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# Copyright 2025 Tencent Inc. and the LlamaFactory team.
#
# This code is inspired by the Tencent's LLaMA-Pro library.
# https://github.com/TencentARC/LLaMA-Pro/blob/main/scripts/block_expansion.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Dict
import fire
import torch
from huggingface_hub import split_torch_state_dict_into_shards
from safetensors.torch import save_file
from tqdm import tqdm
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, PreTrainedModel
from transformers.modeling_utils import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_INDEX_NAME, WEIGHTS_NAME
if TYPE_CHECKING:
from transformers import PretrainedConfig
def change_name(name: str, old_index: int, new_index: int) -> str:
return name.replace(f".{old_index:d}.", f".{new_index:d}.")
def block_expansion(
model_name_or_path: str,
output_dir: str,
num_expand: int,
shard_size: str = "5GB",
save_safetensors: bool = True,
):
r"""
Performs block expansion for LLaMA, Mistral, Qwen2 or Yi models.
Usage: python llama_pro.py --model_name_or_path meta-llama/Llama-2-7b-hf --output_dir llama2_pro --num_expand 8
"""
config: "PretrainedConfig" = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True)
num_layers = getattr(config, "num_hidden_layers")
if num_layers % num_expand != 0:
raise ValueError(f"`num_layers` {num_layers} should be divisible by `num_expand` {num_expand}.")
setattr(config, "num_hidden_layers", num_layers + num_expand)
config.save_pretrained(output_dir)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
tokenizer.save_pretrained(output_dir)
print(f"Expanding model of {num_layers} layers to {num_layers + num_expand} layers.")
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path, torch_dtype="auto", device_map="cpu", trust_remote_code=True, low_cpu_mem_usage=True
)
assert isinstance(model, PreTrainedModel) # type hint
if save_safetensors and getattr(model.config, "tie_word_embeddings", False):
del model.lm_head # safetensors does not allow shared weights
split = num_layers // num_expand
layer_cnt = 0
state_dict = model.state_dict()
output_state_dict: Dict[str, "torch.Tensor"] = OrderedDict()
for i in range(num_layers):
for key, value in state_dict.items():
if f".{i:d}." in key:
output_state_dict[change_name(key, i, layer_cnt)] = value
print(f"Add layer {layer_cnt} copied from layer {i}.")
layer_cnt += 1
if (i + 1) % split == 0:
for key, value in state_dict.items():
if f".{i:d}." in key:
if "down_proj" in key or "o_proj" in key:
output_state_dict[change_name(key, i, layer_cnt)] = torch.zeros_like(value)
else:
output_state_dict[change_name(key, i, layer_cnt)] = torch.clone(value)
print(f"Add layer {layer_cnt} expanded from layer {i}.")
layer_cnt += 1
for key, value in state_dict.items():
if key not in output_state_dict:
output_state_dict[key] = value
weights_name = SAFE_WEIGHTS_NAME if save_safetensors else WEIGHTS_NAME
filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
state_dict_split = split_torch_state_dict_into_shards(
output_state_dict, filename_pattern=filename_pattern, max_shard_size=shard_size
)
for shard_file, tensors in tqdm(state_dict_split.filename_to_tensors.items(), desc="Save weights"):
shard = {tensor: output_state_dict[tensor].contiguous() for tensor in tensors}
if save_safetensors:
save_file(shard, os.path.join(output_dir, shard_file), metadata={"format": "pt"})
else:
torch.save(shard, os.path.join(output_dir, shard_file))
if not state_dict_split.is_sharded:
print(f"Model weights saved in {os.path.join(output_dir, weights_name)}.")
else:
index = {
"metadata": state_dict_split.metadata,
"weight_map": state_dict_split.tensor_to_filename,
}
index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
with open(os.path.join(output_dir, index_name), "w", encoding="utf-8") as f:
json.dump(index, f, indent=2, sort_keys=True)
print(f"Model weights saved in {output_dir}.")
print("- Fine-tune this model with:")
print(f"model_name_or_path: {output_dir}")
print("finetuning_type: freeze")
print(f"freeze_trainable_layers: {num_expand}")
print("use_llama_pro: true")
if __name__ == "__main__":
fire.Fire(block_expansion)
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