[Bugfix] Fix Kimi K3 NVFP4 MoE weight conversion OOM

[wzhao18]

Purpose

Currently when running Kimi k3 nvfp4, it throws ~15000 lines of warning like the following due to fragmented memory during quantization post processing. In some cases, it also fatally fails with OOM.

[rank6]:[W904 12:06:06.083917542 CUDACachingAllocator.cpp:3933] memory allocation failed with OOM on device 6 while trying to allocate 20971520 bytes (free: 16252928, total: 287428640768).
[rank6]:[W904 12:06:06.087318728 CUDACachingAllocator.cpp:3933] memory allocation failed with OOM on device 6 while trying to allocate 20971520 bytes (free: 16252928, total: 287428640768).

This PR fixes it by optimizing nvfp4 MoE weight post processing memory usage. It preallocate the final FlashInfer TRT-LLM shuffled tensors and populate them one expert at a time. This avoids allocating tensors for per-expert result, which fails under _scoped_allocator_max_split(max_split_size_mb=20) as large blocks cannot be split up.

Test Plan

Test Result

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Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

[claude]

Claude Code Review

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[coderabbitai]

📝 Summary

Summary by CodeRabbit

• Performance
  • Improved FP4 mixture-of-experts weight preparation efficiency, reducing repeated processing and temporary allocations.
  • Reduced memory usage during model loading by releasing temporary accelerator cache before post-load transformations.

Walkthrough

The PR optimizes FP4 MoE weight shuffling with cached indices and preallocated buffers. Model loading now clears the accelerator cache before post-load weight transformations.

Changes

Model Loading Updates

Layer / File(s)	Summary
FP4 MoE weight preparation vllm/model_executor/layers/quantization/utils/flashinfer_fp4_moe.py	The preparation function caches permutation indices by tensor shape, writes results into preallocated buffers, and copies interleaved scale data into scratch buffers.
Loader cache cleanup vllm/model_executor/model_loader/base_loader.py	load_model calls torch.accelerator.empty_cache() after weight loading and before post-load transformations.

Estimated code review effort: 3 (Moderate) | ~20 minutes

Merge Risk: 🟡 Moderate · up to 148cd

HPU model loading can fail before post-load processing, so the cleanup path should be made backend-safe before merge.

Suggested reviewers: fxmarty-amd

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 2 functions across 2 files.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Title check	✅ Passed	The title clearly identifies the bugfix and the affected Kimi K3 NVFP4 MoE weight conversion path. It matches the main changes that reduce memory use and prevent OOM failures.
Description check	✅ Passed	The description explains the fragmented-memory warnings and OOM failures, identifies the NVFP4 MoE post-processing optimization, and describes the preallocation approach. The empty test plan and test …

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Actionable comments posted: 1

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Inline comments:
In `@vllm/model_executor/model_loader/base_loader.py`:
- Line 77: Update BaseModelLoader.load_model to guard the
torch.accelerator.empty_cache() call when HPU is active, using an HPU-safe
cleanup path or skipping it, while preserving cache cleanup and allowing
finalize_layerwise_processing() and process_weights_after_loading() to run.

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ℹ️ Review info

⚙️ Run configuration

Configuration used: Repository UI

Review profile: CHILL

Plan: Team

Run ID: ef99501a-7831-42a1-93df-5da4a284b105

📥 Commits

Reviewing files that changed from the base of the PR and between 8f269a9 and 148cd13.

📒 Files selected for processing (2)

• vllm/model_executor/layers/quantization/utils/flashinfer_fp4_moe.py
• vllm/model_executor/model_loader/base_loader.py

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[mgoin]

/ci run

[github-actions]

✅ Triggered Buildkite CI #87358 for commit 148cd134a7b5.

[wzhao18]

/ci retry

[github-actions]

✅ Queued 3 failed job(s) for retry in Buildkite CI #87358.

[wzhao18]

/ci cancel

[github-actions]

✅ Requested cancellation of 1 CI build for wzhao/fix-k3-nvfp4-load: #87358.

[wzhao18]

/ci run

[github-actions]

✅ Triggered Buildkite CI #87373 for commit 2d3f78a47b64.