diff --git a/python/tvm/relay/op/nn/_nn.py b/python/tvm/relay/op/nn/_nn.py
index 37ee6b6e929f..6ae86c0786e5 100644
--- a/python/tvm/relay/op/nn/_nn.py
+++ b/python/tvm/relay/op/nn/_nn.py
@@ -78,6 +78,17 @@ def legalize_dense(attrs, inputs, types):
 reg.register_pattern("nn.dense", reg.OpPattern.OUT_ELEMWISE_FUSABLE)
 
 
+@reg.register_alter_op_layout("nn.dense")
+def alter_op_layout_dense(attrs, inputs, tinfos, out_type):
+    """Alternate the layout of dense"""
+    return topi.nn.dense_alter_layout(attrs, inputs, tinfos, out_type)
+
+
+# dense_pack
+reg.register_strategy("nn.contrib_dense_pack", strategy.dense_pack_strategy)
+reg.register_pattern("nn.contrib_dense_pack", reg.OpPattern.OUT_ELEMWISE_FUSABLE)
+
+
 # fifo_buffer
 @reg.register_compute("nn.fifo_buffer")
 def compute_fifo_buffer(attrs, inputs, out_type):
@@ -1130,6 +1141,25 @@ def dense_shape_func(attrs, inputs, _):
     return ret
 
 
+@script
+def _dense_pack_shape_func(data_shape, weight_shape):
+    out = output_tensor((data_shape.shape[0],), "int64")
+    for i in const_range(out.shape[0] - 1):
+        out[i] = data_shape[i]
+    out[out.shape[0] - 1] = weight_shape[0] * weight_shape[2]
+
+    return out
+
+
+@reg.register_shape_func("nn.contrib_dense_pack", False)
+def dense_pack_shape_func(attrs, inputs, _):
+    """
+    Shape function for dense_pack op.
+    """
+    ret = [_dense_pack_shape_func(inputs[0], inputs[1])]
+    return ret
+
+
 @script
 def _batch_matmul_shape_func(data_shape, weight_shape):
     out = output_tensor((data_shape.shape[0],), "int64")
diff --git a/python/tvm/relay/op/nn/nn.py b/python/tvm/relay/op/nn/nn.py
index 562cee5f53bb..0c233a6e3b53 100644
--- a/python/tvm/relay/op/nn/nn.py
+++ b/python/tvm/relay/op/nn/nn.py
@@ -1435,6 +1435,39 @@ def dense(data, weight, units=None, out_dtype=""):
     return _make.dense(data, weight, units, out_dtype)
 
 
+def contrib_dense_pack(data, weight, units=None, out_dtype=""):
+    """Dense operator.
+    Applies a linear transformation
+
+    .. math::
+
+    `Y = X * W^T`
+
+    Parameters
+    ----------
+    data : tvm.relay.Expr
+        The input data to the operator,
+        of shape `(d_1, d_2, ..., d_n, units_in)`.
+
+    weight : tvm.relay.Expr
+        The transformed weight expressions, 3-D matrix,
+        of shape `(units // pack_weight_tile, units_in, pack_weight_tile)`.
+
+    units : int, optional
+        Number of hidden units of the dense transformation.
+
+    out_dtype : str, optional
+        Specifies the output data type for mixed precision dense,
+        of shape `(d_1, d_2, ..., d_n, units)`.
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The computed result.
+    """
+    return _make.contrib_dense_pack(data, weight, units, out_dtype)
+
+
 def fifo_buffer(data, buffer, axis):
     """FIFO buffer to enable computation reuse in CNNs with sliding indow input
 
diff --git a/python/tvm/relay/op/strategy/generic.py b/python/tvm/relay/op/strategy/generic.py
index 3ad75faf4bc1..f35303895ddc 100644
--- a/python/tvm/relay/op/strategy/generic.py
+++ b/python/tvm/relay/op/strategy/generic.py
@@ -731,6 +731,19 @@ def dense_strategy(attrs, inputs, out_type, target):
     return strategy
 
