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[#12954][fix] AutoDeploy: Fix Gemma4 MoE config (disable multi_stream_moe, lower free_gpu_memory_fraction)#12955

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suyoggupta merged 1 commit intoNVIDIA:mainfrom
nv-auto-deploy:sg/gemma4-moe-config-fix
Apr 12, 2026
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[#12954][fix] AutoDeploy: Fix Gemma4 MoE config (disable multi_stream_moe, lower free_gpu_memory_fraction)#12955
suyoggupta merged 1 commit intoNVIDIA:mainfrom
nv-auto-deploy:sg/gemma4-moe-config-fix

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@suyoggupta suyoggupta commented Apr 11, 2026

Summary

Config-only change — no code modifications.

Verified scores

Model: google/gemma-4-26B-A4B-it (bf16, single H100 80GB)

Benchmark Score
MMLU 75%
GSM8K 90%

Test plan

  • TRTLLM_ACCURACY_NO_REFERENCE=1 pytest tests/integration/defs/accuracy/test_llm_api_autodeploy.py::TestGemma4MoE::test_bf16 -s -v

🤖 Generated with Claude Code

Summary by CodeRabbit

  • Chores
    • Updated model registry example configuration with adjusted GPU memory allocation parameters for KV cache operations.
    • Disabled a transform feature in the example configuration, pending resolution of an upstream dependency issue.

…stream_moe, lower free_gpu_memory_fraction)

Disable multi_stream_moe to fix accuracy regression (GSM8K 80% -> 90%)
when combined with MLIR elementwise fusion and monolithic CUDA graphs.
See NVIDIA#12954 for root cause analysis and proposed fix.

Lower free_gpu_memory_fraction from 0.8 to 0.4 to prevent OOM during
piecewise CUDA graph capture on 80GB GPUs.

Verified scores on google/gemma-4-26B-A4B-it (bf16, single GPU):
  - MMLU: 75%
  - GSM8K: 90%

Signed-off-by: Suyog Gupta <41447211+suyoggupta@users.noreply.github.com>
@suyoggupta suyoggupta requested a review from a team as a code owner April 11, 2026 23:46
@suyoggupta suyoggupta requested a review from marinayanov April 11, 2026 23:46
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coderabbitai bot commented Apr 11, 2026

No actionable comments were generated in the recent review. 🎉

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Reviewing files that changed from the base of the PR and between d68a94e and 2262183.

📒 Files selected for processing (1)
  • examples/auto_deploy/model_registry/configs/gemma4_moe.yaml

📝 Walkthrough

Walkthrough

The PR updates a Gemma-4 MoE model configuration file by reducing the KV cache GPU memory fraction from 0.8 to 0.4, disabling the multi-stream MoE feature, and adding a TODO comment referencing a pending TensorRT-LLM issue.

Changes

Cohort / File(s) Summary
Configuration Update
examples/auto_deploy/model_registry/configs/gemma4_moe.yaml
Reduced kv_cache_config.free_gpu_memory_fraction to 0.4, disabled multi_stream_moe.enabled, and added TODO comment noting the feature remains disabled pending upstream resolution.

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~2 minutes

🚥 Pre-merge checks | ✅ 3
✅ Passed checks (3 passed)
Check name Status Explanation
Title check ✅ Passed The title accurately and specifically describes the main changes: disabling multi_stream_moe and lowering free_gpu_memory_fraction in the Gemma4 MoE config, with proper issue reference [#12954] and [fix] type.
Description check ✅ Passed The PR description includes a clear summary of changes with specific metrics, verified test results, and test plan details. It covers the what, why, and how of the fix.
Docstring Coverage ✅ Passed No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.

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/bot skip --"autodeploy config only change"

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/bot skip --comment "autodeploy config only change"

@suyoggupta suyoggupta enabled auto-merge (squash) April 11, 2026 23:53
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PR_Github #42822 [ skip ] triggered by Bot. Commit: 2262183 Link to invocation

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PR_Github #42822 [ skip ] completed with state SUCCESS. Commit: 2262183
Skipping testing for commit 2262183

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@suyoggupta suyoggupta merged commit 9f9feb9 into NVIDIA:main Apr 12, 2026
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