Single GPU Billion-scale Model Training via Parameter-Efficient Finetuning¶
As pointed out by a recent paper from Stanford Institute for Human-Centered Artificial Intelligence, AI is undergoing a paradigm shift with the rise of “foundation models”, i.e., giant models that are trained on a diverse collection of datasets generally in a self-supervised way. These foundation models, which are the key of AutoMM, can be easily adapted to down-stream applications. However, as the size of these foundation models grows, finetuning these models becomes increasingly difficult. Following is a figure from the Microsoft research blog that demonstrates the trend:
The goal of AutoMM is to help anyone solve machine learning problems via open source foundation models, including these giant models. To finetune these large-scale models, we adopt the recently popularized parameter-efficient finetuning technique. The idea is to either finetune a small subset of the weights in the foundation model (e.g., BitFit), or adding a tiny tunable structure on top of the fixed backbone (e.g., Prompt Tuning, LoRA, Adapter, MAM Adapter, IA^3). These techniques can effectively reduce the peak memory usage and model training time, while maintaining the performance.
In this tutorial, we introduce how to apply parameter-efficient finetuning in MultiModalPredictor.
We first introduce how to adopt the "ia3_bias" algorithm for parameter-efficient finetuning. Afterwards, we show how you can simply combine "ia3_bias"
and gradient checkpointing to finetune the XL-variant of Google’s FLAN-T5 via a single NVIDIA T4 GPU.
Prepare Dataset¶
The Cross-Lingual Amazon Product Review Sentiment dataset contains Amazon product reviews in four languages.
Here, we load the English and German fold of the dataset. In the label column, 0 means negative sentiment and 1 means positive sentiment.
For the purpose of demonstration, we downsampled the training data to 1000 samples. We will train the model on the English dataset and
directly evaluate its performance on the German and Japanese test set.
!wget --quiet https://automl-mm-bench.s3.amazonaws.com/multilingual-datasets/amazon_review_sentiment_cross_lingual.zip -O amazon_review_sentiment_cross_lingual.zip
!unzip -q -o amazon_review_sentiment_cross_lingual.zip -d .
import os
import shutil
os.environ["TRANSFORMERS_CACHE"] = "cache"
def clear_cache():
if os.path.exists("cache"):
shutil.rmtree("cache")
clear_cache()
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
train_en_df = pd.read_csv("amazon_review_sentiment_cross_lingual/en_train.tsv",
sep="\t",
header=None,
names=["label", "text"]) \
.sample(1000, random_state=123).reset_index(drop=True)
test_en_df = pd.read_csv("amazon_review_sentiment_cross_lingual/en_test.tsv",
sep="\t",
header=None,
names=["label", "text"]) \
.sample(200, random_state=123).reset_index(drop=True)
test_de_df = pd.read_csv("amazon_review_sentiment_cross_lingual/de_test.tsv",
sep="\t", header=None, names=["label", "text"]) \
.sample(200, random_state=123).reset_index(drop=True)
test_jp_df = pd.read_csv('amazon_review_sentiment_cross_lingual/jp_test.tsv',
sep='\t', header=None, names=['label', 'text']) \
.sample(200, random_state=123).reset_index(drop=True)
train_en_df.head(5)
| label | text | |
|---|---|---|
| 0 | 0 | This is a film that literally sees little wron... |
| 1 | 0 | This music is pretty intelligent, but not very... |
| 2 | 0 | One of the best pieces of rock ever recorded, ... |
| 3 | 0 | Reading the posted reviews here, is like revis... |
| 4 | 1 | I've just finished page 341, the last page. It... |
test_jp_df.head(5)
| label | text | |
|---|---|---|
| 0 | 1 | 原作はビクトル・ユーゴの長編小説だが、私が子供の頃読んだのは短縮版の「ああ無情」。それでもこ... |
| 1 | 1 | ほかの作品のレビューにみんな書いているのに、何故この作品について書いている人が一人しかいない... |
| 2 | 0 | 一番の問題点は青島が出ていない事でしょう。 ＴＶ番組では『芸人が出ていればバラエティだから... |
| 3 | 0 | 昔、 りんたろう監督によるアニメ「カムイの剣」があった。 「カムイの剣」…を観た人なら本作... |
| 4 | 1 | 以前のアルバムを聴いていないのでなんとも言えないが、クラシックなメタルを聞いてきた耳には、と... |
Finetuning Multilingual Model with IA3 + BitFit¶
In AutoMM, to enable efficient finetuning, just specify the optim.peft to be "ia3_bias".
