[Bugfix][GLM] Invalidate merged KDA convolution weights after refit

[ItsRoy69]

Purpose

Fixes #55087.

GLM-5.3 KDA lazily merges the q/k/v convolution weights into a single cached tensor for the fused causal-conv path. During online RL or refit workflows, the underlying q/k/v convolution weights can be updated after warmup, but the derived merged tensor may remain stale. Subsequent inference can then continue using old or dummy weights even after the model weights have been refreshed.

This change:

• adds explicit invalidation for the merged KDA convolution-weight cache
• invalidates the KDA cache before GLM-5.3 model weight loading
• covers both the direct model-loading path and the top-level CausalLM loading path
• keeps the merged-weight cache for normal inference when weights are unchanged
• retains version/storage checks as additional protection against direct tensor mutations
• preserves the hot-path behavior by rebuilding only when needed

This PR is a follow-up to #53906, which introduced the GLM-5.3 implementation.

Root cause

The KDA layer builds a single merged q|k|v convolution weight tensor and caches it in _merged_conv_weight. That cache was not always invalidated when the source q/k/v conv weights were replaced. In online-RL refit/update flows, this allowed stale merged weights to persist across reloads and be reused in later inference steps.

Fix

• add a version-aware merged-weight cache key in the KDA layer
• rebuild the merged tensor when any source conv weight changes
• add explicit invalidation hooks for the merged KDA cache
• invoke cache invalidation before GLM-5.3 weight loading so the next access rebuilds from fresh weights

Test Plan

Run the focused regression test on a CUDA-enabled environment:

pytest tests/models/glm5next/test_kda.py -v

Run the relevant GLM-5.3 model tests:

pytest tests/models/glm5next -v

Run static validation:

python -m py_compile \
  vllm/models/glm5next/nvidia/kda.py \
  vllm/models/glm5next/nvidia/model.py

ruff check \
  vllm/models/glm5next/nvidia/kda.py \
  vllm/models/glm5next/nvidia/model.py

git diff --check

Test Result

Static validation completed successfully:

• Python compilation passed.
• Ruff passed.
• git diff --check passed.

The full CUDA/model pytest suite was not run locally because this environment does not have the repo virtualenv, PyTorch, or CUDA runtime available.

CUDA validation is still required on the GLM-5.3 PR validation environment. The focused regression test should verify that:

1. the merged convolution weight is built and cached
2. a refit using param.data.copy_() updates a source convolution weight
3. explicit invalidation clears the stale merged tensor
4. the next access rebuilds the merged tensor from the updated weights

Test Environment

Local environment:

• Windows
• no repository virtualenv available
• no PyTorch installation available
• no CUDA runtime available

Scope

Changed files:

• kda.py
• model.py
• test_kda.py

No documentation update is required because this is an internal cache invalidation fix.

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Checklist

• The purpose of the PR is documented and links issue [Bug]: GLM-5.3 KDA keeps stale merged convolution weights after refit #55087.
• The root cause and fix are clearly described.
• The test plan includes focused, model-level, and static validation commands.
• Local environment limitations are documented.
• No documentation update is required for this internal implementation fix.

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📥 Commits

Reviewing files that changed from the base of the PR and between 30e670a and 2271e2d.

📒 Files selected for processing (3)

• tests/models/glm5next/test_kda.py
• vllm/models/glm5next/nvidia/kda.py
• vllm/models/glm5next/nvidia/model.py

🚧 Files skipped from review as they are similar to previous changes (1)

• vllm/models/glm5next/nvidia/model.py

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📝 Summary

Summary by CodeRabbit

• Bug Fixes

  • Ensured merged attention convolution weights refresh correctly after model weights are updated.
  • Prevented stale cached weights during model and causal language model weight loading.
  • Preserved reuse of cached merged weights when source weights have not changed.

• Tests

  • Added coverage verifying refreshed convolution weights reflect updated source values and return a new tensor.
  • Added coverage confirming cached weights are reused when no invalidation is required.

Walkthrough

The change adds explicit invalidation for cached merged GLM-5 KDA convolution weights. Weight-loading paths clear the caches before loading weights, and tests verify cache reuse and rebuilding after a k-convolution weight refit.

Changes

KDA cache refresh

Layer / File(s)	Summary
Merged convolution weight cache vllm/models/glm5next/nvidia/kda.py, tests/models/glm5next/test_kda.py	Glm5NextLinearAttention lazily caches merged q, k, and v convolution weights. _forward uses the cache helper. Explicit invalidation clears the cache, and tests cover reuse and rebuilding after a k-weight refit.
Model weight-load invalidation vllm/models/glm5next/nvidia/model.py	Glm5NextModel invalidates KDA caches before model-level and causal-LM weight loading.

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

Merge Risk: ⚪ Minimal · up to f1373

GLM-5.3 KDA merged convolution weights are cleared before weights are replaced, so subsequent inference rebuilds them from current q/k/v weights. The change preserves reuse for unchanged weights and has no identified merge-blocking risk.

🚥 Pre-merge checks | ✅ 3 | ❌ 2

❌ Failed checks (2 warnings)

Check name	Status	Explanation	Resolution
Linked Issues check	⚠️ Warning	The PR adds explicit invalidation before model weight loading, but the implementation summary states that automatic version and storage checks were removed. Direct refit updates can still leave the ca…	Retain automatic validation based on source-weight changes, or invoke invalidate_kda_conv_weight_caches from every refit or weight-replacement path. Add a regression test that updates source weights without a separate manual invalidation ca…
Docstring Coverage	⚠️ Warning	Docstring coverage is 20.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 15 functions across 3 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (3 passed)

Check name	Status	Explanation
Out of Scope Changes check	✅ Passed	The changes are limited to KDA merged-weight caching, model loading invalidation, and focused regression tests. No unrelated code changes are identified.
Title check	✅ Passed	The title clearly summarizes the primary change: invalidating cached merged KDA convolution weights after refit.
Description check	✅ Passed	The description directly explains the stale-cache bug, the invalidation fix, affected loading paths, tests, and validation limits. It is related to the changeset.

Full details: Linked Issues check

Explanation

The PR adds explicit invalidation before model weight loading, but the implementation summary states that automatic version and storage checks were removed. Direct refit updates can still leave the cached tensor stale unless the caller invokes invalidation, and no evidence shows that the online-RL refit path invokes it. This does not fully satisfy issue #55087.

Resolution

Retain automatic validation based on source-weight changes, or invoke invalidate_kda_conv_weight_caches from every refit or weight-replacement path. Add a regression test that updates source weights without a separate manual invalidation call and verifies that the merged tensor rebuilds [#55087].

• Fix all pre-merge checks with AI

✨ Finishing Touches 💡 1

🛠️ Fix failing CI checks 💡

• Create stacked PR
• Commit on current branch

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