# AMD Quark

Quantization can effectively reduce memory and bandwidth usage, accelerate computation and improve
throughput while with minimal accuracy loss. vLLM can leverage [Quark](https://quark.docs.amd.com/latest/),
the flexible and powerful quantization toolkit, to produce performant quantized models to run on AMD GPUs. Quark has specialized support for quantizing large language models with weight,
activation and kv-cache quantization and cutting-edge quantization algorithms like
AWQ, GPTQ, Rotation and SmoothQuant.

## Quark Installation

Before quantizing models, you need to install Quark. The latest release of Quark can be installed with pip:

```bash
pip install amd-quark
```

You can refer to [Quark installation guide](https://quark.docs.amd.com/latest/install.html)
for more installation details.

Additionally, install `vllm` and `lm-evaluation-harness` for evaluation:

```bash
pip install vllm git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]
```

## Quantization Process

After installing Quark, we will use an example to illustrate how to use Quark.
The Quark quantization process can be listed for 5 steps as below:

1. Load the model
2. Prepare the calibration dataloader
3. Set the quantization configuration
4. Quantize the model and export
5. Evaluation in vLLM

### 1. Load the Model

Quark uses [Transformers](https://huggingface.co/docs/transformers/en/index)
to fetch model and tokenizer.

??? code

    ```python
    from transformers import AutoTokenizer, AutoModelForCausalLM

    MODEL_ID = "meta-llama/Llama-2-70b-chat-hf"
    MAX_SEQ_LEN = 512

    model = AutoModelForCausalLM.from_pretrained(
        MODEL_ID,
        device_map="auto",
        dtype="auto",
    )
    model.eval()

    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, model_max_length=MAX_SEQ_LEN)
    tokenizer.pad_token = tokenizer.eos_token
    ```

### 2. Prepare the Calibration Dataloader

Quark uses the [PyTorch Dataloader](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html)
to load calibration data. For more details about how to use calibration datasets efficiently, please refer
to [Adding Calibration Datasets](https://quark.docs.amd.com/latest/pytorch/calibration_datasets.html).

??? code

    ```python
    from datasets import load_dataset
    from torch.utils.data import DataLoader

    BATCH_SIZE = 1
    NUM_CALIBRATION_DATA = 512

    # Load the dataset and get calibration data.
    dataset = load_dataset("mit-han-lab/pile-val-backup", split="validation")
    text_data = dataset["text"][:NUM_CALIBRATION_DATA]

    tokenized_outputs = tokenizer(
        text_data,
        return_tensors="pt",
        padding=True,
        truncation=True,
        max_length=MAX_SEQ_LEN,
    )
    calib_dataloader = DataLoader(
        tokenized_outputs['input_ids'],
        batch_size=BATCH_SIZE,
        drop_last=True,
    )
    ```

### 3. Set the Quantization Configuration

We need to set the quantization configuration, you can check
[quark config guide](https://quark.docs.amd.com/latest/pytorch/user_guide_config_description.html)
for further details. Here we use FP8 per-tensor quantization on weight, activation,
kv-cache and the quantization algorithm is AutoSmoothQuant.

!!! note
    Note the quantization algorithm needs a JSON config file and the config file is located in
    [Quark Pytorch examples](https://quark.docs.amd.com/latest/pytorch/pytorch_examples.html),
    under the directory `examples/torch/language_modeling/llm_ptq/models`. For example,
    AutoSmoothQuant config file for Llama is
    `examples/torch/language_modeling/llm_ptq/models/llama/autosmoothquant_config.json`.

??? code

    ```python
    from quark.torch.quantization import (Config, QuantizationConfig,
                                        FP8E4M3PerTensorSpec,
                                        load_quant_algo_config_from_file)

    # Define fp8/per-tensor/static spec.
    FP8_PER_TENSOR_SPEC = FP8E4M3PerTensorSpec(
        observer_method="min_max",
        is_dynamic=False,
    ).to_quantization_spec()

    # Define global quantization config, input tensors and weight apply FP8_PER_TENSOR_SPEC.
    global_quant_config = QuantizationConfig(
        input_tensors=FP8_PER_TENSOR_SPEC,
        weight=FP8_PER_TENSOR_SPEC,
    )

