Increase mem eff backward performance (#101847)
# Summary
This is another upstream which is much smaller than the previous.
This bumps the kernel versions from xformers
Current: [6425fd0cacb1a6579aa2f0c4a570b737cb10e9c3](https://github.com/facebookresearch/xformers/commit/6425fd0cacb1a6579aa2f0c4a570b737cb10e9c3)
With this PR: [1d635e193e169fc677b2e7fa42dad7ebe88eec9e](https://github.com/facebookresearch/xformers/commit/1d635e193e169fc677b2e7fa42dad7ebe88eec9e)
### Notable Changes:
- Drastically improve the BW pass in multiple cases (especially when B*numHeads < 100)
- H100 Support: *Warning* While these kernels have been added, we don't have the CI/CD machines to test.
- Enables a deterministic mode.
## Specific Changes
- Updates to the backward kernel.
- Added num_splits_key which we hard code to -1. (This is a another performance knob that we set to the heuristic)
- Update gen_code and kernels to produce h100 instantiations.
### Due Diligence Checks:
* CUDA_lib size: No changes in size
#### Peformance
* Micro Benchmark: (batch_size: 1, num_heads=25, seq_len=4096, embed_dim = 64 | grid:[1,25,1]block: [128,1,1])
* MemEfficientAttention Backward Kernel: 27.972 ms
* After the updated Xformers code(https://github.com/pytorch/pytorch/pull/100583): 23.958 ms
* With this PR: 4.085 ms
* Ran micro benchmarks on sdpa_forw().sum().backward() over a range of dtypes, and input shapes
* Geo_mean increase -> 1.17x
* Max increase -> 2.95x
* min_increase -> 0.8x
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101847
Approved by: https://github.com/cpuhrsch
