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"""
Usage:
python convert_hf_to_easylm.py \
--checkpoint_dir /path/hf_format_dir/ \
--output_file /path/easylm_format.stream \
--model_size 7b \
--streaming
"""
import time
from pathlib import Path
import argparse
import mlxu
import torch
import flax
from EasyLM.checkpoint import StreamingCheckpointer
LLAMA_STANDARD_CONFIGS = {
'1b': {
'dim': 2048,
'intermediate_size': 5504,
'n_layers': 22,
'n_heads': 16,
'norm_eps': 1e-6,
},
'3b': {
'dim': 3200,
'intermediate_size': 8640,
'n_layers': 26,
'n_heads': 32,
'norm_eps': 1e-6,
},
"7b": {
"dim": 4096,
"intermediate_size": 11008,
"n_layers": 32,
"n_heads": 32,
"norm_eps": 1e-6,
},
"13b": {
"dim": 5120,
"intermediate_size": 13824,
"n_layers": 40,
"n_heads": 40,
"norm_eps": 1e-6,
},
"30b": {
"dim": 6656,
"intermediate_size": 17920,
"n_layers": 60,
"n_heads": 52,
"norm_eps": 1e-6,
},
"65b": {
"dim": 8192,
"intermediate_size": 22016,
"n_layers": 80,
"n_heads": 64,
"norm_eps": 1e-5,
},
}
def inverse_permute(params, w):
n_layers = params["n_layers"]
n_heads = params["n_heads"]
dim = params["dim"]
reshaped_w = w.reshape(n_heads, 2, dim // n_heads // 2, dim)
transposed_w = reshaped_w.transpose(0, 2, 1, 3)
inverted_w = transposed_w.reshape(dim, dim)
return inverted_w
def main(args):
start = time.time()
params = LLAMA_STANDARD_CONFIGS[args.model_size]
ckpt_paths = sorted(Path(args.checkpoint_dir).glob("*.bin"))
ckpt = {}
for i, ckpt_path in enumerate(ckpt_paths):
checkpoint = torch.load(ckpt_path, map_location="cpu")
for k, v in checkpoint.items():
if k.startswith("model."):
k = k[6:]
ckpt[k] = v
print(f"Start convert weight to easylm format...")
jax_weights = {
"transformer": {
"wte": {"embedding": ckpt["embed_tokens.weight"].numpy()},
"ln_f": {"kernel": ckpt["norm.weight"].numpy()},
"h": {
"%d"
% (layer): {
"attention": {
"wq": {
"kernel": inverse_permute(
params,
ckpt[f"layers.{layer}.self_attn.q_proj.weight"].numpy(),
).transpose()
},
"wk": {
"kernel": inverse_permute(
params,
ckpt[f"layers.{layer}.self_attn.k_proj.weight"].numpy(),
).transpose()
},
"wv": {
"kernel": ckpt[f"layers.{layer}.self_attn.v_proj.weight"]
.numpy()
.transpose()
},
"wo": {
"kernel": ckpt[f"layers.{layer}.self_attn.o_proj.weight"]
.numpy()
.transpose()
},
},
"feed_forward": {
"w1": {
"kernel": ckpt[f"layers.{layer}.mlp.gate_proj.weight"]
.numpy()
.transpose()
},
"w2": {
"kernel": ckpt[f"layers.{layer}.mlp.down_proj.weight"]
.numpy()
.transpose()
},
"w3": {
"kernel": ckpt[f"layers.{layer}.mlp.up_proj.weight"]
.numpy()
.transpose()
},
},
"attention_norm": {
"kernel": ckpt[f"layers.{layer}.input_layernorm.weight"].numpy()
},
"ffn_norm": {
"kernel": ckpt[
f"layers.{layer}.post_attention_layernorm.weight"
].numpy()
},
}
for layer in range(params["n_layers"])
},
},
"lm_head": {"kernel": ckpt["lm_head.weight"].numpy().transpose()},
}
print(f"Convert weight to easylm format finished...")
print(f"Start to save...")
if args.streaming:
StreamingCheckpointer.save_train_state_to_file(jax_weights, args.output_file)
else:
with mlxu.open_file(args.output_file, "wb") as fout:
fout.write(flax.serialization.msgpack_serialize(jax_weights, in_place=True))
print(
f"Save finished!!! take time: {time.time() - start} save path: {args.output_file}"
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="hf to easylm format script")
parser.add_argument(
"--checkpoint_dir",
type=str,
help="Need to be converted model weight dir. it is a dir",
)
parser.add_argument(
"--output_file", type=str, help="Save model weight file path, it is a file."
)
parser.add_argument(
"--model_size",
type=str,
default="7b",
choices=["7b", "13b", "30b", "65b"],
help="model size",
)
parser.add_argument(
"--streaming",
action="store_true",
default=True,
help="whether is model weight saved stream format",
)
args = parser.parse_args()
print(f"checkpoint_dir: {args.checkpoint_dir}")
print(f"output_file: {args.output_file}")
print(f"model_size: {args.model_size}")
print(f"streaming: {args.streaming}")
main(args)
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