    # Define quantization config for kv-cache layers, output tensors apply FP8_PER_TENSOR_SPEC.
    KV_CACHE_SPEC = FP8_PER_TENSOR_SPEC
    kv_cache_layer_names_for_llama = ["*k_proj", "*v_proj"]
    kv_cache_quant_config = {
        name: QuantizationConfig(
            input_tensors=global_quant_config.input_tensors,
            weight=global_quant_config.weight,
            output_tensors=KV_CACHE_SPEC,
        )
        for name in kv_cache_layer_names_for_llama
    }
    layer_quant_config = kv_cache_quant_config.copy()

    # Define algorithm config by config file.
    LLAMA_AUTOSMOOTHQUANT_CONFIG_FILE = "examples/torch/language_modeling/llm_ptq/models/llama/autosmoothquant_config.json"
    algo_config = load_quant_algo_config_from_file(LLAMA_AUTOSMOOTHQUANT_CONFIG_FILE)

    EXCLUDE_LAYERS = ["lm_head"]
    quant_config = Config(
        global_quant_config=global_quant_config,
        layer_quant_config=layer_quant_config,
        kv_cache_quant_config=kv_cache_quant_config,
        exclude=EXCLUDE_LAYERS,
        algo_config=algo_config,
    )
    ```

### 4. Quantize the Model and Export

Then we can apply the quantization. After quantizing, we need to freeze the
quantized model first before exporting. Note that we need to export model with format of
HuggingFace `safetensors`, you can refer to
[HuggingFace format exporting](https://quark.docs.amd.com/latest/pytorch/export/quark_export_hf.html)
for more exporting format details.

??? code

    ```python
    import torch
    from quark.torch import ModelQuantizer, ModelExporter
    from quark.torch.export import ExporterConfig, JsonExporterConfig

    # Apply quantization.
    quantizer = ModelQuantizer(quant_config)
    quant_model = quantizer.quantize_model(model, calib_dataloader)

    # Freeze quantized model to export.
    freezed_model = quantizer.freeze(model)

    # Define export config.
    LLAMA_KV_CACHE_GROUP = ["*k_proj", "*v_proj"]
    export_config = ExporterConfig(json_export_config=JsonExporterConfig())
    export_config.json_export_config.kv_cache_group = LLAMA_KV_CACHE_GROUP

    # Model: Llama-2-70b-chat-hf-w-fp8-a-fp8-kvcache-fp8-pertensor-autosmoothquant
    EXPORT_DIR = MODEL_ID.split("/")[1] + "-w-fp8-a-fp8-kvcache-fp8-pertensor-autosmoothquant"
    exporter = ModelExporter(config=export_config, export_dir=EXPORT_DIR)
    with torch.no_grad():
        exporter.export_safetensors_model(
            freezed_model,
            quant_config=quant_config,
            tokenizer=tokenizer,
        )
    ```

### 5. Evaluation in vLLM

Now, you can load and run the Quark quantized model directly through the LLM entrypoint:

??? code

    ```python
    from vllm import LLM, SamplingParams

    # Sample prompts.
    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]
    # Create a sampling params object.
    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

    # Create an LLM.
    llm = LLM(
        model="Llama-2-70b-chat-hf-w-fp8-a-fp8-kvcache-fp8-pertensor-autosmoothquant",
        kv_cache_dtype="fp8",
        quantization="quark",
    )
    # Generate texts from the prompts. The output is a list of RequestOutput objects
    # that contain the prompt, generated text, and other information.
    outputs = llm.generate(prompts, sampling_params)
    # Print the outputs.
    print("\nGenerated Outputs:\n" + "-" * 60)
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt:    {prompt!r}")
        print(f"Output:    {generated_text!r}")
        print("-" * 60)
    ```

Or, you can use `lm_eval` to evaluate accuracy:

```bash
lm_eval --model vllm \
  --model_args pretrained=Llama-2-70b-chat-hf-w-fp8-a-fp8-kvcache-fp8-pertensor-autosmoothquant,kv_cache_dtype='fp8',quantization='quark' \
  --tasks gsm8k
```

## Quark Quantization Script

In addition to the example of Python API above, Quark also offers a
[quantization script](https://quark.docs.amd.com/latest/pytorch/example_quark_torch_llm_ptq.html)
to quantize large language models more conveniently. It supports quantizing models with variety
of different quantization schemes and optimization algorithms. It can export the quantized model
and run evaluation tasks on the fly. With the script, the example above can be:

```bash
python3 quantize_quark.py --model_dir meta-llama/Llama-2-70b-chat-hf \
                          --output_dir /path/to/output \
                          --quant_scheme w_fp8_a_fp8 \
                          --kv_cache_dtype fp8 \
                          --quant_algo autosmoothquant \
                          --num_calib_data 512 \
                          --model_export hf_format \
                          --tasks gsm8k
```

## Using OCP MX (MXFP4, MXFP6) models

vLLM supports loading MXFP4 and MXFP6 models quantized offline through AMD Quark, compliant with [Open Compute Project (OCP) specification](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf).

