Fix Gemma2 dtype issue when storing weights in float16 precision

[Nerogar]

What does this PR do?

This change fixes an exception that can happen when storing Gemma2 weights in float16 format, but using a bfloat16 autocast context. In that case, hidden_states can be in bfloat16 format, while attention_mask is in float16 format. That will lead to the following error

    attention_mask = torch.where(sliding_window_mask, min_dtype, attention_mask)
RuntimeError: value cannot be converted to type at::Half without overflow

Using the min value of the attention_mask instead should be safe, since the value is only used in the torch.where operation.

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