Merge pull request #358 from yu239/multi_binary_cross_entropy

multi_binary_cross_entropy when ids vector is provided

paddle/cuda/include/hl_matrix.h

@@ -126,6 +126,36 @@ extern void hl_matrix_cross_entropy_bp(real* grad_d,
                                        int dimM,
                                        int dimN);
 
+/**
+ * @brief  Matrix multi-binary label cross entropy
+ *
+ * @param[in]   output    input matrix (M x N).
+ * @param[out]  entropy   output matrix (M x 1).
+ * @param[in]   mat       input sparse matrix.
+ * @param[in]   dimM      matrix height.
+ * @param[in]   dimN      matrix width.
+ */
+extern void hl_matrix_multi_binary_cross_entropy(real* output,
+                                                 real* entropy,
+                                                 hl_sparse_matrix_s mat,
+                                                 int dimM,
+                                                 int dimN);
+
+/**
+ * @brief  Matrix multi-binary label cross entropy backprop
+ *
+ * @param[in]   output    input matrix (M x N).
+ * @param[out]  grad      output matrix (M x N).
+ * @param[in]   mat       input sparse matrix.
+ * @param[in]   dimM      matrix height.
+ * @param[in]   dimN      matrix width.
+ */
+extern void hl_matrix_multi_binary_cross_entropy_bp(real* output,
+                                                    real* grad,
+                                                    hl_sparse_matrix_s mat,
+                                                    int dimM,
+                                                    int dimN);
+
 /**
  * @brief  Matrix zero memory.
  *

paddle/cuda/include/stub/hl_matrix_stub.h

@@ -57,6 +57,18 @@ inline void hl_matrix_cross_entropy_bp(real* grad_d,
                                        int dimM,
                                        int dimN) {}
 
+inline void hl_matrix_multi_binary_cross_entropy(real* output,
+                                                 real* entropy,
+                                                 hl_sparse_matrix_s mat,
+                                                 int dimM,
+                                                 int dimN) {}
+
+inline void hl_matrix_multi_binary_cross_entropy_bp(real* output,
+                                                    real* grad,
+                                                    hl_sparse_matrix_s mat,
+                                                    int dimM,
+                                                    int dimN) {}
+
 inline void hl_matrix_zero_mem(real* data, int num) {}
 
 inline void hl_param_relu_forward(real* output,

paddle/cuda/src/hl_cuda_matrix.cu

@@ -18,6 +18,7 @@ limitations under the License. */
 #include "hl_matrix_ops.cuh"
 #include "hl_matrix_apply.cuh"
 #include "hl_sequence.h"
+#include "hl_sparse.ph"
 #include "paddle/utils/Logging.h"
 #include "hl_device_functions.cuh"
 #include "hl_gpu_matrix_kernel.cuh"
@@ -317,6 +318,85 @@ void hl_matrix_classification_error(real* A_d,
   CHECK_SYNC("hl_matrix_classification_error");
 }
 
