[Cherry-pick] fix the computation for dx (grad for x) for prelu operation. (#20949) (#21514)

* fix the computation for dx (grad for x) for prelu operation. (#20949)

* set the default value of alpha for prelu to 0.25, test=develop

* add the call to __syncthreads(), test=develop

* fix the implementation of cpu prelu, test=develop

* repair the implementation of element mode prelu, test=develop

* modify test_prelu_op.py, test=develop

paddle/fluid/operators/math/prelu.cu

@@ -18,108 +18,76 @@ namespace paddle {
 namespace operators {
 namespace math {
 
-static const int CUDA_NUM_THREADS = 1024;
-static const int CUDA_MAX_NUM_BLOCKS = 65535;
-inline static int GET_NUM_BLOCKS(const int N) {
+#define CUDA_NUM_THREADS 1024
+
+// CUDA: grid stride looping
+#define CUDA_KERNEL_LOOP(i, n)                                 \
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
+       i += blockDim.x * gridDim.x)
+
+inline static int PADDLE_GET_BLOCKS(const int N) {
   return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS;
 }
 
 template <typename T>
 __global__ void PReluChannelWiseKernel(const T *input, const T *alpha,
-                                       T *output, int channel,
-                                       size_t spatial_size) {
-  size_t offset = blockIdx.x * spatial_size;
-  const T *in = input + offset;
-  T *out = output + offset;
-  T scale = alpha[blockIdx.x % channel];
-
-  for (size_t i = threadIdx.x; i < spatial_size; i += blockDim.x) {
-    T x = in[i];
-    out[i] = (x > 0) ? x : scale * x;
+                                       T *output, size_t channel_num,
+                                       size_t plane_size, size_t numel) {
+  size_t index;
+  CUDA_KERNEL_LOOP(index, numel) {
+    size_t temp = index / plane_size;
+    size_t channel_index = temp % channel_num;
+    T scale = alpha[channel_index];
+    T x = input[index];
+    output[index] = (x > 0) ? x : scale * x;
   }
 }
 
 template <typename T>
 __global__ void PReluElementWiseKernel(const T *input, const T *alpha,
-                                       T *output, size_t spatial_size) {
-  size_t offset = blockIdx.x * spatial_size;
-  const T *in = input + offset;
-  const T *scale = alpha + offset;
-  T *out = output + offset;
-
-  for (size_t i = threadIdx.x; i < spatial_size; i += blockDim.x) {
-    T x = in[i];
-    out[i] = (x > 0) ? x : scale[i] * x;
+                                       T *output, size_t spatial_size,
+                                       size_t numel) {
+  size_t index;
+  CUDA_KERNEL_LOOP(index, numel) {
+    size_t element_index = index % spatial_size;
+    T scale = alpha[element_index];
+    T x = input[index];
+    output[index] = (x > 0) ? x : scale * x;
   }
 }
 
 template <typename T>
 __global__ void PReluScalarKernel(const T *input, const T *alpha, T *output,
-                                  size_t spatial_size) {
-  size_t offset = blockIdx.x * spatial_size;
-  const T *in = input + offset;
-  T scale = *alpha;
-  T *out = output + offset;
-
-  for (size_t i = threadIdx.x; i < spatial_size; i += blockDim.x) {
-    T x = in[i];
-    out[i] = (x > 0) ? x : scale * x;
+                                  size_t numel) {
+  T scale = alpha[0];
+  size_t index;
+  CUDA_KERNEL_LOOP(index, numel) {
+    T x = input[index];
+    output[index] = (x > 0) ? x : scale * x;
   }
 }
 
