msModelSlim – Ascend Model Compression Tool

MindStudio ModelSlim (msModelSlim) is a model compression tool optimized for Huawei Ascend AI processors. It supports quantization and compression for LLMs, MoE models, and multimodal models.

Quick Start

One-Click Quantization (V1 – Recommended)

V1 automatic quantization uses configuration files from lab_practice/ directory.

# Basic W8A8 quantization for Qwen2.5-7B-Instruct
# Config files located at: lab_practice/<model_series>/<model>-<quant_type>-v1.yaml
msmodelslim quant \
    --model_path /path/to/Qwen2.5-7B-Instruct \
    --save_path /path/to/output \
    --device npu \
    --model_type Qwen2.5-7B-Instruct \
    --config_path /path/to/msmodelslim/lab_practice/qwen2.5/qwen2.5-7b-w8a8-v1.yaml \
    --trust_remote_code True

# MoE model quantization (Qwen3-30B-A3B W4A8)
msmodelslim quant \
    --model_path /path/to/Qwen3-30B-A3B \
    --save_path /path/to/output \
    --device npu \
    --model_type Qwen3-30B \
    --config_path /path/to/msmodelslim/lab_practice/qwen3_moe/qwen3-30b-w4a8-v1.yaml \
    --trust_remote_code True

# Multi-device distributed quantization
msmodelslim quant \
    --model_path /path/to/model \
    --save_path /path/to/output \
    --device npu:0,1,2,3 \
    --model_type Qwen2.5-72B-Instruct \
    --config_path /path/to/msmodelslim/lab_practice/qwen2.5/qwen2.5-72b-w8a8c8-v1.yaml \
    --trust_remote_code True

Note: Find config files in lab_practice/ directory of msmodelslim repository:

• Structure: lab_practice/<model_series>/<model>-<quant_type>-v1.yaml
• Example: lab_practice/qwen2.5/qwen2.5-7b-w8a8-v1.yaml

Traditional Quantization (V0)

cd msmodelslim
python3 example/Qwen/quant_qwen.py \
    --model_path /path/to/Qwen2.5-7B-Instruct \
    --save_directory /path/to/output \
    --calib_file example/common/boolq.jsonl \
    --w_bit 8 --a_bit 8 \
    --device_type npu \
    --trust_remote_code True

Installation

Prerequisites

• Python: 3.8+ (3.9+ recommended for some environments)
• CANN: 8.2.RC1+ (8.3.RC1 or 8.5.0 recommended)
• PyTorch Ascend: Ascend Extension for PyTorch

Install Steps

# 1. Clone repository
git clone https://gitcode.com/Ascend/msmodelslim.git
cd msmodelslim

# 2. Run installation script
bash install.sh

# 3. For Atlas 300I Duo (sparse quantization support)
cd ${PYTHON_SITE_PACKAGES}/msmodelslim/pytorch/weight_compression/compress_graph/
sudo bash build.sh ${CANN_INSTALL_PATH}/ascend-toolkit/latest
chmod -R 550 build

Note: Do not run msmodelslim commands from within the source directory to avoid module path conflicts.

See references/installation.md for detailed environment setup.

Quantization Types

Quantization Type Selection

BFLOAT16 Model Notes

For models with torch_dtype=bfloat16 weights (e.g., Qwen3-30B-A3B):

If you encounter AclNN_Parameter_Error(EZ1001): Tensor self not implemented for DT_BFLOAT16, this is likely a Docker image issue, not a msmodelslim limitation.

Quick Diagnosis:

# Test if torch_npu works correctly
python3 -c "import torch; import torch_npu; a = torch.tensor(1).npu(); print('NPU OK')"

If this fails, your Docker image has compatibility issues. Try:

1. Use a different/updated Docker image
2. Reinstall torch_npu matching your CANN version
3. Ensure CANN 8.3.RC1+ for BF16 support

Container Setup: See ascend-docker for proper Docker container creation with NPU device mappings. Refer to references/docker-setup.md for msmodelslim-specific container configuration.

Algorithm Selection

Outlier Suppression Algorithms

Quantization Algorithms

Quick Selection Guide

• Beginners: Use one-click quantization with --config_path pointing to lab_practice/ config files
• Precision priority: QuaRot + AutoRound
• Long sequence: FA3 + KVCache Quant
• Custom model: See references/model-integration.md

See references/quantization-algorithms.md for algorithm details.

Supported Models

Large Language Models

MoE Models

Multimodal Models

See references/model-support.md for complete support matrix.

