jit-models

This project exists to turn PyTorch models into Torch JIT, which can be run in any language with libtorch bindings.

How to develop

The best way is to import the inference code and work backwards.

Try to comment out all of the model so forward only returns the input, then begin opening things up.

Scripting vs tracing

Scripting transforms the code. It can contain branches.

Tracing evaluates the codepath with example data, fixing the branches taken.

The two techniques can be combined in the various nodes in the module tree.

Model-specific details

glow-tts notes

Only two changes!

FlowGenerator.forward (the root module) needs the following changes:

Hardcode a few constructor args. These come from inference.py's arg set:

def forward(self, x, x_lengths, y=None, y_lengths=None, g=None, gen=False, noise_scale=1., length_scale=1.):
	y = None
	y_lengths = None
	noise_scale = .667
	length_scale = 1.0 
	gen = True

Return only the mel output at the end of the forward branch used in tracing:

#return (y, y_m, y_logs, logdet), attn, logw, logw_, x_m, x_logs 
return y