diff --git a/machine/jobs/build_nmt_engine.py b/machine/jobs/build_nmt_engine.py index 75f13b42..5e4d208a 100644 --- a/machine/jobs/build_nmt_engine.py +++ b/machine/jobs/build_nmt_engine.py @@ -25,7 +25,7 @@ def run(args: dict) -> None: progress: Optional[Callable[[ProgressStatus], None]] = None check_canceled: Optional[Callable[[], None]] = None - task = None + task: Optional[Task] = None if args["clearml"]: task = Task.init() diff --git a/machine/jobs/huggingface/hugging_face_nmt_model_factory.py b/machine/jobs/huggingface/hugging_face_nmt_model_factory.py index d1c9af9b..9b0e3356 100644 --- a/machine/jobs/huggingface/hugging_face_nmt_model_factory.py +++ b/machine/jobs/huggingface/hugging_face_nmt_model_factory.py @@ -99,6 +99,7 @@ def create_engine(self) -> TranslationEngine: batch_size=self._config.huggingface.generate_params.batch_size, truncation=TruncationStrategy.LONGEST_FIRST, oom_batch_size_backoff_mult=self._config.huggingface.generate_params.oom_batch_size_backoff_mult, + output_attentions=False, ) def save_model(self) -> Path: diff --git a/machine/jobs/settings.yaml b/machine/jobs/settings.yaml index fe9a0590..b1ee5402 100644 --- a/machine/jobs/settings.yaml +++ b/machine/jobs/settings.yaml @@ -10,14 +10,15 @@ default: do_train: true optim: adamw_torch warmup_steps: 1000 - per_device_train_batch_size: 16 - gradient_accumulation_steps: 4 + per_device_train_batch_size: 64 + gradient_accumulation_steps: 1 label_smoothing_factor: 0.2 group_by_length: true gradient_checkpointing: true lr_scheduler_type: cosine learning_rate: 0.0002 fp16: true + tf32: true save_strategy: no max_steps: 5000 generate_params: diff --git a/machine/translation/huggingface/hugging_face_nmt_model_trainer.py b/machine/translation/huggingface/hugging_face_nmt_model_trainer.py index 2d8641c7..abbbf7b2 100644 --- a/machine/translation/huggingface/hugging_face_nmt_model_trainer.py +++ b/machine/translation/huggingface/hugging_face_nmt_model_trainer.py @@ -4,20 +4,27 @@ import os import re from pathlib import Path -from typing import Any, Callable, List, Optional, Union, cast +from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import torch # pyright: ignore[reportMissingImports] +from accelerate import Accelerator # pyright: ignore[reportMissingImports] +from accelerate.utils.memory import should_reduce_batch_size # pyright: ignore[reportMissingImports] from datasets.arrow_dataset import Dataset from sacremoses import MosesPunctNormalizer from torch import Tensor # pyright: ignore[reportMissingImports] +from torch.nn import Module # pyright: ignore[reportMissingImports] +from torch.optim.lr_scheduler import LambdaLR # pyright: ignore[reportMissingImports] +from torch.optim.optimizer import Optimizer # pyright: ignore[reportMissingImports] from torch.utils.checkpoint import checkpoint # pyright: ignore[reportMissingImports] # noqa: F401 from transformers import ( AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer, DataCollatorForSeq2Seq, + EvalPrediction, M2M100ForConditionalGeneration, M2M100Tokenizer, + MBart50Tokenizer, MBart50TokenizerFast, MBartTokenizer, MBartTokenizerFast, @@ -25,13 +32,13 @@ NllbTokenizerFast, PreTrainedModel, PreTrainedTokenizer, + PreTrainedTokenizerBase, PreTrainedTokenizerFast, Seq2SeqTrainer, Seq2SeqTrainingArguments, TrainerCallback, set_seed, ) -from transformers.models.mbart50 import MBart50Tokenizer from transformers.trainer_callback import TrainerControl, TrainerState from transformers.trainer_utils import get_last_checkpoint from transformers.training_args import TrainingArguments @@ -315,7 +322,7 @@ def preprocess_function(examples): pad_to_multiple_of=8 if self._training_args.fp16 else None, ) - self._trainer = Seq2SeqTrainer( + self._trainer = AutoGradientAccumulationStepsSeq2SeqTrainer( model=model, args=self._training_args, train_dataset=cast(Any, train_dataset), @@ -372,10 +379,12 @@ def __init__( max_steps: Optional[int], progress: Optional[Callable[[ProgressStatus], None]], check_canceled: Optional[Callable[[], None]], + update_frequency: Optional[int] = None, ) -> None: - self._max_steps = max_steps + self._max_steps = max_steps if max_steps is not None else 0 