├── CMakeLists.txt ├── LICENSE ├── README.md ├── TwoLayerNetwork.cpp ├── TwoLayerNetwork.py └── plot_data.py /CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 3.0 FATAL_ERROR) 2 | project(TwoLayerNetwork) 3 | 4 | find_package(Torch REQUIRED) 5 | set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}") 6 | 7 | add_executable(TwoLayerNetwork TwoLayerNetwork.cpp) 8 | target_link_libraries(TwoLayerNetwork "${TORCH_LIBRARIES}") 9 | set_property(TARGET TwoLayerNetwork PROPERTY CXX_STANDARD 14) 10 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PyTorchCppFrontEnd 2 | PyTorch 1.5 C++ frontend API Example code 3 | 4 | To run this code: 5 | Follow the steps outlined here https://pytorch.org/tutorials/advanced/cpp_frontend.html to install libtorch 6 | ``` 7 | //If you need e.g. CUDA 9.0 support, please replace "cpu" with "cu90" in the URL below. 8 | wget https://download.pytorch.org/libtorch/nightly/cpu/libtorch-shared-with-deps-latest.zip 9 | unzip libtorch-shared-with-deps-latest.zip 10 | ``` 11 | build and run commands 12 | ``` 13 | //build the example 14 | mkdir build 15 | cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch 16 | // run the example 17 | cmake --build . --config Release 18 | ``` 19 | Here is the compile output on Ubuntu 18.04. 20 | ``` 21 | $ cmake -DCMAKE_PREFIX_PATH=~/pytorch/libtorch 22 | CMake Warning: 23 | No source or binary directory provided. Both will be assumed to be the 24 | same as the current working directory, but note that this warning will 25 | become a fatal error in future CMake releases. 26 | 27 | 28 | -- The C compiler identification is GNU 7.5.0 29 | -- The CXX compiler identification is GNU 7.5.0 30 | -- Check for working C compiler: /usr/bin/cc 31 | -- Check for working C compiler: /usr/bin/cc -- works 32 | -- Detecting C compiler ABI info 33 | -- Detecting C compiler ABI info - done 34 | -- Detecting C compile features 35 | -- Detecting C compile features - done 36 | -- Check for working CXX compiler: /usr/bin/c++ 37 | -- Check for working CXX compiler: /usr/bin/c++ -- works 38 | -- Detecting CXX compiler ABI info 39 | -- Detecting CXX compiler ABI info - done 40 | -- Detecting CXX compile features 41 | -- Detecting CXX compile features - done 42 | -- Looking for pthread.h 43 | -- Looking for pthread.h - found 44 | -- Looking for pthread_create 45 | -- Looking for pthread_create - not found 46 | -- Looking for pthread_create in pthreads 47 | -- Looking for pthread_create in pthreads - not found 48 | -- Looking for pthread_create in pthread 49 | -- Looking for pthread_create in pthread - found 50 | -- Found Threads: TRUE 51 | -- Found Torch: /home/yasasri/pytorch/libtorch/lib/libtorch.so 52 | -- Configuring done 53 | -- Generating done 54 | -- Build files have been written to: /home/yasasri/code/PyTorchCppFrontEnd 55 | (base) yasasri@yasasri-MacBookPro:~/code/PyTorchCppFrontEnd$ cmake --build . --config Release 56 | Scanning dependencies of target TwoLayerNetwork 57 | [ 50%] Building CXX object CMakeFiles/TwoLayerNetwork.dir/TwoLayerNetwork.cpp.o 58 | [100%] Linking CXX executable TwoLayerNetwork 59 | [100%] Built target TwoLayerNetwork 60 | 61 | ``` 62 | 63 | C++ example output. 64 | ``` 65 | $ ./TwoLayerNetwork 66 | epoch = 99 loss = 0.322818 67 | [ CPUFloatType{} ] 68 | epoch = 199 loss = 0.28491 69 | [ CPUFloatType{} ] 70 | epoch = 299 loss = 0.253744 71 | [ CPUFloatType{} ] 72 | epoch = 399 loss = 0.228085 73 | [ CPUFloatType{} ] 74 | epoch = 499 loss = 0.206759 75 | [ CPUFloatType{} ] 76 | ``` 77 | 78 | Python example output. 79 | ``` 80 | $ python3 TwoLayerNetwork.py 81 | epoch = 99, loss = 1.8510469198226929 82 | epoch = 199, loss = 0.04883603751659393 83 | epoch = 299, loss = 0.002786928554996848 84 | epoch = 399, loss = 0.00019486852397676557 85 | epoch = 499, loss = 1.488259840698447e-05 86 | ``` 87 | 88 | References: 89 | * https://pytorch.org/blog/pytorch-1-dot-5-released-with-new-and-updated-apis/ 90 | * Justin Johnson. Learning PyTorch with Examples. 