[docs] Add autoround doc

README.md

@@ -37,6 +37,7 @@ Big updates have landed in LLM Compressor! To get a more in-depth look, check ou
 
 Some of the exciting new features include:
 
+* **AutoRound Quantization Support**: Added [`AutoRoundModifier`](examples/autoround/llama3_example.py) for quantization using [AutoRound](https://aclanthology.org/2024.findings-emnlp.662.pdf), an advanced post-training algorithm that optimizes rounding and clipping ranges through sign-gradient descent. This approach combines the efficiency of post-training quantization with the adaptability of parameter tuning, delivering robust compression for large language models while maintaining strong performance.
 * **Qwen3 Next and Qwen3 VL MoE Quantization Support**: Quantize the Qwen3 Next and Qwen3 VL MoE models and seamlessly run the models in vLLM. Examples for [NVFP4](examples/quantization_w4a4_fp4/qwen3_next_example.py) and [FP8](examples/quantization_w8a8_fp8/qwen3_next_example.py) Quantization have been added for the Qwen3-Next-80B-A3B-Instruct. For the Qwen3 VL MoE, support has been added for the datafree pathway, specifically [FP8 Quantization](examples/quantization_w8a8_fp8/qwen3_vl_moe_fp8_example.py) (e.g channel-wise and block-wise quantization). NOTE: these models are not supported in tranformers<=4.56.2. You may need to install transformers from source.
 * **Quantization with Multiple Modifiers**: Multiple quantization modifiers can now be applied to the same model for mixed-precision quantization, for example applying AWQ W4A16 to a model's `self_attn` layers and GPTQ W8A8 to its `mlp` layers. This is an advanced usage of `llm-compressor` and an active area of research. See the [non-uniform quantization support](examples/quantization_non_uniform) section for more detail and [example usage](examples/quantization_non_uniform/quantization_multiple_modifiers.py).
 * **QuIP and SpinQuant-style Transforms**: The newly added [`QuIPModifier`](examples/transform/quip_example.py) and [`SpinQuantModifier`](examples/transform/spinquant_example.py) allow users to quantize their models after injecting hadamard weights into the computation graph, reducing quantization error and greatly improving accuracy recovery for low bit weight and activation quantization.
@@ -55,6 +56,7 @@ Some of the exciting new features include:
 * AWQ
 * SmoothQuant
 * SparseGPT
+* AutoRound
 
 ### When to Use Which Optimization
 
@@ -78,6 +80,7 @@ Applying quantization with `llmcompressor`:
 * [Weight only quantization to `fp4`](examples/quantization_w4a16_fp4/llama3_example.py)
 * [Weight only quantization to `int4` using GPTQ](examples/quantization_w4a16/README.md)
 * [Weight only quantization to `int4` using AWQ](examples/awq/README.md)
+* [Weight only quantization to `int4` using AutoRound](examples/autoround/README.md)
 * [Quantizing MoE LLMs](examples/quantizing_moe/README.md)
 * [Quantizing Vision-Language Models](examples/multimodal_vision/README.md)
 * [Quantizing Audio-Language Models](examples/multimodal_audio/README.md)

docs/getting-started/compress.md

@@ -33,6 +33,7 @@ Compression schemes use quantization methods including the following:
 | **AWQ** | Uses channelwise scaling to better preserve important outliers in weights and activations | Better accuracy recovery with faster runtime than GPTQ |
 | **SmoothQuant** | Smooths outliers in activations by folding them into weights, ensuring better accuracy for weight and activation quantized models | Good accuracy recovery with minimal calibration time; composable with other methods |
 | **Round-To-Nearest (RTN)** | Simple quantization technique that rounds each value to the nearest representable level in the target precision. | Provides moderate accuracy recovery in most scenarios. Computationally cheap and fast to implement, making it suitable for real-time or resource-constrained environments. |
+| **AutoRound** | AutoRound optimizes rounding and clipping ranges via sign-gradient descent. | Delivers leading 4-bit and superior sub-4-bit accuracy compared to GPTQ/AWQ, with runtime faster than GPTQ and on par with AWQ. |
 
 For this guide, we'll use `GPTQ` composed with `SmoothQuant` to create an `INT W8A8` quantized model. This combination provides a good balance for performance, accuracy, and compatability across a wide range of hardware.
 

