Dellve CuDNN Documentation

Transcription

1 Dellve CuDNN Documentation Release DELLveTeam May 02, 2017

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3 Contents 1 Install Requirements 3 2 Dellve CuDNN Framework 5 3 Dellve CuDNN Operations 7 4 API Reference 11 5 Contributing 13 6 Licensing 15 i

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5 Dellve CuDNN Documentation, Release Dellve CuDNN is one of the tools that are used as an extension to the main project Dellve Deep on GitHub. This tool utilizes NVIDIA cudnn operations as benchmark and stress test tools. You can install Dellve CuDNN from our GitHub repo: pip install git+ Contents 1

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7 CHAPTER 1 Install Requirements Dellve CuDNN requires CUDA and CuDNN before setup. Important: These environment variables must be set before installation: CUDA_PATH: Path to CUDA library CUDNN_PATH: Path to CUDNN library CUDA Setup CuDNN Setup 3

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9 CHAPTER 2 Dellve CuDNN Framework This framework uses a cpp to python framework PyBind11 to create functions in cpp and allows them to be controlled by Python side. The CPP code uses an open source Python wrapper: PyCuDNN to make calls to CuDNN simpler. class DELLve::BenchmarkController Controller class that speficies the actual runtime execution of each tool in Dellve CuDNN. Provides functions that allows profiling of current status of the exeuction. Assumes that BenchmarkDriver has been ran with the function of current operation as desired. Subclassed by DELLve::BenchmarkDriver< A > Public Functions void startbenchmark(int deviceid, int numruns) Starts a benchmark tool. Sets device to desired GPU. Warms up the test by running a singular operation and synchronizing. Then, runs the operation the specified number of times. Through each run, calculates time it took to run the current method and sets the progress of the current run by the number of loops ran. This is done in a different thread so that external calls can be made to this class to profile the current status. Parameters deviceid: - Device ID to run test on numruns: - Number of repeats void startstresstool(int deviceid, int seconds) Starts a stress test tool. Sets device to desired GPU. Starts the clock and runs the benchmark until desired elapsed time has been reached. Through each run, sets the progress of the test by the number of seconds elapsed. 5

10 Dellve CuDNN Documentation, Release This is also done in a separate thread so that external calls can be made to this class to profile the current status. Parameters deviceid: - Device ID to run test on seconds: - Number of seconds to run the test float getprogress() const Returns the progress set by the tests. Return float - Progress of the current tool. int getavgtimemicro() const Returns the average time taken to run the current problem set in the benchmarks. Should not be called by Stress Tools. Return int - Average time in microsends taken for each benchmark. 6 Chapter 2. Dellve CuDNN Framework

11 CHAPTER 3 Dellve CuDNN Operations Following are all of the operations supported by Dellve CuDNN. template <typename T> DELLve::Benchmark DELLve::Activation::forward(int w, int h, int c, int n) CuDNN Activation Forward Build 4D tensors using NCHW provided. Fill the input with random data using the curand library. Then, set up the function that will run the operation with forward propagation. Parameters w: - Width of each feature map h: - Height of each feature map c: - Number of feature maps per image n: - Number of feature maps template <typename T> DELLve::Benchmark DELLve::Activation::backward(int w, int h, int c, int n) CuDNN Activation Backward Build 4D tensors using NCHW provided. Fill the input with random data using the curand library. Then, set up the function that will run the operation with backward propagation. See See forward for parameter details template <typename T> DELLve::Benchmark DELLve::Softmax::forward(int w, int h, int c, int n, std::string alg) CuDNN Softmax Forward Build 4D tensors using NCHW provided. Fill the input with random data using the curand library. Then, set up the function that will run the operation with forward propagation with algorithm specified. Parameters w: - Width of each feature map 7

