some bugs fix

paddle/cuda/include/hl_tensor_ops.h

-/**
- * hl_tensor_ops.h
- *
- * Author: hedaoyuan (hedaoyuan@baidu.com)
- * Created on: 2016-06-06
- *
- * Copyright (c) Baidu.com, Inc. All Rights Reserved
- *
- */
+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
+
+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. */
 
 #ifndef HL_TENSOR_OPS_H_
 #define HL_TENSOR_OPS_H_

paddle/math/TensorApply.h

-/**
- * TensorApply.h
- *
- * Author: hedaoyuan (hedaoyuan@baidu.com)
- * Created on: 2016-06-06
- *
- * Copyright (c) Baidu.com, Inc. All Rights Reserved
- *
- */
+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
+
+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. */
 
 #pragma once
 

paddle/math/TensorEvaluate.h

-/**
- * TensorEvaluate.h
- *
- * Author: hedaoyuan (hedaoyuan@baidu.com)
- * Created on: 2016-06-06
- *
- * Copyright (c) Baidu.com, Inc. All Rights Reserved
- *
- */
+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
+
+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. */
 
 #pragma once
 

paddle/math/TensorExpression.h

-/**
- * TensorExpression.h
- *
- * Author: hedaoyuan (hedaoyuan@baidu.com)
- * Created on: 2016-06-06
- *
- * Copyright (c) Baidu.com, Inc. All Rights Reserved
- *
- */
+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
+
+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. */
 
 #pragma once
 #include <cstddef>

paddle/math/TrainingAlgorithmOp.cu

-/**
- * TrainingAlgorithmOp.cu
- *
- * Author: hedaoyuan (hedaoyuan@baidu.com)
- * Created on: 2016-06-29
- *
- * Copyright (c) Baidu.com, Inc. All Rights Reserved
- *
- */
+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
+
+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 "paddle/utils/Logging.h"
 #include "BaseMatrix.h"

paddle/math/TrainingAlgorithmOp.h

-/**
- * TrainingAlgorithmOp.h
- *
- * Author: hedaoyuan (hedaoyuan@baidu.com)
- * Created on: 2016-06-29
- *
- * Copyright (c) Baidu.com, Inc. All Rights Reserved
- *
- */
+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
+
+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. */
 
 #pragma once
 

paddle/math/tests/OriginalOptimizerApi.h

-/**
- * OriginalOptimizerApi.h
- *
- * Author: hedaoyuan (hedaoyuan@baidu.com)
- * Created on: 2016-06-29
- *
- * Copyright (c) Baidu.com, Inc. All Rights Reserved
- */
+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
+
+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. */
 
 #pragma once
 

paddle/math/tests/TensorCheck.h

@@ -9,6 +9,7 @@
 
 #include <gtest/gtest.h>
 #include "paddle/math/Matrix.h"
+
 using namespace paddle;  // NOLINT
 using namespace std;     // NOLINT
 
@@ -105,50 +106,9 @@ void TensorCheckEqual(const GpuVectorT<T>& vector1,
   TensorCheckEqual(cpu1, cpu2);
 }
 
-int VectorCheckErr(const Vector& vector1, const Vector& vector2) {
-  CHECK(vector1.getSize() == vector2.getSize());
-
-  const real* data1 = vector1.getData();
-  const real* 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 (fabs(a - b) > FLAGS_max_diff) {
-      if ((fabsf(a - b) / fabsf(a)) > (FLAGS_max_diff / 10.0f)) {
-        count++;
-      }
-    }
-  }
-
-  return count;
-}
-
-#define INIT_UNARY(A1, A2)                  \
-    Tensor A1(height, width);               \
-    Tensor A2(height, width);               \
-    A1.randomizeUniform();                  \
-    A2.copyFrom(A1)
-#define INIT_BINARY(A1, A2, B)              \
-    INIT_UNARY(A1, A2);                     \
-    Tensor B(height, width);                \
-    B.randomizeUniform()
-#define INIT_TERNARY(A1, A2, B, C)          \
-    INIT_BINARY(A1, A2, B);                 \
-    Tensor C(height, width);                \
-    C.randomizeUniform()
-#define INIT_QUATERNARY(A1, A2, B, C, D)    \
-    INIT_TERNARY(A1, A2, B, C);             \
-    Tensor D(height, width);                \
-    D.randomizeUniform()
-
 // Performance Check
 #ifdef PADDLE_DISABLE_TIMER
 