from autogluon.multimodal import MultiModalPredictor
import uuid
model_path = f"./tmp/{uuid.uuid4().hex}-multilingual_ia3"
predictor = MultiModalPredictor(label="label",
path=model_path)
predictor.fit(train_en_df,
presets="multilingual",
hyperparameters={
"optim.peft": "ia3_bias",
"optim.lr_decay": 0.9,
"optim.lr": 3e-03,
"optim.end_lr": 3e-03,
"optim.max_epochs": 2,
"optim.warmup_steps": 0,
"env.batch_size": 32,
})
/home/ci/opt/venv/lib/python3.11/site-packages/mmengine/optim/optimizer/zero_optimizer.py:11: DeprecationWarning: `TorchScript` support for functional optimizers is deprecated and will be removed in a future PyTorch release. Consider using the `torch.compile` optimizer instead.
from torch.distributed.optim import \
=================== System Info ===================
AutoGluon Version: 1.2b20241213
Python Version: 3.11.9
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP Tue Sep 24 10:00:37 UTC 2024
CPU Count: 8
Pytorch Version: 2.5.1+cu124
CUDA Version: 12.4
Memory Avail: 28.44 GB / 30.95 GB (91.9%)
Disk Space Avail: 181.20 GB / 255.99 GB (70.8%)
===================================================
AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).
2 unique label values: [0, 1]
If 'binary' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])
AutoMM starts to create your model. ✨✨✨
To track the learning progress, you can open a terminal and launch Tensorboard:
```shell
# Assume you have installed tensorboard
tensorboard --logdir /home/ci/autogluon/docs/tutorials/multimodal/advanced_topics/tmp/42d2524ebfeb4b1cbbb48df5918af57e-multilingual_ia3
```
Seed set to 0
GPU Count: 1
GPU Count to be Used: 1
Using bfloat16 Automatic Mixed Precision (AMP)
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
| Name | Type | Params | Mode
---------------------------------------------------------------------------
0 | model | HFAutoModelForTextPrediction | 278 M | train
1 | validation_metric | BinaryAUROC | 0 | train
2 | loss_func | CrossEntropyLoss | 0 | train
---------------------------------------------------------------------------
122 K Trainable params
278 M Non-trainable params
278 M Total params
1,112.955 Total estimated model params size (MB)
28 Modules in train mode
213 Modules in eval mode
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[6], line 7
4 model_path = f"./tmp/{uuid.uuid4().hex}-multilingual_ia3"
5 predictor = MultiModalPredictor(label="label",
6 path=model_path)
----> 7 predictor.fit(train_en_df,
8 presets="multilingual",
9 hyperparameters={
10 "optim.peft": "ia3_bias",
11 "optim.lr_decay": 0.9,
12 "optim.lr": 3e-03,
13 "optim.end_lr": 3e-03,
14 "optim.max_epochs": 2,
15 "optim.warmup_steps": 0,
16 "env.batch_size": 32,
17 })
File ~/autogluon/multimodal/src/autogluon/multimodal/predictor.py:529, in MultiModalPredictor.fit(self, train_data, presets, tuning_data, max_num_tuning_data, id_mappings, time_limit, save_path, hyperparameters, column_types, holdout_frac, teacher_predictor, seed, standalone, hyperparameter_tune_kwargs, clean_ckpts, predictions, labels, predictors)
526 assert isinstance(predictors, list)
527 learners = [ele if isinstance(ele, str) else ele._learner for ele in predictors]
--> 529 self._learner.fit(
530 train_data=train_data,
531 presets=presets,
532 tuning_data=tuning_data,
533 max_num_tuning_data=max_num_tuning_data,
534 time_limit=time_limit,
535 save_path=save_path,
536 hyperparameters=hyperparameters,
537 column_types=column_types,
538 holdout_frac=holdout_frac,
539 teacher_learner=teacher_learner,
540 seed=seed,
541 standalone=standalone,
542 hyperparameter_tune_kwargs=hyperparameter_tune_kwargs,
543 clean_ckpts=clean_ckpts,
544 id_mappings=id_mappings,
545 predictions=predictions,
546 labels=labels,
547 learners=learners,
548 )
550 return self
File ~/autogluon/multimodal/src/autogluon/multimodal/learners/base.py:665, in BaseLearner.fit(self, train_data, presets, tuning_data, time_limit, save_path, hyperparameters, column_types, holdout_frac, teacher_learner, seed, standalone, hyperparameter_tune_kwargs, clean_ckpts, **kwargs)
658 self.fit_sanity_check()
659 self.prepare_fit_args(
660 time_limit=time_limit,
661 seed=seed,
662 standalone=standalone,
663 clean_ckpts=clean_ckpts,
664 )
--> 665 fit_returns = self.execute_fit()
666 self.on_fit_end(
667 training_start=training_start,
668 strategy=fit_returns.get("strategy", None),
(...)
