def apply_chunking_to_forward(
    forward_fn: Callable[..., torch.Tensor], chunk_size: int, chunk_dim: int, *input_tensors
) -> torch.Tensor:
    """
    This function chunks the `input_tensors` into smaller input tensor parts of size `chunk_size` over the dimension
    `chunk_dim`. It then applies a layer `forward_fn` to each chunk independently to save memory.

    If the `forward_fn` is independent across the `chunk_dim` this function will yield the same result as directly
    applying `forward_fn` to `input_tensors`.

    Args:
        forward_fn (`Callable[..., torch.Tensor]`):
            The forward function of the model.
        chunk_size (`int`):
            The chunk size of a chunked tensor: `num_chunks = len(input_tensors[0]) / chunk_size`.
        chunk_dim (`int`):
            The dimension over which the `input_tensors` should be chunked.
        input_tensors (`Tuple[torch.Tensor]`):
            The input tensors of `forward_fn` which will be chunked

    Returns:
        `torch.Tensor`: A tensor with the same shape as the `forward_fn` would have given if applied`.


    Examples:

    ```python
    # rename the usual forward() fn to forward_chunk()
    def forward_chunk(self, hidden_states):
        hidden_states = self.decoder(hidden_states)
        return hidden_states


    # implement a chunked forward function
    def forward(self, hidden_states):
        return apply_chunking_to_forward(self.forward_chunk, self.chunk_size_lm_head, self.seq_len_dim, hidden_states)
    ```"""

    assert len(input_tensors) > 0, f"{input_tensors} has to be a tuple/list of tensors"

    # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
    num_args_in_forward_chunk_fn = len(inspect.signature(forward_fn).parameters)
    if num_args_in_forward_chunk_fn != len(input_tensors):
        raise ValueError(
            f"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(input_tensors)} input "
            "tensors are given"
        )

    if chunk_size > 0:
        tensor_shape = input_tensors[0].shape[chunk_dim]
        for input_tensor in input_tensors:
            if input_tensor.shape[chunk_dim] != tensor_shape:
                raise ValueError(
                    f"All input tenors have to be of the same shape: {tensor_shape}, "
                    f"found shape {input_tensor.shape[chunk_dim]}"
                )

        if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
            raise ValueError(
                f"The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk "
                f"size {chunk_size}"
            )

        num_chunks = input_tensors[0].shape[chunk_dim] // chunk_size

        # chunk input tensor into tuples
        input_tensors_chunks = tuple(input_tensor.chunk(num_chunks, dim=chunk_dim) for input_tensor in input_tensors)
        # apply forward fn to every tuple
        output_chunks = tuple(forward_fn(*input_tensors_chunk) for input_tensors_chunk in zip(*input_tensors_chunks))
        # concatenate output at same dimension
        return torch.cat(output_chunks, dim=chunk_dim)

    return forward_fn(*input_tensors)

    This function chunks the `input_tensors` into smaller input tensor parts of size `chunk_size` over the dimension
    `chunk_dim`. It then applies a layer `forward_fn` to each chunk independently to save memory.

    If the `forward_fn` is independent across the `chunk_dim` this function will yield the same result as directly
    applying `forward_fn` to `input_tensors`.

    Args:
        forward_fn (`Callable[..., torch.Tensor]`):
            The forward function of the model.
        chunk_size (`int`):
            The chunk size of a chunked tensor: `num_chunks = len(input_tensors[0]) / chunk_size`.
        chunk_dim (`int`):
            The dimension over which the `input_tensors` should be chunked.
        input_tensors (`Tuple[torch.Tensor]`):
            The input tensors of `forward_fn` which will be chunked

    Returns:
        `torch.Tensor`: A tensor with the same shape as the `forward_fn` would have given if applied`.


    Examples:

    ```python
    # rename the usual forward() fn to forward_chunk()
    def forward_chunk(self, hidden_states):
        hidden_states = self.decoder(hidden_states)
        return hidden_states


    # implement a chunked forward function
    def forward(self, hidden_states):
        return apply_chunking_to_forward(self.forward_chunk, self.chunk_size_lm_head, self.seq_len_dim, hidden_states)
    ```
dpkd/transformers/src/transformers/pytorch_utils.py