modifications according to comments

Haonan · Haonan · commit 5591292b7a0c · 2016-11-11T21:31:32.000-08:00
diff --git a/paddle/cuda/src/hl_cuda_matrix.cu b/paddle/cuda/src/hl_cuda_matrix.cu
@@ -346,6 +346,7 @@ void hl_matrix_multi_binary_cross_entropy(real* output,
   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);
@@ -385,6 +386,7 @@ void hl_matrix_multi_binary_cross_entropy_bp(real* output,
   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);
@@ -763,7 +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];
   }
 }
@@ -791,7 +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;
diff --git a/paddle/gserver/layers/CostLayer.cpp b/paddle/gserver/layers/CostLayer.cpp
@@ -465,10 +465,7 @@ void MultiBinaryLabelCrossEntropy::forwardImp(Matrix& output, Argument& label,
   MatrixPtr value = nullptr;
   if (label.ids) {
     CHECK(!label.value);
-    value = Matrix::createSparseMatrix(
-        label.ids->getSize(), output.getWidth(), label.ids->getSize(),
-        NO_VALUE, SPARSE_CSR, false, useGpu_);
-    label.idsToSparseMatrix(value);
+    value = label.ids->toOneHotSparseMatrix(output.getWidth(), useGpu_);
   } else {
     CHECK(label.value);
     value = label.value;
@@ -491,10 +488,7 @@ void MultiBinaryLabelCrossEntropy::backwardImp(
   MatrixPtr value = nullptr;
   if (label.ids) {
     CHECK(!value);
-    value = Matrix::createSparseMatrix(
-        label.ids->getSize(), output.getWidth(), label.ids->getSize(),
-        NO_VALUE, SPARSE_CSR, false, useGpu_);
-    label.idsToSparseMatrix(value);
+    value = label.ids->toOneHotSparseMatrix(output.getWidth(), useGpu_);
   } else {
     CHECK(label.value);
     value = label.value;
diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/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;
@@ -544,6 +544,22 @@ TEST(Layer, multi_binary_label) {
   }
 }
 
+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) {
   TestConfig config;
   config.layerConfig.set_type("multi_class_cross_entropy_with_selfnorm");
diff --git a/paddle/math/CpuSparseMatrix.cpp b/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;
     }
diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp
@@ -1269,37 +1269,37 @@ void GpuMatrix::bilinearBackward(const Matrix& out,
 }
 
 void GpuMatrix::multiBinaryLabelCrossEntropy(Matrix& output, Matrix& label) {
-    GpuMatrix* output_ptr = dynamic_cast<GpuMatrix*>(&output);
-    auto label_ptr = dynamic_cast<GpuSparseMatrix*>(&label);
-
-    CHECK(output_ptr && label_ptr) << "Invalid argument pointer";
-    CHECK(label_ptr->format_ == SPARSE_CSR) << "Matrix format not supported";
-    CHECK(height_ == output_ptr->height_ && width_ == 1
-          && output_ptr->width_ == label_ptr->getWidth()
-          && output_ptr->height_ == label_ptr->getHeight())
+    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 = output_ptr->data_;
+    real* output_d = outputPtr->data_;
     real* entropy_d = data_;
-    hl_sparse_matrix_s mat_d = label_ptr->sMatrix_.get();
+    hl_sparse_matrix_s mat_d = labelPtr->sMatrix_.get();
     hl_matrix_multi_binary_cross_entropy(
-        output_d, entropy_d, mat_d, height_, output_ptr->width_);
+        output_d, entropy_d, mat_d, height_, outputPtr->width_);
 }
 
 void GpuMatrix::multiBinaryLabelCrossEntropyBp(Matrix &output, Matrix &label) {
-    GpuMatrix* output_ptr = dynamic_cast<GpuMatrix*>(&output);
-    auto label_ptr = dynamic_cast<GpuSparseMatrix*>(&label);
-
-    CHECK(output_ptr && label_ptr) << "Invalid argument pointer";
-    CHECK(label_ptr->format_ == SPARSE_CSR) << "Matrix format not supported";
-    CHECK(height_ == output_ptr->height_ && width_ == output_ptr->width_
-          && output_ptr->width_ == label_ptr->getWidth()
-          && output_ptr->height_ == label_ptr->getHeight())
+    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 = output_ptr->data_;
+    real* output_d = outputPtr->data_;
     real* grad_d = data_;
-    hl_sparse_matrix_s mat_d = label_ptr->sMatrix_.get();
+    hl_sparse_matrix_s mat_d = labelPtr->sMatrix_.get();
     hl_matrix_multi_binary_cross_entropy_bp(
         output_d, grad_d, mat_d, height_, width_);
 }
diff --git a/paddle/math/Vector.cpp b/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),
diff --git a/paddle/math/Vector.h b/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 @@ class VectorT : public BaseVector<T> {
     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.
    */
diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp
@@ -2232,26 +2232,15 @@ void testMultiBinaryLabelCrossEntropy(int numSamples, int dim) {
   MatrixPtr cpuGrad = std::make_shared<CpuMatrix>(numSamples, dim);
   MatrixPtr gpuGrad = std::make_shared<GpuMatrix>(numSamples, dim);
 
