7391aec984c1e9e0899d51e6b0538483c37ec8fb,torchdiffeq/_impl/adjoint.py,,odeint_adjoint,#,115

Before Change

    params = tuple(func.parameters())
    if adjoint_buffers:
        params = params + tuple(buffer for buffer in func.buffers() if buffer.requires_grad)
    n_tensors = len(y0)

    ys = OdeintAdjointMethod.apply(func, t, rtol, atol, method, options, adjoint_rtol, adjoint_atol, adjoint_method,
                                   adjoint_options, n_tensors, *params, *y0)

After Change

    solution = OdeintAdjointMethod.apply(func, y0, t, rtol, atol, method, options, adjoint_rtol, adjoint_atol,
                                         adjoint_method, adjoint_options, t.requires_grad, *adjoint_params)

    if not tensor_input:
        solution = _flat_to_shape(solution, tuple([len(t), *shape] for shape in shapes))
    return solution

Instances