Switch Windows CI jobs to G5 runners (#91727)
### Changelist
* Change Windows TORCH_CUDA_ARCH_LIST from `7.0` to `8.6` to compatible with NVIDIA A10G TPU
* Correctly disable some tests that requires flash attention, which is not available on Windows at the moment. This has been fixed by https://github.com/pytorch/pytorch/pull/91979
* G5 runner has `AMD EPYC 7R32` CPU, not an Intel one
* This seems to change the behavior of `GetDefaultMobileCPUAllocator` in `cpu_profiling_allocator_test`. This might need to be investigated further (TODO: TRACKING ISSUE). In the meantime, the test has been updated accordingly to use `GetDefaultCPUAllocator` correctly instead of `GetDefaultMobileCPUAllocator` for mobile build
* Also one periodic test `test_cpu_gpu_parity_nn_Conv3d_cuda_float32` fails with Tensor not close error when comparing grad tensors between CPU and GPU. This is fixed by turning off TF32 for the test.
### Performance gain
* (CURRENT) p3.2xlarge - https://hud.pytorch.org/tts shows each Windows CUDA shards (1-5 + functorch) takes about 2 hours to finish (duration)
* (NEW RUNNER) g5.4xlarge - The very rough estimation of the duration is 1h30m for each shard, meaning a half an hour gain (**25%**)
### Pricing
On demand hourly rate:
* (CURRENT) p3.2xlarge: $3.428. Total = Total hours spent on Windows CUDA tests * 3.428
* (NEW RUNNER) g5.4xlarge: $2.36. Total = Total hours spent on Windows CUDA tests * Duration gain (0.75) * 2.36
So the current runner is not only more expensive but is also slower. Switching to G5 runners for Windows should cut down the cost by (3.428 - 0.75 * 2.36) / 3.428 = **~45%**
### Rolling out
https://github.com/pytorch/test-infra/pull/1376 needs to be reviewed and approved to ensure the capacity of the runner before PR can be merged.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91727
Approved by: https://github.com/ZainRizvi, https://github.com/malfet, https://github.com/seemethere
