Feat (nn/sdpa): quantization of scaled dot-product attention

[nickfraser]

Reason for this PR

Make it easier for users to quantize attention layers.

Changes Made in this PR

Achieved by providing:

• A modular equivalent to the torch.nn.functional.scaled_dot_product_attention functional
• A quantized version of this module
• Adding code to convert the three options

Testing Summary

Tests:

• Layer replacement test in LLM entry-point
• Basic accuracy test for OPT
• Basic graph replacement test (covered by LLM entry-point test)
• SDPA & Quant SDPA forward tests

Risk Highlight

Adapted from pseudocode in PyTorch's documentation. Otherwise, this code barely touches any existing code, that shouldn't break any existing Brevitas features.

• This PR includes code from another work (please detail).
• This PR contains API-breaking changes.
• This PR depends on work in another PR (please provide links/details).
• This PR introduces new dependencies (please detail).
• There are coverage gaps not covered by tests.
• Documentation updates required in subsequent PR.

Checklist

• Code comments added to any hard-to-understand areas, if applicable.
• Changes generate no new warnings.
• Updated any relevant tests, if applicable.
• No conflicts with destination dev branch.
• I reviewed my own code changes.
• Initial CI/CD passing.
• 1+ reviews given, and any review issues addressed and approved.
• Post-review full CI/CD passing.

[nickfraser]

We should merge #1088 before this.