+__global__ void KeMatrixMultiBinaryCrossEntropy(real* output,
+                                                real* entropy,
+                                                int* row,
+                                                int* col,
+                                                int dimM,
+                                                int dimN) {
+  int index = blockIdx.x * blockDim.x + threadIdx.x;
+  if (index < dimM) {
+    for (int i = 0; i < dimN; i ++) {
+      entropy[index] -= log(1 - output[index * dimN + i]);
+    }
+    int *row_col = col + row[index];
+    int col_num = row[index + 1] - row[index];
+    for (int i = 0; i < col_num; i ++) {
+      real o = output[index * dimN + row_col[i]];
+      entropy[index] -= log(o / (1 - o));
+    }
+  }
+}
+
+void hl_matrix_multi_binary_cross_entropy(real* output,
+                                          real* entropy,
+                                          hl_sparse_matrix_s csr_mat,
+                                          int dimM,
+                                          int dimN) {
+  CHECK_NOTNULL(output);
+  CHECK_NOTNULL(entropy);
+  CHECK_NOTNULL(csr_mat);
+  CHECK_EQ(csr_mat->format, HL_SPARSE_CSR);
+  int n_threads = 1024;
+  int blocks = (dimM + n_threads - 1) / n_threads;
+  dim3 threads(n_threads);
+  dim3 grid(blocks);
+  hl_csr_matrix mat = (hl_csr_matrix)(csr_mat->matrix);
+  KeMatrixMultiBinaryCrossEntropy<<< grid, threads, 0, STREAM_DEFAULT >>>
+          (output, entropy, mat->csr_row, mat->csr_col, dimM, dimN);
+  CHECK_SYNC("hl_matrix_multi_binary_cross_entropy failed");
+}
+
+__global__ void KeMatrixMultiBinaryCrossEntropyBp(real* output,
+                                                  real* grad,
+                                                  int* row,
+                                                  int* col,
+                                                  int dimM,
+                                                  int dimN) {
+  int row_idx = blockIdx.x * blockDim.x + threadIdx.x;
+  if (row_idx < dimM) {
+    for (int i = 0; i < dimN; i ++) {
+      int index = row_idx * dimN + i;
+      grad[index] += 1.0 / (1 - output[index]);
+    }
+    int col_num = row[row_idx + 1] - row[row_idx];
+    int *row_col = col + row[row_idx];
+    for (int i = 0; i < col_num; i ++) {
+      int index = row_idx * dimN + row_col[i];
+      grad[index] -= 1.0 / (output[index] * (1 - output[index]));
+    }
+  }
+}
+
+void hl_matrix_multi_binary_cross_entropy_bp(real* output,
+                                             real* grad,
+                                             hl_sparse_matrix_s csr_mat,
+                                             int dimM,
+                                             int dimN) {
+  CHECK_NOTNULL(output);
+  CHECK_NOTNULL(grad);
+  CHECK_NOTNULL(csr_mat);
+  CHECK_EQ(csr_mat->format, HL_SPARSE_CSR);
+  int n_threads = 1024;
+  int blocks = (dimM + n_threads - 1) / n_threads;
+  dim3 threads(n_threads);
+  dim3 grid(blocks);
+  hl_csr_matrix mat = (hl_csr_matrix)(csr_mat->matrix);
+  KeMatrixMultiBinaryCrossEntropyBp<<< grid, threads, 0, STREAM_DEFAULT >>>
+          (output, grad, mat->csr_row, mat->csr_col, dimM, dimN);
+  CHECK_SYNC("hl_matrix_multi_binary_cross_entropy_bp failed");
+}
+
 __global__ void KeMatrixCrossEntropy(real* O,
                                      real* E,
                                      int* label,
@@ -685,7 +765,7 @@ __global__ void KeMatrixAddSharedBias(real* A,
   int dim = N / channel;
   if (index < M * N) {
     int i = index % N;
-    i = i / dim; 
+    i = i / dim;
     A[index] += scale * B[i];
   }
 }
@@ -713,7 +793,7 @@ __global__ void KeMatrixCollectSharedBias(real *B,
                                           const int dim,
                                           const int limit,
                                           real scale) {
-  if (dim < limit) { 
+  if (dim < limit) {
     int index = blockIdx.x * blockDim.x + threadIdx.x;
     if (index < channel) {
       real sum = 0.0;

paddle/gserver/layers/CostLayer.cpp

@@ -462,25 +462,43 @@ bool MultiBinaryLabelCrossEntropy::init(const LayerMap& layerMap,
 
 void MultiBinaryLabelCrossEntropy::forwardImp(Matrix& output, Argument& label,
                                               Matrix& target) {
-  if (dynamic_cast<CpuSparseMatrix*>(label.value.get()) ||
-      dynamic_cast<GpuSparseMatrix*>(label.value.get())) {
-    target.multiBinaryLabelCrossEntropy(output, *label.value);
+  MatrixPtr value = nullptr;
+  if (label.ids) {
+    CHECK(!label.value);
+    value = label.ids->toOneHotSparseMatrix(output.getWidth(), useGpu_);
+  } else {
+    CHECK(label.value);
+    value = label.value;
+  }
+
+  if (dynamic_cast<CpuSparseMatrix*>(value.get()) ||
+      dynamic_cast<GpuSparseMatrix*>(value.get())) {
+    target.multiBinaryLabelCrossEntropy(output, *value);
   } else {
     Matrix::resizeOrCreate(targetPerDim_, output.getHeight(), output.getWidth(),
                            false, useGpu_);
 
-    targetPerDim_->binaryLabelCrossEntropy(output, *label.value);
+    targetPerDim_->binaryLabelCrossEntropy(output, *value);
     targetPerDim_->rowSum(target);
   }
 }
 
 void MultiBinaryLabelCrossEntropy::backwardImp(
     Matrix& output, Argument& label, Matrix& outputG) {
-  if (dynamic_cast<CpuSparseMatrix*>(label.value.get()) ||
-      dynamic_cast<GpuSparseMatrix*>(label.value.get())) {
-    outputG.multiBinaryLabelCrossEntropyBp(output, *label.value);
+  MatrixPtr value = nullptr;
+  if (label.ids) {
+    CHECK(!value);
+    value = label.ids->toOneHotSparseMatrix(output.getWidth(), useGpu_);
+  } else {
+    CHECK(label.value);
+    value = label.value;
+  }
+
+  if (dynamic_cast<CpuSparseMatrix*>(value.get()) ||
+      dynamic_cast<GpuSparseMatrix*>(value.get())) {
+    outputG.multiBinaryLabelCrossEntropyBp(output, *value);
   } else {
-    outputG.binaryLabelCrossEntropyBp(output, *label.value);
+    outputG.binaryLabelCrossEntropyBp(output, *value);
   }
 }
 