 
+@override_native_generic_func("dense_pack_strategy")
+def dense_pack_strategy(attrs, inputs, out_type, target):
+    """dense_pack generic strategy"""
+    logger.warning("dense_pack is not optimized for this platform.")
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_dense(topi.nn.dense_pack),
+        wrap_topi_schedule(topi.generic.schedule_dense),
+        name="dense_pack.generic",
+    )
+    return strategy
+
+
 # batch_matmul
 def wrap_compute_batch_matmul(topi_compute, need_auto_scheduler_layout=False):
     """wrap batch_matmul topi compute"""
diff --git a/python/tvm/relay/op/strategy/x86.py b/python/tvm/relay/op/strategy/x86.py
index edfaaeefc5df..f33c45b248d6 100644
--- a/python/tvm/relay/op/strategy/x86.py
+++ b/python/tvm/relay/op/strategy/x86.py
@@ -364,7 +364,6 @@ def conv1d_strategy_cpu(attrs, inputs, out_type, target):
 def dense_strategy_cpu(attrs, inputs, out_type, target):
     """dense x86 strategy"""
     strategy = _op.OpStrategy()
-    m, _ = inputs[0].shape
     same_type = inputs[0].dtype == inputs[1].dtype == out_type.dtype
     dtype = inputs[0].dtype
     u8s8s32 = dtype == "uint8" and inputs[1].dtype == "int8" and out_type.dtype == "int32"
@@ -372,6 +371,13 @@ def dense_strategy_cpu(attrs, inputs, out_type, target):
         wrap_compute_dense(topi.x86.dense_nopack),
         wrap_topi_schedule(topi.x86.schedule_dense_nopack),
         name="dense_nopack.x86",
+        plevel=5,
+    )
+
+    strategy.add_implementation(
+        wrap_compute_dense(topi.x86.dense_pack),
+        wrap_topi_schedule(topi.x86.schedule_dense_pack),
+        name="dense_pack.x86",
         plevel=10,
     )
 
@@ -407,14 +413,18 @@ def dense_strategy_cpu(attrs, inputs, out_type, target):
                 name="dense_mkldnn.x86",
                 plevel=15,
             )
-    with SpecializedCondition(m >= 16):
-        # this implementation may not be well-optimized, so use plevel=5 for now.
-        strategy.add_implementation(
-            wrap_compute_dense(topi.x86.dense_pack),
-            wrap_topi_schedule(topi.x86.schedule_dense_pack),
-            name="dense_pack.x86",
-            plevel=5,
-        )
+    return strategy
+
+
+@dense_pack_strategy.register("cpu")
+def dense_pack_strategy_cpu(attrs, inputs, out_type, target):
+    """dense_pack x86 strategy"""
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_dense(topi.x86.dense_pack),
+        wrap_topi_schedule(topi.x86.schedule_dense_pack),
+        name="dense_pack.x86",
+    )
     return strategy
 
 
diff --git a/python/tvm/topi/nn/dense.py b/python/tvm/topi/nn/dense.py
index bb6ea90c3fcd..e8ec476b86a5 100644
--- a/python/tvm/topi/nn/dense.py
+++ b/python/tvm/topi/nn/dense.py
@@ -14,6 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
+# pylint: disable=invalid-name,unused-argument
 """TVM operator fully connected compute."""
 import tvm
 from tvm import te, auto_scheduler
@@ -104,3 +105,72 @@ def dense_legalize(attrs, inputs, types):
     # not to change by default
     # pylint: disable=unused-argument
     return None
+
+
+def dense_pack(data, weight, bias=None, out_dtype=None):
+    """The default implementation of dense_pack in topi.
+
+    Parameters
+    ----------
+    data : tvm.te.Tensor
+        2-D with shape [batch, in_dim]
+
+    weight : tvm.te.Tensor
+        2-D with shape [out_dim, in_dim]
+
+    bias : Optional[tvm.te.Tensor]
+        1-D with shape [out_dim]
+
+    out_dtype : Optional[str]
+        The output type. This is used for mixed precision.
+
+    Returns
+    -------
+    output : tvm.te.Tensor
+        2-D with shape [batch, out_dim]
+    """
+    if out_dtype is None:
+        out_dtype = data.dtype
+    M, K = get_const_tuple(data.shape)  # batch, in_dim
+    N, _, packw_bn = get_const_tuple(weight.shape)  # out_dim
+    N = N * packw_bn
+
+    idxdiv = tvm.tir.indexdiv
+    idxmod = tvm.tir.indexmod
+    k = te.reduce_axis((0, K), name="k")
+    C = te.compute(
+        (M, N),
+        lambda y, x: te.sum(
+            data[y, k].astype(out_dtype)
+            * weight[idxdiv(x, packw_bn), k, idxmod(x, packw_bn)].astype(out_dtype),
+            axis=k,
+        ),
+        name="T_dense_pack",
+        tag="dense_pack",
+    )
+    if bias is not None:
+        C = te.compute((M, N), lambda i, j: C[i, j] + bias[j].astype(out_dtype), tag=tag.BROADCAST)
+    return C
+
+
+@tvm.target.generic_func
+def dense_alter_layout(attrs, inputs, tinfos, out_type):
+    """Change dense layout.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current convolution
+    inputs : tvm.relay.Expr
+        Grouped input symbols
+    tinfos : list
+        Input shape and dtype
+    out_type: type
+        The output type
+
+    Note
+    ----
+    Unlike other TOPI functions, this function operates on both graph level and operator level.
+    """
+    # not to change by default
+    return None
diff --git a/python/tvm/topi/x86/__init__.py b/python/tvm/topi/x86/__init__.py
index 154511010a1c..bb6a7cdd4122 100644
--- a/python/tvm/topi/x86/__init__.py
+++ b/python/tvm/topi/x86/__init__.py
@@ -39,4 +39,5 @@
 from .conv3d_transpose import *
 from .sparse import *
 from .conv2d_alter_op import *
+from .dense_alter_op import *
 from .scatter import *
diff --git a/python/tvm/topi/x86/dense.py b/python/tvm/topi/x86/dense.py
index 15d7a1a310d6..6011f01c2cb0 100644
--- a/python/tvm/topi/x86/dense.py
+++ b/python/tvm/topi/x86/dense.py
@@ -15,6 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 # pylint: disable=invalid-name,too-many-locals,unused-variable
+# pylint: disable=no-value-for-parameter
 """x86 dense operators"""
 from __future__ import absolute_import as _abs
 import tvm
@@ -26,11 +27,12 @@
 from tvm.contrib import mkldnn
 
