116 lines
4.3 KiB
Python
116 lines
4.3 KiB
Python
# coding=utf-8
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# Performs block expansion for LLaMA, Mistral or Qwen1.5 models.
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# Usage: python llama_pro.py --model_name_or_path meta-llama/Llama-2-7b-hf --output_dir llama2_pro --num_expand 8
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# Inspired by: https://github.com/TencentARC/LLaMA-Pro/blob/main/scripts/block_expansion.py
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import json
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import os
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from collections import OrderedDict
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from typing import TYPE_CHECKING, Optional
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import fire
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import torch
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from safetensors.torch import save_file
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from tqdm import tqdm
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from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
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from transformers.modeling_utils import (
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SAFE_WEIGHTS_INDEX_NAME,
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SAFE_WEIGHTS_NAME,
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WEIGHTS_INDEX_NAME,
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WEIGHTS_NAME,
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shard_checkpoint,
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)
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if TYPE_CHECKING:
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from transformers import PretrainedConfig, PreTrainedModel
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def change_name(name: str, old_index: int, new_index: int) -> str:
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return name.replace(".{:d}.".format(old_index), ".{:d}.".format(new_index))
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def block_expansion(
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model_name_or_path: str,
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output_dir: str,
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num_expand: int,
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shard_size: Optional[str] = "2GB",
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save_safetensors: Optional[bool] = False,
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):
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config: "PretrainedConfig" = AutoConfig.from_pretrained(model_name_or_path)
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num_layers = getattr(config, "num_hidden_layers")
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setattr(config, "num_hidden_layers", num_layers + num_expand)
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config.save_pretrained(output_dir)
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
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tokenizer.save_pretrained(output_dir)
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config: "PretrainedConfig" = AutoConfig.from_pretrained(model_name_or_path) # load the original one
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if save_safetensors:
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setattr(config, "tie_word_embeddings", False) # safetensors does not allow shared weights
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model: "PreTrainedModel" = AutoModelForCausalLM.from_pretrained(
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model_name_or_path,
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config=config,
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torch_dtype="auto",
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trust_remote_code=True,
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low_cpu_mem_usage=True,
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)
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state_dict = model.state_dict()
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if num_layers % num_expand != 0:
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raise ValueError("`num_layers` {} should be divisible by `num_expand` {}.".format(num_layers, num_expand))
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split = num_layers // num_expand
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layer_cnt = 0
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output_state_dict = OrderedDict()
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for i in range(num_layers):
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for key, value in state_dict.items():
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if ".{:d}.".format(i) in key:
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output_state_dict[change_name(key, i, layer_cnt)] = value
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print("Add layer {} copied from layer {}".format(layer_cnt, i))
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layer_cnt += 1
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if (i + 1) % split == 0:
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for key, value in state_dict.items():
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if ".{:d}.".format(i) in key:
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if "down_proj" in key or "o_proj" in key:
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output_state_dict[change_name(key, i, layer_cnt)] = torch.zeros_like(value)
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else:
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output_state_dict[change_name(key, i, layer_cnt)] = torch.clone(value)
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print("Add layer {} expanded from layer {}".format(layer_cnt, i))
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layer_cnt += 1
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for key, value in state_dict.items():
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if key not in output_state_dict:
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output_state_dict[key] = value
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weights_name = SAFE_WEIGHTS_NAME if save_safetensors else WEIGHTS_NAME
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shards, index = shard_checkpoint(output_state_dict, max_shard_size=shard_size, weights_name=weights_name)
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for shard_file, shard in tqdm(shards.items(), desc="Save weights"):
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if save_safetensors:
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save_file(shard, os.path.join(output_dir, shard_file), metadata={"format": "pt"})
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else:
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torch.save(shard, os.path.join(output_dir, shard_file))
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if index is None:
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print("Model weights saved in {}".format(os.path.join(output_dir, weights_name)))
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else:
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index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
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with open(os.path.join(output_dir, index_name), "w", encoding="utf-8") as f:
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json.dump(index, f, indent=2, sort_keys=True)
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print("Model weights saved in {}".format(output_dir))
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print("Fine-tune this model with:")
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print(" --model_name_or_path {} \\".format(output_dir))
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print(" --finetuning_type freeze \\")
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print(" --name_module_trainable all \\")
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print(" --num_layer_trainable {} \\".format(num_expand))
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print(" --use_llama_pro")
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if __name__ == "__main__":
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fire.Fire(block_expansion)
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