671 clean_ckpts=clean_ckpts,
672 )
674 return self
File ~/autogluon/multimodal/src/autogluon/multimodal/learners/base.py:577, in BaseLearner.execute_fit(self)
575 return dict()
576 else:
--> 577 attributes = self.fit_per_run(**self._fit_args)
578 self.update_attributes(**attributes) # only update attributes for non-HPO mode
579 return attributes
File ~/autogluon/multimodal/src/autogluon/multimodal/learners/base.py:1358, in BaseLearner.fit_per_run(self, max_time, save_path, ckpt_path, resume, enable_progress_bar, seed, hyperparameters, advanced_hyperparameters, config, df_preprocessor, data_processors, model, standalone, clean_ckpts)
1339 config = self.post_update_config_per_run(
1340 config=config,
1341 num_gpus=num_gpus,
1342 precision=precision,
1343 strategy=strategy,
1344 )
1345 trainer = self.init_trainer_per_run(
1346 num_gpus=num_gpus,
1347 config=config,
(...)
1355 enable_progress_bar=enable_progress_bar,
1356 )
-> 1358 self.run_trainer(
1359 trainer=trainer,
1360 litmodule=litmodule,
1361 datamodule=datamodule,
1362 ckpt_path=ckpt_path,
1363 resume=resume,
1364 )
1365 self.on_fit_per_run_end(
1366 save_path=save_path,
1367 standalone=standalone,
(...)
1372 model=model,
1373 )
1375 best_score = (
1376 trainer.callback_metrics[f"val_{self._validation_metric_name}"].item()
1377 if f"val_{self._validation_metric_name}" in trainer.callback_metrics
1378 else self._best_score
1379 ) # https://github.com/autogluon/autogluon/issues/4428
File ~/autogluon/multimodal/src/autogluon/multimodal/learners/base.py:1211, in BaseLearner.run_trainer(self, trainer, litmodule, datamodule, ckpt_path, resume, pred_writer, is_train)
1209 warnings.filterwarnings("ignore", filter)
1210 if is_train:
-> 1211 trainer.fit(
1212 litmodule,
1213 datamodule=datamodule,
1214 ckpt_path=ckpt_path if resume else None, # this is to resume training that was broken accidentally
1215 )
1216 else:
1217 blacklist_msgs = []
File ~/opt/venv/lib/python3.11/site-packages/lightning/pytorch/trainer/trainer.py:538, in Trainer.fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
536 self.state.status = TrainerStatus.RUNNING
537 self.training = True
--> 538 call._call_and_handle_interrupt(
539 self, self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
540 )
File ~/opt/venv/lib/python3.11/site-packages/lightning/pytorch/trainer/call.py:47, in _call_and_handle_interrupt(trainer, trainer_fn, *args, **kwargs)
45 if trainer.strategy.launcher is not None:
46 return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
---> 47 return trainer_fn(*args, **kwargs)
49 except _TunerExitException:
50 _call_teardown_hook(trainer)
File ~/opt/venv/lib/python3.11/site-packages/lightning/pytorch/trainer/trainer.py:574, in Trainer._fit_impl(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
567 assert self.state.fn is not None
568 ckpt_path = self._checkpoint_connector._select_ckpt_path(
569 self.state.fn,
570 ckpt_path,
571 model_provided=True,
572 model_connected=self.lightning_module is not None,
573 )
--> 574 self._run(model, ckpt_path=ckpt_path)
576 assert self.state.stopped
577 self.training = False
File ~/opt/venv/lib/python3.11/site-packages/lightning/pytorch/trainer/trainer.py:981, in Trainer._run(self, model, ckpt_path)
976 self._signal_connector.register_signal_handlers()
978 # ----------------------------
