413aeac0c9
Co-authored-by: Michael <13900043+michaelzhang-ai@users.noreply.github.com> Co-authored-by: bingxche <bingxche@amd.com> Co-authored-by: yctseng0211 <yctseng@amd.com>
120 lines
4.0 KiB
YAML
120 lines
4.0 KiB
YAML
# Nightly benchmark configurations for srt-slurm powered runners.
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#
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# Structure mirrors InferenceX nvidia-master.yaml but only includes fields
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# actually needed by the runner — prefill/decode topology details are already
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# encoded in each srt-slurm recipe YAML and are not duplicated here.
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#
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# To add/remove concurrencies: edit conc-list for the relevant search-space entry.
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# To add a new runner: add a new top-level block and create a corresponding
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# nightly-test-<runner>.yml workflow.
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# Never edit workflow YAML files directly for these changes.
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dsr1-fp8-gb200-dynamo-sglang:
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model: deepseek-ai/DeepSeek-R1-0528
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model-prefix: dsr1
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runner: gb200
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precision: fp8
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framework: dynamo-sglang
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multinode: true
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disagg: true
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seq-len-configs:
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- isl: 1024
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osl: 1024
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search-space:
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- conc-list: [1024, 2048, 4096, 6144]
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# https://github.com/NVIDIA/srt-slurm/blob/sglang-nightly-regression/recipes/gb200-fp8/1k1k/max-tpt.yaml
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config_file: recipes/gb200-fp8/1k1k/max-tpt.yaml
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- conc-list: [4096]
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# https://github.com/NVIDIA/srt-slurm/blob/sglang-nightly-regression/recipes/gb200-fp8/1k1k/ultra-tpt.yaml
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config_file: recipes/gb200-fp8/1k1k/ultra-tpt.yaml
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dsr1-fp4-gb200-dynamo-sglang:
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model: nvidia/DeepSeek-R1-0528-NVFP4-v2
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model-prefix: dsr1
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runner: gb200
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precision: fp4
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framework: dynamo-sglang
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multinode: true
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disagg: true
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seq-len-configs:
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- isl: 1024
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osl: 1024
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search-space:
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- conc-list: [512, 2048, 4096, 8192]
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# https://github.com/NVIDIA/srt-slurm/blob/sglang-nightly-regression/recipes/gb200-fp4/1k1k/mid-curve.yaml
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config_file: recipes/gb200-fp4/1k1k/mid-curve.yaml
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# AMD MI355X 2-node 1P1D disaggregation. Driven by
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# scripts/ci/slurm/launch_mi355x.sh, which reads each recipe's `runtime`,
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# `bench`, and `bench.accuracy` sections. Every nightly runs ALL four
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# DeepSeek-V4 model x precision combos below (full matrix; GitHub
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# strategy.matrix.config, fail-fast: false). Each runs a GSM8K accuracy
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# hard-gate before the perf sweep.
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#
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# model_path points at the shared NFS HuggingFace cache dir (models--org--name);
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# the launcher resolves the live snapshot via refs/main, so no hash is hardcoded.
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dsv4flash-fp8-mi355x-sglang:
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model: sgl-project/DeepSeek-V4-Flash-FP8
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model-prefix: dsv4flash
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model_path: /it-share/model_coverage/models--sgl-project--DeepSeek-V4-Flash-FP8
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runner: mi355x
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precision: fp8
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framework: sglang
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multinode: true
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disagg: true
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seq-len-configs:
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- isl: 1024
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osl: 1024
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search-space:
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- conc-list: [1, 8, 16, 32, 64, 128, 256]
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config_file: scripts/ci/slurm/recipes/mi355x-fp8/dsv4flash/1k1k/1p1d.yaml
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dsv4pro-fp8-mi355x-sglang:
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model: sgl-project/DeepSeek-V4-Pro-FP8
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model-prefix: dsv4pro
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model_path: /it-share/model_coverage/models--sgl-project--DeepSeek-V4-Pro-FP8
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runner: mi355x
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precision: fp8
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framework: sglang
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multinode: true
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disagg: true
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seq-len-configs:
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- isl: 1024
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osl: 1024
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search-space:
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- conc-list: [1, 8, 16, 32, 64, 128, 256]
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config_file: scripts/ci/slurm/recipes/mi355x-fp8/dsv4pro/1k1k/1p1d.yaml
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dsv4flash-fp4-mi355x-sglang:
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model: deepseek-ai/DeepSeek-V4-Flash
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model-prefix: dsv4flash
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model_path: /it-share/model_coverage/models--deepseek-ai--DeepSeek-V4-Flash
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runner: mi355x
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precision: fp4
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framework: sglang
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multinode: true
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disagg: true
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seq-len-configs:
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- isl: 1024
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osl: 1024
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search-space:
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- conc-list: [1, 8, 16, 32, 64, 128, 256]
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config_file: scripts/ci/slurm/recipes/mi355x-fp4/dsv4flash/1k1k/1p1d.yaml
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dsv4pro-fp4-mi355x-sglang:
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model: deepseek-ai/DeepSeek-V4-Pro
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model-prefix: dsv4pro
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model_path: /it-share/model_coverage/models--deepseek-ai--DeepSeek-V4-Pro
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runner: mi355x
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precision: fp4
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framework: sglang
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multinode: true
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disagg: true
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seq-len-configs:
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- isl: 1024
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osl: 1024
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search-space:
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- conc-list: [1, 8, 16, 32, 64, 128, 256]
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config_file: scripts/ci/slurm/recipes/mi355x-fp4/dsv4pro/1k1k/1p1d.yaml
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