 from .utils import get_fp32_len
+from .injective import schedule_injective_from_existing
 from .. import generic, tag
 from ..utils import traverse_inline, get_const_tuple
 
 
-def _schedule_dense_pack_template(cfg, s, C):
+def _schedule_dense_pack_template(cfg, s, C, O):
     A, packedB = s[C].op.input_tensors
 
     CC = s.cache_write(C, "global")
@@ -39,9 +41,10 @@ def _schedule_dense_pack_template(cfg, s, C):
 
     yt, yo, yi = cfg["tile_y"].apply(s, C, y)
     xt, xo, xi = cfg["tile_x"].apply(s, C, x)
-    s[C].reorder(yt, xt, yo, xo, yi, xi)
-    xyt = s[C].fuse(yt, xt)
-    s[C].parallel(xyt)
+    s[C].reorder(xt, yt, yo, xo, yi, xi)
+    xyt = s[C].fuse(xt, yt)
+    if C == O:
+        s[C].parallel(xyt)
     xyo = s[C].fuse(yo, xo)
     s[C].unroll(yi)
     s[C].vectorize(xi)
@@ -51,12 +54,27 @@ def _schedule_dense_pack_template(cfg, s, C):
     ko, ki = cfg["tile_k"].apply(s, CC, k)
     s[CC].reorder(ko, ki, y, x)
     s[CC].vectorize(x)
-    s[CC].unroll(y)
-    s[CC].unroll(ki)
 
-    z, y, x = s[packedB].op.axis
-    s[packedB].reorder(z, x, y)
-    s[packedB].parallel(z)
+    tile_inner = cfg["tile_inner"].size[-1]
+    if tile_inner > 1:
+        yo, yi = s[CC].split(y, tile_inner)
+        s[CC].reorder(ko, yo, ki, yi, x)
+        s[CC].unroll(yo)
+        s[CC].unroll(ki)
+        s[CC].unroll(yi)
+    else:
+        s[CC].unroll(ki)
+        s[CC].unroll(y)
+
+    if C != O:
+        y, x = s[O].op.axis
+        yt, yo, yi = cfg["tile_y"].apply(s, O, y)
+        xt, xo, xi = cfg["tile_x"].apply(s, O, x)
+        s[O].reorder(xt, yt, yo, xo, yi, xi)
+        xyt = s[O].fuse(xt, yt)
+        s[C].compute_at(s[O], xyt)
+        s[O].vectorize(xi)
+        s[O].parallel(xyt)
     return s
 