-#define CHECK_VECTORPTR(vector1, vector2)   \
-    EXPECT_EQ(VectorCheckErr(vector1, vector2), 0)
-
 #define EXPRESSION_PERFORMANCE(expression)  \
     expression;
 
@@ -156,8 +116,6 @@ int VectorCheckErr(const Vector& vector1, const Vector& vector2) {
 
 #include "paddle/utils/Stat.h"
 
-#define CHECK_VECTORPTR(vector1, vector2)
-
 #define EXPRESSION_PERFORMANCE(expression) \
   do {\
     char expr[30];\

paddle/math/tests/test_Tensor.cu

-/**
- * test_Tensor.cpp
- *
- * Author: hedaoyuan (hedaoyuan@baidu.com)
- * Created on: 2016-06-06
- *
- * Copyright (c) Baidu.com, Inc. All Rights Reserved
- */
-
-#include <gtest/gtest.h>
-#include "paddle/math/Matrix.h"
-using namespace paddle;  // NOLINT
-using namespace std;     // NOLINT
-
-template<typename Tensor>
-extern void TensorCheckEqual(const Tensor& tensor1, const Tensor& tensor2);
-
-void TensorCheckEqual(const CpuMatrix& matrix1, const CpuMatrix& matrix2) {
-  CHECK(matrix1.getHeight() == matrix2.getHeight());
-  CHECK(matrix1.getWidth() == matrix2.getWidth());
-
-  int height = matrix1.getHeight();
-  int width = matrix1.getWidth();
-  const real* data1 = matrix1.getData();
-  const real* data2 = matrix2.getData();
-  int count = 0;
-  for (int i = 0; i < height; i++) {
-    for (int j = 0; j < width; j++) {
-      if (data1[i * width + j] != data2[i * width + j]) {
-        count++;
-      }
-    }
-  }
-  EXPECT_EQ(count, 0) << "There are " << count << " different element.";
-}
-
-void TensorCheckEqual(const GpuMatrix& matrix1, const GpuMatrix& matrix2) {
-  CpuMatrix cpu1(matrix1.getHeight(), matrix1.getWidth());
-  CpuMatrix cpu2(matrix2.getHeight(), matrix2.getWidth());
-  cpu1.copyFrom(matrix1);
-  cpu2.copyFrom(matrix2);
-  TensorCheckEqual(cpu1, cpu2);
-}
-
-void TensorCheckErr(const CpuMatrix& matrix1, const CpuMatrix& matrix2) {
-  CHECK(matrix1.getHeight() == matrix2.getHeight());
-  CHECK(matrix1.getWidth() == matrix2.getWidth());
-#ifndef PADDLE_TYPE_DOUBLE
-  real err = 1e-5;
-#else
-  real err = 1e-10;
-#endif
+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
 
-  int height = matrix1.getHeight();
-  int width = matrix1.getWidth();
-  const real* data1 = matrix1.getData();
-  const real* data2 = matrix2.getData();
-  int count = 0;
-  for (int i = 0; i < height; i++) {
-    for (int j = 0; j < width; j++) {
-      real a = data1[i * width + j];
-      real b = data2[i * width + j];
-      if (fabs(a - b) > err) {
-        if ((fabsf(a - b) / fabsf(a)) > (err / 10.0f)) {
-          count++;
-        }
-      }
-    }
-  }
-  EXPECT_EQ(count, 0) << "There are " << count << " different element.";
-}
+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
 
