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From wan...@apache.org
Subject [35/60] incubator-singa git commit: SINGA-170 Add Dropout layer and CudnnDropout layer
Date Fri, 03 Jun 2016 07:48:40 GMT
SINGA-170 Add Dropout layer and CudnnDropout layer

Checked code format via cpplint.py.
Tested the compilation and linking for cudnn.
Note, if there are multiple cuda installed, pls configure CUDA_BIN_PATH
to your cuda path (e.g., /usr/local/cuda-7.5) before `cmake ..`.
You need to set the CMAKE_INCLUDE_PATH and CMAKE_LIBRARY_PATH for cudnn.


Project: http://git-wip-us.apache.org/repos/asf/incubator-singa/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-singa/commit/b4918753
Tree: http://git-wip-us.apache.org/repos/asf/incubator-singa/tree/b4918753
Diff: http://git-wip-us.apache.org/repos/asf/incubator-singa/diff/b4918753

Branch: refs/heads/dev
Commit: b4918753cfee52a5ef537e453c953b4c384044d2
Parents: 3a87201
Author: Wei Wang <wangwei@comp.nus.edu.sg>
Authored: Wed May 18 12:03:36 2016 +0800
Committer: Wei Wang <wangwei@comp.nus.edu.sg>
Committed: Wed May 18 12:03:36 2016 +0800

----------------------------------------------------------------------
 include/singa/core/common.h     |  4 ++--
 include/singa/core/device.h     |  1 -
 include/singa/core/tensor.h     | 10 +++++-----
 src/core/tensor/tensor.cc       | 32 ++++++++++++++++----------------
 src/model/layer/cudnn_dropout.h | 10 ++++++----
 src/model/layer/cudnn_utils.h   |  6 +++---
 src/model/layer/dropout.h       | 10 +++++++---
 src/model/layer/rnn.h           | 13 ++++++++++---
 8 files changed, 49 insertions(+), 37 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/b4918753/include/singa/core/common.h
----------------------------------------------------------------------
diff --git a/include/singa/core/common.h b/include/singa/core/common.h
index 4d783fb..2f5b167 100644
--- a/include/singa/core/common.h
+++ b/include/singa/core/common.h
@@ -24,7 +24,7 @@
 
 #ifdef USE_CUDA
 #include <cuda_runtime.h>
-#include "cublas_v2.h"
+#include <cublas_v2.h>
 #ifdef USE_CUDNN
 #include <cudnn.h>
 #endif
@@ -40,7 +40,7 @@ typedef struct _Cuda { } Cuda;
 typedef struct _Cudnn { } Cudnn;
 /// To implement function using opencl libraries
 typedef struct _Opencl { } Opencl;
-}  // namespace lib;
+}  // namespace lib
 
 typedef unsigned char Byte;
 /// Blob reprent a chunk of memory (on device or host) managed by VirtualMemory.

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/b4918753/include/singa/core/device.h
----------------------------------------------------------------------
diff --git a/include/singa/core/device.h b/include/singa/core/device.h
index b96efca..9022041 100644
--- a/include/singa/core/device.h
+++ b/include/singa/core/device.h
@@ -130,7 +130,6 @@ class CppDevice : public Device {
 
   /// Free cpu memory.
   void Free(void* ptr) override;
-
 };
 
 /// a singleton CppDevice as the host for all devices.

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/b4918753/include/singa/core/tensor.h
----------------------------------------------------------------------
diff --git a/include/singa/core/tensor.h b/include/singa/core/tensor.h
index 6c20c4f..88a895b 100644
--- a/include/singa/core/tensor.h
+++ b/include/singa/core/tensor.h
@@ -65,8 +65,8 @@ class Tensor {
  public:
   ~Tensor();
   Tensor();
-  Tensor(Shape&& shape, DataType dtype = kFloat32);
-  Tensor(const Shape& shape, DataType dtype = kFloat32);
+  explicit Tensor(Shape&& shape, DataType dtype = kFloat32);
+  explicit Tensor(const Shape& shape, DataType dtype = kFloat32);
   Tensor(Shape&& shape, Device* dev, DataType dtype = kFloat32);
   Tensor(const Shape& shape, Device* dev, DataType dtype = kFloat32);
 
@@ -278,7 +278,7 @@ Tensor operator/(const Tensor& t, DType x);
 template <typename DType>
 void Div(const Tensor& t, DType x, Tensor* ret);
 
-//================Blas operations============================================
+// ================Blas operations============================================
 // ===== Level 1
 // TODO(wangwei) make amax/amin/asum a member function of tensor
 // void Amax(Tensor, Context* ctx); Get the index of the max value in a vector
@@ -308,7 +308,7 @@ void Mult(DType alpha, const Tensor& lhs, DType beta, const Tensor&
rhs,
 
