#include <iostream>
#include <math.h>
#include <stdio.h>
// CUDA libraries.
#include <cuda.h>
#include <cuda_runtime.h>
// Include associated header file.
#include "../include/T5_NN.cuh"

#define ROW_TILE_WIDTH 2
#define COL_TILE_WIDTH 2

enum ActivationFunctions
{
    ReLU,
    Sigmoid,
    None
};

__global__ void neuralNetworkLayer(const float *A, const float *B, float* C, const 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 k = 0; k < A_cols; k++)
        {
            value += A[row*A_cols + k]*B[k*C_cols + col];
        }
        value += bias[row*C_cols + col];
        printf("Before activation\n");
        printf("C[%d, %d] = %f\n", row, col, value);
        switch (activation)
        {
        case (ReLU):
            C[row*C_cols + col] = (value > 0.) ? value : 0.;
            printf("After activation\n");
            printf("C[%d, %d] = %f\n", row, col, C[row*C_cols + col]);
            break;
        case (Sigmoid):
            C[row*C_cols + col] = exp(value)/(exp(value) + 1);
            printf("After activation\n");
            printf("C[%d, %d] = %f\n", row, col, C[row*C_cols + col]);
            break;
        default:
            C[row*C_cols + col] = value;
            printf("After activation\n");
            printf("C[%d, %d] = %f\n", row, col, C[row*C_cols + col]);
            break;
        }
    }
}

float neuralNetwork(const float x[44])
{
    /**
     * 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=1, bias=True)
     *     (3): 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;
    const float* gpu_x;
    cudaMalloc((void**) &gpu_x, x_size*sizeof(float));
    cudaMemcpy((void**) gpu_x, &x, x_size*sizeof(float), cudaMemcpyHostToDevice);
    
