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# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright:
# Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright:
# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
#
# 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 os
from dataclasses import dataclass, field
import logging
import pathlib
from typing import Dict, Optional, Sequence, List
import torch
import transformers
import sys
from salmonn_trainer import SALMONNTrainer, get_state
from dataset import make_supervised_data_module, DataArguments
from model import SALMONN
from utils import print_trainable_parameters
import wandb
@dataclass
class ModelArguments:
ckpt_path: Optional[str] = field(default='./salmonn_7b_v0.pth')
whisper_path: Optional[str] = field(default='./whisper-large-v2')
beats_path: Optional[str] = field(default='./BEATs_iter3_plus_AS2M_finetuned_on_AS2M_cpt2.pt')
vicuna_path: Optional[str] = field(default='./vicuna-7b-v1.5')
version: Optional[str] = field(default="v0")
device: Optional[str] = field(default='cuda')
@dataclass
class TrainingArguments(transformers.TrainingArguments):
output_dir: Optional[str] = field(default='./checkpoints/')
optim: str = field(default="adamw_torch")
bf16: bool = True
fp16: bool = False
lora_alpha: int = 32
model_max_length: int = 2048
use_cache: bool = False
gradient_checkpointing: bool = False
def train():
parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
compute_dtype = (torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
wandb.init(project='SALMONN', name=training_args.run_name)
model = SALMONN(
model_args.ckpt_path, model_args.whisper_path, model_args.beats_path, model_args.vicuna_path,
lora_alpha=training_args.lora_alpha, compute_dtype=compute_dtype
).cuda()
print_trainable_parameters(model, vb=0)
data_module = make_supervised_data_module(tokenizer=model.tokenizer, data_args=data_args)
trainer = SALMONNTrainer(model=model, tokenizer=model.tokenizer, args=training_args, **data_module)
trainer.train()
# Only save Adapter
weight_to_save = get_state(model.named_parameters())
torch.save(weight_to_save, os.path.join(training_args.output_dir, f'salomnn_7b.bin'))
trainer.save_state()
if __name__ == "__main__":
train()
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