tpu-mlir-test 2.49

Machine learning compiler based on MLIR for Sophgo TPU v2.49-20240426.

TPU-MLIR

For Chinese version: README.

TPU-MLIR is an open-source machine-learning compiler based on MLIR for TPU. This project provides a complete toolchain, which can convert pre-trained neural networks from different frameworks into binary files bmodel that can be efficiently operated on TPUs.

SOPHGO aims to become a leading global provider of general-purpose computing power. SOPHGO focuses on the research, development, and promotion of computing products such as AI and RISC-V CPUs, and has built a comprehensive application matrix covering the 'cloud, edge, and endpoint' scenarios with its self-developed products. SOPHGO provides computing products and integrated solutions for applications such as smart cities, intelligent computing centers, smart security, intelligent transportation, safety production, industrial quality inspection, and intelligent terminals. The company has research and development centers in more than 10 cities in China including Beijing, Shanghai, Shenzhen, Qingdao, and Xiamen, as well as in the United States and Singapore.

Currently, supported AI frameworks are PyTorch, ONNX, TFLite and Caffe. Models from other frameworks need to be converted to ONNX models.

Resources

Here are some resources to help you better understand the project:

Index	Documents
01	TPU-MLIR paper
02	TPU-MLIR Technical Reference Manual
03	TPU-MLIR Quick Start

Index	Sharing Sessions
01	TPU-MLIR Paper
02	LayerGroup

Index	Topic	Video Links
01	What is AI Compiler?	AI Compiler Intro
02	MLIR Intro	Basic Syntax (1), Basic Syntax (2), Basic Syntax (3), Dialect Conversion, Pattern Rewriting
03	TPU-MLIR Intro	Overview, Front-end Conversion, Lowering
04	Quantization	Overview, Formula Derivation, Calibration, QAT
05	TPU Memory	Ep1, Ep2
06	TPU-MLIR Practice	To Onnx Format, Graph Optimization, Operator Support, Model Support, Fuse Preprocess, Accuracy Validation

In addition, we also published a series of tasks for any of you interested in our project and would like to develop it with us:

Index	Tasks
01	Rewrite Patterns for PermuteOp
02	Shape Inference Implement
03	mlir2onnx tool Optimize

For More tasks please check Issue.

If you have any questions while doing the tasks above, you can ask or check the existing answers in our Q&A Platform.

How to Build

After cloning the code of this project, it needs to be compiled in docker.

• Download the required image from dockerhub.

docker pull sophgo/tpuc_dev:latest

# myname1234 is just an example, you can set your own name
docker run --privileged --name myname1234 -v $PWD:/workspace -it sophgo/tpuc_dev:latest

After the container is created, the directory of the code in docker should be /workspace/tpu-mlir.

• Building

Run the following command in the project directory:

cd tpu-mlir
source ./envsetup.sh
./build.sh

Usage

Introduce the usage of TPU-MLIR by a simple example of compiling yolov5s.onnx and running it on the BM1684X TPU platform.

The model comes from the official website of yolov5: https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.onnx.

It has been placed in project path regression/model/yolov5s.onnx.

Preparation

Firstly, create a model_yolov5s directory at the same level directory with this project. Then put both model and image files into it.

The operation is as follows:

mkdir model_yolov5s && cd model_yolov5s
cp ${REGRESSION_PATH}/model/yolov5s.onnx .
cp -rf ${REGRESSION_PATH}/dataset/COCO2017 .
cp -rf ${REGRESSION_PATH}/image .
mkdir workspace && cd workspace

Model to MLIR

If the model takes images as input, we need to learn its preprocessing before transforming. No preprocessing needs to be considered if the input is npz file. The preprocessing process is formulated as follows:

$$ y = （x - mean） \times scale, $$

where x represents the input.

The input of the official yolov5 is RGB image. Each value will be multiplied by 1/255. Mean and scale are 0.0, 0.0, 0.0 and 0.0039216, 0.0039216, 0.0039216 respectively.

The model conversion command:

model_transform.py \
    --model_name yolov5s \
    --model_def ../yolov5s.onnx \
    --input_shapes [[1,3,640,640]] \
    --mean 0.0,0.0,0.0 \
    --scale 0.0039216,0.0039216,0.0039216 \
    --keep_aspect_ratio \
    --pixel_format rgb \
    --output_names 350,498,646 \
    --test_input ../image/dog.jpg \
    --test_result yolov5s_top_outputs.npz \
    --mlir yolov5s.mlir

The arguments of model_transform.py:

