Matrix.cpp 148.5 KB
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#include "Matrix.h"
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#include "MathFunctions.h"
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#include "SparseMatrix.h"
#include "SparseRowMatrix.h"

#include <float.h>
#include <algorithm>
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#include <cmath>
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#include <string.h>
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#include "hl_cnn.h"
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#include "hl_gpu.h"
#include "hl_table_apply.h"
#include "hl_top_k.h"
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#include "paddle/utils/Logging.h"
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#include "paddle/function/GemmFunctor.h"
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#include "paddle/utils/ThreadLocal.h"

#include "SIMDFunctions.h"

namespace paddle {

inline real _pow(real a, real beta) { return std::pow(a, beta); }

inline real _square(real a) { return a * a; }

inline real _safelog(real a) { return a > 0.0f ? std::log(a) : -40.0f; }

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Matrix::Matrix(MemoryHandlePtr memHandle,
               size_t height,
               size_t width,
               bool trans,
               bool use_gpu)
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    : BaseMatrix(
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          height,
          width,
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          memHandle ? (reinterpret_cast<real*>(memHandle->getBuf())) : nullptr,
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          trans,
          use_gpu) {
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  elementCnt_ = width * height;
  memoryHandle_ = memHandle;
}

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Matrix::Matrix(
    real* data, size_t height, size_t width, bool trans, bool use_gpu)
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    : BaseMatrix(height, width, data, trans, use_gpu) {
  elementCnt_ = width * height;
}

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Matrix::Matrix(real* data,
               size_t height,
               size_t width,
               size_t stride,
               bool trans,
               bool use_gpu)
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    : BaseMatrix(height, width, stride, data, trans, use_gpu) {
  elementCnt_ = width * height;
}

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MatrixPtr Matrix::createSparseMatrix(real* data,
                                     int* row,
                                     int* col,
                                     size_t height,
                                     size_t width,
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                                     size_t nnz, /* used to allocate space */
                                     SparseValueType valueType, /*value type*/
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                                     SparseFormat format,
                                     bool trans,
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                                     bool useGpu) {
  if (useGpu) {
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    return std::make_shared<GpuSparseMatrix>(
        data, row, col, height, width, nnz, valueType, format, trans);
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  } else {
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    return std::make_shared<CpuSparseMatrix>(
        data, row, col, height, width, nnz, valueType, format, trans);
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  }
}

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MatrixPtr Matrix::createSparseMatrix(size_t height,
                                     size_t width,
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                                     size_t nnz, /* used to allocate space */
                                     SparseValueType valueType, /*value type*/
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                                     SparseFormat format,
                                     bool trans,
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                                     bool useGpu) {
  if (useGpu) {
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    return std::make_shared<GpuSparseMatrix>(
        height, width, nnz, valueType, format, trans);
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  } else {
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    return std::make_shared<CpuSparseMatrix>(
        height, width, nnz, valueType, format, trans);
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  }
}

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MatrixPtr Matrix::create(MemoryHandlePtr memHandle,
                         size_t height,
                         size_t width,
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                         bool trans) {
  if (auto gpuHandle = std::dynamic_pointer_cast<GpuMemoryHandle>(memHandle)) {
    return std::make_shared<GpuMatrix>(gpuHandle, height, width, trans);
  } else if (auto cpuHandle =
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                 std::dynamic_pointer_cast<CpuMemoryHandle>(memHandle)) {
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    return std::make_shared<CpuMatrix>(cpuHandle, height, width, trans);
  } else {
    LOG(FATAL) << "Wrong";
    return nullptr;
  }
}

MatrixPtr Matrix::create(size_t height, size_t width, bool trans, bool useGpu) {
  if (useGpu) {
    return std::make_shared<GpuMatrix>(height, width, trans);
  } else {
    return std::make_shared<CpuMatrix>(height, width, trans);
  }
}

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MatrixPtr Matrix::create(
    real* data, size_t height, size_t width, bool trans, bool useGpu) {
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  if (useGpu) {
    return std::make_shared<GpuMatrix>(data, height, width, trans);
  } else {
    return std::make_shared<CpuMatrix>(data, height, width, trans);
  }
}

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MatrixPtr Matrix::create(real* data,
                         size_t height,
                         size_t width,
                         size_t stride,
                         bool trans,
                         bool useGpu) {
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  if (useGpu) {
    return std::make_shared<GpuMatrix>(data, height, width, stride, trans);
  } else {
    return std::make_shared<CpuMatrix>(data, height, width, stride, trans);
  }
}

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MatrixPtr Matrix::createSparseMatrix(size_t height,
                                     size_t width,
                                     size_t nnz,
                                     SparseValueType valueType,
                                     bool trans,
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                                     bool useGpu) {
  if (useGpu) {
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    return std::make_shared<GpuSparseMatrix>(
        height, width, nnz, valueType, SPARSE_CSR, trans);
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  } else {
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    return std::make_shared<CpuSparseMatrix>(
        height, width, nnz, valueType, SPARSE_CSR, trans);
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  }
}

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void Matrix::resizeOrCreate(
    MatrixPtr& matrix, size_t height, size_t width, bool trans, bool useGpu) {
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  if (!matrix) {
    matrix = Matrix::create(height, width, trans, useGpu);
  } else {
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    CHECK_EQ(matrix->useGpu(), useGpu);
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    matrix->resize(height, width);
  }
}

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void Matrix::resizeOrCreateSparseMatrix(MatrixPtr& matrix,
                                        size_t height,
                                        size_t width,
                                        size_t nnz,
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                                        SparseValueType valueType,
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                                        SparseFormat format,
                                        bool trans,
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                                        bool useGpu) {
  if (!matrix) {
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    matrix = Matrix::createSparseMatrix(
        height, width, nnz, valueType, format, trans, useGpu);
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  } else {
    CHECK(dynamic_cast<CpuSparseMatrix*>(matrix.get()) ||
          dynamic_cast<GpuSparseMatrix*>(matrix.get()));
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    CHECK_EQ(matrix->useGpu(), useGpu);
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    matrix->resize(height, width, nnz, valueType, format);
  }
}

void Matrix::reshape(size_t height, size_t width) {
  CHECK(isContiguous());
  CHECK(height_ * width_ == height * width);
  height_ = height;
  width_ = width;
  stride_ = width_;
}

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MatrixPtr Matrix::subMatrix(size_t startRow,
                            size_t endRow,
                            size_t startCol,
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                            size_t endCol) {
  CHECK_LE(startRow, endRow);
  CHECK_LE(endRow, getHeight());
  CHECK_LE(startCol, endCol);
  CHECK_LE(endCol, getWidth());

  return Matrix::create(getData() + startRow * getStride() + startCol,
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                        endRow - startRow,
                        endCol - startCol,
                        getStride(),
                        trans_,
                        useGpu_);
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}

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void Matrix::setDiag(real value) {
  CHECK(data_ != NULL);
  CHECK_EQ(height_, width_);

  zeroMem();
  BaseMatrix diag(height_, 1, stride_ + 1, data_, false, useGpu_);
  diag.assign(value);
}

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GpuMatrix::GpuMatrix(size_t height, size_t width, bool trans)
    : Matrix(std::make_shared<GpuMemoryHandle>(height * width * sizeof(real)),
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             height,
             width,
             trans,
             true) {}
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GpuMatrix::~GpuMatrix() {}

void GpuMatrix::zeroMem() {
  CHECK(data_ != NULL);
  zero();
}

void GpuMatrix::resetOne() {
  CHECK(data_ != NULL);
  one();
}
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void GpuMatrix::resize(size_t newHeight, size_t newWidth) {
  size_t newSize = newHeight * newWidth;
  if (NULL == memoryHandle_.get() ||
      newSize * sizeof(real) > memoryHandle_->getAllocSize()) {
    memoryHandle_ = std::make_shared<GpuMemoryHandle>(newSize * sizeof(real));
    data_ = reinterpret_cast<real*>(memoryHandle_->getBuf());
  }
  height_ = newHeight;
  width_ = newWidth;
  elementCnt_ = newSize;
  stride_ = width_;
}

real GpuMatrix::getElement(size_t x, size_t y) const {
  real elem = 0;
  hl_memcpy_device2host(&elem, &data_[x * stride_ + y], sizeof(real));
  return elem;
}

real GpuMatrix::getSum() {
  CHECK(isContiguous());
  real sum = 0.0f;
  hl_vector_sum(data_, &sum, height_ * width_);
  return sum;
}

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real GpuMatrix::getMin() {
  CHECK(isContiguous());
  auto vec = GpuVector(height_ * width_, data_);
  return vec.getMin();
}

real GpuMatrix::getMax() {
  CHECK(isContiguous());
  auto vec = GpuVector(height_ * width_, data_);
  return vec.getMax();
}

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void GpuMatrix::accumulateColSum(Matrix& src) {
  CHECK_EQ(getWidth(), src.getWidth());
  CHECK_EQ(getHeight(), (size_t)1);
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  sumCols(src, 1.0, 1.0);
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}

real GpuMatrix::getAbsSum() {
  CHECK(isContiguous());
  real sum = 0.0f;
  hl_vector_abs_sum(data_, &sum, height_ * width_);
  return sum;
}

void GpuMatrix::copyFrom(const Matrix& src) {
  CHECK(isContiguous());
  CHECK(src.isContiguous());
  CHECK(elementCnt_ == src.getElementCnt());

  if (typeid(src) == typeid(CpuMatrix)) {
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    hl_memcpy_host2device(
        data_, const_cast<real*>(src.getData()), sizeof(real) * elementCnt_);
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  } else if (typeid(src) == typeid(GpuMatrix)) {
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    hl_memcpy_device2device(
        data_, const_cast<real*>(src.getData()), sizeof(real) * elementCnt_);
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  } else {
    LOG(FATAL) << "Wrong";
  }
}

void GpuMatrix::copyFrom(const Matrix& src, hl_stream_t stream) {
  CHECK(isContiguous());
  CHECK(src.isContiguous());
  CHECK(elementCnt_ == src.getElementCnt());
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  hl_memcpy_async(this->getData(),
                  const_cast<real*>(src.getData()),
                  sizeof(real) * elementCnt_,
                  stream);
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}

void GpuMatrix::copyFrom(const real* hostSrc, size_t size) {
  CHECK(isContiguous());
  CHECK(size <= elementCnt_);
  hl_memcpy_host2device(data_, const_cast<real*>(hostSrc), sizeof(real) * size);
}

void GpuMatrix::copyFrom(const real* hostSrc, const int64_t* seq) {
  LOG(FATAL) << "not implemented";
}

void GpuMatrix::copyFrom(const IVector& src) {
  CHECK(isContiguous());
  CpuMatrix matrix(src.getSize(), 1, false);
  matrix.copyFrom(src);
  copyFrom(matrix);
}

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void GpuMatrix::copyByRowIndex(Matrix& b, const IVector& rowIndex) {
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  size_t height = getHeight();
  size_t width = getWidth();
  CHECK_EQ(b.getWidth(), width);
  real* dst = getData();
  real* src = b.getData();
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  const int* index = rowIndex.getData();
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  hl_sequence2batch_copy(dst, src, index, width, height, true);
}

MatrixPtr GpuMatrix::clone(size_t height, size_t width, bool useGpu) {
  CHECK(isContiguous());

  if (height == 0 && width == 0) {
    height = height_;
    width = width_;
  }

  CHECK(width && height);

  if (useGpu) {
    return std::make_shared<GpuMatrix>(height, width);
  } else {
    return std::make_shared<CpuMatrix>(height, width);
  }
}

MatrixPtr GpuMatrix::getTranspose() {
  if (memoryHandle_.get() != NULL) {
    MatrixPtr copy_T(
        new GpuMatrix(std::dynamic_pointer_cast<GpuMemoryHandle>(memoryHandle_),
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                      height_,
                      width_,
                      true));
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    return copy_T;
  } else {
    MatrixPtr copy_T(new GpuMatrix(data_, height_, width_, true));
    return copy_T;
  }
}

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void GpuMatrix::transpose(MatrixPtr& matTrans, bool memAlloc) {
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  if (memAlloc) {
    matTrans = std::make_shared<GpuMatrix>(width_, height_);
  } else {
    CHECK(matTrans != NULL);
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    CHECK_EQ(matTrans->getHeight(), width_);
    CHECK_EQ(matTrans->getWidth(), height_);
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  }
  real* dataTrans = matTrans->getData();
  real* data = getData();
  int lda = getStride();
  int ldc = matTrans->getStride();

  hl_matrix_transpose(data, dataTrans, height_, width_, lda, ldc);
}

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void GpuMatrix::rotate(MatrixPtr& matRot, bool memAlloc, bool clockWise) {
  if (memAlloc) {
    matRot = std::make_shared<GpuMatrix>(width_, height_);
  } else {
    CHECK(matRot != NULL);
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    CHECK_EQ(matRot->getHeight(), width_);
    CHECK_EQ(matRot->getWidth(), height_);
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  }

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  real* dataRot = matRot->getData();
  real* data = getData();
  hl_matrix_rotate(data, dataRot, height_, width_, clockWise);
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}

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MatrixPtr GpuMatrix::getInverse() {
  MatrixPtr matInv;
  inverse(matInv, true);
  return matInv;
}

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void GpuMatrix::inverse(MatrixPtr& matInv, bool memAlloc) {
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  CHECK_EQ(height_, width_);

  if (memAlloc) {
    matInv = std::make_shared<GpuMatrix>(height_, width_);
  } else {
    CHECK(matInv != NULL);
  }

  real* data = getData();
  real* dataInv = matInv->getData();
  int lda = getStride();
  int ldc = matInv->getStride();

  hl_matrix_inverse(data, dataInv, height_, lda, ldc);
}

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void GpuMatrix::addBias(Matrix& b, real scale) {
  CHECK(b.getHeight() == 1) << "the Bias should be a vector";
  BaseMatrix::addBias(b, scale);
}

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void GpuMatrix::addSharedBias(Matrix& b, real scale) {
  CHECK(b.getHeight() == 1) << "the Bias should be a vector";
  CHECK_LE(b.getWidth(), getWidth());
  CHECK_EQ(getWidth() % b.getWidth(), 0UL);
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  hl_matrix_add_shared_bias(
      getData(), b.getData(), b.getWidth(), getHeight(), getWidth(), scale);
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}

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void GpuMatrix::collectBias(Matrix& a, real scale) {
  CHECK_EQ(getHeight(), (size_t)1);
  CHECK_EQ(width_, a.getWidth());
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  GpuSparseMatrix* sMatPtr = dynamic_cast<GpuSparseMatrix*>(&a);
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  if (!sMatPtr) {
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    sumCols(a, /* scaleSum= */ scale, /* scaleDest= */ 1);
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  } else {
    real* data = getData();
    hl_sparse_matrix_s A_d = sMatPtr->sMatrix_.get();
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    hl_sparse_matrix_column_sum(data, A_d, sMatPtr->getHeight(), width_, scale);
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  }
}

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void GpuMatrix::collectSharedBias(Matrix& a, real scale) {
  CHECK_EQ(getHeight(), (size_t)1);
  CHECK_EQ(a.getWidth() % getWidth(), 0UL);
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  hl_matrix_collect_shared_bias(
      getData(), a.getData(), getWidth(), a.getHeight(), a.getWidth(), scale);
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}

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void GpuMatrix::sequenceAvgForward(Matrix& a,
                                   const IVector& startsPos,
                                   int mode) {
  size_t height = getHeight();
  size_t width = getWidth();
  CHECK_EQ(height, startsPos.getSize() - 1);
  CHECK_EQ(width, a.getWidth());
  real* dst = getData();
  real* src = a.getData();
  const int* starts = startsPos.getData();

  hl_sequence_avg_forward(dst, src, starts, height, width, mode);
}

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void GpuMatrix::sequenceAvgBackward(Matrix& a,
                                    const IVector& startsPos,
                                    int mode) {
  size_t height = a.getHeight();
  size_t width = getWidth();
  CHECK_EQ(height, startsPos.getSize() - 1);
  CHECK_EQ(width, a.getWidth());
  real* dst = getData();
  real* src = a.getData();
  const int* starts = startsPos.getData();

  hl_sequence_avg_backward(dst, src, starts, height, width, mode);
}

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/* this = scaleAB*(a*b) +  scaleT*this */
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void GpuMatrix::mul(const GpuMatrix& a,
                    const GpuMatrix& b,
                    real scaleAB,
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                    real scaleT) {
  CHECK(!isTransposed()) << "Not supported";

  if (!a.isTransposed() && !b.isTransposed()) {
    CHECK_EQ(width_, b.width_);
    CHECK_EQ(height_, a.height_);
    CHECK_EQ(a.width_, b.height_);
  } else if (a.isTransposed() && !b.isTransposed()) {
    CHECK_EQ(width_, b.width_);
    CHECK_EQ(height_, a.width_);
    CHECK_EQ(a.height_, b.height_);
  } else if (!a.isTransposed() && b.isTransposed()) {
    CHECK_EQ(width_, b.height_);
    CHECK_EQ(height_, a.height_);
    CHECK_EQ(a.width_, b.width_);
  } else {
    LOG(FATAL) << "Is not supported";
  }

  real* A_d = a.data_;
  real* B_d = b.data_;
  real* C_d = data_;
  int dimM = getHeight();
  int dimN = getWidth();
  int dimK = !a.isTransposed() ? a.width_ : a.height_;
  int lda = a.getStride();
  int ldb = b.getStride();
  int ldc = getStride();
  hl_trans_op_t transa = !a.isTransposed() ? HPPL_OP_N : HPPL_OP_T;
  hl_trans_op_t transb = !b.isTransposed() ? HPPL_OP_N : HPPL_OP_T;

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  hl_matrix_mul(A_d,
                transa,
                B_d,
                transb,
                C_d,
                dimM,
                dimN,
                dimK,
                scaleAB,
                scaleT,
                lda,
                ldb,
                ldc);
}

void GpuMatrix::mul(const GpuSparseMatrix& a,
                    const GpuMatrix& b,
                    real scaleAB,
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                    real scaleT) {
  CHECK(isContiguous());
  CHECK(b.isContiguous());
  CHECK(b.useGpu_ == true) << "Matrix type are not equal";
  CHECK(!trans_ && !b.trans_) << "not supported";

  if (!a.trans_) {
    CHECK(width_ == b.width_ && height_ == a.height_ && a.width_ == b.height_)
        << "Matrix dimensions are not equal";
  } else {
    CHECK(width_ == b.width_ && height_ == a.width_ && a.height_ == b.height_)
        << "Matrix dimensions are not equal";
  }
  hl_trans_op_t transA = a.trans_ ? HPPL_OP_T : HPPL_OP_N;
  hl_sparse_matrix_s A_d = a.sMatrix_.get();
  real* B_d = b.data_;
  real* C_d = data_;
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  hl_matrix_csr_mul_dense(A_d,
                          transA,
                          B_d,
                          HPPL_OP_N,
                          C_d,
                          height_,
                          width_,
                          b.height_,
                          scaleAB,
                          scaleT);
}

void GpuMatrix::mul(const GpuMatrix& a,
                    const GpuSparseMatrix& b,
                    real scaleAB,
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                    real scaleT) {
  CHECK(isContiguous());
  CHECK(a.isContiguous());
  CHECK(a.useGpu_ == true) << "Matrix type are not equal";

  hl_sparse_matrix_s B_d = b.sMatrix_.get();
  real* A_d = a.data_;
  real* C_d = data_;
  hl_trans_op_t transB = b.trans_ ? HPPL_OP_T : HPPL_OP_N;
  if (!b.trans_) {
    CHECK(width_ == b.width_ && height_ == a.height_ && a.width_ == b.height_)
        << "Matrix dimensions are not equal";
  } else {
    CHECK(width_ == b.height_ && height_ == a.height_ && a.width_ == b.width_)
        << "Matrix dimensions are not equal";
  }
  if (b.format_ == SPARSE_CSC) {
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    hl_matrix_dense_mul_csc(A_d,
                            HPPL_OP_N,
                            B_d,
                            transB,
                            C_d,
                            height_,
                            width_,
                            a.width_,
                            scaleAB,
                            scaleT);
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  } else {
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    hl_matrix_dense_mul_csr(A_d,
                            HPPL_OP_N,
                            B_d,
                            transB,
                            C_d,
                            height_,
                            width_,
                            a.width_,
                            scaleAB,
                            scaleT);
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  }
}

/* this = a*b */
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void GpuMatrix::mul(const Matrix& a, const Matrix& b) { mul(a, b, 1.0, 0.0); }
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void GpuMatrix::mul(const Matrix& a,
                    const Matrix& b,
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                    real scaleAB,
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                    real scaleT) {
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  const auto a_ptr = dynamic_cast<const GpuMatrix*>(&a);
  const auto b_ptr = dynamic_cast<const GpuMatrix*>(&b);
  const auto a_ptr_s = dynamic_cast<const GpuSparseMatrix*>(&a);
  const auto b_ptr_s = dynamic_cast<const GpuSparseMatrix*>(&b);
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  if (a_ptr && b_ptr) {
    mul(*a_ptr, *b_ptr, scaleAB, scaleT);
  } else if (a_ptr_s && b_ptr) {
    mul(*a_ptr_s, *b_ptr, scaleAB, scaleT);
  } else if (a_ptr && b_ptr_s) {
    mul(*a_ptr, *b_ptr_s, scaleAB, scaleT);
  } else {
    LOG(FATAL) << "Not supported";
  }
}

