2x more memory efficient Graph-based RNN-T
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Thank you for improving NeMo's documentation! [🤖]: Hi @artbataev 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/12328248321) for this PR just finished successfully

So it might be time to merge this PR or get some approvals

I'm just a bot so I'll leave it you what to do next.

//cc @pablo-garay @ko3n1g
# What does this PR do ?

- reduces memory usage of Graph-Transducer by 2 times: Triton-based implementation of log-softmax operation for RNN-T 
  - idea: store log probabilities only for blank/target labels (needed to compute RNN-T loss), not for all logits
  - if we calculate full logits (Joint tensor), this is the optimal implementation: most of the memory is used by `logits/+gradients` (2x logits)
  - previous implementation required `logits/+gradients`, `log_probs/+gradients` (4x logits)
- adds an option to return RNN-T graphs from `forward` along with the loss value for further usage (e.g., using alignment)

**Example**: bfloat16, 15 sec, 8x subsampling (~FastConformer), 3 words/sec, vocab size 1024, batch size 32

|                         | Logits  | Extra Memory | Time     |
| ----------------------- | ------- | ------------ | -------- |
| Without Triton (before) | 1.04 GB | 3.20 GB      | 0.08 sec |
| **With Triton (here)**  | 1.04 GB | **1.13 GB**  | **0.06 sec** |

3x extra memory reduction, 2x total memory reduction (4.24 GB -> 2.17 GB).

Convergence: stable, see https://wandb.ai/nvidia/Graph_RNNT_Optimized

**Collection**: [ASR]

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