examples/autoround/README.md

@@ -0,0 +1,141 @@
+# `AutoRound` Quantization
+
+`llm-compressor` supports [AutoRound](https://aclanthology.org/2024.findings-emnlp.662.pdf), an advanced quantization technique that delivers **high-accuracy**, **low-bit quantization**. The quantized results are fully compatible with `compressed-tensors` and can be served directly with vLLM.
+
+AutoRound introduces three trainable parameters (V, α, and β) to optimize rounding values and clipping ranges during quantization. The method processes each decoder layer sequentially, using block-wise output reconstruction error as the training objective to fine-tune these parameters. This approach combines the efficiency of post-training quantization with the adaptability of parameter tuning, delivering robust compression for large language models while maintaining strong performance.
+
+## Installation
+
+To get started, install:
+
+```bash
+git clone https://github.com/vllm-project/llm-compressor.git
+cd llm-compressor
+pip install -e .
+```
+
+## Quickstart
+
+The example includes an end-to-end script for applying the AutoRound quantization algorithm.
+
+```bash
+python3 llama3_example.py
+```
+
+The resulting model `Meta-Llama-3-8B-Instruct-W4A16-G128-AutoRound` is ready to be loaded into vLLM.
+
+## Code Walkthrough
+
+Now, we will step through the code in the example. There are four steps:
+1) Load model
+2) Prepare calibration data
+3) Apply quantization
+4) Evaluate accuracy in vLLM
+
+### 1) Load Model
+
+Load the model using `AutoModelForCausalLM` for handling quantized saving and loading. 
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+```
+
+### 2) Prepare Calibration Data
+
+When quantizing model weights with AutoRound, you’ll need a small set of sample data to run the algorithm. By default, we are using [NeelNanda/pile-10k](https://huggingface.co/datasets/NeelNanda/pile-10k) as our calibration dataset.
+Recommended starting points:
+- 128 samples — typically sufficient for stable calibration (increase if accuracy degrades).
+- 2048 sequence length — a good baseline for most LLMs.
+- 200 tuning steps — usually enough to converge (increase if accuracy drops).
+
+```python
+# Select calibration dataset.
+from auto_round.calib_dataset import get_dataset
+
+NUM_CALIBRATION_SAMPLES = 128
+MAX_SEQUENCE_LENGTH = 2048
+
+# Get aligned calibration dataset.
+ds = get_dataset(
+    tokenizer=tokenizer,
+    seqlen=MAX_SEQUENCE_LENGTH,
+    nsamples=NUM_CALIBRATION_SAMPLES,
+)
+```
+
+### 3) Apply Quantization
+
+With the dataset ready, we will now apply AutoRound quantization to the model.
+
+```python
+from llmcompressor import oneshot
+from llmcompressor.modifiers.autoround import AutoRoundModifier
+
+# Configure the quantization algorithm to run.
+recipe = AutoRoundModifier(
+    targets="Linear", scheme="W4A16", ignore=["lm_head"], iters=200
+)
+
+# Apply quantization.
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    # disable shuffling to get slightly better mmlu score
+    shuffle_calibration_samples=False,
+)
+
+
+# Save to disk compressed.
+SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-W4A16-G128-AutoRound"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+tokenizer.save_pretrained(SAVE_DIR)
+```
+
+We have successfully created an `int4` model!
+
+### 4) Evaluate Accuracy
+
+With the model created, we can now load and run in vLLM (after installing).
+
+```python
+from vllm import LLM
+model = LLM("./Meta-Llama-3-8B-Instruct-W4A16-G128-AutoRound")
+```
+
+We can evaluate accuracy with `lm_eval` (`pip install lm-eval==0.4.9.1`):
+> Note: quantized models can be sensitive to the presence of the `bos` token. `lm_eval` does not add a `bos` token by default, so make sure to include the `add_bos_token=True` argument when running your evaluations.
+
+Run the following to test accuracy on GSM-8K:
+
+```bash
+lm_eval --model vllm \
+  --model_args pretrained="./Meta-Llama-3-8B-Instruct-W4A16-G128-AutoRound",add_bos_token=true \
+  --tasks gsm8k \
+  --num_fewshot 5 \
+  --limit 1000 \
+  --batch_size 'auto'
+```
+
+We can see the resulting scores look good!
+
+```bash
+| Tasks | Version | Filter           | n-shot | Metric      |     | Value |     | Stderr |
+| ----- | ------: | ---------------- | -----: | ----------- | --- | ----: | --- | -----: |
+| gsm8k |       3 | flexible-extract |      5 | exact_match | ↑   | 0.737 | ±   | 0.0139 |
+|       |         | strict-match     |      5 | exact_match | ↑   | 0.736 | ±   | 0.0139 |
+```
+> Note: quantized model accuracy may vary slightly due to nondeterminism.
+
+### Known Issues
+Currently, `llm-compressor` supports applying AutoRound only on the `wNa16` quantization schemes. Support for additional schemes is planned. You can follow progress in the [RFC](https://github.com/vllm-project/llm-compressor/issues/1968).
+
+### Questions or Feature Request?
+
+Please open up an issue on [vllm-project/llm-compressor](https://github.com/vllm-project/llm-compressor) or [intel/auto-round](https://github.com/intel/auto-round).