gptq

# GPTQ (Generative Pre-trained Transformer Quantization)

Post-training quantization method that compresses LLMs to 4-bit with minimal accuracy loss using group-wise quantization.

## When to use GPTQ

**Use GPTQ when:**
- Need to fit large models (70B+) on limited GPU memory
- Want 4× memory reduction with <2% accuracy loss
- Deploying on consumer GPUs (RTX 4090, 3090)
- Need faster inference (3-4× speedup vs FP16)

**Use AWQ instead when:**
- Need slightly better accuracy (<1% loss)
- Have newer GPUs (Ampere, Ada)
- Want Marlin kernel support (2× faster on some GPUs)

**Use bitsandbytes instead when:**
- Need simple integration with transformers
- Want 8-bit quantization (less compression, better quality)
- Don't need pre-quantized model files

## Quick start

### Installation

```bash
# Install AutoGPTQ
pip install auto-gptq

# With Triton (Linux only, faster)
pip install auto-gptq[triton]

# With CUDA extensions (faster)
pip install auto-gptq --no-build-isolation

# Full installation
pip install auto-gptq transformers accelerate
```

### Load pre-quantized model

```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM

# Load quantized model from HuggingFace
model_name = "TheBloke/Llama-2-7B-Chat-GPTQ"

model = AutoGPTQForCausalLM.from_quantized(
    model_name,
    device="cuda:0",
    use_triton=False  # Set True on Linux for speed
)

tokenizer = AutoTokenizer.from_pretrained(model_name)

# Generate
prompt = "Explain quantum computing"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0]))
```

### Quantize your own model

```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
from datasets import load_dataset

# Load model
model_name = "meta-llama/Llama-2-7b-chat-hf"
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Quantization config
quantize_config = BaseQuantizeConfig(
    bits=4,              # 4-bit quantization
    group_size=128,      # Group size (recommended: 128)
    desc_act=False,      # Activation order (False for CUDA kernel)
    damp_percent=0.01    # Dampening factor
)

# Load model for quantization
model = AutoGPTQForCausalLM.from_pretrained(
    model_name,
    quantize_config=quantize_config
)

# Prepare calibration data
dataset = load_dataset("c4", split="train", streaming=True)
calibration_data = [
    tokenizer(example["text"])["input_ids"][:512]
    for example in dataset.take(128)
]

# Quantize
model.quantize(calibration_data)

# Save quantized model
model.save_quantized("llama-2-7b-gptq")
tokenizer.save_pretrained("llama-2-7b-gptq")

# Push to HuggingFace
model.push_to_hub("username/llama-2-7b-gptq")
```

## Group-wise quantization

**How GPTQ works**:
1. **Group weights**: Divide each weight matrix into groups (typically 128 elements)
2. **Quantize per-group**: Each group has its own scale/zero-point
3. **Minimize error**: Uses Hessian information to minimize quantization error
4. **Result**: 4-bit weights with near-FP16 accuracy

**Group size trade-off**:

| Group Size | Model Size | Accuracy | Speed | Recommendation |
|------------|------------|----------|-------|----------------|
| -1 (per-column) | Smallest | Best | Slowest | Research only |
| 32 | Smaller | Better | Slower | High accuracy needed |
| **128** | Medium | Good | **Fast** | **Recommended default** |
| 256 | Larger | Lower | Faster | Speed critical |
| 1024 | Largest | Lowest | Fastest | Not recommended |

**Example**:
```
Weight matrix: [1024, 4096] = 4.2M elements

Group size = 128:
- Groups: 4.2M / 128 = 32,768 groups
- Each group: own 4-bit scale + zero-point
- Result: Better granularity → better accuracy
```

## Quantization configurations

### Standard 4-bit (recommended)

```python
from auto_gptq import BaseQuantizeConfig

config = BaseQuantizeConfig(
    bits=4,              # 4-bit quantization
    group_size=128,      # Standard group size
    desc_act=False,      # Faster CUDA kernel
    damp_percent=0.01    # Dampening factor
)
```

**Performance**:
- Memory: 4× reduction (70B model: 140GB → 35GB)
- Accuracy: ~1.5% perplexity increase
- Speed: 3-4× faster than FP16

### High accuracy (3-bit with larger groups)

```python
config = BaseQuantizeConfig(
    bits=3,              # 3-bit (more compression)
    group_size=128,      # Keep standard group size
    desc_act=True,       # Better accuracy (slower)
    damp_percent=0.01
)
```

**Trade-off**:
- Memory: 5× reduction
- Accuracy: ~3% perplexity increase
- Speed: 5× faster (but less accurate)

### Maximum accuracy (4-bit with small groups)

```python
config = BaseQuantizeConfig(
    bits=4,
    group_size=32,       # Smaller groups (better accuracy)
    desc_act=True,       # Activation reordering
    damp_percent=0.005   # Lower dampening
)
```

**Trade-off**:
- Memory: 3.5× reduction (slightly larger)
- Accuracy: ~0.8% perplexity increase (best)
- Speed: 2-3× faster (kernel overhead)

## Kernel backends

### ExLlamaV2 (default, fastest)

```python
model = AutoGPTQForCausalLM.from_quantized(
    model_name,
    device="cuda:0",
    use_exllama=True,      # Use ExLlamaV2
    exllama_config={"version": 2}
)
```

**Performance**: 1.5-2× faster than Triton

### Marlin (Ampere+ GPUs)

```python
# Quantize with Marlin format
config = BaseQuantizeConfig(
    bits=4,
    group_size=128,
    desc_act=False  # Required for Marlin
)

model.quantize(calibration_data, use_marlin=True)

# Load with Marlin
model = AutoGPTQForCausalLM.from_quantized(
    model_name,
    device="cuda:0",
    use_marlin=True  # 2× faster on A100/H100
)
```

**Requirements**:
- NVIDIA Ampere or newer (A100, H100, RTX 40xx)
- Compute capability ≥ 8.0

### Triton (Linux only)

```python
model = AutoGPTQForCausalLM.from_quantized(
    model_name,
    device="cuda:0",
    use_triton=True  # Linux only
)
```

**Performance**: 1.2-1.5× faster than CUDA backend

## Integration