vllm.model_executor.models.gpt2 ¶

class GPT2ForSequenceClassification(nn.Module, SupportsCrossEncoding):
    """GPT2 Model for sequence classification.

    This class expands GPT2Model with pooling and score functions - last token
    is being used for classification.

    Attributes:
        transformer: An instance of GPT2Model used for forward operations.
        score: A layer for calculating logits.
        _pooler: An instance of Pooler used for pooling operations.
    """

    is_pooling_model = True

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        self.transformer = GPT2Model(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "gpt2")
        )
        self.score = nn.Linear(
            config.n_embd,
            config.num_labels,
            bias=False,
            dtype=vllm_config.model_config.head_dtype,
        )

        pooler_config = vllm_config.model_config.pooler_config
        assert pooler_config is not None

        self.pooler = DispatchPooler.for_seq_cls(pooler_config, classifier=self.score)

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.transformer.embed_input_ids(input_ids)

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights)

    def forward(
        self,
        input_ids: torch.Tensor | None,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor:
        hidden_states = self.transformer(
            input_ids=input_ids,
            position_ids=positions,
            inputs_embeds=inputs_embeds,
            intermediate_tensors=intermediate_tensors,
        )
        return hidden_states