Merge pull request #4437 from luotao1/warning

fix compiler warning from MKLDNNLayer and so on

Author: luotao1

paddle/framework/lod_tensor_test.cu

@@ -36,8 +36,8 @@ TEST(LoDTensor, LoDInGPU) {
   lod_tensor.mutable_data<float>(place);
 
   lod_tensor.set_lod(src_lod);
-  CHECK_EQ(lod_tensor.lod_element(0, 2), 4);
-  CHECK_EQ(lod_tensor.lod_element(0, 4), 8);
+  CHECK_EQ(lod_tensor.lod_element(0, 2), 4UL);
+  CHECK_EQ(lod_tensor.lod_element(0, 4), 8UL);
 
   auto lod = lod_tensor.lod();
 

paddle/gserver/layers/MKLDNNConvLayer.cpp

@@ -28,7 +28,7 @@ bool MKLDNNConvLayer::init(const LayerMap& layerMap,
   if (!MKLDNNLayer::init(layerMap, parameterMap)) {
     return false;
   }
-  CHECK_EQ(inputLayers_.size(), 1) << "Only support one input layer yet";
+  CHECK_EQ(inputLayers_.size(), 1UL) << "Only support one input layer yet";
   CHECK_EQ(inputLayers_.size(), parameters_.size());
   CHECK(config_.shared_biases()) << "Only support shared biases yet";
 

paddle/gserver/layers/MKLDNNFcLayer.cpp

@@ -28,7 +28,7 @@ bool MKLDNNFcLayer::init(const LayerMap& layerMap,
     return false;
   }
 
-  CHECK_EQ(inputLayers_.size(), 1) << "Only support one input layer yet";
+  CHECK_EQ(inputLayers_.size(), 1UL) << "Only support one input layer yet";
   CHECK_EQ(inputLayers_.size(), parameters_.size());
   CHECK(!parameters_[0]->isSparse()) << "Do not support sparse yet";
 

paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp

@@ -228,7 +228,7 @@ void genGroundTruth(vector<SingleBeamExpansion>& beamExpansions,
         curBeam.groundTruth[j] = *(start + n);
         curBeam.inBeam[j] = 1;
       } else {
-        CHECK_LE(curBeam.rowIdxInBeam[j] + 1,
+        CHECK_LE((size_t)curBeam.rowIdxInBeam[j] + 1,
                  curBeam.subSeqStartPos.size() - 1);
         int start = curBeam.subSeqStartPos[curBeam.rowIdxInBeam[j]];
         int end = curBeam.subSeqStartPos[curBeam.rowIdxInBeam[j] + 1];

paddle/operators/multiplex_op.cu

@@ -42,7 +42,7 @@ class MultiplexGPUKernel : public framework::OpKernel {
     for (auto i = 0; i < rows; i++) {
       int32_t k = index[i];
       PADDLE_ENFORCE_GE(k, 0, "index must be nonnegative.");
-      PADDLE_ENFORCE_LT(k, ins.size(),
+      PADDLE_ENFORCE_LT((size_t)k, ins.size(),
                         "index exceeds the number of candidate tensors.");
       memory::Copy(place, out->data<T>() + i * cols, place,
                    ins[k]->data<T>() + i * cols, cols * sizeof(T), stream);