The scheme currently only supports dynamic quantization for activations.

Example usage, after installing the latest AMD Quark release:

```bash
vllm serve fxmarty/qwen_1.5-moe-a2.7b-mxfp4 --tensor-parallel-size 1
# or, for a model using fp6 activations and fp4 weights:
vllm serve fxmarty/qwen1.5_moe_a2.7b_chat_w_fp4_a_fp6_e2m3 --tensor-parallel-size 1
```

A simulation of the matrix multiplication execution in MXFP4/MXFP6 can be run on devices that do not support OCP MX operations natively (e.g. AMD Instinct MI325, MI300 and MI250), dequantizing weights from FP4/FP6 to half precision on the fly, using a fused kernel. This is useful e.g. to evaluate FP4/FP6 models using vLLM, or alternatively to benefit from the ~2.5-4x memory savings (compared to float16 and bfloat16).

To generate offline models quantized using MXFP4 data type, the easiest approach is to use AMD Quark's [quantization script](https://quark.docs.amd.com/latest/pytorch/example_quark_torch_llm_ptq.html), as an example:

```bash
python quantize_quark.py --model_dir Qwen/Qwen1.5-MoE-A2.7B-Chat \
    --quant_scheme w_mxfp4_a_mxfp4 \
    --output_dir qwen_1.5-moe-a2.7b-mxfp4 \
    --skip_evaluation \
    --model_export hf_format \
    --group_size 32
```

The current integration supports [all combination of FP4, FP6_E3M2, FP6_E2M3](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/utils/ocp_mx_utils.py) used for either weights or activations.

## Using Quark Quantized layerwise Auto Mixed Precision (AMP) Models

vLLM also supports loading layerwise mixed precision model quantized using AMD Quark. Currently, mixed scheme of {MXFP4, FP8} is supported, where FP8 here denotes for FP8 per-tensor scheme. More mixed precision schemes are planned to be supported in a near future, including

- Unquantized Linear and/or MoE layer(s) as an option for each layer, i.e., mixed of {MXFP4, FP8, BF16/FP16}
- MXFP6 quantization extension, i.e., {MXFP4, MXFP6, FP8, BF16/FP16}

Although one can maximize serving throughput using the lowest precision supported on a given device (e.g. MXFP4 for AMD Instinct MI355, FP8 for AMD Instinct MI300), these aggressive schemes can be detrimental to accuracy recovering from quantization on target tasks. Mixed precision allows to strike a balance between maximizing accuracy and throughput.

There are two steps to generate and deploy a mixed precision model quantized with AMD Quark, as shown below.

### 1. Quantize a model using mixed precision in AMD Quark

Firstly, the layerwise mixed-precision configuration for a given LLM model is searched and then quantized using AMD Quark. We will provide a detailed tutorial with Quark APIs later.

As examples, we provide some ready-to-use quantized mixed precision model to show the usage in vLLM and the accuracy benefits. They are:

- amd/Llama-2-70b-chat-hf-WMXFP4FP8-AMXFP4FP8-AMP-KVFP8
- amd/Mixtral-8x7B-Instruct-v0.1-WMXFP4FP8-AMXFP4FP8-AMP-KVFP8
- amd/Qwen3-8B-WMXFP4FP8-AMXFP4FP8-AMP-KVFP8

### 2. inference the quantized mixed precision model in vLLM

Models quantized with AMD Quark using mixed precision can natively be reload in vLLM, and e.g. evaluated using lm-evaluation-harness as follow:

```bash
lm_eval --model vllm \
    --model_args pretrained=amd/Llama-2-70b-chat-hf-WMXFP4FP8-AMXFP4FP8-AMP-KVFP8,tensor_parallel_size=4,dtype=auto,gpu_memory_utilization=0.8,trust_remote_code=False \
    --tasks mmlu \
    --batch_size auto
```
