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[Doc] Update Batch-level DP docs (#25757)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Signed-off-by: yewentao256 <zhyanwentao@126.com>
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Cyrus Leung 2025-09-26 17:37:40 +08:00 committed by yewentao256
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docs/configuration/optimization.md
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@ -139,9 +139,9 @@ there is relatively little gain from TP. On the other hand, TP incurs significan
overhead because of all-reduce being performed after every layer.
Given this, it may be advantageous to instead shard the batched input data using TP, essentially
performing batch-level DP. This has been shown to improve the throughput by around 10% for
performing batch-level DP. This has been shown to improve the throughput and TTFT by around 10% for
`tensor_parallel_size=8`. For vision encoders that use hardware-unoptimized Conv3D operations,
batch-level DP can provide another 40% increase to throughput compared to regular TP.
batch-level DP can provide another 40% improvement compared to regular TP.
Nevertheless, since the weights of the multi-modal encoder are replicated across each TP rank,
there will be a minor increase in memory consumption and may cause OOM if you can barely fit the model already.
@ -172,14 +172,15 @@ Batch-level DP needs to be implemented on a per-model basis,
and enabled by setting `supports_encoder_tp_data = True` in the model class.
Regardless, you need to set `mm_encoder_tp_mode="data"` in engine arguments to use this feature.
Known supported models:
Known supported models (with corresponding benchmarks):
- GLM-4.5V GLM-4.1V (<gh-pr:23168>)
- dots_ocr (<gh-pr:25466>)
- GLM-4.1V or above (<gh-pr:23168>)
- InternVL (<gh-pr:23909>)
- Kimi-VL (<gh-pr:23817>)
- Llama4 (<gh-pr:18368>)
- MiniCPM-V-2.5 or above (<gh-pr:23327>, <gh-pr:23948>)
- Qwen2.5-VL (<gh-pr:22742>)
- Qwen2-VL or above (<gh-pr:22742>, <gh-pr:24955>, <gh-pr:25445>)
- Step3 (<gh-pr:22697>)
## Input Processing