paddle/gserver/tests/test_LayerGrad.cpp

@@ -528,7 +528,7 @@ TEST(Layer, multi_cross) {
   }
 }
 
-TEST(Layer, multi_binary_label) {
+TEST(Layer, multi_binary_label_sparse_mat) {
   TestConfig config;
   config.layerConfig.set_type("multi_binary_label_cross_entropy");
   config.biasSize = 0;
@@ -538,9 +538,26 @@ TEST(Layer, multi_binary_label) {
   config.layerConfig.add_inputs();
   config.layerConfig.add_inputs();
 
-  // Not support GPU now
-  testLayerGrad(config, "multi_binary_label_cross_entropy", 100,
-                /* trans */ false, /* useGpu */ false);
+  for (auto useGpu : {false, true}) {
+      testLayerGrad(config, "multi_binary_label_cross_entropy", 100,
+                    /* trans */ false, useGpu);
+  }
+}
+
+TEST(layer, multi_binary_label_id) {
+  TestConfig config;
+  config.layerConfig.set_type("multi_binary_label_cross_entropy");
+  config.biasSize = 0;
+
+  config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
+  config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
+  config.layerConfig.add_inputs();
+  config.layerConfig.add_inputs();
+
+  for (auto useGpu : {false, true}) {
+      testLayerGrad(config, "multi_binary_label_cross_entropy", 100,
+                    /* trans */ false, useGpu);
+  }
 }
 
 TEST(Layer, multi_cross_with_selfnorm) {

paddle/math/CpuSparseMatrix.cpp

@@ -409,9 +409,6 @@ void CpuSparseMatrix::setRow(size_t row, size_t colNum,
   if (format_ == SPARSE_CSR) {
     CHECK_LT(row, height_);
     CHECK(NULL != cols);
-    for (size_t i = row; i < height_; i++) {
-      CHECK_EQ(rows_[i + 1], rows_[i]);
-    }
     if (0 == row) {
       rows_[row] = 0;
     }

paddle/math/Matrix.cpp

@@ -1268,6 +1268,42 @@ void GpuMatrix::bilinearBackward(const Matrix& out,
   }
 }
 
+void GpuMatrix::multiBinaryLabelCrossEntropy(Matrix& output, Matrix& label) {
+    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";
+
+    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_);
+}
+
+void GpuMatrix::multiBinaryLabelCrossEntropyBp(Matrix &output, Matrix &label) {
+    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_ == outputPtr->width_
+          && outputPtr->width_ == labelPtr->getWidth()
+          && outputPtr->height_ == labelPtr->getHeight())
+            << "Matrix dimensions are not equal";
+
+    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_);
+}
+
 /**
  * CpuMatrix
  */

paddle/math/Matrix.h

@@ -1303,6 +1303,10 @@ public:
                         const size_t numChannels,
                         const real ratioH,
                         const real ratioW);
+
+  void multiBinaryLabelCrossEntropy(Matrix& output, Matrix& label);
+
+  void multiBinaryLabelCrossEntropyBp(Matrix& output, Matrix& label);
 };
 
 class CpuMatrix : public Matrix {

paddle/math/Vector.cpp

@@ -21,6 +21,7 @@ limitations under the License. */
 #include "paddle/utils/ThreadLocal.h"
 #include "paddle/utils/Thread.h"
 #include "paddle/utils/Flags.h"
+#include "Matrix.h"
 #include "hl_gpu.h"
 #include "hl_table_apply.h"
 
@@ -73,6 +74,31 @@ std::shared_ptr<VectorT<T>> VectorT<T>::create(size_t size,
   }
 }
 
+template <>
+MatrixPtr VectorT<real>::toOneHotSparseMatrix(size_t idRange, bool useGpu) {
+    LOG(FATAL) << "Wrong for real vector";
+    return nullptr;
+}
+
+template <>
+MatrixPtr VectorT<int>::toOneHotSparseMatrix(size_t idRange, bool useGpu) {
+  int height = getSize();
+  int width = idRange;
+  MatrixPtr mat = Matrix::createSparseMatrix(
+      height, idRange, height, NO_VALUE, SPARSE_CSR, false, useGpu);
+
+  CpuIVector cpuIds(height);
+  cpuIds.copyFrom(*this);
+  int *idData = cpuIds.getData();
+
+  for (int i = 0; i < height; i ++) {
+    const unsigned int id = idData[i];
+    CHECK_LT(id, width);
+    mat->setRow(i, 1, &id, nullptr);
+  }
+  return mat;
+}
+
 template <class T>
 GpuVectorT<T>::GpuVectorT(size_t size)
     : VectorT<T>(size, std::make_shared<GpuMemoryHandle>(sizeof(T) * size),

paddle/math/Vector.h

@@ -37,6 +37,8 @@ class BaseVector;
 
 class SyncThreadPool;
 
