Add more comments, also add __must_check.

paddle/gserver/activations/ActivationFunction.cpp

@@ -69,11 +69,11 @@ static ClassRegistrar<ActivationFunction> gActivationRegistrar;
 class IdentityActivation : public ActivationFunction {
 public:
   static const std::string name;
-  Status forward(Argument& act) {
+  Status __must_check forward(Argument& act) {
     (void)act;
     return Status();
   }
-  Status backward(Argument& act) {
+  Status __must_check backward(Argument& act) {
     (void)act;
     return Status();
   }
@@ -92,11 +92,11 @@ static InitFunction __reg_activation__identity([] {
  * \f]
  */
 BEGIN_DEFINE_ACTIVATION(sigmoid)
-Status forward(Argument& act) {
+Status __must_check forward(Argument& act) {
   act.value->sigmoid(*act.value);
   return Status();
 }
-Status backward(Argument& act) {
+Status __must_check backward(Argument& act) {
   act.grad->sigmoidDerivative(*act.value);
   return Status();
 }
@@ -115,12 +115,12 @@ MatrixPtr sftMaxDot_;
 MatrixPtr one_;
 
 public:
-Status forward(Argument& act) {
+Status __must_check forward(Argument& act) {
   act.value->softmax(*act.value);
   return Status();
 }
 
-Status backward(Argument& act) {
+Status __must_check backward(Argument& act) {
   MatrixPtr outputV = act.value;
   MatrixPtr outputG = act.grad;
 
@@ -167,7 +167,7 @@ ACTIVATION_CLASS_NAME(softmax) softmax_;
 Argument argument_;
 
 public:
-Status forward(Argument& act) {
+Status __must_check forward(Argument& act) {
   if (act.value->getWidth() != 1UL) {
     return Status(
         "Input width for each timestep of sequence softmax should be 1");
@@ -191,7 +191,7 @@ Status forward(Argument& act) {
   return Status();
 }
 
-Status backward(Argument& act) {
+Status __must_check backward(Argument& act) {
   if (act.value->getWidth() != 1UL) {
     return Status(
         "Input width for each timestep of sequence softmax should be 1");
@@ -207,7 +207,8 @@ Status backward(Argument& act) {
     argument_.value->setData(act.value->getData() + offset, 1UL, size);
     argument_.grad->setData(act.grad->getData() + offset, 1UL, size);
 
-    softmax_.backward(argument_);
+    Status status = softmax_.backward(argument_);
+    if (!status.isOK()) return status;
   }
   return Status();
 }
@@ -224,12 +225,12 @@ END_DEFINE_ACTIVATION(sequence_softmax)
  *    0 otherwise.
  */
 BEGIN_DEFINE_ACTIVATION(relu)
-Status forward(Argument& act) {
+Status __must_check forward(Argument& act) {
   act.value->relu(*act.value);
   return Status();
 }
 
-Status backward(Argument& act) {
+Status __must_check backward(Argument& act) {
   act.grad->reluDerivative(*act.value);
   return Status();
 }
@@ -249,12 +250,12 @@ END_DEFINE_ACTIVATION(relu)
  * TODO(yuyang18): Remove magic number 24 or make it configuable.
  */
 BEGIN_DEFINE_ACTIVATION(brelu)
-Status forward(Argument& act) {
+Status __must_check forward(Argument& act) {
   act.value->brelu(*act.value);
   return Status();
 }
 
-Status backward(Argument& act) {
+Status __must_check backward(Argument& act) {
   act.grad->breluDerivative(*act.value);
   return Status();
 }
@@ -267,12 +268,12 @@ END_DEFINE_ACTIVATION(brelu)
  * \f]
  */
 BEGIN_DEFINE_ACTIVATION(tanh)
-Status forward(Argument& act) {
+Status __must_check forward(Argument& act) {
   act.value->tanh(*act.value);
   return Status();
 }
 
