Zhou TaichangandClaude Opus 5
856d19f42c
Signed-off-by: tzhouam <tzhouam@connect.ust.hk> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
46 lines
1.7 KiB
YAML
46 lines
1.7 KiB
YAML
# Benchmark deploy config: LingBot-World 2.0 on one GPU, eager.
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#
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# This is the portable smoke configuration -- it matches the shipped
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# vllm_omni/deploy/lingbot_world_v2_stepwise.yaml so a number measured here is
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# comparable to the default deployment. It is NOT a realtime configuration: one
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# GPU runs all five forwards of every AR block serially and nothing is compiled,
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# so expect a chunk interval several times the real-time deadline.
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#
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# Use configs/usp4_compiled.yaml for the realtime topology.
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#
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# vllm serve robbyant/lingbot-world-v2-14b-causal-fast-diffusers \
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# --omni --deploy-config benchmarks/lingbot_world/configs/single_gpu_eager.yaml \
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# --port 8000
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pipeline: lingbot_world
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async_chunk: false
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distributed_executor_backend: mp
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dtype: bfloat16
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stages:
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- stage_id: 0
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devices: "0"
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# AR-Diffusion binds one session per runner invocation; the runner rejects
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# anything larger.
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max_num_seqs: 1
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enforce_eager: true
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model_class_name: LingBotWorldCausalDMDPipeline
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engine_backend: vllm_omni.experimental.ar_diffusion.engine.ARDiffusionEngine
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step_execution: true
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streaming_output: true
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default_sampling_params:
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# The causal-fast checkpoint pins a four-step DMD schedule; this sizes the
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# step budget before the pipeline is reached.
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num_inference_steps: 4
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output_type: pil
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extra_args:
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flow_shift: 5.0
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model_config:
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# The AR cache geometry is fixed at load time, so a benchmark request must
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# ask for exactly this resolution.
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ar_diffusion_height: 480
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ar_diffusion_width: 832
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ar_diffusion_kv_config:
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gpu_memory_fraction: 0.6
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warmup_cudagraph: true
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