# tpu-mlir **Repository Path**: codepool/tpu-mlir ## Basic Information - **Project Name**: tpu-mlir - **Description**: 本项目是算能智能AI芯片的TPU编译器工程。该工程提供了一套完整的工具链，其可以将不 同框架下预训练的神经网络，转化为可以在算能TPU上高效运算的二进制文件bmodel。 - **Primary Language**: C++ - **License**: BSD-2-Clause - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 11 - **Created**: 2023-06-21 - **Last Updated**: 2024-06-04 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ![](./docs/assets/sophgo_chip.png) # TPU-MLIR For Chinese version: [README_cn.md](https://github.com/sophgo/tpu-mlir/blob/master/README_cn.md). 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. TPU-MLIR is originally developed by SOPHGO. This company is committed to becoming the world's leading general computing power provider. SOPHGO has inherited the technology, patents, products and customers of BITMAIN in the AI field for many years and focused on the R&D, promotion and application of artificial intelligence chips and related products. It has two major brands: SOPHON and CVITEK. With the self-developed chips as the core, SOPHGO has created a matrix of computing power products, which covers the whole scene of "cloud, edge and terminal" and provides computing power products and overall solutions for urban brains, intelligent computing centers, intelligent security, intelligent transportation, safety production, industrial quality inspection, intelligent terminals and others. For technical details of this project, please refer to: [TPU-MLIR Technical Reference Manual](https://tpumlir.org/en/docs/developer_manual/index.html). We also have a paper introducing the design, . Currently, the project supports BM1684X. BM1684, CV183x, CV182x, Mars, and other chips will be supported in the future. # How to Build After cloning the code of this project, it needs to be compiled in docker. * Download the required image from [dockerhub](https://hub.docker.com/r/sophgo/tpuc_dev). ``` shell docker pull sophgo/tpuc_dev:latest # myname1234 just a 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: ``` shell cd tpu-mlir source ./envsetup.sh ./build.sh ``` # How to Test ``` shell # This project contains the yolov5s.onnx model, which can be used directly for verification pushd regression ./run_model.sh yolov5s popd ``` **Options:** If you want to verify more networks, you need to clone them first. Please refer to . After cloning, the model path should be `/workspace/model-zoo`, and then use the following command to verify: ``` shell # This step can also be skipped since its execution time is quite long pushd regression ./run_all.sh popd ``` # 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: . 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: ``` shell 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: ``` shell 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 (e.g., `.onnx`, `.tflite` or `.prototxt` files) | | 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 | | 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 F32 bmodel Convert the mlir file to the F32 bmodel by the following command: ``` shell model_deploy.py \ --mlir yolov5s.mlir \ --quantize F32 \ --chip bm1684x \ --test_input yolov5s_in_f32.npz \ --test_reference yolov5s_top_outputs.npz \ --model yolov5s_1684x_f32.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 | | model | Yes | Name of output model file (including path) | ## 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 or asymmetric 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: ``` shell 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: ``` shell 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_sym.bmodel ``` To the INT8 asymmetric quantized model: ``` shell model_deploy.py \ --mlir yolov5s.mlir \ --quantize INT8 \ --asymmetric \ --calibration_table yolov5s_cali_table \ --chip bm1684x \ --test_input yolov5s_in_f32.npz \ --test_reference yolov5s_top_outputs.npz \ --tolerance 0.90,0.55 \ --model yolov5s_1684x_int8_asym.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: ``` shell detect_yolov5.py \ --input ../image/dog.jpg \ --model ../yolov5s.onnx \ --output dog_onnx.jpg ``` * F32 bmodel: ``` shell detect_yolov5.py \ --input ../image/dog.jpg \ --model yolov5s_1684x_f32.bmodel \ --output dog_f32.jpg ``` * INT8 **symmetric quantized** bmodel: ``` shell detect_yolov5.py \ --input ../image/dog.jpg \ --model yolov5s_1684x_int8_sym.bmodel \ --output dog_int8_sym.jpg ``` * INT8 **asymmetric quantized** bmodel: ``` shell detect_yolov5.py \ --input ../image/dog.jpg \ --model yolov5s_1684x_int8_asym.bmodel \ --output dog_int8_asym.jpg ``` Outputs of four different models are compared below: ![](./docs/quick_start/assets/yolov5s.png) # Auxiliary Tools ## Model Inference Tool `model_runner.py` Supports bmodel/mlir/onnx/tflite. ``` shell 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`: ``` shell model_tool --info resnet18_1684x_f32.bmodel ```