    // Initialize x_0 and allocate device memory for it
    int x_0_rows = 32, x_0_cols = 1;
    int x_0_size = x_0_rows*x_0_cols;
    float* gpu_x_0;
    cudaMalloc((void**) &gpu_x_0, x_0_size*sizeof(float));
    float x_0[32] = { 0. };
    cudaMemcpy((void**) gpu_x_0, &x_0, x_0_size*sizeof(float), cudaMemcpyHostToDevice);
    // Initialize bias_0 and allocate device memory for it
    int bias_0_rows = 32, bias_0_cols = 1;
    int bias_0_size = bias_0_rows*bias_0_cols;
    const float* gpu_bias_0;
    cudaMalloc((void**) &gpu_bias_0, bias_0_size*sizeof(float));
    const float bias_0[32] = {
         0.03852194175124168396,-0.68359977006912231445,-0.02970946952700614929,-0.31669190526008605957, 0.65455710887908935547,-0.08243317902088165283, 0.77493119239807128906,-0.56777644157409667969, 0.61407601833343505859, 0.21595512330532073975,-0.50334465503692626953, 0.61400628089904785156, 0.20319238305091857910,-0.05957625061273574829, 0.69607305526733398438,-0.72484016418457031250,-0.16477963328361511230,-0.17504660785198211670,-0.08536337316036224365,-0.53083413839340209961, 0.75410842895507812500,-0.50379949808120727539, 0.10314983129501342773, 0.88234001398086547852, 0.56091010570526123047,-0.58347439765930175781,-0.21314881742000579834, 0.22321066260337829590,-0.25960403680801391602,-0.41537967324256896973, 0.28511583805084228516,-0.66923546791076660156
    };
    cudaMemcpy((void**) gpu_bias_0, &bias_0, bias_0_size*sizeof(float), cudaMemcpyHostToDevice);
    // Initialize wgtT_0 and allocate device memory for it
    int wgtT_0_rows = 32, wgtT_0_cols = 44;
    int wgtT_0_size = wgtT_0_rows*wgtT_0_cols;
    const float* gpu_wgtT_0;
    cudaMalloc((void**) &gpu_wgtT_0, wgtT_0_size*sizeof(float));
    const float wgtT_0[44*32] = {
         0.24434363842010498047,-5.03902482986450195312,-0.10222853720188140869,-3.35312747955322265625, 2.65749907493591308594, 0.57255202531814575195, 3.29177832603454589844,-4.44903612136840820312, 5.04968976974487304688,-0.26169544458389282227,-5.35388278961181640625, 4.27396440505981445312, 0.39891564846038818359, 1.97598469257354736328, 4.07329940795898437500,-4.21473503112792968750,-0.11291780322790145874,-2.42647981643676757812,-6.46653938293457031250,-4.07020092010498046875, 4.03720378875732421875,-6.61832189559936523438,-0.81019723415374755859, 3.26421093940734863281, 3.70748186111450195312,-3.48381614685058593750,-2.90266227722167968750,-1.94850862026214599609, 1.77125823497772216797,-2.68539571762084960938, 3.20678925514221191406,-3.07838296890258789062,
        -0.04825218021869659424,-0.10645966231822967529, 0.02519210055470466614, 0.14094869792461395264,-0.20830595493316650391,-0.06521792709827423096,-0.41145098209381103516,-0.03774781525135040283, 0.39318224787712097168, 0.00795115530490875244,-0.23981563746929168701,-0.02616370655596256256,-0.14371669292449951172, 0.12075826525688171387, 1.60685491561889648438, 0.22865271568298339844, 0.06103347614407539368,-0.00887715630233287811, 0.07140446454286575317, 0.48695957660675048828, 0.22347226738929748535,-0.08093263953924179077,-0.03641089424490928650,-0.07067172974348068237,-1.73670589923858642578,-0.05346067622303962708, 0.18525636196136474609, 0.13769163191318511963, 0.47169497609138488770,-0.08360671997070312500,-0.07463672012090682983,-0.17268230020999908447,
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        -0.04834418371319770813, 0.07526406645774841309, 0.01217281725257635117, 0.14074754714965820312,-0.03681468591094017029, 0.04610479623079299927,-0.16923576593399047852,-0.05000643804669380188, 0.27488636970520019531, 0.03927890211343765259,-0.02261483669281005859,-0.10129822045564651489,-0.10503809154033660889,-0.38941049575805664062, 0.19461014866828918457, 0.01576416008174419403, 0.13846614956855773926,-0.02773262746632099152, 0.03338358923792839050, 0.15352201461791992188, 0.09728088974952697754,-0.13071790337562561035,-0.03546205908060073853,-0.05661747604608535767,-0.34468367695808410645,-0.10603389143943786621, 0.15347193181514739990, 0.15398626029491424561, 0.16927455365657806396,-0.07558812201023101807, 0.00499083194881677628, 0.08886094391345977783,
