And also: > 'compile then execute' is not enough for all the deep learning workload. For > example, using our partial evaluator to specialize > training/validation/testing data mean we must compile only after we had > loaded all the data.
So in DL, common practice is that we specify the input shape in an ad-hoc way. Particularly, in MNIST, we know that our input is in shape `(batch_size, 784)`. For more complicated workloads, like models containing complicated control flow. I don't really think loading all the data would suffice. Probably compilation should happen in basic block level if say the IR is in CFG (so you need jit) -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/dmlc/tvm/issues/4054#issuecomment-538060918