/* this = this* b */
void GpuMatrix::rightMul(Matrix& b) { rightMul(b, 1.0, 0.0); }

/* this = scaleAB*(this*b) +  scaleT*this */
void GpuMatrix::rightMul(Matrix& b, real scaleAB, real scaleT) {
  CHECK(dynamic_cast<GpuMatrix*>(&b));
  CHECK(!isTransposed()) << "Not supported";
  CHECK(!b.isTransposed()) << "Not supported";
  mul(*this, *dynamic_cast<GpuMatrix*>(&b), scaleAB, scaleT);
}

/* this = a*this */
void GpuMatrix::leftMul(Matrix& a) { leftMul(a, 1.0, 0.0); }

/* this = scaleAB*(a*this) +  scaleT*this */
void GpuMatrix::leftMul(Matrix& a, real scaleAB, real scaleT) {
  CHECK(dynamic_cast<GpuMatrix*>(&a));
  CHECK(!isTransposed()) << "Not supported";
  CHECK(!a.isTransposed()) << "Not supported";
  mul(*dynamic_cast<GpuMatrix*>(&a), *this, scaleAB, scaleT);
}

void GpuMatrix::selectRows(Matrix& table, IVector& ids) {
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#ifdef PADDLE_WITH_CUDA
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  CHECK(dynamic_cast<GpuMatrix*>(&table));
  CHECK(table.useGpu());
  CHECK(ids.useGpu());
  CHECK_EQ(getHeight(), ids.getSize());
  CHECK_EQ(getWidth(), table.getWidth());
  size_t numSamples = getHeight();
  size_t dim = getWidth();
  real* a = getData();
  size_t tableSize = table.getHeight();
  int* index = ids.getData();

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  hl_matrix_select_rows(a,
                        stride_,
                        table.getData(),
                        table.stride_,
                        index,
                        numSamples,
                        tableSize,
                        dim);
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#endif
}

void GpuMatrix::addToRows(Matrix& table, IVector& ids) {
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#ifdef PADDLE_WITH_CUDA
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  CHECK(dynamic_cast<GpuMatrix*>(&table));
  CHECK(table.useGpu());
  CHECK(ids.useGpu());
  CHECK_EQ(getHeight(), ids.getSize());
  CHECK_EQ(getWidth(), table.getWidth());
  size_t numSamples = getHeight();
  size_t dim = getWidth();
  real* a = getData();
  size_t tableSize = table.getHeight();
  int* index = ids.getData();

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  hl_matrix_add_to_rows(table.getData(),
                        table.stride_,
                        a,
                        stride_,
                        index,
                        numSamples,
                        tableSize,
                        dim);
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#endif
}

void GpuMatrix::colMerge(Matrix& src) {
  CHECK(src.height_ == height_);
  if (!trans_ && !src.trans_) {
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    sumRows(src, /* scaleSum= */ 1, /* scaleDest= */ 0);
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  } else {
    LOG(FATAL) << "Is not supported";
  }
}

void GpuMatrix::rowSum(Matrix& sum) {
  CHECK_EQ(sum.getHeight(), getHeight());
  CHECK_EQ(sum.getWidth(), (size_t)1);

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  sum.sumRows(*this, /* scaleSum= */ 1, /* scaleDest= */ 0);
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}

void GpuMatrix::rowMax(Matrix& max) {
  CHECK_EQ(max.getHeight(), getHeight());
  CHECK_EQ(max.getWidth(), (size_t)1);

  max.maxRows(*this);
}

void GpuMatrix::rowMax(IVector& maxIds, Matrix& maxVal) {
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#ifdef PADDLE_WITH_CUDA
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  CHECK(maxIds.useGpu() && maxVal.useGpu()) << "Matrix type are not equal";
  size_t numSamples = getHeight();
  size_t beam = maxVal.getWidth();
  CHECK_EQ(maxIds.getSize(), numSamples * beam);
  CHECK_EQ(maxVal.getHeight(), numSamples);
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  CHECK_EQ(maxVal.getWidth(), beam);
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  hl_matrix_top_k(maxVal.getData(),
                  maxVal.getStride(),
                  maxIds.getData(),
                  this->getData(),
                  this->getStride(),
                  this->getWidth(),
                  beam,
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                  numSamples);
#endif
}

void GpuMatrix::colMax(Matrix& max) {
  CHECK_EQ(max.getWidth(), getWidth());
  CHECK_EQ(max.getHeight(), (size_t)1);

  max.maxCols(*this);
}

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void GpuMatrix::colMax(IVector& maxIds, Matrix& maxVal) {
  LOG(FATAL) << "Is not supported";
}

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void GpuMatrix::maxoutForward(Matrix& a,
                              IVector& id,
                              size_t channels,
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                              size_t groups) {
  CHECK(dynamic_cast<GpuMatrix*>(&a));
  CHECK(dynamic_cast<GpuIVector*>(&id));
  CHECK_EQ(a.getHeight(), getHeight());

  size_t size = getWidth();
  size_t batchSize = getHeight();
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  const real* input = a.getData();
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  real* output = getData();
  int* idForGpu = id.getData();

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  hl_maxout_forward(
      input, output, idForGpu, batchSize, size, size / channels, groups);
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}

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void GpuMatrix::maxoutBackward(Matrix& a,
                               IVector& id,
                               size_t channels,
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                               size_t groups) {
  CHECK(dynamic_cast<GpuMatrix*>(&a));
  CHECK(dynamic_cast<GpuIVector*>(&id));
  CHECK_EQ(a.getHeight(), getHeight());

  size_t size = a.getWidth();
  size_t batchSize = getHeight();
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  real* input = getData();
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  const real* output = a.getData();
  const int* idForGpu = id.getData();

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  hl_maxout_backward(
      input, output, idForGpu, batchSize, size, size / channels, groups);
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}

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/*calulate the error of classification */
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void GpuMatrix::classificationError(Matrix& output,
                                    IVector& label,
                                    size_t topkSize) {
  auto gpuOutput = dynamic_cast<GpuMatrix*>(&output);
  auto gpuLabel = dynamic_cast<GpuIVector*>(&label);
  size_t numSamples = this->getHeight();
  GpuMatrixPtr gpuTopVal = std::make_shared<GpuMatrix>(numSamples, topkSize);
  GpuIVectorPtr gpuTopIds = std::make_shared<GpuIVector>(numSamples * topkSize);

  CHECK(gpuOutput && gpuLabel) << "Invalid argument pointer";
  CHECK(gpuTopVal && gpuTopIds) << "Allocate GPU memory failed";
  CHECK(gpuLabel->getSize() == numSamples) << "Vector size is not equal";
  CHECK(numSamples == gpuOutput->getHeight() && this->getWidth() == 1)
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      << "Matrix dimensions are not equal";

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  size_t dim = gpuOutput->getWidth();
  hl_matrix_classification_error(gpuTopVal->getData(),
                                 gpuTopVal->getStride(),
                                 gpuTopIds->getData(),
                                 gpuOutput->getData(),
                                 gpuOutput->getStride(),
                                 dim,
                                 topkSize,
                                 numSamples,
                                 gpuLabel->getData(),
                                 this->getData());
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}

/* copy -log(output[i * width + label]) to this->data[i] */
void GpuMatrix::oneHotCrossEntropy(Matrix& output, IVector& label) {
  GpuMatrix* output_ptr = dynamic_cast<GpuMatrix*>(&output);
  GpuIVector* label_ptr = dynamic_cast<GpuIVector*>(&label);

  CHECK(output_ptr && label_ptr) << "Invalid argument pointer";

  CHECK(height_ == label.getSize() && width_ == 1 && height_ == output.height_)
      << "Matrix dimensions are not equal";

  real* A_d = output_ptr->data_;
  real* C_d = data_;
  int* label_d = label_ptr->getData();

  hl_matrix_cross_entropy(A_d, C_d, label_d, height_, output.width_);
}

/* calculate the error of outputV according to label */
void GpuMatrix::oneHotCrossEntropyBp(Matrix& outputV, IVector& label) {
  GpuMatrix* output_ptr = dynamic_cast<GpuMatrix*>(&outputV);
  GpuIVector* label_ptr = dynamic_cast<GpuIVector*>(&label);

  CHECK(output_ptr && label_ptr) << "Invalid argument pointer";

  CHECK(height_ == output_ptr->height_ && width_ == output_ptr->width_)
      << "Matrix dimensions are not equal";

  real* output_d = output_ptr->data_;
  real* grad_d = data_;
  int* label_d = label_ptr->getData();

  hl_matrix_cross_entropy_bp(grad_d, output_d, label_d, height_, width_);
}

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void GpuMatrix::oneHotCrossEntropyWithSelfNorm(Matrix& output,
                                               IVector& label,
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                                               real alpha) {
  LOG(FATAL) << "Not implemented";
}

void GpuMatrix::oneHotCrossEntropyWithSelfNormBp(Matrix& outputV,
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                                                 IVector& label,
                                                 real alpha) {
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  LOG(FATAL) << "Not implemented";
}

void GpuMatrix::softmax(Matrix& output) {
  CHECK(output.useGpu()) << "Matrix type are not equal";

  size_t height = getHeight();
  size_t width = getWidth();
  CHECK(height == output.getHeight() && width == output.getWidth())
      << "Matrix dimensions are not equal";

  real* inputData = getData();
  real* outputData = output.getData();
  hl_matrix_softmax(inputData, outputData, height, width);
}

void GpuMatrix::sequenceSoftmax(Matrix& output, const IVector& index) {
  CHECK_EQ(getWidth(), 1UL);
  CHECK_EQ(output.getWidth(), 1UL);
  CHECK(isContiguous());

  real* inputData = getData();
  real* outputData = output.getData();
  auto starts = index.getData();
  int numSequences = index.getSize() - 1;
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  hl_sequence_softmax_forward(inputData, outputData, starts, numSequences);
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}

void GpuMatrix::softmaxDerivative(Matrix& output, Matrix& sftmaxSum) {
  CHECK(output.useGpu_ == true && sftmaxSum.useGpu_ == true)
      << "Matrix type are not equal";

  CHECK(height_ == output.height_ && width_ == output.width_ &&
        height_ == sftmaxSum.height_)
      << "Matrix dimensions are not equal";

  real* output_d = output.data_;
  real* sftmaxSum_d = sftmaxSum.data_;
  real* grad_d = data_;
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  hl_matrix_softmax_derivative(grad_d, output_d, sftmaxSum_d, height_, width_);
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}

void GpuMatrix::softmaxBackward(Matrix& outputV) {
  CHECK(outputV.useGpu()) << "Matrix type are not equal";

  size_t height = getHeight();
  size_t width = getWidth();
  CHECK(height == outputV.getHeight() && width == outputV.getWidth())
      << "Matrix dimensions are not equal";

  real* output_grad = getData();
  real* output_value = outputV.getData();
  hl_softmax_backward(output_value, output_grad, height, width);
}

void GpuMatrix::sumOfSquares(Matrix& output, Matrix& label) {
  CHECK_EQ(label.getHeight(), height_);
  CHECK_EQ(output.getHeight(), height_);
  CHECK_EQ(label.getWidth(), output.getWidth());
  CHECK_EQ((size_t)1, width_);

  auto labelptr = dynamic_cast<GpuSparseMatrix*>(&label);
  if (labelptr) {
    LOG(FATAL) << "not supported: GpuSparseMatrix as label";
  }

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  BaseMatrix::sumOfSquaredDiffs(output,
                                label,
                                /* scaleSum= */ 1,
                                /* scaleDest= */ 1);
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}

void GpuMatrix::sumOfSquaresBp(Matrix& outputV, Matrix& label) {
  add2(outputV, label, 1, 2, -2);
}

void GpuMatrix::tanh(Matrix& output) { BaseMatrix::tanh(output); }

void GpuMatrix::tanhDerivative(Matrix& output) {
  BaseMatrix::tanhDerivative(output);
}

void GpuMatrix::softrelu(Matrix& output) { BaseMatrix::softrelu(output); }

void GpuMatrix::softreluDerivative(Matrix& output) {
  BaseMatrix::softreluDerivative(output);
}

void GpuMatrix::scaledTanh(Matrix& output, real p1, real p2) {
  BaseMatrix::scaledTanh(output, p1, p2);
}

void GpuMatrix::randomizeUniform() {
  CHECK(isContiguous());
  real* data = data_;
  size_t size = height_ * width_;

  hl_rand(data, size);
}

void GpuMatrix::print(std::ostream& os) const {
  CHECK(isContiguous());
  CpuMatrix cpuMat(getHeight(), getWidth());
  cpuMat.copyFrom(*this);
  cpuMat.print(os);
}

void GpuMatrix::print(std::ostream& os, size_t height, size_t width) const {
  CHECK(isContiguous());
  CpuMatrix cpuMat(getHeight(), getWidth());
  cpuMat.copyFrom(*this);
  cpuMat.print(os, height, width);
}

void GpuMatrix::check(std::ostream& os, Matrix& refMat, bool printDiff) {
  CHECK(isContiguous());
  CHECK(height_ == refMat.getHeight());
  CHECK(width_ == refMat.getWidth());
  CpuMatrix cpuRef(height_, width_);
  GpuMatrix gpuRef(height_, width_);
  cpuRef.copyFrom(refMat);
  gpuRef.copyFrom(*this);
  size_t diffCnt = 0;
  for (size_t i = 0; i < height_; ++i) {
    for (size_t j = 0; j < width_; ++j) {
      real a = gpuRef.getElement(i, j);
      real b = cpuRef.getElement(i, j);
      if (fabs(a - b) > 0.00001) {
        ++diffCnt;
        if (printDiff) {
          os << "ref= " << a << "  check= " << b << std::endl;
        }
      }
    }
  }
  LOG(INFO) << "the  diffCnt is " << diffCnt;
}

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void GpuMatrix::maxPoolForward(Matrix& inputMat,
                               size_t imgSizeH,
                               size_t imgSizeW,
                               size_t channels,
                               size_t sizeX,
                               size_t sizeY,
                               size_t strideH,
                               size_t strideW,
                               size_t outputH,
                               size_t outputW,
                               size_t paddingH,
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                               size_t paddingW) {
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  CHECK(inputMat.useGpu_ == true) << "Matrix type are not equal";

  real* inputData = inputMat.getData();
  size_t frameNum = inputMat.getHeight();
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  CHECK(imgSizeH * imgSizeW * channels == inputMat.getWidth());
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  CHECK(height_ == inputMat.getHeight());
  CHECK(width_ == outputH * outputW * channels);

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  hl_maxpool_forward(frameNum,
                     inputData,
                     channels,
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                     imgSizeH,
                     imgSizeW,
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                     outputH,
                     outputW,
                     sizeX,
                     sizeY,
                     strideH,
                     strideW,
                     paddingH,
                     paddingW,
                     data_,
                     getStride());
}

void GpuMatrix::maxPoolBackward(Matrix& inputMat,
                                size_t imgSizeH,
                                size_t imgSizeW,
                                Matrix& outGrad,
                                Matrix& outV,
                                size_t sizeX,
                                size_t sizeY,
                                size_t strideH,
                                size_t strideW,
                                size_t outputH,
                                size_t outputW,
                                real scaleTargets,
                                real scaleOutput,
                                size_t paddingH,
                                size_t paddingW) {
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  CHECK(inputMat.useGpu_ == true && outGrad.useGpu_ == true &&
        outV.useGpu_ == true)
      << "Matrix type are not equal";

  real* inputData = inputMat.getData();
  real* outData = outV.getData();
  real* outDiff = outGrad.getData();
  size_t frameNum = inputMat.getHeight();
  size_t channels = outV.getWidth() / outputH / outputW;
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  CHECK(imgSizeH * imgSizeW * channels == inputMat.getWidth());
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  CHECK(height_ == inputMat.getHeight());
  CHECK(outGrad.getHeight() == outV.getHeight() &&
        outGrad.getWidth() == outV.getWidth());

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  hl_maxpool_backward(frameNum,
                      inputData,
                      outData,
                      outDiff,
                      channels,
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                      imgSizeH,
                      imgSizeW,
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                      outputH,
                      outputW,
                      sizeX,
                      sizeY,
                      strideH,
                      strideW,
                      paddingH,
                      paddingW,
                      scaleTargets,
                      scaleOutput,
                      data_,
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                      outGrad.getStride());
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}

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void GpuMatrix::avgPoolForward(Matrix& inputMat,
                               size_t imgSizeH,
                               size_t imgSizeW,
                               size_t channels,
                               size_t sizeX,
                               size_t sizeY,
                               size_t strideH,
                               size_t strideW,
                               size_t outputH,
                               size_t outputW,
                               size_t paddingH,
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                               size_t paddingW) {
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  CHECK(inputMat.useGpu_ == true) << "Matrix type are not equal";

  real* inputData = inputMat.getData();
  size_t frameNum = inputMat.getHeight();
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  CHECK(imgSizeH * imgSizeW * channels == inputMat.getWidth());
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  CHECK(height_ == inputMat.getHeight());
  CHECK(width_ == outputH * outputW * channels);

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  hl_avgpool_forward(frameNum,
                     inputData,
                     channels,
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                     imgSizeH,
                     imgSizeW,
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                     outputH,
                     outputW,
                     sizeX,
                     sizeY,
                     strideH,
                     strideW,
                     paddingH,
                     paddingW,
                     data_,
                     getStride());
}

void GpuMatrix::avgPoolBackward(Matrix& outGrad,
                                size_t imgSizeH,
                                size_t imgSizeW,
                                size_t sizeX,
                                size_t sizeY,
                                size_t strideH,
                                size_t strideW,
                                size_t outputH,
                                size_t outputW,
                                real scaleTargets,
                                real scaleOutput,
                                size_t paddingH,
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                                size_t paddingW) {
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  CHECK(outGrad.useGpu_ == true) << "Matrix type are not equal";

  real* outDiff = outGrad.getData();
  size_t frameNum = outGrad.getHeight();
  size_t channels = outGrad.getWidth() / outputH / outputW;
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  CHECK(imgSizeH * imgSizeW * channels == width_);
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  CHECK(height_ == outGrad.getHeight());
  CHECK(outGrad.getWidth() == outputH * outputW * channels);

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  hl_avgpool_backward(frameNum,
                      outDiff,
                      channels,
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                      imgSizeH,
                      imgSizeW,
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                      outputH,
                      outputW,
                      sizeX,
                      sizeY,
                      strideH,
                      strideW,
                      paddingH,
                      paddingW,
                      scaleTargets,
                      scaleOutput,
                      data_,
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                      outGrad.getStride());
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}

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void GpuMatrix::maxPool3DForward(Matrix& inputMat,
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                                 Matrix& maxPoolIdx,
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                                 size_t channels,
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                                 size_t imgSizeD,
                                 size_t imgSizeH,
                                 size_t imgSizeW,
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                                 size_t outputD,
                                 size_t outputH,
                                 size_t outputW,
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                                 size_t sizeZ,
                                 size_t sizeY,
                                 size_t sizeX,
                                 size_t strideD,
                                 size_t strideH,
                                 size_t strideW,
                                 size_t paddingD,
                                 size_t paddingH,
                                 size_t paddingW) {
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  CHECK(inputMat.useGpu_) << "Matrix type are not correct";
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  real* inputData = inputMat.getData();
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  real* maxPoolIdxData = maxPoolIdx.getData();
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  size_t num = inputMat.getHeight();
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  CHECK(imgSizeD * imgSizeH * imgSizeW * channels == inputMat.getWidth());
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  CHECK(height_ == inputMat.getHeight());
  CHECK(width_ == outputD * outputH * outputW * channels);

  hl_maxpool3D_forward(num,
                       inputData,
                       channels,
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                       imgSizeD,
                       imgSizeH,
                       imgSizeW,
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                       outputD,
                       outputH,
                       outputW,
                       sizeZ,
                       sizeY,
                       sizeX,
                       strideD,
                       strideH,
                       strideW,
                       paddingD,
                       paddingH,
                       paddingW,
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                       getData(),
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                       maxPoolIdxData,
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                       getStride());
}

