def generate_from_model_and_ref()

in trl/trainer/kto_trainer.py [0:0]

• 37 lines of code
• 5 McCabe index (conditional complexity)

    def generate_from_model_and_ref(self, model, batch: dict[str, torch.LongTensor]) -> tuple[str, str]:
        """Generate samples from the model and reference model for the given batch of inputs."""

        # If one uses `generate_during_eval` with peft + bf16, we need to explicitly call generate with
        # the torch amp context manager as some hidden states are silently casted to full precision.
        generate_context_manager = (
            autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext()
        )

        with generate_context_manager:
            policy_output = model.generate(
                input_ids=batch["prompt_input_ids"],
                attention_mask=batch["prompt_attention_mask"],
                max_length=self.max_length,
                do_sample=True,
                pad_token_id=self.processing_class.pad_token_id,
            )

            # if reference_output in batch use that otherwise use the reference model
            if "reference_output" in batch:
                reference_output = batch["reference_output"]
            else:
                if self.ref_model is None:
                    with self.null_ref_context():
                        reference_output = self.model.generate(
                            input_ids=batch["prompt_input_ids"],
                            attention_mask=batch["prompt_attention_mask"],
                            max_length=self.max_length,
                            do_sample=True,
                            pad_token_id=self.processing_class.pad_token_id,
                        )
                else:
                    reference_output = self.ref_model.generate(
                        input_ids=batch["prompt_input_ids"],
                        attention_mask=batch["prompt_attention_mask"],
                        max_length=self.max_length,
                        do_sample=True,
                        pad_token_id=self.processing_class.pad_token_id,
                    )

        policy_output = pad_to_length(policy_output, self.max_length, self.processing_class.pad_token_id)
        policy_output_decoded = self.processing_class.batch_decode(policy_output, skip_special_tokens=True)

        reference_output = pad_to_length(reference_output, self.max_length, self.processing_class.pad_token_id)
        reference_output_decoded = self.processing_class.batch_decode(reference_output, skip_special_tokens=True)

        return policy_output_decoded, reference_output_decoded