91 | * Using the PyTorch C++ Frontend 92 | -------------------------------------------------------------------------------- /TwoLayerNetwork.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | // N is batch size; D_in is input dimension 5 | // H is hidden dimension; D_out is output dimension 6 | const int64_t N = 64; 7 | const int64_t D_in = 1000; 8 | const int64_t H = 100; 9 | const int64_t D_out = 10; 10 | 11 | struct TwoLayerNet : torch::nn::Module { 12 | TwoLayerNet() : linear1(D_in, H), linear2(H, D_out) { 13 | register_module("linear1", linear1); 14 | register_module("linear2", linear2); 15 | } 16 | torch::Tensor forward(torch::Tensor x) { 17 | x = torch::relu(linear1->forward(x)); 18 | x = linear2->forward(x); 19 | return x; 20 | } 21 | torch::nn::Linear linear1; 22 | torch::nn::Linear linear2; 23 | }; 24 | 25 | int main() { 26 | torch::manual_seed(1); 27 | 28 | torch::Tensor x = torch::rand({N, D_in}); 29 | torch::Tensor y = torch::rand({N, D_out}); 30 | // change this to torch::kCUDA if GPU is available 31 | torch::Device device(torch::kCPU); 32 | 33 | TwoLayerNet model; 34 | model.to(device); 35 | 36 | float_t learning_rate = 1e-4; 37 | torch::optim::SGD optimizer( 38 | model.parameters(), torch::optim::SGDOptions(learning_rate)); 39 | 40 | // number of ecpochs = 500 41 | for (size_t epoch = 1; epoch <= 500; ++epoch) { 42 | optimizer.zero_grad(); 43 | auto y_pred = model.forward(x); 44 | // need to use y.detach() instead of y 45 | // see issue: https://github.com/pytorch/pytorch/issues/16830 46 | auto loss = torch::mse_loss(y_pred, y.detach()); 47 | if (epoch%100 == 99) 48 | std::cout << "epoch = " << epoch << " " << "loss = " << loss << "\n"; 49 | loss.backward(); 50 | optimizer.step(); 51 | } 52 | } 53 | -------------------------------------------------------------------------------- /TwoLayerNetwork.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | class TwoLayerNet(torch.nn.Module): 4 | 5 | def __init__(self, D_in, H, D_out): 6 | super(TwoLayerNet, self).__init__() 7 | self.linear1 = torch.nn.Linear(D_in, H) 8 | self.linear2 = torch.nn.Linear(H, D_out) 9 | 10 | def forward(self, x): 11 | h_relu = self.linear1(x).clamp(min=0) 12 | y_pred = self.linear2(h_relu) 13 | return y_pred 14 | 15 | # N is batch size; D_in is input dimension 16 | # H is hidden dimension; D_out is output dimension 17 | N, D_in, H, D_out = 64, 1000, 100, 10 18 | 19 | # Create random input and output data 20 | x = torch.randn(N, D_in) 21 | y = torch.randn(N, D_out) 22 | 23 | # nn package to define model and loss function 24 | model = TwoLayerNet(D_in, H, D_out) 25 | 26 | loss_fn = torch.nn.MSELoss(reduction='sum') 27 | learning_rate = 1e-4 28 | optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) 29 | for t in range(500): 30 | # Forward pass: compute predicted y 31 | y_pred = model(x) 32 | 33 | # Compute and print loss 34 | loss = loss_fn(y_pred, y) 35 | if t%100 == 99: 36 | print("epoch = {}, loss = {}".format(t, loss.item())) 37 | 38 | #Zero the gradients before running the backward pass 39 | optimizer.zero_grad() 40 | 41 | # Backpropagate the loss 42 | loss.backward() 43 | 44 | # Update weights 45 | optimizer.step() 46 | -------------------------------------------------------------------------------- /plot_data.py: -------------------------------------------------------------------------------- 1 | """ 2 | Simple Python code to plot the benchmark performance data. 3 | """ 4 | import numpy as np 5 | import matplotlib.pyplot as plt 6 | 7 | x = [500, 1000, 1500, 2500, 5000, 50000] # epochs 8 | y1 = [5.14, 8.05, 9.54, 15.83, 30.46, 275.37] # Python 9 | y2 = [1.99, 3.31, 5.04, 8.15, 14.82, 140.76] # C++ 10 | 11 | fig, ax = plt.subplots() 12 | 13 | line1, = ax.plot(x, y1, label='Python') 14 | 15 | line2, = ax.plot(x, y2, label='C++') 16 | 17 | ax.legend() 18 | ax.set_xlabel('number of epochs') 19 | ax.set_ylabel('runtime in secs(ivybridge Ubuntu 18.04)') 20 | plt.show() 21 | --------------------------------------------------------------------------------