Add a AutoCompare and move some test form test_matrixCompare.cpp to test_Matrix.cpp

paddle/math/tests/TensorCheck.h

@@ -13,11 +13,14 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include <cmath>
-#include <gtest/gtest.h>
 #include "paddle/math/Matrix.h"
 
-using namespace paddle;  // NOLINT
-using namespace std;     // NOLINT
+using paddle::Matrix;
+using paddle::CpuMatrix;
+using paddle::GpuMatrix;
+using paddle::VectorT;
+using paddle::CpuVectorT;
+using paddle::GpuVectorT;
 
 namespace autotest {
 
@@ -71,6 +74,53 @@ private:
   CpuMatrix arg_;
 };
 
+template <>
+class CopyToCpu<Matrix> {
+public:
+  explicit CopyToCpu(const Matrix& arg)
+      : arg_(arg.getHeight(), arg.getWidth()) {
+    arg_.copyFrom(arg);
+  }
+  CpuMatrix& copiedArg() { return arg_; }
+
+private:
+  CpuMatrix arg_;
+};
+
+template <typename T>
+class CopyToCpu<CpuVectorT<T>> {
+public:
+  explicit CopyToCpu(const CpuVectorT<T>& arg) : arg_(arg) {}
+  const CpuVectorT<T>& copiedArg() const { return arg_; }
+
+private:
+  const CpuVectorT<T>& arg_;
+};
+
+template <typename T>
+class CopyToCpu<GpuVectorT<T>> {
+public:
+  explicit CopyToCpu(const GpuVectorT<T>& arg) : arg_(arg.getSize()) {
+    arg_.copyFrom(arg);
+  }
+  CpuVectorT<T>& copiedArg() { return arg_; }
+
+private:
+  CpuVectorT<T> arg_;
+};
+
+template <typename T>
+class CopyToCpu<VectorT<T>> {
+public:
+  explicit CopyToCpu(const VectorT<T>& arg) : arg_(arg.getSize()) {
+    arg_.copyFrom(arg);
+  }
+  CpuVectorT<T>& copiedArg() { return arg_; }
+
+private:
+  CpuVectorT<T> arg_;
+};
+
 template <typename AssertEq>
 void TensorCheck(AssertEq compare,
                  const CpuMatrix& matrix1,
@@ -95,10 +145,30 @@ void TensorCheck(AssertEq compare,
   EXPECT_EQ(count, 0) << "There are " << count << " different element.";
 }
 
+template <typename AssertEq, class T>
+void TensorCheck(AssertEq compare,
+                 const CpuVectorT<T>& vector1,
+                 const CpuVectorT<T>& vector2) {
+  CHECK(vector1.getSize() == vector2.getSize());
+
+  const T* data1 = vector1.getData();
+  const T* data2 = vector2.getData();
+  size_t size = vector1.getSize();
+  int count = 0;
+  for (size_t i = 0; i < size; i++) {
+    real a = data1[i];
+    real b = data2[i];
+    if (!compare(a, b)) {
+      count++;
+    }
+  }
+  EXPECT_EQ(count, 0) << "There are " << count << " different element.";
+}
+
 template <typename AssertEq, typename Tensor1, typename Tensor2>
-extern void TensorCheck(AssertEq compare,
-                        const Tensor1& tensor1,
-                        const Tensor2& tensor2) {
+void TensorCheck(AssertEq compare,
+                 const Tensor1& tensor1,
+                 const Tensor2& tensor2) {
   TensorCheck(compare,
               CopyToCpu<Tensor1>(tensor1).copiedArg(),
               CopyToCpu<Tensor2>(tensor2).copiedArg());
@@ -116,4 +186,24 @@ void TensorCheck(AssertEq compare, size_t args1, size_t args2) {
                           << ", args2 = " << args2;
 }
 
+template <typename Tensor1, typename Tensor2>
+void TensorCheckEqual(const Tensor1& tensor1, const Tensor2& tensor2) {
+  AssertEqual compare(0);
+  TensorCheck(compare,
+              CopyToCpu<Tensor1>(tensor1).copiedArg(),
+              CopyToCpu<Tensor2>(tensor2).copiedArg());
+}
+
+template <typename Tensor1, typename Tensor2>
+void TensorCheckErr(const Tensor1& tensor1, const Tensor2& tensor2) {
+#ifndef PADDLE_TYPE_DOUBLE
+  AssertEqual compare(1e-3);
+#else
+  AssertEqual compare(1e-10);
+#endif
+  TensorCheck(compare,
+              CopyToCpu<Tensor1>(tensor1).copiedArg(),
+              CopyToCpu<Tensor2>(tensor2).copiedArg());
+}
+
 }  // namespace autotest