-template <typename T>
-static inline void PReluChannelWise(cudaStream_t stream, const T *input,
-                                    const T *alpha, T *output,
-                                    std::vector<int> input_shape) {
-  size_t unroll = input_shape[0] * input_shape[1];
-  size_t spatial_size = input_shape[2] * input_shape[3];
-  CHECK_LT(unroll, CUDA_MAX_NUM_BLOCKS);
-  PReluChannelWiseKernel<<<unroll, CUDA_NUM_THREADS, 0, stream>>>(
-      input, alpha, output, input_shape[1], spatial_size);
-}
-
-template <typename T>
-static inline void PReluElementWise(cudaStream_t stream, const T *input,
-                                    const T *alpha, T *output,
-                                    std::vector<int> input_shape) {
-  size_t unroll = input_shape[0] * input_shape[1];
-  size_t spatial_size = input_shape[2] * input_shape[3];
-  CHECK_LT(unroll, CUDA_MAX_NUM_BLOCKS);
-  PReluElementWiseKernel<<<unroll, CUDA_NUM_THREADS, 0, stream>>>(
-      input, alpha, output, spatial_size);
-}
-
-template <typename T>
-static inline void PReluScalar(cudaStream_t stream, const T *input,
-                               const T *alpha, T *output,
-                               std::vector<int> input_shape) {
-  size_t unroll = input_shape[0] * input_shape[1];
-  size_t spatial_size = input_shape[2] * input_shape[3];
-  CHECK_LT(unroll, CUDA_MAX_NUM_BLOCKS);
-  PReluScalarKernel<<<unroll, CUDA_NUM_THREADS, 0, stream>>>(
-      input, alpha, output, spatial_size);
-}
-
 template <typename T>
 void PreluChannelWiseDirectCUDAFunctor<T>::operator()(
     cudaStream_t stream, const T *input, const T *alpha, T *output,
     std::vector<int> input_shape) {
-  size_t unroll = input_shape[0] * input_shape[1];
-  size_t spatial_size = input_shape[2] * input_shape[3];
-  CHECK_LT(unroll, CUDA_MAX_NUM_BLOCKS);
-  PReluChannelWiseKernel<<<unroll, CUDA_NUM_THREADS, 0, stream>>>(
-      input, alpha, output, input_shape[1], spatial_size);
+  size_t plane_size = input_shape[2] * input_shape[3];
+  size_t spatial_size = input_shape[1] * plane_size;
+  size_t numel = input_shape[0] * spatial_size;
+  PReluChannelWiseKernel<<<PADDLE_GET_BLOCKS(numel), CUDA_NUM_THREADS, 0,
+                           stream>>>(input, alpha, output, input_shape[1],
+                                     plane_size, numel);
 }
 
 template <typename T>
 void PreluElementWiseDirectCUDAFunctor<T>::operator()(
     cudaStream_t stream, const T *input, const T *alpha, T *output,
     std::vector<int> input_shape) {
-  size_t unroll = input_shape[0] * input_shape[1];
-  size_t spatial_size = input_shape[2] * input_shape[3];
-  CHECK_LT(unroll, CUDA_MAX_NUM_BLOCKS);
-  PReluElementWiseKernel<<<unroll, CUDA_NUM_THREADS, 0, stream>>>(
-      input, alpha, output, spatial_size);
+  size_t plane_size = input_shape[2] * input_shape[3];
+  size_t spatial_size = input_shape[1] * plane_size;
+  size_t numel = input_shape[0] * spatial_size;
+  PReluElementWiseKernel<<<PADDLE_GET_BLOCKS(numel), CUDA_NUM_THREADS, 0,
+                           stream>>>(input, alpha, output, spatial_size, numel);
 }
 
