dev.md

Developing Inferno scripts (with VSCode)

Inferno syntax highlighting and LSP server

• Go to your local checkout of the inferno repo (e.g. ~/inferno) and run nix build .#vscode-inferno-lsp-server
  • Then in VSCode press ctrl+shift+P and run Install from VSIX
  • In the window, navigate to ~/inferno/result and select inferno-lsp-server.vsix
  • Do the same after for the VSIX created using nix build .#vscode-inferno-syntax-highlighting
• Run nix build .#inferno-lsp-server --print-out-paths (nix build .#inferno-ml-lsp-server for inferno-ml)
  • Copy the store path (nix/store/...) that is printed to stdout to your clipboard
  • Open VSCode, press ctrl + shift + P and search for Open User Settings
  • Search for Inferno, find the Inferno LSP extension tab and open it
  • Paste the directory you copied into the Path to the inferno-lsp-server executable field
  • Be sure to append /bin/inferno-lsp-server (/bin/inferno-ml-lsp-server for inferno-ml) to the end of the directory, then restart VSCode
• Create a new file with an .inferno extension
  • If you begin typing an Inferno command such as Array.argmax, the autocomplete box should appear
• Next, add the following to your tasks.json file in VSCode:

  "tasks": [
          {
              "label": "inferno",
              "type": "shell",
              "command": "cd ~/inferno; nix run .#inferno -- ${file}",
              "problemMatcher": [],
              "group": {
                  "kind": "build",
                  "isDefault": true
              }
          }
      ]
  

  • Change "command": "cd ~/inferno; if your Inferno location is different
  • Change nix run .#inferno -- ${file} to nix run .#inferno-ml -- ${file} for inferno-ml
• You should now be able to build .inferno scripts using ctrl + shift + B in VSCode

Examples

Try saving the following into an .inferno file and compiling it.

let arr = [1, 2, 3, 4] in
let sum = Array.sum arr in
sum

The file should compile and output

10.0

if you're using inferno-ml, you can also try the following

let arr = ML.ones ML.#int [10] in
let arr2 = ML.ones ML.#int [10] in
let arr3 = ML.stack 0 [arr, arr2] in
arr3

The file should compile and output

Tensor Int64 [2,10] [[ 1,  1,  1,  1,  1,  1,  1,  1,  1,  1],
                     [ 1,  1,  1,  1,  1,  1,  1,  1,  1,  1]]

Importing a TorchScript model into Inferno

In an Inferno script, you can load the model using the ML.unsafeLoadScript function. For instance,

let model = ML.unsafeLoadScript "path/to/model/<model_name>.ts.pt" in ...

You can pass arguments of type array of tensor to the model by passing them to ML.forward along with your model.

For example:

let outputs = ML.forward model inputs in ...

where inputs is an array of tensors. This line would assign the return value to outputs, but any other assignment (pattern matching on the return value, for instance) would also work.

Guidelines for model compatability with Inferno

For general information on how you should convert your Python files to TorchScript models, please see the online documentation for TorchScript: https://pytorch.org/docs/stable/jit.html

Inferno's only extra requirement is that a TorchScript model should only take tensors in or out. Standard non-tensor Python types are only allowed internally.

Be sure to use the correct version of TorchScript. You can get this by building the Python interpreter by running nix build .#pytorch and selecting it in VSCode. To do this open VSCode and press ctrl + shift + P and search Python: select interpreter. Choose the one with a nix/store/ path.

If you have other Python libraries that you'd like included in the Nix shell environment, you can add them to devShells..pytorch, defined in nix/dev-shells.nix:

{ 
  # ../nix/dev-shells.nix
  #
  # ...
  pytorch =
    pkgs.mkShell {
      packages = [
        (
          self.legacyPackages.${system}.stable.python3.withPackages (
            ps: with ps; [
              pytorch-bin
              torchvision-bin
              torchaudio-bin
              # Add library name here
            ]
          )
        )
      ];
    };
  # ...
}

You may also consider adding another Python shell environment to our flake's devShells (defined in nix/dev-shells.nix).

The correct version of TorchScript can also be found here if you'd like to install it locally.