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83ddd7e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | # 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
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"""Perform 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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