THCTensorMathScan.cu

#include <THC/THCTensorMath.h>
#include <THC/THCGeneral.h>
#include <THC/THCBlas.h>
#include <THC/THCTensorCopy.h>
#include <THC/THCApply.cuh>
#include <THC/THCReduce.cuh>
#include <THC/THCNumerics.cuh>
#include <THC/THCTensorMathReduce.cuh>
#include <thrust/scan.h>
#include <thrust/execution_policy.h>

/* Perform an inclusive scan along an outer dimension of a tensor.
 *
 * - num_orows is the size of the flattened outer dimensions;
 * - num_irows is the size of the flattened inner dimensions;
 * - row_size is the size of the dimension along which to compute the variance;
 *
 * The dimensions to the outside and inside of the specified dimension are considered as flattened.
 * Thread blocks with the same blockIdx.y process an "outer row" (i.e. an element of the flattened
 * outer dimensions, which contains several "inner rows").
 * Each thread processes a single inner row at a time.
 */
template<typename T, class BinaryOp>
__global__ void THCTensor_kernel_scanOuterDim(T *tgt_, T *src_,
                                              unsigned num_orows, unsigned num_irows, unsigned row_size,
                                              T init, BinaryOp binary_op)
{
  for (unsigned orow = blockIdx.x; orow < num_orows; orow += gridDim.x) {
    for (unsigned irow = blockIdx.y * blockDim.x + threadIdx.x; irow < num_irows; irow += gridDim.y * blockDim.x) {
      T *src = src_ + orow * row_size * num_irows + irow;
      T *tgt = tgt_ + orow * row_size * num_irows + irow;
      T acc = init;

      for (unsigned col = 0; col < row_size; ++col) {
        acc = binary_op(acc, *src);
        *tgt = acc;

        src += num_irows;
        tgt += num_irows;
      }
    }
  }
}

/* Perform an inclusive scan along the innermost dimension of a tensor.
 *
 * - num_rows is the size of the flattened outer dimensions;
 * - row_size is the size of the innermost dimension;
 *
 * The outer dimensions of the tensor are considered as a single dimension, i.e. the tensor is
 * considered as having 'num_rows' rows of size 'row_size'.
 * Each thread block processes one or more sets of contiguous rows (processing multiple rows
 * per thread block is quicker than processing a single row, especially for short rows).
 */
template<typename T, int num_threads_x, int num_threads_y, class BinaryFunction>
__global__ void THCTensor_kernel_scanInnermostDim(T *tgt_, T *src_,
                                                  unsigned num_rows, unsigned row_size,
                                                  T init, BinaryFunction binary_op)
{
  __shared__ T sbuf[num_threads_y][2 * num_threads_x];

  T* row_buf = sbuf[threadIdx.y];

  for (unsigned block_row = blockIdx.x * blockDim.y;
       block_row < num_rows;
       block_row += blockDim.y * gridDim.x) {
    unsigned row = block_row + threadIdx.y;
    T block_total = init;

    T *row_src = src_ + row * row_size;
    T *row_tgt = tgt_ + row * row_size;

    // Perform scan on one block at a time, keeping track of the total value of
    // all blocks processed so far.
    for (unsigned block_col = 0; block_col < row_size; block_col += 2 * num_threads_x) {
      // Load data into shared memory (two values per thread).
      unsigned col1 = block_col + threadIdx.x;
      unsigned col2 = block_col + num_threads_x + threadIdx.x;
      if (row < num_rows) {
        if (col1 < row_size) {
          row_buf[threadIdx.x] = row_src[col1];
        } else {
          row_buf[threadIdx.x] = init;
        }

        if (col2 < row_size) {
          row_buf[num_threads_x + threadIdx.x] = row_src[col2];
        } else {
          row_buf[num_threads_x + threadIdx.x] = init;
        }

        // Add the total value of all previous blocks to the first value of this block.
        if (threadIdx.x == 0) {
          row_buf[0] = binary_op(row_buf[0], block_total);
        }
      }
      __syncthreads();

      // Parallel reduction (up-sweep).
      for (unsigned s = num_threads_x, d = 1; s >= 1; s >>= 1, d <<= 1) {
        if (row < num_rows && threadIdx.x < s) {
          unsigned offset = (2 * threadIdx.x + 1) * d - 1;
          row_buf[offset + d] = binary_op(row_buf[offset], row_buf[offset + d]);
        }
        __syncthreads();
      }

      // Down-sweep.
      for (unsigned s = 2, d = num_threads_x / 2; d >= 1; s <<= 1, d >>= 1) {
        if (row < num_rows && threadIdx.x < s - 1) {
          unsigned offset = 2 * (threadIdx.x + 1) * d - 1;
          row_buf[offset + d] = binary_op(row_buf[offset], row_buf[offset + d]);
        }
        __syncthreads();
      }

      // Write back to output.
      if (row < num_rows) {
        if (col1 < row_size) row_tgt[col1] = row_buf[threadIdx.x];
        if (col2 < row_size) row_tgt[col2] = row_buf[num_threads_x + threadIdx.x];
      }
      block_total = row_buf[2 * num_threads_x - 1];
      __syncthreads();
    }
  }
}

#include <THC/generic/THCTensorMathScan.cu>
#include <THC/THCGenerateAllTypes.h>

#include <THC/generic/THCTensorMathScan.cu>
#include <THC/THCGenerateBoolType.h>