 template <typename T>
@@ -127,11 +95,11 @@ void PreluScalarDirectCUDAFunctor<T>::operator()(cudaStream_t stream,
                                                  const T *input, const T *alpha,
                                                  T *output,
                                                  std::vector<int> input_shape) {
-  size_t unroll = input_shape[0] * input_shape[1];
-  size_t spatial_size = input_shape[2] * input_shape[3];
-  CHECK_LT(unroll, CUDA_MAX_NUM_BLOCKS);
-  PReluScalarKernel<<<unroll, CUDA_NUM_THREADS, 0, stream>>>(
-      input, alpha, output, spatial_size);
+  size_t plane_size = input_shape[2] * input_shape[3];
+  size_t spatial_size = input_shape[1] * plane_size;
+  size_t numel = input_shape[0] * spatial_size;
+  PReluScalarKernel<<<PADDLE_GET_BLOCKS(numel), CUDA_NUM_THREADS, 0, stream>>>(
+      input, alpha, output, numel);
 }
 
 template class PreluChannelWiseDirectCUDAFunctor<float>;

paddle/fluid/operators/math/prelu.h

@@ -42,6 +42,7 @@ class PreluScalarDirectCUDAFunctor {
   void operator()(cudaStream_t stream, const T *input, const T *alpha,
                   T *output, std::vector<int> input_shape);
 };
+
 #endif
 
 }  // namespace math

paddle/fluid/operators/prelu_op.cc

@@ -42,10 +42,21 @@ class PReluOp : public framework::OperatorWithKernel {
                      "equal to the number of channels, should be %d",
                      x_dim[1]);
     } else if (mode == "element") {
-      PADDLE_ENFORCE(product(ctx->GetInputDim("Alpha")) == product(x_dim),
+      auto alpha_dim = ctx->GetInputDim("Alpha");
+      auto alpha_rank = alpha_dim.size();
+      auto x_rank = x_dim.size();
+      size_t x_product = 1;
+      size_t alpha_product = 1;
+      PADDLE_ENFORCE_EQ(alpha_rank, x_rank,
+                        "For element-wise mode, rank of weight Alpha must be ",
+                        "equal to the rank of input.");
+      for (int64_t i = x_rank - 1; i > 0; i--) {
+        x_product *= x_dim[i];
+        alpha_product *= alpha_dim[i];
+      }
+      PADDLE_ENFORCE_EQ(x_product, alpha_product,
                         "For element-wise mode, size of weight Alpha must be "
-                     "equal to the number of input, should be %d",
-                     product(x_dim));
+                        "equal to the number of input.");
     } else {
       PADDLE_THROW("Unkown mode %s", mode);
     }

paddle/fluid/operators/prelu_op.cu

@@ -20,11 +20,19 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-static const int CUDA_NUM_THREADS = 1024;
-static const int CUDA_MAX_NUM_BLOCKS = 65535;
-
 using Tensor = framework::Tensor;
 
+#define CUDA_NUM_THREADS 1024
+
+// CUDA: grid stride looping
+#define CUDA_KERNEL_LOOP(i, n)                                 \
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
+       i += blockDim.x * gridDim.x)
+
+inline static int PADDLE_GET_BLOCKS(const int N) {
+  return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS;
+}
+
 template <typename DeviceContext, typename T>
 class CUDAPReluKernel : public framework::OpKernel<T> {
  public:
@@ -59,71 +67,47 @@ class CUDAPReluKernel : public framework::OpKernel<T> {
   }
 };
 
-namespace prelu {
-struct ElementWiseMode {};
-struct ChannelMode {};
-struct ScalarMode {};
-} /* namespace prelu */
-
-template <typename T, typename M>
-struct AlphaFunctor {
-  HOSTDEVICE inline T operator()(const T* alpha, size_t channel,
-                                 size_t spatial_size, size_t idx) const {}
-};
-
-template <typename T>
-struct AlphaFunctor<T, prelu::ElementWiseMode> {
-  HOSTDEVICE inline T operator()(const T* alpha, size_t channel,
-                                 size_t spatial_size, size_t idx) const {
-    return alpha[blockIdx.x * spatial_size + idx];
-  }
-};
+enum PRELU_MODE { Element, Channel, Scalar };
 