+class Matrix;
+
 template<class T>
 class BaseVector : public BaseMatrixT<T> {
 public:
@@ -155,6 +157,12 @@ public:
     subVecFrom(src, interval.first, interval.second - interval.first);
   }
 
+  /**
+   * convert the vector to a sparse one_hot matrix of width idRange
+   * only applies to IVector
+   */
+  std::shared_ptr<Matrix> toOneHotSparseMatrix(size_t idRange, bool useGpu);
+
   /**
    * This function will crash if the size of src and dest is different.
    */

paddle/math/tests/test_matrixCompare.cpp

@@ -2208,7 +2208,6 @@ void testCollectSharedBias(int numSamples, int dim, int channel) {
   MatrixCheckErr(*cpuBias, *check);
 }
 
-
 TEST(Matrix, sharedBias) {
   for (auto numSamples : {1, 100, 520}) {
     for (auto dim : {100 * 16, 100 * 32}) {
@@ -2222,6 +2221,60 @@ TEST(Matrix, sharedBias) {
   }
 }
 
+void testMultiBinaryLabelCrossEntropy(int numSamples, int dim) {
+  MatrixPtr output = std::make_shared<CpuMatrix>(numSamples, dim);
+  MatrixPtr cpuOutput = std::make_shared<CpuMatrix>(numSamples, dim);
+  MatrixPtr gpuOutput = std::make_shared<GpuMatrix>(numSamples, dim);
+
+  MatrixPtr cpuEntropy = std::make_shared<CpuMatrix>(numSamples, 1);
+  MatrixPtr gpuEntropy = std::make_shared<GpuMatrix>(numSamples, 1);
+
+  MatrixPtr cpuGrad = std::make_shared<CpuMatrix>(numSamples, dim);
+  MatrixPtr gpuGrad = std::make_shared<GpuMatrix>(numSamples, dim);
+
+  MatrixPtr cpuLabel = std::make_shared<CpuSparseMatrix>
+          (numSamples, dim, numSamples, NO_VALUE, SPARSE_CSR, false);
+  MatrixPtr gpuLabel = std::make_shared<GpuSparseMatrix>
+          (numSamples, dim, numSamples, NO_VALUE, SPARSE_CSR, false);
+  for (int i = 0; i < numSamples; i ++) {
+    const unsigned int id = rand() % dim; // NOLINT
+    cpuLabel->setRow(i, 1, &id, nullptr);
+    gpuLabel->setRow(i, 1, &id, nullptr);
+  }
+
+  output->randomizeUniform();
+  cpuOutput->zeroMem();
+  output->softmax(*cpuOutput);
+  gpuOutput->copyFrom(*cpuOutput);
+
+  cpuEntropy->zeroMem();
+  gpuEntropy->zeroMem();
+  cpuEntropy->multiBinaryLabelCrossEntropy(*cpuOutput, *cpuLabel);
+  gpuEntropy->multiBinaryLabelCrossEntropy(*gpuOutput, *gpuLabel);
+
+  MatrixPtr check1 = std::make_shared<CpuMatrix>(numSamples, 1);
+  check1->copyFrom(*gpuEntropy);
+  MatrixCheckErr(*cpuEntropy, *check1);
+
+  cpuGrad->zeroMem();
+  gpuGrad->zeroMem();
+  cpuGrad->multiBinaryLabelCrossEntropyBp(*cpuOutput, *cpuLabel);
+  gpuGrad->multiBinaryLabelCrossEntropyBp(*gpuOutput, *gpuLabel);
+
+  MatrixPtr check2 = std::make_shared<CpuMatrix>(numSamples, dim);
+  check2->copyFrom(*gpuGrad);
+  MatrixCheckErr(*cpuGrad, *check2);
+}
+
+TEST(Matrix, multiBinaryCrossEntropy) {
+  for (auto numSamples : {100, 1000, 10000}) {
+    for (auto dim : {100, 1000, 10000}) {
+      VLOG(3) << " numSamples=" << numSamples << " dim=" << dim;
+      testMultiBinaryLabelCrossEntropy(numSamples, dim);
+    }
+  }
+}
+
 int main(int argc, char** argv) {
   testing::InitGoogleTest(&argc, argv);
   initMain(argc, argv);