From b8725ef7c392a577ac6648457e3945920e3370c0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Fri, 8 Nov 2024 15:39:45 +0000 Subject: [PATCH 01/14] update transformers --- poetry.lock | 276 ++++++++++++++++++++++++++----------------------- pyproject.toml | 2 +- 2 files changed, 148 insertions(+), 130 deletions(-) diff --git a/poetry.lock b/poetry.lock index 2cfb7dd..472be9d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -378,8 +378,8 @@ files = [ lazy-object-proxy = ">=1.4.0" typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""} wrapt = [ - {version = ">=1.14,<2", markers = "python_version >= \"3.11\""}, {version = ">=1.11,<2", markers = "python_version < \"3.11\""}, + {version = ">=1.14,<2", markers = "python_version >= \"3.11\""}, ] [[package]] @@ -3793,10 +3793,10 @@ files = [ 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"scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"] flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] ftfy = ["ftfy"] @@ -6799,27 +6816,28 @@ natten = ["natten (>=0.14.6,<0.15.0)"] onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"] onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"] optuna = ["optuna"] -quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.4.4)", "urllib3 (<2.0.0)"] +quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "libcst", "rich", "ruff (==0.5.1)", "urllib3 (<2.0.0)"] ray = ["ray[tune] (>=2.7.0)"] retrieval = ["datasets (!=2.5.0)", "faiss-cpu"] -ruff = ["ruff (==0.4.4)"] +ruff = ["ruff (==0.5.1)"] sagemaker = ["sagemaker (>=2.31.0)"] sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"] serving = ["fastapi", "pydantic", "starlette", "uvicorn"] sigopt = ["sigopt"] sklearn = ["scikit-learn"] speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] -testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"] +testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"] tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"] tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] +tiktoken = ["blobfile", "tiktoken"] timm = ["timm (<=0.9.16)"] -tokenizers = ["tokenizers (>=0.19,<0.20)"] -torch = ["accelerate (>=0.21.0)", "torch"] +tokenizers = ["tokenizers (>=0.20,<0.21)"] +torch = ["accelerate (>=0.26.0)", "torch"] torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"] -torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"] -video = ["av (==9.2.0)", "decord (==0.6.0)"] +torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.20,<0.21)", "torch", "tqdm (>=4.27)"] +video = ["av (==9.2.0)"] vision = ["Pillow (>=10.0.1,<=15.0)"] [[package]] @@ -7786,4 +7804,4 @@ gpu = ["faiss-gpu", "vllm"] [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "28fbc02cd29c1a1d09e00a4eb6b5804842e68bded1b2106a4587063d266ec070" +content-hash = "f33937802211e05f000c1a3afae293441b14054d10a752e80b4f4f9fe841aa48" diff --git a/pyproject.toml b/pyproject.toml index 9c2b472..653d705 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,7 +13,7 @@ keywords = ["alignment", "llm", "dpo", "ppo", "rlhf"] [tool.poetry.dependencies] python = "^3.10" -transformers = "4.43.1" +transformers = ">=4.44" peft = "0.8.2" typer ="^0.9.0" wandb ="^0.15.3" From c2a553a90fb9fe4baa55706c453762511dc60326 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Sat, 9 Nov 2024 14:58:48 +0000 Subject: [PATCH 02/14] add liger kernel --- poetry.lock | 3687 ++++++++++++++++++++++++++---------------------- pyproject.toml | 3 +- 2 files changed, 1988 insertions(+), 1702 deletions(-) diff --git a/poetry.lock b/poetry.lock 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["pytest-mypy"] [extras] deepspeed = ["accelerate", "deepspeed"] -gpu = ["faiss-gpu", "vllm"] +gpu = ["faiss-gpu", "liger-kernel-nightly", "vllm"] [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "f33937802211e05f000c1a3afae293441b14054d10a752e80b4f4f9fe841aa48" +content-hash = "cb14ed96b020f8642ba193fde29702162e9d09d6b520b52cf042a7094c0d0fab" diff --git a/pyproject.toml b/pyproject.toml index 653d705..ecb1dda 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -53,6 +53,7 @@ faiss-gpu = { version = "^1.7.2", optional = true } deepspeed = { version = "0.12", optional = true } accelerate = { version = "0.29.0", optional = true } vllm = {version = "0.5.3", optional = true} +liger-kernel-nightly = {version = "^0.4.0.dev20241109021846", optional = true} [tool.poetry.dev-dependencies] @@ -70,7 +71,7 @@ annotated-types = "^0.6.0" [tool.poetry.extras] deepspeed = ["deepspeed", "accelerate"] -gpu = ["faiss-gpu", "xformers", "vllm"] +gpu = ["faiss-gpu", "xformers", "vllm", "liger-kernel-nightly"] [build-system] requires = ["poetry-core>=1.0.0"] From c46598df1b6ed109f33c44bc4918cb54e2eef72f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Mon, 11 Nov 2024 12:31:42 +0300 Subject: [PATCH 03/14] in progress --- turbo_alignment/dataset/chat/chat.py | 2 ++ turbo_alignment/pipelines/inference/chat.py | 2 ++ turbo_alignment/pipelines/train/dpo.py | 33 +++++++++++---------- turbo_alignment/pipelines/train/sft.py | 27 +++++++++-------- turbo_alignment/trainers/dpo.py | 7 +++-- 5 files changed, 39 insertions(+), 32 deletions(-) diff --git a/turbo_alignment/dataset/chat/chat.py b/turbo_alignment/dataset/chat/chat.py index ebdede9..acc8d4c 100755 --- a/turbo_alignment/dataset/chat/chat.py +++ b/turbo_alignment/dataset/chat/chat.py @@ -299,6 +299,8 @@ def _encode( inference=inference, random_cut=random_cut, ) + if len(input_ids) >= 8000: + raise ValueError(f'{len(input_ids)=}, which is >=8000') except ValueError as ex: output.append(None) diff --git a/turbo_alignment/pipelines/inference/chat.py b/turbo_alignment/pipelines/inference/chat.py index 93b975c..aa08fef 100755 --- a/turbo_alignment/pipelines/inference/chat.py +++ b/turbo_alignment/pipelines/inference/chat.py @@ -44,6 +44,8 @@ def _get_single_inference_settings( dtype='bfloat16', tensor_parallel_size=model_inference_settings.tensor_parallel_size, enable_lora=enable_lora, + gpu_memory_utilization=0.9, + disable_custom_all_reduce=True, ) else: diff --git a/turbo_alignment/pipelines/train/dpo.py b/turbo_alignment/pipelines/train/dpo.py index 155f6d3..22cbea3 100755 --- a/turbo_alignment/pipelines/train/dpo.py +++ b/turbo_alignment/pipelines/train/dpo.py @@ -36,22 +36,23 @@ def _get_cherry_pick_callback( tokenizer: PreTrainedTokenizerBase, **kwargs, ) -> ChatCherryPickCallback: - cherry_pick_settings = experiment_settings.cherry_pick_settings - - cherry_pick_datasets = DatasetLoader[InferenceChatDataset](InferenceChatDataset).load_datasets( - cherry_pick_settings.dataset_settings, tokenizer=tokenizer, strategy=DatasetStrategy.INFERENCE - ) - - metrics = [ - Metric.by_name(metric.type)(MetricSettingsRegistry.by_name(metric.type)(**metric.parameters)) - for metric in cherry_pick_settings.metric_settings - ] - - return ChatCherryPickCallback( - cherry_pick_settings=cherry_pick_settings, - datasets=cherry_pick_datasets, - metrics=metrics, - ) + return None + # cherry_pick_settings = experiment_settings.cherry_pick_settings + + # cherry_pick_datasets = DatasetLoader[InferenceChatDataset](InferenceChatDataset).load_datasets( + # cherry_pick_settings.dataset_settings, tokenizer=tokenizer, strategy=DatasetStrategy.INFERENCE + # ) + + # metrics = [ + # Metric.by_name(metric.type)(MetricSettingsRegistry.by_name(metric.type)(**metric.parameters)) + # for metric in cherry_pick_settings.metric_settings + # ] + + # return ChatCherryPickCallback( + # cherry_pick_settings=cherry_pick_settings, + # datasets=cherry_pick_datasets, + # metrics=metrics, + # ) @staticmethod def _get_training_args(experiment_settings: DPOTrainExperimentSettings) -> DPOTrainingArguments: diff --git a/turbo_alignment/pipelines/train/sft.py b/turbo_alignment/pipelines/train/sft.py index a1bddec..800359d 100755 --- a/turbo_alignment/pipelines/train/sft.py +++ b/turbo_alignment/pipelines/train/sft.py @@ -37,22 +37,23 @@ def _get_cherry_pick_callback( tokenizer: PreTrainedTokenizerBase, **kwargs, ) -> ChatCherryPickCallback: - cherry_pick_settings = experiment_settings.cherry_pick_settings + return None + # cherry_pick_settings = experiment_settings.cherry_pick_settings - cherry_pick_datasets = DatasetLoader[InferenceChatDataset](InferenceChatDataset).load_datasets( - cherry_pick_settings.dataset_settings, tokenizer=tokenizer, strategy=DatasetStrategy.INFERENCE - ) + # cherry_pick_datasets = DatasetLoader[InferenceChatDataset](InferenceChatDataset).load_datasets( + # cherry_pick_settings.dataset_settings, tokenizer=tokenizer, strategy=DatasetStrategy.INFERENCE + # ) - metrics = [ - Metric.by_name(metric.type)(MetricSettingsRegistry.by_name(metric.type)(**metric.parameters)) - for metric in cherry_pick_settings.metric_settings - ] + # metrics = [ + # Metric.by_name(metric.type)(MetricSettingsRegistry.by_name(metric.type)(**metric.parameters)) + # for metric in cherry_pick_settings.metric_settings + # ] - return ChatCherryPickCallback( - cherry_pick_settings=cherry_pick_settings, - datasets=cherry_pick_datasets, - metrics=metrics, - ) + # return ChatCherryPickCallback( + # cherry_pick_settings=cherry_pick_settings, + # datasets=cherry_pick_datasets, + # metrics=metrics, + # ) @staticmethod def _get_training_args(experiment_settings: SftTrainExperimentSettings) -> TrainingArguments: diff --git a/turbo_alignment/trainers/dpo.py b/turbo_alignment/trainers/dpo.py index 490e6d1..693baa6 100755 --- a/turbo_alignment/trainers/dpo.py +++ b/turbo_alignment/trainers/dpo.py @@ -749,9 +749,9 @@ def _compute_metrics( metrics[f'{prefix_name}grad_term'] = ( (self.dpo_loss_registry.beta * F.sigmoid(rejected_rewards - chosen_rewards)).detach().cpu().mean().item() ) - metrics[f'{prefix_name}grad_term_std'] = ( - (self.dpo_loss_registry.beta * F.sigmoid(rejected_rewards - chosen_rewards)).detach().cpu().std().item() - ) + # metrics[f'{prefix_name}grad_term_std'] = ( + # (self.dpo_loss_registry.beta * F.sigmoid(rejected_rewards - chosen_rewards)).detach().cpu().std().item() + # ) return metrics @@ -791,6 +791,7 @@ def compute_loss( model: PreTrainedModel | nn.Module, inputs: dict[str, torch.Tensor | Any], return_outputs=False, + num_items_in_batch=None, ) -> torch.Tensor | tuple[torch.Tensor, dict[str, float]]: loss, metrics = self.get_batch_metrics(model, inputs, train_eval='train') From d41b1700936ec7478b202aef01e5265c7c9b6e84 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Mon, 11 Nov 2024 13:49:17 +0000 Subject: [PATCH 04/14] in progress --- turbo_alignment/cli/train.py | 14 ++ turbo_alignment/common/data/io.py | 4 +- turbo_alignment/pipelines/train/__init__.py | 1 + turbo_alignment/pipelines/train/ref_model.py | 170 +++++++++++++++++++ turbo_alignment/trainers/dpo.py | 1 + turbo_alignment/trainers/ref_model.py | 170 +++++++++++++++++++ 6 files changed, 358 insertions(+), 2 deletions(-) create mode 100755 turbo_alignment/pipelines/train/ref_model.py create mode 100755 turbo_alignment/trainers/ref_model.py diff --git a/turbo_alignment/cli/train.py b/turbo_alignment/cli/train.py index da109ff..1be4243 100755 --- a/turbo_alignment/cli/train.py +++ b/turbo_alignment/cli/train.py @@ -33,6 +33,20 @@ def train_dpo_entrypoint( pipelines.TrainDPOStrategy().run(experiment_settings) +@app.command(name='train_ref_model', help='Run DPO pipeline') +def train_ref_entrypoint( + experiment_settings_path: Path = typer.Option( + ..., + '--experiment_settings_path', + exists=True, + help='Path to experiment config file', + ) +) -> None: + print('ok') + experiment_settings = pipeline_settings.DPOTrainExperimentSettings.parse_file(experiment_settings_path) + pipelines.ref_model_DPOStrategy().run(experiment_settings) + + @app.command(name='train_kto', help='Run KTO pipeline') def train_kto_entrypoint( experiment_settings_path: Path = typer.Option( diff --git a/turbo_alignment/common/data/io.py b/turbo_alignment/common/data/io.py index f1c738f..fe62323 100755 --- a/turbo_alignment/common/data/io.py +++ b/turbo_alignment/common/data/io.py @@ -18,8 +18,8 @@ def read_jsonl(path: Path): return [json.loads(line) for line in f] -def write_jsonl(records: Any, path: Path) -> None: - with path.open('w', encoding='utf-8') as f: +def write_jsonl(records: Any, path: Path, mode='w') -> None: + with path.open(mode, encoding='utf-8') as f: for r in records: f.write(json.dumps(r, ensure_ascii=False) + '\n') diff --git a/turbo_alignment/pipelines/train/__init__.py b/turbo_alignment/pipelines/train/__init__.py index ac24318..ae47d96 100755 --- a/turbo_alignment/pipelines/train/__init__.py +++ b/turbo_alignment/pipelines/train/__init__.py @@ -6,3 +6,4 @@ from .rag import TrainRAGStrategy from .rm import TrainRMStrategy from .sft import TrainSFTStrategy +from .ref_model import ref_model_DPOStrategy diff --git a/turbo_alignment/pipelines/train/ref_model.py b/turbo_alignment/pipelines/train/ref_model.py new file mode 100755 index 0000000..f36dd23 --- /dev/null +++ b/turbo_alignment/pipelines/train/ref_model.py @@ -0,0 +1,170 @@ +from typing import Callable + +from torch.utils.data import Dataset +from transformers import PreTrainedModel, PreTrainedTokenizerBase +from transformers.data.data_collator import DataCollatorMixin +from torch.utils.data import ConcatDataset, Dataset + +from turbo_alignment.cherry_picks.chat import ChatCherryPickCallback +from turbo_alignment.common.logging import get_project_logger +from turbo_alignment.common.tf.loaders.model import load_model +from turbo_alignment.common.tf.special_tokens_setter import SpecialTokensSetter +from turbo_alignment.constants import TRAINER_LOGS_FOLDER +from turbo_alignment.dataset.chat.chat import InferenceChatDataset +from turbo_alignment.dataset.loader import DatasetLoader +from turbo_alignment.dataset.pair_preferences import PairPreferenceDataCollator +from turbo_alignment.metrics.metric import Metric +from turbo_alignment.metrics.registry import MetricSettingsRegistry +from turbo_alignment.pipelines.train.base import BaseTrainStrategy +from turbo_alignment.settings.datasets.base import DatasetStrategy +from turbo_alignment.settings.pipelines.train.dpo import DPOTrainExperimentSettings +from turbo_alignment.trainers.dpo import DPOTrainingArguments +from turbo_alignment.trainers.ref_model import ref_model_DPOTrainer + +logger = get_project_logger() + + +class ref_model_DPOStrategy(BaseTrainStrategy[DPOTrainExperimentSettings]): + @staticmethod + def _get_data_collator( + experiment_settings: DPOTrainExperimentSettings, + tokenizer: PreTrainedTokenizerBase, + **kwargs, + ) -> Callable: + return PairPreferenceDataCollator(tokenizer=tokenizer, add_labels=True) + + @staticmethod + def _get_cherry_pick_callback( + experiment_settings: DPOTrainExperimentSettings, + tokenizer: PreTrainedTokenizerBase, + **kwargs, + ) -> ChatCherryPickCallback: + return None + + @staticmethod + def _get_training_args(experiment_settings: DPOTrainExperimentSettings) -> DPOTrainingArguments: + return DPOTrainingArguments( + output_dir=str(experiment_settings.log_path / TRAINER_LOGS_FOLDER), + label_names=[], + remove_unused_columns=False, + **experiment_settings.trainer_settings.dict(), + ) + + @staticmethod + def _get_trainer( + training_args: DPOTrainingArguments, + experiment_settings: DPOTrainExperimentSettings, + model: PreTrainedModel, + tokenizer: PreTrainedTokenizerBase, + train_dataset: Dataset, + val_dataset: Dataset, + data_collator: Callable, + output_path='logits_outputs.jsonl', + ): + + extra_args = {} + if experiment_settings.trainer_settings.use_ref_model: + ref_model = load_model(experiment_settings.model_settings, tokenizer) + for _, param in ref_model.named_parameters(): + param.requires_grad = False + + extra_args['ref_model'] = ref_model + + if experiment_settings.trainer_settings.use_sft_model: + sft_model = load_model(experiment_settings.model_settings, tokenizer) + for _, param in sft_model.named_parameters(): + param.requires_grad = False + + extra_args['sft_model'] = sft_model + + return ref_model_DPOTrainer( + model=ref_model, + args=training_args, + train_dataset=train_dataset, + eval_dataset=val_dataset, + callbacks=[], + data_collator=data_collator, + tokenizer=tokenizer, + output_path='logits_outputs.jsonl', + **extra_args, + ) + + def _dataset_and_collator_sanity_check(self, dataset: Dataset, collator: DataCollatorMixin) -> None: + logger.info(f'Train sample example:\n{dataset[0]}') + + logger.info( + 'Input-w check: {input_ids}'.format( + input_ids=collator([dataset[0], dataset[1]])['inputs_w']['input_ids'][0] + ) + ) + logger.info( + 'Mask-w check: {mask}'.format(mask=collator([dataset[0], dataset[1]])['inputs_w']['attention_mask'][0]) + ) + logger.info( + 'Input-l check: {input_ids}'.format( + input_ids=collator([dataset[0], dataset[1]])['inputs_l']['input_ids'][0] + ) + ) + logger.info( + 'Mask-l check: {mask}'.format(mask=collator([dataset[0], dataset[1]])['inputs_l']['attention_mask'][0]) + ) + + + + def run(self, experiment_settings: DPOTrainExperimentSettings) -> None: + training_args = self._get_training_args(experiment_settings) + + self.tokenizer = self._load_tokenizer(experiment_settings) + + logger.info('Tokenizer is loaded!') + + additional_special_tokens = self._get_additional_special_tokens(experiment_settings) + logger.info(f'Special tokens: {additional_special_tokens}') + special_tokens_setter = SpecialTokensSetter(self.tokenizer, experiment_settings.special_tokens_settings) + special_tokens_setter.set_all() + special_tokens_setter.set_custom_tokens(additional_special_tokens) + + logger.info('Special tokens added!') + + logger.info('Model is loaded!') + + train_dataset: ConcatDataset = ConcatDataset( + datasets=DatasetLoader().load_datasets( + experiment_settings.train_dataset_settings, + tokenizer=self.tokenizer, + strategy=DatasetStrategy.TRAIN, + ) + ) + + val_dataset: ConcatDataset = ConcatDataset( + datasets=DatasetLoader().load_datasets( + experiment_settings.val_dataset_settings, + tokenizer=self.tokenizer, + strategy=DatasetStrategy.TRAIN, + ) + ) + + data_collator = self._get_data_collator(experiment_settings, self.tokenizer) + + self.trainer = self._get_trainer( + training_args, + experiment_settings, + None, + self.tokenizer, + train_dataset, + val_dataset, + data_collator, + output_path='logits_outputs.jsonl' + ) + + if self.trainer.accelerator.is_main_process: + self._dataset_and_collator_sanity_check(train_dataset, data_collator) + + self._add_trainer_callbacks(experiment_settings) + + + print('👀'*15, 'ref_model') + + self.trainer.train() + + # self.trainer.save_model() diff --git a/turbo_alignment/trainers/dpo.py b/turbo_alignment/trainers/dpo.py index 490e6d1..73df9df 100755 --- a/turbo_alignment/trainers/dpo.py +++ b/turbo_alignment/trainers/dpo.py @@ -791,6 +791,7 @@ def compute_loss( model: PreTrainedModel | nn.Module, inputs: dict[str, torch.Tensor | Any], return_outputs=False, + num_items_in_batch=None, ) -> torch.Tensor | tuple[torch.Tensor, dict[str, float]]: loss, metrics = self.get_batch_metrics(model, inputs, train_eval='train') diff --git a/turbo_alignment/trainers/ref_model.py b/turbo_alignment/trainers/ref_model.py new file mode 100755 index 0000000..cfa3471 --- /dev/null +++ b/turbo_alignment/trainers/ref_model.py @@ -0,0 +1,170 @@ +from pathlib import Path +from typing import Any, Callable, Literal + +import gc + +import torch +from torch import nn +from torch.utils.data import Dataset +from transformers import ( + PreTrainedModel, + PreTrainedTokenizerBase, + Trainer, + TrainerCallback, +) + +from turbo_alignment.common.data.io import write_jsonl +from turbo_alignment.common.logging import get_project_logger +from turbo_alignment.constants import DISABLE_LOSS_LABEL +from turbo_alignment.trainers.utils import ( + concatenated_inputs, + prepare_model, +) + +logger = get_project_logger() + + + +class ref_model_DPOTrainer(Trainer): + """ + Inspired by https://github.com/huggingface/trl/blob/main/trl/trainer/dpo_trainer.py + + """ + + def __init__( + self, + model: PreTrainedModel | nn.Module, + data_collator: Callable, + args, + train_dataset: Dataset, + eval_dataset: Dataset, + ref_model: PreTrainedModel | nn.Module | None = None, + sft_model: PreTrainedModel | nn.Module | None = None, + tokenizer: PreTrainedTokenizerBase | None = None, + callbacks: list[TrainerCallback] | None = None, + **kwargs, + ): + self.data_collator = data_collator + + self.output_path = kwargs.pop('output_path') + + self.average_log_prob = args.average_log_prob + + super().__init__( + model=model, + args=args, + data_collator=data_collator, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + tokenizer=tokenizer, + callbacks=callbacks, + **kwargs, + ) + self.model = prepare_model(ref_model, self.accelerator, self.is_deepspeed_enabled) + + + def _get_batch_logps( + self, + logits: torch.Tensor, + labels: torch.Tensor, + average_log_prob: bool = False, + ) -> torch.Tensor: + if logits.shape[:-1] != labels.shape: + raise ValueError('Logits (batch and sequence length dim) and labels must have the same shape.') + + labels = labels[:, 1:].clone() + logits = logits[:, :-1, :] + loss_mask = labels != DISABLE_LOSS_LABEL + + labels[labels == DISABLE_LOSS_LABEL] = 0 + + per_token_logps = torch.gather(logits.log_softmax(-1), dim=2, index=labels.unsqueeze(2)).squeeze(2) + + if average_log_prob: + return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1) + return (per_token_logps * loss_mask).sum(-1) + + def concatenated_forward( + self, model: nn.Module, batch: dict[str, Any] + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor | None]: + concatenated_batch = concatenated_inputs(batch, device=self.accelerator.device) + + precomputed_margins: torch.Tensor | None = concatenated_batch.pop('margin', None) + + all_logits = model( + concatenated_batch['input_ids'], + attention_mask=concatenated_batch['attention_mask'], + ).logits.to(torch.float32) + all_logps = self._get_batch_logps( + all_logits, + concatenated_batch['labels'], + average_log_prob=self.average_log_prob, + ) + chosen_idxs = batch['inputs_w']['input_ids'].shape[0] + rejected_idx = batch['inputs_l']['input_ids'].shape[0] + + chosen_logps = all_logps[:chosen_idxs] + rejected_logps = all_logps[chosen_idxs : chosen_idxs + rejected_idx] + + chosen_logits = all_logits[:chosen_idxs] + rejected_logits = all_logits[chosen_idxs:] + return chosen_logps, rejected_logps, chosen_logits, rejected_logits, precomputed_margins + + def _get_logps(self, model: nn.Module | None, batch: dict[str, Any]) -> tuple[torch.Tensor, torch.Tensor]: + with torch.no_grad(): + if model is not None: + (chosen_logps, rejected_logps, *_) = self.concatenated_forward(model, batch) + else: + with self.accelerator.unwrap_model(self.model).disable_adapter(): + ( + chosen_logps, + rejected_logps, + *_, + ) = self.concatenated_forward(self.model, batch) + + return chosen_logps, rejected_logps + + def get_batch_metrics( + self, + model: nn.Module, + batch: dict[str, Any], + train_eval: Literal['train', 'eval'] = 'train', + ) -> tuple[torch.Tensor, dict[str, float]]: + + reference_chosen_logps, reference_rejected_logps = self._get_logps(self.model, batch) + return reference_chosen_logps, reference_rejected_logps + + def compute_loss( + self, + model: PreTrainedModel | nn.Module, + inputs: dict[str, torch.Tensor | Any], + return_outputs=False, + num_items_in_batch=None, + ) -> torch.Tensor | tuple[torch.Tensor, dict[str, float]]: + reference_chosen_logps, reference_rejected_logps = self.get_batch_metrics(self.model, inputs, train_eval='train') + + if self.accelerator.is_main_process: + data = { + 'inputs_w': inputs['inputs_w']['input_ids'].tolist(), + 'inputs_l': inputs['inputs_l']['input_ids'].tolist(), + 'chosen_logps': reference_chosen_logps.tolist(), + 'rejected_logps': reference_rejected_logps.tolist(), + } + write_jsonl([data], Path(self.output_path), 'a+') + + gc.collect() + torch.cuda.empty_cache() + return torch.tensor([0.0], requires_grad=True) + + def prediction_step( + self, + model: PreTrainedModel | nn.Module, + inputs: dict[str, torch.Tensor | Any], + prediction_loss_only: bool, + ignore_keys: list[str] | None = None, + ) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor | None]: + + if prediction_loss_only: + return torch.tensor([0.0]).detach(), None, None + + return torch.tensor([0.0]).detach(), torch.tensor([0.0]), torch.tensor([0.0]) From 10738ecce34e15fb728eac5b30695e794035f68f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Mon, 11 Nov 2024 15:20:34 +0000 Subject: [PATCH 05/14] in progress --- .../dataset/pair_preferences/collators.py | 6 ++++ .../dataset/pair_preferences/models.py | 2 ++ .../pair_preferences/pair_preference.py | 4 ++- turbo_alignment/metrics/distinctness.py | 1 + turbo_alignment/metrics/registry.py | 1 + turbo_alignment/pipelines/train/__init__.py | 2 +- turbo_alignment/pipelines/train/ref_model.py | 11 ++----- turbo_alignment/trainers/dpo.py | 33 ++++++++++++------- turbo_alignment/trainers/ref_model.py | 16 +++------ 9 files changed, 43 insertions(+), 33 deletions(-) diff --git a/turbo_alignment/dataset/pair_preferences/collators.py b/turbo_alignment/dataset/pair_preferences/collators.py index d19b16c..2763cb7 100755 --- a/turbo_alignment/dataset/pair_preferences/collators.py +++ b/turbo_alignment/dataset/pair_preferences/collators.py @@ -52,4 +52,10 @@ def __call__(self, examples: list[dict[str, dict[str, Any]]]) -> dict[str, Any]: if 'precomputed_margin' in examples[0] and examples[0]['precomputed_margin'] is not None: batch['precomputed_margin'] = torch.tensor([ex['precomputed_margin'] for ex in examples]) + if 'ref_chosen_logps' in examples[0] and examples[0]['ref_chosen_logps'] is not None: + batch['ref_chosen_logps'] = torch.tensor([ex['ref_chosen_logps'] for ex in examples]) + + if 'ref_rejected_logps' in examples[0] and examples[0]['ref_rejected_logps'] is not None: + batch['ref_rejected_logps'] = torch.tensor([ex['ref_rejected_logps'] for ex in examples]) + return batch diff --git a/turbo_alignment/dataset/pair_preferences/models.py b/turbo_alignment/dataset/pair_preferences/models.py index 4cb099b..efafef7 100755 --- a/turbo_alignment/dataset/pair_preferences/models.py +++ b/turbo_alignment/dataset/pair_preferences/models.py @@ -12,3 +12,5 @@ class PairPreferenceRecord(DatasetRecord): answer_w: ChatMessage answer_l: ChatMessage precomputed_margin: float | None = None + ref_chosen_logps: float | None = None + ref_rejected_logps: float | None = None diff --git a/turbo_alignment/dataset/pair_preferences/pair_preference.py b/turbo_alignment/dataset/pair_preferences/pair_preference.py index fc6ff70..8adb80f 100755 --- a/turbo_alignment/dataset/pair_preferences/pair_preference.py +++ b/turbo_alignment/dataset/pair_preferences/pair_preference.py @@ -72,7 +72,7 @@ def convert_records(self, records: list[PairPreferenceRecord]) -> list[dict[str, if not (chosen_record and rejected_record): continue - ignore_keys = ['precomputed_margin'] + ignore_keys = ['precomputed_margin', 'ref_chosen_logps', 'ref_rejected_logps'] if not self._add_labels: ignore_keys.append('labels') @@ -85,6 +85,8 @@ def convert_records(self, records: list[PairPreferenceRecord]) -> list[dict[str, 'inputs_w': chosen_tokens, 'inputs_l': rejected_tokens, 'precomputed_margin': record.precomputed_margin, + 'ref_chosen_logps': record.ref_chosen_logps, + 'ref_rejected_logps': record.ref_rejected_logps, } ) diff --git a/turbo_alignment/metrics/distinctness.py b/turbo_alignment/metrics/distinctness.py index a1ff2bd..4f10de2 100755 --- a/turbo_alignment/metrics/distinctness.py +++ b/turbo_alignment/metrics/distinctness.py @@ -1,4 +1,5 @@ from collections import defaultdict + from transformers.tokenization_utils_base import PreTrainedTokenizerBase from turbo_alignment.metrics.metric import Metric diff --git a/turbo_alignment/metrics/registry.py b/turbo_alignment/metrics/registry.py index d9f5abd..c99195c 100755 --- a/turbo_alignment/metrics/registry.py +++ b/turbo_alignment/metrics/registry.py @@ -1,4 +1,5 @@ from enum import Enum + from pydantic import field_validator from turbo_alignment.common.registry import Registrable diff --git a/turbo_alignment/pipelines/train/__init__.py b/turbo_alignment/pipelines/train/__init__.py index ae47d96..050e80e 100755 --- a/turbo_alignment/pipelines/train/__init__.py +++ b/turbo_alignment/pipelines/train/__init__.py @@ -4,6 +4,6 @@ from .kto import TrainKTOStrategy from .multimodal import TrainMultimodalStrategy from .rag import TrainRAGStrategy +from .ref_model import ref_model_DPOStrategy from .rm import TrainRMStrategy from .sft import TrainSFTStrategy -from .ref_model import ref_model_DPOStrategy diff --git a/turbo_alignment/pipelines/train/ref_model.py b/turbo_alignment/pipelines/train/ref_model.py index f36dd23..c92f9e2 100755 --- a/turbo_alignment/pipelines/train/ref_model.py +++ b/turbo_alignment/pipelines/train/ref_model.py @@ -1,9 +1,8 @@ from typing import Callable -from torch.utils.data import Dataset +from torch.utils.data import ConcatDataset, Dataset from transformers import PreTrainedModel, PreTrainedTokenizerBase from transformers.data.data_collator import DataCollatorMixin -from torch.utils.data import ConcatDataset, Dataset from turbo_alignment.cherry_picks.chat import ChatCherryPickCallback from turbo_alignment.common.logging import get_project_logger @@ -61,7 +60,6 @@ def _get_trainer( data_collator: Callable, output_path='logits_outputs.jsonl', ): - extra_args = {} if experiment_settings.trainer_settings.use_ref_model: ref_model = load_model(experiment_settings.model_settings, tokenizer) @@ -109,8 +107,6 @@ def _dataset_and_collator_sanity_check(self, dataset: Dataset, collator: DataCol 'Mask-l check: {mask}'.format(mask=collator([dataset[0], dataset[1]])['inputs_l']['attention_mask'][0]) ) - - def run(self, experiment_settings: DPOTrainExperimentSettings) -> None: training_args = self._get_training_args(experiment_settings) @@ -154,7 +150,7 @@ def run(self, experiment_settings: DPOTrainExperimentSettings) -> None: train_dataset, val_dataset, data_collator, - output_path='logits_outputs.jsonl' + output_path='logits_outputs.jsonl', ) if self.trainer.accelerator.is_main_process: @@ -162,8 +158,7 @@ def run(self, experiment_settings: DPOTrainExperimentSettings) -> None: self._add_trainer_callbacks(experiment_settings) - - print('👀'*15, 'ref_model') + print('👀' * 15, 'ref_model') self.trainer.train() diff --git a/turbo_alignment/trainers/dpo.py b/turbo_alignment/trainers/dpo.py index 693baa6..249c3f0 100755 --- a/turbo_alignment/trainers/dpo.py +++ b/turbo_alignment/trainers/dpo.py @@ -26,9 +26,9 @@ from turbo_alignment.settings.pipelines.train.dpo import ( APODownLossSettings, APOZeroLossSettings, + ASFTLossSettings, CPOLossSettings, DPOLossesType, - ASFTLossSettings, HingeLossSettings, IPOLossSettings, KTOLossSettings, @@ -575,12 +575,18 @@ def _get_batch_logps( return (per_token_logps * loss_mask).sum(-1) def concatenated_forward( - self, model: nn.Module, batch: dict[str, Any] + self, + model: nn.Module, + batch: dict[str, Any], + get_from_dataset=False, ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor | None]: concatenated_batch = concatenated_inputs(batch, device=self.accelerator.device) precomputed_margins: torch.Tensor | None = concatenated_batch.pop('margin', None) + if get_from_dataset: + return concatenated_batch.pop('ref_chosen_logps'), concatenated_batch.pop('ref_rejected_logps') + all_logits = model( concatenated_batch['input_ids'], attention_mask=concatenated_batch['attention_mask'], @@ -601,16 +607,19 @@ def concatenated_forward( return chosen_logps, rejected_logps, chosen_logits, rejected_logits, precomputed_margins def _get_logps(self, model: nn.Module | None, batch: dict[str, Any]) -> tuple[torch.Tensor, torch.Tensor]: - with torch.no_grad(): - if model is not None: - (chosen_logps, rejected_logps, *_) = self.concatenated_forward(model, batch) - else: - with self.accelerator.unwrap_model(self.model).disable_adapter(): - ( - chosen_logps, - rejected_logps, - *_, - ) = self.concatenated_forward(self.model, batch) + if self.ref_model is None: + chosen_logps, rejected_logps = self.concatenated_forward(model, batch, get_from_dataset=True) + else: + with torch.no_grad(): + if model is not None: + (chosen_logps, rejected_logps, *_) = self.concatenated_forward(model, batch) + else: + with self.accelerator.unwrap_model(self.model).disable_adapter(): + ( + chosen_logps, + rejected_logps, + *_, + ) = self.concatenated_forward(self.model, batch) return chosen_logps, rejected_logps diff --git a/turbo_alignment/trainers/ref_model.py b/turbo_alignment/trainers/ref_model.py index cfa3471..4dd5d93 100755 --- a/turbo_alignment/trainers/ref_model.py +++ b/turbo_alignment/trainers/ref_model.py @@ -1,8 +1,7 @@ +import gc from pathlib import Path from typing import Any, Callable, Literal -import gc - import torch from torch import nn from torch.utils.data import Dataset @@ -16,15 +15,11 @@ from turbo_alignment.common.data.io import write_jsonl from turbo_alignment.common.logging import get_project_logger from turbo_alignment.constants import DISABLE_LOSS_LABEL -from turbo_alignment.trainers.utils import ( - concatenated_inputs, - prepare_model, -) +from turbo_alignment.trainers.utils import concatenated_inputs, prepare_model logger = get_project_logger() - class ref_model_DPOTrainer(Trainer): """ Inspired by https://github.com/huggingface/trl/blob/main/trl/trainer/dpo_trainer.py @@ -62,7 +57,6 @@ def __init__( ) self.model = prepare_model(ref_model, self.accelerator, self.is_deepspeed_enabled) - def _get_batch_logps( self, logits: torch.Tensor, @@ -130,7 +124,6 @@ def get_batch_metrics( batch: dict[str, Any], train_eval: Literal['train', 'eval'] = 'train', ) -> tuple[torch.Tensor, dict[str, float]]: - reference_chosen_logps, reference_rejected_logps = self._get_logps(self.model, batch) return reference_chosen_logps, reference_rejected_logps @@ -141,7 +134,9 @@ def compute_loss( return_outputs=False, num_items_in_batch=None, ) -> torch.Tensor | tuple[torch.Tensor, dict[str, float]]: - reference_chosen_logps, reference_rejected_logps = self.get_batch_metrics(self.model, inputs, train_eval='train') + reference_chosen_logps, reference_rejected_logps = self.get_batch_metrics( + self.model, inputs, train_eval='train' + ) if self.accelerator.is_main_process: data = { @@ -163,7 +158,6 @@ def prediction_step( prediction_loss_only: bool, ignore_keys: list[str] | None = None, ) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor | None]: - if prediction_loss_only: return torch.tensor([0.0]).detach(), None, None From beaab562aa85af2a3833279cb11922ec18664eb9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Mon, 11 Nov 2024 16:42:26 +0000 Subject: [PATCH 06/14] in progress --- turbo_alignment/trainers/ref_model.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/turbo_alignment/trainers/ref_model.py b/turbo_alignment/trainers/ref_model.py index 4dd5d93..e441b75 100755 --- a/turbo_alignment/trainers/ref_model.py +++ b/turbo_alignment/trainers/ref_model.py @@ -149,7 +149,7 @@ def compute_loss( gc.collect() torch.cuda.empty_cache() - return torch.tensor([0.0], requires_grad=True) + return torch.tensor([0.0], requires_grad=True).to(self.accelerator.device) def prediction_step( self, @@ -159,6 +159,10 @@ def prediction_step( ignore_keys: list[str] | None = None, ) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor | None]: if prediction_loss_only: - return torch.tensor([0.0]).detach(), None, None + return torch.tensor([0.0]).to(self.accelerator.device).detach(), None, None - return torch.tensor([0.0]).detach(), torch.tensor([0.0]), torch.tensor([0.0]) + return ( + torch.tensor([0.0]).to(self.accelerator.device).detach(), + torch.tensor([0.0]).to(self.accelerator.device), + torch.tensor([0.0]).to(self.accelerator.device), + ) From 582c427ced38bc2b212bc7a56a40c63b2c5916eb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Tue, 19 Nov 2024 10:16:58 +0000 Subject: [PATCH 07/14] in progress --- turbo_alignment/cli/inference.py | 11 ++ .../pipelines/inference/__init__.py | 1 + .../pipelines/inference/metrics.py | 176 ++++++++++++++++++ .../settings/pipelines/inference/__init__.py | 1 + .../settings/pipelines/inference/metrics.py | 18 ++ 5 files changed, 207 insertions(+) create mode 100644 turbo_alignment/pipelines/inference/metrics.py create mode 100644 turbo_alignment/settings/pipelines/inference/metrics.py diff --git a/turbo_alignment/cli/inference.py b/turbo_alignment/cli/inference.py index 5bb7868..4968a36 100755 --- a/turbo_alignment/cli/inference.py +++ b/turbo_alignment/cli/inference.py @@ -10,6 +10,7 @@ from turbo_alignment.settings.pipelines.inference.chat import ( ChatInferenceExperimentSettings, ) +from turbo_alignment.settings.pipelines.inference.metrics import MetricsSettings from turbo_alignment.settings.pipelines.inference.multimodal import ( MultimodalInferenceExperimentSettings, ) @@ -66,3 +67,13 @@ def infer_rag_entrypoint( ) -> None: inference_settings = RAGInferenceExperimentSettings.parse_file(inference_settings_path) pipelines.RAGInferenceStrategy().run(inference_settings) + + +@app.command(name='run_metrics', help='Run metrics evaluation') +def infer_metrics( + inference_settings_path: Path = typer.Option( + ..., '--inference_settings_path', exists=True, help='Path to inference config file' + ) +): + inference_settings = MetricsSettings.parse_file(inference_settings_path) + pipelines.MetricsInferenceStrategy().run(inference_settings) diff --git a/turbo_alignment/pipelines/inference/__init__.py b/turbo_alignment/pipelines/inference/__init__.py index c2408b0..45fc3c1 100755 --- a/turbo_alignment/pipelines/inference/__init__.py +++ b/turbo_alignment/pipelines/inference/__init__.py @@ -1,5 +1,6 @@ from .chat import ChatInferenceStrategy from .classification import ClassificationInferenceStrategy +from .metrics import MetricsInferenceStrategy from .multimodal import MultimodalInferenceStrategy from .rag import RAGInferenceStrategy from .rm import RMInferenceStrategy diff --git a/turbo_alignment/pipelines/inference/metrics.py b/turbo_alignment/pipelines/inference/metrics.py new file mode 100644 index 0000000..e4525b4 --- /dev/null +++ b/turbo_alignment/pipelines/inference/metrics.py @@ -0,0 +1,176 @@ +import gc +import json +import math +import os +from typing import Dict, List + +import loguru +import torch +import vllm +from accelerate import Accelerator +from accelerate.utils import set_seed +from transformers import AutoModelForSequenceClassification, AutoTokenizer + +from turbo_alignment.common.tf.loaders import load_model, load_tokenizer +from turbo_alignment.dataset.base import BaseDataset +from turbo_alignment.dataset.chat import ChatDatasetRecord +from turbo_alignment.dataset.loader import DatasetLoader +from turbo_alignment.generators.vllm_chat import VLLMChatGenerator +from turbo_alignment.metrics import Metric, MetricSettingsRegistry +from turbo_alignment.metrics.registry import KLType +from turbo_alignment.metrics.utils import get_logits +from turbo_alignment.pipelines.base import BaseStrategy +from turbo_alignment.settings.datasets import DatasetStrategy +from turbo_alignment.settings.pipelines.inference.metrics import MetricsSettings + + +class MetricsInferenceStrategy(BaseStrategy): + def run(self, experiment_settings: MetricsSettings) -> None: + accelerator = Accelerator() + set_seed(seed=0, device_specific=False) + experiment_settings.save_path.mkdir(parents=True, exist_ok=True) + + metrics = [ + Metric.by_name(metric.type)(MetricSettingsRegistry.by_name(metric.type)(**metric.parameters)) + for metric in experiment_settings.metric_settings + ] + + model_inference_settings = experiment_settings.inference_settings + tokenizer = load_tokenizer( + model_inference_settings.tokenizer_settings, + model_inference_settings.model_settings, + ) + + model = load_model(model_inference_settings.model_settings, tokenizer) + model = accelerator.prepare_model(model, device_placement=True, evaluation_mode=True) + + sft = load_model(model_inference_settings.model_settings, tokenizer) # fix + sft = accelerator.prepare_model(sft, device_placement=True, evaluation_mode=True) + + vllm_model = vllm.LLM( + model=model_inference_settings.model_settings.model_path.absolute().as_posix(), + dtype='bfloat16', + gpu_memory_utilization=0.3, + tensor_parallel_size=model_inference_settings.tensor_parallel_size, + # max_model_len=model_inference_settings.max_model_len, + ) + + dataset = DatasetLoader[BaseDataset](BaseDataset).load_datasets( + experiment_settings.dataset_settings, + tokenizer=tokenizer, + strategy=DatasetStrategy.INFERENCE, + )[0] + + generation_settings = experiment_settings.inference_settings.generation_settings[0] + + batch_size = model_inference_settings.batch + generator = VLLMChatGenerator( + model=vllm_model, + tokenizer=tokenizer, + transformers_settings=generation_settings.transformers_settings, + custom_generation_settings=generation_settings.custom_settings, + batch=batch_size, + return_logits=True, + ) + + input_records = [dataset[idx] for idx in range(len(dataset))] + records_batches = [ + input_records[i * batch_size : (i + 1) * batch_size] for i in range(math.ceil(len(dataset) / batch_size)) + ] + + original_records_batches: list[list[ChatDatasetRecord]] = [ + [dataset.get_original_record_by_id(r['id']) for r in batch] for batch in records_batches + ] + + num_gens = generation_settings.transformers_settings.num_return_sequences + + metrics_kwargs = {} + + with open(os.path.join(experiment_settings.save_path, 'metrics.jsonl'), 'w') as f: + for i, (records_batch, original_records_batch) in enumerate( + zip(records_batches, original_records_batches) + ): + loguru.logger.info('batch {}/{}', i + 1, len(records_batches)) + + generation_outputs = generator._generate_from_batch( + records_batch, + original_records_batch, + dataset.source.name, + ) + + string_answers = [[answer.content for answer in g.answers] for g in generation_outputs] + string_labels = [[g.messages[-1].content] * len(g.answers) for g in generation_outputs] + + flattened_answers = [answer for g in generation_outputs for answer in g.answers] + answer_tokens_ids = [answer.answer_token_ids.cpu() for answer in flattened_answers] + input_tokens_ids = [answer.input_token_ids.cpu() for answer in flattened_answers] + + logits = get_logits(input_tokens_ids, answer_tokens_ids, model) + sft_logits = get_logits(input_tokens_ids, answer_tokens_ids, sft) + metrics_kwargs[KLType.SFT_MODEL] = sft_logits + + batch_metrics = [{} for _ in range(batch_size)] + + for i in range(len(batch_metrics)): + batch_metrics[i]['context'] = [] + batch_metrics[i]['label'] = [] + for idx in range(len(original_records_batch[i].messages[:-1])): + batch_metrics[i]['context'].append( + { + 'content': original_records_batch[i].messages[idx].content, + 'role': original_records_batch[i].messages[idx].role, + } + ) + + batch_metrics[i]['label'] = [ + { + 'content': original_records_batch[i].messages[-1].content, + 'role': original_records_batch[i].messages[-1].role, + } + ] + + batch_metrics[i]['answers'] = [ + {'id': idx, 'content': string_answers[i][idx]} for idx in range(len(string_answers[i])) + ] + + for metric in metrics: + metric_results = metric.compute( + dataset=dataset, + references=string_labels, + predictions=string_answers, + accelerator=accelerator, + tokenizer=tokenizer, + dataset_name=dataset.source.name, + answer_tokens_ids=answer_tokens_ids, + input_token_ids=input_tokens_ids, + logits=logits, + metrics_kwargs=metrics_kwargs, + )[0].element_wise_scores + for scores in metric_results: + metric_name = scores.label.split('@@', 1)[-1] + metric_values = scores.values + if metric_name == 'reward': + metric_values = [m[0] for m in metric_values] + if metric_name in ['reward', 'kl_with_sft', 'length', 'perplexity']: + metric_values = [ + metric_values[i * num_gens : (i + 1) * num_gens] + for i in range(len(metric_values) // num_gens) + ] + + for i in range(len(metric_values)): + batch_metrics[i][metric_name] = metric_values[i] + + for item in batch_metrics: + if len(item) == 0: + continue + json.dump(item, f) + f.write('\n') + + del batch_metrics + del generation_outputs + del flattened_answers + del string_answers + del string_labels + del logits + del sft_logits + gc.collect() diff --git