 template <typename T>
-struct AlphaFunctor<T, prelu::ChannelMode> {
-  HOSTDEVICE inline T operator()(const T* alpha, size_t channel,
-                                 size_t spatial_size, size_t idx) const {
-    return alpha[blockIdx.x % channel];
-  }
-};
-
-template <typename T>
-struct AlphaFunctor<T, prelu::ScalarMode> {
-  HOSTDEVICE inline T operator()(const T* alpha, size_t channel,
-                                 size_t spatial_size, size_t idx) const {
-    return alpha[0];
-  }
-};
-
-template <typename T, typename M>
-__global__ void PReluGradElementWiseKernel(const T* x_ptr, const T* y_ptr,
+__global__ void PReluOpGradKernel(const T* x_ptr, const T* y_ptr,
                                   const T* alpha_ptr, const T* dy_ptr,
-                                           T* dx_ptr, T* dalpha_ptr,
-                                           size_t channel,
-                                           size_t spatial_size) {
-  size_t offset = blockIdx.x * spatial_size;
-  AlphaFunctor<T, M> alpha_func;
-
-  for (size_t i = threadIdx.x; i < spatial_size; i += blockDim.x) {
-    T y = y_ptr[offset + i];
-    T x = x_ptr[offset + i];
-    T dy = dy_ptr[offset + i];
-    T alpha = alpha_func(alpha_ptr, channel, spatial_size, i);
-    if (dx_ptr != nullptr) dx_ptr[offset + i] = (y > 0) ? dy : alpha * dy;
-    if (dalpha_ptr != nullptr) dalpha_ptr[offset + i] = (x > 0) ? 0 : x * dy;
+                                  T* dx_ptr, T* dalpha_ptr, size_t channel_num,
+                                  size_t plane_size, size_t spatial_size,
+                                  size_t numel, PRELU_MODE mode) {
+  size_t index;
+  CUDA_KERNEL_LOOP(index, numel) {
+    T scale;
+    if (mode == Element) {
+      size_t element_index = index % spatial_size;
+      scale = alpha_ptr[element_index];
+    } else if (mode == Channel) {
+      size_t temp = index / plane_size;
+      size_t channel_index = temp % channel_num;
+      scale = alpha_ptr[channel_index];
+    } else {
+      scale = alpha_ptr[0];
+    }
+    T x = x_ptr[index];
+    T dy = dy_ptr[index];
+    if (dx_ptr != nullptr) dx_ptr[index] = (x > 0) ? dy : scale * dy;
+    if (dalpha_ptr != nullptr) dalpha_ptr[index] = (x > 0) ? 0 : x * dy;
   }
 }
 
-template <typename T, typename M>
-class PreluGradElementwiseFunctor {
+template <typename T>
+class PreluOpGradFunctor {
  public:
   void operator()(cudaStream_t stream, const T* x, const T* y, const T* alpha,
-                  const T* dy, T* dx, T* dalpha, std::vector<int> input_shape) {
-    size_t unroll = input_shape[0] * input_shape[1];
-    size_t spatial_size = input_shape[2] * input_shape[3];
-    CHECK_LT(unroll, CUDA_MAX_NUM_BLOCKS);
-    PReluGradElementWiseKernel<T, M><<<unroll, CUDA_NUM_THREADS, 0, stream>>>(
-        x, y, alpha, dy, dx, dalpha, input_shape[1], spatial_size);
+                  const T* dy, T* dx, T* dalpha, std::vector<int> input_shape,
+                  PRELU_MODE mode) {
+    size_t plane_size = input_shape[2] * input_shape[3];
+    size_t spatial_size = plane_size * input_shape[1];
+    size_t numel = spatial_size * input_shape[0];
+    PReluOpGradKernel<
+        T><<<PADDLE_GET_BLOCKS(numel), CUDA_NUM_THREADS, 0, stream>>>(
+        x, y, alpha, dy, dx, dalpha, input_shape[1], plane_size, spatial_size,
+        numel, mode);
   }
 };
 
