RWKV-Runner/backend-python/convert_safetensors.py

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import json
import os
import sys
import copy
import torch
from safetensors.torch import load_file, save_file
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--input", type=str, help="Path to input pth model")
parser.add_argument(
"--output",
type=str,
default="./converted.st",
help="Path to output safetensors model",
)
args = parser.parse_args()
def rename_key(rename, name):
for k, v in rename.items():
if k in name:
name = name.replace(k, v)
return name
def convert_file(pt_filename: str, sf_filename: str, transpose_names=[], rename={}):
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loaded = torch.load(pt_filename, map_location="cpu")
if "state_dict" in loaded:
loaded = loaded["state_dict"]
loaded = {k: v.clone().half() for k, v in loaded.items()}
# for k, v in loaded.items():
# print(f'{k}\t{v.shape}\t{v.dtype}')
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# For tensors to be contiguous
for k, v in loaded.items():
for transpose_name in transpose_names:
if transpose_name in k:
loaded[k] = v.transpose(0, 1)
loaded = {rename_key(rename, k).lower(): v.contiguous() for k, v in loaded.items()}
for k, v in loaded.items():
print(f"{k}\t{v.shape}\t{v.dtype}")
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dirname = os.path.dirname(sf_filename)
os.makedirs(dirname, exist_ok=True)
save_file(loaded, sf_filename, metadata={"format": "pt"})
reloaded = load_file(sf_filename)
for k in loaded:
pt_tensor = loaded[k]
sf_tensor = reloaded[k]
if not torch.equal(pt_tensor, sf_tensor):
raise RuntimeError(f"The output tensors do not match for key {k}")
if __name__ == "__main__":
try:
convert_file(
args.input,
args.output,
["lora_A"],
{"time_faaaa": "time_first", "lora_A": "lora.0", "lora_B": "lora.1"},
)
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print(f"Saved to {args.output}")
except Exception as e:
with open("error.txt", "w") as f:
f.write(str(e))