eac38dbe9694bdfa6c2050528d8cc6a64747e933,pymanopt/autodiff/backends/_autograd.py,_AutogradBackend,compute_gradient,#_AutogradBackend#Any#Any#,38

Before Change

    @Backend._assert_backend_available
    def compute_gradient(self, function, arguments):
        flattened_arguments = flatten_arguments(arguments)
        if len(flattened_arguments) == 1:
            return autograd.grad(function)
        // XXX: This path handles cases where the signature hint looks like
        //      "@Autograd(("x", "y"))". This is potentially unnecessary as
        //      tests also pass if we instead use "@Autograd". Revisit this
        //      once we ported more complicated examples to autograd.
        if len(arguments) == 1:
            @functools.wraps(function)
            def unary_function(arguments):
                return function(*arguments)
            return autograd.grad(unary_function)

        // Turn `function` into a function accepting a single argument which
        // gets unpacked when the function is called. This is necessary for
        // autograd to compute and return the gradient for each input in the
        // input tuple/list and return it in the same grouping.
        // In order to unpack arguments correctly, we also need a signature hint
        // in the form of `arguments`. This is because autograd wraps tuples and
        // lists in `SequenceBox` types which are not derived from tuple or list
        // so we cannot detect nested arguments automatically.
        unary_function = unpack_arguments(function, signature=arguments)
        return autograd.grad(unary_function)

    @staticmethod
    def _compute_nary_hessian_vector_product(function):
        gradient = autograd.grad(function)

After Change

    def compute_gradient(self, function, arguments):
        num_arguments = len(arguments)
        gradient = autograd.grad(function, argnum=list(range(num_arguments)))
        if num_arguments > 1:
            return gradient
        return self._unpack_return_value(gradient)

    @Backend._assert_backend_available
    def compute_hessian_vector_product(self, function, arguments):
        num_arguments = len(arguments)

Instances