Skip to content

Huggingface

HuggingFace Trainer Mixin

HuggingFaceTrainerProtocol

Bases: Protocol

Source code in src/fed_rag/trainers/huggingface/mixin.py 
34
35
36
37
38
39
40
41
42
@runtime_checkable
class HuggingFaceTrainerProtocol(Protocol):
    train_dataset: "Dataset"
    training_arguments: Optional["TrainingArguments"]

    def model(
        self,
    ) -> Union["SentenceTransformer", "PreTrainedModel", "PeftModel"]:
        pass  # pragma: no cover

HuggingFaceTrainerMixin

Bases: BaseModel, ABC

HuggingFace Trainer Mixin.

Source code in src/fed_rag/trainers/huggingface/mixin.py 
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
class HuggingFaceTrainerMixin(BaseModel, ABC):
    """HuggingFace Trainer Mixin."""

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
    )
    train_dataset: "Dataset"
    training_arguments: Optional["TrainingArguments"] = None

    def __init__(
        self,
        train_dataset: "Dataset",
        training_arguments: Optional["TrainingArguments"] = None,
        **kwargs: Any,
    ):
        if not _has_huggingface:
            msg = (
                f"`{self.__class__.__name__}` requires `huggingface` extra to be installed. "
                "To fix please run `pip install fed-rag[huggingface]`."
            )
            raise MissingExtraError(msg)
        super().__init__(
            train_dataset=train_dataset,
            training_arguments=training_arguments,
            **kwargs,
        )

    @property
    @abstractmethod
    def hf_trainer_obj(self) -> "Trainer":
        """A ~transformers.Trainer object."""

hf_trainer_obj abstractmethod property 

hf_trainer_obj

A ~transformers.Trainer object.

HuggingFace LM-Supervised Retriever Trainer

HuggingFaceTrainerForLSR

Bases: HuggingFaceTrainerMixin, BaseRetrieverTrainer

HuggingFace LM-Supervised Retriever Trainer.

Source code in src/fed_rag/trainers/huggingface/lsr.py 
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
class HuggingFaceTrainerForLSR(HuggingFaceTrainerMixin, BaseRetrieverTrainer):
    """HuggingFace LM-Supervised Retriever Trainer."""

    _hf_trainer: Optional["SentenceTransformerTrainer"] = PrivateAttr(
        default=None
    )

    def __init__(
        self,
        rag_system: RAGSystem,
        train_dataset: "Dataset",
        training_arguments: Optional["TrainingArguments"] = None,
        **kwargs: Any,
    ):
        super().__init__(
            train_dataset=train_dataset,
            rag_system=rag_system,
            training_arguments=training_arguments,
            **kwargs,
        )

    @model_validator(mode="after")
    def set_private_attributes(self) -> "HuggingFaceTrainerForLSR":
        # if made it to here, then this import is available
        from sentence_transformers import SentenceTransformer

        # validate rag system
        _validate_rag_system(self.rag_system)

        # validate model
        if not isinstance(self.model, SentenceTransformer):
            raise TrainerError(
                "For `HuggingFaceTrainerForLSR`, attribute `model` must be of type "
                "`~sentence_transformers.SentenceTransformer`."
            )

        self._hf_trainer = LSRSentenceTransformerTrainer(
            model=self.model,
            args=self.training_arguments,
            data_collator=DataCollatorForLSR(rag_system=self.rag_system),
            train_dataset=self.train_dataset,
        )

        return self

    def train(self, **kwargs: Any) -> TrainResult:
        output: TrainOutput = self.hf_trainer_obj.train(**kwargs)
        return TrainResult(loss=output.training_loss)

    def evaluate(self) -> TestResult:
        # TODO: implement this
        raise NotImplementedError

    @property
    def hf_trainer_obj(self) -> "SentenceTransformerTrainer":
        return self._hf_trainer

LSRSentenceTransformerTrainer

Bases: SentenceTransformerTrainer

Source code in src/fed_rag/trainers/huggingface/lsr.py 
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
class LSRSentenceTransformerTrainer(SentenceTransformerTrainer):
    def __init__(
        self,
        *args: Any,
        data_collator: DataCollatorForLSR,
        loss: Optional[LSRLoss] = None,
        **kwargs: Any,
    ):
        if not _has_huggingface:
            msg = (
                f"`{self.__class__.__name__}` requires `huggingface` extra to be installed. "
                "To fix please run `pip install fed-rag[huggingface]`."
            )
            raise MissingExtraError(msg)

        # set loss
        if loss is None:
            loss = LSRLoss()
        else:
            if not isinstance(loss, LSRLoss):
                raise InvalidLossError(
                    "`LSRSentenceTransformerTrainer` must use ~fed_rag.loss.LSRLoss`."
                )

        if not isinstance(data_collator, DataCollatorForLSR):
            raise InvalidDataCollatorError(
                "`LSRSentenceTransformerTrainer` must use ~fed_rag.data_collators.DataCollatorForLSR`."
            )

        super().__init__(
            *args, loss=loss, data_collator=data_collator, **kwargs
        )

    def collect_scores(
        self, inputs: dict[str, torch.Tensor | Any]
    ) -> tuple[torch.Tensor, torch.Tensor]:
        if "retrieval_scores" not in inputs:
            raise MissingInputTensor(
                "Collated `inputs` are missing key `retrieval_scores`"
            )

        if "lm_scores" not in inputs:
            raise MissingInputTensor(
                "Collated `inputs` are missing key `lm_scores`"
            )

        retrieval_scores = inputs.get("retrieval_scores")
        lm_scores = inputs.get("lm_scores")

        return retrieval_scores, lm_scores

    def compute_loss(
        self,
        model: "SentenceTransformer",
        inputs: dict[str, torch.Tensor | Any],
        return_outputs: bool = False,
        num_items_in_batch: Any | None = None,
    ) -> torch.Tensor | tuple[torch.Tensor, dict[str, Any]]:
        """Compute LSR loss.

