scs4onnx 1.0.7

A very simple tool that compresses the overall size of the ONNX model by aggregating duplicate constant values as much as possible. Simple Constant value Shrink for ONNX.

scs4onnx

A very simple tool that compresses the overall size of the ONNX model by aggregating duplicate constant values as much as possible. Simple Constant value Shrink for ONNX.

Key concept

• If the same constant tensor is found by scanning the entire graph for Constant values, it is aggregated into a single constant tensor.
• Ignore scalar values.
• Ignore variables.
• Finally, create a Fork of onnx-simplifier and merge this process just before the onnx file output process

1. Setup

1-1. HostPC

### option
$ echo export PATH="~/.local/bin:$PATH" >> ~/.bashrc \
&& source ~/.bashrc

### run
$ pip install -U onnx \
&& python3 -m pip install -U onnx_graphsurgeon --index-url https://pypi.ngc.nvidia.com \
&& pip install -U scs4onnx

1-2. Docker

### docker pull
$ docker pull pinto0309/scs4onnx:latest

### docker build
$ docker build -t pinto0309/scs4onnx:latest .

### docker run
$ docker run --rm -it -v `pwd`:/workdir pinto0309/scs4onnx:latest
$ cd /workdir

2. CLI Usage

$ scs4onnx -h

usage:
scs4onnx [-h] [--mode {shrink,npy}] [--non_verbose] input_onnx_file_path output_onnx_file_path

positional arguments:
  input_onnx_file_path
                        Input onnx file path.
  output_onnx_file_path
                        Output onnx file path.

optional arguments:
  -h, --help            show this help message and exit
  --mode {shrink,npy}   Constant Value Compression Mode.
                        shrink: Share constant values inside the model as much as possible.
                                The model size is slightly larger because
                                some shared constant values remain inside the model,
                                but performance is maximized.
                        npy:    Outputs constant values used repeatedly in the model to an
                                external file .npy. Instead of the smallest model body size,
                                the file loading overhead is greater.
                        Default: shrink
  --non_verbose         Do not show all information logs. Only error logs are displayed.

3. In-script Usage

$ python
>>> from scs4onnx import shrinking
>>> help(shrinking)

Help on function shrinking in module scs4onnx.onnx_shrink_constant:

shrinking(
  input_onnx_file_path: Union[str, NoneType] = '',
  output_onnx_file_path: Union[str, NoneType] = '',
  onnx_graph: Union[onnx.onnx_ml_pb2.ModelProto, NoneType] = None,
  mode: Union[str, NoneType] = 'shrink',
  non_verbose: Union[bool, NoneType] = False
) -> Tuple[onnx.onnx_ml_pb2.ModelProto, str]

    Parameters
    ----------
    input_onnx_file_path: Optional[str]
        Input onnx file path.
        Either input_onnx_file_path or onnx_graph must be specified.

    output_onnx_file_path: Optional[str]
        Outpu onnx file path.
        If output_onnx_file_path is not specified, no .onnx file is output.

    onnx_graph: Optional[onnx.ModelProto]
        onnx.ModelProto.
        Either input_onnx_file_path or onnx_graph must be specified.
        onnx_graph If specified, ignore input_onnx_file_path and process onnx_graph.

    mode: Optional[str]
        Constant Value Compression Mode.
        'shrink': Share constant values inside the model as much as possible.
            The model size is slightly larger because some shared constant values remain
            inside the model, but performance is maximized.
        'npy': Outputs constant values used repeatedly in the model to an external file .npy.
            Instead of the smallest model body size, the file loading overhead is greater.
        Default: shrink

    non_verbose: Optional[bool]
        Do not show all information logs. Only error logs are displayed.
        Default: False

    Returns
    -------
    shrunken_graph: onnx.ModelProto
        Shrunken onnx ModelProto

    npy_file_paths: List[str]
        List of paths to externally output .npy files.
        An empty list is always returned when in 'shrink' mode.

3. CLI Execution

$ scs4onnx input.onnx output.onnx --mode shrink

4. In-script Execution

4-1. When an onnx file is used as input

If output_onnx_file_path is not specified, no .onnx file is output.

from scs4onnx import shrinking

shrunk_graph, npy_file_paths = shrinking(
  input_onnx_file_path='input.onnx',
  output_onnx_file_path='output.onnx',
  mode='npy',
  non_verbose=False
)

4-2. When entering the onnx.ModelProto

onnx_graph If specified, ignore input_onnx_file_path and process onnx_graph.

from scs4onnx import shrinking

shrunk_graph, npy_file_paths = shrinking(
  onnx_graph=graph,
  mode='npy',
  non_verbose=True
)

5. Sample

5-1. shrink mode sample

• 297.8MB -> 67.4MB

  

5-2. npy mode sample

• 297.8MB -> 21.3MB

  

5-3. .npy file view

$ python
>>> import numpy as np
>>> param = np.load('gmflow_sintel_480x640_shrunken_exported_1646.npy')
>>> param.shape
(8, 1200, 1200)
>>> param
array([[[   0.,    0.,    0., ...,    0.,    0.,    0.],
        [   0.,    0.,    0., ...,    0.,    0.,    0.],
        [   0.,    0.,    0., ...,    0.,    0.,    0.],
        ...,
        [-100., -100., -100., ...,    0.,    0.,    0.],
        [-100., -100., -100., ...,    0.,    0.,    0.],
        [-100., -100., -100., ...,    0.,    0.,    0.]]], dtype=float32)

6. Reference

1. https://docs.nvidia.com/deeplearning/tensorrt/onnx-graphsurgeon/docs/index.html
2. https://github.com/NVIDIA/TensorRT/tree/main/tools/onnx-graphsurgeon