/*
Copyright (c) 2020 - 2021 Advanced Micro Devices, Inc. All rights reserved.

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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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*/

// Test Description:
/* This test implements sum reduction kernel, first with each threads own rank
   as input and comparing the sum with expected sum output derieved from n(n-1)/2
   formula. The second part, partitions this parent group into child subgroups
   a.k.a tiles using using tiled_partition() collective operation. This can be called
   with a static tile size, passed in templated non-type variable-tiled_partition<tileSz>,
   or in runtime as tiled_partition(thread_group parent, tileSz). This test covers both these
   cases.
   This test tests functionality of cg group partitioning, (static and dynamic) and its respective
   API's size(), thread_rank(), and sync().
*/

#include <hip_test_common.hh>
#include <hip/hip_cooperative_groups.h>
#include <stdio.h>
#include <vector>

using namespace cooperative_groups;

#define ASSERT_EQUAL(lhs, rhs) assert(lhs == rhs)

#define NUM_ELEMS 10000000
#define NUM_THREADS_PER_BLOCK 512
#define WAVE_SIZE 32

/* Test coalesced group's functionality.
 *
 */

__device__ int atomicAggInc(int *ptr) {
   coalesced_group g = coalesced_threads();
   int prev;
   // elect the first active thread to perform atomic add
   if (g.thread_rank() == 0) {
     prev = atomicAdd(ptr, g.size());
   }
   // broadcast previous value within the warp
   // and add each active thread’s rank to it
   prev = g.thread_rank() + g.shfl(prev, 0);
   return prev;
}

__global__ void kernel_shfl (int * dPtr, int *dResults, int srcLane, int cg_sizes) {
  int id = threadIdx.x + blockIdx.x * blockDim.x;
  if (id % cg_sizes == 0) {
    coalesced_group const& g = coalesced_threads();
    int rank = g.thread_rank();
    int val = dPtr[rank];
    dResults[rank] = g.shfl(val, srcLane);
    return;
  }
}

__global__ void kernel_shfl_any_to_any (int *randVal, int *dsrcArr, int *dResults, int cg_sizes) {

 int id = threadIdx.x + blockIdx.x * blockDim.x;

 if (id % cg_sizes == 0) {
    coalesced_group const& g = coalesced_threads();
    int rank = g.thread_rank();
    int val = randVal[rank];
    dResults[rank] = g.shfl(val, dsrcArr[rank]);
    return;
  }

}

__global__ void filter_arr(int *dst, int *nres, const int *src, int n) {
  int id = threadIdx.x + blockIdx.x * blockDim.x;

  for (int i = id; i < n; i += gridDim.x * blockDim.x) {
    if (src[i] > 0) dst[atomicAggInc(nres)] = src[i];
  }
}

/* Parallel reduce kernel.
 *
 * Step complexity: O(log n)
 * Work complexity: O(n)
 *
 * Note: This kernel works only with power of 2 input arrays.
 */
__device__ int reduction_kernel(coalesced_group g, int* x, int val) {
  int lane = g.thread_rank();
  int sz = g.size();

  for (int i = g.size() / 2; i > 0; i /= 2) {
    // use lds to store the temporary result
    x[lane] = val;
    // Ensure all the stores are completed.
    g.sync();

    if (lane < i) {
      val += x[lane + i];
    }
    // It must work on one tiled thread group at a time,
    // and it must make sure all memory operations are
    // completed before moving to the next stride.
    // sync() here just does that.
    g.sync();
  }

  // Choose the 0'th indexed thread that holds the reduction value to return
  if (g.thread_rank() == 0) {
    return val;
  }
  // Rest of the threads return no useful values
  else {
    return -1;
  }
}

__global__ void kernel_cg_coalesced_group_partition(unsigned int tileSz, int* result,
                                                  bool isGlobalMem, int* globalMem, int cg_sizes) {

  int id = threadIdx.x + blockIdx.x * blockDim.x;
  if (id % cg_sizes == 0) {
    coalesced_group threadBlockCGTy = coalesced_threads();
    int threadBlockGroupSize = threadBlockCGTy.size();

    int* workspace = NULL;

    if (isGlobalMem) {
      workspace = globalMem;
    } else {
      // Declare a shared memory
      extern __shared__ int sharedMem[];
      workspace = sharedMem;
    }

    int input, outputSum, expectedOutput;