-  auto cpuRows = IVector::create(numSamples + 1, false);
-  auto cpuCols = IVector::create(numSamples, false);
-  auto gpuRows = IVector::create(numSamples + 1, true);
-  auto gpuCols = IVector::create(numSamples, true);
-  cpuRows->setElement(0, 0);
-  gpuRows->setElement(0, 0);
-  for (int i = 0; i < numSamples; i ++) {
-    int id = rand() % dim; // NOLINT
-    cpuRows->setElement(i + 1, i + 1);
-    gpuRows->setElement(i + 1, i + 1);
-    cpuCols->setElement(i, id);
-    gpuCols->setElement(i, id);
-  }
-
   MatrixPtr cpuLabel = std::make_shared<CpuSparseMatrix>
-          (nullptr, cpuRows->getData(), cpuCols->getData(),
-           numSamples, dim, numSamples, NO_VALUE, SPARSE_CSR, false);
+          (numSamples, dim, numSamples, NO_VALUE, SPARSE_CSR, false);
   MatrixPtr gpuLabel = std::make_shared<GpuSparseMatrix>
-          (nullptr, gpuRows->getData(), gpuCols->getData(),
-           numSamples, dim, numSamples, NO_VALUE, SPARSE_CSR, false);
+          (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();
@@ -2278,8 +2267,8 @@ void testMultiBinaryLabelCrossEntropy(int numSamples, int dim) {
 }
 
 TEST(Matrix, multiBinaryCrossEntropy) {
-  for (auto numSamples : {1, 100, 500}) {
-    for (auto dim : {1000, 10000, 100000}) {
+  for (auto numSamples : {100, 1000, 10000}) {
+    for (auto dim : {100, 1000, 10000}) {
       VLOG(3) << " numSamples=" << numSamples << " dim=" << dim;
       testMultiBinaryLabelCrossEntropy(numSamples, dim);
     }
diff --git a/paddle/parameter/Argument.cpp b/paddle/parameter/Argument.cpp
@@ -572,42 +572,4 @@ void Argument::subArgFrom(const Argument& input, size_t offset, size_t height,
   }
 }
 
-void Argument::idsToSparseMatrix(MatrixPtr sparse_mat) {
-  int height = ids->getSize();
-  int width = sparse_mat->getWidth();
-
-  CpuIVector cpu_ids(height);
-  cpu_ids.copyFrom(*ids);
-  int *id_data = cpu_ids.getData();
-
-  int *rows = nullptr;
-  int *cols = nullptr;
-  if (sparse_mat->useGpu()) {
-      auto gpu_sparse_mat =
-              dynamic_cast<GpuSparseMatrix*>(sparse_mat.get());
-      rows = gpu_sparse_mat->rows_;
-      cols = gpu_sparse_mat->cols_;
-  } else {
-      rows = sparse_mat->getRows();
-      cols = sparse_mat->getCols();
-  }
-
-  rows[0] = 0;
-  for (int i = 0; i < height; i ++) {
-    int id = id_data[i];
-    CHECK_LT(id, width);
-    rows[i + 1] = i + 1;
-    cols[i] = id;
-  }
-
-  if (sparse_mat->useGpu()) {
-    auto gpu_sparse_mat =
-            dynamic_cast<GpuSparseMatrix*>(sparse_mat.get());
-    hl_memcpy_csr_matrix(gpu_sparse_mat->sMatrix_.get(),
-                         nullptr, rows, cols,
-                         HPPL_STREAM_DEFAULT);
-    hl_stream_synchronize(HPPL_STREAM_DEFAULT);
-  }
-}
-
 }  // namespace paddle
diff --git a/paddle/parameter/Argument.h b/paddle/parameter/Argument.h
@@ -286,12 +286,6 @@ struct Argument {
    sequence has sub-sequence degrades to a sequence.
    */
   void degradeSequence(const Argument& input, bool useGpu);
-
-  /*
-    @brief    convert the ids vector to value as a sparse matrix
-    @param[out]   the output sparse_mat (already allocated)
-  */
-  void idsToSparseMatrix(MatrixPtr sparse_mat);
 };
 
 }  // namespace paddle
