Fix calculations in gru_unit_op to be consistent with gru_op

paddle/operators/gru_unit_op.h

@@ -146,35 +146,27 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
     auto* weight_grad =
         context.Output<Tensor>(framework::GradVarName("Weight"));
     auto* bias_grad = context.Output<Tensor>(framework::GradVarName("Bias"));
-    input_grad->mutable_data<T>(context.GetPlace());
-    hidden_prev_grad->mutable_data<T>(context.GetPlace());
-    weight_grad->mutable_data<T>(context.GetPlace());
     Tensor gate_grad;
-    gate_grad.mutable_data<T>(input->dims(), context.GetPlace());
     Tensor reset_hidden_prev_grad;
-    reset_hidden_prev_grad.mutable_data<T>(reset_hidden_prev->dims(),
-                                           context.GetPlace());
-
-    int batch_size = input->dims()[0];
-    int frame_size = hidden_prev->dims()[1];
 
     const T* hidden_prev_data = hidden_prev->data<T>();
-    T* hidden_prev_grad_data = hidden_prev_grad->data<T>();
     const T* weight_data = weight->data<T>();
-    T* weight_grad_data = weight_grad->data<T>();
-    T* gate_grad_data = gate_grad.data<T>();
+    T* gate_grad_data =
+        gate_grad.mutable_data<T>(input->dims(), context.GetPlace());
     const T* reset_hidden_prev_data = reset_hidden_prev->data<T>();
-    T* reset_hidden_prev_grad_data = reset_hidden_prev_grad.data<T>();
+    T* reset_hidden_prev_grad_data = reset_hidden_prev_grad.mutable_data<T>(
+        reset_hidden_prev->dims(), context.GetPlace());
 
     auto h_p = EigenMatrix<T>::From(*hidden_prev);
     auto g = EigenMatrix<T>::From(*gate);
     auto d_h = EigenMatrix<T>::From(*hidden_grad);
-    auto d_x = EigenMatrix<T>::From(*input_grad);
-    auto d_h_p = EigenMatrix<T>::From(*hidden_prev_grad);
     auto d_g = EigenMatrix<T>::From(gate_grad);
     auto d_r_h_p = EigenMatrix<T>::From(reset_hidden_prev_grad);
     auto place = context.GetEigenDevice<Place>();
 
+    int batch_size = input->dims()[0];
+    int frame_size = hidden_prev->dims()[1];
+
     Eigen::array<int, 2> extents({{batch_size, frame_size}});
     Eigen::array<int, 2> u_offsets({{0, 0}});
     auto u = g.slice(u_offsets, extents);  // update gate
@@ -195,28 +187,42 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
                          gate_grad_data + frame_size * 2, frame_size * 3,
                          weight_data + frame_size * frame_size * 2, frame_size,
                          0, reset_hidden_prev_grad_data, frame_size);
+    // backward for unactivated reset gate
+    ActGradCompute(context.Attr<int>("gate_activation"), place, r, r,
+                   d_g.slice(r_offsets, extents), d_r_h_p * h_p);
+    // backward for weight
+    if (weight_grad) {
+      T* weight_grad_data = weight_grad->mutable_data<T>(context.GetPlace());
       // backward for state_weight
       math::gemm<Place, T>(
           context.device_context(), true, false, frame_size, frame_size,
           batch_size, 1, reset_hidden_prev_data, frame_size,
           gate_grad_data + frame_size * 2, frame_size * 3, 0,
           weight_grad_data + frame_size * frame_size * 2, frame_size);
-    // backward for unactivated reset gate
-    ActGradCompute(context.Attr<int>("gate_activation"), place, r, r,
-                   d_g.slice(r_offsets, extents), d_r_h_p * h_p);
+
       // backward for update_gate_weight and reset_gate_weight
       math::gemm<Place, T>(context.device_context(), true, false, frame_size,
                            frame_size * 2, batch_size, 1, hidden_prev_data,
                            frame_size, gate_grad_data, frame_size * 3, 0,
                            weight_grad_data, frame_size * 2);
+    }
     // backward for hidden_prev
+    if (hidden_prev_grad) {
+      T* hidden_prev_grad_data =
+          hidden_prev_grad->mutable_data<T>(context.GetPlace());
+      auto d_h_p = EigenMatrix<T>::From(*hidden_prev_grad);
       d_h_p.device(place) = d_r_h_p * r + d_h * (u.constant(T(1)) - u);
       math::gemm<Place, T>(context.device_context(), false, true, batch_size,
                            frame_size, frame_size * 2, 1, gate_grad_data,
                            frame_size * 3, weight_data, frame_size * 2, 1,
                            hidden_prev_grad_data, frame_size);
+    }
     // backward for input
+    if (input_grad) {
+      input_grad->mutable_data<T>(context.GetPlace());
+      auto d_x = EigenMatrix<T>::From(*input_grad);
       d_x.device(place) = d_g;
+    }
     // backward for bias
     if (bias_grad) {
       bias_grad->mutable_data<T>(context.GetPlace());

python/paddle/v2/fluid/tests/test_gru_unit_op.py

@@ -28,8 +28,8 @@ def relu(x):
 
 
 class TestGRUUnitOp(OpTest):
-    batch_size = 3
-    frame_size = 5
+    batch_size = 5
+    frame_size = 10
     activate = {
         GRUActivationType.identity: identity,
         GRUActivationType.sigmoid: sigmoid,
@@ -92,9 +92,7 @@ class TestGRUUnitOp(OpTest):
         self.check_output()
 
     def test_check_grad(self):
-        self.check_grad(
-            ['Input', 'HiddenPrev', 'Weight'], ['Hidden'],
-            max_relative_error=0.007)
+        self.check_grad(['Input', 'HiddenPrev', 'Weight'], ['Hidden'])
 
 
 class TestGRUUnitOpWithBias(TestGRUUnitOp):
@@ -110,9 +108,12 @@ class TestGRUUnitOpWithBias(TestGRUUnitOp):
         }
 
     def test_check_grad(self):
+        self.check_grad(['Input', 'HiddenPrev', 'Weight', 'Bias'], ['Hidden'])
+
+    def test_check_grad_ingore_input(self):
         self.check_grad(
-            ['Input', 'HiddenPrev', 'Weight', 'Bias'], ['Hidden'],
-            max_relative_error=0.007)
+            ['HiddenPrev', 'Weight', 'Bias'], ['Hidden'],
+            no_grad_set=set('Input'))
 
 
 if __name__ == '__main__':