    // input to reduction, for each thread, is its' rank in the group
    input = threadBlockCGTy.thread_rank();

    expectedOutput = (threadBlockGroupSize - 1) * threadBlockGroupSize / 2;

    outputSum = reduction_kernel(threadBlockCGTy, workspace, input);

    if (threadBlockCGTy.thread_rank() == 0) {
      printf(" Sum of all ranks 0..%d in coalesced_group is %d\n\n",
             (int)threadBlockCGTy.size() - 1, outputSum);
      printf(" Creating %d groups, of tile size %d threads:\n\n",
             (int)threadBlockCGTy.size() / tileSz, tileSz);
    }

    threadBlockCGTy.sync();

    coalesced_group tiledPartition = tiled_partition(threadBlockCGTy, tileSz);

    // This offset allows each group to have its own unique area in the workspace array
    int workspaceOffset = threadBlockCGTy.thread_rank() - tiledPartition.thread_rank();

    outputSum = reduction_kernel(tiledPartition, workspace + workspaceOffset, input);

    if (tiledPartition.thread_rank() == 0) {
      printf(
          "   Sum of all ranks 0..%d in this tiledPartition group is %d. Corresponding parent thread "
          "rank: %d\n",
          tiledPartition.size() - 1, outputSum, input);

        result[input / (tileSz)] = outputSum;
    }
    return;
  }
}

__global__ void kernel_coalesced_active_groups() {
  thread_block threadBlockCGTy = this_thread_block();
  int threadBlockGroupSize = threadBlockCGTy.size();

  // input to reduction, for each thread, is its' rank in the group
  int input = threadBlockCGTy.thread_rank();

  if (threadBlockCGTy.thread_rank() == 0) {
    printf(" Creating odd and even set of active thread groups based on branch divergence\n\n");
  }

  threadBlockCGTy.sync();

  // Group all active odd threads
  if (threadBlockCGTy.thread_rank() % 2) {
    coalesced_group activeOdd = coalesced_threads();

    if (activeOdd.thread_rank() == 0) {
      printf(" ODD: Size of odd set of active threads is %d."
             " Corresponding parent thread_rank is %d.\n\n",
               activeOdd.size(), threadBlockCGTy.thread_rank());
    }
  }
  else { // Group all active even threads
    coalesced_group activeEven = coalesced_threads();

    if (activeEven.thread_rank() == 0) {
      printf(" EVEN: Size of even set of active threads is %d."
             " Corresponding parent thread_rank is %d.",
               activeEven.size(), threadBlockCGTy.thread_rank());
    }
  }
  return;
}

void printResults(int* ptr, int size) {
  for (int i = 0; i < size; i++) {
    std::cout << ptr[i] << " ";
  }
  std::cout << '\n';
}

void compareResults(int* cpu, int* gpu, int size) {
  for (unsigned int i = 0; i < size / sizeof(int); i++) {
    if (cpu[i] != gpu[i]) {
      INFO(" results do not match.");
    }
  }
}

static void test_active_threads_grouping() {
  hipError_t err;
  int blockSize = 1;
  int threadsPerBlock = WAVE_SIZE;

  // Launch Kernel
    hipLaunchKernelGGL(kernel_coalesced_active_groups, blockSize, threadsPerBlock, 0, 0);

    err = hipDeviceSynchronize();
    if (err != hipSuccess) {
      fprintf(stderr, "Failed to launch kernel (error code %s)!\n", hipGetErrorString(err));
    }
  printf("\n...PASSED.\n\n");
}

// Search if the sum exists in the expected results array
void verifyResults(int* hPtr, int* dPtr, int size) {
  int i = 0, j = 0;
  for (i = 0; i < size; i++) {
    for (j = 0; j < size; j++) {
      if (hPtr[i] == dPtr[j]) {
        break;
      }
    }
    if (j == size) {
      INFO(" Result verification failed!");
    }
  }
}


static void test_group_partition(unsigned int tileSz, bool useGlobalMem) {
  hipError_t err;
  int blockSize = 1;
  int threadsPerBlock = WAVE_SIZE;

  std::vector<unsigned int> cg_sizes = {1, 2, 3};
  for (auto i : cg_sizes) {

    int numTiles = ((blockSize * threadsPerBlock) / i) / tileSz;