paddle/math/tests/TestUtils.h

@@ -14,21 +14,19 @@ limitations under the License. */
 
 /**
  * TestUtils.h is used to automatically compare CPU and GPU code is consistent.
- *
- * Auto compare BaseMatrix member function:
- * Use case:
- * a. void BaseMatrix::tanh(BaseMatrixT& b);
- *   Compare method: BaseMatrixCompare<0>(&BaseMatrix::tanh);
- *
- * b.
- *
+ * Refer test_Matrix.cpp and test_BaseMatrix.cpp for how to use autotest.
 */
 
 #include <gtest/gtest.h>
 #include "paddle/math/Matrix.h"
+#include "paddle/math/SparseMatrix.h"
 #include "TensorCheck.h"
 
-using namespace paddle;  // NOLINT
+using paddle::BaseMatrix;
+using paddle::CpuIVector;
+using paddle::GpuIVector;
+using paddle::CpuSparseMatrix;
+using paddle::GpuSparseMatrix;
 
 namespace autotest {
 
@@ -196,9 +194,7 @@ template <bool AsRowVector,
           typename R,
           typename... Args,
           typename AssertEq>
-void BaseMatrixCompare(R (C::*f)(Args...),
-                       AssertEq compare,
-                       bool checkArgs = false) {
+void BaseMatrixCompare(R (C::*f)(Args...), AssertEq compare) {
   for (auto height : {1, 11, 73, 128, 200, 330}) {
     for (auto width : {1, 3, 32, 100, 512, 1000}) {
       CpuMatrix obj1(AsRowVector ? 1 : height, AsColVector ? 1 : width);
@@ -227,17 +223,91 @@ void BaseMatrixCompare(R (C::*f)(Args...),
       call(obj2, f, std::get<I>(tuple2)...);
 
       TensorCheck(compare, obj1, obj2);
-      if (checkArgs) {
-        checkTuple(tuple1, tuple2, compare);
-      }
     }
   }
 }
 
+template <typename T>
+class ReturnType {
+public:
+  typedef T type;
+};
+
+template <>
+class ReturnType<CpuMatrix> {
+public:
+  typedef GpuMatrix type;
+};
+
+template <>
+class ReturnType<CpuIVector> {
+public:
+  typedef GpuIVector type;
+};
+
+template <>
+class ReturnType<CpuSparseMatrix> {
+public:
+  typedef GpuSparseMatrix type;
+};
+
+template <typename T>
+typename ReturnType<T>::type autoArgs(T v) {
+  return v;
+}
+
+template <>
+GpuMatrix autoArgs(CpuMatrix v) {
+  GpuMatrix a(v.getHeight(), v.getWidth());
+  a.copyFrom(v);
+  return a;
+}
+
+template <>
+GpuIVector autoArgs(CpuIVector v) {
+  GpuIVector a(v.getSize());
+  a.copyFrom(v);
+  return a;
+}
+
+template <>
+GpuSparseMatrix autoArgs(CpuSparseMatrix v) {
+  GpuSparseMatrix a(v.getHeight(),
+                    v.getWidth(),
+                    v.getElementCnt(),
+                    v.getValueType(),
+                    v.getFormat());
+
+  a.copyFrom(v, HPPL_STREAM_DEFAULT);
+  hl_stream_synchronize(HPPL_STREAM_DEFAULT);
+  return a;
+}
+
+class AutoCompare {
+public:
+  AutoCompare(size_t height, size_t width)
+      : cpu(height, width), gpu(height, width) {
+    init(cpu);
+    copy(gpu, cpu);
+  }
+
+  template <typename C, typename R, typename... FArgs, typename... Args>
+  void operator()(R (C::*f)(FArgs...), Args&&... args) {
+    call(cpu, f, args...);
+    call(gpu, f, autoArgs(args)...);
+
+    TensorCheckErr(cpu, gpu);
+  }
+
+protected:
+  CpuMatrix cpu;
+  GpuMatrix gpu;
+};
+
 }  // namespace autotest
 
 template <std::size_t... I, typename C, typename R, typename... Args>
-void BaseMatrixCompare(R (C::*f)(Args...), bool checkArgs = false) {
+void BaseMatrixCompare(R (C::*f)(Args...)) {
   static_assert(sizeof...(I) == sizeof...(Args),
                 "size of parameter packs are not equal");
 
@@ -247,7 +317,7 @@ void BaseMatrixCompare(R (C::*f)(Args...), bool checkArgs = false) {
   autotest::AssertEqual compare(1e-10);
 #endif
 