@@ -162,7 +146,7 @@ class CUDAPReluGradKernel : public framework::OpKernel<T> {
 
     T* dalpha_tmp_ptr;
     Tensor dalpha_tmp;
-    if (mode == "element" || dalpha_ptr == nullptr) {
+    if (dalpha_ptr == nullptr) {
       dalpha_tmp_ptr = dalpha_ptr;
     } else {
       auto& dev_ctx = context.template device_context<DeviceContext>();
@@ -170,25 +154,24 @@ class CUDAPReluGradKernel : public framework::OpKernel<T> {
       dalpha_tmp_ptr = dalpha_tmp.mutable_data<T>(context.GetPlace());
     }
 
+    PRELU_MODE m;
     if (mode == "element") {
-      PreluGradElementwiseFunctor<T, prelu::ElementWiseMode> prelu_grad;
-      prelu_grad(stream, x_ptr, y_ptr, alpha_ptr, dy_ptr, dx_ptr,
-                 dalpha_tmp_ptr, input_shape);
+      m = Element;
     } else if (mode == "channel") {
-      PreluGradElementwiseFunctor<T, prelu::ChannelMode> prelu_grad;
-      prelu_grad(stream, x_ptr, y_ptr, alpha_ptr, dy_ptr, dx_ptr,
-                 dalpha_tmp_ptr, input_shape);
+      m = Channel;
     } else {
-      PreluGradElementwiseFunctor<T, prelu::ScalarMode> prelu_grad;
-      prelu_grad(stream, x_ptr, y_ptr, alpha_ptr, dy_ptr, dx_ptr,
-                 dalpha_tmp_ptr, input_shape);
+      m = Scalar;
     }
+    PreluOpGradFunctor<T> prelu_grad;
+    prelu_grad(stream, x_ptr, y_ptr, alpha_ptr, dy_ptr, dx_ptr, dalpha_tmp_ptr,
+               input_shape, m);
 
-    if (mode == "element" || dalpha_tmp_ptr == nullptr) return;
+    if (dalpha_tmp_ptr == nullptr) return;
 
     std::vector<int> reduce_dims;
     for (size_t i = 0; i < input_shape.size(); i++) {
       if (mode == "channel" && i == 1) continue;
+      if (mode == "element" && i != 0) continue;
       reduce_dims.push_back(i);
     }
 

