fix(critical): DeltaNet NaN 99.98% — clamp gate logits before exp to prevent overflow

Docker log reveals: 'NaN in prefill GatedDeltaNet layer 0 (frac=0.9998)'
Every DeltaNet (linear attention) layer produces 99.98% NaN values.
nan_to_num replaces them with zeros, destroying model output quality.
This is the root cause of d10_thinking_disable_ctk gibberish output.

Root cause: g.cumsum(dim=-1) accumulates unbounded gate logits.
When fed to exp(), large values overflow to Inf, which propagates
as NaN through subsequent matmul and forward_sub operations.

Fix: Clamp cumulative gate logits to [-20, 20] before any exp().
Range keeps exp in [~2e-9, ~5e8] — safe for float32 accumulation.
Inspired by CCCL dispatch_reduce_deterministic.cuh: numerical
stability requires bounded intermediate values (RFA pattern).

Also in this log:
- FusedMoE: 'vllm_moe_topk_softmax' not in ixformer → PyTorch fallback
  (expected, cannot fix without BI-V100 kernel rebuild)
- OOM at end of sub168: 31.72 GiB GPU with 30.86 GiB allocated

CCCL input: dispatch_reduce_deterministic.cuh RFA pattern,
tuning_batch_memcpy.cuh (small=128t×4buf, large=256t×32B)

qwen3_6_scripts/qwen3_5.py

@@ -112,6 +112,11 @@ def _torch_chunk_gated_delta_rule(
         diagonal=0)
 
     g = g.cumsum(dim=-1)
+    # Clamp gate logits to prevent exp overflow → NaN cascade.
+    # CCCL dispatch_reduce_deterministic.cuh: numerical stability requires
+    # bounded intermediate values. Gate logit range [-20, 20] keeps exp
+    # in [~2e-9, ~5e8] — safe for float32 accumulation.
+    g = g.clamp(-20.0, 20.0)
     decay_mask = ((g.unsqueeze(-1) - g.unsqueeze(-2)).tril().exp().float()).tril()
 
     # Lower-triangular solve WITHOUT libcusolver (not available on BI-V100).