diff --git a/paddle/fluid/operators/math/depthwise_conv.cu b/paddle/fluid/operators/math/depthwise_conv.cu
index 2c686c8ba7f56e70b283c160b1715869ac15b904..882b914f94fe454710f590d6aa0452627a88af75 100644
--- a/paddle/fluid/operators/math/depthwise_conv.cu
+++ b/paddle/fluid/operators/math/depthwise_conv.cu
@@ -45,67 +45,106 @@ __device__ __inline__ void CudaAtomicAddWithWarp(T* sum, T value) {
 // A Cuda kernel to compute the depthwise convolution forward pass
 // in NCHW format.
 template <typename T, bool fuse_relu_before_conv>
-__device__ __inline__ void KernelDepthwiseConv(ARG_DEFINE_KernelDepthwiseConv) {
-  for (int w_out = threadIdx.x; w_out < output_width; w_out += blockDim.x) {
-    for (int h_out = threadIdx.y; h_out < output_height; h_out += blockDim.y) {
-      const int batch = blockIdx.y;
-      const int c_out = blockIdx.x;
-
-      const int c_in = c_out / filter_multiplier;
-      const T* weight = filter_data + c_out * filter_height * filter_width;
-      T value = 0;
-      const int h_in_start = -padding_height + h_out * stride_height;
-      const int w_in_start = -padding_width + w_out * stride_width;
-      const int h_in_end = h_in_start + filter_height * dilate_height;
-      const int w_in_end = w_in_start + filter_width * dilate_width;
-
-      int in_offset;
-      if (data_layout != DataLayout::kNHWC) {
-        in_offset =
-            ((batch * input_channels + c_in) * input_height) * input_width;
-      } else {
-        in_offset = batch * input_height * input_width * input_channels;
+__device__ __inline__ void KernelDepthwiseConvNCHW(
+    ARG_DEFINE_KernelDepthwiseConv) {
+  int idx = threadIdx.x + blockIdx.x * blockDim.x;
+  if (idx >= (output_channels * batch_size * output_height * output_width))
+    return;
+
+  const int w_out = idx % output_width;
+  const int h_out = (idx / output_width) % output_height;
+  const int c_out = (idx / output_width / output_height) % output_channels;
+  const int batch = idx / output_width / output_height / output_channels;
+
+  const int c_in = c_out / filter_multiplier;
+  const T* weight = filter_data + c_out * filter_height * filter_width;
+  T value = 0;
+  const int h_in_start = -padding_height + h_out * stride_height;
+  const int w_in_start = -padding_width + w_out * stride_width;
+  const int h_in_end = h_in_start + filter_height * dilate_height;
+  const int w_in_end = w_in_start + filter_width * dilate_width;
+
+  int in_offset =
+      ((batch * input_channels + c_in) * input_height) * input_width;
+
+  const int h_end = h_in_end < input_height ? h_in_end : input_height;
+  const int w_end = w_in_end < input_width ? w_in_end : input_width;
+  const int h_start = h_in_start > 0 ? h_in_start : 0;
+  const int w_start = w_in_start > 0 ? w_in_start : 0;
+  int weight_offset = 0;
+
+#pragma unroll
+  for (int h_in = h_in_start; h_in < h_in_end; h_in += dilate_height) {
+#pragma unroll
+    for (int w_in = w_in_start; w_in < w_in_end; w_in += dilate_width) {
+      if (h_in >= h_start && h_in < h_end && w_in >= w_start && w_in < w_end) {
+        int offset = in_offset + h_in * input_width + w_in;
+        T in_data = input_data[offset];
+        if (fuse_relu_before_conv) {
+          value += weight[weight_offset] * max(0.0f, in_data);
+        } else {
+          value += weight[weight_offset] * in_data;
+        }
       }
+      weight_offset++;
+    }
+  }
+  int index = batch * output_channels * output_height * output_width +
+              c_out * output_height * output_width + h_out * output_width +
+              w_out;
+  output_data[index] = value;
+}
 
-      const int h_end = h_in_end < input_height ? h_in_end : input_height;
-      const int w_end = w_in_end < input_width ? w_in_end : input_width;
-      const int h_start = h_in_start > 0 ? h_in_start : 0;
-      const int w_start = w_in_start > 0 ? w_in_start : 0;
-      int weight_offset = 0;
-
-      for (int h_in = h_in_start; h_in < h_in_end; h_in += dilate_height) {
-        for (int w_in = w_in_start; w_in < w_in_end; w_in += dilate_width) {
-          if (h_in >= h_start && h_in < h_end && w_in >= w_start &&
-              w_in < w_end) {
-            int offset;
-            if (data_layout != DataLayout::kNHWC) {
-              offset = in_offset + h_in * input_width + w_in;
-            } else {
-              offset = in_offset +
-                       (h_in * input_width + w_in) * input_channels + c_in;
-            }
-            if (fuse_relu_before_conv) {
-              value += weight[weight_offset] * max(0.0f, input_data[offset]);
-            } else {
-              value += weight[weight_offset] * input_data[offset];
-            }
-          }
-          weight_offset++;
+// A Cuda kernel to compute the depthwise convolution forward pass
+// in NHWC format.
