Fuse ScaledSum and its backward BatchScale (#16517)
### Fuse ScaledSum and its backward BatchScale
For deberta models, there is a pattern
a / scalar_0 + b / scalar_1 + c / scalar_2
We can fuse this into ScaledSum operator, taking 2(or 3) inputs, and
2(or 3) attributes scalar, generating one output.
For the backward, the gradient of a, b and c will be computed with
BatchScale.
### Benchmark on 8x32GV100
```bash
torchrun --nproc_per_node=8 examples/onnxruntime/training/language-modeling/run_mlm.py --model_name_or_path microsoft/deberta-v3-large --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --num_train_epochs 10 --do_train --overwrite_output_dir --output_dir ./outputs/ --seed 1137 --fp16 --report_to none --optim adamw_ort_fused --max_steps 400 --logging_steps 1 --use_module_with_loss --deepspeed aml_ds_config_zero_1.json --per_device_train_batch_size 10
```
#### Main Branch
```
Total overhead: 127954ms where export takes 116489ms.
epoch = 14.29
train_loss = 4.9803
train_runtime = 0:10:27.29
train_samples = 2223
train_samples_per_second = 51.013
train_steps_per_second = 0.638
throughput per GPU = 14.29* 2223/ (627.29 - 127.954) / 8 (gpu) = 7.952 samples/second
```
#### This PR
```
Total overhead: 128761ms where export takes 118510ms.
***** train metrics *****
epoch = 14.29
train_loss = 4.6144
train_runtime = 0:10:04.31
train_samples = 2223
train_samples_per_second = 52.953
train_steps_per_second = 0.662
throughput per GPU = 14.29*2223 / (604.31 - 128.761) / 8 = 8.350 samples/second
```
5.x% performance gains.
