diff --git a/aten/src/ATen/native/mkldnn/Conv.cpp b/aten/src/ATen/native/mkldnn/Conv.cpp
index 7ba6b320ad7..160ef9d9a0c 100644
--- a/aten/src/ATen/native/mkldnn/Conv.cpp
+++ b/aten/src/ATen/native/mkldnn/Conv.cpp
@@ -708,7 +708,7 @@ Tensor _mkldnn_convolution_transpose(
 
   if (bias.defined()) {
     const ideep::tensor b = itensor_from_tensor(bias);
-    ideep::convolution_transpose_forward::compute(
+    ideep::convolution_transpose_forward::compute_v3(
         x,
         w,
         b,
@@ -719,9 +719,10 @@ Tensor _mkldnn_convolution_transpose(
         padding_r(padding, output_padding),
         dilation.vec(),
         groups,
+        use_channels_last,
         op_attr);
   } else {
-    ideep::convolution_transpose_forward::compute(
+    ideep::convolution_transpose_forward::compute_v3(
         x,
         w,
         output_sizes,
@@ -731,6 +732,7 @@ Tensor _mkldnn_convolution_transpose(
         padding_r(padding, output_padding),
         dilation.vec(),
         groups,
+        use_channels_last,
         op_attr);
   }
   if (input.is_mkldnn()) {
@@ -738,7 +740,6 @@ Tensor _mkldnn_convolution_transpose(
   } else if (!use_channels_last) {
     return mkldnn_to_dense(MKLDNNTensor(y, input.options()));
   } else {
-    TORCH_INTERNAL_ASSERT(y.get_desc().is_nhwc());
     return output;
   }
 }
@@ -923,65 +924,18 @@ Tensor mkldnn_convolution_transpose(
     IntArrayRef dilation,
     int64_t groups)
 {
-  // See [Note: hacky wrapper removal for optional tensor]
-  c10::MaybeOwned<Tensor> bias_maybe_owned = at::borrow_from_optional_tensor(bias_opt);
-  const Tensor& bias = *bias_maybe_owned;
-
-  if (input.scalar_type() == ScalarType::BFloat16) {
-    TORCH_CHECK(mkldnn_bf16_device_check(),
-        "mkldnn_convolution_transpose: bf16 path needs the cpu support avx512bw, avx512vl and avx512dq");
-  }
-
   bool use_channels_last = mkldnn_conv_use_channels_last(input, weight);
-  auto memory_format = mkldnn_convolution_memory_format(input.ndimension(), use_channels_last);
-
-  auto output_sizes = conv_input_size(input.sizes(), weight.sizes(), padding, output_padding, stride, dilation, groups);
-  auto output = at::empty({0}, input.options());
-
-  const ideep::tensor x = itensor_from_tensor(input);
-  ideep::tensor w = itensor_from_tensor(weight);
-  // mkldnn transposed convolution has weight in logical order of OIHW or OIDHW,
-  // while PyTorch has IOHW or IODHW, `._tranpose()` switches strides (no memory copy).
-  w.transpose_(0, 1);
-
-  ideep::tensor y;
-  if (use_channels_last) {
-    output.resize_(output_sizes, memory_format);
-    y = itensor_from_tensor(output);
-  }
-  if (bias.defined()) {
-    const ideep::tensor b = itensor_from_tensor(bias);
-    ideep::convolution_transpose_forward::compute(
-        x,
-        w,
-        b,
-        output_sizes,
-        y,
-        stride.vec(),
-        padding.vec(),
-        padding_r(padding, output_padding),
-        dilation.vec(),
-        groups);
-  } else {
-    ideep::convolution_transpose_forward::compute(
-        x,
-        w,
-        output_sizes,
-        y,
-        stride.vec(),
-        padding.vec(),
-        padding_r(padding, output_padding),
-        dilation.vec(),
-        groups);
-  }
-
-  if (input.is_mkldnn()) {
-    return MKLDNNTensor(y, input.options());
-  } else if (!use_channels_last) {
-    return mkldnn_to_dense(MKLDNNTensor(y, input.options()));
-  } else {
-    return output;
-  }
+  return _mkldnn_convolution_transpose(
+      input,
+      weight,
+      bias_opt,
+      padding,
+      output_padding,
+      stride,
+      dilation,
+      groups,
+      use_channels_last
+  );
 }
 
 Tensor mkldnn_convolution_transpose_backward_input(
diff --git a/test/test_mkldnn.py b/test/test_mkldnn.py
index 821eec3dcad..a0669967329 100644
--- a/test/test_mkldnn.py
+++ b/test/test_mkldnn.py
@@ -427,6 +427,16 @@ class TestMkldnn(TestCase):
     def test_conv_transpose3d(self):
         self._test_conv_transpose_base(dim=3)
 
+    def test_conv_transposed2d_ic1_nhwc(self):
+        x = torch.ones([1, 1, 2, 2]).contiguous(memory_format=torch.channels_last)
+        model = torch.nn.ConvTranspose2d(in_channels=1, out_channels=2, kernel_size=[5, 5]).eval()
+        model.weight.data = torch.ones([1, 2, 5, 5]).contiguous(memory_format=torch.channels_last)
+        with torch.no_grad():
+            y = model(x)
+            with torch.backends.mkldnn.flags(enabled=False):
+                y_ref = model(x)
+            self.assertEqual(y, y_ref)
+
     def test_conv2d_legacy_jit_model(self):
         """
         MKLDNN integration used to serialize models with 5d weight for grouped