979 # RUN THE TRAINER
980 # ----------------------------
--> 981 results = self._run_stage()
983 # ----------------------------
984 # POST-Training CLEAN UP
985 # ----------------------------
986 log.debug(f"{self.__class__.__name__}: trainer tearing down")
File ~/opt/venv/lib/python3.11/site-packages/lightning/pytorch/trainer/trainer.py:1023, in Trainer._run_stage(self)
1021 if self.training:
1022 with isolate_rng():
-> 1023 self._run_sanity_check()
1024 with torch.autograd.set_detect_anomaly(self._detect_anomaly):
1025 self.fit_loop.run()
File ~/opt/venv/lib/python3.11/site-packages/lightning/pytorch/trainer/trainer.py:1052, in Trainer._run_sanity_check(self)
1049 call._call_callback_hooks(self, "on_sanity_check_start")
1051 # run eval step
-> 1052 val_loop.run()
1054 call._call_callback_hooks(self, "on_sanity_check_end")
1056 # reset logger connector
File ~/opt/venv/lib/python3.11/site-packages/lightning/pytorch/loops/utilities.py:178, in _no_grad_context.<locals>._decorator(self, *args, **kwargs)
176 context_manager = torch.no_grad
177 with context_manager():
--> 178 return loop_run(self, *args, **kwargs)
File ~/opt/venv/lib/python3.11/site-packages/lightning/pytorch/loops/evaluation_loop.py:135, in _EvaluationLoop.run(self)
133 self.batch_progress.is_last_batch = data_fetcher.done
134 # run step hooks
--> 135 self._evaluation_step(batch, batch_idx, dataloader_idx, dataloader_iter)
136 except StopIteration:
137 # this needs to wrap the `*_step` call too (not just `next`) for `dataloader_iter` support
138 break
File ~/opt/venv/lib/python3.11/site-packages/lightning/pytorch/loops/evaluation_loop.py:396, in _EvaluationLoop._evaluation_step(self, batch, batch_idx, dataloader_idx, dataloader_iter)
390 hook_name = "test_step" if trainer.testing else "validation_step"
391 step_args = (
392 self._build_step_args_from_hook_kwargs(hook_kwargs, hook_name)
393 if not using_dataloader_iter
394 else (dataloader_iter,)
395 )
--> 396 output = call._call_strategy_hook(trainer, hook_name, *step_args)
398 self.batch_progress.increment_processed()
400 if using_dataloader_iter:
401 # update the hook kwargs now that the step method might have consumed the iterator
File ~/opt/venv/lib/python3.11/site-packages/lightning/pytorch/trainer/call.py:319, in _call_strategy_hook(trainer, hook_name, *args, **kwargs)
316 return None
318 with trainer.profiler.profile(f"[Strategy]{trainer.strategy.__class__.__name__}.{hook_name}"):
--> 319 output = fn(*args, **kwargs)
321 # restore current_fx when nested context
322 pl_module._current_fx_name = prev_fx_name
File ~/opt/venv/lib/python3.11/site-packages/lightning/pytorch/strategies/strategy.py:411, in Strategy.validation_step(self, *args, **kwargs)
409 if self.model != self.lightning_module:
410 return self._forward_redirection(self.model, self.lightning_module, "validation_step", *args, **kwargs)
--> 411 return self.lightning_module.validation_step(*args, **kwargs)
File ~/autogluon/multimodal/src/autogluon/multimodal/optim/lit_module.py:381, in LitModule.validation_step(self, batch, batch_idx)
365 def validation_step(self, batch, batch_idx):
366 """
367 Per validation step. This function is registered by LightningModule.
368 Refer to https://lightning.ai/docs/pytorch/stable/common/lightning_module.html#validation-loop
(...)
