[Unity][IR][UX] Privacy annotation in Relax

python/tvm/script/parser/relax/entry.py

@@ -45,9 +45,11 @@
 # this formulation allows us to support having @R.function
 # appear as a decorator by itself or to have optional arguments
 # like @R.function(pure=False)
-def function(f: Optional[FType] = None, pure: bool = True) -> Union[Function, FType]:
+def function(
+    f: Optional[FType] = None, pure: bool = True, private: bool = False
+) -> Union[Function, FType]:
     # pylint: disable=unused-argument
-    # (pure isn't used here, but is used later in parsing)
+    # (pure and private aren't used here, but are used later in parsing)
 
     # need to inspect the stack first because is_defined_in_class expects the outer class
     # to be in a particular position in the stack

python/tvm/script/parser/relax/parser.py

@@ -21,7 +21,7 @@
 from typing import Any, Dict, Optional
 
 from tvm import relax, tir
-from tvm.ir import GlobalVar, structural_equal
+from tvm.ir import GlobalVar, make_node, structural_equal
 from tvm.relax import Expr, StructInfo
 from tvm.relax.utils import convert_to_expr
 from tvm.script.ir_builder.relax.frame import BlockFrame
@@ -178,7 +178,8 @@ def visit_function_def(self: Parser, node: doc.FunctionDef) -> None:
         local_func_var = relax.Var(node.name, relax.FuncStructInfo(params_sinfo, ret_sinfo))
         self.var_table.add(node.name, local_func_var)
 
-    purity = find_purity_annotation(node)
+    purity = find_decorator_annotation(node, "pure")
+    # don't handle the privacy annotation here because it's only relevant for global funcs
 
     with self.var_table.with_frame():
         with self.with_dispatch_token("relax"):
@@ -204,20 +205,19 @@ def visit_function_def(self: Parser, node: doc.FunctionDef) -> None:
                 self.visit_body(node.body)
 
 
-def find_purity_annotation(node: doc.FunctionDef) -> bool:
+def find_decorator_annotation(node: doc.FunctionDef, annotation: str, default: bool = True) -> bool:
     """
-    Check the value of `pure` in the function decorator.
-    Returns the annotated purity if present, otherwise defaulting to True.
-    This allows for specifying the purity in the function signature.
+    Check the value of given annotation (argument name) in the function decorator.
+    Returns the value of the annotation if present, otherwise giving the default value.
     """
-    # look for the pure argument in the function decorator
+    # look for the named argument in the function decorator
     for dec in node.decorator_list:
         if not isinstance(dec, doc.Call) or dec.func.attr != "function":
             continue
         for keyword in dec.keywords:
-            if keyword.arg == "pure":
+            if keyword.arg == annotation:
                 return keyword.value.value
-    return True
+    return default
 
 
 @dispatch.register(token="relax", type_name="tvm_declare_function")
@@ -238,8 +238,15 @@ def visit_tvm_declare_function(self: Parser, node: doc.FunctionDef) -> GlobalVar
             param_sinfo = eval_struct_info(self, arg.annotation, eval_str=True)
             params.append(relax.Var(arg.arg, param_sinfo))
 
-    is_pure = find_purity_annotation(node)
-    func_signature = relax.Function.create_empty(params, ret_sinfo, is_pure=is_pure)
+    is_pure = find_decorator_annotation(node, "pure")
+
+    # if the global function is not private, then use its name as the global symbol
+    is_private = find_decorator_annotation(node, "private", default=False)
+    attrs = None
+    if not is_private:
+        attrs = make_node("DictAttrs", global_symbol=node.name)
+
+    func_signature = relax.Function.create_empty(params, ret_sinfo, is_pure=is_pure, attrs=attrs)
     return I.decl_function(node.name, func_signature)
 
 

src/relax/transform/gradient.cc

@@ -333,7 +333,9 @@ class GradientMutator : private ExprMutator {
     }
 
     GradientMutator mutator(mod, require_grads_value, target_index);
-    Function new_func_transformed = Downcast<Function>(mutator.VisitExpr(new_func));
+    // remove the global symbol if the original had one (the adjoint does not need a global symbol)
+    Function new_func_transformed =
+        WithoutAttr(Downcast<Function>(mutator.VisitExpr(new_func)), tvm::attr::kGlobalSymbol);
 
     IRModule new_module = GetRef<IRModule>(mod.CopyOnWrite());
     new_module->Add(GlobalVar(func_name + "_adjoint"), new_func_transformed);

src/relax/transform/lift_transform_params.cc

@@ -251,7 +251,10 @@ class TransformParamsLifter : public ExprMutator {
     lift_plan_ = planner.Plan(func, num_input);
 