-  autotest::BaseMatrixCompare<false, false, I...>(f, compare, checkArgs);
+  autotest::BaseMatrixCompare<false, false, I...>(f, compare);
 }
 
 template <std::size_t... I, typename C, typename R, typename... Args>

paddle/math/tests/test_BaseMatrix.cpp

@@ -15,7 +15,7 @@ limitations under the License. */
 #ifndef PADDLE_ONLY_CPU
 /**
  * This test file compares the implementation of CPU and GPU function
- * in BaseMatrix.cpp.
+ * in BaseMatrix.cpp or Matrix.cpp.
  */
 
 #include <gtest/gtest.h>
@@ -188,17 +188,22 @@ TEST(BaseMatrix, Other) {
   BaseMatrixCompare<0, 1, 2>(&BaseMatrix::rowScale);
   BaseMatrixCompare<0, 1, 2>(&BaseMatrix::rowDotMul);
   BaseMatrixCompare<0, 1, 2, 3>(&BaseMatrix::binaryClassificationError);
+
+  BaseMatrixCompare<0, 1>(&Matrix::sumOfSquaresBp);
 }
 
 TEST(BaseMatrix, Aggregate) {
   BaseMatrixAsColVector<0>(&BaseMatrix::maxRows);
   BaseMatrixAsColVector<0>(&BaseMatrix::minRows);
   BaseMatrixAsColVector<0, 1, 2>(&BaseMatrix::sumRows);
+  BaseMatrixAsColVector<0, 1>(&Matrix::sumOfSquares);
 
   BaseMatrixAsRowVector<0>(&BaseMatrix::maxCols);
   BaseMatrixAsRowVector<0>(&BaseMatrix::minCols);
   BaseMatrixAsRowVector<0, 1>(&BaseMatrix::addDotMulVMM);
   BaseMatrixAsRowVector<0, 1, 2>(&BaseMatrix::sumCols);
+  BaseMatrixAsRowVector<0, 1>(
+      static_cast<void (Matrix::*)(Matrix&, real)>(&Matrix::collectBias));
 }
 
 int main(int argc, char** argv) {

paddle/math/tests/test_Matrix.cpp

@@ -14,25 +14,295 @@ limitations under the License. */
 
 #ifndef PADDLE_ONLY_CPU
 /**
- * This test file compares the implementation of CPU and GPU function
- * in Matrix.cpp.
+ * This test file use AutoCompare to compares the implementation
+ * of CPU and GPU member function in Matrix.cpp.
+ *
+ * 1. Constructs an AutoCompare object, a AutoCompare object contains
+ *    a CpuMatrix and a GpuMatrix;
+ * 2. Initializes the required parameters for the member function.
+ *    Only need to initialize the CPU parameters.
+ * 3. Use the operator() template for testing. In the operator() will call back
+ *    member functions, and compare the results.
+ *
+ * use case:
+ *  AutoCompare test(...);
+ *  Init Argument arg1,arg2...
+ *  test(function, arg1, arg2....)
+ *
  */
 
 #include <gtest/gtest.h>
 #include "TestUtils.h"
 
-using namespace paddle;  // NOLINT
+using paddle::CpuMatrix;
+using paddle::SparseValueType;
+using paddle::SparseFormat;
+using paddle::NO_VALUE;
+using paddle::SPARSE_CSR;
+using paddle::initMain;
+using autotest::TensorCheckEqual;
+using autotest::TensorCheckErr;
+using autotest::AutoCompare;
 
