Raise value error when it falls back to native pytorch softmax and attn_mask is None
# What does this PR do ?

When softmax fused kernel is [not available](https://github.com/NVIDIA/apex/blob/85e9eddece9d4ac72b48c2407f8162f2173e1bf4/apex/transformer/functional/fused_softmax.py#L222), it will fall back to pytorch softmax forward in NeMo.

In apex, the mask is override based on your `attn_layer_type`. So it will always be set to either [causal or padding type of masking](https://github.com/NVIDIA/apex/blob/85e9eddece9d4ac72b48c2407f8162f2173e1bf4/apex/transformer/functional/fused_softmax.py#L205). While when it falls back to NeMo, it will actually do None masking fwd. This will cause in issues in training and inference when your tensor shape doesn't fit kernel or you are using 32 precision. 

We propose few improvement:
1. In apex, the user should be warned, no matter what they set in mask, it will be override based on `attn_layer_type`.
2. In every NeMo models, the attn mask should always be set when it's trying to use `MatchedScaleMaskSoftmax`.

**Collection**: Megatron models use fused softmax from apex, but attn_mask is None.

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
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```python
# Add a code snippet demonstrating how to use this 
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**Pre checks**:
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- [ ] New Feature
- [ ] Bugfix
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# Additional Information
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