a/turbo_alignment/settings/pipelines/inference/__init__.py b/turbo_alignment/settings/pipelines/inference/__init__.py index 61d35d5..4290757 100755 --- a/turbo_alignment/settings/pipelines/inference/__init__.py +++ b/turbo_alignment/settings/pipelines/inference/__init__.py @@ -1,4 +1,5 @@ from .base import InferenceExperimentSettings from .chat import ChatInferenceExperimentSettings +from .metrics import MetricsSettings from .multimodal import MultimodalInferenceExperimentSettings from .rag import RAGInferenceExperimentSettings diff --git a/turbo_alignment/settings/pipelines/inference/metrics.py b/turbo_alignment/settings/pipelines/inference/metrics.py new file mode 100644 index 0000000..5d17c8f --- /dev/null +++ b/turbo_alignment/settings/pipelines/inference/metrics.py @@ -0,0 +1,18 @@ +from pathlib import Path + +from turbo_alignment.settings.base import ExtraFieldsNotAllowedBaseModel +from turbo_alignment.settings.metric import MetricSettings +from turbo_alignment.settings.pipelines.inference.base import ( + INFERENCE_DATASETS_SETTINGS, +) +from turbo_alignment.settings.pipelines.inference.chat import ( + ChatSingleModelInferenceSettings, +) + + +class MetricsSettings(ExtraFieldsNotAllowedBaseModel): + inference_settings: ChatSingleModelInferenceSettings + metric_settings: list[MetricSettings] = [] + + dataset_settings: INFERENCE_DATASETS_SETTINGS + save_path: Path From 4eae9511a93d8a092e4ce4428559a61cc70d7ee5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Tue, 26 Nov 2024 17:10:42 +0000 Subject: [PATCH 08/14] some fixes --- .../common/tf/special_tokens_setter.py | 9 +++++---- turbo_alignment/dataset/chat/chat.py | 4 ++-- turbo_alignment/generators/rm.py | 18 ++++++------------ .../settings/tf/special_tokens_setter.py | 2 +- turbo_alignment/trainers/rm.py | 8 +++++++- 5 files changed, 21 insertions(+), 20 deletions(-) diff --git a/turbo_alignment/common/tf/special_tokens_setter.py b/turbo_alignment/common/tf/special_tokens_setter.py index b1aaeb6..040bc34 100755 --- a/turbo_alignment/common/tf/special_tokens_setter.py +++ b/turbo_alignment/common/tf/special_tokens_setter.py @@ -13,12 +13,12 @@ def __init__(self, tokenizer: PreTrainedTokenizerBase, special_tokens_settings: self._special_tokens_settings = special_tokens_settings self._special_tokens_already_set: bool = False - def setBOS(self, bos_token: str) -> None: + def setBOS(self, bos_token: str | None) -> None: if self._tokenizer.bos_token_id is None: logger.info('Model does not have bos_token_id') self._tokenizer.add_special_tokens(special_tokens_dict={'bos_token': bos_token}) - assert self._tokenizer.bos_token_id is not None - logger.info(f'Created bos_token_id = {self._tokenizer.bos_token_id}') + if self._tokenizer.bos_token_id is not None: + logger.info(f'Created bos_token_id = {self._tokenizer.bos_token_id}') else: logger.info(f'Model has bos_token_id = {self._tokenizer.bos_token_id}') @@ -94,9 +94,10 @@ def set_custom_tokens(self, tokens: list[str]) -> None: assert added_tokens == len(tokens) def setup_model_config(self, model: PreTrainedModel) -> None: - model.config.bos_token_id = self._tokenizer.bos_token_id model.config.eos_token_id = self._tokenizer.eos_token_id + if self._tokenizer.bos_token_id is not None: + model.config.bos_token_id = self._tokenizer.bos_token_id if self._tokenizer.pad_token_id is not None: model.config.pad_token_id = self._tokenizer.pad_token_id if self._tokenizer.sep_token_id is not None: diff --git a/turbo_alignment/dataset/chat/chat.py b/turbo_alignment/dataset/chat/chat.py index 8267486..064a653 100755 --- a/turbo_alignment/dataset/chat/chat.py +++ b/turbo_alignment/dataset/chat/chat.py @@ -247,8 +247,8 @@ def _truncate_and_merge( input_ids = input_ids[cut_index:] labels = labels[-len(input_ids) :] - input_ids = np.concatenate((np.array([self.tokenizer.bos_token_id]), input_ids)) - labels = np.concatenate((np.array([DISABLE_LOSS_LABEL]), labels)) + # input_ids = np.concatenate((np.array([self.tokenizer.bos_token_id]), input_ids)) + # labels = np.concatenate((np.array([DISABLE_LOSS_LABEL]), labels)) return input_ids, labels, conversation.get_prompt_repr(left_bound, right_bound) diff --git a/turbo_alignment/generators/rm.py b/turbo_alignment/generators/rm.py index ce9a9c8..ca0f38d 100755 --- a/turbo_alignment/generators/rm.py +++ b/turbo_alignment/generators/rm.py @@ -59,21 +59,15 @@ def _generate_from_batch( if len(merged_inputs) == 0: return [] + assert self._micro_batch == 1, 'batch size should be 1' + rewards = [] with torch.no_grad(): - input_ids = nn.utils.rnn.pad_sequence( - [item['input_ids'] for item in merged_inputs], - padding_value=self._tokenizer.pad_token_id, - batch_first=True, - ) - attn_mask = nn.utils.rnn.pad_sequence( - [item['attention_mask'] for item in merged_inputs], - padding_value=0, - batch_first=True, - ) + input_ids = [item['input_ids'].unsqueeze(0) for item in merged_inputs] + attn_mask = [item['attention_mask'].unsqueeze(0) for item in merged_inputs] for i in range(0, len(input_ids), self._micro_batch): - input_ids_batch = input_ids[i : i + self._micro_batch].to(self.device) - attn_mask_batch = attn_mask[i : i + self._micro_batch].to(self.device) + input_ids_batch = input_ids[i].to(self.device) + attn_mask_batch = attn_mask[i].to(self.device) rewards.extend(self._model(input_ids=input_ids_batch, attention_mask=attn_mask_batch).logits.cpu()) rewards = torch.cat(rewards, dim=0) diff --git a/turbo_alignment/settings/tf/special_tokens_setter.py b/turbo_alignment/settings/tf/special_tokens_setter.py index fc6ab7e..ae5f506 100755 --- a/turbo_alignment/settings/tf/special_tokens_setter.py +++ b/turbo_alignment/settings/tf/special_tokens_setter.py @@ -2,7 +2,7 @@ class SpecialTokensSettings(ExtraFieldsNotAllowedBaseModel): - bos_token: str + bos_token: str | None eos_token: str pad_token: str | None = None unk_token: str | None = None diff --git a/turbo_alignment/trainers/rm.py b/turbo_alignment/trainers/rm.py index 46d9326..ccaf996 100755 --- a/turbo_alignment/trainers/rm.py +++ b/turbo_alignment/trainers/rm.py @@ -31,7 +31,13 @@ def concatenated_forward(self, model: nn.Module, batch: dict[str, Any]) -> tuple return chosen_rewards, rejected_rewards - def compute_loss(self, model, inputs, return_outputs=False) -> tuple[torch.Tensor, dict[str, Any]] | torch.Tensor: + def compute_loss( + self, + model, + inputs, + return_outputs=False, + num_items_in_batch=None, + ) -> tuple[torch.Tensor, dict[str, Any]] | torch.Tensor: rewards_w, rewards_l = self.concatenated_forward(model, inputs) loss = -torch.nn.functional.logsigmoid(rewards_w - rewards_l).mean() From c036b7597f316d3f18fb75239a16ca2641daaf9d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Tue, 26 Nov 2024 18:02:45 +0000 Subject: [PATCH 09/14] fix --- turbo_alignment/common/tf/special_tokens_setter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/turbo_alignment/common/tf/special_tokens_setter.py b/turbo_alignment/common/tf/special_tokens_setter.py index 040bc34..9fac385 100755 --- a/turbo_alignment/common/tf/special_tokens_setter.py +++ b/turbo_alignment/common/tf/special_tokens_setter.py @@ -16,8 +16,8 @@ def __init__(self, tokenizer: PreTrainedTokenizerBase, special_tokens_settings: def setBOS(self, bos_token: str | None) -> None: if self._tokenizer.bos_token_id is None: logger.info('Model does not have bos_token_id') - self._tokenizer.add_special_tokens(special_tokens_dict={'bos_token': bos_token}) if