-TEST(Matrix, Matrix) {
-  BaseMatrixCompare<0>(&Matrix::softmax, true);
-  BaseMatrixCompare<0, 1>(&Matrix::sumOfSquaresBp);
+void testBilinearFwdBwd(int numSamples,
+                        int imgSizeH,
+                        int imgSizeW,
+                        int channels) {
+  int inWidth = imgSizeH * imgSizeW * channels;
+  int outWidth = 2 * imgSizeH * 2 * imgSizeW * channels;
+  real ratioH = 0.5;
+  real ratioW = 0.5;
+
+  AutoCompare forward(numSamples, outWidth);
+  CpuMatrix arg1(numSamples, inWidth);
+  arg1.randomizeUniform();
+  forward(&Matrix::bilinearForward,
+          arg1,
+          imgSizeH,
+          imgSizeW,
+          2 * imgSizeH,
+          2 * imgSizeW,
+          channels,
+          ratioH,
+          ratioW);
+
+  AutoCompare backward(numSamples, inWidth);
+  CpuMatrix arg2(numSamples, outWidth);
+  arg2.randomizeUniform();
+  backward(&Matrix::bilinearBackward,
+           arg2,
+           2 * imgSizeH,
+           2 * imgSizeW,
+           imgSizeH,
+           imgSizeW,
+           channels,
+           ratioH,
+           ratioW);
+}
+
+TEST(Matrix, BilinearFwdBwd) {
+  for (auto numSamples : {5, 10}) {
+    for (auto channels : {8, 16}) {
+      for (auto imgSizeH : {14, 28}) {
+        for (auto imgSizeW : {16, 30}) {
+          VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
+                  << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
+          testBilinearFwdBwd(numSamples, imgSizeH, imgSizeW, channels);
+        }
+      }
+    }
+  }
+}
+
+void testMatrixAddBias(int height, int width, real scale) {
+  AutoCompare test(height, width);
+  CpuMatrix arg1(1, width);
+  arg1.randomizeUniform();
+  test(static_cast<void (Matrix::*)(Matrix&, real)>(&Matrix::addBias),
+       arg1,
+       scale);
+}
+
+void testMatrixAddDotMulMMV(int height, int width) {
+  AutoCompare test(height, width);
+  CpuMatrix arg1(height, width);
+  CpuMatrix arg2(1, width);
+  arg1.randomizeUniform();
+  arg2.randomizeUniform();
+  test(&BaseMatrix::addDotMulMMV, arg1, arg2);
+}
+
+TEST(Matrix, unary) {
+  for (auto height : {1, 3, 11, 73, 128, 200, 330}) {
+    for (auto width : {1, 3, 32, 100, 512, 1000, 3210}) {
+      VLOG(3) << " height=" << height << " width=" << width;
+      testMatrixAddBias(height, width, 1.0);
+      testMatrixAddBias(height, width, 3.5);
+      testMatrixAddDotMulMMV(height, width);
+    }
+  }
+}
+
+void testMatrixAddAtOffset(int height, int width1, int width2, int offset) {
+  AutoCompare test(height, width2);
+  CpuMatrix arg1(height, width1);
+  arg1.randomizeUniform();
+  test(&Matrix::addAtOffset, arg1, offset);
+}
+
+void testMatrixAssignAtOffset(int height, int width1, int width2, int offset) {
+  AutoCompare test(height, width2);
+  CpuMatrix arg1(height, width1);
+  arg1.randomizeUniform();
+  test(&Matrix::assignAtOffset, arg1, offset);
+}
+
+TEST(Matrix, AtOffset) {
+  for (auto height : {1, 11, 73, 128, 200}) {
+    for (auto width1 : {1, 32, 100, 512, 1000}) {
+      for (auto width2 : {1, 32, 100, 512, 1000}) {
+        int columnOffset = 0;
+        int offset = std::abs(width1 - width2);
+        if (offset) {
+          columnOffset = std::rand() % offset;
+        }
+        VLOG(3) << " height=" << height << " width1=" << width1
+                << " width2=" << width2 << " columnOffset = " << columnOffset;
+        testMatrixAddAtOffset(height, width1, width2, columnOffset);
+        testMatrixAssignAtOffset(height, width1, width2, columnOffset);
+      }
+    }
+  }
+}
+
+void testMatrixSelectRows(int numSamples, int tableSize, int inputDim) {
+  AutoCompare test(numSamples, inputDim);
+  CpuMatrix arg1(tableSize, inputDim);
+  CpuIVector arg2(numSamples);
+  arg1.randomizeUniform();
+  arg2.rand(tableSize);
+  test(&Matrix::selectRows, arg1, arg2);
+}
+
+TEST(Matrix, tableProjection) {
+  for (auto numSamples : {10, 100, 1000, 10000, 80000}) {
+    for (auto tableSize : {10, 100}) {
+      for (auto inputDim : {20, 50}) {
+        VLOG(3) << " numSamples=" << numSamples << " tableSize=" << tableSize
+                << " inputDim=" << inputDim;
+        testMatrixSelectRows(numSamples, tableSize, inputDim);
+      }
+    }
+  }
+}
+
+void testMatrixCopyByRowIndex(int outHeight, int inHeight, int width) {
+  AutoCompare test(outHeight, width);
+  CpuMatrix arg1(inHeight, width);
+  CpuIVector arg2(outHeight);
+  arg1.randomizeUniform();
+  arg2.rand(inHeight);
+  test(&Matrix::copyByRowIndex, arg1, arg2);
 }
 
