[Quantization] Modify the logic of BNB double quantization

[jeejeelee]

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Purpose

For BNB's double quantization, two dequantizations need to be executed. This PR moves the first dequantization to the weight loading stage, so that only one dequantization needs to be executed during inference, which can slightly improve inference efficiency

Test Plan

Test Result

(Optional) Documentation Update

[github-actions]

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[gemini-code-assist]

Summary of Changes

Hello @jeejeelee, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request optimizes the handling of BitsAndBytes (BNB) double quantization by shifting one of the two necessary dequantization steps from inference time to the model loading phase. This change aims to slightly improve inference efficiency by pre-processing the quantized constants, ensuring that only a single dequantization operation is required during the actual forward pass.

Highlights

• Performance Optimization: I've moved the first dequantization step for BitsAndBytes (BNB) double quantization from inference time to the model weight loading stage. This change aims to reduce computational overhead during actual inference by pre-processing the quantized constants.
• New Utility Function: I've introduced a new helper function, dequantize_dq, within bitsandbytes_loader.py to encapsulate the logic for dequantizing double-quantized constants during weight loading.

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[gemini-code-assist]

Code Review

The pull request modifies the logic of BNB double quantization by moving the first dequantization to the weight loading stage to improve inference efficiency. The code changes involve adding a dequantize_dq function and calling it during weight loading. The review suggests adding comments to improve code clarity and understanding.

[gemini-code-assist]

Consider adding a comment to explain the purpose of adding quant_state.offset to absmax.

            absmax = dequantize_blockwise(quant_state.absmax,
                                          quant_state.state2)
            absmax += quant_state.offset # Apply offset after dequantization

[gemini-code-assist]

Consider adding a comment explaining why this conversion is necessary.

            if absmax.dtype != torch.float32:
                absmax = absmax.float() # Ensure absmax is float32 for consistency