tt-xla

Compare two nightly GitHub Actions runs and classify failures as new, persisting, or fixed

Stash local changes, pull latest main, and display a structured commit summary. For tt_forge_models uplift commits, shows the affected models from the submodule log.

Analyze a PyTorch model, then design and implement an optimal multi-device sharding strategy for Tenstorrent hardware that minimizes collective-communication (CCL) ops. A strategy is both which parallelism to use (tensor / sequence / data) and how to shard under it - e.g. Megatron column→row is one of several tensor-parallel schemes. Use whenever the user asks to shard a model, distribute it across devices, write Shardy annotations, reduce CCLs, or analyze a model for tensor/sequence parallelism. Trigger even when the user only says "sharding strategy" or names a model with a device count, without asking for a full plan.

Analyze CI benchmark workflow runs from GitHub Actions for the tt-xla project. Produces a markdown report covering failed jobs (with root-cause error extraction via logs and Glean), successful model performance metrics (samples/sec, TTFT, device perf), perf regressions/improvements vs previous nightly, and the full dependency commit chain (tt-xla, tt-mlir, tt-metal). Use this skill whenever the user wants to analyze a CI run, review nightly benchmark results, investigate CI failures, check benchmark performance from a workflow run, or asks about "latest nightly" results. Also trigger when the user pastes a GitHub Actions run URL or mentions a run ID in the context of performance analysis, or asks about perf regressions.

Review and fix language correctness and formatting in documentation files.

Triage one tt-forge-models training test failing with a bfloat16 dtype-mismatch RuntimeError (e.g. "mat1 and mat2 must have the same dtype, but got Float and BFloat16", "'<op>' not implemented for 'BFloat16'"). For cross-dtype operands, attempts a minimal loader fix propagating `dtype_override` into the offending tensor constructor, then re-runs CPU + pytest and updates the YAML (passing -> EXPECTED_PASSING; new failure -> KNOWN_FAILURE_XFAIL). For op-not-implemented (no PyTorch kernel), goes straight to KNOWN_FAILURE_XFAIL with the verbatim error. Updates every training entry sharing the affected loader. Never edits inference YAML or `dynamic_loader.py`.

Triage one tt-forge-models training test stuck at FAILED_FE_COMPILATION with reason "tt-forge-models doesn't implement unpack_forward_output for this model." Inspects the model's forward output, registers a handler or writes a per-loader override, and updates the YAML.

Use when auditing a TTNN model's IR for missed op fusion opportunities — both direct TTNN fusions (a fused ttnn op already exists) and theoretical fusions (the pattern is a single kernel in torch/triton/cuda)

Analyze a GitHub Actions run and summarize failures

Analyzes, debugs and proposes fixes for graph breaks in PyTorch/XLA model compilation. Use when a model generates more graphs than expected during compilation, the user mentions "graph break", or when debugging excessive graph generation in tt-xla pipelines.

Code review skill specialized for tt-xla (Python + C++ PJRT plugin for Tenstorrent hardware). Covers C++ memory safety, PJRT API patterns, Python test standards, and project-specific conventions.

Create Excalidraw diagram JSON files that make visual arguments. Use when the user wants to visualize workflows, architectures, or concepts.