Add vLLM v0.18.1 source tree with KV transfer abort fix
third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:
vllm/v1/core/sched/scheduler.py:
Replace fatal assert with graceful skip when KV transfer callback
arrives for an already-aborted request during PD disaggregated serving.
Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
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third_party/vllm/docs/cli/bench/mm_processor.md
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# vllm bench mm-processor
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## Overview
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`vllm bench mm-processor` profiles the multimodal input processor pipeline of
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vision-language models. It measures per-stage latency from the HuggingFace
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processor through to the encoder forward pass, helping you identify
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preprocessing bottlenecks and understand how different image resolutions or
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item counts affect end-to-end request time.
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The benchmark supports two data sources: synthetic random multimodal inputs
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(`random-mm`) and HuggingFace datasets (`hf`). Warmup requests are run before
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measurement to ensure stable results.
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## Quick Start
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```bash
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vllm bench mm-processor \
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--model Qwen/Qwen2-VL-7B-Instruct \
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--dataset-name random-mm \
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--num-prompts 50 \
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--random-input-len 300 \
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--random-output-len 40 \
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--random-mm-base-items-per-request 2 \
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--random-mm-limit-mm-per-prompt '{"image": 3, "video": 0}' \
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--random-mm-bucket-config '{(256, 256, 1): 0.7, (720, 1280, 1): 0.3}'
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```
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## Measured Stages
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| Stage | Description |
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| ----- | ----------- |
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| `get_mm_hashes_secs` | Time spent hashing multimodal inputs |
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| `get_cache_missing_items_secs` | Time spent looking up the processor cache |
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| `apply_hf_processor_secs` | Time spent in the HuggingFace processor |
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| `merge_mm_kwargs_secs` | Time spent merging multimodal kwargs |
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| `apply_prompt_updates_secs` | Time spent updating prompt tokens |
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| `preprocessor_total_secs` | Total preprocessing time |
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| `encoder_forward_secs` | Time spent in the encoder model forward pass |
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| `num_encoder_calls` | Number of encoder invocations per request |
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The benchmark also reports end-to-end latency (TTFT + decode time) per
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request. Use `--metric-percentiles` to select which percentiles to report
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(default: p99) and `--output-json` to save results.
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For more examples (HF datasets, warmup, JSON output), see
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[Benchmarking CLI — Multimodal Processor Benchmark](../../benchmarking/cli.md#multimodal-processor-benchmark).
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## JSON CLI Arguments
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--8<-- "docs/cli/json_tip.inc.md"
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## Arguments
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--8<-- "docs/generated/argparse/bench_mm_processor.inc.md"
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