[Feature] Support GLM-5.2 source-layer DSA indexer training

[sjyouuuuug]

Motivation

Enable training of GLM-5.2 source-layer DSA indexers with attention-derived KL supervision.

Changes

• Add a cuDNN DSA indexer KL-loss operator and custom backward integration.
• Separate differentiable indexer feature projection from no-grad Top-k selection.
• Construct sparse attention teacher and indexer student distributions.
• Aggregate source-layer indexer losses at the model boundary.
• Keep physical MTP indexers frozen.
• Add a GLM-5.2 training configuration and launch script.
• Add tests for KL construction, gradient boundaries, score scaling, query masking, source/shared-layer behavior, and MTP freezing.

Validation

• Indexer loss CPU tests: 10 passed.
• Source-indexer attention tests: 10 passed.
• Multi-GPU tiny training completed all configured training steps.
• Indexer loss decreased during the 300-step training run.

Current limitations

• IndexShare reuses source-layer Top-k indices, but shared-layer teacher supervision is not included.
• Indexer training currently requires:
  • SP_SIZE=1
  • INTRA_LAYER_MICRO_BATCH=1
  • the cudnn_dsa backend

Results