 
@@ -83,11 +101,11 @@ def _schedule_dense_nopack_template(cfg, s, C):
 
 def _default_dense_pack_config(cfg, M, N, K):
     # Generate default schedule for dynamic shape.
-    if isinstance(M, tvm.tir.Var):
+    if isinstance(M, (tvm.tir.Var, tvm.tir.Any)):
         M = 16
-    if isinstance(N, tvm.tir.Var):
+    if isinstance(N, (tvm.tir.Var, tvm.tir.Any)):
         N = 16
-    if isinstance(K, tvm.tir.Var):
+    if isinstance(K, (tvm.tir.Var, tvm.tir.Any)):
         K = 16
 
     vec_width = get_fp32_len()
@@ -116,15 +134,16 @@ def _default_dense_pack_config(cfg, M, N, K):
     cfg["tile_y"] = SplitEntity([MM // tiley_oi, tiley_oi, tiley_ii])
     cfg["tile_x"] = SplitEntity([NN // tilex_oi, tilex_oi, tilex_ii])
     cfg["tile_k"] = SplitEntity([K, 1])
+    cfg["tile_inner"] = SplitEntity([M // tiley_ii, tiley_ii])
 
 
 def _default_dense_nopack_config(cfg, M, N, K):
     # Generate default schedule for dynamic shape.
-    if isinstance(M, tvm.tir.Var):
+    if isinstance(M, (tvm.tir.Var, tvm.tir.Any)):
         M = 16
-    if isinstance(N, tvm.tir.Var):
+    if isinstance(N, (tvm.tir.Var, tvm.tir.Any)):
         N = 16
-    if isinstance(K, tvm.tir.Var):
+    if isinstance(K, (tvm.tir.Var, tvm.tir.Any)):
         K = 16
 
     vec_width = get_fp32_len()
@@ -146,9 +165,15 @@ def dense_nopack(cfg, data, weight, bias=None, out_dtype=None):
     M, K = get_const_tuple(data.shape)
     N, _ = get_const_tuple(weight.shape)
     # create tuning space
-    cfg.define_split("tile_y", 32 if isinstance(M, tvm.tir.Var) else M, num_outputs=2)
-    cfg.define_split("tile_x", 32 if isinstance(N, tvm.tir.Var) else N, num_outputs=2)
-    cfg.define_split("tile_k", 32 if isinstance(K, tvm.tir.Var) else K, num_outputs=2)
+    cfg.define_split(
+        "tile_y", 32 if isinstance(M, (tvm.tir.Var, tvm.tir.Any)) else M, num_outputs=2
+    )
+    cfg.define_split(
+        "tile_x", 32 if isinstance(N, (tvm.tir.Var, tvm.tir.Any)) else N, num_outputs=2
+    )
+    cfg.define_split(
+        "tile_k", 32 if isinstance(K, (tvm.tir.Var, tvm.tir.Any)) else K, num_outputs=2
+    )
     if cfg.is_fallback:
         _default_dense_nopack_config(cfg, M, N, K)
 
@@ -184,23 +209,46 @@ def _callback(op):
 
 @autotvm.register_topi_compute("dense_pack.x86")
 def dense_pack(cfg, data, weight, bias=None, out_dtype=None):
-    """Compute dense with packing"""
+    """Compute dense with transformed weight."""
     if out_dtype is None:
         out_dtype = data.dtype
     M, K = get_const_tuple(data.shape)  # batch, in_dim
-    N, _ = get_const_tuple(weight.shape)  # out_dim
+    if len(weight.shape) == 3:
+        N, _, packw_bn = get_const_tuple(weight.shape)  # out_dim
+        N = N * packw_bn
+    else:
+        N, _ = get_const_tuple(weight.shape)  # out_dim
     # create tuning space
-    cfg.define_split("tile_y", M, num_outputs=3)
-    cfg.define_split("tile_x", N, num_outputs=3)
-    cfg.define_split("tile_k", K, num_outputs=2)
+    cfg.define_split(
+        "tile_y", 32 if isinstance(M, (tvm.tir.Var, tvm.tir.Any)) else M, num_outputs=3
+    )
+    cfg.define_split(
+        "tile_x", 32 if isinstance(N, (tvm.tir.Var, tvm.tir.Any)) else N, num_outputs=3
+    )
+    cfg.define_split(
+        "tile_k", 32 if isinstance(K, (tvm.tir.Var, tvm.tir.Any)) else K, num_outputs=2
+    )
+    cfg.define_split(
+        "tile_inner",
+        32 if isinstance(M, (tvm.tir.Var, tvm.tir.Any)) else M,
+        num_outputs=2,
+        filter=lambda y: y.size[-1] <= 16,
+    )
     if cfg.is_fallback:
         _default_dense_pack_config(cfg, M, N, K)
 
