#include // CUDA libraries #include #include // Include associated header file #include "T5_DNN.cuh" enum ActivationFunctions { ReLU, Sigmoid, None }; __global__ void neuralNetworkLayer(float *A, float *B, float* C, float* bias, int A_cols, int C_rows, int C_cols, ActivationFunctions activation) { /** * Computes the result (matrix C) of a single layer of a neural network: * C = activation(AB + bias) * where B is the matrix of weights and A is the input vector. This code is stolen from * here: https://github.com/charitha22/workspace/blob/master/cuda/mm/naive_matrix_multiply.cu */ int row = blockIdx.y*blockDim.y + threadIdx.y; int col = blockIdx.x*blockDim.x + threadIdx.x; if (row < C_rows && col < C_cols) { float value = 0.; for (int inner = 0; inner < A_cols; ++inner) { value += A[row*A_cols + inner]*B[inner*C_cols + col]; } value += bias[row*C_cols + col]; switch (activation) { case (ReLU): C[row*C_cols + col] = (value > 0.) ? value : 0.; break; case (Sigmoid): C[row*C_cols + col] = exp(value)/(exp(value) + 1); break; default: C[row*C_cols + col] = value; break; } } } float neuralNetwork(float* x) { /** * Auto-generated from the following PyTorch (v1.12.1+cu113) model: * DNN( * (layers): Sequential( * (0): Linear(in_features=44, out_features=32, bias=True) * (1): ReLU() * (2): Linear(in_features=32, out_features=32, bias=True) * (3): ReLU() * (4): Linear(in_features=32, out_features=1, bias=True) * (5): Sigmoid() * ) * ) * * Implements the calculation of the discriminant for a simple neural network * with some CUDA acceleration */ // Initialize x and allocate device memory for it int x_rows = 1, x_cols = 44; int x_size = x_rows*x_cols; float* gpu_x; cudaMalloc(&gpu_x, x_size*sizeof(float)); cudaMemcpy(gpu_x, x, x_size*sizeof(float), cudaMemcpyHostToDevice); // Initialize x_0 and allocate device memory for it int x_0_rows = 1, x_0_cols = 32; int x_0_size = x_0_rows*x_0_cols; float* gpu_x_0; cudaMalloc(&gpu_x_0, x_0_size*sizeof(float)); float x_0[32] = { 0. }; cudaMemcpy(gpu_x_0, x_0, x_0_size*sizeof(float), cudaMemcpyHostToDevice); // Initialize bias_0 and allocate device memory for it int bias_0_rows = 1, bias_0_cols = 32; int bias_0_size = bias_0_rows*bias_0_cols; float* gpu_bias_0; cudaMalloc(&gpu_bias_0, bias_0_size*sizeof(float)); float bias_0[32] = { -0.113411694765090942382812500000,-0.644476950168609619140625000000,-0.995468139648437500000000000000, 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1.754723429679870605468750000000,-0.150131121277809143066406250000,-0.871158242225646972656250000000, 0.424302846193313598632812500000,-0.616618812084197998046875000000,-0.015744073316454887390136718750,-1.272865653038024902343750000000, 0.544318437576293945312500000000,-0.121996097266674041748046875000,-1.136966586112976074218750000000, 0.570699095726013183593750000000, 0.925056755542755126953125000000,-0.724550187587738037109375000000,-1.055687665939331054687500000000,-2.147436618804931640625000000000,-0.070856578648090362548828125000, 1.950405955314636230468750000000, 0.332398861646652221679687500000, 0.825570225715637207031250000000 }; cudaMemcpy(gpu_wgtT_0, wgtT_0, wgtT_0_size*sizeof(float), cudaMemcpyHostToDevice); // (0): Linear(in_features=44, out_features=32, bias=True) => x = x*W_T + b dim3 x_0_block(x_0_cols, x_0_rows, 1); dim3 x_0_grid((x_0_cols + x_0_block.x - 1)/x_0_block.x, (x_0_rows + x_0_block.y - 1)/x_0_block.y); neuralNetworkLayer<<>>(gpu_x, gpu_wgtT_0, gpu_x_0, gpu_bias_0, x_cols, x_0_rows, x_0_cols, ReLU); // Wait for GPU to finish before accessing on host cudaDeviceSynchronize(); // Get results cudaMemcpy(x_0, gpu_x_0, x_0_size*sizeof(float), cudaMemcpyDeviceToHost); // Clean up cudaFree(gpu_x); cudaFree(gpu_bias_0); cudaFree(gpu_wgtT_0); // Initialize x_2 and allocate device memory for it int x_2_rows = 1, x_2_cols = 32; int x_2_size = x_2_rows*x_2_cols; float* gpu_x_2; cudaMalloc(&gpu_x_2, x_2_size*sizeof(float)); float