[Layout] Unify dense op input layout

include/tvm/relay/attrs/nn.h

@@ -1017,8 +1017,8 @@ struct DensePackAttrs : public tvm::AttrsNode<DensePackAttrs> {
         .set_default(NullValue<DataType>())
         .describe("Output data type, set to explicit type under mixed precision setting");
     TVM_ATTR_FIELD(weight_layout)
-        .set_default("NK")
-        .describe("Dimension ordering of weight. Packed layouts, such as NK8n, are possible.");
+        .set_default("NC")
+        .describe("Dimension ordering of weight. Packed layouts, such as NC8n, are possible.");
   }
 };
 

python/tvm/relay/op/nn/nn.py

@@ -1548,7 +1548,7 @@ def dense(data, weight, units=None, out_dtype=""):
     return _make.dense(data, weight, units, out_dtype)
 
 
-def contrib_dense_pack(data, weight, weight_layout="NK", units=None, out_dtype=""):
+def contrib_dense_pack(data, weight, weight_layout="NC", units=None, out_dtype=""):
     """Dense operator.
     Applies a linear transformation with packed weight
 
@@ -1567,7 +1567,7 @@ def contrib_dense_pack(data, weight, weight_layout="NK", units=None, out_dtype="
         of shape `(units // pack_weight_tile, units_in, pack_weight_tile)`.
 
     weight_layout: str
-        The layout of weight, such as "NK" or "NK8n".
+        The layout of weight, such as "NC" or "NC8n".
 
     units : int, optional
         Number of hidden units of the dense transformation.

python/tvm/topi/x86/dense_alter_op.py

@@ -47,7 +47,7 @@ def _alter_dense_layout(attrs, inputs, tinfos, out_type):
             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
+            weight_layout = "NC%dn" % packw_bn
             new_weight = te.placeholder(
                 (N // packw_bn, K, packw_bn),
                 dtype=weight_tensor.dtype,

src/relay/op/nn/nn.cc

@@ -210,7 +210,7 @@ InferCorrectLayoutOutput DenseInferCorrectLayout(const Attrs& attrs,
                                                  const Array<Layout>& new_in_layouts,
                                                  const Array<Layout>& old_in_layouts,
                                                  const Array<tvm::relay::Type>& old_in_types) {
-  return InferCorrectLayoutOutput({"NC", "NK"}, {"NC"}, attrs);
+  return InferCorrectLayoutOutput({"NC", "NC"}, {"NC"}, attrs);
 }
 
 TVM_REGISTER_GLOBAL("relay.op.nn._make.dense").set_body_typed(MakeDense);

tests/python/frontend/pytorch/test_forward.py

@@ -1576,6 +1576,13 @@ class LinearNoBias(Module):
         def forward(self, input, weight):
             return F.linear(input, weight)
 
+    class LinearNested(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+
+        def forward(self, x, y, z):
+            return F.linear(x, F.linear(y, z))
+
     input2d = torch.rand([2, 2]).float()
     input3d = torch.rand([4, 3, 2]).float()
     weight1d = torch.rand([2]).float()
@@ -1595,6 +1602,9 @@ def forward(self, input, weight):
     verify_model(LinearNoBias(), input_data=[input2d, weight1d])
     # 3D input, 2D weight, no bias
     verify_model(LinearNoBias(), input_data=[input3d, weight3x2])
+
+    verify_model(LinearNested(), input_data=[torch.randn(10, 10) for _ in range(3)])
+
     # TODO: Add the following cases when matmul(1D, _) is supported by TVM
     # 1D input, 2D weight, 1D bias
     # 1D input, 2D weight, no bias

tests/python/relay/test_pass_alter_op_layout.py

@@ -1317,8 +1317,8 @@ def before():
     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)
+        target_layout = "NC16n"
+        weight_transform = relay.layout_transform(weight, "NC", target_layout)
         y = relay.nn.contrib_dense_pack(
             x, weight_transform, target_layout, units=None, out_dtype="float32"
         )
@@ -1387,8 +1387,8 @@ def expected():
         squeeze = relay.squeeze(pool, axis=[2, 3])
         dense = relay.nn.contrib_dense_pack(
             relay.layout_transform(squeeze, "NC8c", "NC"),
-            relay.layout_transform(dense_weight, "NK", "NK16n"),
-            "NK16n",
+            relay.layout_transform(dense_weight, "NC", "NC16n"),
+            "NC16n",
             out_dtype="float32",
         )
         return relay.Function(analysis.free_vars(dense), dense)