-    packw_bn = cfg["tile_x"].size[-1]
-    packw_shape = (N // packw_bn, K, packw_bn)
-    packw = te.compute(
-        packw_shape, lambda z, y, x: weight[z * packw_bn + x, y], name="packed_weight"
-    )
+    if len(weight.shape) == 2:
+        packw_bn = cfg["tile_x"].size[-1]
+        packw_shape = (N // packw_bn, K, packw_bn)
+        if autotvm.GLOBAL_SCOPE.in_tuning:
+            # Directly use modified data layout placeholder.
+            packw = tvm.te.placeholder(packw_shape, weight.dtype, name="packed_weight")
+        else:
+            packw = te.compute(
+                packw_shape, lambda z, y, x: weight[z * packw_bn + x, y], name="packed_weight"
+            )
+    else:
+        packw = weight
 
     idxdiv = tvm.tir.indexdiv
     idxmod = tvm.tir.indexmod
@@ -226,7 +274,7 @@ def schedule_dense_pack(cfg, outs):
 
     def _callback(op):
         if "dense_pack" in op.tag:
-            _schedule_dense_pack_template(cfg, s, op.output(0))
+            _schedule_dense_pack_template(cfg, s, op.output(0), outs[0])
 
     traverse_inline(s, outs[0].op, _callback)
     return s
@@ -276,7 +324,19 @@ def dense_mkl(cfg, data, weight, bias=None, out_dtype=None):
 @autotvm.register_topi_schedule("dense_mkl.x86")
 def schedule_dense_mkl(_, outs):
     """Create schedule for dense_mkl"""
-    return generic.schedule_extern(outs)
+    # return generic.schedule_extern(outs)
+    s = te.create_schedule([x.op for x in outs])
+    te.schedule.AutoInlineInjective(s)
+
+    def _callback(op):
+        if "broadcast" in op.tag or "injective" in op.tag or "elemwise" in op.tag:
+            schedule_injective_from_existing(s, op.output(0))
+
+    # traverse_inline(s, outs[0].op, _callback)
+    for out in outs:
+        if "dense" not in out.op.name:
+            schedule_injective_from_existing(s, out)
+    return s
 
 
 @autotvm.register_topi_compute("dense_mkldnn.x86")
diff --git a/python/tvm/topi/x86/dense_alter_op.py b/python/tvm/topi/x86/dense_alter_op.py
new file mode 100644
index 000000000000..5e15c8bf5368
--- /dev/null
+++ b/python/tvm/topi/x86/dense_alter_op.py
@@ -0,0 +1,68 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name,unused-variable,unused-argument,no-member
+"""Dense alter op functions for x86"""
+
+import tvm
+from tvm import te
+from tvm import relay
+from tvm import autotvm
+from .dense import _default_dense_pack_config
+from ..utils import get_const_tuple
+from ..nn import dense_alter_layout
+
+
+@dense_alter_layout.register(["cpu", "arm_cpu"])
+def _alter_dense_layout(attrs, inputs, tinfos, out_type):
+    target = tvm.target.Target.current(allow_none=False)
+    dispatch_ctx = autotvm.task.DispatchContext.current
+    data_tensor, weight_tensor = tinfos
+    out_dtype = out_type.dtype
+    M, K = get_const_tuple(data_tensor.shape)
+    N, _ = get_const_tuple(weight_tensor.shape)
+
+    impl, outs = relay.backend.compile_engine.select_implementation(
+        relay.op.get("nn.dense"), attrs, tinfos, out_type, target
+    )
+    workload = autotvm.task.get_workload(outs)
+    if workload:
+        cfg = dispatch_ctx.query(target, workload)
+        topi_impl = workload[0]
+        if topi_impl == "dense_pack.x86":
+            if cfg.is_fallback:
+                _default_dense_pack_config(cfg, M, N, K)
+            packw_bn = cfg["tile_x"].size[-1]
+            weight_layout = "NK%dn" % packw_bn
+            new_weight = te.placeholder(
+                (N // packw_bn, K, packw_bn),
+                dtype=weight_tensor.dtype,
+            )
+            # Relay dense doesn't have bias.
+            new_workload = autotvm.task.args_to_workload(
+                [
+                    data_tensor,
+                    new_weight,
+                    None,
+                    out_dtype,
+                ],
+                topi_impl,
+            )
+            dispatch_ctx.update(target, new_workload, cfg)
+            weight_transform = relay.layout_transform(inputs[1], "NK", weight_layout)
+            return relay.nn.contrib_dense_pack(inputs[0], weight_transform, None, out_dtype)
+
+    return None
diff --git a/src/relay/op/nn/nn.cc b/src/relay/op/nn/nn.cc
index 8ace82be9ff8..3e3d94c614c3 100644
--- a/src/relay/op/nn/nn.cc
+++ b/src/relay/op/nn/nn.cc
@@ -186,6 +186,33 @@ RELAY_REGISTER_OP("nn.dense")
     .set_support_level(1)
     .add_type_rel("Dense", DenseRel<DenseAttrs>);
 