x_2[32] = { 0. }; cudaMemcpy(gpu_x_2, x_2, x_2_size*sizeof(float), cudaMemcpyHostToDevice); // Initialize bias_2 and allocate device memory for it int bias_2_rows = 1, bias_2_cols = 32; int bias_2_size = bias_2_rows*bias_2_cols; float* gpu_bias_2; cudaMalloc(&gpu_bias_2, bias_2_size*sizeof(float)); float bias_2[32] = { 0.368473380804061889648437500000, 0.219935327768325805664062500000,-1.153472304344177246093750000000, 0.189746916294097900390625000000,-0.067594930529594421386718750000,-0.204528257250785827636718750000, 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0.010026044212281703948974609375,-0.743237376213073730468750000000 }; cudaMemcpy(gpu_wgtT_2, wgtT_2, wgtT_2_size*sizeof(float), cudaMemcpyHostToDevice); // (2): Linear(in_features=32, out_features=32, bias=True) => x = x*W_T + b dim3 x_2_block(x_2_cols, x_2_rows, 1); dim3 x_2_grid((x_2_cols + x_2_block.x - 1)/x_2_block.x, (x_2_rows + x_2_block.y - 1)/x_2_block.y); neuralNetworkLayer<<>>(gpu_x_0, gpu_wgtT_2, gpu_x_2, gpu_bias_2, x_0_cols, x_2_rows, x_2_cols, ReLU); // Wait for GPU to finish before accessing on host cudaDeviceSynchronize(); // Get results cudaMemcpy(x_2, gpu_x_2, x_2_size*sizeof(float), cudaMemcpyDeviceToHost); // Clean up cudaFree(gpu_x_0); cudaFree(gpu_bias_2); cudaFree(gpu_wgtT_2); // Initialize x_4 and allocate device memory for it int x_4_rows = 1, x_4_cols = 1; int x_4_size = x_4_rows*x_4_cols; float* gpu_x_4; cudaMalloc(&gpu_x_4, x_4_size*sizeof(float)); float x_4[1] = { 0. }; cudaMemcpy(gpu_x_4, x_4, x_4_size*sizeof(float), cudaMemcpyHostToDevice); // Initialize bias_4 and allocate device memory for it int bias_4_rows = 1, bias_4_cols = 1; int bias_4_size = bias_4_rows*bias_4_cols; float* gpu_bias_4; cudaMalloc(&gpu_bias_4, bias_4_size*sizeof(float)); float bias_4[1] = { 0.503246486186981201171875000000 }; cudaMemcpy(gpu_bias_4, bias_4, bias_4_size*sizeof(float), cudaMemcpyHostToDevice); // Initialize wgtT_4 and allocate device memory for it int wgtT_4_rows = 32, wgtT_4_cols = 1; int wgtT_4_size = wgtT_4_rows*wgtT_4_cols; float* gpu_wgtT_4; cudaMalloc(&gpu_wgtT_4, wgtT_4_size*sizeof(float)); float wgtT_4[32*1] = { 0.762260854244232177734375000000, -0.109718091785907745361328125000, 0.486676990985870361328125000000, -0.293269395828247070312500000000, 0.216582491993904113769531250000, 0.822565138339996337890625000000, -0.724595606327056884765625000000, 0.046157058328390121459960937500, -0.169900581240653991699218750000, 0.307012289762496948242187500000, 0.337143152952194213867187500000, -0.263579487800598144531250000000, 0.396959900856018066406250000000, 0.216389715671539306640625000000, -0.275450080633163452148437500000, 0.836189270019531250000000000000, 0.284820050001144409179687500000, 0.425763964653015136718750000000, -0.123780108988285064697265625000, -0.143616050481796264648437500000, -1.113721966743469238281250000000, -1.111571550369262695312500000000, 0.215610042214393615722656250000, 0.202986225485801696777343750000, -0.006719581782817840576171875000, -0.399531364440917968750000000000, -0.191530168056488037109375000000, -1.715734243392944335937500000000, -0.379245012998580932617187500000, 0.026375815272331237792968750000, -0.781431794166564941406250000000, -0.085395559668540954589843750000 }; cudaMemcpy(gpu_wgtT_4, wgtT_4, wgtT_4_size*sizeof(float), cudaMemcpyHostToDevice); // (4): Linear(in_features=32, out_features=1, bias=True) => x = x*W_T + b dim3 x_4_block(x_4_cols, x_4_rows, 1); dim3 x_4_grid((x_4_cols + x_4_block.x - 1)/x_4_block.x, (x_4_rows + x_4_block.y - 1)/x_4_block.y); neuralNetworkLayer<<>>(gpu_x_2, gpu_wgtT_4, gpu_x_4, gpu_bias_4, x_2_cols, x_4_rows, x_4_cols, Sigmoid); // Wait for GPU to finish before accessing on host cudaDeviceSynchronize(); // Get results cudaMemcpy(x_4, gpu_x_4, x_4_size*sizeof(float), cudaMemcpyDeviceToHost); // Clean up cudaFree(gpu_x_2); cudaFree(gpu_bias_4); cudaFree(gpu_wgtT_4); cudaFree(gpu_x_4); return x_4[0]; }