[GitHub] [incubator-mxnet] mightydeveloper edited a comment on issue #14607: split_and_load can now handle num_ctx > num_data. Github Issue #13909

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mightydeveloper edited a comment on issue #14607: split_and_load can now handle num_ctx > num_data. Github Issue #13909
URL: https://github.com/apache/incubator-mxnet/pull/14607#issuecomment-479461422
 
 
   @wkcn 
   In the example above, `losses` only contains loss terms that are forwarded by real data samples. (meaning that unnecessary(possibly fake) context loss are not appended from the previous for loop.)
   So I believe when we do `loss.backward()`, only the contexts that are appended from the previous for loop will run and calculate gradients for previously marked variables from previous for loop. 
   
   For example, suppose we have 3 examples left from the dataset and have 5 GPU contexts.
   We would only call `losses.append(loss_fn(out, l))` 3 times, marking variables in 0, 1, 2 GPU contexts.
   So when we call 
   ```
   for loss in losses:
       loss.backward()
   ```
   only gradients for GPU 0, 1, 2 will be calculated and when we eventually call `trainer.step()`, the weights will be updated.
   Does this answer help you? (I might have misunderstood your concern)
   
   + Also, I noticed that I have an indentation error in my original PR request description so I just edited it.

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