wehub-resource-sync
111 lines
3.7 KiB
Markdown
111 lines
3.7 KiB
Markdown
# Request-Level Batching
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Request-level batching lets diffusion serving combine multiple compatible
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logical requests into one pipeline `forward()` call. Each prompt remains a
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separate request with its own `request_id`, sampling parameters, seed, output,
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error, and abort state. The scheduler decides which requests can run together.
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!!! warning "Prompt List Semantics"
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Diffusion request-level batching does not support a top-level packed
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list-prompt request. Submit multiple prompts as independent requests and let
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the scheduler batch compatible in-flight requests. Multimodal payloads stay
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inside a single prompt dict, for example
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`{"prompt": "...", "multi_modal_data": {"image": image}}`.
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## Enablement
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Increase `max_num_seqs` above `1` to allow the request scheduler to keep more
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than one compatible request active:
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```bash
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vllm serve Qwen/Qwen-Image --omni \
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--port 8091 \
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--max-num-seqs 4
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```
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For bursty online traffic, you can also set a small admission wait window. This
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lets the engine wait briefly before the first `schedule()` of a new wave so
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nearby compatible requests can arrive and share the same fused forward pass:
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```bash
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vllm serve Qwen/Qwen-Image --omni \
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--port 8091 \
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--max-num-seqs 4 \
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--request-batch-max-wait-ms 20
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```
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`--request-batch-max-wait-ms 0` is the default and disables admission waiting,
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so there is no added wait latency.
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For deploy YAMLs, configure the diffusion stage engine args:
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```yaml
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stage_args:
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- stage_id: 0
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stage_type: diffusion
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engine_args:
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max_num_seqs: 4
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request_batch_max_wait_ms: 20
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```
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## Compatibility
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Only pipelines that declare request-batch support use the fused request-batch
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path. The engine validates that the pipeline `forward()` uses the request-batch
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contract and returns `list[DiffusionOutput]`. Pipelines that do not support this
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contract do not use fused `pipeline.forward(batch)`; scheduled requests are
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executed through per-request worker calls.
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The scheduler batches only compatible requests. Compatibility is based on
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shape-sensitive and guidance-sensitive sampling fields, including resolution,
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frame count, CFG settings, output count, and LoRA identity. Requests with
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different LoRA adapters or scales are kept in separate batches so the worker
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activates one adapter per batch.
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Request-level batching applies when `step_execution=False`. For the separate
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step-wise runtime, see [Step Execution](step_execution.md).
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## Tuning
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- `max_num_seqs` caps the number of active compatible requests in one scheduler
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wave.
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- `request_batch_max_wait_ms` is an upper bound on extra admission wait before a
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new wave starts. Keep it small for latency-sensitive serving; values such as
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`10` to `50` ms are a practical starting range for bursty HTTP ingress.
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- `0` disables admission waiting and preserves the lowest first-request latency.
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- FIFO ordering is conservative: an incompatible request at the front of the
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waiting queue can block later compatible requests from joining the current
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batch.
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## Python API
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When constructing `Omni`, pass the same engine arguments:
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```python
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from vllm_omni.entrypoints.omni import Omni
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omni = Omni(
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model="Qwen/Qwen-Image",
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max_num_seqs=4,
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request_batch_max_wait_ms=20.0,
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)
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outputs = omni.generate(
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[
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"a cup of coffee on a table",
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"a toy dinosaur on a sandy beach",
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"a fox waking up in bed and yawning",
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]
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)
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```
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`Omni.generate([...])` submits each list item as its own logical diffusion
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request. The runtime may batch those requests internally when their sampling
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parameters are compatible.
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## For Contributors
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For implementation details and model-author guidance, see
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[Request-Level Batching for Diffusion](../../design/feature/diffusion_request_level_batching.md).
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