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void GpuMatrix::maxPool3DBackward(Matrix& outGrad,
                                  Matrix& maxPoolIdx,
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                                  size_t imgSizeD,
                                  size_t imgSizeH,
                                  size_t imgSizeW,
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                                  size_t outputD,
                                  size_t outputH,
                                  size_t outputW,
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                                  size_t sizeZ,
                                  size_t sizeY,
                                  size_t sizeX,
                                  size_t strideD,
                                  size_t strideH,
                                  size_t strideW,
                                  size_t paddingD,
                                  size_t paddingH,
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                                  size_t paddingW,
                                  real scaleTargets,
                                  real scaleOutput) {
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  CHECK(outGrad.useGpu_ && maxPoolIdx.useGpu_) << "Matrix type are not equal";
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  real* outDiff = outGrad.getData();
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  real* maxPoolIdxData = maxPoolIdx.getData();
  size_t frameNum = getHeight();
  size_t channels = outGrad.getWidth() / outputD / outputH / outputW;
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  CHECK(imgSizeD * imgSizeH * imgSizeW * channels == getWidth());
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  CHECK(outGrad.getHeight() == maxPoolIdx.getHeight() &&
        outGrad.getWidth() == maxPoolIdx.getWidth());
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  hl_maxpool3D_backward(frameNum,
                        outDiff,
                        channels,
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                        imgSizeD,
                        imgSizeH,
                        imgSizeW,
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                        outputD,
                        outputH,
                        outputW,
                        sizeZ,
                        sizeY,
                        sizeX,
                        strideD,
                        strideH,
                        strideW,
                        paddingD,
                        paddingH,
                        paddingW,
                        scaleTargets,
                        scaleOutput,
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                        getData(),
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                        maxPoolIdxData,
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                        outGrad.getStride());
}

void GpuMatrix::avgPool3DForward(Matrix& inputMat,
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                                 size_t channels,
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                                 size_t imgSizeD,
                                 size_t imgSizeH,
                                 size_t imgSizeW,
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                                 size_t outputD,
                                 size_t outputH,
                                 size_t outputW,
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                                 size_t sizeZ,
                                 size_t sizeY,
                                 size_t sizeX,
                                 size_t strideD,
                                 size_t strideH,
                                 size_t strideW,
                                 size_t paddingD,
                                 size_t paddingH,
                                 size_t paddingW) {
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  CHECK(inputMat.useGpu_) << "Matrix type are not equal";
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  real* inputData = inputMat.getData();
  size_t frameNum = inputMat.getHeight();
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  CHECK(imgSizeD * imgSizeH * imgSizeW * channels == inputMat.getWidth());
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  CHECK(height_ == inputMat.getHeight());
  CHECK(width_ == outputD * outputH * outputW * channels);

  hl_avgpool3D_forward(frameNum,
                       inputData,
                       channels,
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                       imgSizeD,
                       imgSizeH,
                       imgSizeW,
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                       outputD,
                       outputH,
                       outputW,
                       sizeZ,
                       sizeY,
                       sizeX,
                       strideD,
                       strideH,
                       strideW,
                       paddingD,
                       paddingH,
                       paddingW,
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                       getData(),
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                       getStride());
}

void GpuMatrix::avgPool3DBackward(Matrix& outGrad,
                                  size_t imgSizeD,
                                  size_t imgSizeH,
                                  size_t imgSizeW,
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                                  size_t outputD,
                                  size_t outputH,
                                  size_t outputW,
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                                  size_t sizeZ,
                                  size_t sizeY,
                                  size_t sizeX,
                                  size_t strideD,
                                  size_t strideH,
                                  size_t strideW,
                                  size_t paddingD,
                                  size_t paddingH,
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                                  size_t paddingW,
                                  real scaleTargets,
                                  real scaleOutput) {
  CHECK(outGrad.useGpu_) << "Matrix type are not equal";
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  real* outDiff = outGrad.getData();
  size_t frameNum = outGrad.getHeight();
  size_t channels = outGrad.getWidth() / outputD / outputH / outputW;
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  CHECK(imgSizeD * imgSizeH * imgSizeW * channels == width_);
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  CHECK(height_ == outGrad.getHeight());
  CHECK(outGrad.getWidth() == outputD * outputH * outputW * channels);

  hl_avgpool3D_backward(frameNum,
                        outDiff,
                        channels,
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                        imgSizeD,
                        imgSizeH,
                        imgSizeW,
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                        outputD,
                        outputH,
                        outputW,
                        sizeZ,
                        sizeY,
                        sizeX,
                        strideD,
                        strideH,
                        strideW,
                        paddingD,
                        paddingH,
                        paddingW,
                        scaleTargets,
                        scaleOutput,
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                        getData(),
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                        outGrad.getStride());
}

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void GpuMatrix::maxSequenceForward(Matrix& input,
                                   const IVector& sequence,
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                                   IVector& index) {
  CHECK(dynamic_cast<GpuMatrix*>(&input));
  CHECK(dynamic_cast<const GpuIVector*>(&sequence));
  CHECK(dynamic_cast<GpuIVector*>(&index));

  real* outData = getData();
  real* inputData = input.getData();
  const int* starts = sequence.getData();
  int* maxIndex = index.getData();
  size_t numSequences = getHeight();
  size_t dim = getWidth();

  CHECK_EQ(dim, input.getWidth());
  CHECK_EQ(numSequences, sequence.getSize() - 1);
  CHECK_EQ(numSequences * dim, index.getSize());

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  hl_max_sequence_forward(
      inputData, starts, outData, maxIndex, numSequences, dim);
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}

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void GpuMatrix::maxSequenceBackward(Matrix& outputGrad,
                                    const IVector& sequence,
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                                    IVector& index) {
  CHECK(dynamic_cast<GpuMatrix*>(&outputGrad));
  CHECK(dynamic_cast<const GpuIVector*>(&sequence));
  CHECK(dynamic_cast<GpuIVector*>(&index));

  real* inputGrad = getData();
  real* outGrad = outputGrad.getData();
  int* maxIndex = index.getData();
  size_t dim = getWidth();
  size_t numSequences = sequence.getSize() - 1;

  CHECK_EQ(dim, outputGrad.getWidth());
  CHECK_EQ(numSequences, outputGrad.getHeight());
  CHECK_EQ(numSequences * dim, index.getSize());

  hl_max_sequence_backward(outGrad, maxIndex, inputGrad, numSequences, dim);
}

void GpuMatrix::paramReluForward(Matrix& data, Matrix& W) {
  CHECK(data.useGpu_ == true && W.useGpu_ == true)
      << "Matrix type are not equal";
  real* input = data.getData();
  real* w = W.getData();
  size_t numElements = data.getWidth();
  size_t numSamples = data.getHeight();
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  size_t paraSize = W.getHeight() * W.getWidth();
  CHECK(!(numElements % paraSize));  // this check from ParameterReluLayer::init
  size_t partial_sum = numElements / paraSize;
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  real* output = getData();
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  hl_param_relu_forward(output, input, w, numElements, numSamples, partial_sum);
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}

void GpuMatrix::paramReluBackwardW(Matrix& oGrad, Matrix& data) {
  CHECK(oGrad.useGpu_ == true && data.useGpu_ == true)
      << "Matrix type are not equal";
  real* ograd = oGrad.getData();
  real* input = data.getData();
  real* wgrad = data_;
  size_t numElements = data.getWidth();
  size_t numSamples = data.getHeight();
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  size_t paraSize = this->getHeight() * this->getWidth();
  CHECK(!(numElements % paraSize));  // this check from ParameterReluLayer::init
  size_t partial_sum = numElements / paraSize;
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  hl_param_relu_backward_w(
      wgrad, ograd, input, numElements, numSamples, partial_sum);
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}

void GpuMatrix::paramReluBackwardDiff(Matrix& oGrad, Matrix& data, Matrix& W) {
  real* diff = data_;
  real* input = data.getData();
  real* ograd = oGrad.getData();
  real* w = W.getData();
  size_t numElements = data.getWidth();
  size_t numSamples = data.getHeight();
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  size_t paraSize = W.getHeight() * W.getWidth();
  CHECK(!(numElements % paraSize));  // this check from ParameterReluLayer::init
  size_t partial_sum = numElements / paraSize;
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  hl_param_relu_backward_diff(
      ograd, input, w, diff, numElements, numSamples, partial_sum);
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}

void GpuMatrix::addColumnVector(const Matrix& b) {
  BaseMatrix::addColVector(const_cast<Matrix&>(b));
}

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void GpuMatrix::bilinearForward(const Matrix& in,
                                const size_t inImgH,
                                const size_t inImgW,
                                const size_t outImgH,
                                const size_t outImgW,
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                                const size_t numChannels,
                                const real ratioH,
                                const real ratioW) {
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  CHECK(dynamic_cast<const GpuMatrix*>(&in));

  const size_t outputW = getWidth();
  const size_t outputH = getHeight();
  const size_t inputW = in.getWidth();
  const size_t inputH = in.getHeight();

  real* outData = getData();
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  const real* inData = in.getData();
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  if (inImgH == outImgW && inImgW == outImgW) {
    this->copyFrom(in);
  } else {
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    hl_bilinear_forward(inData,
                        inImgH,
                        inImgW,
                        inputH,
                        inputW,
                        outData,
                        outImgH,
                        outImgW,
                        outputH,
                        outputW,
                        numChannels,
                        ratioH,
                        ratioW);
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  }
}

void GpuMatrix::bilinearBackward(const Matrix& out,
                                 const size_t outImgH,
                                 const size_t outImgW,
                                 const size_t inImgH,
                                 const size_t inImgW,
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                                 const size_t numChannels,
                                 const real ratioH,
                                 const real ratioW) {
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  CHECK(dynamic_cast<const GpuMatrix*>(&out));

  const size_t inputW = getWidth();
  const size_t inputH = getHeight();
  const size_t outputW = out.getWidth();
  const size_t outputH = out.getHeight();

  real* inGrad = getData();
  const real* outGrad = out.getData();

  if (outImgH == inImgH && outImgW == inImgW) {
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    this->add(const_cast<Matrix&>(out));
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  } else {
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    hl_bilinear_backward(inGrad,
                         inImgH,
                         inImgW,
                         inputH,
                         inputW,
                         outGrad,
                         outImgH,
                         outImgW,
                         outputH,
                         outputW,
                         numChannels,
                         ratioH,
                         ratioW);
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  }
}

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void GpuMatrix::multiBinaryLabelCrossEntropy(Matrix& output, Matrix& label) {
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  GpuMatrix* outputPtr = dynamic_cast<GpuMatrix*>(&output);
  auto labelPtr = dynamic_cast<GpuSparseMatrix*>(&label);

  CHECK(outputPtr && labelPtr) << "Invalid argument pointer";
  CHECK(labelPtr->format_ == SPARSE_CSR) << "Matrix format not supported";
  CHECK(height_ == outputPtr->height_ && width_ == 1 &&
        outputPtr->width_ == labelPtr->getWidth() &&
        outputPtr->height_ == labelPtr->getHeight())
      << "Matrix dimensions are not equal";
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  real* output_d = outputPtr->data_;
  real* entropy_d = data_;
  hl_sparse_matrix_s mat_d = labelPtr->sMatrix_.get();
  hl_matrix_multi_binary_cross_entropy(
      output_d, entropy_d, mat_d, height_, outputPtr->width_);
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}

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void GpuMatrix::multiBinaryLabelCrossEntropyBp(Matrix& output, Matrix& label) {
  GpuMatrix* outputPtr = dynamic_cast<GpuMatrix*>(&output);
  auto labelPtr = dynamic_cast<GpuSparseMatrix*>(&label);
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  CHECK(outputPtr && labelPtr) << "Invalid argument pointer";
  CHECK(labelPtr->format_ == SPARSE_CSR) << "Matrix format not supported";
  CHECK(height_ == outputPtr->height_ && width_ == outputPtr->width_ &&
        outputPtr->width_ == labelPtr->getWidth() &&
        outputPtr->height_ == labelPtr->getHeight())
      << "Matrix dimensions are not equal";
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  real* output_d = outputPtr->data_;
  real* grad_d = data_;
  hl_sparse_matrix_s mat_d = labelPtr->sMatrix_.get();
  hl_matrix_multi_binary_cross_entropy_bp(
      output_d, grad_d, mat_d, height_, width_);
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}

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void GpuMatrix::vol2Col(real* dataSrc,
                        int channels,
                        int depth,
                        int height,
                        int width,
                        int filterD,
                        int filterH,
                        int filterW,
                        int strideD,
                        int strideH,
                        int strideW,
                        int paddingD,
                        int paddingH,
                        int paddingW) {
  hl_matrix_vol2Col(dataSrc,
                    channels,
                    depth,
                    height,
                    width,
                    filterD,
                    filterH,
                    filterW,
                    strideD,
                    strideH,
                    strideW,
                    paddingD,
                    paddingH,
                    paddingW,
                    getData());
}

void GpuMatrix::col2Vol(real* dataDst,
                        int channels,
                        int depth,
                        int height,
                        int width,
                        int filterD,
                        int filterH,
                        int filterW,
                        int strideD,
                        int strideH,
                        int strideW,
                        int paddingD,
                        int paddingH,
                        int paddingW,
                        real alpha,
                        real beta) {
  hl_matrix_col2Vol(dataDst,
                    channels,
                    depth,
                    height,
                    width,
                    filterD,
                    filterH,
                    filterW,
                    strideD,
                    strideH,
                    strideW,
                    paddingD,
                    paddingH,
                    paddingW,
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                    getData(),
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                    alpha,
                    beta);
}
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/**
 * CpuMatrix
 */

CpuMatrix::CpuMatrix(size_t height, size_t width, bool trans)
    : Matrix(std::make_shared<CpuMemoryHandle>(height * width * sizeof(real)),
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             height,
             width,
             trans,
             false) {}
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CpuMatrix::~CpuMatrix() {}

void CpuMatrix::zeroMem() {
  CHECK(data_ != NULL);
  if (isContiguous()) {
    memset(data_, 0, height_ * width_ * sizeof(real));
  } else {
    BaseMatrix::zero();
  }
}
void CpuMatrix::resetOne() {
  CHECK(data_ != NULL);
  BaseMatrix::one();
}

void CpuMatrix::copyFrom(const Matrix& src) {
  CHECK(isContiguous());
  if (typeid(src) == typeid(GpuMatrix)) {
    CHECK(src.isContiguous());
    CHECK(elementCnt_ == src.getElementCnt());
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    hl_memcpy_device2host(
        data_, const_cast<real*>(src.getData()), sizeof(real) * elementCnt_);
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  } else if (typeid(src) == typeid(CpuMatrix) ||
             typeid(src) == typeid(SharedCpuMatrix)) {
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    CHECK(src.isContiguous());
    CHECK(elementCnt_ == src.getElementCnt());
    memcpy(data_, src.getData(), sizeof(real) * elementCnt_);
  } else if (typeid(src) == typeid(CpuSparseMatrix)) {
    CHECK_GE(elementCnt_, src.getElementCnt());
    copyFrom(dynamic_cast<CpuSparseMatrix&>(const_cast<Matrix&>(src)));
  } else {
    LOG(FATAL) << "Wrong";
  }
}

void CpuMatrix::copyFrom(CpuSparseMatrix& src) {
  CHECK(isContiguous());
  CHECK(height_ == src.getHeight());
  CHECK(width_ == src.getWidth());
  memset(data_, 0, sizeof(real) * height_ * width_);
  if (src.getValueType() == FLOAT_VALUE) {
    if (src.getFormat() == SPARSE_CSC) {
      int* rows = src.getRows();
      real* vals = src.getValue();
      for (size_t i = 0; i < width_; i++) {
        for (size_t j = src.getColStartIdx(i); j < src.getColStartIdx(i + 1);
             j++) {
          data_[rows[j] * width_ + i] = vals[j];
        }
      }
    } else {
      int* cols = src.getCols();
      real* vals = src.getValue();
      for (size_t i = 0; i < height_; i++) {
        for (size_t j = src.getRowStartIdx(i); j < src.getRowStartIdx(i + 1);
             j++) {
          data_[i * width_ + cols[j]] = vals[j];
        }
      }
    }
  } else {
    if (src.getFormat() == SPARSE_CSC) {
      int* rows = src.getRows();
      for (size_t i = 0; i < width_; i++) {
        for (size_t j = src.getColStartIdx(i); j < src.getColStartIdx(i + 1);
             j++) {
          data_[rows[j] * width_ + i] = 1.0;
        }
      }
    } else {
      int* cols = src.getCols();
      for (size_t i = 0; i < height_; i++) {
        for (size_t j = src.getRowStartIdx(i); j < src.getRowStartIdx(i + 1);
             j++) {
          data_[i * width_ + cols[j]] = 1.0;
        }
      }
    }
  }
}

void CpuMatrix::copyFrom(const Matrix& src, hl_stream_t stream) {
  CHECK(isContiguous());
  CHECK(src.isContiguous());
  CHECK(elementCnt_ == src.getElementCnt());
  if (typeid(src) == typeid(GpuMatrix)) {
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    hl_memcpy_async(this->getData(),
                    const_cast<real*>(src.getData()),
                    sizeof(real) * elementCnt_,
                    stream);
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    // There is a need to add synchronization to ensure that the data is copied.
    hl_stream_synchronize(stream);
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  } else if (typeid(src) == typeid(CpuMatrix)) {
    memcpy(data_, src.getData(), sizeof(real) * elementCnt_);
  } else {
    LOG(FATAL) << "Wrong";
  }
}

void CpuMatrix::copyFrom(const real* cpuSrc, size_t size) {
  CHECK(isContiguous());
  CHECK(size <= elementCnt_);
  memcpy(data_, cpuSrc, sizeof(real) * size);
}

void CpuMatrix::copyFrom(const real* cpuSrc, const int64_t* seq) {
  CHECK(isContiguous());
  for (size_t i = 0; i < height_; i++) {
    memcpy(data_ + i * width_, cpuSrc + seq[i] * width_, sizeof(real) * width_);
  }
}

void CpuMatrix::copyFrom(const IVector& src) {
  CHECK(isContiguous());
  CHECK(elementCnt_ == src.getSize())
      << "the src and dst should have same size.";
  const int* cpuSrc = NULL;
  IVectorPtr tmp;
  if (src.useGpu()) {
    CpuIVector tmp(src.getSize());
    tmp.copyFrom(src);
    cpuSrc = tmp.getData();
  } else {
    cpuSrc = src.getData();
  }
  for (size_t i = 0; i < elementCnt_; ++i) {
    data_[i] = cpuSrc[i];
  }
}

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void CpuMatrix::copyByRowIndex(Matrix& b, const IVector& rowIndex) {
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  size_t height = getHeight();
  size_t width = getWidth();
  CHECK_EQ(b.getWidth(), width);
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  const int* index = rowIndex.getData();
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  for (size_t i = 0; i < height; i++) {
    CHECK_LT(static_cast<size_t>(index[i]), b.getHeight());
    real* src = b.getData() + index[i] * width;
    real* dst = getData() + i * width;
    memcpy(dst, src, sizeof(real) * width);
  }
}

MatrixPtr CpuMatrix::clone(size_t height, size_t width, bool useGpu) {
  CHECK(isContiguous());

  if (height == 0 && width == 0) {
    height = height_;
    width = width_;
  }

  CHECK(width && height);

  if (useGpu) {
    return std::make_shared<GpuMatrix>(height, width);
  } else {
    return std::make_shared<CpuMatrix>(height, width);
  }
}

void CpuMatrix::resize(size_t newHeight, size_t newWidth) {
  size_t newSize = newHeight * newWidth;
  if (NULL == memoryHandle_.get() ||
      newSize * sizeof(real) > memoryHandle_->getAllocSize()) {
    memoryHandle_ = std::make_shared<CpuMemoryHandle>(newSize * sizeof(real));
    data_ = reinterpret_cast<real*>(memoryHandle_->getBuf());
  }

  height_ = newHeight;
  width_ = newWidth;
  elementCnt_ = newSize;
  stride_ = width_;
}

real CpuMatrix::getElement(size_t x, size_t y) const {
  return data_[x * stride_ + y];
}

real CpuMatrix::getSum() {
  CHECK(isContiguous());
  double sum = 0;
  for (size_t i = 0; i < height_; ++i) {
    for (size_t j = 0; j < width_; ++j) {
      sum += data_[i * width_ + j];
    }
  }
  return sum;
}

void CpuMatrix::accumulateColSum(Matrix& src) {
  CHECK_EQ(getWidth(), src.getWidth());
  CHECK_EQ(getHeight(), (size_t)1);

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  sumCols(src, /* scaleSum= */ 1, /* scaleDest= */ 1);
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}

real CpuMatrix::getAbsSum() {
  CHECK(isContiguous());
  double sum = 0;
  for (size_t i = 0; i < height_; ++i) {
    for (size_t j = 0; j < width_; ++j) {
      sum += fabs(data_[i * width_ + j]);
    }
  }
  return sum;
}

MatrixPtr CpuMatrix::getTranspose() {
  if (memoryHandle_.get() != NULL) {
    return std::make_shared<CpuMatrix>(
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        std::dynamic_pointer_cast<CpuMemoryHandle>(memoryHandle_),
        height_,
        width_,
        true);
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  } else {
    MatrixPtr copy_T(new CpuMatrix(data_, height_, width_, true));
    return copy_T;
  }
}

