[Relay][Topi][CPU] Dense with weight transform

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")

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
 

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"""

python/tvm/relay/op/strategy/x86.py

@@ -364,14 +364,20 @@ 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"
     strategy.add_implementation(
         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
 
 

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

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 *