Feature request: don't specify attn_flash. Select when possible

[pfeatherstone]

Rather than manually specifying attn_flash, why not allow the code to adaptively figure out if possible in the forward() method?
As far as i can tell you can use it when there is no fancy relative positional bias and not doing an ONNX export.
I feel like it would be a case of doing something like:

if not onnx_export and not t5_rel_bias and not ...:
     return F.scaled_dot_product_attention(...)
else:
    return some_vanilla_sdpa(...)

I don't know if there is way to introspect if we're doing an ONNX export. If not, we could simply add a parameter to the forward() method like onnx_exporting

[pfeatherstone]

Or at least, it would be nice if you could specify attn_flash in forward() not in the constructor of the transformer. Then during training i can set to True, and during ONNX export (also during training), i can set to False. Alternatively, I can recreate a model and copy the weights during export.

[pfeatherstone]

So, the reason for this is: I would like to train with scaled_dot_product_attention() but when I'm doing my checkpointing, i export to ONNX, which doesn't support Flash. So I have to create a fresh model with Flash disable, port the weights, then do the ONNX export. If attn_flash were specified in the forward method, i wouldn't have to copy the model, i could just change the arguments.
Also, in the forward pass, you could check whether Flash is possible depending on:

• sparse_topk == False
• talking_heads == False
• torch.__version__ >= 2.0.0
• either attn_mask is not None or is_causal==True (not both)
• isinstance(rel_pos, RelativePositionBias) == False
• isinstance(rel_pos, DynamicPositionBias) == False
• not residual_attn and not cross_residual_attn
• somehow detect if doing onnx export

If either of these is false, set flash = False.
Then basically you never have to worry about it and the model will always do the right thing without the user having to worry about it.