Implement Flash Attention 2 for webgpu EP (#23576)
### Description
This change implements FlashAttention 2 for the webgpu EP for the MHA
operator.
Numbers from Alderlake device show a 2.2x speed up for prefill, which
considering that Attention is 50% of prefill phase (other 50% being
MatMul) implies 4x speed up for Attention with this implementation. This
is inline with the expected perf gain of 2-4x with FlashAttention over
regular attention.
```
Baseline
PS C:\onnxruntime> C:\model_benchmark\model_benchmark.exe -i C:\Phi-3.5-mini-instruct-onnx-web\Phi-3.5-mini-instruct-onnx-web\ -l 1000
Batch size: 1, prompt tokens: 1001, tokens to generate: 128
Prompt processing (time to first token):
avg (us): 9.54997e+06 <<<<<
avg (tokens/s): 104.817
p50 (us): 9.49218e+06
stddev (us): 251442
n: 5 * 1001 token(s)
------
With FlashAttention 2
PS C:\onnxruntime> C:\model_benchmark\model_benchmark.exe -i C:\Phi-3.5-mini-instruct-onnx-web\Phi-3.5-mini-instruct-onnx-web\ -l 1000
Batch size: 1, prompt tokens: 1001, tokens to generate: 128
Prompt processing (time to first token):
avg (us): 4.27937e+06 <<<<<
avg (tokens/s): 233.913
p50 (us): 4.27687e+06
stddev (us): 5344.1
n: 5 * 1001 token(s)
```
### Motivation and Context
On integrated GPUs memory bandwidth is premium, Flash attention makes
softmax computation (and therefore output attention vector computation)
a running operation instead of maintaining full QKt attention scores in
memory. As a result, we see significant improvements in prefill speed -
200% speed up measured here.
This change uses techniques from co-operative matrix multiply to use
registers from a subgroup for fast in register matrix multiply. Without
the co-operative matrix multiply technique ALD showed about 6.0s prefill
time.
Tested on ALD/TGL intel integrated and Nvidia 4070.
### Future Work
- Fine tuning and profiling optimizations.
- Current implement is for prefill only, a generation phase optimized
FA2 implementation is possible, however attention is a tiny part of the
generation phase.
