[RELAY] Stop_fusion annotation (#2624)

python/tvm/relay/op/annotation/annotation.py

@@ -29,3 +29,19 @@ def on_device(data, device):
         raise ValueError("device is expected to be the type of TVMContext or "
                          "str, but received %s" % (type(device)))
     return _make.on_device(data, device)
+
+
+def stop_fusion(data):
+    """Annotate an expression to prevent it being fused with previous expressions.
+
+    Parameters
+    ----------
+    data : tvm.relay.Expr
+        The expression to be annotated.
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The annotated expression.
+    """
+    return _make.stop_fusion(data)

src/relay/op/annotation/annotation.cc

@@ -9,6 +9,7 @@
 #include <tvm/relay/expr.h>
 #include <tvm/relay/op.h>
 #include <tvm/relay/op_attr_types.h>
+#include <topi/elemwise.h>
 
 #include "../type_relations.h"
 #include "../../pass/alter_op_layout.h"
@@ -37,6 +38,31 @@ RELAY_REGISTER_OP("on_device")
 .set_attr<FInferCorrectLayout>("FInferCorrectLayout",
                                ElemwiseArbitraryLayout);
 
+Expr StopFusion(Expr data) {
+  static const Op& op = Op::Get("annotation.stop_fusion");
+  return CallNode::make(op, {data}, Attrs{}, {});
+}
+
+TVM_REGISTER_API("relay.op.annotation._make.stop_fusion")
+.set_body_typed<Expr(Expr)>([](Expr data) {
+    return StopFusion(data);
+});
+
+RELAY_REGISTER_OP("annotation.stop_fusion")
+.describe(R"code(Annotate an expression to prevent it being fused with previous expressions.)code"
+TVM_ADD_FILELINE)
+.set_num_inputs(1)
+.add_argument("data", "Tensor", "The input data.")
+.add_type_rel("Identity", IdentityRel)
+.set_support_level(10)
+.set_attr<TOpPattern>("TOpPattern", kOpaque)
+.set_attr<TOpIsStateful>("TOpIsStateful", false)
+.set_attr<FInferCorrectLayout>("FInferCorrectLayout", ElemwiseArbitraryLayout)
+.set_attr<FTVMCompute>("FTVMCompute",
+                       [](const Attrs& attrs, const Array<Tensor>& inputs,
+                          const Type& out_dtype, const Target& target) -> Array<Tensor> {
+                         return {topi::identity(inputs[0])};
+                       });
 
 }  // namespace relay
 }  // namespace tvm

src/relay/pass/fuse_ops.cc

@@ -741,10 +741,14 @@ class FuseMutator : private ExprMutator {
   }
   // Transform calls.
   Expr VisitExpr_(const CallNode* call) {
+    static const Op& stop_fusion = Op::Get("annotation.stop_fusion");
     if (call->op.as<OpNode>()) {
       // If it is a primitive op call
       // then we must have a group assignment for it already.
       CHECK(gmap_.count(call));
+      if (call->op.same_as(stop_fusion)) {
+        return ExprMutator::VisitExpr(call->args[0]);
+      }
       auto* ret_group = gmap_.at(call)->FindRoot();
       Array<Expr> new_args = GetNewArguments(call->args, ret_group);
 

src/relay/pass/pattern_util.h

@@ -329,6 +329,8 @@ Expr MakeConcatenate(Expr data, int axis);
 
 Expr MakeStridedSlice(Expr data, Array<Integer> begin, Array<Integer> end, Array<Integer> strides);
 
+Expr StopFusion(Expr data);
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_PASS_PATTERN_UTIL_H_

tests/python/relay/test_pass_fuse_ops.py

@@ -220,9 +220,41 @@ def test_tuple_strided_slice():
     print(zz.astext())
 
 
+def test_stop_fusion():
+    def before(dshape):
+        x = relay.var("x", shape=dshape)
+        y = relay.add(x, relay.const(1, "float32"))
+        y = relay.annotation.stop_fusion(y)
+        z = relay.exp(y)
+        return relay.Function([x], z)
+
+    def expected(dshape):
+        x = relay.var("p0", shape=dshape)
+        y = relay.add(x, relay.const(1, "float32"))
+        f1 = relay.Function([x], y)
+
+        x = relay.var("p01", shape=dshape)
+        y = relay.exp(x)
+        f2 = relay.Function([x], y)
+
+        x = relay.var("x", shape=dshape)
+        y = relay.Call(f1, [x])
+        z = relay.Call(f2, [y])
+        return relay.Function([x], z)
+
+    dshape = (10, 20)
+    z = before(dshape)
+    z = relay.ir_pass.infer_type(z)
+    z = relay.ir_pass.fuse_ops(z)
+    z = relay.ir_pass.infer_type(z)
+    after = relay.ir_pass.infer_type(expected(dshape))
+    assert relay.ir_pass.alpha_equal(z, after)
+
+
 if __name__ == "__main__":
     test_fuse_simple()
     test_conv2d_fuse()
     test_concatenate()
     test_tuple_root()
     test_tuple_strided_slice()
+    test_stop_fusion()