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void CpuMatrix::transpose(MatrixPtr& matTrans, bool memAlloc) {
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  if (memAlloc) {
    matTrans = std::make_shared<CpuMatrix>(width_, height_);
  } else {
    CHECK(matTrans != NULL);
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    CHECK_EQ(matTrans->getHeight(), width_);
    CHECK_EQ(matTrans->getWidth(), height_);
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  }
  real* dataTrans = matTrans->getData();
  real* data = getData();
  int lda = getStride();
  int ldc = matTrans->getStride();

  for (size_t i = 0; i < height_; i++) {
    for (size_t j = 0; j < width_; j++) {
      dataTrans[j * ldc + i] = data[i * lda + j];
    }
  }
}

1903 1904 1905 1906 1907
void CpuMatrix::rotate(MatrixPtr& matRot, bool memAlloc, bool clockWise) {
  if (memAlloc) {
    matRot = std::make_shared<CpuMatrix>(width_, height_);
  } else {
    CHECK(matRot != NULL);
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    CHECK_EQ(matRot->getHeight(), width_);
    CHECK_EQ(matRot->getWidth(), height_);
1910 1911 1912 1913 1914 1915 1916
  }
  real* dataRot = matRot->getData();
  real* data = getData();

  for (size_t i = 0; i < height_; i++) {
    for (size_t j = 0; j < width_; j++) {
      if (clockWise) {
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        dataRot[j * height_ + i] = data[(height_ - i - 1) * width_ + j];
1918
      } else {
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        dataRot[j * height_ + i] = data[i * width_ + (width_ - j - 1)];
1920 1921 1922 1923 1924
      }
    }
  }
}

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MatrixPtr CpuMatrix::getInverse() {
  MatrixPtr matInv;
  inverse(matInv, true);
  return matInv;
}

1931
void CpuMatrix::inverse(MatrixPtr& matInv, bool memAlloc) {
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  CHECK_EQ(height_, width_);

  if (memAlloc) {
    matInv = std::make_shared<CpuMatrix>(height_, width_);
  } else {
    CHECK(matInv != NULL);
  }

  CHECK_EQ(height_, matInv->getHeight());
  CHECK_EQ(width_, matInv->getWidth());
  matInv->copyFrom(*this);

  real* data = getData();
  real* dataInv = matInv->getData();
  int ldc = matInv->getStride();

  if (height_ == 1) {
    CHECK_NE(*data, 0);
    *dataInv = 1.0 / (*data);
    return;
  }

  /* Compute the LU decomposition of the matrix */
  std::vector<int> ipiv(height_);
  CBLAS_ORDER order = (matInv->isTransposed() ? CblasColMajor : CblasRowMajor);
  int info = getrf<real>(order, height_, height_, dataInv, ldc, ipiv.data());
  CHECK_EQ(info, 0);

  /* Compute the inverse of the matrix given its LU decompsotion */
  info = getri<real>(order, height_, dataInv, ldc, ipiv.data());
  CHECK_EQ(info, 0);
}

1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975
void CpuMatrix::maxPoolForward(Matrix& inputMat,
                               size_t imgSizeH,
                               size_t imgSizeW,
                               size_t channels,
                               size_t sizeX,
                               size_t sizeY,
                               size_t strideH,
                               size_t strideW,
                               size_t outputH,
                               size_t outputW,
                               size_t paddingH,
1976
                               size_t paddingW) {
1977 1978 1979
  real* inputData = inputMat.getData();
  real* outData = data_;
  size_t num = inputMat.getHeight();
1980 1981 1982
  size_t inLength = imgSizeH * imgSizeW;
  size_t outLength = outputH * outputW;
  CHECK(inLength == inputMat.getWidth() / channels);
1983
  CHECK_EQ(num, this->getHeight());
1984
  CHECK_EQ(channels * outLength, this->getWidth());
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  size_t outStride = getStride();
1986 1987

  /* initialize the data_ */
1988 1989
  for (size_t i = 0; i < height_; i++) {
    for (size_t j = 0; j < width_; j++) {
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      outData[i * outStride + j] = -(real)FLT_MAX;
1991
    }
1992 1993 1994
  }

  /* pool max one by one */
1995 1996
  for (size_t n = 0; n < num; ++n) {  // frame by frame
    if (!isContiguous()) {
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      outData = data_ + n * outStride;
1998
    }
1999 2000
    for (size_t c = 0; c < channels; ++c) {  // channel by channel
      for (size_t ph = 0; ph < outputH; ++ph) {
2001 2002 2003
        int hstart = ph * strideH - paddingH;
        int hend = std::min(hstart + sizeY, imgSizeH);
        hstart = std::max(hstart, 0);
2004
        for (size_t pw = 0; pw < outputW; ++pw) {
2005
          int wstart = pw * strideW - paddingW;
2006
          int wend = std::min(wstart + sizeX, imgSizeW);
2007 2008 2009
          wstart = std::max(wstart, 0);
          for (int h = hstart; h < hend; ++h) {
            for (int w = wstart; w < wend; ++w) {
2010 2011
              outData[ph * outputW + pw] = std::max(
                  outData[ph * outputW + pw], inputData[h * imgSizeW + w]);
2012 2013 2014 2015 2016
            }
          }
        }
      }
      // compute offset
2017 2018
      inputData += inLength;
      outData += outLength;
2019 2020 2021 2022
    }
  }
}

2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037
void CpuMatrix::maxPoolBackward(Matrix& image,
                                size_t imgSizeH,
                                size_t imgSizeW,
                                Matrix& outGrad,
                                Matrix& outV,
                                size_t sizeX,
                                size_t sizeY,
                                size_t strideH,
                                size_t strideW,
                                size_t outputH,
                                size_t outputW,
                                real scaleTargets,
                                real scaleOutput,
                                size_t paddingH,
                                size_t paddingW) {
2038
  size_t num = image.getHeight();
2039 2040 2041 2042
  size_t inLength = imgSizeH * imgSizeW;
  size_t outLength = outputH * outputW;
  size_t channels = size_t(width_ / inLength);
  CHECK(image.getWidth() == inLength * channels);
2043 2044 2045 2046 2047 2048 2049 2050
  CHECK(image.getHeight() == height_ && image.getWidth() == width_);
  CHECK(outV.getHeight() == outGrad.getHeight() &&
        outV.getWidth() == outGrad.getWidth());

  real* tgtGrad = data_;
  real* inData = image.getData();
  real* otData = outV.getData();
  real* otGrad = outGrad.getData();
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  size_t outStride = outV.getStride();
  real* origOutData = otData;
  real* origOutGrad = otGrad;

2056
  for (size_t n = 0; n < num; ++n) {
2057
    if (!outV.isContiguous()) {
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      otData = origOutData + n * outStride;
      otGrad = origOutGrad + n * outStride;
2060
    }
2061 2062
    for (size_t c = 0; c < channels; ++c) {
      for (size_t ph = 0; ph < outputH; ++ph) {
2063 2064 2065
        int hstart = ph * strideH - paddingH;
        int hend = std::min(hstart + sizeY, imgSizeH);
        hstart = std::max(hstart, 0);
2066
        for (size_t pw = 0; pw < outputW; ++pw) {
2067 2068 2069 2070 2071
          int wstart = pw * strideW - paddingW;
          int wend = std::min(wstart + sizeX, imgSizeW);
          wstart = std::max(wstart, 0);
          for (int h = hstart; h < hend; ++h) {
            for (int w = wstart; w < wend; ++w) {
2072 2073 2074
              tgtGrad[h * imgSizeW + w] =
                  scaleTargets * tgtGrad[h * imgSizeW + w] +
                  scaleOutput * otGrad[ph * outputW + pw] *
2075
                      (inData[h * imgSizeW + w] == otData[ph * outputW + pw]);
2076 2077 2078 2079 2080
            }
          }
        }
      }
      // offset
2081 2082 2083 2084
      inData += inLength;
      tgtGrad += inLength;
      otData += outLength;
      otGrad += outLength;
2085 2086 2087 2088
    }
  }
}

2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099
void CpuMatrix::avgPoolForward(Matrix& input,
                               size_t imgSizeH,
                               size_t imgSizeW,
                               size_t channels,
                               size_t sizeX,
                               size_t sizeY,
                               size_t strideH,
                               size_t strideW,
                               size_t outputH,
                               size_t outputW,
                               size_t paddingH,
2100
                               size_t paddingW) {
2101 2102
  // The main loop
  size_t num = input.getHeight();
2103 2104 2105 2106
  size_t inLength = imgSizeH * imgSizeW;
  size_t outLength = outputH * outputW;
  CHECK(inLength * channels == input.getWidth());
  CHECK(outLength * channels * num == height_ * width_);
2107 2108 2109 2110
  real* tgtData = data_;
  real* inData = input.getData();

  for (size_t n = 0; n < num; ++n) {
2111 2112 2113
    if (!isContiguous()) {
      tgtData = data_ + n * getStride();
    }
2114 2115
    for (size_t c = 0; c < channels; ++c) {
      for (size_t ph = 0; ph < outputH; ++ph) {
2116 2117 2118
        int hstart = ph * strideH - paddingH;
        int hend = std::min(hstart + sizeY, imgSizeH);
        hstart = std::max(hstart, 0);
2119
        for (size_t pw = 0; pw < outputW; ++pw) {
2120
          int wstart = pw * strideW - paddingW;
2121
          int wend = std::min(wstart + sizeX, imgSizeW);
2122
          wstart = std::max(wstart, 0);
2123
          tgtData[ph * outputW + pw] = 0;  // clear
2124 2125
          for (int h = hstart; h < hend; ++h) {
            for (int w = wstart; w < wend; ++w) {
2126
              tgtData[ph * outputW + pw] += inData[h * imgSizeW + w];
2127 2128
            }
          }
2129 2130
          int poolSize = (hend - hstart) * (wend - wstart);
          CHECK(poolSize);
2131
          tgtData[ph * outputW + pw] /= poolSize;
2132 2133 2134
        }
      }
      // compute offset
2135 2136
      inData += inLength;
      tgtData += outLength;
2137 2138 2139 2140
    }
  }
}

2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153
void CpuMatrix::avgPoolBackward(Matrix& input,
                                size_t imgSizeH,
                                size_t imgSizeW,
                                size_t sizeX,
                                size_t sizeY,
                                size_t strideH,
                                size_t strideW,
                                size_t outputH,
                                size_t outputW,
                                real scaleTargets,
                                real scaleOutput,
                                size_t paddingH,
                                size_t paddingW) {
2154 2155
  size_t num = input.getHeight();
  size_t channels = input.getWidth() / outputH / outputW;
2156 2157 2158
  size_t inLength = imgSizeH * imgSizeW;
  size_t outLength = outputH * outputW;
  CHECK(inLength * channels == getWidth());
2159 2160 2161 2162
  real* inData = input.getData();
  real* outData = getData();

  for (size_t n = 0; n < num; ++n) {
2163 2164 2165
    if (!input.isContiguous()) {
      inData = input.getData() + n * input.getStride();
    }
2166 2167
    for (size_t c = 0; c < channels; ++c) {
      for (size_t ph = 0; ph < outputH; ++ph) {
2168 2169 2170
        int hstart = ph * strideH - paddingH;
        int hend = std::min(hstart + sizeY, imgSizeH);
        hstart = std::max(hstart, 0);
2171
        for (size_t pw = 0; pw < outputW; ++pw) {
2172
          int wstart = pw * strideW - paddingW;
2173
          int wend = std::min(wstart + sizeX, imgSizeW);
2174
          wstart = std::max(wstart, 0);
2175
          int poolSize = (hend - hstart) * (wend - wstart);
2176 2177 2178 2179 2180
          CHECK(poolSize);

          for (int h = hstart; h < hend; ++h) {
            for (int w = wstart; w < wend; ++w) {
              outData[h * imgSizeW + w] += inData[ph * outputW + pw] / poolSize;
2181 2182 2183 2184 2185
            }
          }
        }
      }
      // offset
2186 2187
      outData += inLength;
      inData += outLength;
2188 2189 2190 2191
    }
  }
}

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void CpuMatrix::maxPool3DForward(Matrix& inputMat,
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                                 Matrix& maxPoolIdx,
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                                 size_t channels,
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                                 size_t imgSizeD,
                                 size_t imgSizeH,
                                 size_t imgSizeW,
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                                 size_t outputD,
                                 size_t outputH,
                                 size_t outputW,
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                                 size_t sizeZ,
                                 size_t sizeY,
                                 size_t sizeX,
                                 size_t strideD,
                                 size_t strideH,
                                 size_t strideW,
                                 size_t paddingD,
                                 size_t paddingH,
                                 size_t paddingW) {
  real* inputData = inputMat.getData();
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  real* outData = getData();
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  real* maxPoolIdxData = maxPoolIdx.getData();
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  size_t num = inputMat.getHeight();
2214 2215 2216
  size_t inLength = imgSizeH * imgSizeW * imgSizeD;
  size_t outLength = outputH * outputW * outputD;
  CHECK(inLength == inputMat.getWidth() / channels);
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  CHECK_EQ(num, this->getHeight());
2218
  CHECK_EQ(channels * outLength, this->getWidth());
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  size_t outStride = getStride();

  /* initialize the data_ */
  for (size_t i = 0; i < height_; i++) {
    for (size_t j = 0; j < width_; j++) {
      outData[(i)*outStride + j] = -(real)FLT_MAX;
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      maxPoolIdxData[(i)*outStride + j] = -1;
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    }
  }

  /* pool max one by one */
  for (size_t n = 0; n < num; ++n) {  // frame by frame
    if (!isContiguous()) {
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      outData = getData() + n * outStride;
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      maxPoolIdxData = maxPoolIdx.getData() + n * outStride;
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    }
    for (size_t c = 0; c < channels; ++c) {  // channel by channel
      for (size_t pd = 0; pd < outputD; ++pd) {
2237 2238 2239
        int dstart = pd * strideD - paddingD;
        int dend = std::min(dstart + sizeZ, imgSizeD);
        dstart = std::max(dstart, 0);
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        for (size_t ph = 0; ph < outputH; ++ph) {
2241 2242 2243
          int hstart = ph * strideH - paddingH;
          int hend = std::min(hstart + sizeY, imgSizeH);
          hstart = std::max(hstart, 0);
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          for (size_t pw = 0; pw < outputW; ++pw) {
            int wstart = pw * strideW - paddingW;
2246
            int wend = std::min(wstart + sizeX, imgSizeW);
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            wstart = std::max(wstart, 0);
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            int maxIdx = -1;
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            real maxOutData = outData[(pd * outputH + ph) * outputW + pw];
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            for (int d = dstart; d < dend; ++d) {
              for (int h = hstart; h < hend; ++h) {
                for (int w = wstart; w < wend; ++w) {
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                  if (maxOutData <
2254 2255 2256
                      inputData[(d * imgSizeH + h) * imgSizeW + w]) {
                    maxOutData = inputData[(d * imgSizeH + h) * imgSizeW + w];
                    maxIdx = (d * imgSizeH + h) * imgSizeW + w;
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                  }
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                }
              }
            }
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            outData[(pd * outputH + ph) * outputW + pw] = maxOutData;
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            maxPoolIdxData[(pd * outputH + ph) * outputW + pw] = maxIdx;
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          }
        }
      }
      // compute offset
2267 2268 2269
      inputData += inLength;
      outData += outLength;
      maxPoolIdxData += outLength;
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    }
  }
}

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void CpuMatrix::maxPool3DBackward(Matrix& outGrad,
                                  Matrix& maxPoolIdx,
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                                  size_t imgSizeD,
                                  size_t imgSizeH,
                                  size_t imgSizeW,
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                                  size_t outputD,
                                  size_t outputH,
                                  size_t outputW,
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                                  size_t sizeZ,
                                  size_t sizeY,
                                  size_t sizeX,
                                  size_t strideD,
                                  size_t strideH,
                                  size_t strideW,
                                  size_t paddingD,
                                  size_t paddingH,
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                                  size_t paddingW,
                                  real scaleTargets,
                                  real scaleOutput) {
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  size_t num = getHeight();
2294 2295 2296
  size_t inLength = imgSizeH * imgSizeW * imgSizeD;
  size_t outLength = outputH * outputW * outputD;
  size_t channels = size_t(width_ / inLength);
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  CHECK(maxPoolIdx.getHeight() == outGrad.getHeight() &&
        maxPoolIdx.getWidth() == outGrad.getWidth());
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  real* tgtGrad = getData();
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  real* otGrad = outGrad.getData();
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  real* maxPoolIdxData = maxPoolIdx.getData();
  size_t outStride = outGrad.getStride();
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  for (size_t n = 0; n < num; ++n) {
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    if (!outGrad.isContiguous()) {
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      otGrad = outGrad.getData() + n * outStride;
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      maxPoolIdxData = maxPoolIdx.getData() + n * outStride;
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    }
    for (size_t c = 0; c < channels; ++c) {
      for (size_t pd = 0; pd < outputD; ++pd) {
        for (size_t ph = 0; ph < outputH; ++ph) {
          for (size_t pw = 0; pw < outputW; ++pw) {
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            const size_t index = (pd * outputH + ph) * outputW + pw;
            const size_t tgtIdx = static_cast<size_t>(maxPoolIdxData[index]);
            tgtGrad[tgtIdx] =
                scaleTargets * tgtGrad[tgtIdx] + scaleOutput * otGrad[index];
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          }
        }
      }
      // offset
2322 2323 2324
      tgtGrad += inLength;
      otGrad += outLength;
      maxPoolIdxData += outLength;
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    }
  }
}

void CpuMatrix::avgPool3DForward(Matrix& input,
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                                 size_t channels,
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                                 size_t imgSizeD,
                                 size_t imgSizeH,
                                 size_t imgSizeW,
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                                 size_t outputD,
                                 size_t outputH,
                                 size_t outputW,
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                                 size_t sizeZ,
                                 size_t sizeY,
                                 size_t sizeX,
                                 size_t strideD,
                                 size_t strideH,
                                 size_t strideW,
                                 size_t paddingD,
                                 size_t paddingH,
                                 size_t paddingW) {
  // The main loop
  size_t num = input.getHeight();
2348 2349 2350 2351
  size_t inLength = imgSizeH * imgSizeW * imgSizeD;
  size_t outLength = outputH * outputW * outputD;
  CHECK(inLength * channels == input.getWidth());
  CHECK(outLength * channels * num == height_ * width_);
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  real* tgtData = getData();
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  real* inData = input.getData();

  for (size_t n = 0; n < num; ++n) {
    if (!isContiguous()) {
      tgtData = data_ + n * getStride();
    }
    for (size_t c = 0; c < channels; ++c) {
      for (size_t pd = 0; pd < outputD; ++pd) {
2361 2362 2363
        int dstart = pd * strideD - paddingD;
        int dend = std::min(dstart + sizeZ, imgSizeD);
        dstart = std::max(dstart, 0);
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        for (size_t ph = 0; ph < outputH; ++ph) {
2365 2366 2367
          int hstart = ph * strideH - paddingH;
          int hend = std::min(hstart + sizeY, imgSizeH);
          hstart = std::max(hstart, 0);
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          for (size_t pw = 0; pw < outputW; ++pw) {
            int wstart = pw * strideW - paddingW;
2370
            int wend = std::min(wstart + sizeX, imgSizeW);
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            wstart = std::max(wstart, 0);

            tgtData[(pd * outputH + ph) * outputW + pw] = 0;  // clear
            for (int d = dstart; d < dend; ++d) {
              for (int h = hstart; h < hend; ++h) {
                for (int w = wstart; w < wend; ++w) {
                  tgtData[(pd * outputH + ph) * outputW + pw] +=
2378
                      inData[(d * imgSizeH + h) * imgSizeW + w];
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                }
              }
            }
2382 2383
            int poolSize = (dend - dstart) * (hend - hstart) * (wend - wstart);
            CHECK(poolSize);
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            tgtData[(pd * outputH + ph) * outputW + pw] /= poolSize;
          }
        }
      }
      // compute offset
2389 2390
      inData += inLength;
      tgtData += outLength;
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    }
  }
}

void CpuMatrix::avgPool3DBackward(Matrix& input,
                                  size_t imgSizeD,
                                  size_t imgSizeH,
                                  size_t imgSizeW,
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                                  size_t outputD,
                                  size_t outputH,
                                  size_t outputW,
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                                  size_t sizeZ,
                                  size_t sizeY,
                                  size_t sizeX,
                                  size_t strideD,
                                  size_t strideH,
                                  size_t strideW,
                                  size_t paddingD,
                                  size_t paddingH,
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                                  size_t paddingW,
                                  real scaleTargets,
                                  real scaleOutput) {
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  size_t num = input.getHeight();
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  size_t inLength = imgSizeH * imgSizeW * imgSizeD;
  size_t outLength = outputH * outputW * outputD;
  size_t channels = input.getWidth() / outLength;
  CHECK(inLength * channels == getWidth());
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  real* inData = input.getData();
  real* outData = getData();

  for (size_t n = 0; n < num; ++n) {
    if (!input.isContiguous()) {
      inData = input.getData() + n * input.getStride();
    }
    for (size_t c = 0; c < channels; ++c) {
      for (size_t pd = 0; pd < outputD; ++pd) {
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        int dstart = pd * strideD - paddingD;
        int dend = std::min(dstart + sizeZ, imgSizeD);
        dstart = std::max(dstart, 0);
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        for (size_t ph = 0; ph < outputH; ++ph) {
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          int hstart = ph * strideH - paddingH;
          int hend = std::min(hstart + sizeY, imgSizeH);
          hstart = std::max(hstart, 0);
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          for (size_t pw = 0; pw < outputW; ++pw) {
            int wstart = pw * strideW - paddingW;
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            int wend = std::min(wstart + sizeX, imgSizeW);
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            wstart = std::max(wstart, 0);
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            int poolSize = (dend - dstart) * (hend - hstart) * (wend - wstart);
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            CHECK(poolSize);
            for (int d = dstart; d < dend; ++d) {
              for (int h = hstart; h < hend; ++h) {
                for (int w = wstart; w < wend; ++w) {
                  outData[(d * imgSizeH + h) * imgSizeW + w] +=
                      inData[(pd * outputH + ph) * outputW + pw] / poolSize;
                }
              }
            }
          }
        }
      }
      // offset
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      outData += inLength;
      inData += outLength;
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    }
  }
}

