AWQ Quantization: Standard vs Dynamic Heuristic

Comparison of two AWQ quantization approaches for 4-bit weight quantization of Large Language Models.

Methods

1. Standard AWQ (awq_stand_xl.py)

• Group-wise asymmetric quantization [0, 15]
• L2 salience metric: E[X²] for channel importance
• No heuristic rounding: Uses nearest rounding
• Batched sequential processing: Memory-efficient layer-by-layer quantization
• Special lm_head handling: Splits large layers into chunks to avoid OOM

2. Dynamic Heuristic AWQ (awq_dh_xl.py)

• All features from Standard AWQ, PLUS:
• Heuristic-guided rounding: Global greedy correction to minimize quantization error
• Dynamic outlier detection: Kneedle algorithm adaptively identifies outliers per layer
• Flip constraint: Limits flips per output channel to max_flip_percent (default: 1%)
• Outlier masking: Protects high-activation channels from flipping

Usage

Quantize with Standard AWQ

python awq_stand_xl.py \
    --model-path ./models/Mistral-7B-v0.3 \
    --output-dir ./quantized_models/Mistral-7B-v0.3_awq_standard \
    --n-calib 128 \
    --layer-batch-size 16

Quantize with Dynamic Heuristic AWQ

python awq_dh_xl.py \
    --model-path ./models/Mistral-7B-v0.3 \
    --output-dir ./quantized_models/Mistral-7B-v0.3_awq_dh \
    --n-calib 128 \
    --knee-tolerance 0.0 \
    --max-flip-percent 0.01 \
    --layer-batch-size 16

Evaluate and Compare

python compare_awq_slicing.py \
    --heuristic-path ./quantized_models/Mistral-7B-v0.3_awq_dh \
    --standard-path ./quantized_models/Mistral-7B-v0.3_awq_standard \
    --n-samples 2000

Key Parameters

Parameter	Default	Description
--n-calib	128	Number of calibration samples
--n-grid	20	Grid search points for α (scaling factor)
--group-size	128	Quantization group size
--layer-batch-size	16	Layers per batch (higher = more memory)
--lmhead-chunks	4	Split lm_head into N chunks (higher = less memory)
--knee-tolerance	0.0	Outlier detection tolerance (Dynamic AWQ only)
--max-flip-percent	0.01	Max flips per output channel = 1% of in_features (Dynamic AWQ only)

Results

Mistral-7B-v0.3

Perplexity (↓ lower is better):

Dataset	Origin	Standard AWQ	Dynamic AWQ
WikiText-2	4.8454	4.9778	4.9689
C4	7.6040	7.7892	7.7830

Improvement: Dynamic AWQ achieves 0.89% better WikiText-2 and 0.62% better C4 perplexity vs Standard AWQ.

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Llama-3-8B

Perplexity (↓ lower is better):

Dataset	Origin	Standard AWQ	Dynamic AWQ
WikiText-2	5.4425	6.7386	6.6863
C4	8.6383	10.4595	10.3014

Improvement: Dynamic AWQ achieves 5.23% better C4 perplexity vs Standard AWQ. Note: Dynamic outlier detection found 0.65% outliers on average (vs fixed 5%).

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Llama-2-7B

Perplexity (↓ lower is better):

Dataset	Origin	Standard AWQ	Dynamic AWQ
WikiText-2	4.9712	5.1280	5.1270
C4	6.5748	6.7986	6.7983

Improvement: Marginal improvements; both methods perform similarly on this model.

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Qwen2.5-7B

Perplexity (↓ lower is better):

Dataset	Origin	Standard AWQ	Dynamic AWQ
WikiText-2	23.1382	24.0180	23.3029
C4	36.1769	37.5713	36.4447

Improvement: Dynamic AWQ achieves 7.15% better WikiText-2 and 11.27% better C4 perplexity vs Standard AWQ. Significant gains on this model family.

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Summary

Dynamic Heuristic AWQ consistently outperforms Standard AWQ across all tested models:

• Mistral-7B-v0.3: +0.6-0.9% improvement
• Llama-3-8B: +5.2% improvement (C4)
• Llama-2-7B: Marginal improvement
• Qwen2.5-7B: +7-11% improvement

Key Advantages of Dynamic AWQ:

1. Adaptive outlier detection: Kneedle algorithm adjusts per layer (vs fixed 5%)
2. Flip constraint: Prevents over-correction (limits to 1% per channel)
3. Better optimization: Global greedy rounding reduces quantization error

Hardware Requirements

• GPU: 16GB+ VRAM recommended (tested on A100/V100)
  • Model: ~5-7GB
  • Activations: ~3-5GB per batch
  • Peak: ~14-20GB with default settings
• CPU fallback: Supported but 10-20× slower
• Storage: ~15GB for models and cached datasets

Memory Optimization

For limited VRAM (8-12GB):

python awq_dh_xl.py \
    --model-path ./models/Mistral-7B-v0.3 \
    --layer-batch-size 8 \           # Reduce batch size
    --lmhead-chunks 8 \               # Split lm_head more
    --n-calib 64 \                    # Fewer calibration samples
    --calib-dataset wikitext2-simple  # Smaller dataset

Citation

If you use this code, please cite:

Dynamic Heuristic AWQ: Adaptive Quantization with Kneedle-based Outlier Detection

License

MIT License