Custom Model Integration

Quick Overview

1. Create adapter file: msmodelslim/model/my_model/model_adapter.py
2. Define adapter class: Inherit TransformersModel + interface classes
3. Implement interfaces: handle_dataset, init_model, generate_model_visit, etc.
4. Register model: Add to config/config.ini

Example

from msmodelslim.model.interface_hub import ModelSlimPipelineInterfaceV1
from msmodelslim.model.common.transformers import TransformersModel

class MyModelAdapter(TransformersModel, ModelSlimPipelineInterfaceV1):
    def handle_dataset(self, dataset, device):
        return self._get_tokenized_data(dataset, device)
    
    def init_model(self, device):
        return self._load_model(device)
    
    def generate_model_visit(self, model):
        from msmodelslim.model.common.layer_wise_forward import generated_decoder_layer_visit_func
        yield from generated_decoder_layer_visit_func(model)
    
    def generate_model_forward(self, model, inputs):
        from msmodelslim.model.common.layer_wise_forward import transformers_generated_forward_func
        yield from transformers_generated_forward_func(model, inputs)

See references/model-integration.md and scripts/model_adapter_template.py for complete guide.

Precision Auto-Tuning

Sensitive Layer Analysis

# Analyze model sensitivity
msmodelslim analyze --model_path /path/to/model --model_type Qwen2.5-7B-Instruct

Analysis Algorithms:

• std: Standard deviation based (recommended for general use)
• quantile: Quantile/IQR based (for long-tail distributions)
• kurtosis: Kurtosis based (for extreme value detection)

Auto-Tuning Strategy

Standing High: Binary search to minimize fallback layers while maintaining precision.

# Use auto-tuning config
msmodelslim quant \
    --model_path /path/to/model \
    --save_path /path/to/output \
    --model_type Qwen2.5-7B-Instruct \
    --config_path /path/to/auto_tuning_config.yaml

See references/precision-tuning.md for tuning strategies.

Deployment

vLLM-Ascend

# Online service
vllm serve /path/to/quantized-model \
    --served-model-name "Qwen2.5-7B-w8a8" \
    --max-model-len 4096 \
    --quantization ascend

# Offline inference (Python)
from vllm import LLM, SamplingParams

llm = LLM(
    model="/path/to/quantized-model",
    max_model_len=4096,
    quantization="ascend"
)
outputs = llm.generate(["Hello"], SamplingParams(temperature=0.6))

MindIE

# Deploy with MindIE
# See MindIE documentation for details

Weight Conversion

# Convert to AutoAWQ/AutoGPTQ format
python3 example/common/ms_to_vllm.py --input /path/to/quantized --output /path/to/converted

See references/deployment.md for deployment details.

Output Files

After quantization, the output directory contains:

output/
├── config.json                      # Original model config
├── generation_config.json           # Generation config
├── quant_model_description.json     # Quantization description
├── quant_model_weight_w8a8.safetensors  # Quantized weights
├── tokenizer_config.json            # Tokenizer config
├── tokenizer.json                   # Tokenizer vocabulary
└── vocab.json                       # Vocabulary (if applicable)

Troubleshooting

Common Issues

Q: Out of memory during quantization?

# Use layer-by-layer quantization (default in V1)
# Or use CPU quantization
msmodelslim quant --device cpu ...

Q: Precision degradation after quantization?

• Use higher precision type (W8A8 instead of W4A8)
• Check lab_practice/ for best practice configs
• Enable outlier suppression algorithms
• See references/precision-tuning.md

Q: Model type not supported?

• Check references/model-support.md
• Implement custom adapter: references/model-integration.md

Q: How to enable debug logging?

export MSMODELSLIM_LOG_LEVEL=DEBUG
msmodelslim quant ...

Scripts & Assets

Scripts

• scripts/check_env.sh – Environment check
• scripts/quantize_model.sh – Quantization template
• scripts/model_adapter_template.py – Model adapter template

Config Templates (assets/)

• assets/quant_config_w8a8.yaml – W8A8 config
• assets/quant_config_w4a8.yaml – W4A8 config
• assets/quant_config_sparse.yaml – Sparse quantization
• assets/quant_config_pdmix.yaml – PDMIX config

Official References

• Documentation: https://msmodelslim.readthedocs.io/zh-cn/latest/
• GitCode Repository: https://gitcode.com/Ascend/msmodelslim
• vLLM-Ascend: https://docs.vllm.ai/projects/ascend/en/latest/
• Huawei Ascend: https://www.hiascend.com/document

Related Skills

• atc-model-converter – Model conversion for Ascend
• npu-smi – NPU device management
• hccl-test – HCCL performance testing