paddle/fluid/operators/prelu_op.h

@@ -45,8 +45,10 @@ class PReluKernel : public framework::OpKernel<T> {
         o_ptr[i] = x_ptr[i] > 0 ? x_ptr[i] : alpha_ptr[index] * x_ptr[i];
       }
     } else if (mode == "element") {
+      int temp = numel / dim[0];
       for (i = 0; i < numel; i++) {
-        o_ptr[i] = x_ptr[i] > 0 ? x_ptr[i] : alpha_ptr[i] * x_ptr[i];
+        index = i % temp;
+        o_ptr[i] = x_ptr[i] > 0 ? x_ptr[i] : alpha_ptr[index] * x_ptr[i];
       }
     } else {
       for (i = 0; i < numel; i++) {
@@ -64,12 +66,10 @@ class PReluGradKernel : public framework::OpKernel<T> {
     auto* dx = context.Output<Tensor>(framework::GradVarName("X"));
     auto* dout = context.Input<Tensor>(framework::GradVarName("Out"));
     auto* dalpha = context.Output<Tensor>(framework::GradVarName("Alpha"));
-    auto* out = context.Input<Tensor>("Out");
     auto* alpha = context.Input<Tensor>("Alpha");
     const T* alpha_ptr = alpha->data<T>();
     const T* x_ptr = x->data<T>();
     const T* dout_ptr = dout->data<T>();
-    const T* out_ptr = out->data<T>();
     std::string mode = context.Attr<std::string>("mode");
     int numel = x->numel();
     auto dim = x->dims();
@@ -83,15 +83,18 @@ class PReluGradKernel : public framework::OpKernel<T> {
           temp = numel / (dim[0] * dim[1]);
           index = (i / temp) % dim[1];
           dx_ptr[i] =
-              out_ptr[i] > 0 ? dout_ptr[i] : alpha_ptr[index] * dout_ptr[i];
+              x_ptr[i] > 0 ? dout_ptr[i] : alpha_ptr[index] * dout_ptr[i];
         }
       } else if (mode == "element") {
+        temp = numel / dim[0];
         for (i = 0; i < numel; i++) {
-          dx_ptr[i] = out_ptr[i] > 0 ? dout_ptr[i] : alpha_ptr[i] * dout_ptr[i];
+          index = i % temp;
+          dx_ptr[i] =
+              x_ptr[i] > 0 ? dout_ptr[i] : alpha_ptr[index] * dout_ptr[i];
         }
       } else {
         for (i = 0; i < numel; i++) {
-          dx_ptr[i] = out_ptr[i] > 0 ? dout_ptr[i] : alpha_ptr[0] * dout_ptr[i];
+          dx_ptr[i] = x_ptr[i] > 0 ? dout_ptr[i] : alpha_ptr[0] * dout_ptr[i];
         }
       }
     }
@@ -105,15 +108,17 @@ class PReluGradKernel : public framework::OpKernel<T> {
         for (i = 0; i < numel; i++) {
           temp = numel / (dim[0] * dim[1]);
           index = (i / temp) % dim[1];
-          dalpha_ptr[index] += out_ptr[i] > 0 ? 0 : x_ptr[i] * dout_ptr[i];
+          dalpha_ptr[index] += x_ptr[i] > 0 ? 0 : x_ptr[i] * dout_ptr[i];
         }
       } else if (mode == "element") {
+        temp = numel / dim[0];
         for (i = 0; i < numel; i++) {
-          dalpha_ptr[i] += out_ptr[i] > 0 ? 0 : x_ptr[i] * dout_ptr[i];
+          index = i % temp;
+          dalpha_ptr[index] += x_ptr[i] > 0 ? 0 : x_ptr[i] * dout_ptr[i];
         }
       } else {
         for (i = 0; i < numel; i++) {
-          dalpha_ptr[0] += out_ptr[i] > 0 ? 0 : x_ptr[i] * dout_ptr[i];
+          dalpha_ptr[0] += x_ptr[i] > 0 ? 0 : x_ptr[i] * dout_ptr[i];
         }
       }
     }

paddle/fluid/operators/reduce_ops/cub_reduce.h

@@ -66,6 +66,7 @@ __global__ void ReduceKernel2D(const Tx* x, Ty* y, ReduceOp reducer,
   Ty reduce_var = init;
   for (int idx_y = threadIdx.x; idx_y < reduce_num; idx_y += BlockDim)
     reduce_var = reducer(reduce_var, transformer(x[idx_x + idx_y]));
+  __syncthreads();
 
   reduce_var =
       cub::BlockReduce<Ty, BlockDim>(temp_storage).Reduce(reduce_var, reducer);
@@ -113,6 +114,7 @@ __global__ void ReduceKernel(const Tx* x, Ty* y, ReduceOp reducer,
     for (int k = 0; k < Rank; ++k) idx_x += (sub_index[k] * x_strides[k]);
     reduce_var = static_cast<Ty>(reducer(reduce_var, transformer(x[idx_x])));
   }
+  __syncthreads();
 
   reduce_var =
       cub::BlockReduce<Ty, BlockDim>(temp_storage).Reduce(reduce_var, reducer);

