Reduce the Cuda Graph memory footprint when running with DBO

[SageMoore]

Purpose

This PR reduces the memory footprint of cudagraphs when running with DBO by only constructing non-dbo cudagraphs for shapes that DBO doesn't support.

Test Plan

lm_eval deepseek-ai/DeepSeek-V2-Lite

|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.3633|±  |0.0278|
|     |       |strict-match    |     5|exact_match|↑  |0.3567|±  |0.0277|

Test Result

Sizes before

(EngineCore_DP4 pid=82745) INFO 09-26 08:51:47 [gpu_model_runner.py:3443] Graph capturing finished in 75 secs, took -4.58 GiB
(EngineCore_DP14 pid=80459) INFO 09-26 08:51:47 [gpu_model_runner.py:3443] Graph capturing finished in 75 secs, took -4.57 GiB

Sizes after

(EngineCore_DP0 pid=675149) INFO 09-26 18:32:37 [v1/worker/gpu_model_runner.py:3443] Graph capturing finished in 28 secs, took 3.43 GiB
(EngineCore_DP1 pid=675150) INFO 09-26 18:32:37 [v1/worker/gpu_model_runner.py:3443] Graph capturing finished in 28 secs, took 3.43 GiB

This size can be further reduced by running with full cudagraphs only

(EngineCore_DP1 pid=3384884) INFO 09-26 16:37:22 [gpu_model_runner.py:3375] Graph capturing finished in 18 secs, took 1.99 GiB
(EngineCore_DP0 pid=3384883) INFO 09-26 16:37:22 [gpu_model_runner.py:3375] Graph capturing finished in 18 secs, took 1.99 GiB

Which is around what non-DBO is with both styles of cudagraphs turned on

(EngineCore_DP0 pid=3307545) INFO 09-26 16:24:57 [gpu_model_runner.py:3375] Graph capturing finished in 18 secs, took 2.12 GiB
(EngineCore_DP1 pid=3307546) INFO 09-26 16:24:57 [gpu_model_runner.py:3375] Graph capturing finished in 18 secs, took 2.12 GiB

[gemini-code-assist]

Code Review

This pull request introduces an effective optimization to reduce the memory footprint of CUDA graphs when using Dynamic Batching and Overlapping (DBO). The main change avoids capturing both microbatched and non-microbatched graphs for the same shape, instead capturing only the appropriate graph type. This directly contributes to lower memory usage. A corresponding change correctly handles runtime scenarios where microbatching is aborted, by falling back to eager execution to prevent graph mismatches. The implementation is clean, logical, and appears to be correct. I have not identified any issues of high or critical severity.