self._progress = progress self._check_canceled = check_canceled + self._update_frequency = update_frequency if update_frequency is not None else max((self._max_steps // 100), 1) def on_train_begin(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs) -> None: if self._check_canceled is not None: @@ -387,6 +396,9 @@ def on_train_begin(self, args: TrainingArguments, state: TrainerState, control: ) def on_step_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs) -> None: + if (state.global_step % self._update_frequency) != 0: + return + if self._check_canceled is not None: self._check_canceled() @@ -398,6 +410,73 @@ def on_step_end(self, args: TrainingArguments, state: TrainerState, control: Tra ) +class AutoGradientAccumulationStepsSeq2SeqTrainer(Seq2SeqTrainer): + def __init__( + self, + model: Union[PreTrainedModel, Module], + args: Seq2SeqTrainingArguments, + data_collator: Any, + train_dataset: Optional[Dataset] = None, + eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None, + tokenizer: Optional[PreTrainedTokenizerBase] = None, + model_init: Optional[Callable[[], PreTrainedModel]] = None, + compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None, + callbacks: Optional[List[TrainerCallback]] = None, + optimizers: Tuple[Optional[Optimizer], Optional[LambdaLR]] = (None, None), + preprocess_logits_for_metrics: Optional[Callable[[Tensor, Tensor], Tensor]] = None, + ): + super().__init__( + model, + args, + data_collator, + train_dataset, # type: ignore + eval_dataset, # type: ignore + tokenizer, + model_init, + compute_metrics, + callbacks, + optimizers, # type: ignore + preprocess_logits_for_metrics, + ) + + def _inner_training_loop( + self, batch_size=None, args=None, resume_from_checkpoint=None, trial=None, ignore_keys_for_eval=None + ): + inner_training_loop = find_executable_batch_size(super()._inner_training_loop, batch_size, self.accelerator) + return inner_training_loop( + args=args, + resume_from_checkpoint=resume_from_checkpoint, + trial=trial, + ignore_keys_for_eval=ignore_keys_for_eval, + ) + + +def find_executable_batch_size(function: Callable, starting_batch_size, accelerator: Accelerator): + batch_size = starting_batch_size + + def decorator(*args, **kwargs): + nonlocal batch_size + gc.collect() + torch.cuda.empty_cache() + + while True: + if batch_size == 0: + raise RuntimeError("No executable batch size found, reached zero.") + try: + return function(batch_size, *args, **kwargs) + except Exception as e: + if should_reduce_batch_size(e): + gc.collect() + torch.cuda.empty_cache() + batch_size //= 2 + accelerator.gradient_accumulation_steps = accelerator.gradient_accumulation_steps * 2 + kwargs["args"].gradient_accumulation_steps = accelerator.gradient_accumulation_steps + else: + raise + + return decorator + + def add_lang_code_to_tokenizer(tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast], lang_code: str): if isinstance(tokenizer, M2M100Tokenizer): lang_token = "__" + lang_code + "__" diff --git a/poetry.lock b/poetry.lock index 860194ff..152f61de 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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type = ["pytest-mypy"] [extras] @@ -5138,6 +5084,6 @@ sentencepiece = ["sentencepiece"] thot = ["sil-thot"] [metadata] -lock-version = "2.1" +lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "f56942f52a117fba35a5f43ee631c386ff95dd270301805558064d0228253624" +content-hash = "2409d4fd7dabc4baa971c5e5d0c65d2d540a973772af6ee50e60cc97ea9d962b" diff --git a/pyproject.toml b/pyproject.toml index 6b0eb09d..b1030b46 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -64,7 +64,7 @@ urllib3 = "<2" sentencepiece = "^0.2.0" sil-thot = "^3.4.6" -transformers = ">=4.38.0,<4.46" +transformers = "4.46.2" datasets = "^2.4.0" sacremoses = "^0.0.53" @@ -89,8 +89,9 @@ decoy = "^2.1.0" pep8-naming = "^0.14.1" [tool.poetry.group.gpu.dependencies] -torch = { version = "2.4.0", markers = "sys_platform == 'win32' or sys_platform == 'linux'" } -accelerate = { version = "^0.26.1", markers = "sys_platform == 'win32' or sys_platform == 'linux'" } +torch = { version = "^2.4", markers = 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