[moe training] update tests for torchtitan moe refactor

[danielvegamyhre]

Stacked PRs:

• [moe training] add memory bandwidth calculations to kernel benchmarking scripts #2769
• [moe training] refactor to share benchmarking and profiling utils #2767
• [moe training] update bench script to compare fp8 dynamic quant scaled_grouped_mm fwd+bwd against bf16 #2765
• [moe training] remove duplicate benchmark script #2762
• [moe training] use llama4 shapes for kernel benchmarks #2756
• [moe training] fix scaling type bug; refactor distributed tests #2749
• [moe training] work around wrap_triton bug by using normal custom ops instead for fp8 rowwise kernels  #2734
• ->[moe training] update tests for torchtitan moe refactor #2733

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[moe training] update tests for torchtitan moe refactor

Summary

• After llama4 and DSV3 MoE implementations were refactored to use 1 unified/generic MoE implementation in torchtitan (unify moe implementation for llama4 and deepseek_v3 torchtitan#1534) we need to update our integration test which tests single device e2e training + torch.compile

[pytorch-bot]

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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/2733

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