diff --git a/csrc/gpu/aten/operators/BatchNorm.cpp b/csrc/gpu/aten/operators/BatchNorm.cpp
index 110365edc..cf596e03b 100644
--- a/csrc/gpu/aten/operators/BatchNorm.cpp
+++ b/csrc/gpu/aten/operators/BatchNorm.cpp
@@ -584,12 +584,14 @@ void batch_norm_elementwise(
               batch_norm_elemt_channels_first_template<
                   scalar_t,
                   accscalar_t,
-                  int32_t>(out, self, *weight, *bias, mean_, invstd_);
+                  int32_t>(
+                  out, self.contiguous(), *weight, *bias, mean_, invstd_);
             } else {
               batch_norm_elemt_channels_first_template<
                   scalar_t,
                   scalar_t,
-                  int32_t>(out, self, *weight, *bias, mean_, invstd_);
+                  int32_t>(
+                  out, self.contiguous(), *weight, *bias, mean_, invstd_);
             }
           });
       return;
@@ -607,7 +609,16 @@ void batch_norm_elementwise(
           (!mean_.defined() || mean_.is_contiguous()) &&
           (!invstd_.defined() || invstd_.is_contiguous())) {
         batch_norm_elemt_channels_last_template(
-            out, self, *weight, *bias, mean_, invstd_);
+            out,
+            // It is a WA to fix Mobile-SSD convergence issue.
+            // TODO: Fully support: Check and convert activations with any
+            // shapes to align with kernel required memory layout.
+            self.dim() == 4 ? self.contiguous(at::MemoryFormat::ChannelsLast)
+                            : self,
+            *weight,
+            *bias,
+            mean_,
+            invstd_);
         return;
       }
     }
@@ -2858,13 +2869,25 @@ Tensor batch_norm_elementwise_backward_train(
                   scalar_t,
                   accscalar_t,
                   int32_t>(
-                  grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu);
+                  grad_out.contiguous(),
+                  input.contiguous(),
+                  mean,
+                  invstd,
+                  weight,
+                  sum_dy,
+                  sum_dy_xmu);
             } else {
               return batch_norm_backward_elemt_channels_first_template<
                   scalar_t,
                   scalar_t,
                   int32_t>(
-                  grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu);
+                  grad_out.contiguous(),
+                  input.contiguous(),
+                  mean,
+                  invstd,
+                  weight,
+                  sum_dy,
+                  sum_dy_xmu);
             }
           });
     }
@@ -2872,7 +2895,17 @@ Tensor batch_norm_elementwise_backward_train(
       if ((!weight.defined() || weight.is_contiguous()) &&
           mean.is_contiguous() && invstd.is_contiguous()) {
         return batch_norm_backward_elemt_channels_last_template(
-            grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu);
+            // It is a WA to fix Mobile-SSD convergence issue.
+            grad_out.dim() == 4
+                ? grad_out.contiguous(at::MemoryFormat::ChannelsLast)
+                : grad_out,
+            input.dim() == 4 ? input.contiguous(at::MemoryFormat::ChannelsLast)
+                             : input,
+            mean,
+            invstd,
+            weight,
+            sum_dy,
+            sum_dy_xmu);
       }
     }
     case Impl::General: {
@@ -3091,7 +3124,18 @@ std::tuple<Tensor, Tensor, Tensor, Tensor> batch_norm_backward_reduce_dispatch(
       (!weight.defined() || weight.is_contiguous()) && mean.is_contiguous() &&
       invstd.is_contiguous()) {
     return batch_norm_backward_reduce_channels_last_template(
-        grad_output, input, mean, invstd, weight, input_g, weight_g, bias_g);
+        // It is a WA to fix Mobile-SSD convergence issue.
+        grad_output.dim() == 4
+            ? grad_output.contiguous(at::MemoryFormat::ChannelsLast)
+            : grad_output,
+        input.dim() == 4 ? input.contiguous(at::MemoryFormat::ChannelsLast)
+                         : input,
+        mean,
+        invstd,
+        weight,
+        input_g,
+        weight_g,
+        bias_g);
   }
   return IPEX_DISPATCH_FLOATING_TYPES_AND2(
       kHalf,
@@ -3282,8 +3326,8 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> native_batch_norm_backward(
                   scalar_t,
                   accscalar_t,
                   int32_t>(
-                  grad_output,
-                  input,
+                  grad_output.contiguous(),
+                  input.contiguous(),
                   *weight,
                   *running_mean,
                   *running_var,
@@ -3297,8 +3341,8 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> native_batch_norm_backward(
                   scalar_t,
                   scalar_t,
                   int32_t>(
-                  grad_output,
-                  input,
+                  grad_output.contiguous(),
+                  input.contiguous(),
                   *weight,
                   *running_mean,
                   *running_var,
@@ -3913,7 +3957,17 @@ Tensor batch_norm_backward_elemt_dispatch(
       batch_norm_use_channels_last_kernels(self) &&
       batch_norm_use_channels_last_kernels(input)) {
     return batch_norm_backward_elemt_channels_last_template(
-        self, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count);
+        // It is a WA to fix Mobile-SSD convergence issue.
+        self.dim() == 4 ? self.contiguous(at::MemoryFormat::ChannelsLast)
+                        : self,
+        input.dim() == 4 ? input.contiguous(at::MemoryFormat::ChannelsLast)
+                         : input,
+        mean,
+        invstd,
+        weight,
+        sum_dy,
+        sum_dy_xmu,
+        count);
   }
 
   return IPEX_DISPATCH_FLOATING_TYPES_AND2(
@@ -3938,13 +3992,27 @@ Tensor batch_norm_backward_elemt_dispatch(
                 scalar_t,
                 accscalar_t,
                 int32_t>(
-                self, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count);
+                self.contiguous(),
+                input.contiguous(),
+                mean,
+                invstd,
+                weight,
+                sum_dy,
+                sum_dy_xmu,
+                count);
           } else {
             return batch_norm_backward_elemt_channels_first_template<
                 scalar_t,
                 scalar_t,
                 int32_t>(
-                self, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count);
+                self.contiguous(),
+                input.contiguous(),
+                mean,
+                invstd,
+                weight,
+                sum_dy,
+                sum_dy_xmu,
+                count);
           }
         } else {
           if (is_half_float || is_bfloat16_float) {
@@ -3952,13 +4020,27 @@ Tensor batch_norm_backward_elemt_dispatch(
                 scalar_t,
                 accscalar_t,
                 int64_t>(
-                self, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count);
+                self.contiguous(),
+                input.contiguous(),
+                mean,
+                invstd,
+                weight,
+                sum_dy,
+                sum_dy_xmu,
+                count);
           } else {
             return batch_norm_backward_elemt_channels_first_template<
                 scalar_t,
                 scalar_t,
                 int64_t>(
-                self, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count);
+                self.contiguous(),
+                input.contiguous(),
+                mean,
+                invstd,
+                weight,
+                sum_dy,
+                sum_dy_xmu,
+                count);
           }
         }
       });