DiT engines: replace BF16 with FP16-mixed (FP32 islands) + add FP32 variants

Drop dit_bf16.trt for all three SA3 DiTs (sm-music, sm-sfx, sa3-m). BF16
quantization error compounds over 8 pingpong sampling steps, drifting
cos-sim vs PT FP32 from 0.99 (single step) to 0.81 (final latent) and
producing audibly degraded output.

Replace with dit_fp16mixed.trt (the canonical from now on): FP16 trunk
with FP32 islands around every RMSNorm chain (Pow+ReduceMean+Sqrt+Mul),
every attention Softmax, and the RoPE region (anything reachable from a
Cast(to=FP32) feeding Cos/Sin/Einsum). 140 RMSNorms + 40 Softmaxes per
sm-music block, more for medium. Built with STRONGLY_TYPED so TRT honors
the explicit dtypes (no auto-promotion). Matches MLX's "FP16 with implicit
FP32 reductions via fused kernels" recipe.

Per-step cos-sim vs PT FP32: 0.99997 single-step, 0.998 over 8 steps.
RMS-curve correlation: 0.998. Audio basically indistinguishable from
FP32 PyTorch reference at 1/15 the wall time (43 ms vs 630 ms).

Also adds dit_fp32.trt (1.8 GB sm-*, 5.8 GB medium) for users who want
bit-for-bit parity with the PyTorch reference at the cost of ~3x slower
inference.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

tensorRT/sm_90/sa3-m/{dit_bf16.trt → dit_fp16mixed.trt}

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tensorRT/sm_90/sa3-sm-music/{dit_bf16.trt → dit_fp16mixed.trt}

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tensorRT/sm_90/sa3-sm-sfx/{dit_bf16.trt → dit_fp16mixed.trt}

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