-void TensorCheckErr(const GpuMatrix& matrix1, const GpuMatrix& matrix2) {
-  CpuMatrix cpu1(matrix1.getHeight(), matrix1.getWidth());
-  CpuMatrix cpu2(matrix2.getHeight(), matrix2.getWidth());
-  cpu1.copyFrom(matrix1);
-  cpu2.copyFrom(matrix2);
-  TensorCheckErr(cpu1, cpu2);
-}
+    http://www.apache.org/licenses/LICENSE-2.0
 
-template<class T>
-void TensorCheckEqual(const CpuVectorT<T>& vector1,
-                      const CpuVectorT<T>& vector2) {
-  CHECK(vector1.getSize() == vector2.getSize());
+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. */
 
-  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++) {
-    if (data1[i] != data2[i]) {
-      count++;
-    }
-  }
-  EXPECT_EQ(count, 0) << "There are " << count << " different element.";
-}
+#include <gtest/gtest.h>
+#include "paddle/math/Matrix.h"
+#include "TensorCheck.h"
 
-template<class T>
-void TensorCheckEqual(const GpuVectorT<T>& vector1,
-                      const GpuVectorT<T>& vector2) {
-  CpuVectorT<T> cpu1(vector1.getSize());
-  CpuVectorT<T> cpu2(vector2.getSize());
-  cpu1.copyFrom(vector1);
-  cpu2.copyFrom(vector2);
-  TensorCheckEqual(cpu1, cpu2);
-}
+using namespace paddle;  // NOLINT
+using namespace std;     // NOLINT
 
 #define INIT_UNARY(A1, A2)                  \
     Tensor A1(height, width);               \

paddle/math/tests/test_TrainingAlgorithm.cpp

-/**
- * test_TrainingAlgorithm.cpp
- *
- * Author: hedaoyuan (hedaoyuan@baidu.com)
- * Created on: 2016-06-29
- *
- * Copyright (c) Baidu.com, Inc. All Rights Reserved
- */
+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
+
+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 <gtest/gtest.h>
 #include "paddle/utils/Util.h"
@@ -44,6 +49,26 @@ private:
     }\
   } while (0)
 
+int VectorCheckErr(const Vector& vector1, const Vector& vector2) {
+  CHECK(vector1.getSize() == vector2.getSize());
+
+  const real* data1 = vector1.getData();
+  const real* 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 (fabs(a - b) > FLAGS_max_diff) {
+      if ((fabsf(a - b) / fabsf(a)) > (FLAGS_max_diff / 10.0f)) {
+        count++;
+      }
+    }
+  }
+
+  return count;
+}
+
 int VectorCheckErr(const VectorPtr& vector1, const VectorPtr& vector2) {
   VectorPtr tmp1;
   VectorPtr tmp2;
@@ -52,6 +77,17 @@ int VectorCheckErr(const VectorPtr& vector1, const VectorPtr& vector2) {
   return VectorCheckErr(*tmp1, *tmp2);
 }
 
+#ifdef PADDLE_DISABLE_TIMER
+
+#define CHECK_VECTORPTR(vector1, vector2)   \
+    EXPECT_EQ(VectorCheckErr(vector1, vector2), 0)
+
+#else
+
+#define CHECK_VECTORPTR(vector1, vector2)
+
+#endif
+
 typedef std::function<void(size_t size, bool useGpu)> testMatrixFunc;
 
 void testCase(testMatrixFunc matrixFunc) {

paddle/math/tests/test_lazyAssign.cu

@@ -27,7 +27,16 @@ void testMatrixCase(testMatrixFunc matrixFunc) {
 
 template<typename Tensor>
 void testLazyAssign(int height, int width) {
-  INIT_QUATERNARY(A1, A2, B, C, D);
+  Tensor A1(height, width);
+  Tensor A2(height, width);
+  Tensor B(height, width);
+  Tensor C(height, width);
+  Tensor D(height, width);
+  A1.randomizeUniform();
+  B.randomizeUniform();
+  C.randomizeUniform();
+  D.randomizeUniform();
+  A2.copyFrom(A1);
 
   EXPRESSION_PERFORMANCE(A1 = B + C; A1 = A1 * D;);