8e12624641eab7a7e73497ea96124bb39aca1c01,memcnn/models/revop.py,InvertibleCheckpointFunction,forward,#,13

Before Change

        ctx.num_bwd_passes = num_bwd_passes

        with torch.no_grad():
            x = input_t.detach()  // Makes a detached copy which shares the storage
            output = ctx.fn(x)

        detached_output = output.detach_()  // Detaches y in-place (inbetween computations can now be discarded)

        // store these tensor nodes for backward pass
        ctx.input_t = [input_t] * num_bwd_passes

After Change

        if not isinstance(output, tuple):
            output = (output,)

        // Detaches y in-place (inbetween computations can now be discarded)
        detached_output = tuple([element.detach_() for element in output])

Instances