 // tempalte<typename DType> T Dot(const Tensor& lhs, const Tensor& rhs);
 
-//================Random operations==========================================
+// ================Random operations==========================================
 /// For each element x set x = 1 if random() < p; otherwise x = 1.
 void Bernoulli(float p, Tensor* t);
 /// Fill in Tensor 't' following uniform distribution.
@@ -316,7 +316,7 @@ void Uniform(float low, float high, Tensor* t);
 /// Fill in Tensor 't' following Gaussian distribution.
 void Gaussian(float mean, float std, Tensor* t);
 
-//================Neural Net operations======================================
+// ================Neural Net operations======================================
 /* following API of cudnn, e.g., conv, pool, lrn, batchnorm, softmax
 void ConvFwd(const ConvConf& conf, const Tensor& x, const Tensor& w, Tensor*
y);
 void ConvBwdBias(const ConvConf& conf, const Tensor& dy, Tensor* db);

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/b4918753/src/core/tensor/tensor.cc
----------------------------------------------------------------------
diff --git a/src/core/tensor/tensor.cc b/src/core/tensor/tensor.cc
index cd62a38..0e5570d 100644
--- a/src/core/tensor/tensor.cc
+++ b/src/core/tensor/tensor.cc
@@ -381,7 +381,7 @@ GenBinaryTensorFunction(Pow, Pow);
 #define EltwiseTensorScalarFn(fn, t, x, ret)                               \
   do {                                                                     \
     TYPE_LIB_SWITCH(t.data_type(), DType, t.device()->device_lib(), Lib, { \
-      static_assert(std::is_same<SType, DType>::value,                            
\
+      static_assert(std::is_same<SType, DType>::value,                     \
                     "The Scalar type must match the Tensor data type");    \
       ret->device()->Exec(                                                 \
           [t, x, ret](Context* ctx) {                                      \
@@ -436,8 +436,8 @@ template Tensor Mult<float>(float alpha, const Tensor& lhs,
float beta,
     const Tensor& rhs);
 
 template <typename SType>
-void Mult(SType alpha, const Tensor& A, SType beta, const Tensor& B, Tensor* C)
-{
+void Mult(SType alpha, const Tensor& A, SType beta, const Tensor& B,
+          Tensor* C) {
   CHECK_EQ(A.shape().size(), 2u);
   bool transA = A.transpose();
   size_t m = transA ? A.shape()[1] : A.shape()[0], n = 0;
@@ -445,14 +445,14 @@ void Mult(SType alpha, const Tensor& A, SType beta, const Tensor&
B, Tensor* C)
     n = C->Size();
     TYPE_LIB_SWITCH(A.data_type(), DType, A.device()->device_lib(), Lib, {
       static_assert(std::is_same<SType, DType>::value,
-        "The scalar type must be the same as the tensor data type");
+                    "The scalar type must be the same as the tensor data type");
       C->device()->Exec(
-        [transA, m, n, alpha, A, beta, B, C](Context* ctx) {
-        GEMV<DType, Lib>(transA, m, n, alpha, A.blob(),
-          B.blob(), beta, C->blob(), ctx);
-        },
-        {A.blob(), B.blob()}, {C->blob()});
-      });
+          [transA, m, n, alpha, A, beta, B, C](Context* ctx) {
+            GEMV<DType, Lib>(transA, m, n, alpha, A.blob(), B.blob(), beta,
+                             C->blob(), ctx);
+          },
+          {A.blob(), B.blob()}, {C->blob()});
+    });
   } else {
     CHECK(!C->transpose());
     bool transB = B.transpose();
@@ -462,15 +462,15 @@ void Mult(SType alpha, const Tensor& A, SType beta, const Tensor&
B, Tensor* C)
     CHECK_EQ(A.Size(), m * k);
     CHECK_EQ(B.Size(), n * k);
     TYPE_LIB_SWITCH(A.data_type(), DType, A.device()->device_lib(), Lib, {
-        static_assert(std::is_same<SType, DType>::value,
-          "The scalar type must be the same as the tensor data type");
-        C->device()->Exec(
+      static_assert(std::is_same<SType, DType>::value,
+                    "The scalar type must be the same as the tensor data type");
+      C->device()->Exec(
           [transA, transB, m, n, k, alpha, A, beta, B, C](Context* ctx) {
-          GEMM<DType, Lib>(transA, transB, m, n, k, alpha, A.blob(),
-            B.blob(), beta, C->blob(), ctx);
+            GEMM<DType, Lib>(transA, transB, m, n, k, alpha, A.blob(), B.blob(),
+                             beta, C->blob(), ctx);
           },
           {A.blob(), B.blob()}, {C->blob()});
-        });
+    });
   }
 }
 template void Mult<float>(float alpha, const Tensor& lhs, float beta,

