103 lines
6.5 KiB
Markdown
103 lines
6.5 KiB
Markdown
# Qwen3-30B-A3B Community vLLM Harness Ablation, 2026-05-02
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## Goal
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Run a fresh dash0 experiment on the community vLLM latest release with the local community model:
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`/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B`
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The comparison is:
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| Variant | Spec | Harness |
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| --- | --- | --- |
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| no-harness | `configs/examples/dash0_qwen30b_a3b_community_vllm020_noharness.json` | disabled via `llm.use_harness=false` |
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| harness | `configs/examples/dash0_qwen30b_a3b_community_vllm020_harness.json` | enabled, including deterministic stop proposal |
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Both specs start from the same base vLLM configuration. The base contains only serving access fields: `host`, `port`, and `served-model-name`. It does not set performance flags such as TP, DP, EP, max model length, prefix cache, chunked prefill, max-num-seqs, max-num-batched-tokens, or gpu-memory-utilization. The first trial therefore measures community vLLM defaults for this model.
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The launch environment sets `HOME=/tmp/wjh` and `XDG_CACHE_HOME=/tmp/wjh/.cache` so vLLM, torch.compile, and FlashInfer build caches land on dash0 local storage instead of CPFS. This is a startup/cache placement choice, not a vLLM performance flag.
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## vLLM Install
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PyPI reports `vllm==0.20.0` as the current community release checked on 2026-05-02. The dash0 runtime venv is on local rootfs rather than CPFS, because installing torch/CUDA wheels into CPFS was I/O-bound:
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`/tmp/wjh/venvs/vllm-0.20.0-cu129`
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The first plain `pip install vllm==0.20.0` smoke pulled `torch 2.11.0+cu130` and failed on dash0's driver (`570.133.20`, CUDA 12.9). The active install uses the vLLM 0.20.0 GitHub release `+cu129` wheel and the PyTorch CUDA 12.9 index, matching the vLLM documented CUDA 12.9 install path for this driver.
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Install log:
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`/home/admin/cpfs/wjh/aituner/aituner/logs/install_vllm_0.20.0_20260502.log`
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## Workload
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The experiment reuses the 0-8k chat window that has already been used for qwen27b harness work:
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| Field | Value |
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| --- | --- |
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| window | `chat_w20260311_1000` |
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| source rows | 32606 |
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| input filter | 0 to 8192 tokens |
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| completion tokens | fixed 128 via `trace.completion_tokens_override` |
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| max requests per probe | 512 |
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| replay time scale | 0.1 |
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| target pass rate | 0.95 |
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| TTFT SLO | 2s up to 4k, 4s up to 32k, 6s above |
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| TPOT SLO | 50ms |
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| search high | 0.125 sampling_u |
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| max probes per trial | 4 |
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The `max_requests_per_probe=512` cap keeps the fresh community-vLLM ablation practical while preserving a real trace-shaped replay, SLO scoring, and binary-search threshold probe. A trace-only count check gives 31 to 65 selected requests across the six binary-search thresholds, avoiding the invalid low-cap case where early thresholds can select zero requests.
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The first full-output attempt showed why a bounded replay is needed for a 12-iteration ablation: at the first threshold (`0.0625`), 31 selected requests contained 14,849 output tokens with `out_max=2981`. That makes one probe too slow to finish a full no-harness/harness pair. The first out128 attempt with `replay_time_scale=1.0` was still bounded by real window time, so each probe waited close to the original window duration. The active ablation therefore fixes output length at 128 tokens, uses `replay_time_scale=0.1`, and limits each trial to four binary-search probes. `load_trace_requests` scales both request arrivals and the window duration, so reported request rates are the actual compressed replay request rates. This changes the load/decode mix, so the result should be interpreted as a community-vLLM harness convergence test under a bounded, time-compressed chat replay, not as a full-output production benchmark.
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## Harness Update Under Test
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This run tests a stricter early-stop harness:
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- The harness still injects L-C-A workload features, recent trial diagnostics, active bottleneck, legal topology candidates, tested signatures, and knob-family rules.
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- A strong incumbent no longer means immediate stop. It means "validate nearby alternatives".
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- Deterministic stop is allowed only after completed validation evidence says continuing is unlikely to be useful:
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- the incumbent beats baseline by a generic large-gain ratio,
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- at least two post-incumbent validation trials have run,
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- those validation trials did not produce a feasible per-GPU improvement,
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- the validation covered topology and runtime families, or accumulated at least three post-incumbent validation attempts.
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- If the stop guard fires, `study tune` writes `harness-stop-XXXX` and exits without spending another GPU trial or asking the LLM for another proposal.
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- A single-family all-infeasible plateau is not enough to stop deterministically. It only blocks repeating that family; the LLM must either justify a different family or later satisfy the validation/convergence stop rule.
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This is a generic harness rule, not a testcase-specific threshold. It does not depend on qwen27b, qwen235b, qwen30b, a fixed TP/DP value, or a hardcoded SLO number.
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## Unit Tests
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Local test command:
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```bash
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PYTHONPATH=src python3 -m unittest tests.test_core_flow -q
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```
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Result: passed, 74 tests.
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The added coverage checks:
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| Test | Purpose |
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| --- | --- |
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| `test_harness_does_not_stop_immediately_after_strong_incumbent` | strong incumbent requires validation first |
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| `test_harness_stop_after_post_incumbent_validation_is_exhausted` | deterministic stop after validation exhaustion |
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| `test_cli_tune_uses_harness_stop_before_llm` | `study tune` can stop without calling the LLM or launching another GPU trial |
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| `test_prompt_can_disable_harness_for_ablation` | no-harness prompt removes structured harness context |
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## Experiment Tracking
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Pending dash0 runs:
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| Variant | tmux session | Log | Study root |
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| --- | --- | --- | --- |
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| no-harness | `qwen30b_vllm020_noharness_out128_scale01_20260502` | `logs/qwen30b_vllm020_noharness_out128_scale01_20260502.log` | `.aituner-community-vllm020/dash0-qwen30b-a3b-community-vllm020-chat-0-8k-out128-scale01-noharness` |
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| harness | `qwen30b_vllm020_harness_out128_scale01_20260502` | `logs/qwen30b_vllm020_harness_out128_scale01_20260502.log` | `.aituner-community-vllm020/dash0-qwen30b-a3b-community-vllm020-chat-0-8k-out128-scale01-harness` |
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The harness run should be judged by best-so-far `request_rate_per_gpu` per tuning iteration, plus whether it stops only after validation evidence. The no-harness run should use the same trial budget so the ablation exposes whether the early-stop harness saves iterations without hiding a later better point.
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## Results
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Pending. This section will be filled after the dash0 experiments finish.
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