GANQ minimizes LLM quantization error for faster, more accurate inference without retraining.
GPU-adaptive algorithm enhances LLM weight quantization, boosting both accuracy and speed.
Paper - https://arxiv.org/abs/2501.12956
Original Problem: 😞:
→ LLMs are resource-intensive, hindering deployment.
→ Current weight quantization methods often degrade performance or require extensive retraining.
→ Existing hardware lacks native mixed-precision support, leading to inefficiencies.
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Solution in this Paper: 🤔:
→ GANQ is a layer-wise, post-training, non-uniform quantization method optimized for lookup table-based mixed-precision General Matrix Multiplication.
→ GANQ uses a GPU-adaptive optimization algorithm. This minimizes discrepancies between quantized and original layer outputs.
→ GANQ is inherently parallel, exploiting GPUs' power. It is compatible with existing techniques for outlier management.
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Key Insights from this Paper: 💡:
→ Non-uniform quantization better captures LLM weight distributions than uniform methods.
→ Optimizing for lookup table-based mixed-precision General Matrix Multiplication significantly boosts inference efficiency.
→ GPU-parallel, row-wise computation makes non-uniform quantization practical for large models.
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Results: ✅:
→ On WikiText2, GANQ achieves lower perplexity than other methods across various model sizes, even surpassing FP16 performance in some instances (OPT-2.7B: 12.33 perplexity vs. 12.47 for FP16).
→ For LLaMA-2-7B, GANQ maintains accuracy comparable to the full-precision model with 4-bit quantization (64.23% average accuracy vs. 64.47% for FP16) and shows minimal drop with 3-bit quantization (62.22%).
→ Achieves up to 2.57× speedup on a NVIDIA RTX 4090 GPU.
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