Method floating_point_ops

src/transformers/trainer.py:3909–3926

For models that inherit from [`PreTrainedModel`], uses that method to compute the number of floating point operations for every backward + forward pass. If using another model, either implement such a method in the model or subclass and override this method. Args:

(self, inputs: dict[str, torch.Tensor | Any])

            self.current_flos = 0

    def floating_point_ops(self, inputs: dict[str, torch.Tensor | Any]) -> int:
        """
        For models that inherit from [`PreTrainedModel`], uses that method to compute the number of floating point
        operations for every backward + forward pass. If using another model, either implement such a method in the
        model or subclass and override this method.

        Args:
            inputs (`dict[str, torch.Tensor | Any]`):
                The inputs and targets of the model.

        Returns:
            `int`: The number of floating-point operations.
        """
        if (main_input := getattr(self.model, "main_input_name", "input_ids")) in inputs and hasattr(
            self.model, "num_parameters"
        ):
            return 6 * inputs[main_input].numel() * self.model.num_parameters(exclude_embeddings=True)
        return 0

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