379 Index of mini-batch.
380 """
--> 381 output, loss = self._shared_step(batch)
382 if self.model_postprocess_fn:
383 output = self.model_postprocess_fn(output)
File ~/autogluon/multimodal/src/autogluon/multimodal/optim/lit_module.py:305, in LitModule._shared_step(self, batch)
303 self.mixup_fn.mixup_enabled = self.training & (self.current_epoch < self.hparams.mixup_off_epoch)
304 batch, label = multimodel_mixup(batch=batch, model=self.model, mixup_fn=self.mixup_fn)
--> 305 output = run_model(self.model, batch)
306 loss = self._compute_loss(output=output, label=label)
307 return output, loss
File ~/autogluon/multimodal/src/autogluon/multimodal/models/utils.py:863, in run_model(model, batch, trt_model)
861 output_vec = pure_model(*tuple(input_vec))
862 else:
--> 863 output_vec = model(*tuple(input_vec))
865 output = pure_model.get_output_dict(*output_vec)
866 else:
File ~/opt/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1736, in Module._wrapped_call_impl(self, *args, **kwargs)
1734 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1735 else:
-> 1736 return self._call_impl(*args, **kwargs)
File ~/opt/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1747, in Module._call_impl(self, *args, **kwargs)
1742 # If we don't have any hooks, we want to skip the rest of the logic in
1743 # this function, and just call forward.
1744 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1745 or _global_backward_pre_hooks or _global_backward_hooks
1746 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1747 return forward_call(*args, **kwargs)
1749 result = None
1750 called_always_called_hooks = set()
File ~/autogluon/multimodal/src/autogluon/multimodal/models/hf_text.py:230, in HFAutoModelForTextPrediction.forward(self, text_token_ids, text_segment_ids, text_valid_length, text_column_names, text_column_indices)
228 else:
229 if "token_type_ids" in self.tokenizer.model_input_names:
--> 230 outputs = self.model(
231 input_ids=text_token_ids,
232 token_type_ids=text_segment_ids,
233 attention_mask=text_masks,
234 )
235 else:
236 outputs = self.model(
237 input_ids=text_token_ids,
238 attention_mask=text_masks,
239 )
File ~/opt/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1736, in Module._wrapped_call_impl(self, *args, **kwargs)
1734 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1735 else:
-> 1736 return self._call_impl(*args, **kwargs)
File ~/opt/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1747, in Module._call_impl(self, *args, **kwargs)
1742 # If we don't have any hooks, we want to skip the rest of the logic in
1743 # this function, and just call forward.
1744 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1745 or _global_backward_pre_hooks or _global_backward_hooks
1746 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1747 return forward_call(*args, **kwargs)
1749 result = None
1750 called_always_called_hooks = set()
File ~/opt/venv/lib/python3.11/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:871, in DebertaV2Model.forward(self, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict)
861 token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
863 embedding_output = self.embeddings(
864 input_ids=input_ids,
865 token_type_ids=token_type_ids,
(...)
868 inputs_embeds=inputs_embeds,
869 )
--> 871 encoder_outputs = self.encoder(
872 embedding_output,
873 attention_mask,
874 output_hidden_states=True,
875 output_attentions=output_attentions,
876 return_dict=return_dict,
877 )
878 encoded_layers = encoder_outputs[1]
880 if self.z_steps > 1:
File ~/opt/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1736, in Module._wrapped_call_impl(self, *args, **kwargs)
1734 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1735 else:
-> 1736 return self._call_impl(*args, **kwargs)
File ~/opt/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1747, in Module._call_impl(self, *args, **kwargs)
1742 # If we don't have any hooks, we want to skip the rest of the logic in
1743 # this function, and just call forward.
