init_crossover.py

# Copyright 2023-present the HuggingFace Inc. team.
#
# 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 torch
import os
from peft import CrossoverConfig, get_peft_model
from transformers import AutoTokenizer, AutoModelForCausalLM
import argparse

parser = argparse.ArgumentParser(description="Merge Adapter to Base Model")
parser.add_argument("--base_model_path", type=str, help="The name or path of the fp32/16 base model.")
parser.add_argument("--output_dir", type=str, default="clover_model")
parser.add_argument("--bits", type=str, default="fp32", choices=["bf16", "fp16", "fp32"])
parser.add_argument("--init_weights", type=str, default="kaiming", help="(`['kaiming', 'gaussian', 'orthogonal']`)")
parser.add_argument('--target_modules', nargs='+', help='', required=True)
parser.add_argument("--block_size", type=int)
parser.add_argument("--alpha", type=int)
parser.add_argument("--dropout", type=float)
args = parser.parse_args()
print(args)

model = AutoModelForCausalLM.from_pretrained(
    args.base_model_path,
    torch_dtype=(
        torch.float16
        if args.bits == "fp16"
        else (torch.bfloat16 if args.bits == "bf16" else torch.float32)
    ),
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(args.base_model_path)
crossover_config = CrossoverConfig(
    init_crossover_weights=args.init_weights,
    target_modules=args.target_modules,
    block_size=args.block_size,
    alpha=args.alpha,
    dropout=args.dropout,
    task_type="CAUSAL_LM",
)
peft_model = get_peft_model(model, crossover_config)
print(peft_model.get_nb_trainable_parameters())

# Save crossover modules:
peft_model.peft_config["default"].init_crossover_weights = 'kaiming'
peft_model.save_pretrained(os.path.join(args.output_dir, "crossover_init"))
# Save residual model:
peft_model = peft_model.unload()
peft_model.save_pretrained(args.output_dir)
# Save the tokenizer:
tokenizer.save_pretrained(args.output_dir)