In the past, I have been using Theano where I could select the convolution strategy (see: Theano convolution strategies. 12). When I reimplement my model in PyTorch the memory usage is significantly higher, suggesting the convolution strategy is different.
In PyTorch, I can find the definitions in this github line. 14
My question is two-fold:

Am I correct if I deduce that IMPLICIT_PRECOMP_GEMM is the default strategy in PyTorch?
Can I select the strategy manually (preferably per convolution) or can this only be done through the benchmarking function?