    // numTiles = 0 when partitioning is possible. The below statement is to avoid
    // out-of-bounds error and still evaluate failure case.
    numTiles = (numTiles == 0) ? 1 : numTiles;

    // Build an array of expected reduction sum output on the host
    // based on the sum of their respective thread ranks to use for verification
    int* expectedSum = new int[numTiles];
    int temp = 0, sum = 0;
    for (int i = 1; i <= numTiles; i++) {
      sum = temp;
      temp = (((tileSz * i) - 1) * (tileSz * i)) / 2;
      expectedSum[i-1] = temp - sum;
    }

    int* dResult = NULL;
    hipMalloc(&dResult, sizeof(int) * numTiles);

    int* globalMem = NULL;
    if (useGlobalMem) {
      hipMalloc((void**)&globalMem, threadsPerBlock * sizeof(int));
    }

    int* hResult = NULL;
    hipHostMalloc(&hResult, numTiles * sizeof(int), hipHostMallocDefault);
    memset(hResult, 0, numTiles * sizeof(int));

    // Launch Kernel
    if (useGlobalMem) {
      hipLaunchKernelGGL(kernel_cg_coalesced_group_partition, blockSize, threadsPerBlock, 0, 0, tileSz,
                         dResult, useGlobalMem, globalMem, i);

      err = hipDeviceSynchronize();
      if (err != hipSuccess) {
        fprintf(stderr, "Failed to launch kernel (error code %s)!\n", hipGetErrorString(err));
      }
    } else {
      hipLaunchKernelGGL(kernel_cg_coalesced_group_partition, blockSize, threadsPerBlock,
                         threadsPerBlock * sizeof(int), 0, tileSz, dResult, useGlobalMem, globalMem, i);

      err = hipDeviceSynchronize();
      if (err != hipSuccess) {
        fprintf(stderr, "Failed to launch kernel (error code %s)!\n", hipGetErrorString(err));
      }
    }

    hipMemcpy(hResult, dResult, numTiles * sizeof(int), hipMemcpyDeviceToHost);
    verifyResults(expectedSum, hResult, numTiles);
    // Free all allocated memory on host and device
    hipFree(dResult);
    hipFree(hResult);
    if (useGlobalMem) {
      hipFree(globalMem);
    }
    delete[] expectedSum;

    printf("\n...PASSED.\n\n");
  }
}
static void test_shfl_any_to_any() {

  std::vector<unsigned int> cg_sizes = {1, 2, 3};
  for (auto i : cg_sizes) {

    hipError_t err;
    int blockSize = 1;
    int threadsPerBlock = WAVE_SIZE;

    int totalThreads = blockSize * threadsPerBlock;
    int group_size = (totalThreads + i - 1) / i;
    int group_size_in_bytes = group_size * sizeof(int);

    int* hPtr = NULL;
    int* dPtr = NULL;
    int* dsrcArr = NULL;
    int* dResults = NULL;
    int* srcArr = (int*)malloc(group_size_in_bytes);
    int* srcArrCpu = (int*)malloc(group_size_in_bytes);

    std::cout << "Testing coalesced_groups shfl any-to-any\n" <<std::endl;

    int arrSize = blockSize * threadsPerBlock * sizeof(int);

    hipHostMalloc(&hPtr, arrSize);
    // Fill up the array
    for (int i = 0; i < WAVE_SIZE; i++) {
      hPtr[i] = rand() % 1000;
    }

    // Fill up the random array
    for (int i = 0; i < group_size; i++) {
      srcArr[i] = rand() % 1000;
      srcArrCpu[i] = srcArr[i] % group_size;
    }