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/**
 * Input: one or more sequences. Each sequence contains some instances.
 * Output: output size is the number of input sequences (NOT input instances).
 * output[i] is set to max_{for each instance in this sequence}{input[i]}
 */
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void CpuMatrix::maxSequenceForward(Matrix& input,
                                   const IVector& sequence,
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                                   IVector& index) {
  CHECK(dynamic_cast<CpuMatrix*>(&input));
  CHECK(dynamic_cast<const CpuIVector*>(&sequence));
  CHECK(dynamic_cast<CpuIVector*>(&index));

  real* outData = getData();
  real* inputData = input.getData();
  const int* starts = sequence.getData();
  int* maxIndex = index.getData();
  size_t numSequences = getHeight();
  size_t dim = getWidth();

  CHECK_EQ(dim, input.getWidth());
  CHECK_EQ(numSequences, sequence.getSize() - 1);
  CHECK_EQ(starts[numSequences], (int)input.getHeight());
  CHECK_EQ(numSequences * dim, index.getSize());

  for (size_t sequenceId = 0; sequenceId < numSequences; ++sequenceId) {
    // current sequence, loop for each input instance
    // (1) first instance: do not need compare, copy value to outV directly
    for (size_t k = 0; k < dim; ++k) {
      outData[sequenceId * dim + k] = inputData[starts[sequenceId] * dim + k];
      maxIndex[sequenceId * dim + k] = starts[sequenceId];
    }
    // (2) other instance in same sequence
    for (int insId = starts[sequenceId] + 1; insId < starts[sequenceId + 1];
         ++insId) {
      // insId is the index on all instances
      for (size_t k = 0; k < dim; ++k) {
        // for each dim
        if (inputData[insId * dim + k] > outData[sequenceId * dim + k]) {
          // update max value and record index
          outData[sequenceId * dim + k] = inputData[insId * dim + k];
          maxIndex[sequenceId * dim + k] = insId;
        }
      }
    }
  }
}

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void CpuMatrix::maxSequenceBackward(Matrix& outputGrad,
                                    const IVector& sequence,
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                                    IVector& index) {
  CHECK(dynamic_cast<CpuMatrix*>(&outputGrad));
  CHECK(dynamic_cast<const CpuIVector*>(&sequence));
  CHECK(dynamic_cast<CpuIVector*>(&index));

  real* inputGrad = getData();
  real* outGrad = outputGrad.getData();
  int* maxIndex = index.getData();
  size_t dim = getWidth();
  size_t numSequences = sequence.getSize() - 1;

  CHECK_EQ(dim, outputGrad.getWidth());
  CHECK_EQ(numSequences, outputGrad.getHeight());
  CHECK_EQ(numSequences * dim, index.getSize());

  for (size_t sequenceId = 0; sequenceId < numSequences; ++sequenceId) {
    // current sequence
    for (size_t j = 0; j < dim; ++j) {
      // each dim
      int insId = maxIndex[sequenceId * dim + j];
      inputGrad[insId * dim + j] += outGrad[sequenceId * dim + j];
    }
  }
}

inline void vecAddTo(real* a, const real* b, size_t len) {
  for (unsigned int i = 0; i < len; ++i) {
    a[i] += b[i];
  }
}

inline void vecAddTo(real* a, const real* b, real scaleB, size_t len) {
  for (unsigned int i = 0; i < len; ++i) {
    a[i] += scaleB * b[i];
  }
}

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inline void colVecAddTo(
    real* a, const real* b, size_t len, size_t aWidth, size_t bWidth) {
2546 2547 2548 2549 2550
  for (unsigned int i = 0; i < len; ++i) {
    a[i * aWidth] += b[i * bWidth];
  }
}

2551 2552
inline void colVecAddTo(
    real* a, real* b, real c, size_t len, size_t aWidth, size_t bWidth) {
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  for (unsigned int i = 0; i < len; ++i) {
    a[i * aWidth] += b[i * bWidth] * c;
  }
}

void CpuMatrix::addBias(Matrix& b, real scale) {
  CHECK(b.useGpu_ == false) << "Matrix type are not equal";

  CHECK_EQ(b.getHeight(), (size_t)1);
  CHECK_EQ(width_, b.getWidth());
  real* aData = getData();
  real* bData = b.getData();
  size_t numSamples = getHeight();
  size_t dim = getWidth();

  if (scale == 1 && getStride() % 32 == 0) {  // use libaddto
    // @TODO(yuyang18) Make input addr can be unaligned.
    // So merge this if and else
    CHECK_EQ((size_t)aData % 32, 0UL);
    CHECK_EQ((size_t)bData % 32, 0UL);
    for (size_t i = 0; i < numSamples; i++) {
      simd::addTo(aData + i * getStride(), bData, dim);
    }
  } else {
    for (size_t i = 0; i < numSamples; i++) {
      for (size_t j = 0; j < dim; j++) {
        aData[i * getStride() + j] += scale * bData[j];
      }
    }
  }
}

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void CpuMatrix::addSharedBias(Matrix& b, real scale) {
  CHECK_EQ(b.getHeight(), (size_t)1);
  real* aData = getData();
  real* bData = b.getData();
  size_t numSamples = getHeight();
  size_t channel = b.getWidth();
  CHECK_EQ(getWidth() % channel, 0UL);
  size_t dim = getWidth() / channel;

  for (size_t i = 0; i < numSamples; i++) {
    for (size_t c = 0; c < channel; c++) {
      for (size_t j = 0; j < dim; j++) {
        aData[i * getStride() + c * dim + j] += scale * bData[c];
      }
    }
  }
}

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void CpuMatrix::collectBias(Matrix& a, real scale) {
  CHECK_EQ(getHeight(), (size_t)1);
  CHECK_EQ(width_, a.getWidth());
  CpuSparseMatrix* aptr = dynamic_cast<CpuSparseMatrix*>(&a);
  if (!aptr) {
2608
    sumCols(a, /* scaleSum= */ scale, /* scaleDest= */ 1);
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  } else {
    size_t nnz = aptr->getElementCnt();
    int* cols = aptr->getCols();
    real* A = aptr->getValue();
    real* B = getData();
    for (size_t i = 0; i < nnz; i++) {
      B[cols[i]] += scale * A[i];
    }
  }
}

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void CpuMatrix::collectSharedBias(Matrix& a, real scale) {
  CHECK_EQ(getHeight(), (size_t)1);
  real* B = getData();
  real* A = a.getData();
  size_t numSamples = a.getHeight();
  size_t channel = getWidth();
  CHECK_EQ(a.getWidth() % channel, 0UL);
  size_t dim = a.getWidth() / channel;
  for (size_t i = 0; i < numSamples; i++) {
    for (size_t c = 0; c < channel; c++) {
      for (size_t j = 0; j < dim; j++) {
        B[c] += scale * A[i * channel * dim + c * dim + j];
      }
    }
  }
}

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void CpuMatrix::sequenceAvgForward(Matrix& a,
                                   const IVector& startsPos,
                                   int mode) {
  size_t height = getHeight();
  size_t width = getWidth();
  CHECK_EQ(height, startsPos.getSize() - 1);
  CHECK_EQ(width, a.getWidth());
  real* dst = getData();
  real* src = a.getData();
  const int* starts = startsPos.getData();
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  MatrixPtr outMtx = Matrix::create(nullptr, 1, width, false, false);
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  MatrixPtr dataMtx = Matrix::create(nullptr, 1, width, false, false);
  for (size_t i = 0; i < height; i++) {
    int sequenceLength = starts[i + 1] - starts[i];
    if (0 == sequenceLength) {
      // empty sequence
      continue;
    }
    outMtx->setData(dst + i * width);
    dataMtx->setData(src + starts[i] * width, sequenceLength, width);
    if (mode == 0) {
      // plain average
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      outMtx->sumCols(*dataMtx,
                      (real)1 / (real)sequenceLength,
                      /* scaleDest= */ 1);
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    } else if (mode == 1) {
      // sum instead of average
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      outMtx->sumCols(*dataMtx, /* scaleSum= */ 1, /* scaleDest= */ 1);
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    } else if (mode == 2) {
      // divide by square root of sequenceLength
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      outMtx->sumCols(*dataMtx,
                      (real)1 / std::sqrt(sequenceLength),
                      /* scaleDest= */ 1);
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    } else {
      LOG(FATAL) << "should not reach here";
    }
  }
}

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void CpuMatrix::sequenceAvgBackward(Matrix& a,
                                    const IVector& startsPos,
                                    int mode) {
  size_t height = a.getHeight();
  size_t width = getWidth();
  CHECK_EQ(height, startsPos.getSize() - 1);
  CHECK_EQ(width, a.getWidth());
  real* dst = getData();
  real* src = a.getData();
  const int* starts = startsPos.getData();
  MatrixPtr outMtx = Matrix::create(nullptr, 1, width, false, false);
  MatrixPtr dataMtx = Matrix::create(nullptr, 1, width, false, false);
  for (size_t i = 0; i < height; ++i) {
    int sequenceLength = starts[i + 1] - starts[i];
    if (0 == sequenceLength) {
      // empty sequence
      continue;
    }
    outMtx->setData(dst + starts[i] * width, sequenceLength, width);
    dataMtx->setData(src + i * width);
    if (mode == 0) {
      // plain average
      outMtx->addBias(*dataMtx, 1.0f / sequenceLength);
    } else if (mode == 1) {
      // sum instead of average
      outMtx->addBias(*dataMtx, 1.0f);
    } else if (mode == 2) {
      // divide by square root of sequenceLength
      outMtx->addBias(*dataMtx, 1.0f / std::sqrt(sequenceLength));
    } else {
      LOG(FATAL) << "should not reach here";
    }
  }
}

2711
/* this = scaleAB*(a*b) + scaleT*this*/
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void CpuMatrix::mul(const Matrix& a,
                    const Matrix& b,
2714
                    real scaleAB,
2715 2716
                    real scaleT) {
  CHECK(!isTransposed()) << "Not supported";
2717 2718 2719 2720
  const auto a_ptr = dynamic_cast<const CpuMatrix*>(&a);
  const auto b_ptr = dynamic_cast<const CpuMatrix*>(&b);
  const auto a_ptr_s = dynamic_cast<const CpuSparseMatrix*>(&a);
  const auto b_ptr_s = dynamic_cast<const CpuSparseMatrix*>(&b);
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  if (a_ptr && b_ptr) {
    mul((CpuMatrix*)a_ptr, (CpuMatrix*)b_ptr, scaleAB, scaleT);
  } else if (a_ptr_s && b_ptr) {
    mul((CpuSparseMatrix*)a_ptr_s, (CpuMatrix*)b_ptr, scaleAB, scaleT);
  } else if (a_ptr && b_ptr_s) {
    mul((CpuMatrix*)a_ptr, (CpuSparseMatrix*)b_ptr_s, scaleAB, scaleT);
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  } else {
    LOG(FATAL) << "Not supported";
  }
}

2733 2734 2735
void CpuMatrix::mul(CpuSparseMatrix* a,
                    CpuMatrix* b,
                    real scaleAB,
2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749
                    real scaleT) {
  if (dynamic_cast<CacheRowCpuMatrix*>(b)) {
    return mul(a, dynamic_cast<CacheRowCpuMatrix*>(b), this, scaleAB, scaleT);
  } else if (dynamic_cast<SparseRowCpuMatrix*>(b)) {
    return mul(a, dynamic_cast<SparseRowCpuMatrix*>(b), this, scaleAB, scaleT);
  } else {
    return mul(a, b, this, scaleAB, scaleT);
  }
}

void CpuMatrix::mul(CpuMatrix* a, CpuMatrix* b, real scaleAB, real scaleT) {
  CHECK(!isTransposed()) << "Not supported";

  size_t a_col, b_col, a_row, b_row;
2750
  bool a_trans, b_trans;
2751 2752 2753
  if (!a->isTransposed()) {
    a_col = a->getWidth();
    a_row = a->getHeight();
2754
    a_trans = false;
2755 2756 2757
  } else {
    a_col = a->getHeight();
    a_row = a->getWidth();
2758
    a_trans = true;
2759 2760 2761 2762
  }
  if (!b->isTransposed()) {
    b_col = b->getWidth();
    b_row = b->getHeight();
2763
    b_trans = false;
2764 2765 2766
  } else {
    b_col = b->getHeight();
    b_row = b->getWidth();
2767
    b_trans = true;
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  }

  CHECK_EQ(a_col, b_row);
  CHECK_EQ(a_row, getHeight());
  CHECK_EQ(b_col, getWidth());

  real* A = a->getData();
  real* B = b->getData();
  real* C = getData();

  int M = getHeight();
  int N = getWidth();
  int K = a_col;
  int lda = a->getStride();
  int ldb = b->getStride();
  int ldc = getStride();
2784
  BlasGemm<DEVICE_TYPE_CPU, real>::compute(
2785
      a_trans, b_trans, M, N, K, scaleAB, A, lda, B, ldb, scaleT, C, ldc);
2786 2787
}

2788 2789
void CpuMatrix::mul(
    CpuMatrix* a, CpuMatrix* b, CpuSparseMatrix* c, real scaleAB, real scaleT) {
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  CHECK(!c->isTransposed()) << "Not supported";
  CHECK_EQ(c->getValueType(), FLOAT_VALUE);

  real* A = a->getData();
  real* B = b->getData();
  real* C = c->getValue();
  int* rows = c->getRows();
  int* cols = c->getCols();
  size_t height = c->getHeight();
  size_t width = c->getWidth();
  if (scaleT == 0) {
    c->zeroMem();
  }

  if (!a->isTransposed() && !b->isTransposed()) {
    size_t m = a->getWidth();
    CHECK_EQ(b->getHeight(), m);
    CHECK_EQ(a->getHeight(), height);
    CHECK_EQ(b->getWidth(), width);
    if (c->getFormat() == SPARSE_CSC) {
      for (size_t i = 0; i < width; i++) {
        size_t start = c->getColStartIdx(i);
        size_t end = c->getColStartIdx(i + 1);
        for (size_t j = start; j < end; j++) {
          real sum = 0;
          size_t rowIdx = rows[j];
          for (size_t k = 0; k < m; k++) {
            sum += A[rowIdx * m + k] * B[k * width + i];
          }
          C[j] = scaleAB * sum + scaleT * C[j];
        }
      }
    } else {
      for (size_t i = 0; i < height; i++) {
        size_t start = c->getRowStartIdx(i);
        size_t end = c->getRowStartIdx(i + 1);
        for (size_t j = start; j < end; j++) {
          real sum = 0;
          size_t colIdx = cols[j];
          for (size_t k = 0; k < m; k++) {
            sum += A[i * m + k] * B[k * width + colIdx];
          }
          C[j] = scaleAB * sum + scaleT * C[j];
        }
      }
    }
  } else if (a->isTransposed() && !b->isTransposed()) {
    size_t m = a->getHeight();
    CHECK_EQ(m, b->getHeight());
    CHECK_EQ(b->getWidth(), width);
    CHECK_EQ(a->getWidth(), height);

    if (c->getFormat() == SPARSE_CSC) {
      for (size_t i = 0; i < width; i++) {
        size_t start = c->getColStartIdx(i);
        size_t end = c->getColStartIdx(i + 1);
        for (size_t j = start; j < end; j++) {
          real sum = 0;
          size_t rowIdx = rows[j];
          for (size_t k = 0; k < m; k++) {
            sum += A[k * height + rowIdx] * B[k * width + i];
          }
          C[j] = scaleAB * sum + scaleT * C[j];
        }
      }
    } else {
      for (size_t i = 0; i < height; i++) {
        int start = c->getRowStartIdx(i);
        int end = c->getRowStartIdx(i + 1);
        for (int j = start; j < end; j++) {
          real sum = 0;
          size_t colIdx = cols[j];
          for (size_t k = 0; k < m; k++) {
            sum += A[k * height + i] * B[k * width + colIdx];
          }
          C[j] = scaleAB * sum + scaleT * C[j];
        }
      }
    }
  } else if (!a->isTransposed() && b->isTransposed()) {
    size_t m = a->getWidth();
    CHECK_EQ(b->getWidth(), m);
    CHECK_EQ(a->getHeight(), height);
    CHECK_EQ(b->getHeight(), width);
    if (c->getFormat() == SPARSE_CSR) {
      for (size_t i = 0; i < height; i++) {
        size_t start = c->getRowStartIdx(i);
        size_t end = c->getRowStartIdx(i + 1);
        for (size_t j = start; j < end; j++) {
          real sum = 0;
          size_t colIdx = cols[j];
          for (size_t k = 0; k < m; k++) {
            sum += A[i * m + k] * B[colIdx * m + k];
          }
          C[j] = scaleAB * sum + scaleT * C[j];
        }
      }
    } else {
      LOG(FATAL) << "Not supported csc format "
                    "when a is not trans and b is trans";
    }
  } else {
    LOG(FATAL) << "Not supported";
  }
}

2896 2897 2898
void CpuMatrix::mul(CpuMatrix* a,
                    CpuSparseMatrix* b,
                    real scaleAB,
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                    real scaleT) {
  CHECK(!trans_) << "Not supported";
  CHECK(!a->isTransposed()) << "Not supported";
  CHECK(scaleT == 0 || scaleT == 1);

