AMCT Large Model MXQUANT Quantization

1 Quantization Prerequisites

1.1 Install Dependencies

The dependency packages for this sample can be found in requirements.txt

Note that the torch_npu package version needs to match the Python and torch package versions, and the CANN package needs to be installed

1.2 Model and Dataset Preparation

This sample uses Llama2-7b, qwen2-7b, and qwen3-8b models, pileval data, and wikitext2 dataset as examples. Data is loaded online, and models need to be downloaded by users themselves and the model path needs to be specified when executing the script.

Note: The quantization data type combination mxfp8_e4m3fn * mxfp8_e4m3fn only supports quantizing original data type torch.bfloat16. Please modify the data type when getting the model in the src/utils.py file.

1.3 Simple Quantization Configuration

The quantization configuration used in this sample is built into the tool and can be obtained and used in the following ways:

MXFP8_E4M3FN * MXFP8_E4M3FN full quantization configuration: from amct_pytorch import MXFP8_QUANT_CFG

MXFP4_E2M1 weight-only quantization configuration:

cfg = {
    'batch_num': 1,
    'quant_cfg': {
        'weights': {
            'type': 'mxfp4_e2m1',
            'symmetric': True,
            'strategy': 'group',
            'group_size': 32
        },
    },
    'algorithm': {'mxquant'},
    'skip_layers': {'lm_head'}
}

If you need to modify the detailed configuration, please refer to the documentation to construct the required quantization configuration dict.

The MXQUANT algorithm supports weight-only quantization and full quantization. The supported quantization types and quantization configurations are:

| Field | Type | Description | Value Range | Notes | |:--| :-: | :-- | :-: | :-: | :-- | |batch_num|uint32|Number of batches used for quantization |1|/| |skip_layers|str|Layers to skip quantization |/|Skip quantization layers support fuzzy matching. When the configured string is a layer name substring or matches the layer name, skip quantization for that layer and do not generate quantization configuration. The string must contain numbers or letters| |weights.type|str|Quantized weight type|'mxfp8_e4m3fn'/'mxfp4_e2m1'|/| |weights.symmetric|bool|Symmetric quantization|TRUE|Only supports symmetric quantization| |weights.strategy|str|Quantization granularity|'group'|/| |inputs.type|str|Quantized activation type|'mxfp8_e4m3fn'|Full quantization scenario does not support configuring weight quantization type mxfp4_e2m1| |inputs.symmetric|bool|Symmetric quantization|TRUE|Only supports symmetric quantization| |inputs.strategy|str|Quantization granularity|'group'|/| |algorithm|dict|Quantization algorithm configuration used|{'mxquant'}|/|

2 Quantization Example

2.1 Use Interface Method to Call

step 1. Please execute the following command in the current directory to run the sample program. Users need to modify the model and dataset paths in the sample program according to actual conditions:

python3 src/run_llama2_samples.py --model_path=/data/Llama2_7b_hf/

python3 src/run_qwen_samples.py --model_path=/data/Qwen2-7b/

python3 src/run_qwen_samples.py --model_path=/data/Qwen3-8b/

If the following information appears, it indicates that quantization is successful:

Test time taken:  1.0 min  59.24865388870239 s
Score:  5.477707

Where Score is the quantized model PPL. For specific values, refer to the following table:

Model	Calibration Set	Dataset	Pre-quantization PPL	Post-mxfp8_e4m3fn*mxfp8_e4m3fn quantization PPL
LLAMA2-7B	pileval	wikitext2	5.472	5.523
QWEN2-7B	pileval	wikitext2	7.137	7.197
QWEN3-8B	pileval	wikitext2	9.715	9.934

After inference succeeds, a quantization log file ./amct_log/amct_pytorch.log is generated in the current directory