diff --git a/python/tvm/relay/op/strategy/x86.py b/python/tvm/relay/op/strategy/x86.py
index f13373b794e3..836a92d7421e 100644
--- a/python/tvm/relay/op/strategy/x86.py
+++ b/python/tvm/relay/op/strategy/x86.py
@@ -214,11 +214,11 @@ def conv2d_strategy_cpu(attrs, inputs, out_type, target):
             assert kernel_layout == "HWIO"
             if not is_auto_scheduler_enabled():
                 logger.warning("group_conv2d is not optimized for x86 with autotvm.")
-            strategy.add_implementation(
-                wrap_compute_conv2d(topi.nn.group_conv2d_nhwc, has_groups=True),
-                wrap_topi_schedule(topi.generic.schedule_group_conv2d_nhwc),
-                name="group_conv2d_nhwc.generic",
-            )
+                strategy.add_implementation(
+                    wrap_compute_conv2d(topi.nn.group_conv2d_nhwc, has_groups=True),
+                    wrap_topi_schedule(topi.generic.schedule_group_conv2d_nhwc),
+                    name="group_conv2d_nhwc.generic",
+                )
         else:
             raise RuntimeError("Unsupported group_conv2d layout {}".format(layout))
     return strategy
diff --git a/python/tvm/topi/arm_cpu/group_conv2d.py b/python/tvm/topi/arm_cpu/group_conv2d.py
index a8fb4c6920a9..99f4d5c946d6 100644
--- a/python/tvm/topi/arm_cpu/group_conv2d.py
+++ b/python/tvm/topi/arm_cpu/group_conv2d.py
@@ -23,11 +23,11 @@
 from tvm import te
 from tvm.autotvm.task.space import SplitEntity, OtherOptionEntity
 
-from ..util import get_const_tuple
+from ..utils import get_const_tuple
 from ..nn.pad import pad
 from .. import tag
 
-from ..nn.util import infer_pad
+from ..nn.utils import infer_pad
 from ..nn.conv2d import _get_workload as _get_conv2d_workload
 
 
@@ -62,8 +62,8 @@ def _get_default_config(cfg, data, kernel, strides, padding, groups, out_dtype,
 def _fallback_schedule(cfg, wkl):
     simd_width = 4  # assume ARM SIMD Width is 4
     pad_left, pad_right = wkl.padl, wkl.padr
-    stride_w = wkl.wstride
-    out_width = (wkl.width + pad_left + pad_right - wkl.wkernel) // stride_w + 1
+    stride_w = wkl.stride_w
+    out_width = (wkl.width + pad_left + pad_right - wkl.kernel_w) // stride_w + 1
     groups = wkl.groups
     kernels_per_group = wkl.out_filter // groups
     kernel_depth = wkl.in_filter // groups
diff --git a/python/tvm/topi/nn/conv2d.py b/python/tvm/topi/nn/conv2d.py
index a00df1fdbf0f..80f87f86736c 100644
--- a/python/tvm/topi/nn/conv2d.py
+++ b/python/tvm/topi/nn/conv2d.py
@@ -174,14 +174,10 @@ def _get_workload(data, kernel, stride, padding, dilation, out_dtype, data_layou
     else:
         KH, KW, CIG, CO = get_const_tuple(kernel.shape)
 
-<<<<<<< HEAD
     pt, pl, pb, pr = get_pad_tuple(padding, (get_const_int(KH), get_const_int(KW)))
     dilation_h, dilation_w = (
         dilation if isinstance(dilation, (tuple, list)) else (dilation, dilation)
     )
-=======
-    HPAD, WPAD, _, _ = get_pad_tuple(padding, (get_const_int(KH), get_const_int(KW)))
->>>>>>> c57bd780a (Update conv2d.py)
     GRPS = CI // CIG
     if isinstance(stride, (tuple, list)):
         HSTR, WSTR = stride
diff --git a/python/tvm/topi/x86/group_conv2d.py b/python/tvm/topi/x86/group_conv2d.py
index 8d946ba0dd98..02dc058b39cc 100644
--- a/python/tvm/topi/x86/group_conv2d.py
+++ b/python/tvm/topi/x86/group_conv2d.py
@@ -23,12 +23,12 @@
 from tvm import te
 from tvm.autotvm.task.space import SplitEntity, OtherOptionEntity
 
-from .util import get_fp32_len
-from ..util import get_const_tuple
+from .utils import get_fp32_len
+from ..utils import get_const_tuple
 from ..nn.pad import pad
 from .. import tag
 
-from ..nn.util import infer_pad
+from ..nn.utils import infer_pad
 from ..nn.conv2d import _get_workload as _get_conv2d_workload
 
 
@@ -63,8 +63,8 @@ def _get_default_config(cfg, data, kernel, strides, padding, groups, out_dtype,
 def _fallback_schedule(cfg, wkl):
     simd_width = get_fp32_len()
     pad_left, pad_right = wkl.padl, wkl.padr
-    stride_w = wkl.wstride
-    out_width = (wkl.width + pad_left + pad_right - wkl.wkernel) // stride_w + 1
+    stride_w = wkl.stride_w
+    out_width = (wkl.width + pad_left + pad_right - wkl.kernel_w) // stride_w + 1
     groups = wkl.groups
     kernels_per_group = wkl.out_filter // groups
     kernel_depth = wkl.in_filter // groups