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         0.00900506321340799332, 0.07721171528100967407,-0.10661669820547103882, 0.07701881974935531616, 0.04388131946325302124, 0.12341000139713287354,-0.04209913313388824463,-0.08790724724531173706, 0.31444954872131347656, 0.39023309946060180664, 0.04955331981182098389, 0.13356526196002960205, 0.53133130073547363281,-0.43041345477104187012, 0.03642638027667999268,-0.25743582844734191895, 0.04361768811941146851, 0.28915968537330627441, 0.36393448710441589355, 0.08175972104072570801, 0.08054135739803314209,-0.13465435802936553955, 0.05955012515187263489,-0.01323404163122177124,-0.21579831838607788086,-0.04355644807219505310, 0.27617537975311279297, 0.77407145500183105469,-0.20442590117454528809,-0.17099456489086151123,-0.29983979463577270508,-0.08078791201114654541,
        -0.15478859841823577881,-0.34651660919189453125, 0.04654807224869728088,-0.23069304227828979492, 0.11919578164815902710,-0.05163815617561340332,-0.27604719996452331543,-0.10965692251920700073,-0.11210840195417404175, 0.26922005414962768555, 0.13901515305042266846,-0.05186912789940834045,-0.41968080401420593262, 0.46570315957069396973,-0.28665241599082946777, 0.13917754590511322021,-0.10500495135784149170,-0.42121627926826477051,-0.10068048536777496338,-0.24871715903282165527, 0.00726440688595175743, 0.16173197329044342041, 0.40519514679908752441,-0.04243832081556320190,-0.65925985574722290039, 0.08753348886966705322,-0.61628711223602294922,-0.09744511544704437256, 0.11248546093702316284,-0.16221149265766143799, 0.33842653036117553711,-0.24435727298259735107,
        -0.02526188828051090240, 0.06788796931505203247,-0.14319723844528198242,-0.05669433623552322388, 0.12730553746223449707, 0.16660954058170318604, 0.12822824716567993164,-0.09630632400512695312,-0.20904904603958129883, 0.07087226957082748413,-0.27900525927543640137, 0.09906103461980819702,-0.23674108088016510010,-0.01149685773998498917, 0.79857212305068969727, 0.44303125143051147461,-0.03626188263297080994, 0.03666086494922637939,-0.04171967133879661560,-0.19430102407932281494, 0.02452385425567626953, 0.43649160861968994141, 0.05663725361227989197, 0.18356178700923919678,-0.43165603280067443848,-0.06758291274309158325,-0.28488451242446899414, 0.03660047799348831177, 0.22659502923488616943, 0.50858795642852783203, 0.26921525597572326660,-0.03643934428691864014,
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        -0.03059375099837779999,-0.25724253058433532715, 0.00602599466219544411,-0.14678683876991271973, 0.37239637970924377441,-0.05134842172265052795, 0.09310068190097808838,-0.32196703553199768066,-0.04786385595798492432, 0.02811163850128650665,-0.19626164436340332031,-0.04066625237464904785, 0.20980824530124664307,-0.16851967573165893555, 0.04907176643610000610,-0.46210846304893493652,-0.00895109027624130249,-0.20731063187122344971, 0.06110817566514015198,-0.08846010267734527588, 0.24961054325103759766, 0.04888059198856353760,-0.18008437752723693848,-0.02476035244762897491,-0.40538185834884643555,-0.19478136301040649414,-0.19400753080844879150, 0.11014720052480697632,-0.17551538348197937012,-0.17190222442150115967, 0.10577572882175445557,-0.29165399074554443359,
         0.42229983210563659668, 4.52885675430297851562,-0.14197731018066406250, 2.12838983535766601562,-6.02166795730590820312,-0.34019878506660461426,-3.74112868309020996094, 3.09538197517395019531,-3.56549978256225585938, 1.60634970664978027344, 3.69623541831970214844,-4.24451303482055664062, 1.35054862499237060547, 0.25217163562774658203,-2.19799542427062988281, 4.94915294647216796875, 0.14265151321887969971, 1.68758690357208251953, 2.29178857803344726562, 2.00793647766113281250,-4.23850536346435546875, 4.27112817764282226562,-0.38069161772727966309,-4.14938211441040039062,-2.44637799263000488281, 3.71292281150817871094, 1.47118568420410156250, 0.34412112832069396973, 0.46766865253448486328, 5.05903768539428710938,-0.67437064647674560547, 5.30771780014038085938,
