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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 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)
-
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