2de822661c
The old min_new_tokens penalizer updated logits through boolean-mask indexing. That indexing is data-dependent and can force synchronization on the decode hot path. Use an elementwise torch.where followed by inplace add so the operation stays tensorized and avoids the mask-index update path. Constraint: Keep the numeric behavior for active rows equivalent without multiplying by zero, which would turn -inf penalties into NaN. Rejected: Expanding the mask and assigning through logits[mask] | this is the synchronization pattern being removed. Confidence: high Scope-risk: narrow Directive: Do not reintroduce boolean-mask writes in decode-step penalty paths without profiling the synchronization behavior. Tested: RED/GREEN local pytest test/registered/unit/sampling/test_min_new_tokens_penalizer.py Tested: RED/GREEN remote pytest in cjy-glm5-new for min_new_tokens penalizer together with related regression tests Tested: git diff --check; py_compile for min_new_tokens.py Not-tested: Full decode throughput benchmark
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.