12 Dellve CuDNN Documentation, Release h: - Height of each feature map c: - Number of feature maps per image n: - Number of feature maps alg: - Algorithm to run. Can be fast, accurate, or log template <typename T> DELLve::Benchmark DELLve::Softmax::backward(int w, int h, int c, int n, std::string alg) CuDNN Softmax Backward Build 4D tensors using NCHW provided. Fill the input with random data using the curand library. Then, set up the function that will run the operation with backward propagation with algorithm specified. See See forward for parameter details template <typename T> DELLve::Benchmark DELLve::Convolution::forward(int w, int h, int c, int n, int k, int r, int s, int padw, int padh, int stridev, int strideh) CuDNN Convolution Forward Build 4D tensors using NCHW and KCRS provided for input and filter respectively. Then, create the output tensor by calculating the forward output dimensions of convolution. Finally, set up the workspace required and return the function that will run the operation with forward propagation. Parameters w: - Width of input image h: - Height of input image c: - Number of channels per input image n: - Number of input images k: - Number of filters r: - Height of filter s: - Width of filter padw: - Width of zero padding padh: - Height of zero padding stridev: - Filter vertical stride strideh: - Filter horizontal stride template <typename T> DELLve::Benchmark DELLve::Convolution::backwardData(int w, int h, int c, int n, int k, int r, int s, int padw, int padh, int stridew, int strideh) CuDNN Convolution Backward Data Build 4D tensors using NCHW and KCRS provided for input and filter respectively. Then, create the output tensor by calculating the forward output dimensions of convolution. Finally, set up the workspace required and return the function that will run the operation with backward propagation respective to data. See See forward for parameter details template <typename T> 8 Chapter 3. Dellve CuDNN Operations

13 Dellve CuDNN Documentation, Release DELLve::Benchmark DELLve::Convolution::backwardFilter(int w, int h, int c, int n, int k, int r, int s, int padw, int padh, int stridew, int strideh) CuDNN Convolution Backward Filter Build 4D tensors using NCHW and KCRS provided for input and filter respectively. Then, create the output tensor by calculating the forward output dimensions of convolution. Finally, set up the workspace required and return the function that will run the operation with backward propagation respective to filter. See See forward for parameter details template <typename T> DELLve::Benchmark DELLve::Pooling::forward(int w, int h, int c, int n, int winh, int winw, int padh, int padw, int vstride, int hstride, std::string mode) CuDNN Pooling Forward Build 4D tensors using NCHW provided. Fill the input with random data using the curand library. Then, calculate the forward output dimensions and set up a 4d tensor for output. Finally, return the function to run the operation with forward propagation. Parameters w: - Width of each feature map h: - Height of each feature map c: - Number of feature maps per image n: - Number of feature maps winh: - Height of pooling window winw: - Width of pooling window padh: - Height of the zero padding padw: - Width of the zero padding vstride: - Pooling vertical stride hstride: - Pooling horizontal stride mode: - Pooling mode ot run. Can be max, avgpad, avgnopad template <typename T> DELLve::Benchmark DELLve::Pooling::backward(int w, int h, int c, int n, int winh, int winw, int padh, int padw, int hstride, int wstride, std::string mode) CuDNN Pooling Backward Build 4D tensors using NCHW provided. Fill the input with random data using the curand library. Then, calculate the backward output dimensions and set up a 4d tensor for output. Finally, return the function to run the operation with backward propagation. See See forward for parameter details 9

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15 CHAPTER 4 API Reference 11

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17 CHAPTER 5 Contributing This section is coming soon. Please check back later. 13

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19 CHAPTER 6 Licensing Dellve CuDNN is licensed to you under the MIT License: Important: Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the Software ), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED AS IS, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PAR- TICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFT- WARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 15

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21 Index D DELLve::Activation::backward (C++ function), 7 DELLve::Activation::forward (C++ function), 7 DELLve::BenchmarkController (C++ class), 5 DELLve::BenchmarkController::getAvgTimeMicro (C++ function), 6 DELLve::BenchmarkController::getProgress (C++ function), 6 DELLve::BenchmarkController::startBenchmark (C++ function), 5 DELLve::BenchmarkController::startStressTool (C++ function), 5 DELLve::Convolution::backwardData (C++ function), 8 DELLve::Convolution::backwardFilter (C++ function), 8 DELLve::Convolution::forward (C++ function), 8 DELLve::Pooling::backward (C++ function), 9 DELLve::Pooling::forward (C++ function), 9 DELLve::Softmax::backward (C++ function), 8 DELLve::Softmax::forward (C++ function), 7 17