-Status backward(Argument& act) {
+Status __must_check backward(Argument& act) {
   act.grad->tanhDerivative(*act.value);
   return Status();
 }
@@ -290,12 +291,12 @@ real a, b;
 
 public:
 ACTIVATION_CLASS_NAME(stanh)() : a(1.7159), b(2. / 3.) {}
-Status forward(Argument& act) {
+Status __must_check forward(Argument& act) {
   act.value->scaledTanh(*act.value, a, b);
   return Status();
 }
 
-Status backward(Argument& act) {
+Status __must_check backward(Argument& act) {
   act.grad->scaledTanhDerivative(*act.value, a, b);
   return Status();
 }
@@ -308,12 +309,12 @@ END_DEFINE_ACTIVATION(stanh)
  * \f]
  */
 BEGIN_DEFINE_ACTIVATION(softrelu)
-Status forward(Argument& act) {
+Status __must_check forward(Argument& act) {
   act.value->softrelu(*act.value);
   return Status();
 }
 
-Status backward(Argument& act) {
+Status __must_check backward(Argument& act) {
   act.grad->softreluDerivative(*act.value);
   return Status();
 }
@@ -332,7 +333,7 @@ END_DEFINE_ACTIVATION(softrelu)
  *     0   if z=0
  */
 BEGIN_DEFINE_ACTIVATION(abs)
-Status forward(Argument& act) {
+Status __must_check forward(Argument& act) {
   SetDevice device(act.deviceId);
   Matrix::resizeOrCreate(act.in,
                          act.value->getHeight(),
@@ -345,7 +346,7 @@ Status forward(Argument& act) {
   return Status();
 }
 
-Status backward(Argument& act) {
+Status __must_check backward(Argument& act) {
   act.grad->absDerivative(*act.in);
   return Status();
 }
@@ -358,7 +359,7 @@ END_DEFINE_ACTIVATION(abs)
  * \f]
  */
 BEGIN_DEFINE_ACTIVATION(square)
-Status forward(Argument& act) {
+Status __must_check forward(Argument& act) {
   SetDevice device(act.deviceId);
   Matrix::resizeOrCreate(act.in,
                          act.value->getHeight(),
@@ -371,7 +372,7 @@ Status forward(Argument& act) {
   return Status();
 }
 
-Status backward(Argument& act) {
+Status __must_check backward(Argument& act) {
   act.grad->squareDerivative(*act.in);
   return Status();
 }
@@ -384,12 +385,12 @@ END_DEFINE_ACTIVATION(square)
  * \f]
  */
 BEGIN_DEFINE_ACTIVATION(exponential)
-Status forward(Argument& act) {
+Status __must_check forward(Argument& act) {
   act.value->exp2(*act.value);
   return Status();
 }
 
-Status backward(Argument& act) {
+Status __must_check backward(Argument& act) {
   act.grad->expDerivative(*act.value);
   return Status();
 }
@@ -402,7 +403,7 @@ END_DEFINE_ACTIVATION(exponential)
  * \f]
  */
 BEGIN_DEFINE_ACTIVATION(log)
-Status forward(Argument& act) {
+Status __must_check forward(Argument& act) {
   SetDevice device(act.deviceId);
   Matrix::resizeOrCreate(act.in,
                          act.value->getHeight(),
@@ -415,7 +416,7 @@ Status forward(Argument& act) {
   return Status();
 }
 
-Status backward(Argument& act) {
+Status __must_check backward(Argument& act) {
   act.grad->dotDiv(*act.grad, *act.in);
   return Status();
 }

paddle/gserver/activations/ActivationFunction.h

@@ -49,7 +49,7 @@ public:
    *
    * Usually, act is Layer::output_
    */
-  virtual Status forward(Argument& act) = 0;
+  virtual Status __must_check forward(Argument& act) = 0;
 
