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[#9098][feat] Simple sharding latent experts#9099

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lucaslie merged 3 commits intoNVIDIA:mainfromnv-auto-deploy:gk/sharding_mole
Nov 19, 2025

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@greg-kwasniewski1greg-kwasniewski1 commentedNov 12, 2025
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Added explicit simple sharding (column + all_gather) for latent projections in Mixture of Latent Experts for nemotron family of models.

Summary by CodeRabbit

  • New Features

    • Enhanced model configuration with support for latent projection handling and improved projection settings for advanced neural architecture components.
  • Chores

    • Removed internal documentation comments from configuration files.

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Signed-off-by: greg-kwasniewski1 <213329731+greg-kwasniewski1@users.noreply.github.com>
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coderabbitaibot commentedNov 12, 2025
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📝 Walkthrough

Walkthrough

Removes TODO comments from a configuration file and adds MOE latent projection handling to the NemotronH model patches with explicit tensor parallelism plan entries for mamba SSM and attention projections.

Changes

Cohort / File(s)Change Summary
Comment removal
tensorrt_llm/_torch/auto_deploy/config/default.yaml
Removed two TODO comment lines related to sharding parameters and run_shape_prop timing. No functional changes.
MOE latent projection configuration
tensorrt_llm/_torch/auto_deploy/models/patches/nemotron_h.py
Added fc1_latent_proj and fc2_latent_proj entries to _nemotron_h_base_model_tp_plan dictionary with gather-based shard configuration. Updated base TP plan to explicitly define mamba SSM layer (in_proj, out_proj) and attention projections (q_proj, k_proj, v_proj, o_proj) alongside latent projection entries.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~8 minutes

  • The default.yaml change is a straightforward comment removal with no logic impact.
  • The nemotron_h.py change involves adding new configuration entries to an existing dictionary structure; verify that the new latent projection entries (fc1_latent_proj, fc2_latent_proj) align with the model's architecture and existing sharding strategy.

Pre-merge checks and finishing touches

❌ Failed checks (1 inconclusive)
Check nameStatusExplanationResolution
Description check❓ InconclusiveThe PR description provides a clear summary of the change (added explicit sharding for latent projections in MOE for nemotron models), but the template sections for 'Description' and 'Test Coverage' are incomplete/empty.Complete the 'Description' section with more detail on why this change is needed, and fill the 'Test Coverage' section with specific tests that validate the latent projection sharding behavior.
✅ Passed checks (2 passed)
Check nameStatusExplanation
Docstring Coverage✅ PassedNo functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.
Title check✅ PassedThe title clearly summarizes the main change: adding simple sharding for latent experts in the Nemotron model family, which aligns with the code changes in nemotron_h.py.
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PR_Github #24329 [ run ] triggered by Bot. Commit:9ca9b35

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PR_Github #24329 [ run ] completed with stateSUCCESS. Commit:9ca9b35
/LLM/main/L0_MergeRequest_PR pipeline #18358 completed with status: 'SUCCESS'

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@lucaslie can you please approve?

@lucaslielucaslie linked an issueNov 13, 2025 that may beclosed by this pull request
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@lucaslielucaslie changed the title[TRTLLM-9098][feat] Simple sharding latent experts[#9098][feat] Simple sharding latent expertsNov 13, 2025
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/bot run --reuse-test

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PR_Github #24608 [ run ] triggered by Bot. Commit:3a2b887

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PR_Github #24608 [ run ] completed with stateSUCCESS. Commit:3a2b887
/LLM/main/L0_MergeRequest_PR pipeline #18575 completed with status: 'SUCCESS'

@lucaslielucaslie merged commit7905d6c intoNVIDIA:mainNov 19, 2025
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@github-project-automationgithub-project-automationbot moved this fromIn review toDone inAutoDeploy BoardNov 19, 2025
lkomali pushed a commit to lkomali/TensorRT-LLM that referenced this pull requestNov 19, 2025
Signed-off-by: greg-kwasniewski1 <213329731+greg-kwasniewski1@users.noreply.github.com>Signed-off-by: lkomali <lkomali@nvidia.com>
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@lucaslielucaslielucaslie approved these changes

@suyogguptasuyogguptasuyoggupta approved these changes

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[Feature]: Sharding support for latent MoE

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