[RFC]Feedback collection about TensorRT-LLM 1.0 Release Planning and API Compatibility Commitment

Dear community,

Since TensorRT-LLM's initial GitHub release in October 2023, we have made substantial progress over the past eighteen months. We extend our sincere gratitude for the community's exceptional support and valuable feedback, which have been instrumental in advancing the framework.

With the recent introduction of the PyTorch backend in the 0.17 release and our adoption of a GitHub-first development process, we are now preparing for the 1.0 release. This milestone will formalize our commitment to API backward compatibility. Previously, we intentionally delayed declaring 1.0 status given the rapidly evolving nature of this domain, as we wanted to avoid premature commitments that might require breaking changes.

Proposed Scope for 1.0 API Compatibility:

To ensure enforceable backward compatibility for TensorRT-LLM 1.0 APIs, we have implemented an automated protection mechanism. Specifically:

• The reference_committed directory contains APIs with formal backward compatibility commitments.
• The reference directory includes additional APIs currently under automated protection but not yet formally committed. Based on community feedback, we will progressively migrate select APIs from references to references_committed.

We welcome your input on two key matters:

• Feedback regarding the proposed API compatibility coverage

• Specific feature requests for inclusion in the TensorRT-LLM 1.0 release

Your insights will directly influence our roadmap prioritization.

Best regards,
The TensorRT-LLM Engineering Team

Start by reviewing tests/unittest/api_stability/references_committed and tests/unittest/api_stability/references to understand the protected APIs and proposed compatibility boundary. The issue requests community feedback on APIs to commit and features for TensorRT-LLM 1.0; done means providing specific, actionable feedback or feature requests rather than making a defined code change.