   /**
    * @brief Backward propagaion
@@ -58,7 +58,7 @@ public:
    * - Before calling backward(), act.grad = dE / dy, where E is the error/cost
    * - After backward() returns, act.grad = dE / dx = (dE/dy) * (dy/dx)
    */
-  virtual Status backward(Argument& act) = 0;
+  virtual Status __must_check backward(Argument& act) = 0;
 
   virtual const std::string& getName() const = 0;
 };

paddle/gserver/layers/Layer.cpp

@@ -336,7 +336,7 @@ void Layer::showOutputStats() {
 void Layer::forwardActivation() {
   /* activation */
   auto status = activation_->forward(output_);
-  CHECK(status.isOK()) << status.what();
+  status.check();
 
   /* dropout */
   if (config_.drop_rate() > 0) {
@@ -375,7 +375,7 @@ void Layer::backwardActivation() {
   }
 
   auto status = activation_->backward(output_);
-  CHECK(status.isOK()) << status.what();
+  status.check();
 }
 
 void Layer::forwardDropOut() {

paddle/gserver/layers/MDLstmLayer.cpp

@@ -506,9 +506,12 @@ void MDLstmLayer::forwardGate2OutputSequence(int start,
           *frameState_[start + preOffsetV[i]].value, *checkFgOneDim, 1.0, 1.0);
     }
   }
-  activationGate_->forward(frameInputGate_[idxCurr]);
-  activationGate_->forward(frameForgetGate_[idxCurr]);
-  activation_->forward(frameInputNode_[idxCurr]);
+  auto status = activationGate_->forward(frameInputGate_[idxCurr]);
+  status.check();
+  status = activationGate_->forward(frameForgetGate_[idxCurr]);
+  status.check();
+  status = activation_->forward(frameInputNode_[idxCurr]);
+  status.check();
 
   frameState_[idxCurr].value->zeroMem();
   for (int i = 0; i < numDims_; i++) {
@@ -530,10 +533,12 @@ void MDLstmLayer::forwardGate2OutputSequence(int start,
 
   frameOutputGate_[idxCurr].value->addDotMul(
       *frameState_[idxCurr].value, *checkOg_, 1.0, 1.0);
-  activationGate_->forward(frameOutputGate_[idxCurr]);
+  status = activationGate_->forward(frameOutputGate_[idxCurr]);
+  status.check();
 
   framePreOutput_[idxCurr].value->copyFrom(*(frameState_[idxCurr].value));
-  activationState_->forward(framePreOutput_[idxCurr]);
+  status = activationState_->forward(framePreOutput_[idxCurr]);
+  status.check();
 
   frameOutput_[idxCurr].value->dotMul(*framePreOutput_[idxCurr].value,
                                       *frameOutputGate_[idxCurr].value);
@@ -640,12 +645,12 @@ void MDLstmLayer::backwardGate2OutputSequence(int start,
 
   framePreOutput_[idxCurr].grad->dotMul(*frameOutput_[idxCurr].grad,
                                         *frameOutputGate_[idxCurr].value);
-  activationState_->backward(framePreOutput_[idxCurr]);
+  activationState_->backward(framePreOutput_[idxCurr]).check();
   frameState_[idxCurr].grad->copyFrom(*(framePreOutput_[idxCurr].grad));
 
   frameOutputGate_[idxCurr].grad->dotMul(*frameOutput_[idxCurr].grad,
                                          *framePreOutput_[idxCurr].value);
-  activationGate_->backward(frameOutputGate_[idxCurr]);
+  activationGate_->backward(frameOutputGate_[idxCurr]).check();
 
   frameState_[idxCurr].grad->addDotMul(
       *frameOutputGate_[idxCurr].grad, *checkOg_, 1.0, 1.0);
@@ -702,9 +707,9 @@ void MDLstmLayer::backwardGate2OutputSequence(int start,
     }
   }
 
-  activationGate_->backward(frameInputGate_[idxCurr]);
-  activationGate_->backward(frameForgetGate_[idxCurr]);
-  activation_->backward(frameInputNode_[idxCurr]);
+  activationGate_->backward(frameInputGate_[idxCurr]).check();
+  activationGate_->backward(frameForgetGate_[idxCurr]).check();
+  activation_->backward(frameInputNode_[idxCurr]).check();
 
   if (bias_->getWGrad()) {
     for (int i = 0; i < numDims_; i++) {

paddle/gserver/layers/NCELayer.cpp

@@ -193,7 +193,8 @@ public:
       forwardOneInput(l);
     }
 
-    activation_->forward(sampleOut_);
+    auto status = activation_->forward(sampleOut_);
+    status.check();
 
     forwardCost();
   }
@@ -207,7 +208,8 @@ public:
 
     backwardCost();
 
