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import os
import json
from argparse import ArgumentParser
from glob import glob
from tqdm import tqdm
import torch
from safetensors.torch import load_file, save_file
from huggingface_hub import snapshot_download
from kernel import weight_quant
def main(bf16_path, int8_path, model_name="deepseek-ai/DeepSeek-R1"):
torch.set_default_dtype(torch.bfloat16)
os.makedirs(int8_path, exist_ok=True)
model_index_file = os.path.join(int8_path, "model.safetensors.index.json")
if not os.path.exists(model_index_file):
snapshot_download(
repo_id=model_name,
allow_patterns=["model.safetensors.index.json"],
local_dir=int8_path,
local_dir_use_symlinks=False
)
print(f"model index file downloaded to {model_index_file}")
with open(model_index_file, "r") as f:
model_index = json.load(f)
weight_map = model_index["weight_map"]
scale_count = len([key for key in weight_map.keys() if key.endswith("_scale_inv")])
safetensor_files = list(glob(os.path.join(bf16_path, "*.safetensors")))
safetensor_files.sort()
quant_count = 0
for safetensor_file in tqdm(safetensor_files):
file_name = os.path.basename(safetensor_file)
state_dict = load_file(safetensor_file, device="cuda")
new_state_dict = {}
for weight_name, weight in state_dict.items():
scale_inv_name = f"{weight_name}_scale_inv"
if scale_inv_name in weight_map:
assert weight.element_size() == 2
quant_count += 1
int8_weight, scale_inv = weight_quant(weight)
new_state_dict[weight_name] = int8_weight
new_state_dict[scale_inv_name] = scale_inv
else:
new_state_dict[weight_name] = weight
new_safetensor_file = os.path.join(int8_path, file_name)
save_file(new_state_dict, new_safetensor_file)
assert quant_count == scale_count
print(f"{quant_count} weights are quantized.")
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--input-bf16-hf-path", type=str, required=True)
parser.add_argument("--output-int8-hf-path", type=str, required=True)
parser.add_argument("--model-name", type=str, default="deepseek-ai/DeepSeek-R1")
args = parser.parse_args()
main(args.input_bf16_hf_path, args.output_int8_hf_path, args.model_name)
print("done")
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