Create zero_delay_qkv_benchmark.py
Author: itsmenick212
Pull request: yes

Implemented Zero-Delay QKV Compression with benchmarking for HuggingFace Transformers to optimize inference runtime. 

- Added `CombinedQKVProjection` class to replace separate query, key, and value projections with a single linear layer.
- Modified BERT's attention mechanism by replacing `query`, `key`, and `value` layers with the combined QKV projection.
- Introduced a `replace_attn_qkv_with_combined` function to automate the replacement process across all layers.
- Added a benchmarking utility to compare inference runtime performance before and after QKV compression.
- Provided a complete example script for testing and benchmarking with dummy data.

This change aims to demonstrate runtime optimizations for inference-heavy use cases in Transformer models.

# What does this PR do?

Implemented Zero-Delay QKV Compression with benchmarking for HuggingFace Transformers to optimize inference runtime. 

- Added `CombinedQKVProjection` class to replace separate query, key, and value projections with a single linear layer.
- Modified BERT's attention mechanism by replacing `query`, `key`, and `value` layers with the combined QKV projection.
- Introduced a `replace_attn_qkv_with_combined` function to automate the replacement process across all layers.
- Added a benchmarking utility to compare inference runtime performance before and after QKV compression.
- Provided a complete example script for testing and benchmarking with dummy data.

This change aims to demonstrate runtime optimizations for inference-heavy use cases in Transformer models.


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Library:

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- generate: @zucchini-nlp (visual-language models) or @gante (all others)
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Documentation: @stevhliu

HF projects:

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- diffusers: [different repo](https://github.com/huggingface/diffusers)
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Comments:
Implemented Zero-Delay QKV Compression with benchmarking for HuggingFace Transformers to optimize inference runtime. 

- Added `CombinedQKVProjection` class to replace separate query, key, and value projections with a single linear layer.
- Modified BERT's attention mechanism by replacing `query`, `key`, and `value` layers with the combined QKV projection.
- Introduced a `replace_attn_qkv_with_combined` function to automate the replacement process across all layers.
- Added a benchmarking utility to compare inference runtime performance before and after QKV compression.
- Provided a complete example script for testing and benchmarking with dummy data.

This change aims to demonstrate runtime optimizations for inference-heavy use cases in Transformer models.