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[ROCm][Perf] Reuse compiled FlyDSL host launchers - #64008
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Cache one compiled callable per factory launcher. Use the existing runtime ABI for dynamic tensor dimensions and launch arguments. Co-authored-by: OpenAI Codex <noreply@openai.com> Signed-off-by: Felix Li <felix.li@amd.com>
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✅ @coderfeli, CI is now available for this PR.
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✅ Triggered Buildkite CI #97610 for commit |
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✅ Queued 2 failed job(s) for retry in Buildkite CI #97610. |
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✅ Triggered Buildkite CI #98016 for commit |
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Hi @coderfeli, the pre-commit checks have failed. Please run: uv pip install pre-commit>=4.5.1
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✅ Triggered Buildkite CI #98199 for commit |
…el_tests Main's vllm-project#64101 adds DSv4.1 mono decode at TP8 with its own blocking step and TP8 cases in the tests this branch moved; fold them into the moved tests and run them in the DSv4.1 Mono I/O (now mi355_8) and Nightly steps. Main's vllm-project#64008 launches the mono kernels through _run_compiled, so the numerics test taps K1 there instead of wrapping the launcher. Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: Andreas Karatzas <akaratza@amd.com>
Overview
Reduce CPU overhead for warm FlyDSL launches by reusing compiled host launchers instead of repeating JIT argument binding and cache-key construction. Covers DeepSeek V4/V4.1 mono decode, Kimi K3 specialized decode, TurboQuant and UltraQuant.
Claims
Validation
Measured on gfx950 with PyTorch 2.10.0 and FlyDSL 0.4.0: 30 alternating batches of 100 launches, with GPU synchronization outside timed sections. Times are median (min–max), µs.
Existing GPU correctness suites: 68 passed before and after. Dynamic batches 1→2→4→8→3→1 reuse the same compiled callable and match reference outputs. Dynamic arguments, row strides, compile-only and graph replay checks passed; pre-commit including mypy passed.
Validation used a source-only editable installation. Full-model accuracy, TP serving and end-to-end performance were not measured because the target checkpoints and a complete ROCm extension build are unavailable.
Details
Targets host dispatch during warm eager execution and graph capture. The results measure host enqueue time; steady graph-replay speedup was not measured. Open-PR searches found no duplicate fix for these call sites.
Pull Request Checklist
I used vLLM's
/pr-checklistskill. (Mandatory for agents, optional for humans).AI assistance was used during the creation of this PR.
Design Fit: Minimizes impact on core components, reuses existing functionality, and justifies added complexity.
Testing and Validation: Validates the change and ensures any added tests are meaningful and reliable, with CI coverage or documented CI resource constraints and validation performed outside CI.
Code Quality and Style: Keeps code and comments clear and concise, and updates relevant documentation and examples.
Pull Request Contents: Includes a brief summary and relevant links, supports claims with evidence, explains root causes and implementation trade-offs, and follows the contributing guide.
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