+// relay.nn.contrib_dense_pack
+// Positional relay function to create dense_pack operator used by frontend FFI.
+Expr MakeDensePack(Expr data, Expr weight, IndexExpr units, DataType out_dtype) {
+  auto attrs = make_object<DenseAttrs>();
+  attrs->units = units;
+  attrs->out_dtype = out_dtype;
+  static const Op& op = Op::Get("nn.contrib_dense_pack");
+  return Call(op, {data, weight}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op.nn._make.contrib_dense_pack").set_body_typed(MakeDensePack);
+
+RELAY_REGISTER_OP("nn.contrib_dense_pack")
+    .describe(R"code(Applies a linear transformation: :math:`Y = XW^T`.
+
+- **data**: `(x1, x2, ..., xn, input_dim)`
+- **weight**: `(units // pack_weight_tile, input_dim, pack_weight_tile)`
+- **out**: `(x1, x2, ..., xn, units)`.
+
+)code" TVM_ADD_FILELINE)
+    .set_attrs_type<DenseAttrs>()
+    .set_num_inputs(2)
+    .add_argument("data", "nD Tensor", "Input data.")
+    .add_argument("weight", "3D Tensor", "Packed weight matrix.")
+    .set_support_level(10)
+    .add_type_rel("DensePack", DensePackRel<DenseAttrs>);
+
 // relay.leaky_relu
 TVM_REGISTER_NODE_TYPE(LeakyReluAttrs);
 
diff --git a/src/relay/op/nn/nn.h b/src/relay/op/nn/nn.h
index 9b9cff2dba81..c00e2e02b369 100644
--- a/src/relay/op/nn/nn.h
+++ b/src/relay/op/nn/nn.h
@@ -31,6 +31,8 @@
 
 #include <utility>
 
+#include "../op_common.h"
+
 namespace tvm {
 namespace relay {
 
@@ -88,6 +90,29 @@ bool DenseRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
   return true;
 }
 
+template <typename AttrType>
+bool DensePackRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+                  const TypeReporter& reporter) {
+  ICHECK_EQ(types.size(), 3);
+  const auto* data = types[0].as<TensorTypeNode>();
+  const auto* weight = types[1].as<TensorTypeNode>();
+  if (data == nullptr || weight == nullptr) return false;
+
+  const AttrType* param = attrs.as<AttrType>();
+  ICHECK(param != nullptr);
+
+  Array<tvm::PrimExpr> oshape = data->shape;
+  oshape.Set((oshape.size() - 1), weight->shape[0] * weight->shape[2]);
+
+  DataType out_dtype = param->out_dtype;
+  if (out_dtype.bits() == 0) {
+    out_dtype = data->dtype;
+  }
+  // assign output type
+  reporter->Assign(types[2], TensorType(oshape, out_dtype));
+  return true;
+}
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_OP_NN_NN_H_
diff --git a/tests/python/relay/test_autotvm_task_extraction.py b/tests/python/relay/test_autotvm_task_extraction.py
index da71ac37f695..d6bfd8d0ec11 100644
--- a/tests/python/relay/test_autotvm_task_extraction.py
+++ b/tests/python/relay/test_autotvm_task_extraction.py
@@ -60,9 +60,9 @@ def test_task_extraction():
     tasks = autotvm.task.extract_from_program(
         mod["main"], target=target, params=params, ops=(dense,)
     )
-    assert len(tasks) == 1
+    assert len(tasks) == 2
     tasks = autotvm.task.extract_from_program(mod, target=target, params=params, ops=(dense,))
-    assert len(tasks) == 1
+    assert len(tasks) == 2
 