    /* Fill cpu results array so that we can verify with gpu computation */
    int* cpuResultsArr = (int*)malloc(group_size_in_bytes);
    for(int i = 0; i < group_size; i++) {
      cpuResultsArr[i] = hPtr[srcArrCpu[i]];
    }

    //printf("Array passed to GPU for computation\n");
    //printResults(hPtr, WAVE_SIZE);
    hipMalloc(&dPtr, group_size_in_bytes);
    hipMalloc(&dResults, group_size_in_bytes);

    hipMalloc(&dsrcArr, group_size_in_bytes);
    hipMemcpy(dsrcArr, srcArr, group_size_in_bytes, hipMemcpyHostToDevice);

    hipMemcpy(dPtr, hPtr, group_size_in_bytes, hipMemcpyHostToDevice);
    // Launch Kernel
    hipLaunchKernelGGL(kernel_shfl_any_to_any, blockSize, threadsPerBlock,
                       threadsPerBlock * sizeof(int), 0 , dPtr, dsrcArr, dResults, i);
    hipMemcpy(hPtr, dResults, group_size_in_bytes, hipMemcpyDeviceToHost);
    err = hipDeviceSynchronize();
    if (err != hipSuccess) {
      fprintf(stderr, "Failed to launch kernel (error code %s)!\n", hipGetErrorString(err));
    }
    //printf("GPU results: \n");
    //printResults(hPtr, group_size);
    //printf("Printing cpu to be verified array\n");
    //printResults(cpuResultsArr, group_size);
    //printf("Printing srcLane array that was passed\n");
    //printResults(srcArr, group_size);
    //printf("Printing srcLane array on the CPU\n");
    //printResults(srcArrCpu, group_size);
    compareResults(hPtr, cpuResultsArr, group_size_in_bytes);
    std::cout << "Results verified!\n";

    hipFree(hPtr);
    hipFree(dPtr);
    free(srcArr);
    free(srcArrCpu);
    free(cpuResultsArr);
  }
}
static void test_shfl_broadcast() {

  std::vector<unsigned int> cg_sizes = {1, 2, 3};
  for (auto i : cg_sizes) {

    hipError_t err;
    int blockSize = 1;
    int threadsPerBlock = WAVE_SIZE;

    int totalThreads = blockSize * threadsPerBlock;
    int group_size = (totalThreads + i - 1) / i;
    int group_size_in_bytes = group_size * sizeof(int);

    int* hPtr = NULL;
    int* dPtr = NULL;
    int* dResults = NULL;
    int srcLane = rand() % 1000;
    int srcLaneCpu = 0;
    std::cout << "Testing coalesced_groups shfl with srcLane " << srcLane << '\n'
              << " and group size " << i <<std::endl;

    int arrSize = blockSize * threadsPerBlock * sizeof(int);

    hipHostMalloc(&hPtr, arrSize);
    // Fill up the array
    for (int i = 0; i < WAVE_SIZE; i++) {
      hPtr[i] = rand() % 1000;
    }


    /* Fill cpu results array so that we can verify with gpu computation */
    srcLaneCpu = hPtr[srcLane % group_size];

    int* cpuResultsArr = (int*)malloc(sizeof(int) * group_size);
    for (int i = 0; i < group_size; i++) {
      cpuResultsArr[i] = srcLaneCpu;
    }
    printf("Array passed to GPU for computation\n");
    printResults(hPtr, WAVE_SIZE);
    hipMalloc(&dPtr, group_size_in_bytes);
    hipMalloc(&dResults, group_size_in_bytes);

    hipMemcpy(dPtr, hPtr, group_size_in_bytes, hipMemcpyHostToDevice);
    // Launch Kernel
    hipLaunchKernelGGL(kernel_shfl, blockSize, threadsPerBlock,
                       threadsPerBlock * sizeof(int), 0, dPtr, dResults, srcLane, i);
    hipMemcpy(hPtr, dResults, group_size_in_bytes, hipMemcpyDeviceToHost);
    err = hipDeviceSynchronize();
    if (err != hipSuccess) {
      fprintf(stderr, "Failed to launch kernel (error code %s)!\n", hipGetErrorString(err));
    }
    printf("GPU results: \n");
    printResults(hPtr, group_size);
    printf("Printing cpu to be verified array\n");
    printResults(cpuResultsArr, group_size);