-    activation_->backward(sampleOut_);
+    auto status = activation_->backward(sampleOut_);
+    status.check();
 
     if (biases_->getWGrad()) {
       backwardBias(callback);

paddle/gserver/layers/RecurrentLayer.cpp

@@ -217,21 +217,22 @@ void RecurrentLayer::forwardOneSequence(int start, int length) {
     if (prevOutput_) {
       frameOutput_[start].value->mul(*prevOutput_, *weight_->getW(), 1, 1);
     }
-    activation_->forward(frameOutput_[start]);
+    activation_->forward(frameOutput_[start]).check();
+
     for (int i = 1; i < length; ++i) {
       frameOutput_[start + i].value->mul(
           *frameOutput_[start + i - 1].value, *weight_->getW(), 1, 1);
-      activation_->forward(frameOutput_[start + i]);
+      activation_->forward(frameOutput_[start + i]).check();
     }
     if (prevOutput_) {
       prevOutput_->assign(*frameOutput_[start + length - 1].value);
     }
   } else {
-    activation_->forward(frameOutput_[start + length - 1]);
+    activation_->forward(frameOutput_[start + length - 1]).check();
     for (int i = length - 2; i >= 0; --i) {
       frameOutput_[start + i].value->mul(
           *frameOutput_[start + i + 1].value, *weight_->getW(), 1, 1);
-      activation_->forward(frameOutput_[start + i]);
+      activation_->forward(frameOutput_[start + i]).check();
     }
   }
 }
@@ -280,11 +281,11 @@ void RecurrentLayer::backwardOneSequence(int start, int length) {
   MatrixPtr weightT = weight_->getW()->getTranspose();
   if (!reversed_) {
     for (int i = length - 1; i > 0; --i) {
-      activation_->backward(frameOutput_[start + i]);
+      activation_->backward(frameOutput_[start + i]).check();
       frameOutput_[start + i - 1].grad->mul(
           *frameOutput_[start + i].grad, *weightT, 1, 1);
     }
-    activation_->backward(frameOutput_[start]);
+    activation_->backward(frameOutput_[start]).check();
     if (weight_->getWGrad()) {
       weight_->getWGrad()->mul(
           *output_.value->subMatrix(start, length - 1)->getTranspose(),
@@ -294,11 +295,11 @@ void RecurrentLayer::backwardOneSequence(int start, int length) {
     }
   } else {
     for (int i = 0; i < length - 1; ++i) {
-      activation_->backward(frameOutput_[start + i]);
+      activation_->backward(frameOutput_[start + i]).check();
       frameOutput_[start + i + 1].grad->mul(
           *frameOutput_[start + i].grad, *weightT, 1, 1);
     }
-    activation_->backward(frameOutput_[start + length - 1]);
+    activation_->backward(frameOutput_[start + length - 1]).check();
     if (weight_->getWGrad()) {
       weight_->getWGrad()->mul(
           *output_.value->subMatrix(start + 1, length - 1)->getTranspose(),
@@ -333,7 +334,7 @@ void RecurrentLayer::forwardBatch(int batchSize,
       }
       Argument arg;
       arg.value = batch2;
-      activation_->forward(arg);
+      activation_->forward(arg).check();
     }
   }
   batchValue_->copyBackSeq(*output_.value);
@@ -363,7 +364,7 @@ void RecurrentLayer::backwardBatch(int batchSize,
       Argument arg;
       arg.value = batch1;
       arg.grad = batch2;
-      activation_->backward(arg);
+      activation_->backward(arg).check();
 