1744 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1745 or _global_backward_pre_hooks or _global_backward_hooks
1746 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1747 return forward_call(*args, **kwargs)
1749 result = None
1750 called_always_called_hooks = set()
File ~/opt/venv/lib/python3.11/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:675, in DebertaV2Encoder.forward(self, hidden_states, attention_mask, output_hidden_states, output_attentions, query_states, relative_pos, return_dict)
665 output_states, attn_weights = self._gradient_checkpointing_func(
666 layer_module.__call__,
667 next_kv,
(...)
672 output_attentions,
673 )
674 else:
--> 675 output_states, attn_weights = layer_module(
676 next_kv,
677 attention_mask,
678 query_states=query_states,
679 relative_pos=relative_pos,
680 rel_embeddings=rel_embeddings,
681 output_attentions=output_attentions,
682 )
684 if output_attentions:
685 all_attentions = all_attentions + (attn_weights,)
File ~/opt/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1736, in Module._wrapped_call_impl(self, *args, **kwargs)
1734 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1735 else:
-> 1736 return self._call_impl(*args, **kwargs)
File ~/opt/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1747, in Module._call_impl(self, *args, **kwargs)
1742 # If we don't have any hooks, we want to skip the rest of the logic in
1743 # this function, and just call forward.
1744 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1745 or _global_backward_pre_hooks or _global_backward_hooks
1746 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1747 return forward_call(*args, **kwargs)
1749 result = None
1750 called_always_called_hooks = set()
File ~/opt/venv/lib/python3.11/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:443, in DebertaV2Layer.forward(self, hidden_states, attention_mask, query_states, relative_pos, rel_embeddings, output_attentions)
434 def forward(
435 self,
436 hidden_states,
(...)
441 output_attentions: bool = False,
442 ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
--> 443 attention_output, att_matrix = self.attention(
444 hidden_states,
445 attention_mask,
446 output_attentions=output_attentions,
447 query_states=query_states,
448 relative_pos=relative_pos,
449 rel_embeddings=rel_embeddings,
450 )
451 intermediate_output = self.intermediate(attention_output)
452 layer_output = self.output(intermediate_output, attention_output)
File ~/opt/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1736, in Module._wrapped_call_impl(self, *args, **kwargs)
1734 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1735 else:
-> 1736 return self._call_impl(*args, **kwargs)
File ~/opt/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1747, in Module._call_impl(self, *args, **kwargs)
1742 # If we don't have any hooks, we want to skip the rest of the logic in
1743 # this function, and just call forward.
1744 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1745 or _global_backward_pre_hooks or _global_backward_hooks
1746 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1747 return forward_call(*args, **kwargs)
1749 result = None
1750 called_always_called_hooks = set()
File ~/opt/venv/lib/python3.11/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:376, in DebertaV2Attention.forward(self, hidden_states, attention_mask, output_attentions, query_states, relative_pos, rel_embeddings)
367 def forward(
368 self,
369 hidden_states,
(...)
374 rel_embeddings=None,
375 ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
--> 376 self_output, att_matrix = self.self(
377 hidden_states,
378 attention_mask,
379 output_attentions,
380 query_states=query_states,
381 relative_pos=relative_pos,
382 rel_embeddings=rel_embeddings,
383 )
384 if query_states is None:
385 query_states = hidden_states
File ~/opt/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1736, in Module._wrapped_call_impl(self, *args, **kwargs)
1734 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1735 else:
-> 1736 return self._call_impl(*args, **kwargs)
File ~/opt/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1747, in Module._call_impl(self, *args, **kwargs)
1742 # If we don't have any hooks, we want to skip the rest of the logic in
1743 # this function, and just call forward.