+template <typename T, bool fuse_relu_before_conv>
+__device__ __inline__ void KernelDepthwiseConvNHWC(
+    ARG_DEFINE_KernelDepthwiseConv) {
+  int idx = threadIdx.x + blockIdx.x * blockDim.x;
+  if (idx >= (output_channels * batch_size * output_height * output_width))
+    return;
+
+  const int c_out = idx % output_channels;
+  const int w_out = (idx / output_channels) % output_width;
+  const int h_out = (idx / output_channels / output_width) % output_height;
+  const int batch = idx / output_width / output_height / output_channels;
+
+  const int c_in = c_out / filter_multiplier;
+  const T* weight = filter_data + c_out * filter_height * filter_width;
+  T value = 0;
+  const int h_in_start = -padding_height + h_out * stride_height;
+  const int w_in_start = -padding_width + w_out * stride_width;
+  const int h_in_end = h_in_start + filter_height * dilate_height;
+  const int w_in_end = w_in_start + filter_width * dilate_width;
+
+  const int h_end = h_in_end < input_height ? h_in_end : input_height;
+  const int w_end = w_in_end < input_width ? w_in_end : input_width;
+  const int h_start = h_in_start > 0 ? h_in_start : 0;
+  const int w_start = w_in_start > 0 ? w_in_start : 0;
+  int weight_offset = 0;
+
+#pragma unroll
+  for (int h_in = h_in_start; h_in < h_in_end; h_in += dilate_height) {
+#pragma unroll
+    for (int w_in = w_in_start; w_in < w_in_end; w_in += dilate_width) {
+      if (h_in >= h_start && h_in < h_end && w_in >= w_start && w_in < w_end) {
+        int offset = ((batch * input_height + h_in) * input_width + w_in) *
+                         output_channels +
+                     c_in;
+        T in_data = input_data[offset];
+        if (fuse_relu_before_conv) {
+          value += weight[weight_offset] * max(0.0f, in_data);
+        } else {
+          value += weight[weight_offset] * in_data;
         }
       }
-      int index;
-      if (data_layout != DataLayout::kNHWC) {
-        index = ((batch * gridDim.x + c_out) * output_height + h_out) *
-                    output_width +
-                w_out;
-      } else {
-        index = ((batch * output_height + h_out) * output_width + w_out) *
-                    gridDim.x +
-                c_out;
-      }
-      output_data[index] = value;
+      weight_offset++;
     }
   }
+  int index = batch * output_channels * output_height * output_width +
+              h_out * output_width * output_channels + w_out * output_channels +
+              c_out;
+  output_data[index] = value;
 }
 
 template <typename T, int c_filter, bool fuse_relu_before_conv>
@@ -183,36 +222,37 @@ __device__ __inline__ void KernelDepthwiseConvCFilter(
 template <typename T, int c_filter_multiplier, int c_stride, int c_filter,
           bool fuse_relu_before_conv>
 __global__ void KernelDepthwiseConvSp(ARG_DEFINE_KernelDepthwiseConv) {
-  if (c_filter_multiplier == 0) {
-    if (c_filter == -1)
-      KernelDepthwiseConv<T, fuse_relu_before_conv>(
-          input_data, filter_data, batch_size, output_channels, output_height,
-          output_width, input_channels, input_height, input_width,
-          filter_multiplier, filter_height, filter_width, stride_height,
-          stride_width, padding_height, padding_width, dilate_height,
-          dilate_width, output_data, data_layout);
-    else
-      KernelDepthwiseConvCFilter<T, c_filter, fuse_relu_before_conv>(
-          input_data, filter_data, batch_size, output_channels, output_height,
-          output_width, input_channels, input_height, input_width,
-          filter_multiplier, filter_height, filter_width, stride_height,