       if (n != 0) {
         batch1 = batchGrad_->getBatchValue(n - 1, batch2->getHeight());

paddle/gserver/layers/SelectiveFullyConnectedLayer.cpp

@@ -192,7 +192,8 @@ void SelectiveFullyConnectedLayer::forward(PassType passType) {
                                nnz,
                                /*trans=*/false,
                                /*useGpu=*/useGpu_);
-    activation_->forward(arg);
+    //! TODO(yuyang18): Why we cannot invoke forwardActivation here?
+    activation_->forward(arg).check();
   } else /* train and test in train, not generating */ {
     // during training, this layer output value is *Matrix*, which is input of
     // eg. multi-class-cross-entropy

paddle/gserver/tests/test_WarpCTCLayer.cpp

@@ -148,11 +148,11 @@ LayerPtr createCTCLayer(string name,
 
   ActivationFunction* softmaxActivation = ActivationFunction::create("softmax");
 
-  softmaxActivation->forward(dataLayer->getOutput());
+  softmaxActivation->forward(dataLayer->getOutput()).check();
   layer->forward(PASS_GC);
 
   layer->backward();
-  softmaxActivation->backward(dataLayer->getOutput());
+  softmaxActivation->backward(dataLayer->getOutput()).check();
 
   return layer;
 }

paddle/utils/Compiler.h

@@ -0,0 +1,25 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+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
+
+#ifdef __GNUC__
+#define GCC_VERSION \
+  (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__)
+#else
+#define GCC_VERSION
+#endif
+
+#if GCC_VERSION >= 30400
+#define __must_check __attribute__((warn_unused_result))
+#else
+#define __must_check
+#endif

paddle/utils/Status.h

@@ -14,9 +14,11 @@ limitations under the License. */
 
 #pragma once
 
+#include <glog/logging.h>
 #include <stdio.h>
 #include <memory>
 #include <string>
+#include "Compiler.h"
 
 namespace paddle {
 
@@ -29,8 +31,55 @@ namespace paddle {
  * There are two styles to return status in Paddle.
  *
  * 1. Return Status
+ *    When method return a status, the return must use `__must_check` attribute.
+ *    Example as below.
+ * @code{cpp}
+ * Status __must_check foo();
  *
+ * Status __must_check bar() {
+ *   // do something.
+ *   Status s = foo();  // invoke other method return status.
+ *   if (!s.isOK()) return s;
+ *   // do something else.
+ *   return Status();
+ * }
+ * @endcode{cpp}
  *
+ * 2. Return by parameter.
+ *    It is another way to return a status, by using a pointer parameter.
+ *    Example as below.
+ *
+ * @code{cpp}
+ * Status bar();
+ *
+ * int foo(Status* status) {
+ *   // Do something.
+ *   Status s = bar();
+ *   if (!s.isOK()) {
+ *     *status = s;
+ *     return 0;
+ *   }
+ *   // Do something else.
+ *   if (someInternalErrorHappend) {
+ *     status->setByPrintf("Some dimension is too large, %d", dimension);
+ *     return 0;
+ *   }
+ *   // End of method.
+ *   return someValue;
+ * }
+ *
+ * Status foobar() {
+ *   Status s;
+ *   // do something.
+ *   foo(&s);
+ *   if (!s.isOK()) return s;
+ * }
+ * @endcode{cpp}
+ *
+ *
+ * Currently there is a helper method 'check' in status, because Paddle always
+ * use log(FATAL) or CHECK to make program exit before. When we clean all
+ * log(FATAL) and CHECK in Paddle, 'check' method will be removed.
  */
 class Status final : public std::exception {
 public:
@@ -92,6 +141,13 @@ public:
    */
   inline bool isOK() const noexcept { return errMsg_ == nullptr; }
 
+  /**
+   * @brief check this status by glog.
+   * @note It is a temp method used during cleaning Paddle code. It will be
+   *       removed later.
+   */
+  inline void check() const { CHECK(isOK()) << what(); }
+
 private:
   std::shared_ptr<std::string> errMsg_;
 };