        NOTE: the forward pass of the model is taken care of in the DataCollatorForLSR.

        Args:
            model (SentenceTransformer): _description_
            inputs (dict[str, torch.Tensor  |  Any]): _description_
            return_outputs (bool, optional): _description_. Defaults to False.
            num_items_in_batch (Any | None, optional): _description_. Defaults to None.

        Raises:
            NotImplementedError: _description_

        Returns:
            torch.Tensor | tuple[torch.Tensor, dict[str, Any]]: _description_
        """
        retrieval_scores, lm_scores = self.collect_scores(inputs)
        loss = self.loss(retrieval_scores, lm_scores)

        # inputs are actually the outputs of RAGSystem's "forward" pass
        return (loss, inputs) if return_outputs else loss

compute_loss 

compute_loss(
    model,
    inputs,
    return_outputs=False,
    num_items_in_batch=None,
)

Compute LSR loss.

NOTE: the forward pass of the model is taken care of in the DataCollatorForLSR.

Parameters:

Name Type Description Default
model SentenceTransformer

description

 required
inputs dict[str, Tensor | Any]

description

 required
return_outputs bool

description. Defaults to False.

 False
num_items_in_batch Any | None

description. Defaults to None.

 None

Raises:

Type Description
NotImplementedError

description

Returns:

Type Description
Tensor | tuple[Tensor, dict[str, Any]]

torch.Tensor | tuple[torch.Tensor, dict[str, Any]]: description
Source code in src/fed_rag/trainers/huggingface/lsr.py 
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
def compute_loss(
    self,
    model: "SentenceTransformer",
    inputs: dict[str, torch.Tensor | Any],
    return_outputs: bool = False,
    num_items_in_batch: Any | None = None,
) -> torch.Tensor | tuple[torch.Tensor, dict[str, Any]]:
    """Compute LSR loss.

    NOTE: the forward pass of the model is taken care of in the DataCollatorForLSR.

    Args:
        model (SentenceTransformer): _description_
        inputs (dict[str, torch.Tensor  |  Any]): _description_
        return_outputs (bool, optional): _description_. Defaults to False.
        num_items_in_batch (Any | None, optional): _description_. Defaults to None.

    Raises:
        NotImplementedError: _description_

    Returns:
        torch.Tensor | tuple[torch.Tensor, dict[str, Any]]: _description_
    """
    retrieval_scores, lm_scores = self.collect_scores(inputs)
    loss = self.loss(retrieval_scores, lm_scores)

    # inputs are actually the outputs of RAGSystem's "forward" pass
    return (loss, inputs) if return_outputs else loss

HuggingFace Retrieval-Augmented Generator Trainer

HuggingFaceTrainerForRALT

Bases: HuggingFaceTrainerMixin, BaseGeneratorTrainer

HuggingFace Trainer for Retrieval-Augmented LM Training/Fine-Tuning.

Source code in src/fed_rag/trainers/huggingface/ralt.py 
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
class HuggingFaceTrainerForRALT(HuggingFaceTrainerMixin, BaseGeneratorTrainer):
    """HuggingFace Trainer for Retrieval-Augmented LM Training/Fine-Tuning."""

    _hf_trainer: Optional["Trainer"] = PrivateAttr(default=None)

    def __init__(
        self,
        rag_system: RAGSystem,
        train_dataset: "Dataset",
        training_arguments: Optional["TrainingArguments"] = None,
        **kwargs: Any,
    ):
        if not _has_huggingface:
            msg = (
                f"`{self.__class__.__name__}` requires `huggingface` extra to be installed. "
                "To fix please run `pip install fed-rag[huggingface]`."
            )
            raise MissingExtraError(msg)

        if training_arguments is None:
            training_arguments = _get_default_training_args()
        else:
            training_arguments.remove_unused_columns = (
                False  # pragma: no cover
            )

        super().__init__(
            train_dataset=train_dataset,
            rag_system=rag_system,
            training_arguments=training_arguments,
            **kwargs,
        )

    @model_validator(mode="after")
    def set_private_attributes(self) -> "HuggingFaceTrainerForRALT":
        # if made it to here, then this import is available
        from transformers import Trainer

        # validate rag system
        _validate_rag_system(self.rag_system)

        self._hf_trainer = Trainer(
            model=self.model,
            args=self.training_arguments,
            data_collator=DataCollatorForRALT(rag_system=self.rag_system),
            train_dataset=self.train_dataset,
        )

        return self

    def train(self, **kwargs: Any) -> TrainResult:
        output: TrainOutput = self.hf_trainer_obj.train(**kwargs)
        return TrainResult(loss=output.training_loss)

    def evaluate(self) -> TestResult:
        # TODO: implement this
        raise NotImplementedError

    @property
    def hf_trainer_obj(self) -> "Trainer":
        return self._hf_trainer