Resume unit testing.

paddle/operators/cross_entropy_op.cu

@@ -23,8 +23,6 @@ template <typename T>
 __global__ void CrossEntropyGradientKernel(T* dX, const T* dY, const T* X,
                                            const int64_t* label, const int N,
                                            const int D) {
-  // TOOD(qingqing) define CUDA_1D_KERNEL_LOOP macro in a common file.
-  // CUDA_1D_KERNEL_LOOP(i, N) {
   for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
        i += blockDim.x * gridDim.x) {
     int idx = i * D + label[i];

paddle/operators/math/math_function.cu

@@ -240,7 +240,7 @@ void axpy<platform::GPUPlace, float>(const platform::DeviceContext& context,
   PADDLE_ENFORCE(platform::dynload::cublasSaxpy(
       reinterpret_cast<const platform::CUDADeviceContext&>(context)
           .cublas_handle(),
-      n, alpha, x, 1, y, 1));
+      n, &alpha, x, 1, y, 1));
 }
 
 template <>
@@ -250,7 +250,7 @@ void axpy<platform::GPUPlace, double>(const platform::DeviceContext& context,
   PADDLE_ENFORCE(platform::dynload::cublasDaxpy(
       reinterpret_cast<const platform::CUDADeviceContext&>(context)
           .cublas_handle(),
-      n, alpha, x, 1, y, 1));
+      n, &alpha, x, 1, y, 1));
 }
 
 template struct SetConstant<platform::GPUPlace, float>;
@@ -270,7 +270,7 @@ DEFINE_GPU_TRANS(6);
 
 struct TensorSetConstantGPU {
   TensorSetConstantGPU(const platform::DeviceContext& context,
-                    framework::Tensor* tensor, float value)
+                       framework::Tensor* tensor, float value)
       : context_(context), tensor_(tensor), value_(value) {}
 
   template <typename T>

paddle/operators/sequence_conv_op.h

@@ -65,10 +65,8 @@ class SequenceConvKernel : public framework::OpKernel<T> {
                         padding_trainable, context_start, context_length,
                         context_stride, up_pad, down_pad);
 
-    context.device_context().Finish();
     math::matmul<Place, T>(context.device_context(), col, false, filter, false,
                            static_cast<T>(1.0), out, static_cast<T>(0.0));
-    context.device_context().Finish();
   }
 };
 

python/paddle/v2/framework/tests/test_lstm_op.py

@@ -180,7 +180,6 @@ class TestLstmOp(OpTest):
             ['Input', 'Weight', 'Bias'], ['Hidden'], max_relative_error=5e-4)
 
 
-"""
 class TestLstmOpHasInitial(TestLstmOp):
     def set_argument(self):
         self.lod = [[0, 2, 5, 7]]
@@ -281,7 +280,7 @@ class TestLstmOpNotUsePeepholes(TestLstmOp):
         self.has_initial_state = False
         self.is_reverse = True
         self.use_peepholes = False
-"""
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/v2/framework/tests/test_seq_conv.py

@@ -122,7 +122,7 @@ class TestSeqProject(OpTest):
                 max_relative_error=0.05,
                 no_grad_set=set(['X', 'Filter']))
 
-    def not_test_check_grad_Filter(self):
+    def test_check_grad_Filter(self):
         self.check_grad(
             ['Filter'],
             'Out',
@@ -165,33 +165,34 @@ class TestSeqProject(OpTest):
         self.output_represention = 8  # output feature size
 
 
-#class TestSeqProjectCase1(TestSeqProject):
-#    def init_test_case(self):
-#        self.input_row = 11
-#        self.context_start = -1
-#        self.context_length = 3
-#        self.padding_trainable = True
-#        self.context_stride = 1
-#
-#        self.input_size = [self.input_row, 23]
-#        self.lod = [[0, 4, 5, 8, self.input_row]]
-#        self.output_represention = 8  # output feature size
-#
-#
-#class TestSeqProjectCase2(TestSeqProject):
-#    def init_test_case(self):
-#        self.input_row = 25
-#        self.context_start = 2
-#        self.context_length = 3
-#        self.padding_trainable = True
-#        self.context_stride = 1
-#
-#        self.input_size = [self.input_row, 23]
-#        idx = range(self.input_size[0])
-#        del idx[0]
-#        self.lod = [[0] + np.sort(random.sample(idx, 8)).tolist() +
-#                    [self.input_size[0]]]
-#        self.output_represention = 8  # output feature size
+class TestSeqProjectCase1(TestSeqProject):
+    def init_test_case(self):
+        self.input_row = 11
+        self.context_start = -1
+        self.context_length = 3
+        self.padding_trainable = True
+        self.context_stride = 1
+
+        self.input_size = [self.input_row, 23]
+        self.lod = [[0, 4, 5, 8, self.input_row]]
+        self.output_represention = 8  # output feature size
+
+
+class TestSeqProjectCase2(TestSeqProject):
+    def init_test_case(self):
+        self.input_row = 25
+        self.context_start = 2
+        self.context_length = 3
+        self.padding_trainable = True
+        self.context_stride = 1
+
+        self.input_size = [self.input_row, 23]
+        idx = range(self.input_size[0])
+        del idx[0]
+        self.lod = [[0] + np.sort(random.sample(idx, 8)).tolist() +
+                    [self.input_size[0]]]
+        self.output_represention = 8  # output feature size
+
 
 if __name__ == '__main__':
     unittest.main()