tzhouamandClaude Opus 5
c4e61133fb
Two corrections and the paperwork they imply.
The fused-RMSNorm registration defaulted to *on*, which contradicts the
measurement it was committed with: those kernels are 3.6x and 4.5x faster in
isolation but measured 72.7 tok/s against 74.0 end to end, because inductor
already fuses the native norm into the residual add and an opaque call breaks
that fusion. The implementations stay registered, so the provider remains
selectable through `kernel_config.ir_op_priority` on a machine where the trade
lands differently; only the default ranking is left alone now, and the test
asserts both states rather than just the opt-in one.
The four switches this branch added (VLLM_OMNI_AOT_CACHE_SHIM,
_CPU_FUSED_NORMS, _CPU_RELEASE_MEMORY, _SPARK_GATE_REDUCTION) were not in the
reviewed environment-variable inventory, so
`test_statically_resolvable_environment_accesses_are_classified` failed. They
are classified now, the reviewed count moves 28 -> 32 with a note saying why,
and each has a row in docs/configuration/environment_variables.md carrying the
measurement that justifies its default.
`VLLM_OMNI_CPU_FAST_SAMPLER` and `VLLM_OMNI_STEP_STATS_SYNC` remain
unclassified and that test still fails on them. They arrived in 6ce8de1b, in
files this branch does not touch, so they are left to whoever owns that work
rather than documented from a guess.
Under `-m "core_model and cpu"`: 2593 passed before these fixes with 9
failures, of which two were mine. The rest are CUDA-only tests in a CPU venv
("Torch not compiled with CUDA enabled") plus test_qnn_export.py, which
another session is actively editing.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Configuration Options
This section lists the most common options for running vLLM-Omni.
For options within a vLLM Engine, please refer to the vLLM 0.28 configuration guide.
Each model defines fixed topology in a registered PipelineConfig and runtime
overrides in a deploy YAML.
For process-level settings shared by the CLI, deploy YAML, workers, and model integrations, see Environment Variables.
For a specific example, see the Qwen2.5-Omni deploy config. The matching frozen pipeline topology lives at vllm_omni/model_executor/models/qwen2_5_omni/pipeline.py.
For an introduction, see Pipeline and deploy configurations.
Memory Configuration
- GPU Memory Calculation and Configuration - Guide on how to calculate memory requirements and set up
gpu_memory_utilizationfor optimal performance
Optimization Features
- Diffusion Features Overview - Complete overview of all diffusion model features and supported models