     mod, params, _ = get_network("resnet-18", batch_size=1)
     mod_list.append(mod)
@@ -70,13 +70,13 @@ def test_task_extraction():
     tasks = autotvm.task.extract_from_program(
         mod["main"], target=target, params=params, ops=(conv2d, dense)
     )
-    assert len(tasks) == 13
+    assert len(tasks) == 14
     tasks = autotvm.task.extract_from_program(
         mod, target=target, params=params, ops=(conv2d, dense)
     )
-    assert len(tasks) == 13
+    assert len(tasks) == 14
     tasks = autotvm.task.extract_from_program(mod, target=target, params=params)
-    assert len(tasks) == 13
+    assert len(tasks) == 14
 
     mod, params, _ = get_network("resnet3d-18", batch_size=1)
     tasks = autotvm.task.extract_from_program(mod, target=target, params=params, ops=(conv3d,))
@@ -88,7 +88,7 @@ def test_task_extraction():
     tasks = autotvm.task.extract_from_program(
         mod, target=target, params=params, ops=(conv2d, dense)
     )
-    assert len(tasks) == 20
+    assert len(tasks) == 21
 
     mod, params, _ = get_network("dcgan", batch_size=1)
     tasks = autotvm.task.extract_from_program(
diff --git a/tests/python/relay/test_pass_alter_op_layout.py b/tests/python/relay/test_pass_alter_op_layout.py
index 58c279d750ec..41186884bdb2 100644
--- a/tests/python/relay/test_pass_alter_op_layout.py
+++ b/tests/python/relay/test_pass_alter_op_layout.py
@@ -18,7 +18,7 @@
 import pytest
 
 import tvm
-from tvm import relay
+from tvm import relay, topi
 from tvm.relay import transform, analysis
 from tvm.relay.testing.temp_op_attr import TempOpAttr
 from tvm.relay.testing import run_infer_type
@@ -1248,6 +1248,34 @@ def expected():
     assert tvm.ir.structural_equal(a, b, map_free_vars=True), "Actual = \n" + str(a)
 
 
+def test_alter_op_dense():
+    def before():
+        x = relay.var("x", shape=(32, 64))
+        weight = relay.var("weight", shape=(48, 64))
+        y = relay.nn.dense(x, weight)
+        y = relay.Function(analysis.free_vars(y), y)
+        return y
+
+    def expected():
+        x = relay.var("x", shape=(32, 64))
+        weight = relay.var("weight", shape=(48, 64))
+        target_layout = "NK16n"
+        weight_transform = relay.layout_transform(weight, "NK", target_layout)
+        y = relay.nn.contrib_dense_pack(x, weight_transform, units=None, out_dtype="float32")
+        y = relay.Function(analysis.free_vars(y), y)
+        return y
+
+    for target, _ in tvm.testing.enabled_targets():
+        with tvm.target.Target(target):
+            with TempOpAttr(
+                "nn.dense", "FTVMAlterOpLayout", topi.x86.dense_alter_op._alter_dense_layout
+            ):
+                a = before()
+                a = run_opt_pass(a, transform.AlterOpLayout())
+                b = run_opt_pass(expected(), transform.InferType())
+                assert tvm.ir.structural_equal(a, b)
+
+
 if __name__ == "__main__":
     test_alter_op()
     test_alter_return_none()
@@ -1269,3 +1297,4 @@ def expected():
     test_alter_layout_nhwc_arm()
     test_alter_layout_nhwc_int8_aarch64()
     test_alter_op_with_global_var()
+    test_alter_op_dense()
diff --git a/tutorials/micro/micro_tflite.py b/tutorials/micro/micro_tflite.py
index c28918380265..c979216d0c6b 100644
--- a/tutorials/micro/micro_tflite.py
+++ b/tutorials/micro/micro_tflite.py
@@ -195,7 +195,7 @@
 # Now, compile the model for the target:
 
 with tvm.transform.PassContext(
-    opt_level=3, config={"tir.disable_vectorize": True}, disabled_pass=["FuseOps"]
+    opt_level=3, config={"tir.disable_vectorize": True}, disabled_pass=["FuseOps", "AlterOpLayout"]
 ):
     graph, c_mod, c_params = relay.build(mod, target=TARGET, params=params)