torchscript-models/README.md

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.

Porting guide

Here are some quick learnings on how to port models so that they work as TorchScript.

Expected integer literal for index:

    x_org = x                                                           
    for i in range(self.n_layers):
       x = self.conv_layers[i](x * x_mask)
           ~~~~~~~~~~~~~~~~~~ <--- HERE    

Seems like we can't use non-constant indices, per this ticket and this one.

It's suggested to turn the loop into an enumerate. They also suggest this.

For iteration over two lists using the same index, use zip. This,

for i in range(self.n_layers):
  x = self.conv_layers[i](x * x_mask)
  x = self.norm_layers[i](x)
  x = self.relu_drop(x)

Becomes this,

for conv_layer, norm_layer in zip(self.conv_layers, self.norm_layers):
  x = conv_layer(x * x_mask)
  x = norm_layer(x)
  x = self.relu_drop(x)

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