h.\d+.mlp.c_proj.bias", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/mlp/c_proj/b"
else:
print("Unrecognized variable name. %s", name)
# 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 = 0;
if use_f16:
if (name == "model/wte" or name == "model/lm_head" or name[-2:] == "/g" or name[-2:] == "/w") and n_dims == 2:
print(" Converting to float16")
data = data.astype(np.float16)
ftype = 1
else:
print(" Converting to float32")
data = data.astype(np.float32)
ftype = 0
"model/h.*/attn/c_attn/w"
"model/h.*/attn/c_proj/w"
"model/h.*/mlp/c_fc/w"
"model/h.*/mlp/c_proj/w"
if name[-14:] == "/attn/c_attn/w" or name[-14:] == "/attn/c_attn/b":
print(" Duplicate K,V heads to use MHA instead of MQA")
embed_dim = hparams["n_embd"]
head_dim = embed_dim // hparams["n_head"]
# ((n_heads + 2) * head_dim, hidden_dim) -> (3 * n_heads * head_dim, hidden_dim)
q, k ,v = np.split(data, (hparams["n_head"] * head_dim, (hparams["n_head"] + 1) * head_dim), axis=0)
# duplicate k, v along the first axis (head_dim, hidden_dim) -> (n_heads * head_dim, hidden_dim)
if len(k.shape) == 2:
k = np.tile(k, (hparams["n_head"], 1))
v = np.tile(v, (hparams["n_head"], 1))
elif len(k.shape) == 1:
k = np.tile(k, (hparams["n_head"]))
v = np.tile(v, (hparams["n_head"]))
# concat q, k, v along the first axis (n_heads * head_dim, hidden_dim) -> (3 * n_heads * head_dim, hidden_dim)
data = np.concatenate((q, k, v), axis=0)
# header
str = name.encode('utf-8')
fout.write(struct.pack("iii", n_dims, len(str), ftype))
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("")
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