lite-converter

name	lite-converter
description	Model conversion pipeline, parser development, optimization passes and quantization. Use when converting models to .ms, writing parser code, implementing optimizer passes, or configuring quantization.
paths	["mindspore-lite/tools/converter/**","mindspore-lite/tools/optimizer/**","mindspore-lite/schema/**","mindspore-lite/tools/schema_gen/**"]

MindSpore Lite Model Conversion and Optimization

Conversion Pipeline

Input Model (MindIR/TF/Caffe/ONNX/TFLite/PyTorch)
  -> Parse (framework-specific Parser) -> Unified MindIR (ANF Graph)
  -> Import -> Internal graph representation
  -> Optimize (Constant Folding, Op Fusion, Format Transform, Parallel Split, Redundant elimination)
  -> Quantize (optional: Weight / Full / Mixed Precision)
  -> Export (.ms for LiteRT or .mindir for ExtendRT)

Conversion Tool Usage

# MindIR -> .ms (device-side)
./converter_lite --fmk=MINDIR --modelFile=model.mindir --outputFile=model

# TensorFlow / TFLite / Caffe / ONNX / PyTorch -> .ms
./converter_lite --fmk=TF --modelFile=model.pb --outputFile=model
./converter_lite --fmk=TFLITE --modelFile=model.tflite --outputFile=model
./converter_lite --fmk=CAFFE --modelFile=model.prototxt --weightFile=model.caffemodel --outputFile=model
./converter_lite --fmk=ONNX --modelFile=model.onnx --outputFile=model
./converter_lite --fmk=PYTORCH --modelFile=model.pt --outputFile=model

# With quantization
./converter_lite --fmk=MINDIR --modelFile=model.mindir --outputFile=model \
  --quantType=WeightQuant --bitNum=8

### Key Parameters

| Parameter | Description |
|------|------|
| `--fmk` | Input framework: MINDIR/TF/TFLITE/CAFFE/ONNX/PYTORCH |
| `--modelFile` | Input model file path |
| `--weightFile` | Caffe weight file (Caffe only) |
| `--outputFile` | Output file path (without extension) |
| `--quantType` | WeightQuant / FullQuant / NoQuant |
| `--bitNum` | Quantization bits: 1-8 (default 8) |
| `--optimize` | ascend_oriented / general / none |
| `--configFile` | Quantization or runtime config file |
| `--inputShape` | Dynamic shape input (e.g., `input1:1,3,224,224;input2:1,3,256,256`) |

## Parser Development

mindspore-lite/tools/converter/parser/ onnx/ # onnx_model_parser.cc + per-operator parsers tf/ # tf_model_parser.cc + per-operator parsers tflite/ # tflite_model_parser.cc + per-operator parsers caffe/ # caffe_model_parser.cc + per-operator parsers

### Adding a New Framework Parser

1. Create directory under `tools/converter/parser/`
2. Implement `XxxModelParser` with `Parse()` method (original model -> ANF Graph)
3. Operator mapping: original operator -> MindSpore Primitive
4. Register FMK type in `tools/converter/converter_flags.cc`
5. Add unit tests

### Operator Mapping

Each Parser maps original operators to MindSpore internals:

- **Attribute conversion**: e.g., TF padding format to MindSpore format
- **Data layout**: NHWC <-> NCHW conversion
- **Missing operators**: simulate with composites (e.g., ReduceMean for GlobalAvgPool)

## Converter Directory Structure

mindspore-lite/tools/converter/ adapter/ # Format adapters config_parser/ # Config file parsing converter_lite/ # CLI tool entry point cxx_api/ # Converter C++ API decomposer/ # Operator decomposition import/ # Model import (mindir_importer, primitive_adjust) legacy_optimizer/ # Legacy optimization passes micro/ # Micro code generation ops/ # Operator utilities parser/ # Framework-specific parsers preprocess/ # Data preprocessing pipeline quantizer/ # Quantization implementations calibrator.cc # Calibration data processing full_quant_quantizer/ # Full quantization weight_quantizer/ # Weight-only quantization gptq_quantizer/ # GPTQ quantization fse_encoder/ # FSE encoding huffman_encode/ # Huffman encoding registry/ # Extension registration session/ # Conversion session

## Optimizer Pass Development

mindspore-lite/tools/optimizer/ common/ # Common optimization utilities const_fold/ # Constant folding passes fiss on/ # Operator fission passes (note: directory is "fisson") format/ # Format transform passes fusion/ # Operator fusion passes (conv_bn, conv_activation, matmul_add, etc.) graph/ # Graph-level optimization parallel/ # Parallel split passes

### Writing an Optimization Pass

```cpp
class MyFusionPass : public Pass {
 public:
  bool Run(const FuncGraphPtr &graph) override {
    auto node_list = TopoSort(graph->get_return());
    for (auto &node : node_list) {
      if (!CheckPattern(node)) continue;
      DoFusion(node);
      changed = true;
    }
    return changed;
  }
};

Passes execute during converter phase in a fixed order chain.

Quantization

Post-training Quantization

1. Prepare calibration dataset (100-500 representative samples)
2. Write config file:

[common_quant_param]
quant_type=WEIGHT_QUANT
bit_num=8

[data_preprocess]
calibrate_path=/path/to/calibration/images/
calibrate_size=100

[input_format]
input_type=IMAGE
resize_height=224
resize_width=224

3. Run: ./converter_lite --fmk=MINDIR --modelFile=model.mindir --outputFile=model_quant --configFile=quant_config.cfg

Quantization Types

Type	Description	Use Case
WeightQuant	Weight-only	Reduce model size, minimal accuracy loss
FullQuant	Weight + activation	Maximum compression, needs calibration data
Mixed Precision	Partial INT8 + FP32	Balance accuracy and performance

Export Formats

Format	Target Runtime	Serialization
.ms	LiteRT (device-side)	FlatBuffers, zero-copy deserialization

Schema files: mindspore-lite/schema/ops.fbs (~1.3K lines), model.fbs, ops_types.fbs

Common Conversion Issues

1. Unsupported operator: Check schema/ops.fbs operator list
2. Shape inference failure: Use --inputShape to specify input shapes
3. Quantization accuracy drop: Try mixed precision or more calibration data
4. Caffe missing BatchNorm params: Ensure .caffemodel weight file path is correct