add mkldnn_addto unit test and pass it

paddle/gserver/layers/MKLDNNLayer.cpp

@@ -77,7 +77,7 @@ void MKLDNNLayer::forward(PassType passType) {
       needResetBwd_ = true;
     }
 
-    if (inputLayers_[0]->getType() == "data") {
+    if (inputLayers_[0]->getType() == "data" && inputLayers_.size() == 1) {
       // Update input value data when input layer is "data" type,
       // since the input value data address might be changed.
       CHECK(extInVal_);

paddle/gserver/tests/MKLDNNTester.cpp

@@ -132,7 +132,7 @@ void MKLDNNTester::checkForward() {
   VLOG(MKLDNN_TESTS) << "Check Forward";
   printTopDatas();
   double delta =
-      compareMatrix(dnnLayer_->getOutputValue(), refLayer_->getOutputValue());
+      compareMatrix(refLayer_->getOutputValue(), dnnLayer_->getOutputValue());
   EXPECT_LE(fabs(delta), eps_);
 }
 
@@ -147,7 +147,7 @@ void MKLDNNTester::checkBackwardData() {
     VLOG(MKLDNN_ALL) << "Reference Backward Result: InputGrad " << i;
     printMatrix(refDiff);
 
-    double delta = compareMatrix(dnnDiff, refDiff);
+    double delta = compareMatrix(refDiff, dnnDiff);
     EXPECT_LE(fabs(delta), eps_);
     if (isBN) {
       // the other two inputs in batch norm are for moving mean and var
@@ -177,7 +177,7 @@ void MKLDNNTester::checkBackwardWgts() {
                      << parameters_[REF][i]->getName();
     printVector(ref);
 
-    double delta = compareVector(dnn, ref);
+    double delta = compareVector(ref, dnn);
     EXPECT_LE(fabs(delta), eps_);
   }
 

paddle/gserver/tests/test_MKLDNN.cpp

@@ -271,20 +271,53 @@ TEST(MKLDNNLayer, BatchNormLayer) {
   testBatchNormLayer({16, 32, 16, 16});
 }
 
-struct testActDesc {
+struct testImageDesc {
   int bs, ic, ih, iw;
 };
 
-static void getAddtoConfig(TestConfig& cfg, const testActDesc& pm) {
+static void getAddtoConfig(TestConfig& cfg,
+                           const testImageDesc& pm,
+                           const size_t nInputs = 1) {
   cfg.biasSize = 0;
   cfg.layerConfig.set_type("addto");
   size_t layerSize = pm.ic * pm.ih * pm.iw;
   cfg.layerConfig.set_size(layerSize);
-  cfg.inputDefs.push_back({INPUT_DATA, "layer_0", layerSize, 0});
-  cfg.layerConfig.add_inputs();
+  cfg.layerConfig.set_active_type("relu");
+  for (size_t i = 0; i < nInputs; ++i) {
+    std::stringstream ss;
+    ss << "layer_" << i;
+    cfg.inputDefs.push_back({INPUT_DATA, ss.str(), layerSize, 0});
+    LayerInputConfig* input = cfg.layerConfig.add_inputs();
+    ImageConfig* img_conf = input->mutable_image_conf();
+    img_conf->set_channels(pm.ic);
+    img_conf->set_img_size_y(pm.ih);
+    img_conf->set_img_size(pm.iw);
+  }
+}
+
+void testAddtoLayer(const testImageDesc& pm, const size_t nInputs) {
+  CHECK_GE(nInputs, 1);
+  TestConfig dnnConfig;
+  getAddtoConfig(dnnConfig, pm, nInputs);
+  dnnConfig.layerConfig.set_type("mkldnn_addto");
+  // TODO(TJ): test with bias
+  for (auto withBias : {false}) {
+    if (withBias) {
+      dnnConfig.biasSize = pm.ic * pm.ih * pm.iw;
+    } else {
+      dnnConfig.biasSize = 0;
+    }
+    RUN_MKLDNN_TEST_LAYER(dnnConfig, "addto", pm)
+  }
+}
+
+TEST(MKLDNNLayer, AddtoLayer) {
+  testAddtoLayer({16, 5, 14, 14}, 1);
+  testAddtoLayer({8, 10, 8, 8}, 2);
+  testAddtoLayer({4, 12, 1, 1}, 3);
 }
 
-void testActivation(std::string actType, const testActDesc& pm) {
+void testActivation(std::string actType, const testImageDesc& pm) {
   // TODO(TJ): remove me when paddle support elu activation
   if (actType == "mkldnn_elu") {
     return;