1744 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1745 or _global_backward_pre_hooks or _global_backward_hooks
1746 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1747 return forward_call(*args, **kwargs)
1749 result = None
1750 called_always_called_hooks = set()
File ~/opt/venv/lib/python3.11/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:267, in DisentangledSelfAttention.forward(self, hidden_states, attention_mask, output_attentions, query_states, relative_pos, rel_embeddings)
262 attention_scores = attention_scores.view(
263 -1, self.num_attention_heads, attention_scores.size(-2), attention_scores.size(-1)
264 )
266 attention_mask = attention_mask.bool()
--> 267 attention_scores = attention_scores.masked_fill(~(attention_mask), torch.finfo(query_layer.dtype).min)
268 # bsz x height x length x dimension
269 attention_probs = nn.functional.softmax(attention_scores, dim=-1)
RuntimeError: value cannot be converted to type at::BFloat16 without overflow
The fraction of the tunable parameters is around 0.5% of all parameters. Actually, the model trained purely on English data can achieve good performance on the test sets, even on the German / Japanese test set. It obtained comparable results as full-finetuning as in AutoMM for Text - Multilingual Problems.
score_in_en = predictor.evaluate(test_en_df)
score_in_de = predictor.evaluate(test_de_df)
score_in_jp = predictor.evaluate(test_jp_df)
print('Score in the English Testset:', score_in_en)
print('Score in the German Testset:', score_in_de)
print('Score in the Japanese Testset:', score_in_jp)
Training FLAN-T5-XL on Single GPU¶
By combining gradient checkpointing and parameter-efficient finetuning, it is feasible to finetune
google/flan-t5-xl that has close to two billion parameterswith a single T4 GPU available in
AWS G4 instances.
To turn on gradient checkpointing, you just need to set "model.hf_text.gradient_checkpointing" to True.
To accelerate the training, we downsample the number of training samples to be 200.
# Just for clean the space
clear_cache()
shutil.rmtree(model_path)
from autogluon.multimodal import MultiModalPredictor
train_en_df_downsample = train_en_df.sample(200, random_state=123)
new_model_path = f"./tmp/{uuid.uuid4().hex}-multilingual_ia3_gradient_checkpoint"
predictor = MultiModalPredictor(label="label",
path=new_model_path)
predictor.fit(train_en_df_downsample,
presets="multilingual",
hyperparameters={
"model.hf_text.checkpoint_name": "google/flan-t5-xl",
"model.hf_text.gradient_checkpointing": True,
"model.hf_text.low_cpu_mem_usage": True,
"optim.peft": "ia3_bias",
"optim.lr_decay": 0.9,
"optim.lr": 3e-03,
"optim.end_lr": 3e-03,
"optim.max_epochs": 1,
"optim.warmup_steps": 0,
"env.batch_size": 1,
"env.inference_batch_size_ratio": 1
})
Global seed set to 123
Auto select gpus: [0]
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
| Name | Type | Params
-------------------------------------------------------------------
0 | model | HFAutoModelForTextPrediction | 1.2 B
1 | validation_metric | AUROC | 0
2 | loss_func | CrossEntropyLoss | 0
-------------------------------------------------------------------
203 K Trainable params
1.2 B Non-trainable params
1.2 B Total params
4,894.913 Total estimated model params size (MB)
Epoch 0, global step 20: 'val_roc_auc' reached 0.88802 (best 0.88802), saving model to '/home/ubuntu/autogluon/docs/tutorials/multimodal/advanced_topics/multilingual_ia3_gradient_checkpoint/epoch=0-step=20.ckpt' as top 1
Epoch 0, global step 40: 'val_roc_auc' reached 0.94531 (best 0.94531), saving model to '/home/ubuntu/autogluon/docs/tutorials/multimodal/advanced_topics/multilingual_ia3_gradient_checkpoint/epoch=0-step=40.ckpt' as top 1
`Trainer.fit` stopped: `max_epochs=1` reached.
<autogluon.multimodal.predictor.MultiModalPredictor at 0x7fd58c4dbca0>
score_in_en = predictor.evaluate(test_en_df)
print('Score in the English Testset:', score_in_en)
Score in the English Testset: {'roc_auc': 0.931263189629183}
# Just for clean the space
clear_cache()
shutil.rmtree(new_model_path)
Other Examples¶
You may go to AutoMM Examples to explore other examples about AutoMM.
Customization¶
To learn how to customize AutoMM, please refer to Customize AutoMM.