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 kernel import weight_dequant def main(fp8_path, bf16_path): torch.set_default_dtype(torch.bfloat16) os.makedirs(bf16_path, exist_ok=True) model_index_file = os.path.join(fp8_path, "model.safetensors.index.json") with open(model_index_file, "r") as f: model_index = json.load(f) weight_map = model_index["weight_map"] fp8_weight_names = [] safetensor_files = list(glob(os.path.join(fp8_path, "*.safetensors"))) 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(): if weight_name.endswith("_scale_inv"): continue elif weight.element_size() == 1: scale_inv_name = f"{weight_name}_scale_inv" assert scale_inv_name in state_dict fp8_weight_names.append(weight_name) scale_inv = state_dict[scale_inv_name] new_state_dict[weight_name] = weight_dequant(weight, scale_inv) else: new_state_dict[weight_name] = weight new_safetensor_file = os.path.join(bf16_path, file_name) save_file(new_state_dict, new_safetensor_file) new_model_index_file = os.path.join(bf16_path, "model.safetensors.index.json") for weight_name in fp8_weight_names: scale_inv_name = f"{weight_name}_scale_inv" assert scale_inv_name in weight_map weight_map.pop(scale_inv_name) with open(new_model_index_file, "w") as f: json.dump({"metadata": {}, "weight_map": weight_map}, f, indent=2) if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--input-fp8-hf-path", type=str, required=True) parser.add_argument("--output-bf16-hf-path", type=str, required=True) args = parser.parse_args() main(args.input_fp8_hf_path, args.output_bf16_hf_path)