[experimental chore/agentx-v0.2-aiperf-testing branch] agentic build_replay_cmd: missing --tokenizer-trust-remote-code breaks Kimi-K2.5 (and other custom-code tokenizer models) / agentic build_replay_cmd 缺少 --tokenizer-trust-remote-code 导致 Kimi-K2.5 等自定义 tokenizer 模型失败

benchmark_lib.sh を開いて build_replay_cmd を見つけ、既存の aiperf フラグを報告されている reproducer と比較します。replay オプションと並べて tokenizer の trust オプションを追加し、カスタムコードの tokenizer モデルで agentic-coding sweep を実行します。完了条件は、最初のリクエストで失敗するのではなく、trace_replay にベンチマークデータが含まれることです。

Summary

benchmark_lib.sh::build_replay_cmd does not pass --tokenizer-trust-remote-code to aiperf profile. This makes the launcher unusable for any model whose tokenizer requires executing custom Python code from the HuggingFace repo — for example, Kimi-K2.5-MXFP4 (and likely several other Qwen/Kimi/MiniMax variants).

aiperf bails out at startup of the first trace replay request:

╭─ Tokenizer Error ────────────────────────────────────────────────────────────╮
│  Failed to load tokenizer 'amd/Kimi-K2.5-MXFP4'                              │
│                                                                              │
│  AIPerf needs a tokenizer for accurate client-side token counting and        │
│  synthetic prompt generation.                                                │
│                                                                              │
│  Possible Causes:                                                            │
│    • Tokenizer requires executing custom Python code from HuggingFace        │
│                                                                              │
│  Suggested Fixes:                                                            │
│    • Add: --tokenizer-trust-remote-code                                      │
│    • Skip tokenizer (non-synthetic data only): --use-server-token-count      │
╰──────────────────────────────────────────────────────────────────────────────╯

Notably, build_replay_cmd does already pass --use-server-token-count, but aiperf still calls into the tokenizer for trace dataset preprocessing (not just synthetic generation), so the skip-flag isn't sufficient.

Repro

Run any agentic-coding sweep against a model with a custom-code tokenizer:

podman run ... -e MODEL=amd/Kimi-K2.5-MXFP4 -e TP=4 -e CONC=1 -e OFFLOADING=none \
  -e RESULT_DIR=... \
  ... vllm/vllm-openai-rocm:v0.19.1 \
  /workspace/benchmarks/single_node/agentic/kimik2.5_fp4_mi355x.sh

vLLM server starts cleanly (loads model, /health 200). aiperf then errors immediately on the first request with the tokenizer load failure above. Every CONC point in the sweep produces an empty trace_replay/ directory with no actual benchmark data — sweep_summary.csv has only headers.

Suggested fix

 REPLAY_CMD+=" --output-artifact-dir $result_dir/trace_replay"
+# Required for models whose tokenizer ships custom Python code (Kimi-K2.5,
+# some Qwen/MiniMax variants). aiperf's trace-dataset preprocessing calls
+# AutoTokenizer.from_pretrained even when --use-server-token-count is set,
+# so the trust flag is not optional.
+REPLAY_CMD+=" --tokenizer-trust-remote-code"
 if [ "$duration" -lt 900 ] || [ "${AIPERF_UNSAFE_OVERRIDE:-false}" = "true" ]; then
     REPLAY_CMD+=" --unsafe-override"
 fi

This is safe because the launcher already uses --trust-remote-code for the vLLM server side; aiperf needs the matching flag.

Why MiniMax-M2.5 worked but Kimi-K2.5 didn't

MiniMaxAI/MiniMax-M2.5 uses a standard HF tokenizer (no custom code). amd/Kimi-K2.5-MXFP4 ships with custom tokenizer files (tool_declaration_ts.py, media_utils.py — visible in the v0.19.1 vLLM startup log: "A new version of the following files was downloaded from huggingface.co/amd/Kimi-K2.5-MXFP4"). The current build_replay_cmd only happens to work for the subset of models with stock tokenizers.

Impact

Every Kimi family model in v0.2 (kimik2.5-fp4-mi355x-vllm, kimik2.5-fp4-b200-vllm, kimik2.5-int4-b200-vllm, etc.) is currently un-benchmarkable through the agentic-coding scenario. We did a 4h kimik2.5 FP4 MI355X sweep that produced zero data (all 7 conc points failed identically) before noticing this — flag-fix is one line.

Environment

• Branch: chore/agentx-v0.2-aiperf-testing (tip c8dfb585)
• aiperf submodule: 70fecb2e
• Reproducer image: vllm/vllm-openai-rocm:v0.19.1
• Reproducer model: amd/Kimi-K2.5-MXFP4
• Hardware: AAC1 MI355X (gfx950), TP=4

Companion to issues #1358, #1359, #1360, #1363 (other v0.2 issues found in the same campaign).

中文说明

benchmark_lib.sh 中的 build_replay_cmd 函数未向 aiperf profile 传递 --tokenizer-trust-remote-code 参数。对于 tokenizer 需要执行自定义 Python 代码的模型（如 Kimi-K2.5-MXFP4），aiperf 在启动第一个 trace replay 请求时因 tokenizer 加载失败而报错退出。虽然已传递 --use-server-token-count，但 aiperf 在 trace 数据集预处理阶段仍会调用 tokenizer，该跳过标志不足以解决问题。建议在 REPLAY_CMD 中添加 --tokenizer-trust-remote-code。此问题影响所有 Kimi 系列模型的 agentic-coding 基准测试场景，导致扫描产生零数据。