python/paddle/fluid/dygraph/nn.py

@@ -2146,7 +2146,9 @@ class PRelu(layers.Layer):
         if self._mode == 'channel':
             self._alpha_shape = [1, input.shape[1], 1, 1]
         elif self._mode == 'element':
-            self._alpha_shape = input.shape
+            self._alpha_shape = [
+                1, input.shape[1], input.shape[2], input.shape[3]
+            ]
         self._dtype = self._helper.input_dtype(input)
         self._alpha = self.create_parameter(
             attr=self._param_attr,

python/paddle/fluid/layers/nn.py

@@ -12431,14 +12431,14 @@ def prelu(x, mode, param_attr=None, name=None):
     if mode == 'channel':
         alpha_shape = [1, x.shape[1], 1, 1]
     elif mode == 'element':
-        alpha_shape = x.shape
+        alpha_shape = [1, x.shape[1], x.shape[2], x.shape[3]]
     dtype = helper.input_dtype(input_param_name='x')
     alpha = helper.create_parameter(
         attr=helper.param_attr,
         shape=alpha_shape,
         dtype='float32',
         is_bias=False,
-        default_initializer=Constant(1.0))
+        default_initializer=Constant(0.25))
     out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type="prelu",

python/paddle/fluid/tests/unittests/test_layers.py

@@ -712,7 +712,7 @@ class TestLayer(LayerTest):
             self.assertTrue(
                 np.array_equal(btp1.bias.numpy(), btp2.bias.numpy()))
 
-    def test_prelu(self):
+    def prelu_test(self, mode):
         inp_np = np.ones([5, 200, 100, 100]).astype('float32')
         with self.static_graph():
             data_t = layers.data(
@@ -720,7 +720,6 @@ class TestLayer(LayerTest):
                 shape=[5, 200, 100, 100],
                 dtype="float32",
                 append_batch_size=False)
-            mode = 'channel'
             out = layers.prelu(
                 data_t, mode, param_attr=ParamAttr(initializer=Constant(1.0)))
             static_rlt = self.get_static_graph_result(
@@ -732,7 +731,6 @@ class TestLayer(LayerTest):
                 shape=[5, 200, 100, 100],
                 dtype="float32",
                 append_batch_size=False)
-            mode = 'channel'
             prelu = nn.PRelu(
                 'prelu',
                 mode=mode,
@@ -742,7 +740,6 @@ class TestLayer(LayerTest):
                 feed={"input": inp_np}, fetch_list=[out])[0]
 
         with self.dynamic_graph():
-            mode = 'channel'
             prelu = nn.PRelu(
                 'prelu',
                 mode=mode,
@@ -756,7 +753,6 @@ class TestLayer(LayerTest):
         with self.dynamic_graph():
             inp_np = np.random.randn(5, 200, 100, 100).astype("float32")
             inp = base.to_variable(inp_np)
-            mode = 'channel'
             prelu1 = nn.PRelu(
                 'prelu1',
                 mode=mode,
@@ -779,6 +775,11 @@ class TestLayer(LayerTest):
             self.assertTrue(
                 np.array_equal(prelu1.weight.numpy(), prelu2.weight.numpy()))
 
+    def test_prelu(self):
+        self.prelu_test('channel')
+        self.prelu_test('element')
+        self.prelu_test('all')
+
     def test_embeding(self):
         inp_word = np.array([[[1]]]).astype('int64')
         dict_size = 20

python/paddle/fluid/tests/unittests/test_prelu_op.py

@@ -37,7 +37,8 @@ class PReluTest(OpTest):
             alpha_np = np.random.rand(1, x_np.shape[1], 1, 1).astype("float32")
             self.inputs = {'X': x_np, 'Alpha': alpha_np}
         else:
-            alpha_np = np.random.rand(*x_np.shape).astype("float32")
+            alpha_np = np.random.rand(1, x_np.shape[1], x_np.shape[2], \
+                x_np.shape[3]).astype("float32")
             self.inputs = {'X': x_np, 'Alpha': alpha_np}
 
         out_np = np.maximum(self.inputs['X'], 0.)