  // TODO(yuyang18): Maybe bug implementation here
  CHECK_EQ(scaleAB, static_cast<real>(1.0));

  real* A = a->getData();
  real* B = b->getValue();
  real* C = getData();
  int* rows = b->getRows();
  int* cols = b->getCols();

  if (scaleT == 0) {
    zeroMem();
  }
  if (b->getFormat() == SPARSE_CSC) {
    if (!b->isTransposed()) {
      size_t m = a->getWidth();
      CHECK_EQ(b->getHeight(), m);
      CHECK_EQ(a->getHeight(), height_);
      CHECK_EQ(b->getWidth(), width_);

      if (b->getValueType() == NO_VALUE) {
        for (size_t j = 0; j < b->getWidth(); ++j) {
          int start = b->getColStartIdx(j);
          int end = b->getColStartIdx(j + 1);
          for (int i = start; i < end; ++i) {
            colVecAddTo(C + j, A + rows[i], height_, width_, a->getWidth());
          }
        }
      } else if (b->getValueType() == FLOAT_VALUE) {
        for (size_t j = 0; j < b->getWidth(); ++j) {
          int start = b->getColStartIdx(j);
          int end = b->getColStartIdx(j + 1);
          for (int i = start; i < end; ++i) {
2936 2937
            colVecAddTo(
                C + j, A + rows[i], B[i], height_, width_, a->getWidth());
2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958
          }
        }
      }
    } else /*if (b->isTransposed())*/ {
      size_t m = a->getWidth();
      CHECK_EQ(b->getHeight(), width_);
      CHECK_EQ(a->getHeight(), height_);
      CHECK_EQ(b->getWidth(), m);
      if (b->getValueType() == NO_VALUE) {
        for (size_t i = 0; i < b->getWidth(); ++i) {
          int start = b->getColStartIdx(i);
          int end = b->getColStartIdx(i + 1);
          for (int j = start; j < end; ++j) {
            colVecAddTo(C + rows[j], A + i, height_, width_, a->getWidth());
          }
        }
      } else if (b->getValueType() == FLOAT_VALUE) {
        for (size_t i = 0; i < b->getWidth(); ++i) {
          int start = b->getColStartIdx(i);
          int end = b->getColStartIdx(i + 1);
          for (int j = start; j < end; ++j) {
2959 2960
            colVecAddTo(
                C + rows[j], A + i, B[j], height_, width_, a->getWidth());
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          }
        }
      }
    }
  } else {
    if (!b->isTransposed()) {
      size_t m = a->getWidth();
      CHECK_EQ(b->getHeight(), m);
      CHECK_EQ(a->getHeight(), height_);
      CHECK_EQ(b->getWidth(), width_);

      if (b->getValueType() == NO_VALUE) {
        for (size_t j = 0; j < b->getHeight(); ++j) {
          int start = b->getRowStartIdx(j);
          int end = b->getRowStartIdx(j + 1);
          for (int i = start; i < end; ++i) {
            colVecAddTo(C + cols[i], A + j, height_, width_, a->getWidth());
          }
        }
      } else if (b->getValueType() == FLOAT_VALUE) {
        for (size_t j = 0; j < b->getHeight(); ++j) {
          int start = b->getRowStartIdx(j);
          int end = b->getRowStartIdx(j + 1);
          for (int i = start; i < end; ++i) {
2985 2986
            colVecAddTo(
                C + cols[i], A + j, B[i], height_, width_, a->getWidth());
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          }
        }
      }
    } else /*if (b->isTransposed())*/ {
      size_t m = a->getWidth();
      CHECK_EQ(b->getHeight(), width_);
      CHECK_EQ(a->getHeight(), height_);
      CHECK_EQ(b->getWidth(), m);
      if (b->getValueType() == NO_VALUE) {
        for (size_t i = 0; i < b->getHeight(); ++i) {
          int start = b->getRowStartIdx(i);
          int end = b->getRowStartIdx(i + 1);
          for (int j = start; j < end; ++j) {
            colVecAddTo(C + i, A + cols[j], height_, width_, a->getWidth());
          }
        }
      } else if (b->getValueType() == FLOAT_VALUE) {
        for (size_t i = 0; i < b->getHeight(); ++i) {
          int start = b->getRowStartIdx(i);
          int end = b->getRowStartIdx(i + 1);
          for (int j = start; j < end; ++j) {
3008 3009
            colVecAddTo(
                C + i, A + cols[j], B[j], height_, width_, a->getWidth());
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          }
        }
      }
    }
  }
}

void CpuMatrix::selectRows(Matrix& table, IVector& ids) {
  if (dynamic_cast<CacheRowCpuMatrix*>(&table)) {
    selectRowsImp(*dynamic_cast<CacheRowCpuMatrix*>(&table), ids);
  } else if (dynamic_cast<SparseRowCpuMatrix*>(&table)) {
    selectRowsImp(*dynamic_cast<SparseRowCpuMatrix*>(&table), ids);
  } else {
    CHECK(table.isContiguous());
    selectRowsImp(*dynamic_cast<CpuMatrix*>(&table), ids);
  }
}

void CpuMatrix::selectElements(Matrix& table, IVector& ids) {
  CHECK_EQ(table.getHeight(), ids.getSize());
  CHECK_EQ(getHeight(), ids.getSize());
  CHECK_EQ(getWidth(), 1U);
  real* tableData = table.getData();
  int* idsData = ids.getData();
  for (size_t i = 0; i < table.getHeight(); i++) {
    data_[i] += tableData[i * table.getWidth() + idsData[i]];
  }
}

void CpuMatrix::addElements(Matrix& table, IVector& ids) {
  CHECK_EQ(table.getHeight(), ids.getSize());
  CHECK_EQ(getHeight(), ids.getSize());
  CHECK_EQ(getWidth(), 1U);
  real* tableData = table.getData();
  int* idsData = ids.getData();
  for (size_t i = 0; i < table.getHeight(); i++) {
    tableData[i * table.getWidth() + idsData[i]] += data_[i];
  }
}

// this.row[i] += table.row[ids[i]]
template <typename TableMatType>
void CpuMatrix::selectRowsImp(TableMatType& table, IVector& ids) {
  CHECK(!table.useGpu());
  CHECK(!ids.useGpu());
  CHECK_EQ(getHeight(), ids.getSize());
  CHECK_EQ(getWidth(), table.getWidth());
  size_t numSamples = getHeight();
  size_t dim = getWidth();
  real* a = getData();
  size_t tableSize = table.getHeight();
  int* index = ids.getData();

  for (size_t i = 0; i < numSamples; ++i) {
    if (index[i] == -1) continue;
    CHECK_LT(index[i], (int)tableSize);
    CHECK_GE(index[i], 0);
    vecAddTo(a + i * stride_, table.getRow(index[i]), dim);
  }
}

void CpuMatrix::addToRows(Matrix& table, IVector& ids) {
  if (dynamic_cast<CacheRowCpuMatrix*>(&table)) {
    addToRowsImp(*dynamic_cast<CacheRowCpuMatrix*>(&table), ids);
  } else if (dynamic_cast<SparseAutoGrowRowCpuMatrix*>(&table)) {
    addToRowsImp(*dynamic_cast<SparseAutoGrowRowCpuMatrix*>(&table), ids);
  } else if (dynamic_cast<SparseRowCpuMatrix*>(&table)) {
    addToRowsImp(*dynamic_cast<SparseRowCpuMatrix*>(&table), ids);
  } else {
    CHECK(table.isContiguous());
    addToRowsImp(*dynamic_cast<CpuMatrix*>(&table), ids);
  }
}

// table.row[ids[i]] += this.row[i]
template <typename TableMatType>
void CpuMatrix::addToRowsImp(TableMatType& table, IVector& ids) {
  CHECK(!table.useGpu());
  CHECK(!ids.useGpu());
  CHECK_EQ(getHeight(), ids.getSize());
  CHECK_EQ(getWidth(), table.getWidth());
  size_t numSamples = getHeight();
  size_t dim = getWidth();
  real* a = getData();
  size_t tableSize = table.getHeight();
  int* index = ids.getData();

  for (size_t i = 0; i < numSamples; ++i) {
    if (index[i] == -1) continue;
    CHECK_LT(index[i], (int)tableSize);
    CHECK_GE(index[i], 0);
    vecAddTo(table.getRow(index[i]), a + i * stride_, dim);
  }
}

static ThreadLocal<std::vector<const real*>> threadLocalColArray;

template <typename MatBType, typename MatCType>
3108 3109
void CpuMatrix::mul(
    CpuSparseMatrix* a, MatBType* b, MatCType* c, real scaleAB, real scaleT) {
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  CHECK(!c->isTransposed()) << "Not supported";
  CHECK(!b->isTransposed()) << "Not supported";
  // TODO(yuyang18): Maybe bug implementation here.
  CHECK(scaleAB == 1) << "Not supported";
  CHECK(scaleT == 0 || scaleT == 1) << "Not supported";
  CHECK_EQ(a->getFormat(), SPARSE_CSR) << "Not supported";

  real* B = b->getData();
  real* C = c->getData();
  size_t height = c->getHeight();
  size_t width = c->getWidth();
  int* cols = a->getCols();
  real* values = a->getValue();

  if (scaleT == 0) {
    c->zeroMem();
  }

  if (!a->isTransposed()) {
    size_t m = a->getWidth();
    CHECK_EQ(b->getHeight(), m);
    CHECK_EQ(a->getHeight(), height);
    CHECK_EQ(b->getWidth(), width);

    if (a->getValueType() == NO_VALUE) {
      if (width % 32 == 0) {  // use libaddto
        // @TODO(yuyang18) Make input addr can be unaligned.
        // So merge this if and else
        CHECK_EQ((size_t)B % 32, 0UL);
        CHECK_EQ((size_t)C % 32, 0UL);
        auto& colArray = *threadLocalColArray;
        for (size_t i = 0; i < a->getHeight(); ++i) {
          const int start = a->getRowStartIdx(i);
          const int end = a->getRowStartIdx(i + 1);
          size_t colNum = end - start;
          colArray.resize(colNum);
          for (int j = 0; j < end - start; ++j) {
            colArray[j] = b->getRow(cols[j + start]);
          }
          simd::batchAddTo(c->getRow(i), &colArray[0], colNum, width);
        }

      } else {
        for (size_t i = 0; i < a->getHeight(); ++i) {
          const int start = a->getRowStartIdx(i);
          const int end = a->getRowStartIdx(i + 1);
          for (int j = start; j < end; ++j) {
            vecAddTo(c->getRow(i), b->getRow(cols[j]), width);
          }
        }
      }
    } else if (a->getValueType() == FLOAT_VALUE) {
      for (size_t i = 0; i < a->getHeight(); ++i) {
        const int start = a->getRowStartIdx(i);
        const int end = a->getRowStartIdx(i + 1);
        for (int j = start; j < end; ++j) {
          vecAddTo(c->getRow(i), b->getRow(cols[j]), values[j], width);
        }
      }
    }
  } else /*if (a->isTransposed())*/ {
    size_t m = a->getHeight();
    CHECK_EQ(b->getHeight(), m);
    CHECK_EQ(a->getWidth(), height);
    CHECK_EQ(b->getWidth(), width);
    if (a->getValueType() == NO_VALUE) {
      if (width % 32 == 0) {  // use libaddto
        // @TODO(yuyang18) Make input addr can be unaligned.
        // So merge this if and else
        CHECK_EQ((size_t)B % 32, 0UL);
        CHECK_EQ((size_t)C % 32, 0UL);
        for (size_t i = 0; i < a->getHeight(); ++i) {
          const int start = a->getRowStartIdx(i);
          const int end = a->getRowStartIdx(i + 1);
          for (int j = start; j < end; ++j) {
            simd::addTo(c->getRow(cols[j]), b->getRow(i), width);
          }
        }

      } else {
        for (size_t i = 0; i < a->getHeight(); ++i) {
          const int start = a->getRowStartIdx(i);
          const int end = a->getRowStartIdx(i + 1);
          for (int j = start; j < end; ++j) {
            vecAddTo(c->getRow(cols[j]), b->getRow(i), width);
          }
        }
      }
    } else if (a->getValueType() == FLOAT_VALUE) {
      for (size_t i = 0; i < a->getHeight(); ++i) {
        const int start = a->getRowStartIdx(i);
        const int end = a->getRowStartIdx(i + 1);
        for (int j = start; j < end; ++j) {
          vecAddTo(c->getRow(cols[j]), b->getRow(i), values[j], width);
        }
      }
    }
  }
}

// instantiation mul() called in SparseRowMatrix.cpp
template void CpuMatrix::mul<CpuMatrix, SparseRowCpuMatrix>(
3212 3213 3214 3215
    CpuSparseMatrix* a,
    CpuMatrix* b,
    SparseRowCpuMatrix* c,
    real scaleAB,
3216 3217
    real scaleT);
template void CpuMatrix::mul<CpuMatrix, SparseAutoGrowRowCpuMatrix>(
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    CpuSparseMatrix* a,
    CpuMatrix* b,
    SparseAutoGrowRowCpuMatrix* c,
    real scaleAB,
    real scaleT);
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template void CpuMatrix::mul<CpuMatrix, CacheRowCpuMatrix>(CpuSparseMatrix* a,
                                                           CpuMatrix* b,
                                                           CacheRowCpuMatrix* c,
                                                           real scaleAB,
                                                           real scaleT);

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void SharedCpuMatrix::mul(CpuSparseMatrix* a,
                          CpuMatrix* b,
                          real scaleAB,
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                          real scaleT) {
  CHECK(!isTransposed()) << "Not supported";
  CHECK(!b->isTransposed()) << "Not supported";
  CHECK_EQ(scaleAB, 1) << "Not supported";
  CHECK_EQ(scaleT, 1) << "Not supported";
  CHECK_EQ(a->getFormat(), SPARSE_CSR) << "not supported";

  real* B = b->getData();
  real* C = getData();
  size_t height = getHeight();
  size_t width = getWidth();

  // get real trans
  MatrixPtr aTrans;
  if (a->isTransposed()) {
    aTrans = a->getTmpSparseMatrix(a->getWidth(), a->getHeight());
    a->transpose(aTrans, false);
  }
  a = dynamic_cast<CpuSparseMatrix*>(aTrans.get());

  size_t m = a->getWidth();
  CHECK_EQ(b->getHeight(), m);
  CHECK_EQ(a->getHeight(), height);
  CHECK_EQ(b->getWidth(), width);

  size_t blockSize = (height / blockNum_) + 1;
  CpuMatrixPtr localBuf = *localBuf_;
  if (!localBuf) {
    localBuf = std::make_shared<CpuMatrix>(blockSize, width);
  } else {
    localBuf->resize(blockSize, width);
  }
  localBuf->zeroMem();
  real* localC = localBuf->getData();
  std::vector<int>& blockSeq = *blockSeq_;
  if (blockSeq.size() == 0) {
    for (int k = 0; k < blockNum_; ++k) {
      blockSeq.push_back(k);
    }
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    std::shuffle(
        blockSeq.begin(), blockSeq.end(), ThreadLocalRandomEngine::get());
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  }
  std::vector<int>& localBufRows = *localBufRows_;
  int* cols = a->getCols();
  real* value = a->getValue();

  for (int k = 0; k < blockNum_; ++k) {
    int blockId = blockSeq[k];
    size_t blockBegin = blockId * blockSize;
    size_t blockEnd = (blockId + 1) * blockSize;
    if (blockId == blockNum_ - 1) {
      blockEnd = height;
    }
    if (a->getValueType() == NO_VALUE) {
      for (size_t i = blockBegin; i < blockEnd; ++i) {
        int start = a->getRowStartIdx(i);
        int end = a->getRowStartIdx(i);
        size_t colNum = a->getColNum(i);
        if (colNum == 0) {
          continue;
        }  // skip empty row
        localBufRows.push_back(i);
        size_t bufPos = localBufRows.size() - 1;
        for (int j = start; j < end; ++j) {
          vecAddTo(localC + bufPos * width, B + cols[j] * width, width);
        }
      }
    } else if (a->getValueType() == FLOAT_VALUE) {
      for (size_t i = blockBegin; i < blockEnd; ++i) {
        int start = a->getRowStartIdx(i);
        int end = a->getRowStartIdx(i);
        size_t colNum = a->getColNum(i);
        if (colNum == 0) {
          continue;
        }  // skip empty row
        localBufRows.push_back(i);
        size_t bufPos = localBufRows.size() - 1;
        for (int j = start; j < end; ++j) {
3310 3311
          vecAddTo(
              localC + bufPos * width, B + cols[j] * width, value[j], width);
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        }
      }
    }

    {
      std::lock_guard<std::mutex> guard(*blockLocks_[blockId]);
      for (size_t i = 0; i < localBufRows.size(); ++i) {
        vecAddTo(C + localBufRows[i] * width, localC + i * width, width);
      }
    }
    memset(localC, 0, localBufRows.size() * width * sizeof(real));
    localBufRows.clear();
  }

  VLOG(2) << " B[0]=" << B[0] << " B[1]=" << B[1] << " C[0]=" << C[0]
          << " C[1]=" << C[1];
}

void SharedCpuMatrix::add(Matrix& b, real p1, real p2) {
  CHECK_EQ(blockNum_, 1);
  std::lock_guard<std::mutex> guard(*blockLocks_[0]);
  CpuMatrix::add(b, p1, p2);
}

void SharedCpuMatrix::add(real p1, real p2) {
  CHECK_EQ(blockNum_, 1);
  std::lock_guard<std::mutex> guard(*blockLocks_[0]);
  CpuMatrix::add(p1, p2);
}

void SharedCpuMatrix::initShared(int blockNum) {
  CHECK_GT(height_ * width_, 1UL * 1024 * 1024)
      << "should not share small matrix";
  initBlock(blockNum);
}

void SharedCpuMatrix::initBlock(int blockNum) {
  CHECK_LE(blockNum, 200) << "should not use large block number";
  blockNum_ = blockNum;
  blockLocks_.resize(blockNum);
  for (auto& locker : blockLocks_) {
    locker.reset(new std::mutex);
  }
}

/* Add a (column) vector b to matrix a, column by column */
void CpuMatrix::addColumnVector(const Matrix& b) {
  BaseMatrix::addColVector(const_cast<Matrix&>(b));
}

/* this = a*b */
3363
void CpuMatrix::mul(const Matrix& a, const Matrix& b) {
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  return mul(a, b, 1.0, 0.0);
}

/* this = scaleAB*(this*b) +  scaleT*this */
void CpuMatrix::rightMul(Matrix& b, real scaleAB, real scaleT) {
  (void)b;
  (void)scaleAB;
  (void)scaleT;
  LOG(FATAL) << "Not implemented";
}

/* this = this* b */
void CpuMatrix::rightMul(Matrix& b) { return rightMul(b, 1.0, 0.0); }

/* this = scaleAB*(a*this) +  scaleT*this */
void CpuMatrix::leftMul(Matrix& a, real scaleAB, real scaleT) {
  (void)a;
  (void)scaleAB;
  (void)scaleT;
  LOG(FATAL) << "Not implemented";
}

/* this = a*this) */
void CpuMatrix::leftMul(Matrix& a) { return leftMul(a, 1.0, 0.0); }

void CpuMatrix::colMerge(Matrix& src) { src.rowSum(*this); }

void CpuMatrix::rowSum(Matrix& sum) {
  CHECK_EQ(sum.getHeight(), getHeight());
  CHECK_EQ(sum.getWidth(), (size_t)1);

3395
  sum.sumRows(*this, /* scaleSum= */ 1, /* scaleDest= */ 0);
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}

void CpuMatrix::rowMaxId(IVector& maxIds) {
  CHECK(!maxIds.useGpu()) << "Matrix type are not equal";

  size_t numSamples = getHeight();
  CHECK_EQ(maxIds.getSize(), numSamples);

  real* a = getData();
  int* s = maxIds.getData();
  size_t dim = getWidth();

  for (size_t i = 0; i < numSamples; i++) {
    real sm = a[i * dim];
    int maxId = 0;
    for (size_t j = 1; j < dim; j++) {
      if (a[i * dim + j] > sm) {
        maxId = j;
        sm = a[i * dim + j];
      }
    }
    s[i] = maxId;
  }
}

void CpuMatrix::rowMax(Matrix& max) {
  CHECK_EQ(max.getHeight(), getHeight());
  CHECK_EQ(max.getWidth(), (size_t)1);
  max.maxRows(*this);
}

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/* Get the top k elements of each row of this matrix */
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void CpuMatrix::rowMax(IVector& maxIds, Matrix& maxVal) {
  CHECK(isContiguous());
  CHECK(!maxIds.useGpu() && !maxVal.useGpu()) << "Matrix type are not equal";
  size_t numSamples = getHeight();
  size_t beam = maxVal.getWidth();
  CHECK_EQ(maxIds.getSize(), numSamples * beam);
  CHECK_EQ(maxVal.getHeight(), numSamples);
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  CHECK_EQ(maxVal.getWidth(), beam);
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  real* a = getData();
  int* s = maxIds.getData();
  real* t = maxVal.getData();
  size_t dim = getWidth();
  for (size_t i = 0; i < numSamples; i++) {
    std::vector<std::pair<real, size_t>> vec;
    for (size_t j = 0; j < dim; j++) {
      vec.push_back(std::pair<real, size_t>(a[i * dim + j], j));
    }

    std::partial_sort(
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        vec.begin(),
        vec.begin() + beam,
        vec.end(),
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        [](const std::pair<real, size_t>& l, const std::pair<real, size_t>& r) {
          return l.first > r.first;
        });
    for (size_t j = 0; j < beam; j++) {
      t[i * beam + j] = vec[j].first;
      s[i * beam + j] = vec[j].second;
    }
  }
}

void CpuMatrix::colMax(Matrix& max) {
  CHECK_EQ(max.getWidth(), getWidth());
  CHECK_EQ(max.getHeight(), (size_t)1);
  max.maxCols(*this);
}

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void CpuMatrix::colMax(IVector& maxIds, Matrix& maxVal) {
  CHECK(isContiguous());
  CHECK(!maxIds.useGpu() && !maxVal.useGpu()) << "Matrix type are not equal";
  size_t numSamples = getWidth();
  size_t beam = maxVal.getHeight();
  CHECK_EQ(maxIds.getSize(), numSamples * beam);
  CHECK_EQ(maxVal.getWidth(), numSamples);

  real* a = getData();
  int* s = maxIds.getData();
  real* t = maxVal.getData();
  size_t dim = getHeight();
  for (size_t i = 0; i < numSamples; i++) {
    std::vector<std::pair<real, size_t>> vec;
    for (size_t j = 0; j < dim; j++) {
      vec.push_back(std::pair<real, size_t>(a[i + j * numSamples], j));
    }

    std::partial_sort(
3486 3487 3488
        vec.begin(),
        vec.begin() + beam,
        vec.end(),
3489 3490 3491 3492 3493 3494 3495 3496 3497 3498
        [](const std::pair<real, size_t>& l, const std::pair<real, size_t>& r) {
          return l.first > r.first;
        });
    for (size_t j = 0; j < beam; j++) {
      t[i + j * numSamples] = vec[j].first;
      s[i + j * numSamples] = vec[j].second;
    }
  }
}

