diff --git a/paddle/function/Function.h b/paddle/function/Function.h
index 9ad00c6f370cf64e9cc26f16e62c4d2ddb284003..15eb35b7f7dac1b98f2d8694707d83b84bda0f2e 100644
--- a/paddle/function/Function.h
+++ b/paddle/function/Function.h
@@ -38,7 +38,7 @@ public:
       if (err) {
         *err = Error(e.what());
       } else {
-        LOG(FATAL) << "Cannot get key " << key << "with error " << e.what();
+        LOG(FATAL) << "Cannot get key " << key << " with error " << e.what();
       }
       return T();
     }
diff --git a/paddle/function/PadOpGpu.cu b/paddle/function/PadOpGpu.cu
index 9104b1aca507c526858c2117e0a5db59f535091e..9094f1528433fdcaad3397a991aa8ac6fa04bc01 100644
--- a/paddle/function/PadOpGpu.cu
+++ b/paddle/function/PadOpGpu.cu
@@ -44,9 +44,9 @@ void Pad<DEVICE_TYPE_GPU>(real* outputs,
   size_t nth = num * inC * inH * inW;
   int blockSize = 1024;
   int gridSize = (nth + 1024 - 1) / 1024;
-  int cstart = pad.channelStart, cend = pad.channelEnd;
-  int hstart = pad.heightStart, hend = pad.heightEnd;
-  int wstart = pad.widthStart, wend = pad.widthEnd;
+  int cstart = pad.channel[0], cend = pad.channel[1];
+  int hstart = pad.height[0], hend = pad.height[1];
+  int wstart = pad.width[0], wend = pad.width[1];
   int outC = inC + cstart + cend;
   int outH = inH + hstart + hend;
   int outW = inW + wstart + wend;
@@ -83,9 +83,9 @@ void PadGrad<DEVICE_TYPE_GPU>(real* inGrad,
   int nth = num * inC * inH * inW;
   int blockSize = 1024;
   int gridSize = (nth + 1024 - 1) / 1024;
-  int cstart = pad.channelStart, cend = pad.channelEnd;
-  int hstart = pad.heightStart, hend = pad.heightEnd;
-  int wstart = pad.widthStart, wend = pad.widthEnd;
+  int cstart = pad.channel[0], cend = pad.channel[1];
+  int hstart = pad.height[0], hend = pad.height[1];
+  int wstart = pad.width[0], wend = pad.width[1];
   int outC = inC + cstart + cend;
   int outH = inH + hstart + hend;
   int outW = inW + wstart + wend;
diff --git a/paddle/function/PadOpTest.cpp b/paddle/function/PadOpTest.cpp
index cd22d9113567912f7694e05e5d631e49d940e3ac..f77ac2a8c49c83f2d6c64c2a30b6a2f2eb09ac10 100644
--- a/paddle/function/PadOpTest.cpp
+++ b/paddle/function/PadOpTest.cpp
@@ -24,48 +24,22 @@ TEST(Pad, real) {
         for (size_t imgSizeW : {5, 32, 96}) {
           VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
                   << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
-
-          FunctionCompare compare("Pad",
-                                  FuncConfig()
-                                      .set("cstart", 2)
-                                      .set("cend", 3)
-                                      .set("hstart", 1)
-                                      .set("hend", 2)
-                                      .set("wstart", 3)
-                                      .set("wend", 2));
-          TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW};
-          TensorShape outDims{
-              numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
-          compare.addInputs(BufferArg(VALUE_TYPE_FLOAT, inDims));
-          compare.addOutputs(BufferArg(VALUE_TYPE_FLOAT, outDims, ASSIGN_TO));
-          compare.run();
-        }
-      }
-    }
-  }
-}
-
-TEST(PadGrad, real) {
-  for (size_t numSamples : {5, 32}) {
-    for (size_t channels : {1, 5, 32}) {
-      for (size_t imgSizeH : {5, 33, 100}) {
-        for (size_t imgSizeW : {5, 32, 96}) {
-          VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
-                  << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
-          FunctionCompare compare("PadGrad",
-                                  FuncConfig()
-                                      .set("cstart", 2)
-                                      .set("cend", 3)
-                                      .set("hstart", 1)
-                                      .set("hend", 2)
-                                      .set("wstart", 3)
-                                      .set("wend", 2));
-          TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW};
-          TensorShape outDims{
-              numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
-          compare.addInputs(BufferArg(VALUE_TYPE_FLOAT, outDims));
-          compare.addOutputs(BufferArg(VALUE_TYPE_FLOAT, inDims, ASSIGN_TO));
-          compare.run();
+          for (bool test_grad : {false, true}) {
+            FunctionCompare compare(
+                test_grad ? "PadGrad" : "Pad",
+                FuncConfig()
+                    .set<std::vector<uint32_t>>("channel", {2, 3})
+                    .set<std::vector<uint32_t>>("height", {1, 2})
+                    .set<std::vector<uint32_t>>("width", {3, 2}));
+            TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW};
+            TensorShape outDims{
+                numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
+            compare.addInputs(
+                BufferArg(VALUE_TYPE_FLOAT, test_grad ? outDims : inDims));
+            compare.addOutputs(BufferArg(
+                VALUE_TYPE_FLOAT, test_grad ? inDims : outDims, ASSIGN_TO));
+            compare.run();
+          }
         }
       }
     }