     // Step 2: Add the lifted function to the module
-    builder_->AddFunction(lift_plan_.f_transform_params, new_func_name);
+    // (The lifted function should be public so we add a global symbol to it)
+    auto lift_func =
+        WithAttr(lift_plan_.f_transform_params, tvm::attr::kGlobalSymbol, new_func_name);
+    builder_->AddFunction(lift_func, new_func_name);
 
     // Step 3: Update the current function.
 

src/script/ir_builder/relax/frame.cc

@@ -74,6 +74,18 @@ void FunctionFrameNode::ExitWithScope() {
                              "function scope, if it's defined in a Module";
     const IRModuleFrame& frame = opt_frame.value();
     const String& func_name = name.value_or("");
+    // If the function has already been declared (i.e., it is global), see if there is
+    // already a global symbol defined for it (i.e., it is not private).
+    // If yes, add it to the current function's attributes (unless one was manually defined)
+    if (frame->global_var_map.count(func_name)) {
+      auto decl = frame->functions.at(frame->global_var_map.at(func_name));
+      if (decl->attrs.defined()) {
+        auto attr_dict = decl->attrs.get()->dict;
+        if (attr_dict.count("global_symbol") && !attrs.count("global_symbol")) {
+          func = std::move(WithAttr(func, tvm::attr::kGlobalSymbol, attr_dict.at("global_symbol")));
+        }
+      }
+    }
     if (!frame->global_var_map.count(func_name)) {
       // First time visiting the function.
       ir::DeclFunction(func_name, func);

src/script/printer/relax/function.cc

@@ -52,17 +52,45 @@ TVM_STATIC_IR_FUNCTOR(IRDocsifier, vtable)
       (*f)->func_vars = nullptr;
       // Step 4. Print attributes
       if (n->attrs.defined() && !n->attrs->dict.empty()) {
-        (*f)->stmts.push_back(
-            ExprStmtDoc(Relax(d, "func_attr")  //
-                            ->Call({d->AsDoc<ExprDoc>(n->attrs, n_p->Attr("attrs"))})));
+        // if the function is a global function and has a global symbol,
+        // then don't print the global symbol (it will be implicit from not being private)
+        if (d->frames.size() == 3 && n->attrs->dict.count("global_symbol")) {
+          Map<String, ObjectRef> new_attrs;
+          for (auto kv : n->attrs->dict) {
+            if (kv.first != "global_symbol") {
+              new_attrs.Set(kv.first, kv.second);
+            }
+          }
+          if (!new_attrs.empty()) {
+            (*f)->stmts.push_back(ExprStmtDoc(
+                Relax(d, "func_attr")  //
+                    ->Call({d->AsDoc<ExprDoc>(DictAttrs(new_attrs), n_p->Attr("attrs"))})));
+          }
+        } else {
+          (*f)->stmts.push_back(
+              ExprStmtDoc(Relax(d, "func_attr")  //
+                              ->Call({d->AsDoc<ExprDoc>(n->attrs, n_p->Attr("attrs"))})));
+        }
       }
       // Step 5. Prepare the decorator (include purity if it's impure)
       ExprDoc decorator = Relax(d, "function");
+      Array<ExprDoc, void> pos_args = {};
+      Array<String, void> dec_keys;
+      Array<ExprDoc, void> dec_values;
       if (!n->is_pure) {
-        Array<ExprDoc> pos_args = {};
-        decorator = std::move(decorator->Call(
-            pos_args, {"pure"}, {LiteralDoc::Boolean(false, Optional<ObjectPath>())}));
+        dec_keys.push_back("pure");
+        dec_values.push_back(LiteralDoc::Boolean(false, Optional<ObjectPath>()));
+      }
+      // if the function is global and does not have a global symbol, indicate that it's private
+      if (d->frames.size() == 3 &&
+          (!n->attrs.defined() || !n->attrs->dict.count("global_symbol"))) {
+        dec_keys.push_back("private");
+        dec_values.push_back(LiteralDoc::Boolean(true, Optional<ObjectPath>()));
       }
+      if (dec_keys.size()) {
+        decorator = std::move(decorator->Call(pos_args, dec_keys, dec_values));
+      }
+
       // Step 6. Print body
       Array<StmtDoc> body =
           PrintSeqExpr(Downcast<relax::SeqExpr>(n->body), n_p->Attr("body"), d, /*use_ret=*/true);

tests/python/relax/test_transform_attach_global_symbol.py

@@ -43,7 +43,7 @@ def tir_matmul(x: T.handle, y: T.handle, z: T.handle) -> None:
                         C[vi, vj] = T.float32(0)
                     C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
 