3499 3500 3501
void CpuMatrix::maxoutForward(Matrix& a,
                              IVector& id,
                              size_t channels,
3502 3503 3504 3505 3506 3507 3508 3509
                              size_t groups) {
  CHECK(dynamic_cast<CpuMatrix*>(&a));
  CHECK(dynamic_cast<CpuIVector*>(&id));
  CHECK_EQ(a.getHeight(), getHeight());

  size_t size = getWidth();
  size_t batchSize = getHeight();
  size_t featLen = size / channels;
3510
  const real* input = a.getData();
3511 3512 3513 3514 3515 3516 3517 3518 3519 3520 3521 3522 3523 3524 3525 3526 3527 3528 3529 3530 3531 3532 3533
  int* idForCpu = id.getData();

  MatrixPtr maxInMat, maxOutMat;
  Matrix::resizeOrCreate(maxInMat, groups, size, false, false);
  Matrix::resizeOrCreate(maxOutMat, 1, size, false, false);

  for (size_t batch_idx = 0; batch_idx < batchSize; ++batch_idx) {
    size_t newIndex = batch_idx * size;
    IVectorPtr tmpId = IVector::create(idForCpu + newIndex, size, false);

    for (size_t i = 0; i < channels; ++i) {
      size_t newFeatLen = i * featLen;
      for (size_t j = 0; j < groups; ++j) {
        maxInMat->subMatrix(j, j + 1, newFeatLen, newFeatLen + featLen)
            ->copyFrom(input + (newIndex + newFeatLen) * groups + j * featLen,
                       featLen);
      }
    }
    maxInMat->colMax(*tmpId, *maxOutMat);
    this->subRowMatrix(batch_idx, batch_idx + 1)->copyFrom(*maxOutMat);
  }
}

3534 3535 3536
void CpuMatrix::maxoutBackward(Matrix& a,
                               IVector& id,
                               size_t channels,
3537 3538 3539 3540 3541 3542 3543 3544 3545
                               size_t groups) {
  CHECK(dynamic_cast<CpuMatrix*>(&a));
  CHECK(dynamic_cast<CpuIVector*>(&id));
  CHECK_EQ(a.getHeight(), getHeight());

  size_t size = a.getWidth();
  size_t batchSize = getHeight();
  size_t featLen = size / channels;
  size_t newFeatLen = groups * featLen;
3546 3547
  real* inputG = getData();
  const real* outG = a.getData();
3548 3549 3550 3551 3552 3553 3554 3555 3556 3557 3558 3559 3560 3561
  int* idForCpu = id.getData();

  for (size_t batch_idx = 0; batch_idx < batchSize; ++batch_idx) {
    size_t newIndex = batch_idx * size;
    int* idData = idForCpu + newIndex;

    for (size_t i = 0; i < size; ++i) {
      int gradIdx =
          idData[i] * featLen + (i / featLen) * newFeatLen + i % featLen;
      (inputG + newIndex * groups)[gradIdx] += (outG + newIndex)[i];
    }
  }
}

3562 3563 3564 3565 3566 3567 3568 3569 3570 3571 3572 3573 3574 3575 3576 3577 3578 3579 3580 3581 3582 3583 3584 3585 3586
void CpuMatrix::rowNormalizeL1(Matrix& out) {
  CHECK(!out.useGpu());

  size_t numSamples = getHeight();
  size_t dim = getWidth();
  CHECK_EQ(out.getHeight(), numSamples);
  CHECK_EQ(out.getWidth(), dim);
  real* a = getData();
  real* b = out.getData();
  for (size_t i = 0; i < numSamples; ++i) {
    real s = 0;
    for (size_t j = 0; j < dim; ++j) {
      s += a[i * dim + j];
    }
    // Right now, we just bet that sum won't be zero. If this really happens,
    // we will figure out what should be done then.
    CHECK_GT(s, 0);
    s = 1 / s;
    for (size_t j = 0; j < dim; ++j) {
      b[i * dim + j] = s * a[i * dim + j];
    }
  }
}

/* calulate classification error */
3587 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600
void CpuMatrix::classificationError(Matrix& output,
                                    IVector& label,
                                    size_t topkSize) {
  size_t numSamples = this->getHeight();
  auto cpuOutput = dynamic_cast<CpuMatrix*>(&output);
  auto cpuLabel = dynamic_cast<CpuIVector*>(&label);
  IVectorPtr cpuTopIds = std::make_shared<CpuIVector>(numSamples * topkSize);
  MatrixPtr cpuTopVal = std::make_shared<CpuMatrix>(numSamples, topkSize);

  CHECK(cpuOutput && cpuLabel) << "Invalid argument pointer";
  CHECK(cpuTopIds && cpuTopVal) << "Allocate cpu memory failed";
  CHECK(cpuLabel->getSize() == numSamples) << "Vector size is not equal";
  CHECK(cpuOutput->getHeight() == numSamples && this->getWidth() == 1)
      << "Matrix dimensions are not equal";
3601

3602 3603
  // top k matrix classification
  cpuOutput->rowMax(*cpuTopIds, *cpuTopVal);
3604

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  size_t dim = cpuOutput->getWidth();
  real* result = this->getData();
  int* ids = cpuTopIds->getData();
  int* lbl = cpuLabel->getData();
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  for (size_t i = 0; i < numSamples; ++i) {
    CHECK_GE(lbl[i], 0);
    CHECK_LT((size_t)lbl[i], dim);
3612 3613 3614 3615 3616

    for (size_t j = 0; j < topkSize; ++j) {
      if (ids[j + i * topkSize] == lbl[i]) {
        result[i] = 0;
        break;
3617
      }
3618
      result[i] = 1.0f;
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    }
  }
}

/* copy -log(output[label]) to this->data[i] */
void CpuMatrix::oneHotCrossEntropy(Matrix& output, IVector& label) {
  CHECK(dynamic_cast<CpuMatrix*>(&output));
  CHECK(dynamic_cast<CpuIVector*>(&label));

  size_t numSamples = getHeight();
  size_t dim = output.getWidth();
  CHECK_EQ(label.getSize(), numSamples);
  CHECK_EQ(output.getHeight(), numSamples);
  CHECK_EQ(getWidth(), (size_t)1);

  real* out = output.getData();
  real* cost = getData();
  int* lbl = label.getData();
  for (size_t i = 0; i < numSamples; ++i, out += dim) {
    CHECK_GE(lbl[i], 0);
    CHECK_LT((size_t)lbl[i], dim);
    cost[i] = -std::log(out[lbl[i]]);
  }
}

/* calculate the error of outputV according to label */
void CpuMatrix::oneHotCrossEntropyBp(Matrix& output, IVector& label) {
  CHECK(dynamic_cast<CpuMatrix*>(&output));
  CHECK(dynamic_cast<CpuIVector*>(&label));
  size_t numSamples = getHeight();
  size_t dim = getWidth();
  CHECK_EQ(output.getWidth(), dim);
  real* out = output.getData();
  real* grad = getData();
  int* lbl = label.getData();
  for (size_t i = 0; i < numSamples; ++i, out += dim, grad += dim) {
    grad[lbl[i]] -= 1 / out[lbl[i]];
  }
}

/*
    We implement the matrix functionality in CostLayer.cpp,
    but we define the scalar function here for sanity check
    deletion of the function does not affect anything neverthelss
*/
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void CpuMatrix::oneHotCrossEntropyWithSelfNorm(Matrix& output,
                                               IVector& label,
3666 3667 3668 3669 3670 3671 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683 3684 3685 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695
                                               real alpha) {
  CHECK(dynamic_cast<CpuMatrix*>(&output));
  CHECK(dynamic_cast<CpuIVector*>(&label));

  size_t numSamples = getHeight();
  size_t dim = output.getWidth();
  CHECK_EQ(label.getSize(), numSamples);
  CHECK_EQ(output.getHeight(), numSamples);
  CHECK_EQ(getWidth(), (size_t)1);

  real* out = output.getData();
  real* cost = getData();
  int* lbl = label.getData();
  for (size_t i = 0; i < numSamples; ++i, out += dim) {
    CHECK_GE(lbl[i], 0);
    CHECK_LT((size_t)lbl[i], dim);
    real sum = 0;
    for (size_t j = 0; j < dim; ++j) {
      sum += out[j];
    }
    sum = _safelog(sum);
    cost[i] = -_safelog(out[lbl[i]]) + sum + alpha * _square(sum);
  }
}

/*
    We implement the matrix functionality in CostLayer.cpp,
    but we define the scalar function here for sanity check
    deletion of the function does not affect anything neverthelss
*/
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void CpuMatrix::oneHotCrossEntropyWithSelfNormBp(Matrix& output,
                                                 IVector& label,
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                                                 real alpha) {
  CHECK(dynamic_cast<CpuMatrix*>(&output));
  CHECK(dynamic_cast<CpuIVector*>(&label));
  size_t numSamples = getHeight();
  size_t dim = getWidth();
  CHECK_EQ(output.getWidth(), dim);
  real* out = output.getData();
  real* grad = getData();
  int* lbl = label.getData();

  for (size_t i = 0; i < numSamples; ++i, out += dim, grad += dim) {
    grad[lbl[i]] -= 1 / out[lbl[i]];
    real sum = 0;
    for (size_t j = 0; j < dim; ++j) {
      sum += out[j];
    }
    for (size_t j = 0; j < dim; ++j) {
      if (j == (size_t)lbl[i]) {
        grad[j] += -1 / out[j];
      }
      grad[j] += 1 / sum + 2 * alpha * _safelog(sum) / sum;
    }
  }
}

#define FORWARD_LOOP()                      \
  size_t numSamples = getHeight();          \
  size_t dim = getWidth();                  \
  CHECK_EQ(output.getHeight(), numSamples); \
  CHECK_EQ(output.getWidth(), dim);         \
  const real* in = getData();               \
  real* out = output.getData();             \
  for (size_t i = 0; i < numSamples; ++i, in += dim, out += dim)

#define BACKWARD_LOOP()                     \
  size_t numSamples = getHeight();          \
  size_t dim = getWidth();                  \
  CHECK_EQ(output.getHeight(), numSamples); \
  CHECK_EQ(output.getWidth(), dim);         \
  real* grad = getData();                   \
  real* out = output.getData();             \
  for (size_t i = 0; i < numSamples; ++i, grad += dim, out += dim)

void CpuMatrix::softmax(Matrix& output) {
  CHECK(!output.useGpu());

  const float THRESHOLD = -64.0;

  FORWARD_LOOP() {
    real max = -1.0e20;
    for (size_t j = 0; j < dim; ++j) {
      if (in[j] > max) {
        max = in[j];
      }
    }
    for (size_t j = 0; j < dim; ++j) {
      real a = in[j] - max;
      if (a < THRESHOLD) {
        a = THRESHOLD;
      }
      out[j] = a;
    }
    vExp(dim, out, out);

    real sum = 0;
    for (size_t j = 0; j < dim; ++j) {
      sum += out[j];
    }
    sum = 1 / sum;
    for (size_t j = 0; j < dim; ++j) {
      out[j] *= sum;
    }
  }
}

void CpuMatrix::sequenceSoftmax(Matrix& output, const IVector& index) {
  CHECK_EQ(getWidth(), 1UL);
  CHECK_EQ(output.getWidth(), 1UL);
  CHECK(isContiguous());

3778 3779 3780 3781 3782 3783 3784 3785 3786 3787
  MatrixPtr inTmp = Matrix::create(nullptr,
                                   /* height= */ 1,
                                   1,
                                   /* trans= */ false,
                                   false);
  MatrixPtr outTmp = Matrix::create(nullptr,
                                    /* height= */ 1,
                                    1,
                                    /* trans= */ false,
                                    false);
3788 3789 3790 3791 3792 3793 3794 3795 3796 3797 3798 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 3809 3810 3811 3812 3813 3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825 3826 3827 3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839
  size_t numSequences = index.getSize() - 1;
  auto starts = index.getData();
  for (size_t i = 0; i < numSequences; ++i) {
    size_t offset = starts[i];
    size_t size = starts[i + 1] - starts[i];
    inTmp->setData(getData() + offset, 1UL, size);
    outTmp->setData(output.getData() + offset, 1UL, size);
    inTmp->softmax(*outTmp);
  }
}

void CpuMatrix::softmaxDerivative(Matrix& output, Matrix& sftmaxSum) {
  CHECK(output.useGpu_ == false) << "Matrix type are not equal";
  CHECK_EQ(getHeight(), sftmaxSum.getHeight());

  real* sums = sftmaxSum.getData();

  BACKWARD_LOOP() {
    real sum = sums[i];
    for (size_t j = 0; j < dim; ++j) {
      grad[j] = out[j] * (grad[j] - sum);
    }
  }
}

void CpuMatrix::sumOfSquares(Matrix& output, Matrix& label) {
  CHECK(output.useGpu_ == false && label.useGpu_ == false)
      << "Matrix type are not equal";

  size_t numSamples = getHeight();
  size_t dim = output.getWidth();
  CHECK_EQ(label.getHeight(), numSamples);
  CHECK_EQ(output.getHeight(), numSamples);
  CHECK_EQ(label.getWidth(), dim);
  CHECK_EQ(getWidth(), (size_t)1);
  real* out = output.getData();
  real* cost = getData();

  auto labelptr = dynamic_cast<CpuSparseMatrix*>(&label);
  if (labelptr) {
    // it is a CpuSparseMatrix
    if (labelptr->getFormat() == SPARSE_CSR) {
      // treat label as a SparseMatrix
      for (size_t i = 0; i < numSamples; ++i) {
        for (size_t j = 0; j < dim; ++j) {
          cost[i] += _square(out[i * dim + j]);
        }
      }
      if (labelptr->getValueType() == NO_VALUE) {
        int* cols = labelptr->getCols();
        for (size_t i = 0; i < numSamples; ++i) {
          for (size_t j = labelptr->getRowStartIdx(i);
3840 3841
               j < labelptr->getRowStartIdx(i + 1);
               ++j) {
3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852 3853 3854 3855 3856
            cost[i] += 1.0 - 2.0 * out[i * dim + cols[j]];
            /*
             * explanation of above line: original codes are follows:
             * cost[i] -= _square(out[i * dim + feature.col]);
             * cost[i] += _square(1.0 - out[i * dim + feature.col]);
             */
          }
        }
      } else if (labelptr->getValueType() == FLOAT_VALUE) {
        int* cols = labelptr->getCols();
        real* values = labelptr->getValue();
        for (size_t i = 0; i < numSamples; ++i) {
          real sum1 = 0;
          real sum2 = 0;
          for (size_t j = labelptr->getRowStartIdx(i);
3857 3858
               j < labelptr->getRowStartIdx(i + 1);
               ++j) {
3859 3860 3861 3862 3863 3864 3865 3866 3867 3868 3869 3870 3871 3872 3873 3874 3875 3876 3877 3878 3879
            sum1 += values[j] * values[j];
            sum2 += values[j] * out[i * dim + cols[j]];
            /*
             * explanation of above line: original codes are follows:
             * cost[i] -= _square(out[i * dim + feature.col]);
             * cost[i] += _square(value.col - out[i * dim + feature.col]);
             */
          }
          cost[i] += sum1 - 2.0 * sum2;
        }
      } else {
        LOG(FATAL) << "unsupported sparse matrix value type in sumOfSquares";
        return;
      }
      return;
    } else {
      LOG(FATAL) << "unsupported sparse matrix format in sumOfSquares";
      return;
    }
  }

3880 3881 3882 3883
  BaseMatrix::sumOfSquaredDiffs(output,
                                label,
                                /* scaleSum= */ 1,
                                /* scaleDest= */ 1);
3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 3894 3895 3896 3897 3898 3899 3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 3912
}

/* calculate the error of outputV according to label */
void CpuMatrix::sumOfSquaresBp(Matrix& output, Matrix& label) {
  CHECK(output.useGpu_ == false && label.useGpu_ == false)
      << "Matrix type are not equal";

  size_t numSamples = getHeight();
  size_t dim = getWidth();
  CHECK_EQ(output.getWidth(), dim);
  CHECK_EQ(label.getWidth(), dim);

  real* out = output.getData();
  real* grad = getData();

  auto labelptr = dynamic_cast<CpuSparseMatrix*>(&label);
  if (labelptr) {
    // it is a CpuSparseMatrix
    if (labelptr->getFormat() == SPARSE_CSR) {
      // treat label as a SparseMatrix
      for (size_t i = 0; i < numSamples; ++i) {
        for (size_t j = 0; j < dim; ++j) {
          grad[i * dim + j] += 2.0 * out[i * dim + j];
        }
      }
      if (labelptr->getValueType() == NO_VALUE) {
        int* cols = labelptr->getCols();
        for (size_t i = 0; i < numSamples; ++i) {
          for (size_t j = labelptr->getRowStartIdx(i);
3913 3914
               j < labelptr->getRowStartIdx(i + 1);
               ++j) {
3915 3916 3917 3918 3919 3920 3921 3922 3923 3924 3925 3926 3927 3928
            grad[i * dim + cols[j]] -= 2.0;
            /*
             * explanation of above line: original codes are follows:
             * grad[i * dim + feature.col] -= 2.0 * out[i * dim + feature.col];
             * grad[i * dim + feature.col] += 2.0 * (out[i * dim + feature.col]
             * - 1);
             */
          }
        }
      } else if (labelptr->getValueType() == FLOAT_VALUE) {
        int* cols = labelptr->getCols();
        real* values = labelptr->getValue();
        for (size_t i = 0; i < numSamples; ++i) {
          for (size_t j = labelptr->getRowStartIdx(i);
3929 3930
               j < labelptr->getRowStartIdx(i + 1);
               ++j) {
3931 3932 3933 3934 3935 3936 3937 3938 3939 3940 3941 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 3954 3955 3956 3957 3958 3959 3960 3961 3962 3963
            grad[i * dim + cols[j]] -= 2.0 * values[j];
            /*
             * explanation of above line: original codes are follows:
             * grad[i * dim + feature.col] -= 2.0 * out[i * dim + feature.col];
             * grad[i * dim + feature.col] += 2.0 * (out[i * dim + feature.col]
             * - value.col);
             */
          }
        }
      } else {
        LOG(FATAL) << "unsupported sparse matrix value type in sumOfSquares";
        return;
      }
      return;
    } else {
      LOG(FATAL) << "unsupported sparse matrix format in sumOfSquares";
      return;
    }
  }

  real* lbl = label.getData();
  size_t ld = getStride();
  size_t outLd = output.getStride();
  size_t lblLd = label.getStride();
  CHECK(lbl);
  for (size_t i = 0; i < numSamples;
       ++i, out += outLd, lbl += lblLd, grad += ld) {
    for (size_t j = 0; j < dim; ++j) {
      grad[j] += 2.0 * (out[j] - lbl[j]);  // positive gradient;
    }
  }
}

3964
void CpuMatrix::smoothL1(Matrix& output, Matrix& label, real destScale) {
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  CHECK(output.useGpu_ == false && label.useGpu_ == false)
      << "Matrix type are not equal";

  size_t numSamples = getHeight();
  size_t dim = output.getWidth();
  CHECK_EQ(label.getHeight(), numSamples);
  CHECK_EQ(output.getHeight(), numSamples);
  CHECK_EQ(label.getWidth(), dim);
  CHECK_EQ(getWidth(), (size_t)1);
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  real* cost = getData();
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  real* out = output.getData();
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  real* lbl = label.getData();

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  for (size_t i = 0; i < numSamples; ++i, out += dim, lbl += dim) {
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    for (size_t j = 0; j < dim; ++j) {
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      real absVal = std::fabs(out[j] - lbl[j]);
3982
      cost[i] *= destScale;
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      if (absVal < 1.0)
        cost[i] += 0.5 * absVal * absVal;
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      else
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        cost[i] += absVal - 0.5;
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    }
  }
}

3991
void CpuMatrix::smoothL1Bp(Matrix& output, Matrix& label, real destScale) {
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  CHECK(output.useGpu_ == false && label.useGpu_ == false)
      << "Matrix type are not equal";

  size_t numSamples = getHeight();
  size_t dim = output.getWidth();
  CHECK_EQ(label.getHeight(), numSamples);
  CHECK_EQ(output.getHeight(), numSamples);
  CHECK_EQ(label.getWidth(), dim);
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  CHECK_EQ(getWidth(), dim);

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  real* out = output.getData();
  real* lbl = label.getData();
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  real* grad = getData();
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  for (size_t i = 0; i < numSamples; ++i, out += dim, grad += dim, lbl += dim) {
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    for (size_t j = 0; j < dim; ++j) {
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      real val = out[j] - lbl[j];
4009
      grad[j] *= destScale;
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      if (std::fabs(val) < 1) {
        grad[j] += val;
      } else {
        grad[j] += (real(0) < val) - (val < real(0));
      }
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    }
  }
}