-          stride_width, padding_height, padding_width, dilate_height,
-          dilate_width, output_data, data_layout);
-  } else {
-    if (c_filter == -1)
-      KernelDepthwiseConv<T, fuse_relu_before_conv>(
+  int final_filter_multiplier = filter_multiplier;
+  int h_stride = stride_height;
+  int w_stride = stride_width;
+  if (c_filter_multiplier != 0) {
+    final_filter_multiplier = c_filter_multiplier;
+    h_stride = c_stride;
+    w_stride = c_stride;
+  }
+  if (c_filter == -1) {
+    if (data_layout == DataLayout::kNCHW) {
+      KernelDepthwiseConvNCHW<T, fuse_relu_before_conv>(
           input_data, filter_data, batch_size, output_channels, output_height,
           output_width, input_channels, input_height, input_width,
-          c_filter_multiplier, filter_height, filter_height, c_stride, c_stride,
-          padding_height, padding_width, dilate_height, dilate_width,
+          final_filter_multiplier, filter_height, filter_width, h_stride,
+          w_stride, padding_height, padding_width, dilate_height, dilate_width,
           output_data, data_layout);
-    else
-      KernelDepthwiseConvCFilter<T, c_filter, fuse_relu_before_conv>(
+    } else {
+      KernelDepthwiseConvNHWC<T, fuse_relu_before_conv>(
           input_data, filter_data, batch_size, output_channels, output_height,
           output_width, input_channels, input_height, input_width,
-          c_filter_multiplier, filter_height, filter_height, c_stride, c_stride,
-          padding_height, padding_width, dilate_height, dilate_width,
+          final_filter_multiplier, filter_height, filter_width, h_stride,
+          w_stride, padding_height, padding_width, dilate_height, dilate_width,
           output_data, data_layout);
+    }
+  } else {
+    KernelDepthwiseConvCFilter<T, c_filter, fuse_relu_before_conv>(
+        input_data, filter_data, batch_size, output_channels, output_height,
+        output_width, input_channels, input_height, input_width,
+        final_filter_multiplier, filter_height, filter_width, h_stride,
+        w_stride, padding_height, padding_width, dilate_height, dilate_width,
+        output_data, data_layout);
   }
 }
 
@@ -564,12 +604,22 @@ class DepthwiseConvFunctor<platform::CUDADeviceContext, T,
     dim3 threads(std::min(output_width, thread), blocks, 1);
     dim3 grid(output_channels, batch_size, 1);
     int filter_multiplier = output_channels / input_channels;
+
+    int nums_output =
+        batch_size * output_channels * output_height * output_width;
+    int block_size = 512;
+
 #define check_case(c_filter_multiplier, c_stride, c_filter)                  \
   if (c_filter_multiplier == 0 ||                                            \
       filter_multiplier == c_filter_multiplier &&                            \
           stride_height == stride_width && stride_height == c_stride &&      \
           (ksize_height == ksize_width && ksize_height == c_filter ||        \
            c_filter == -1)) {                                                \
+    if (c_filter == -1) {                                                    \
+      threads.x = block_size;                                                \
+      grid.x = (nums_output + block_size - 1) / block_size;                  \
+      threads.y = threads.z = grid.y = grid.z = 1;                           \
+    }                                                                        \
     KernelDepthwiseConvSp<                                                   \
         T, c_filter_multiplier, c_stride, c_filter,                          \
         fuse_relu_before_conv><<<grid, threads, 0, context.stream()>>>(      \