Mamba inference opt

[wdykas]

What does this PR do ?

Inference optimizations for nano

Cached -exp(A_log.float()) in the Mamba decode path so the three elementwise kernels (float-cast, exp, neg) that ran per-layer per-token are now computed once per inference session. Moved the batch_indices int64 upcast from the per-layer forward into the MambaMetadata buffer allocation, eliminating a fourth small kernel from the hot path.

edit: we added more on here:

Config	Run 1	Run 2	Run 3	Mean	vs baseline	vs previous
Baseline	115.04	114.76	115.64	115.15	—	—
Opt1 (cached A + int64 buf)	116.64	116.94	116.56	116.71	+1.35%	+1.35%
Opt2 (+ pre-expanded A)	117.63	118.47	118.47	118.19	+2.64%	+1.27%

MoE optimization not part of this graph
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[wdykas]

/ok to test a0dda2c

[santhnm2]

LGTM

[wdykas]

/ok to test b67fd23

[wdykas]

/ok to test 8d27f1d

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You can track the progress here: https://github.com/NVIDIA/Megatron-LM/actions/runs/24798603343