Skip to content

Visualizing model internals in a live demo#

We recently added an attention visualization to the live BiDAF demo on allennlp.org. Here's what it looks like:

demo screenshot

The basic components here are reusable, so you can relatively easily get a similar demo for whatever model you're working on. This is a little bit more involved than some of our other how-tos, but if you're feeling adventurous, here's a guide to the changes you need to make to get a live demo that shows model internals, which we've found to be very helpful for understanding what parts of our models need fixing.

Pre-requisites#

  1. You need to have a text-in-json-out demo of your model already running using our simple server. This demo server will act as a backend with which the react server will interact and produce results. See the tutorial on creating a demo for how to do this.
  2. We'll be using a React app to generate HTML and javascript for the fancier demo, so you need to have npm and node.js installed. There are plenty of guides for how to install these on the web, sometimes with one-line shell commands. On MacOS with homebrew, you can just run brew install npm.
  3. Some vague familiarity with React and how JSX components work would be helpful, but we've tried to make it super simple to modify the React components even if you've never seen React before. You'll need to use javascript syntax, but again, even if you're not familiar with javascript, you should be able to get by with just making simple modifications to what's there.

Model plumbing#

If your text in / text out demo already contains "text out" for all of the pieces of your model you want to display in your demo, you can skip this section. If not, keep reading.

You need the model to output whatever information you want to display. We already return a dictionary from Model.forward containing whatever keys you want; you just need to be sure the things you want to visualize are in there (including actual strings instead of integers if you want to have human-readable labels for the rows and columns of your visualizations). For BiDAF, this was straightforward (see https://github.com/allenai/allennlp/pull/692/files):

  • We added question_tokens and passage_tokens fields to the metadata returned for each instance in the DatasetReader, which we use for column and row labels.
  • We added the passage_question_attention tensor to the model's output_dict. No change or anything required here, just add the tensor directly, and the library will sanitize it to a JSON object containing lists of numbers for you.
  • In Model.decode we made sure that the question_tokens and passage_tokens fields got passed along to the output_dict from the metadata we got from the DatasetReader. There are other ways of doing this without going through a metadata field (e.g., change the Predictor to tokenize your data, and pass along the tokens there), but it was easiest this way with BiDAF.

That's all you have to change in the model. All we need is to be able to get the right information in the output dictionary from model.forward(), however you want to make that happen.

After you've made these changes in your model, you should be able to see the outputs you want to visualize in the text output returned by the simple server you set up in the pre-requisites.

Set up the React demo#

We've set up a repository with a stripped-down version of the code that runs the demo on demo.allennlp.org. Clone that repository and copy the demo/ directory to wherever you want. cd to that directory, and run npm install, then npm start. That should show a screen in your terminal that says "Compiled successfully!", directing you to view your demo in a browser at localhost:3000. If you go there, you should see a page that says "Your Model Name" and has some place-holder UI elements.

Customize the React demo#

The demo code that you copied above has two files that you need to modify to make your demo work: src/ModelInput.jsx and src/ModelOutput.jsx. ModelInput.jsx renders the left half of the screen, where you give inputs to the model, and ModelOutput.jsx renders the right half of the screen with the output of your model after you click on the "Run" button.

Modifying ModelInput.jsx#

There are six TODOs in ModelInput.jsx, showing all of the places you might want to modify to get your demo hooked up correctly. These modifications include changing the input elements to accept the inputs that your model expects, adding some canned examples that are easy to run in your demo, and giving a title and description for your model. The most important point to make sure of is that the keys to the dictionary you pass to the runModel function match the keys that your model expects to receive in predict_json.

Once you've modified these, you can check that you've done it correctly by clicking "Run" with an example and making sure that your simple server receives and can respond to the request. You should be running the simple server on port 8000 (which is the default), and you should see the server log a POST request, with a response code 200. If you see an error on your server at this point, you haven't hooked up your variable names correctly. Note that the right side of the page won't render properly yet, and the javascript will probably crash. That's fine, just make sure the backend server worked and refresh the page - we'll fix the output next.

Modifying ModelOutput.jsx#

There are only two TODOs in ModelOutput.jsx that indicate places you need to modify. These pieces pull out the variables you want to visualize from the outputs JSON returned by your model, and then display those variables with fancy components. We've given examples for visualizing text fields and attention maps (both 1D and 2D); if you want to see other kinds of output, you can add whatever React components or raw HTML that you want here.

After you've fixed ModelOutput.jsx to render the actual outputs from your model, that's it, you're done! You should be able to run your demo end-to-end, entering text on the left side of the page, and seeing your results on the right side of the page. Happy demoing!

More general uses#

The basic outline here is quite general and allows for visualizing all kinds of model internals. You just need to make sure your model outputs the internal state you want to visualize, and that the demo code passes that information from the JSON response to the proper React component.

We've used this to visualize the internal workings of an action-based semantic parser. The parser is not quite ready for public consumption, so the code and demo are not yet available, but here's a sneak peak of what you can do with this kind of model visualization:

main parser UI
The main (still rough) parser UI, with model internals
The sequence of predicted actions that generated the logical form above
At each output step, we show the considered actions and their probabilities
At each output step, we also show the model's attention over the question
linking scores
Before decoding, we compute a linking between table entities and question words. This shows part of that linking.