        -0.14371232688426971436,-0.00987537205219268799,-0.15732939541339874268,-0.05880972743034362793,-0.23074963688850402832, 0.08661886304616928101,-0.12933197617530822754,-0.16161398589611053467, 0.16995425522327423096, 0.02062946744263172150,-0.10625309497117996216, 0.06286960095167160034,-0.05241081118583679199,-0.03048302419483661652, 1.01382291316986083984, 0.21470455825328826904,-0.07828563451766967773, 0.02946876548230648041,-0.12548969686031341553, 0.16972661018371582031, 0.03117799572646617889, 0.20129011571407318115, 0.03403444215655326843,-0.07020273804664611816,-1.10789227485656738281,-0.12464692443609237671,-0.02039779536426067352, 0.02634164877235889435, 0.32983544468879699707, 0.22008647024631500244, 0.12264712899923324585,-0.15251138806343078613,
        -0.00089821469737216830, 0.04847634211182594299, 0.10313726961612701416, 0.27356514334678649902, 0.11723536998033523560, 0.11597874760627746582,-0.15621875226497650146,-0.02820404432713985443,-0.21108721196651458740,-0.33240425586700439453, 0.23381942510604858398, 0.02315874584019184113,-0.23514614999294281006, 0.23958508670330047607, 0.08659209311008453369,-0.10960455238819122314,-0.05278063565492630005,-0.30605807900428771973,-0.00090169592294842005, 0.43320739269256591797,-0.08916503936052322388,-0.31843733787536621094, 0.16861867904663085938, 0.10735899209976196289,-0.11669920384883880615,-0.09591776132583618164,-0.82631111145019531250, 0.81757432222366333008, 0.09511033445596694946,-0.31249687075614929199, 0.07797354459762573242, 0.01710882037878036499,
        -0.14294452965259552002,-0.02867908962070941925,-0.07851452380418777466,-0.05926045030355453491, 0.09527369588613510132,-0.15995623171329498291, 0.28317523002624511719,-0.28998354077339172363, 0.06315220147371292114, 0.25686976313591003418,-0.19042341411113739014, 0.07124044746160507202, 0.39058518409729003906,-0.07667887955904006958, 0.50241082906723022461,-0.01002821978181600571, 0.10060171037912368774,-0.02615112438797950745,-0.02585802040994167328,-0.31881141662597656250, 0.10759472101926803589,-0.33721610903739929199,-0.01383331883698701859, 0.27336090803146362305, 0.18783381581306457520,-0.11768177896738052368,-0.04179735481739044189, 0.04530091956257820129,-0.23389823734760284424,-0.01426943950355052948, 0.19921344518661499023,-0.15826721489429473877,
        -0.01060746610164642334, 0.02943025156855583191, 0.06214239448308944702, 0.03097855485975742340, 0.21417155861854553223,-0.54569196701049804688,-0.27380901575088500977, 0.08912359923124313354,-0.46380996704101562500, 0.35422921180725097656,-0.13649468123912811279, 0.14090469479560852051,-0.64408361911773681641,-0.96921014785766601562,-0.83478945493698120117, 0.73681801557540893555, 0.12143266946077346802,-0.43679922819137573242,-0.84324878454208374023, 0.00955482199788093567, 0.03684432059526443481,-0.55930590629577636719,-0.00533925369381904602,-0.44488975405693054199, 0.29417619109153747559, 0.27834591269493103027,-0.40987148880958557129,-0.08529653400182723999,-0.28634107112884521484,-0.87844198942184448242, 0.37983947992324829102, 0.25305867195129394531,
        -0.01902252994477748871, 1.08168709278106689453,-0.01181428972631692886, 0.91100710630416870117,-0.37145060300827026367,-0.49605867266654968262,-0.77371585369110107422, 0.74638861417770385742,-0.99138939380645751953, 1.06320858001708984375, 1.43323183059692382812,-1.40579521656036376953, 0.06541280448436737061,-1.45783603191375732422,-1.09269249439239501953, 1.00108850002288818359,-0.00010333034151699394, 1.68913030624389648438, 0.71818011999130249023, 0.75249463319778442383,-1.38238131999969482422, 1.08419299125671386719,-0.00959701091051101685,-0.63989460468292236328,-0.85440832376480102539, 0.52833604812622070312, 1.09670937061309814453, 0.56011927127838134766,-0.99395751953125000000, 0.77346587181091308594,-1.08128499984741210938, 0.86606216430664062500
    };
    cudaMemcpy((void**) 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
    std::cout << "Layer 0" << std::endl;
    dim3 x_0_dim_grid(ceilf(x_0_cols/(float)COL_TILE_WIDTH), ceilf(x_0_rows/(float)ROW_TILE_WIDTH), 1);
    dim3 x_0_dim_block(COL_TILE_WIDTH, ROW_TILE_WIDTH, 1);
    neuralNetworkLayer<<<x_0_dim_grid, x_0_dim_block>>>(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((void**) &x_0, gpu_x_0, x_0_size*sizeof(float), cudaMemcpyDeviceToHost);
    
