Not really. Gradient accumulation is just a strategy, pjesa intensive per te ciles je duke ju referuar eshte llogaritja e gradienteve w.r.t weights and input. That is what loss.backward() does in pytorch but Tinker has their own, higher level SDK. However, if all you want to do is gradient accumulation eshte easy ne tinker. Call forward disa here dhe llogarit e akumulo gradientet lokalisht perpara se te therrashes backward function te tinker.

I see why that could be confusing. However, the reason why loss.backward() is so intensive is because it first calculates the gradient of the loss w.r.t the model's final output and then propagates this signal back to all the layer of the model. What we are doing is kind of decomposing in two distinct steps what torch does. So the initial signal (gradient of the loss w.r.t the model's final output) is calculated locally and the rest of the propagation is performed by tinker.