-        @R.function
+        @R.function(private=True)
         def main(
             x: R.Tensor(("m", "n"), "float32"), w: R.Tensor(("n", "k"), "float32")
         ) -> R.Tensor:
@@ -74,7 +74,6 @@ def tir_matmul(x: T.handle, y: T.handle, z: T.handle) -> None:
         def main(
             x: R.Tensor(("m", "n"), "float32"), w: R.Tensor(("n", "k"), "float32")
         ) -> R.Tensor:
-            R.func_attr({"global_symbol": "main"})
             m, n, k = T.int64(), T.int64(), T.int64()
             gv0 = R.call_tir(Expected.tir_matmul, (x, w), R.Tensor((m, k), dtype="float32"))
             return gv0
@@ -94,7 +93,7 @@ class Before:
         def tir_zeros(x: T.Buffer((2), "float32")) -> None:
             x[0] = T.float32(0)
 
-        @R.function
+        @R.function(private=True)
         def main() -> R.Tensor:
             gv0 = R.call_tir(Before.tir_zeros, (), R.Tensor((2,), dtype="float32"))
             return gv0
@@ -110,7 +109,6 @@ def tir_zeros(x: T.Buffer((2), "float32")) -> None:
 
         @R.function
         def main() -> R.Tensor:
-            R.func_attr({"global_symbol": "main"})
             gv0 = R.call_tir(Expected.tir_zeros, (), R.Tensor((2,), dtype="float32"))
             return gv0
 

tests/python/relax/test_transform_combine_parallel_matmul.py

@@ -378,6 +378,7 @@ def expected1(
         w2: R.Tensor((2, 640, 640), dtype="float32"),
         w3: R.Tensor((3, 4, 640, 640), dtype="float32"),
     ) -> R.Tensor:
+        R.func_attr({"global_symbol": "main"})
         with R.dataflow():
             lv = R.concat((w0, w2), axis=2)
             lv1 = R.matmul(x, lv, out_dtype="float32")
@@ -458,6 +459,7 @@ def expected1(
         b0: R.Tensor((640,), dtype="float32"),
         b1: R.Tensor((640,), dtype="float32"),
     ) -> R.Tensor:
+        R.func_attr({"global_symbol": "main"})
         with R.dataflow():
             lv = R.concat((w0, w1, w2), axis=1)
             lv1 = R.matmul(x1, lv, out_dtype="float32")
@@ -515,6 +517,7 @@ def expected(
         w3: R.Tensor((640, 640), dtype="float32"),
         w4: R.Tensor((640, 640), dtype="float32"),
     ) -> R.Tensor:
+        R.func_attr({"global_symbol": "main"})
         with R.dataflow():
             lv = R.concat((w0, w1, w2), axis=1)
             lv1 = R.matmul(x1, lv, out_dtype="float32")

tests/python/relax/test_transform_dead_code_elimination.py

@@ -168,7 +168,7 @@ def tir_add(
                     vi, vj = T.axis.remap("SS", [i, j])
                     z[vi, vj] = x[vi, vj] + y[vi, vj]
 
-        @R.function
+        @R.function(private=True)
         def unused_func(x: R.Tensor((16, 16), "float32"), w: R.Tensor((16, 16), "float32")):
             gv0 = R.add(x, w)
             return gv0
@@ -202,7 +202,7 @@ def tir_add(
                     vi, vj = T.axis.remap("SS", [i, j])
                     z[vi, vj] = x[vi, vj] + y[vi, vj]
 
-        @R.function
+        @R.function(private=True)
         def unused_func(x: R.Tensor((16, 16), "float32"), w: R.Tensor((16, 16), "float32")):
             gv0 = R.add(x, w)
             return gv0
@@ -239,7 +239,7 @@ def tir_add(
                     vi, vj = T.axis.remap("SS", [i, j])
                     z[vi, vj] = x[vi, vj] + y[vi, vj]
 
-        @R.function
+        @R.function(private=True)
         def unused_func(x: R.Tensor(("m", "n"), "float32"), w: R.Tensor(("n", "k"), "float32")):
             gv0 = R.add(x, w)
             return gv0
@@ -310,7 +310,7 @@ def unused_func1(
                     vi, vj = T.axis.remap("SS", [i, j])
                     z[vi, vj] = x[vi, vj] + y[vi, vj]
 