    compareResults(hPtr, cpuResultsArr, group_size_in_bytes);
    std::cout << "Results verified!\n";

    hipFree(hPtr);
    hipFree(dPtr);
    free(cpuResultsArr);
  }
}

TEST_CASE("Unit_coalesced_groups") {
  // Use default device for validating the test
  int deviceId;
  HIP_CHECK(hipGetDevice(&deviceId));
  hipDeviceProp_t deviceProperties;
  HIP_CHECK(hipGetDeviceProperties(&deviceProperties, deviceId));
  int maxThreadsPerBlock = deviceProperties.maxThreadsPerBlock;

  std::cout << "Now testing coalesced_groups" << '\n' << std::endl;

  int *data_to_filter, *filtered_data, nres = 0;
  int *d_data_to_filter, *d_filtered_data, *d_nres;

  int numOfBuckets = 5;

  data_to_filter = reinterpret_cast<int *>(malloc(sizeof(int) * NUM_ELEMS));

  // Generate input data.
  for (int i = 0; i < NUM_ELEMS; i++) {
    data_to_filter[i] = rand() % numOfBuckets;
  }


  HIP_CHECK(hipMalloc(&d_data_to_filter, sizeof(int) * NUM_ELEMS));
  HIP_CHECK(hipMalloc(&d_filtered_data, sizeof(int) * NUM_ELEMS));
  HIP_CHECK(hipMalloc(&d_nres, sizeof(int)));

  HIP_CHECK(hipMemcpy(d_data_to_filter, data_to_filter,
                             sizeof(int) * NUM_ELEMS, hipMemcpyHostToDevice));
  hipMemset(d_nres, 0, sizeof(int));

  dim3 dimBlock(NUM_THREADS_PER_BLOCK, 1, 1);
  dim3 dimGrid((NUM_ELEMS / NUM_THREADS_PER_BLOCK) + 1, 1, 1);

  filter_arr<<<dimGrid, dimBlock>>>(d_filtered_data, d_nres, d_data_to_filter,
                                    NUM_ELEMS);


  HIP_CHECK(hipMemcpy(&nres, d_nres, sizeof(int), hipMemcpyDeviceToHost));

  filtered_data = reinterpret_cast<int *>(malloc(sizeof(int) * nres));

  HIP_CHECK(hipMemcpy(filtered_data, d_filtered_data, sizeof(int) * nres,
                             hipMemcpyDeviceToHost));

  int *host_filtered_data =
      reinterpret_cast<int *>(malloc(sizeof(int) * NUM_ELEMS));

  // Generate host output with host filtering code.
  int host_flt_count = 0;
  for (int i = 0; i < NUM_ELEMS; i++) {
    if (data_to_filter[i] > 0) {
      host_filtered_data[host_flt_count++] = data_to_filter[i];
    }
  }

  printf("\nWarp Aggregated Atomics %s \n",
         (host_flt_count == nres) ? "PASSED" : "FAILED");

  // Now, testing shfl collective
  std::cout << "Now testing shfl collective as a broadcast" << '\n' << std::endl;

  for (int i = 0; i < 100; i++) {
    test_shfl_broadcast();
  }


  // Now, testing shfl collective
  std::cout << "Now testing shfl operations any-to-any member lanes" << '\n' << std::endl;

  for (int i = 0; i < 100; i++) {
    test_shfl_any_to_any();
  }

  // Now, pass a already coalesced_group that was partitioned
  /* Test coalesced group partitioning */
  std::cout << "Now testing coalesced_groups partitioning" << '\n' << std::endl;

  int testNo = 1;
  for (int memTy = 0; memTy < 2; memTy++) {
    std::vector<unsigned int> tileSizes = {2, 4, 8, 16, 32};
    for (auto i : tileSizes) {
      std::cout << "TEST " << testNo << ":" << '\n' << std::endl;
      test_group_partition(i, memTy);
      testNo++;
    }
  }

  std::cout << "Now grouping active threads based on branch divergence" << '\n' << std::endl;
  test_active_threads_grouping();
}