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/b4918753/src/model/layer/cudnn_dropout.h
----------------------------------------------------------------------
diff --git a/src/model/layer/cudnn_dropout.h b/src/model/layer/cudnn_dropout.h
index d2b68b9..db0aa15 100644
--- a/src/model/layer/cudnn_dropout.h
+++ b/src/model/layer/cudnn_dropout.h
@@ -16,12 +16,14 @@
  * limitations under the License.
  */
 
-#ifndef SINGA_MODEL_LAYER_CUDNN_DROPOUT_H_
-#define SINGA_MODEL_LAYER_CUDNN_DROPOUT_H_
+#ifndef SRC_MODEL_LAYER_CUDNN_DROPOUT_H_
+#define SRC_MODEL_LAYER_CUDNN_DROPOUT_H_
 #ifdef USE_CUDNN
 // cudnn dropout is added in cudnn 5
 #if CUDNN_MAJOR_VERSION >= 5
-
+#include <utility>
+#include <string>
+#include <vector>
 #include "./dropout.h"
 #include "singa/core/common.h"
 #include "singa/model/layer.h"
@@ -51,4 +53,4 @@ class CudnnDropout : public Dropout {
 }  // namespace
 #endif  // CUDNN_VERSION_MAJOR>=5
 #endif  // USE_CUDNN
-#endif  // SINGA_MODEL_LAYER_CUDNN_DROPOUT_H_
+#endif  // SRC_MODEL_LAYER_CUDNN_DROPOUT_H_

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/b4918753/src/model/layer/cudnn_utils.h
----------------------------------------------------------------------
diff --git a/src/model/layer/cudnn_utils.h b/src/model/layer/cudnn_utils.h
index 92c8df7..298ee5c 100644
--- a/src/model/layer/cudnn_utils.h
+++ b/src/model/layer/cudnn_utils.h
@@ -15,8 +15,8 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifndef SINGA_MODEL_LAYER_CUDNN_BASE_H_
-#define SINGA_MODEL_LAYER_CUDNN_BASE_H_
+#ifndef SRC_MODEL_LAYER_CUDNN_UTILS_H_
+#define SRC_MODEL_LAYER_CUDNN_UTILS_H_
 
 #ifdef USE_CUDNN
 
@@ -82,4 +82,4 @@ inline const char* cudnnGetErrorString(cudnnStatus_t status) {
 
 }  // namespace singa
 #endif  // USE_CUDNN
-#endif  // SINGA_MODEL_LAYER_CUDNN_BASE_H_
+#endif  // SRC_MODEL_LAYER_CUDNN_UTILS_H_

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/b4918753/src/model/layer/dropout.h
----------------------------------------------------------------------
diff --git a/src/model/layer/dropout.h b/src/model/layer/dropout.h
index a6e733a..5efaf6a 100644
--- a/src/model/layer/dropout.h
+++ b/src/model/layer/dropout.h
@@ -15,9 +15,13 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifndef SINGA_MODEL_LAYER_DROPOUT_H_
-#define SINGA_MODEL_LAYER_DROPOUT_H_
+#ifndef SRC_MODEL_LAYER_DROPOUT_H_
+#define SRC_MODEL_LAYER_DROPOUT_H_
+#include <utility>
+#include <string>
+#include <vector>
 #include "singa/model/layer.h"
+
 namespace singa {
 class Dropout : public Layer {
  public:
@@ -55,4 +59,4 @@ class Dropout : public Layer {
   Tensor mask_;
 };
 }  // namespace singa
-#endif  // SINGA_MODEL_LAYER_DROPOUT_H_
+#endif  // SRC_MODEL_LAYER_DROPOUT_H_

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/b4918753/src/model/layer/rnn.h
----------------------------------------------------------------------
diff --git a/src/model/layer/rnn.h b/src/model/layer/rnn.h
index a6ba461..35c86bd 100644
--- a/src/model/layer/rnn.h
+++ b/src/model/layer/rnn.h
@@ -15,9 +15,16 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifndef SINGA_MODEL_LAYER_DROPOUT_H_
-#define SINGA_MODEL_LAYER_DROPOUT_H_
+#ifndef SRC_MODEL_LAYER_RNN_H_
+#define SRC_MODEL_LAYER_RNN_H_
+
+#include <utility>
+#include <string>
+#include <vector>
+#include <stack>
+
 #include "singa/model/layer.h"
+
 namespace singa {
 /// To enable use the same layer multiple times in one iteration in RNN,
 /// the Forward() function pushes the 'input' or 'output' that are
@@ -56,4 +63,4 @@ class RNN : public Layer {
   std::stack<Tensor*> states_;
 };
 }  // namespace singa
-#endif  // SINGA_MODEL_LAYER_DROPOUT_H_
+#endif  // SRC_MODEL_LAYER_RNN_H_


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