-TEST(Matrix, Aggregate) {
-  BaseMatrixAsRowVector<0, 1>(
-      static_cast<void (Matrix::*)(Matrix&, real)>(&Matrix::collectBias));
+TEST(Matrix, copyByRowIndex) {
+  for (auto outHeight : {31, 500, 1000}) {
+    for (auto inHeight : {17, 257, 500, 1200}) {
+      for (auto width : {512, 1024}) {
+        VLOG(3) << outHeight << " " << inHeight << " " << width;
+        testMatrixCopyByRowIndex(outHeight, inHeight, width);
+      }
+    }
+  }
+}
+
+void testCosSim(int heightX, int heightY, int width, real scale) {
+  AutoCompare test(heightX, 1);
+  CpuMatrix arg1(heightX, width);
+  CpuMatrix arg2(heightY, width);
+  arg1.randomizeUniform();
+  arg2.randomizeUniform();
+  arg2.add(-0.5);
+  test(&Matrix::cosSim, arg1, arg2, scale);
+}
+
+TEST(Matrix, cosSim) {
+  for (auto heightX : {10, 100, 1000}) {
+    for (auto heightY : {1, heightX}) {
+      for (auto width : {10, 100, 1000}) {
+        for (auto scale : {1.0, 2.0}) {
+          testCosSim(heightX, heightY, width, scale);
+        }
+      }
+    }
+  }
+}
+
+void testParamReluForward(int height, int width, int w_height, int w_width) {
+  AutoCompare test(height, width);
+  CpuMatrix arg1(height, width);
+  CpuMatrix arg2(w_height, w_width);
+  arg1.randomizeUniform();
+  arg2.randomizeUniform();
+  arg1.add(-0.5);
+  test(&Matrix::paramReluForward, arg1, arg2);
+}
+
+void testParamReluBackwardW(int height, int width, int w_height, int w_width) {
+  AutoCompare test(w_height, w_width);
+  CpuMatrix arg1(height, width);
+  CpuMatrix arg2(height, width);
+  arg1.randomizeUniform();
+  arg2.randomizeUniform();
+  arg2.add(-0.5);
+  test(&Matrix::paramReluBackwardW, arg1, arg2);
+}
+
+TEST(Matrix, paramRelu) {
+  for (auto height : {10, 100}) {
+    for (auto width : {10, 100}) {
+      for (auto w_height : {1, 2}) {
+        for (auto w_width : {1, 2}) {
+          testParamReluForward(height, width, w_height, w_width);
+          testParamReluBackwardW(height, width, w_height, w_width);
+        }
+      }
+    }
+  }
+}
+
+void testAddSharedBias(int numSamples, int dim, int channel) {
+  AutoCompare test(numSamples, dim);
+  CpuMatrix arg1(1, channel);
+  arg1.randomizeUniform();
+  test(&Matrix::addSharedBias, arg1, 1.0);
+}
+
+void testCollectSharedBias(int numSamples, int dim, int channel) {
+  AutoCompare test(1, channel);
+  CpuMatrix arg1(numSamples, dim);
+  arg1.randomizeUniform();
+  test(&Matrix::collectSharedBias, arg1, 1.0);
+}
+
+TEST(Matrix, sharedBias) {
+  for (auto numSamples : {1, 100, 520}) {
+    for (auto dim : {100 * 16, 100 * 32}) {
+      for (auto channel : {8, 16}) {
+        VLOG(3) << " numSamples=" << numSamples << " dim=" << dim
+                << " channel=" << channel;
+        testAddSharedBias(numSamples, dim, channel);
+        testCollectSharedBias(numSamples, dim, channel);
+      }
+    }
+  }
+}
+
+void testMultiBinaryLabelCrossEntropy(int numSamples, int dim) {
+  AutoCompare forward(numSamples, 1);
+  CpuMatrix arg1(numSamples, dim);
+  CpuSparseMatrix arg2(numSamples, dim, numSamples, NO_VALUE, SPARSE_CSR);
+
+  CpuMatrix output1(numSamples, dim);
+  output1.randomizeUniform();
+  output1.softmax(arg1);
+  for (int i = 0; i < numSamples; i++) {
+    const unsigned int id = std::rand() % dim;
+    arg2.setRow(i, 1, &id, nullptr);
+  }
+  forward(&Matrix::multiBinaryLabelCrossEntropy, arg1, arg2);
+
+  AutoCompare backward(numSamples, dim);
+  backward(&Matrix::multiBinaryLabelCrossEntropyBp, arg1, arg2);
+}
 
-  BaseMatrixAsColVector<0, 1>(&Matrix::sumOfSquares);
+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) {