Argument	Required？	Description
model_name	Yes	Model name
model_def	Yes	Model definition file (.onnx,.pt,.tflite or .prototxt)
model_data	No	Specify the model weight file, required when it is caffe model (corresponding to the '.caffemodel' file)
input_shapes	No	The shape of the input, such as [[1,3,640,640]] (a two-dimensional array), which can support multiple inputs
resize_dims	No	The size of the original image to be adjusted to. If not specified, it will be resized to the input size of the model
keep_aspect_ratio	No	Whether to maintain the aspect ratio when resize. False by default. It will pad 0 to the insufficient part when setting
mean	No	The mean of each channel of the image. The default is 0.0,0.0,0.0
scale	No	The scale of each channel of the image. The default is 1.0,1.0,1.0
pixel_format	No	Image type, can be rgb, bgr, gray or rgbd
output_names	No	The names of the output. Use the output of the model if not specified, otherwise use the specified names as the output
test_input	No	The input file for validation, which can be an image, npy or npz. No validation will be carried out if it is not specified
test_result	No	Output file to save validation result
excepts	No	Names of network layers that need to be excluded from validation. Separated by comma
debug	No	if open debug, immediate model file will keep; or will remove after conversion done
mlir	Yes	The output mlir file name (including path)

After converting to mlir file, a ${model_name}_in_f32.npz file containing preprocessed input will be generated.

MLIR to F16 bmodel

Convert the mlir file to the F16 bmodel by the following command:

model_deploy.py \
  --mlir yolov5s.mlir \
  --quantize F16 \
  --chip bm1684x \
  --test_input yolov5s_in_f32.npz \
  --test_reference yolov5s_top_outputs.npz \
  --model yolov5s_1684x_f16.bmodel

The arguments of model_deploy.py:

Argument	Required？	Description
mlir	Yes	Mlir file
quantize	Yes	Quantization type (F32/F16/BF16/INT8)
chip	Yes	The platform that the model will use. Currently only bm1684x is supported. More TPU platforms will be supported in the future
calibration_table	No	The quantization table path. Required when it is INT8 quantization
tolerance	No	Tolerance for the minimum similarity between MLIR quantized and MLIR fp32 inference results
correctnetss	No	Tolerance for the minimum similarity between simulator and MLIR quantized inference results. 0.99,0.90 by default
excepts	No	Names of network layers that need to be excluded from validation. Separated by comma
debug	No	if open debug, immediate model file will keep; or will remove after conversion done
model	Yes	Name of output model file (including path)
dynamic	No	dynamic codegen for to support dynamic shape

MLIR to INT8 bmodel

Before converting to the INT8 model, you need to run calibration to get the calibration table. The number of input data is about 100 to 1000 according to the situation.

Then use the calibration table to generate a symmetric int8 bmodel. It is generally not recommended to use the asymmetric one if the symmetric one already meets the requirements, because the performance of the asymmetric model will be slightly worse than the symmetric model.

Here is an example of the existing 100 images from COCO2017 to perform calibration:

run_calibration.py yolov5s.mlir \
  --dataset ../COCO2017 \
  --input_num 100 \
  -o yolov5s_cali_table

Execute the following command to convert to the INT8 symmetric quantized model:

model_deploy.py \
  --mlir yolov5s.mlir \
  --quantize INT8 \
  --calibration_table yolov5s_cali_table \
  --chip bm1684x \
  --test_input yolov5s_in_f32.npz \
  --test_reference yolov5s_top_outputs.npz \
  --tolerance 0.85,0.45 \
  --model yolov5s_1684x_int8.bmodel

Results Comparison

This project has a yolov5 sample written in python (path: python/samples/detect_yolov5.py) for object detection. Read the code to learn how the model is used:

1. preprocess the input
2. model inference to get output
3. post-process the output

The following code is used to verify the output of onnx/f32/int8 model respectively:

• ONNX model:

detect_yolov5.py \
  --input ../image/dog.jpg \
  --model ../yolov5s.onnx \
  --output dog_origin.jpg

• F16 bmodel:

detect_yolov5.py \
  --input ../image/dog.jpg \
  --model yolov5s_1684x_f16.bmodel \
  --output dog_f16.jpg

• INT8 symmetric quantized bmodel:

detect_yolov5.py \
  --input ../image/dog.jpg \
  --model yolov5s_1684x_int8.bmodel \
  --output dog_int8.jpg

Outputs of different models are compared below:

Auxiliary Tools

Model Inference Tool model_runner.py

Supports bmodel/mlir/pytorch/onnx/tflite/caffe.

model_runner.py \
  --input resnet18_in_f32.npz \
  --model resnet18_1684x_f32.bmodel \
  --output resnet18_output.npz

Tool for bmodel

The bmodel file can be viewed and edited by model_tool:

  model_tool
    --info model_file : show brief model info
    --print model_file : show detailed model info
    --extract model_file : extract one multi-net bmodel to multi one-net bmodels
    --combine file1 .. fileN -o new_file: combine bmodels to one bmodel by filepath
    --combine_dir dir1 .. dirN -o new_dir: combine bmodels to one bmodel by directory path
    --dump model_file start_offset byte_size out_file: dump binary data to file from bmodel

For example, to get basic information of bmodel:

model_tool --info resnet18_1684x_f32.bmodel

Related Links

• Official Network
• Discourse
• Videos