leaveletandClaude Fable 5
0d065a8ab0
The EAGLE bigram fallback built 64K-token keys with an index-based Python comprehension (6.3 ms per call at 64K tokens). zip + islice runs the pairing loop in C and avoids copying the shifted operand: 3.6-4.2 ms per call (~1.6x). Output is byte-identical (same tuples); the tai-kernel fast path is unaffected. Packing bigrams into int64s via numpy was measured and rejected: the list<->array boxing makes it slower (4.1 ms) than zip until token ids are numpy end-to-end, and it would change HiCache storage hash inputs. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Registered Tests
Tests under this directory are auto-discovered by run_suite.py via CI registration decorators.
Where Should I Put My New Test?
No server / engine launch required
| What you're testing | Directory | Requires |
|---|---|---|
| Component logic in isolation (cache, scheduler, config, parser, etc.) | unit/<module>/ |
CPU or GPU |
| CUDA kernel correctness | kernels/ |
GPU |
Server / engine launch required (E2E)
| What you're testing | Directory | Requires |
|---|---|---|
| Model inference correctness | models/, 4-gpu-models/, 8-gpu-models/ |
GPU |
| Feature-specific (OpenAI API, LoRA, speculative, distributed, VLM, etc.) | openai_server/, lora/, spec/, distributed/, ... |
GPU |
| Benchmarks (performance, accuracy, stress) | benchmark/ |
GPU |
| Platform-specific | amd/, ascend/ |
Vendor GPU |
See unit/README.md for unit test conventions.