    // Clean up
    cudaFree((void**) x);
    cudaFree((void**) bias_0);
    cudaFree((void**) wgtT_0);
    
    // Initialize x_2 and allocate device memory for it
    int x_2_rows = 1, x_2_cols = 1;
    int x_2_size = x_2_rows*x_2_cols;
    float* gpu_x_2;
    cudaMalloc((void**) &gpu_x_2, x_2_size*sizeof(float));
    float x_2[1] = { 0. };
    cudaMemcpy((void**) 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 = 1;
    int bias_2_size = bias_2_rows*bias_2_cols;
    const float* gpu_bias_2;
    cudaMalloc((void**) &gpu_bias_2, bias_2_size*sizeof(float));
    const float bias_2[1] = {
        -0.43118447065353393555
    };
    cudaMemcpy((void**) gpu_bias_2, &bias_2, bias_2_size*sizeof(float), cudaMemcpyHostToDevice);
    // Initialize wgtT_2 and allocate device memory for it
    int wgtT_2_rows = 1, wgtT_2_cols = 32;
    int wgtT_2_size = wgtT_2_rows*wgtT_2_cols;
    const float* gpu_wgtT_2;
    cudaMalloc((void**) &gpu_wgtT_2, wgtT_2_size*sizeof(float));
    const float wgtT_2[32*1] = {
        -0.10270550101995468140,
         0.50244033336639404297,
        -0.00174043292645365000,
         0.07954226434230804443,
        -0.54070854187011718750,
        -0.00215496332384645939,
        -0.15524573624134063721,
         0.22512565553188323975,
        -0.34885948896408081055,
         0.19126167893409729004,
         0.44914963841438293457,
        -0.36128515005111694336,
         0.04839605093002319336,
        -0.09916895627975463867,
        -0.72243708372116088867,
         0.32562962174415588379,
        -0.09670914709568023682,
         0.14170570671558380127,
         0.12138047069311141968,
         0.11395114660263061523,
        -0.46724098920822143555,
         0.23605719208717346191,
         0.03084296546876430511,
        -0.17991840839385986328,
        -0.47323790192604064941,
         0.24481520056724548340,
         0.13806425034999847412,
         0.08907321840524673462,
        -0.06194781884551048279,
         0.25962191820144653320,
        -0.15115147829055786133,
         0.52859854698181152344
    };
    cudaMemcpy((void**) gpu_wgtT_2, &wgtT_2, wgtT_2_size*sizeof(float), cudaMemcpyHostToDevice);
    
    // (2): Linear(in_features=32, out_features=1, bias=True) => x = x*W_T + b
    std::cout << "Layer 2" << std::endl;
    dim3 x_2_dim_grid(ceilf(x_2_cols/(float)COL_TILE_WIDTH), ceilf(x_2_rows/(float)ROW_TILE_WIDTH), 1);
    dim3 x_2_dim_block(COL_TILE_WIDTH, ROW_TILE_WIDTH, 1);
    neuralNetworkLayer<<<x_2_dim_grid, x_2_dim_block>>>(gpu_x_0, gpu_wgtT_2, gpu_x_2, gpu_bias_2, x_0_cols, x_2_rows, x_2_cols, Sigmoid);
    // Wait for GPU to finish before accessing on host
    cudaDeviceSynchronize();
    // Get results
    cudaMemcpy((void**) &x_2, gpu_x_2, x_2_size*sizeof(float), cudaMemcpyDeviceToHost);
    
    // Clean up
    cudaFree(x_0);
    cudaFree((void**) bias_2);
    cudaFree((void**) wgtT_2);
    cudaFree(x_2);
    
    return x_2[0];
}