Add fast path of qmean/qstd for quantized CPU (#70172)
Add fast path of qmean and qstd when computation is done in innermost dimensions for quantized CPU. The fast path supports inputs in contiguous memory format.
For example:
```python
X = torch.randn((2,3,4,5), dtype=torch.float)
qX = torch.quantize_per_tensor(X, scale, zero_point, torch_type)
# dim can be: -1, (-1, -2), (-1, -2, -3), (-1, -2, -3, -4), 3, (3, 2), (3, 2, 1), (3, 2, 1, 0) or None
dim = -1
qY = torch.mean(qX, dim) # qY = torch.std(qX, dim)
```
**Performance test results**
Test Env:
- Intel® Xeon® CLX-8260
- 1 instance, 4 cores
- Using Jemalloc
Test method:
Create 4d contiguous tensors as inputs, set `dim` to the innermost two dimensions `(-1, -2)`, then do the following tests
- Quantize inputs and use the fast path
- Quantize inputs and use the reference path
- Use fp32 kernel (no quantization)
Mean: exec time (us) vs. shape
Std: exec time (us) vs. shape
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70172
Approved by: https://github.com/malfet
