MNN Model Analysis Tools

This repository contains Python scripts for analyzing MNN models using different callback mechanisms to collect statistics during model inference.

Scripts Overview

1. get_max_values.py

Collects maximum activation values from MNN model layers during inference.

2. get_thresholds.py

Calculates sparsity thresholds for MNN model layers based on target sparsity levels.

Requirements

pip install datasets torch tqdm

You'll also need to build pymnn

cd /path/to/MNN/pymnn/pip_package
python build_deps.py llm
python setup.py install

Usage

Get Max Values

cd /path/to/MNN/transformers/llm/collect
python get_max_values.py -m <mnn_model_path> [options]

Arguments:

• -m, --mnn-path: Path to MNN model config (required)
• -d, --eval_dataset: Dataset for evaluation (default: 'wikitext/wikitext-2-raw-v1')
• -o, --output-path: Output file path (default: 'max_values.json')
• -l, --length: Sample length for processing (default: 512)

Example:

python get_maxval.py --m /path/to/MNN/transformers/llm/export/model/config.json -o ./max_val_test.json

Get Thresholds

cd /path/to/MNN/transformers/llm/collect
python get_thresholds.py -m <mnn_model_path> [options]

Arguments:

• -m, --mnn-path: Path to MNN model config(required)
• -d, --eval_dataset: Dataset for evaluation (default: 'wikitext/wikitext-2-raw-v1')
• -o, --output-path: Output file path (default: 'thresholds.json')
• -t, --target-sparsity: Target sparsity level (default: 0.5)
• -l, --length: Sample length for processing (default: 512)

Example:

python get_thredsholds.py -m /path/to/MNN/transformers/llm/export/model/config.json -l 1024 -t 0.5 -o ./thresholds_0.5.json

How It Works

Both scripts:

1. Load an MNN model and configure it for analysis
2. Load a text dataset (default: WikiText-2)
3. Tokenize and process the dataset text
4. Run model inference to collect statistics via callbacks
5. Save results to JSON files

The key difference is in the callback configuration:

• Max Values: Uses enable_max_value_callback to collect maximum activation values
• Thresholds: Uses enable_threshold_callback with target sparsity to calculate pruning thresholds

Output

Both scripts generate JSON files containing the collected statistics that can be used for model optimization, pruning, or quantization analysis.