tensorflow / tensorflow/probability
Custom gradients in HMC?
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Description
When passing a target_log_prob_fn that's not built from TF primitives (and hence doesn't allow for automatic differentiation) to the HamiltonianMonteCarlo kernel, is there a way to also pass a custom gradient function? Of course, you lose all the performance benefits of autodif, but this would significantly increase the potential areas of application for TFP's HMC.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the HamiltonianMonteCarlo kernel API and how target_log_prob_fn is currently differentiated. The issue does not name implementation files or tests, so first determine where gradient handling is defined and how a custom gradient would be represented. Done means a supported custom-gradient path with coverage for non-TensorFlow target functions.
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Assessment
- Tech stack
- machine-learning
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100