No, torch.no_grad() does disable tracking of operations. This included inplace operations (which are tracked when using .detach()) and the differentiation.
The only quirk is that requires_grad will still be set for the result of the computation - something that does not strike in some common cases (for example not when evaluating networks withing a with torch.no_grad(): block). As a result of the bug, a graph will be built for stuff following the no_grad scope when it is not required, but no gradient will flow back to the inputs, as requested by no_grad.
You could achieve the same as the post-bugfix behaviour now by manually using .detach() for the inputs or setting .requires_grad_(False) for the outputs of the computation.
Best regards
Thomas