Method move

deepspeed/module_inject/layers.py:406–429

(self, tensor)

                                                                           is not None, dtype)

    def move(self, tensor):
        # TODO: consider the timing of deletion
        # to save host resources when DP > 1。

        # keep_module_on_host is used to keep the module on the host. Checkpoints are loaded to the host first (in some
        # cases it can be done from the disk even to prevent filling host's memory), thus no need to create a new copy.
        if tensor.is_meta:
            # Keep tensor in meta device if tensor is meta.
            return tensor
        else:
            device = 'cpu' if self.__class__.keep_module_on_host else get_accelerator().current_device_name()
            return_new_copy = not self.__class__.keep_module_on_host

            # Using new tensors help in freeing memory (after split for example) was done before by calling clone().
            # Using copy=True instead of clone() will help in case of cpu --> cpu.
            # Otherwise to() will not create a new copy for the view of the full tensor, and it will not be de-referenced.
            cloned_tensor = tensor.to(device, copy=return_new_copy)

            if return_new_copy:
                # free the memory of the original tensor to reduce memory peak
                # Equivalent to directly deleting the tensor reference outside the function.
                # see https://github.com/microsoft/DeepSpeed/pull/4353
                tensor.data = torch.empty(0, device=tensor.device)
            return cloned_tensor


def configure_tensor_parallel_runtime(config):