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void CpuMatrix::tanh(Matrix& output) {
  CHECK(isContiguous());
  CHECK(output.isContiguous());
  size_t numSamples = getHeight();
  size_t dim = getWidth();
  CHECK_EQ(output.getHeight(), numSamples);
  CHECK_EQ(output.getWidth(), dim);
  vTanh(numSamples * dim, getData(), output.getData());
}

void CpuMatrix::tanhDerivative(Matrix& output) {
  BaseMatrix::tanhDerivative(output);
}

void CpuMatrix::softrelu(Matrix& output) {
  CHECK(isContiguous());
  CHECK(output.isContiguous());
  const real THRESHOLD = 40.0;
  FORWARD_LOOP() {  // TODO(yuyang18): SIMD it?
    for (size_t j = 0; j < dim; ++j) {
      real x = in[j];
      if (x > THRESHOLD) {
        x = THRESHOLD;
      } else if (x < -THRESHOLD) {
        x = -THRESHOLD;
      }
      out[j] = x;
    }
  }
  vExp(numSamples * dim, output.getData(), output.getData());
  vLog1p(numSamples * dim, output.getData(), output.getData());
}

void CpuMatrix::softreluDerivative(Matrix& output) {
  CHECK(isContiguous());
  CHECK(output.isContiguous());
  size_t numSamples = getHeight();
  size_t dim = getWidth();
  size_t size = numSamples * dim;
  CHECK_EQ(output.getHeight(), numSamples);
  CHECK_EQ(output.getWidth(), dim);
  real* grad = getData();
  MatrixPtr tmpMat = Matrix::create(numSamples, dim);
  real* tmp = tmpMat->getData();

  vExp(size, output.getData(), tmpMat->getData());

  for (size_t i = 0; i < size; ++i) {
    grad[i] *= (1.0 - 1.0 / tmp[i]);
  }
}

void CpuMatrix::scaledTanh(Matrix& output, real p1, real p2) {
  CHECK(isContiguous());
  CHECK(output.isContiguous());
  size_t numSamples = getHeight();
  size_t dim = getWidth();
  CHECK_EQ(output.getHeight(), numSamples);
  CHECK_EQ(output.getWidth(), dim);

  const real* in = getData();
  real* out = output.getData();

  // out = p2*in
  for (size_t i = 0; i < numSamples * dim; ++i) {
    out[i] = p2 * in[i];
  }

  vTanh(numSamples * dim, out, out);

  // out = p1 * out
  for (size_t i = 0; i < numSamples * dim; ++i) {
    out[i] = p1 * out[i];
  }
}

/* uniform randomization, minimize precision = 1e-5 */
void CpuMatrix::randomizeUniform() {
  CHECK(isContiguous());
  real* data = getData();
  unsigned int* randSeed = ThreadLocalRand::getSeed();
  real recipRandMax = 1.0f / (real)RAND_MAX;
  for (size_t i = 0; i < elementCnt_; ++i) {
    *data++ = rand_r(randSeed) * recipRandMax;
  }
}

void CpuMatrix::print(std::ostream& os) const {
  CHECK(isContiguous());
  for (size_t i = 0; i < height_; ++i) {
    for (size_t j = 0; j < width_; ++j) {
      os << data_[i * width_ + j] << " ";
    }
    os << std::endl;
  }
}

void CpuMatrix::paramReluForward(Matrix& data, Matrix& W) {
  real* input = data.getData();
  real* w = W.getData();
  size_t numElements = data.getWidth();
  size_t numSamples = data.getHeight();
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  size_t paraSize = W.getHeight() * W.getWidth();
  CHECK(!(numElements % paraSize));  // this check from ParameterReluLayer::init
  size_t partial_sum = numElements / paraSize;
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  for (size_t n = 0, k = 0; n < numSamples; ++n) {
    for (size_t i = 0; i < numElements; ++i, ++k) {
      data_[k] = input[k] > 0 ? input[k] : input[k] * w[i / partial_sum];
    }
  }
}

void CpuMatrix::paramReluBackwardW(Matrix& oGrad, Matrix& data) {
  real* ograd = oGrad.getData();
  real* input = data.getData();
  real* wgrad = data_;
  size_t numElements = data.getWidth();
  size_t numSamples = data.getHeight();
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  size_t paraSize = this->getHeight() * this->getWidth();
  CHECK(!(numElements % paraSize));  // this check from ParameterReluLayer::init
  size_t partial_sum = numElements / paraSize;
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  for (size_t n = 0, k = 0; n < numSamples; ++n) {
    for (size_t i = 0; i < numElements; ++i, ++k) {
      wgrad[i / partial_sum] += ograd[k] * (input[k] > 0 ? 0 : input[k]);
    }
  }
}

void CpuMatrix::paramReluBackwardDiff(Matrix& oGrad, Matrix& data, Matrix& W) {
  real* diff = data_;
  real* input = data.getData();
  real* ograd = oGrad.getData();
  real* w = W.getData();
  size_t numElements = data.getWidth();
  size_t numSamples = data.getHeight();
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  size_t paraSize = W.getHeight() * W.getWidth();
  CHECK(!(numElements % paraSize));  // this check from ParameterReluLayer::init
  size_t partial_sum = numElements / paraSize;
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  for (size_t n = 0, k = 0; n < numSamples; ++n) {
    for (size_t i = 0; i < numElements; ++i, ++k) {
      diff[k] += ograd[k] * (input[k] > 0 ? 1 : w[i / partial_sum]);
    }
  }
}

void CpuMatrix::print(std::ostream& os, size_t height, size_t width) const {
  CHECK(isContiguous());
  size_t h = height_ < height ? height_ : height;
  size_t w = width_ < width ? width_ : width;
  os.setf(std::ostream::scientific);
  os << "[";
  for (size_t i = 0; i < h; ++i) {
    for (size_t j = 0; j < w; ++j) {
      os << data_[i * width_ + j] << " ";
    }
    if (i == h - 1) {
      os << "]";
    }
    os << std::endl;
  }
}

void CpuMatrix::printOneRow(std::ostream& os, size_t idx) const {
  CHECK_LT(idx, height_);
  size_t offset = idx * stride_;
  os << data_[offset];
  for (size_t i = 1; i < width_; ++i) {
    os << " " << data_[offset + i];
  }
  os << ";";
}

void CpuMatrix::check(std::ostream& os, Matrix& refMat, bool printDiff) {
  CHECK(isContiguous());
  CHECK(height_ == refMat.getHeight());
  CHECK(width_ == refMat.getWidth());
  CpuMatrix cpuRef(height_, width_);
  cpuRef.copyFrom(refMat);
  size_t diffCnt = 0;
  for (size_t i = 0; i < height_; ++i) {
    for (size_t j = 0; j < width_; ++j) {
      real a = getElement(i, j);
      real b = cpuRef.getElement(i, j);
      if (fabs(a - b) > 0.00001) {
        ++diffCnt;
        if (printDiff) {
          os << "ref= " << a << "  check= " << b << std::endl;
        }
      }
    }
  }
  LOG(INFO) << "the  diffCnt is " << diffCnt;
}

real CpuMatrix::getMin() {
  size_t size = getHeight() * getWidth();
  real* data = getData();
  real res = data[0];
  for (size_t i = 1; i < size; ++i) {
    if (res > data[i]) {
      res = data[i];
    }
  }
  return res;
}

real CpuMatrix::getMax() {
  size_t size = getHeight() * getWidth();
  real* data = getData();
  real res = data[0];
  for (size_t i = 1; i < size; ++i) {
    if (res < data[i]) {
      res = data[i];
    }
  }
  return res;
}

void CpuMatrix::circularConv(Matrix& in0, Matrix& in1) {
  size_t height = this->getHeight();
  size_t width0 = this->getWidth();
  size_t width1 = in1.getWidth();

  CHECK_EQ(height, in0.getHeight());
  CHECK_EQ(width0, in0.getWidth());
  CHECK_EQ(height, in1.getHeight());

  CHECK_EQ(width1 % 2, 1U);

  real* outV = this->getData();
  real* inV0 = in0.getData();
  real* inV1 = in1.getData();

  int leftCtxLen = (width1 - 1) / 2;
  for (size_t x = 0; x < height;
       ++x, outV += width0, inV0 += width0, inV1 += width1) {
    for (size_t i = 0; i < width0; ++i) {  // each dimension of output
      for (size_t j = 0; j < width1; ++j) {
        // iterate over all dimentions of inV1
        int index = i + j - leftCtxLen;
        index = (index + width0) % width0;
        outV[i] += inV0[index] * inV1[j];
      }
    }
  }
}

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void CpuMatrix::circularConvDerivative(
    Matrix& outG, Matrix& in0, Matrix& in1, Matrix& inG0, Matrix& inG1) {
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  size_t height = in0.getHeight();
  size_t width0 = in0.getWidth();
  size_t width1 = in1.getWidth();

  CHECK_EQ(height, in1.getHeight());
  CHECK_EQ(height, inG0.getHeight());
  CHECK_EQ(width0, inG0.getWidth());
  CHECK_EQ(height, inG1.getHeight());
  CHECK_EQ(width1, inG1.getWidth());
  CHECK_EQ(height, outG.getHeight());
  CHECK_EQ(width0, outG.getWidth());

  real* outGV = outG.getData();
  real* inV0 = in0.getData();
  real* inV1 = in1.getData();
  real* inGV0 = inG0.getData();
  real* inGV1 = inG1.getData();

  int leftCtxLen = (width1 - 1) / 2;
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  for (size_t x = 0; x < height; ++x,
              outGV += width0,
              inV0 += width0,
              inV1 += width1,
              inGV0 += width0,
              inGV1 += width1) {
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    for (size_t j = 0; j < width1; ++j) {  // iterate over width1
      for (size_t i = 0; i < width0; ++i) {
        // such over all dimensions of outG
        int index = i + j - leftCtxLen;
        index = (index + width0) % width0;
        inGV0[index] += outGV[i] * inV1[j];
        inGV1[j] += outGV[i] * inV0[index];
      }
    }
  }
}

void CpuMatrix::multiBinaryLabelCrossEntropy(Matrix& output, Matrix& label) {
  CHECK(dynamic_cast<CpuMatrix*>(&output));
  auto labelPtr = dynamic_cast<CpuSparseMatrix*>(&label);
  CHECK(labelPtr);

  size_t numSamples = getHeight();
  size_t dim = output.getWidth();
  CHECK_EQ(numSamples, output.getHeight());
  CHECK_EQ(numSamples, labelPtr->getHeight());
  CHECK_EQ(dim, labelPtr->getWidth());

  real* out = output.getData();
  real* cost = getData();
  for (size_t i = 0; i < numSamples; ++i, out += dim) {
    for (size_t j = 0; j < dim; ++j) {
      CHECK(out[j] > 0 && out[j] < 1.0);
      cost[i] -= std::log(1 - out[j]);
    }

    const int* cols = labelPtr->getRowCols(i);
    for (size_t j = 0; j < labelPtr->getColNum(i); ++j) {
      CHECK_LT(size_t(cols[j]), dim);
      cost[i] -= std::log(out[cols[j]] / (1 - out[cols[j]]));
    }
  }
}

void CpuMatrix::multiBinaryLabelCrossEntropyBp(Matrix& output, Matrix& label) {
  CHECK(dynamic_cast<CpuMatrix*>(&output));
  auto labelPtr = dynamic_cast<CpuSparseMatrix*>(&label);
  CHECK(labelPtr);

  size_t numSamples = getHeight();
  size_t dim = getWidth();
  CHECK_EQ(numSamples, output.getHeight());
  CHECK_EQ(numSamples, labelPtr->getHeight());
  CHECK_EQ(dim, output.getWidth());
  CHECK_EQ(dim, labelPtr->getWidth());

  real* out = output.getData();
  real* grad = getData();
  for (size_t i = 0; i < numSamples; ++i, out += dim, grad += dim) {
    for (size_t j = 0; j < dim; ++j) {
      CHECK(out[j] > 0 && out[j] < 1.0);
      grad[j] += 1.0 / (1 - out[j]);
    }

    const int* cols = labelPtr->getRowCols(i);
    for (size_t j = 0; j < labelPtr->getColNum(i); ++j) {
      CHECK_LT(size_t(cols[j]), dim);
      grad[cols[j]] -= 1.0 / (out[cols[j]] * (1 - out[cols[j]]));
    }
  }
}

/* calculate the classification error for multi binary label */
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void CpuMatrix::classificationErrorMulti(Matrix& output,
                                         Matrix& label,
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                                         real threshold) {
  CHECK(dynamic_cast<CpuMatrix*>(&output));
  auto labelPtr = dynamic_cast<CpuSparseMatrix*>(&label);
  CHECK(labelPtr);

  size_t numSamples = getHeight();
  size_t dim = output.getWidth();
  CHECK_EQ(numSamples, output.getHeight());
  CHECK_EQ(numSamples, labelPtr->getHeight());
  CHECK_EQ(dim, labelPtr->getWidth());

  real* out = output.getData();
  real* result = getData();
  for (size_t i = 0; i < numSamples; ++i, out += dim) {
    real sum = 0.0;
    for (size_t j = 0; j < dim; ++j) {
      if (out[j] >= threshold) {
        sum += 1.0;
      }
    }

    const int* cols = labelPtr->getRowCols(i);
    for (size_t j = 0; j < labelPtr->getColNum(i); ++j) {
      CHECK_LT(size_t(cols[j]), dim);
      if (out[cols[j]] < threshold) {
        sum += 1.0;
      } else {
        sum -= 1.0;
      }
    }
    result[i] = sum / dim;
  }
}

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void CpuMatrix::bilinearForward(const Matrix& in,
                                const size_t inImgH,
                                const size_t inImgW,
                                const size_t outImgH,
                                const size_t outImgW,
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                                const size_t numChannels,
                                const real ratioH,
                                const real ratioW) {
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  CHECK(dynamic_cast<const CpuMatrix*>(&in));

  size_t outputW = getWidth();
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  size_t batchSize = getHeight();
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  size_t inputW = in.getWidth();
  size_t inputH = in.getHeight();
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  size_t inPosOffset = inImgH * inImgW;
  size_t outPosOffset = outImgH * outImgW;
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  (void)(inputH);
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  real* outData = getData();
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  const real* inData = in.getData();
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  if (inImgH == outImgH && inImgW == outImgW) {
    this->copyFrom(in);
  } else {
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    for (size_t k = 0; k < batchSize; ++k) {  // loop for batches
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      for (size_t i = 0; i < outImgH; ++i) {  // loop for images
        size_t h = ratioH * i;
        size_t hid = (h < inImgH - 1) ? 1 : 0;
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        real h1lambda = ratioH * i - h;
        real h2lambda = 1 - h1lambda;
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        for (size_t j = 0; j < outImgW; ++j) {
          size_t w = ratioW * j;
          size_t wid = (w < inImgW - 1) ? 1 : 0;
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          real w1lambda = ratioW * j - w;
          real w2lambda = 1 - w1lambda;
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          // calculate four position for bilinear interpolation
          const real* inPos = &inData[k * inputW + h * inImgW + w];
          real* outPos = &outData[k * outputW + i * outImgW + j];
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          for (size_t c = 0; c < numChannels; ++c) {  // loop for channels
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            // bilinear interpolation
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            outPos[0] =
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                h2lambda * (w2lambda * inPos[0] + w1lambda * inPos[wid]) +
                h1lambda * (w2lambda * inPos[hid * inImgW] +
                            w1lambda * inPos[hid * inImgW + wid]);
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            inPos += inPosOffset;
            outPos += outPosOffset;
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          }
        }
      }
    }
  }
}

void CpuMatrix::bilinearBackward(const Matrix& out,
                                 const size_t outImgH,
                                 const size_t outImgW,
                                 const size_t inImgH,
                                 const size_t inImgW,
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                                 const size_t numChannels,
                                 const real ratioH,
                                 const real ratioW) {
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  CHECK(dynamic_cast<const CpuMatrix*>(&out));

  size_t inputW = getWidth();
  size_t inputH = getHeight();
  size_t outputW = out.getWidth();
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  size_t batchSize = out.getHeight();
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  size_t inPosOffset = inImgH * inImgW;
  size_t outPosOffset = outImgH * outImgW;
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  (void)(inputH);
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  real* inGrad = getData();
  const real* outGrad = out.getData();

  if (inImgH == outImgH && inImgW == outImgW) {
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    this->add(const_cast<Matrix&>(out));
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  } else {
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    for (size_t k = 0; k < batchSize; ++k) {  // loop for batches
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      for (size_t i = 0; i < outImgH; ++i) {  // loop for images
        size_t h = ratioH * i;
        size_t hid = (h < inImgH - 1) ? 1 : 0;
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        real h1lambda = ratioH * i - h;
        real h2lambda = 1 - h1lambda;
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        for (size_t j = 0; j < outImgW; ++j) {
          size_t w = ratioW * j;
          size_t wid = (w < inImgW - 1) ? 1 : 0;
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          real w1lambda = ratioW * j - w;
          real w2lambda = 1 - w1lambda;
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          real* inPos = &inGrad[k * inputW + h * inImgW + w];
          const real* outPos = &outGrad[k * outputW + i * outImgW + j];
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          for (size_t c = 0; c < numChannels; ++c) {  // loop for channels
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            inPos[0] += h2lambda * w2lambda * outPos[0];
            inPos[wid] += h2lambda * w1lambda * outPos[0];
            inPos[hid * inImgW] += h1lambda * w2lambda * outPos[0];
            inPos[hid * inImgW + wid] += h1lambda * w1lambda * outPos[0];
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            inPos += inPosOffset;
            outPos += outPosOffset;
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          }
        }
      }
    }
  }
}

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void CpuMatrix::vol2Col(real* data,
                        int channels,
                        int depth,
                        int height,
                        int width,
                        int filterD,
                        int filterH,
                        int filterW,
                        int strideD,
                        int strideH,
                        int strideW,
                        int paddingD,
                        int paddingH,
                        int paddingW) {
  real* outData = getData();
  int outHeight = (height + 2 * paddingH - filterH) / strideH + 1;
  int outWidth = (width + 2 * paddingW - filterW) / strideW + 1;
  int outDepth = (depth + 2 * paddingD - filterD) / strideD + 1;

  int channelsCol = channels * filterD * filterH * filterW;
  for (int c = 0; c < channelsCol; ++c) {
    int wOffset = c % filterW;
    int hOffset = (c / filterW) % filterH;
    int dOffset = (c / filterW / filterH) % filterD;
    int cIn = c / filterW / filterH / filterD;
    for (int d = 0; d < outDepth; ++d) {
      for (int h = 0; h < outHeight; ++h) {
        for (int w = 0; w < outWidth; ++w) {
          int dPad = d * strideD - paddingD + dOffset;
          int hPad = h * strideH - paddingH + hOffset;
          int wPad = w * strideW - paddingW + wOffset;

          if (hPad >= 0 && hPad < height && wPad >= 0 && wPad < width &&
              dPad >= 0 && dPad < depth)
            outData[((c * outDepth + d) * outHeight + h) * outWidth + w] =
                data[((cIn * depth + dPad) * height + hPad) * width + wPad];
          else
            outData[((c * outDepth + d) * outHeight + h) * outWidth + w] = 0;
        }
      }
    }
  }
}

void CpuMatrix::col2Vol(real* trg,
                        int channels,
                        int depth,
                        int height,
                        int width,
                        int filterD,
                        int filterH,
                        int filterW,
                        int strideD,
                        int strideH,
                        int strideW,
                        int paddingD,
                        int paddingH,
                        int paddingW,
                        real alpha,
                        real beta) {
  real* src = getData();
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  int outDepth = (depth + 2 * paddingD - filterD) / strideD + 1;
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  int outHeight = (height + 2 * paddingH - filterH) / strideH + 1;
  int outWidth = (width + 2 * paddingW - filterW) / strideW + 1;
  int channelsCol = channels * filterD * filterH * filterW;
  for (int c = 0; c < channelsCol; ++c) {
    int wOffset = c % filterW;
    int hOffset = (c / filterW) % filterH;
    int dOffset = (c / filterW / filterH) % filterD;
    int cIm = c / filterW / filterH / filterD;
    for (int d = 0; d < outDepth; ++d) {
      for (int h = 0; h < outHeight; ++h) {
        for (int w = 0; w < outWidth; ++w) {
          int dPad = d * strideD - paddingD + dOffset;
          int hPad = h * strideH - paddingH + hOffset;
          int wPad = w * strideW - paddingW + wOffset;
          if (hPad >= 0 && hPad < height && wPad >= 0 && wPad < width &&
              dPad >= 0 && dPad < depth)
            trg[((cIm * depth + dPad) * height + hPad) * width + wPad] =
                alpha *
                    src[((c * outDepth + d) * outHeight + h) * outWidth + w] +
                beta *
                    trg[((cIm * depth + dPad) * height + hPad) * width + wPad];
        }
      }
    }
  }
}

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////////////////////////////////////////////////////////////////
//               functions executed via cpu                   //
////////////////////////////////////////////////////////////////

void GpuMatrix::selectElements(Matrix& table, IVector& ids) {
  execViaCpu2(&CpuMatrix::selectElements, *this, table, ids);
}
}  // namespace paddle
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