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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):
"""
Converts FP8 weights to BF16 and saves the converted weights.
This function reads FP8 weights from the specified directory, converts them to BF16,
and saves the converted weights to another specified directory. It also updates the
model index file to reflect the changes.
Args:
fp8_path (str): The path to the directory containing the FP8 weights and model index file.
bf16_path (str): The path to the directory where the converted BF16 weights will be saved.
Raises:
KeyError: If a required scale_inv tensor is missing for a weight.
Notes:
- The function assumes that the FP8 weights are stored in safetensor files.
- The function caches loaded safetensor files to optimize memory usage.
- The function updates the model index file to remove references to scale_inv tensors.
"""
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"]
# Cache for loaded safetensor files
loaded_files = {}
fp8_weight_names = []
# Helper function to get tensor from the correct file
def get_tensor(tensor_name):
"""
Retrieves a tensor from the cached safetensor files or loads it from disk if not cached.
Args:
tensor_name (str): The name of the tensor to retrieve.
Returns:
torch.Tensor: The retrieved tensor.
Raises:
KeyError: If the tensor does not exist in the safetensor file.
"""
file_name = weight_map[tensor_name]
if file_name not in loaded_files:
file_path = os.path.join(fp8_path, file_name)
loaded_files[file_name] = load_file(file_path, device="cuda")
return loaded_files[file_name][tensor_name]
safetensor_files = list(glob(os.path.join(fp8_path, "*.safetensors")))
safetensor_files.sort()
for safetensor_file in tqdm(safetensor_files):
file_name = os.path.basename(safetensor_file)
current_state_dict = load_file(safetensor_file, device="cuda")
loaded_files[file_name] = current_state_dict
new_state_dict = {}
for weight_name, weight in current_state_dict.items():
if weight_name.endswith("_scale_inv"):
continue
elif weight.element_size() == 1: # FP8 weight
scale_inv_name = f"{weight_name}_scale_inv"
try:
# Get scale_inv from the correct file
scale_inv = get_tensor(scale_inv_name)
fp8_weight_names.append(weight_name)
new_state_dict[weight_name] = weight_dequant(weight, scale_inv)
except KeyError:
print(f"Warning: Missing scale_inv tensor for {weight_name}, skipping conversion")
new_state_dict[weight_name] = weight
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)
# Memory management: keep only the 2 most recently used files
if len(loaded_files) > 2:
oldest_file = next(iter(loaded_files))
del loaded_files[oldest_file]
torch.cuda.empty_cache()
# Update model index
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"
if 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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