Profile MoE TP-local shards without collectives
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@@ -1,18 +1,18 @@
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version = 1
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[[jobs]]
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name = "qwen30-vllm020-moe-smoke-20260716-v1"
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name = "qwen30-vllm020-moe-smoke-20260716-v2-local-shard"
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gpus = 1
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gpu_model = "H20"
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hosts = ["dash0"]
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command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-qwen30-vllm020-profile-v1 && timeout --signal=TERM --kill-after=30s 1020 bash run_moe_smoke.sh"
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artifacts = ["artifacts/moe-smoke"]
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artifacts = ["artifacts/moe-smoke-v2"]
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[jobs.env]
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HOME = "/tmp/wjh"
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XDG_CACHE_HOME = "/tmp/wjh/.cache"
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VLLM_CACHE_ROOT = "/tmp/wjh/.cache/vllm"
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OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-fleet/artifacts/moe-smoke"
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OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-fleet/artifacts/moe-smoke-v2"
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VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1"
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VLLM_SOURCE = "/home/admin/cpfs/wjh/agentic-kv/third_party/vllm_v20_build"
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MODEL = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B"
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@@ -199,8 +199,12 @@ def main() -> None:
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in_dtype=torch.bfloat16,
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max_num_tokens=max_num_tokens,
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)
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# This process profiles one TP-local weight shard. Keep the global
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# runtime context single-rank so vLLM does not initialize a collective;
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# the action-conditioned shard size remains explicit in moe_config and
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# the real TP2/TP4 all-reduce is profiled in a separate multi-GPU run.
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vllm_config = VllmConfig(
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parallel_config=ParallelConfig(tensor_parallel_size=tp)
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parallel_config=ParallelConfig(tensor_parallel_size=1)
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)
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with set_current_vllm_config(vllm_config):
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backend, experts_cls = select_unquantized_moe_backend(moe_config)
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@@ -348,8 +352,8 @@ def main() -> None:
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"norm_topk_prob": True,
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},
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"measurement_scope": (
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"vLLM modular MoE prepare+FlashInfer CUTLASS experts+finalize; "
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"router linear/top-k and TP all-reduce excluded"
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"one TP-local weight shard: vLLM modular MoE prepare+FlashInfer "
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"CUTLASS experts+finalize; router linear/top-k and TP all-reduce excluded"
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),
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"rows": rows,
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}
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