[Relax] Add FRelaxInferLayout for flip operator (#18637)

## Why

The flip operator lacked layout inference support, preventing it from
participating in layout transformations during the ConvertLayout pass.

  ## How

- Add InferLayoutFlip function that transforms the axis attribute
according to the input layout
  - Register FRelaxInferLayout attribute for relax.flip operator
  - Add test case for conv2d followed by flip with layout conversion

Author: guan404ming

src/relax/op/tensor/manipulate.cc

@@ -2047,11 +2047,44 @@ StructInfo InferStructInfoFlip(const Call& call, const BlockBuilder& ctx) {
   return data_sinfo;
 }
 
+InferLayoutOutput InferLayoutFlip(
+    const Call& call, const ffi::Map<ffi::String, ffi::Array<ffi::String>>& desired_layouts,
+    const VarLayoutMap& var_layout_map) {
+  ICHECK(NoDesiredLayout(call, desired_layouts));
+
+  const auto* attrs = call->attrs.as<FlipAttrs>();
+  ICHECK(attrs != nullptr) << "Invalid Call";
+  const auto* tensor_sinfo = GetStructInfoAs<TensorStructInfoNode>(call->args[0]);
+  ICHECK(tensor_sinfo != nullptr) << "Invalid Call";
+  ICHECK(!tensor_sinfo->IsUnknownNdim()) << "Only support static ndim for now";
+
+  LayoutDecision existing_layout = GetLayoutDecision(var_layout_map, call->args[0]);
+  int ndim = tensor_sinfo->ndim;
+
+  if (existing_layout->layout.ndim() != existing_layout->layout.ndim_primal()) {
+    existing_layout = LayoutDecision(InitialLayout(ndim));
+  }
+
+  int axis = attrs->axis.IntValue();
+  if (axis < 0) {
+    axis += ndim;
+  }
+
+  const int new_axis = FindAxis(existing_layout->layout, axis);
+  ICHECK_GE(new_axis, 0) << "Failed to find transformed axis";
+
+  ObjectPtr<FlipAttrs> new_attrs = ffi::make_object<FlipAttrs>(*attrs);
+  new_attrs->axis = Integer(new_axis);
+
+  return InferLayoutOutput({existing_layout}, {existing_layout}, Attrs(new_attrs));
+}
+
 TVM_REGISTER_OP("relax.flip")
     .set_attrs_type<FlipAttrs>()
     .set_num_inputs(1)
     .add_argument("data", "Tensor", "The input tensor.")
     .set_attr<FInferStructInfo>("FInferStructInfo", InferStructInfoFlip)
+    .set_attr<FRelaxInferLayout>("FRelaxInferLayout", InferLayoutFlip)
     .set_attr<Bool>("FPurity", Bool(true));
 
 /* relax.gather_elements */

tests/python/relax/test_transform_convert_layout.py

@@ -5283,5 +5283,49 @@ def main(
     verify(Input, Expected)
 
 
+def test_conv2d_flip():
+    @I.ir_module
+    class Input:
+        @R.function
+        def main(
+            x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32")
+        ) -> R.Tensor(None, "float32", ndim=4):
+            with R.dataflow():
+                gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
+                gv2: R.Tensor((2, 4, 26, 26), "float32") = R.flip(gv, axis=1)
+                R.output(gv2)
+            return gv2
+
+    @I.ir_module
+    class Expected:
+        @R.function
+        def main(
+            x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32")
+        ) -> R.Tensor(None, dtype="float32", ndim=4):
+            with R.dataflow():
+                lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1])
+                lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1])
+                gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d(
+                    lv,
+                    lv1,
+                    strides=[1, 1],
+                    padding=[0, 0, 0, 0],
+                    dilation=[1, 1],
+                    groups=1,
+                    data_layout="NHWC",
+                    kernel_layout="OHWI",
+                    out_layout="NHWC",
+                    out_dtype="float32",
+                )
+                lv2: R.Tensor((2, 26, 26, 4), dtype="float32") = R.flip(gv, axis=3)
+                gv2: R.Tensor((2, 4, 26, 26), dtype="float32") = R.permute_dims(
+                    lv2, axes=[0, 3, 1, 2]
+                )
+                R.output(gv2)
+            return gv2
+
+    verify(Input, Expected)
+
+
 if __name__ == "__main__":
     tvm.testing.main()