self._tokenizer.bos_token_id is not None: + self._tokenizer.add_special_tokens(special_tokens_dict={'bos_token': bos_token}) logger.info(f'Created bos_token_id = {self._tokenizer.bos_token_id}') else: logger.info(f'Model has bos_token_id = {self._tokenizer.bos_token_id}') From 74ac8a9187d887a7c812f4a76f2faee0087a6019 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Tue, 26 Nov 2024 18:03:21 +0000 Subject: [PATCH 10/14] fix --- turbo_alignment/common/tf/special_tokens_setter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/turbo_alignment/common/tf/special_tokens_setter.py b/turbo_alignment/common/tf/special_tokens_setter.py index 9fac385..bc42b26 100755 --- a/turbo_alignment/common/tf/special_tokens_setter.py +++ b/turbo_alignment/common/tf/special_tokens_setter.py @@ -16,7 +16,7 @@ def __init__(self, tokenizer: PreTrainedTokenizerBase, special_tokens_settings: def setBOS(self, bos_token: str | None) -> None: if self._tokenizer.bos_token_id is None: logger.info('Model does not have bos_token_id') - if self._tokenizer.bos_token_id is not None: + if bos_token is not None: self._tokenizer.add_special_tokens(special_tokens_dict={'bos_token': bos_token}) logger.info(f'Created bos_token_id = {self._tokenizer.bos_token_id}') else: From e0acd61b6e9faec52150ff9b99a7c1ea35fe577e Mon Sep 17 00:00:00 2001 From: oltsy Date: Sun, 1 Dec 2024 02:08:35 +0300 Subject: [PATCH 11/14] Add: nca pair loss --- .../settings/pipelines/train/dpo.py | 6 ++++ turbo_alignment/trainers/dpo.py | 31 +++++++++++++++++++ 2 files changed, 37 insertions(+) diff --git a/turbo_alignment/settings/pipelines/train/dpo.py b/turbo_alignment/settings/pipelines/train/dpo.py index bc85133..3d35fa3 100755 --- a/turbo_alignment/settings/pipelines/train/dpo.py +++ b/turbo_alignment/settings/pipelines/train/dpo.py @@ -26,6 +26,7 @@ class DPOLossesType(str, Enum): APO_DOWN = 'apo_down' ASFT = 'asft' DPOP = 'dpop' + NCA_PAIT = 'nca_pair' class DPOLossSettings(ExtraFieldsNotAllowedBaseModel): @@ -93,6 +94,10 @@ class DPOPLossSettings(DPOLossSettings): lam: float = 0.1 +class NCAPairLossSettings(DPOLossSettings): + loss_type: Literal[DPOLossesType.NCA_PAIT] + + class SyncRefModelSettings(ExtraFieldsNotAllowedBaseModel): sync_ref_model: bool = False alpha: float = 1.0 @@ -114,6 +119,7 @@ class DPOTrainerSettings(TrainerSettings): | APOZeroLossSettings | APODownLossSettings | DPOPLossSettings + | NCAPairLossSettings ) sync_ref_settings: SyncRefModelSettings use_ref_model: bool = True diff --git a/turbo_alignment/trainers/dpo.py b/turbo_alignment/trainers/dpo.py index ef3288f..7473987 100755 --- a/turbo_alignment/trainers/dpo.py +++ b/turbo_alignment/trainers/dpo.py @@ -39,6 +39,7 @@ SimPOLossSettings, SlicHfLossSettings, SyncRefModelSettings, + NCAPairLossSettings ) from turbo_alignment.trainers.utils import ( DPOLossRegistry, @@ -456,6 +457,35 @@ def compute_loss( return loss, chosen_rewards, rejected_rewards +@DPOLossRegistry.register(DPOLossesType.NCA_PAIT) +class NCAPairLoss(DPOLossRegistry): + def __init__(self, *args, beta: float = 0.1, **kwargs) -> None: + self.beta = beta + super().__init__(*args, **kwargs) + + def compute_loss( + self, + policy_chosen_logps: torch.FloatTensor, + policy_rejected_logps: torch.FloatTensor, + reference_chosen_logps: torch.FloatTensor, + reference_rejected_logps: torch.FloatTensor, + precomputed_margins: torch.FloatTensor | None, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + chosen_logratios = policy_chosen_logps - reference_chosen_logps + rejected_logratios = policy_rejected_logps - reference_rejected_logps + + chosen_rewards = self.beta * (policy_chosen_logps - reference_chosen_logps).detach() + rejected_rewards = self.beta * (policy_rejected_logps - reference_rejected_logps).detach() + + loss = ( + -F.logsigmoid(chosen_logratios * self.beta) + - 0.5 * F.logsigmoid(-chosen_logratios * self.beta) + - 0.5 * F.logsigmoid(-rejected_logratios * self.beta) + ) + + return loss, chosen_rewards, rejected_rewards + + @dataclass class DPOTrainingArguments(TrainingArguments): loss_settings: ( @@ -473,6 +503,7 @@ class DPOTrainingArguments(TrainingArguments): | APOZeroLossSettings | APODownLossSettings | DPOPLossSettings + | NCAPairLossSettings ) = field( default_factory=SigmoidLossSettings(loss_type=DPOLossesType.SIGMOID) ) # type: ignore[call-overload] From bdbd3794aafbde9ed9cbca0c9c7cd37c7cfc5a34 Mon Sep 17 00:00:00 2001 From: oltsy Date: Mon, 2 Dec 2024 11:43:51 +0300 Subject: [PATCH 12/14] Fix: linters --- turbo_alignment/trainers/dpo.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/turbo_alignment/trainers/dpo.py b/turbo_alignment/trainers/dpo.py index 7473987..40e9091 100755 --- a/turbo_alignment/trainers/dpo.py +++ b/turbo_alignment/trainers/dpo.py @@ -33,13 +33,13 @@ HingeLossSettings, IPOLossSettings, KTOLossSettings, + NCAPairLossSettings, ORPOLossSettings, SigmoidLossSettings, SigmoidLossWithMarginSettings, SimPOLossSettings, SlicHfLossSettings, SyncRefModelSettings, - NCAPairLossSettings ) from turbo_alignment.trainers.utils import ( DPOLossRegistry, @@ -482,7 +482,7 @@ def compute_loss( - 0.5 * F.logsigmoid(-chosen_logratios * self.beta) - 0.5 * F.logsigmoid(-rejected_logratios * self.beta) ) - + return loss, chosen_rewards, rejected_rewards @@ -853,6 +853,7 @@ def compute_loss( model: PreTrainedModel | nn.Module, inputs: dict[str, torch.Tensor | Any], return_outputs=False, + num_items_in_batch=None, ) -> torch.Tensor | tuple[torch.Tensor, dict[str, float]]: loss, metrics = self.get_batch_metrics(model, inputs, train_eval='train') From 627b1f006e386c2746f81d7fc131d5c91d395aed Mon Sep 17 00:00:00 2001 From: Alexey Malakhov <131314005+alekseymalakhov11@users.noreply.github.com> Date: Fri, 13 Dec 2024 17:25:45 +0300 Subject: [PATCH 13/14] fix name --- turbo_alignment/settings/pipelines/train/dpo.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/turbo_alignment/settings/pipelines/train/dpo.py b/turbo_alignment/settings/pipelines/train/dpo.py index 3d35fa3..c08b8b8 100755 --- a/turbo_alignment/settings/pipelines/train/dpo.py +++ b/turbo_alignment/settings/pipelines/train/dpo.py @@ -26,7 +26,7 @@ class DPOLossesType(str, Enum): APO_DOWN = 'apo_down' ASFT = 'asft' DPOP = 'dpop' - NCA_PAIT = 'nca_pair' + NCA_PAIR = 'nca_pair' class DPOLossSettings(ExtraFieldsNotAllowedBaseModel): @@ -95,7 +95,7 @@ class DPOPLossSettings(DPOLossSettings): class NCAPairLossSettings(DPOLossSettings): - loss_type: Literal[DPOLossesType.NCA_PAIT] + loss_type: Literal[DPOLossesType.NCA_PAIR] class SyncRefModelSettings(ExtraFieldsNotAllowedBaseModel): From cc063a954bb3805ba8dc06961e98a6c757f6bf62 Mon Sep 17 00:00:00 2001 From: Alexey Malakhov <131314005+alekseymalakhov11@users.noreply.github.com> Date: Fri, 13 Dec 2024 17:35:58 +0300 Subject: [PATCH 14/14] fix tests --- turbo_alignment/trainers/dpo.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/turbo_alignment/trainers/dpo.py b/turbo_alignment/trainers/dpo.py index 40e9091..2278eee 100755 --- a/turbo_alignment/trainers/dpo.py +++ b/turbo_alignment/trainers/dpo.py @@ -457,7 +457,7 @@ def compute_loss( return loss, chosen_rewards, rejected_rewards -@DPOLossRegistry.register(DPOLossesType.NCA_PAIT) +@DPOLossRegistry.register(DPOLossesType.NCA_PAIR) class NCAPairLoss(DPOLossRegistry): def __init__(self, *args, beta: float = 0.1, **kwargs) -> None: self.beta = beta