-        @R.function
+        @R.function(private=True)
         def unused_func2(x: R.Tensor((16, 16), "float32"), w: R.Tensor((16, 16), "float32")):
             gv0 = R.add(x, w)
             return gv0

tests/python/relax/test_transform_fuse_ops.py

@@ -938,7 +938,7 @@ def relu(A: T.Buffer((T.int64(1), T.int64(512), T.int64(64), T.int64(64)), "floa
                     T.writes(B[v_i0, v_i1, v_i2, v_i3])
                     B[v_i0, v_i1, v_i2, v_i3] = T.max(A[v_i0, v_i1, v_i2, v_i3], T.float32(0))
 
-        @R.function
+        @R.function(private=True)
         def fused_layer_norm_relu(x: R.Tensor((1, 512, 64, 64), dtype="float32"), mean: R.Tensor((64, 64), dtype="float32"), var: R.Tensor((64, 64), dtype="float32")) -> R.Tensor((1, 512, 64, 64), dtype="float32"):
             R.func_attr({"Primitive": 1})
             cls = Expected
@@ -1080,7 +1080,7 @@ def transpose(rxplaceholder: T.Buffer((T.int64(320), T.int64(1280)), "float32"),
                     T.writes(T_transpose[v_ax0, v_ax1])
                     T_transpose[v_ax0, v_ax1] = rxplaceholder[v_ax1, v_ax0]
 
-        @R.function
+        @R.function(private=True)
         def fused_conv2d_add_add2(inp_0: R.Tensor((2, 320, 64, 64), dtype="float32"), w1: R.Tensor((320, 320, 3, 3), dtype="float32"), lv28: R.Tensor((1, 320, 1, 1), dtype="float32"), lv35: R.Tensor((2, 320, 1, 1), dtype="float32")) -> R.Tensor((2, 320, 64, 64), dtype="float32"):
             R.func_attr({"Primitive": 1})
             cls = Expected
@@ -1091,7 +1091,7 @@ def fused_conv2d_add_add2(inp_0: R.Tensor((2, 320, 64, 64), dtype="float32"), w1
                 R.output(gv)
             return gv
 
-        @R.function
+        @R.function(private=True)
         def fused_matmul_add1(inp_1: R.Tensor((2, 1280), dtype="float32"), lv31: R.Tensor((1280, 320), dtype="float32"), b2: R.Tensor((320,), dtype="float32")) -> R.Tensor((2, 320), dtype="float32"):
             cls = Expected
             R.func_attr({"Primitive": 1})
@@ -1226,7 +1226,7 @@ def transpose1(rxplaceholder: T.Buffer((T.int64(10), T.int64(128)), "float32"),
                     T.writes(T_transpose[v_ax0, v_ax1])
                     T_transpose[v_ax0, v_ax1] = rxplaceholder[v_ax1, v_ax0]
 
-        @R.function
+        @R.function(private=True)
         def fused_matmul1_add1(inp_1: R.Tensor((1, 128), dtype="float32"), lv4: R.Tensor((128, 10), dtype="float32"), linear2_bias: R.Tensor((10,), dtype="float32")) -> R.Tensor((1, 10), dtype="float32"):
             R.func_attr({"Primitive": 1})
             cls = Expected
@@ -1268,7 +1268,7 @@ def main(x: R.Tensor(["n", "m"], "float32")):
 
     @I.ir_module
     class Expected:
-        @R.function
+        @R.function(private=True)
         def fused_add_exp_squeeze(
             x: R.Tensor(["n", "m"], "float32"), p0: R.Tensor([], "float32")
         ) -> R.Tensor(["n", "m"], dtype="float32"):
@@ -1306,7 +1306,7 @@ def main(s: R.Shape(["n"])):
 
     @I.ir_module
     class Expected:
-        @R.function
+        @R.function(private=True)
         def fused_full_trilu_broadcast_to(
             s: R.Shape(["n"]),
         ) -> R.Tensor([1, 1, "n", "n"], "float32"):
@@ -1354,7 +1354,7 @@ def main(s: R.Shape(["n"]), kv_cache: R.Object):
 
     @I.ir_module
     class Expected:
-        @R.function
+        @R.function(private=True)
         def fused_full_trilu_broadcast_to(
             s: R.Shape(["n"]),
         ) -> R.Tensor([1, 1, "n", "n"], "float32"):