[DPL] Support tir_vars field in is_call_tir pattern (#16494)

* [DPL] Support tir_vars field in is_call_tir pattern

* add doc

* lint

* lint

Author: vinx13

python/tvm/relax/dpl/pattern.py

@@ -871,19 +871,23 @@ def is_shape(shape: List[tvm.ir.PrimExpr]) -> "PrimArrPattern":
 def _is_call_tir(
     func_pattern: DFPattern,
     args: Union[List, Tuple, TuplePattern] = None,
+    tir_vars: Optional[DFPattern] = None,
 ) -> CallPattern:
     if args is None:
         args = wildcard()
     elif isinstance(args, (list, tuple)):
         args = TuplePattern(args)
 
-    return is_op("relax.call_tir")(func_pattern, args, add_constraint=False)
+    if tir_vars is None:
+        return is_op("relax.call_tir")(func_pattern, args, add_constraint=False)
+    return is_op("relax.call_tir")(func_pattern, args, tir_vars, add_constraint=False)
 
 
 # Todo(relax-team): Dataflow pattern for StructInfo, and match out_sinfo
 def is_call_tir(
     func_name: str,
     args: Union[List, Tuple, TuplePattern] = None,
+    tir_vars: Optional[DFPattern] = None,
 ) -> CallPattern:
     """
     Syntax sugar for creating a CallPattern for call_tir that calls an function through global var.
@@ -894,14 +898,15 @@ def is_call_tir(
         Name of the CPS function to call.
     args : Union[List[DFPattern], Tuple[DFPattern]], optional
         Arguments in expected call_packed, by default None meaning arbitrary (number of) arguments
-
+    tir_vars : Optional[DFPattern]
+        Pattern to match the tuple of integers that are unpacked when calling the tir func.
     Returns
     -------
     CallPattern
         The resulting CallPattern
     """
     func_pattern = GlobalVarPattern(func_name)
-    return _is_call_tir(func_pattern, args)
+    return _is_call_tir(func_pattern, args, tir_vars)
 
 
 def _is_call_dps_packed(

src/relax/ir/dataflow_pattern.cc

@@ -574,14 +574,18 @@ ConstantPattern IsConst() { return ConstantPattern(make_object<ConstantPatternNo
 WildcardPattern Wildcard() { return WildcardPattern(make_object<WildcardPatternNode>()); }
 ExprPattern IsExpr(const Expr& expr) { return ExprPattern(expr); }
 ExprPattern IsOp(const String& op_name) { return IsExpr(Op::Get(op_name)); }
-CallPattern IsCallTIR(const String& name, Optional<TuplePattern> var_args) {
+CallPattern IsCallTIR(const String& name, Optional<TuplePattern> var_args,
+                      Optional<DFPattern> tir_vars) {
   DFPattern arg_pattern;
   if (!var_args.defined()) {
     arg_pattern = Wildcard();
   } else {
     arg_pattern = var_args.value();
   }
 
+  if (tir_vars.defined()) {
+    return IsOp("relax.call_tir")(GlobalVarPattern(name), arg_pattern, tir_vars.value());
+  }
   return IsOp("relax.call_tir")(GlobalVarPattern(name), arg_pattern);
 }
 

tests/python/relax/test_dataflow_pattern.py

@@ -56,14 +56,27 @@ def tir_relu(x: T.handle, y: T.handle):
                 vi, vj = T.axis.remap("SS", [i, j])
                 B[vi, vj] = T.max(A[vi, vj], 0.0)
 
+    @T.prim_func
+    def tir_zeros(x: T.handle, n: T.int64):
+        T.func_attr({"global_symbol": "tir_zeros"})
+        A = T.match_buffer(x, [n])
+        for i in range(n):
+            with T.block():
+                vi = T.axis.remap("S", [i])
+                A[vi] = 1.0
+
     @R.function
-    def main(x: R.Tensor((32, 32), "float32"), w: R.Tensor((32, 32), "float32")) -> R.Tensor:
+    def main(x: R.Tensor((32, 32), "float32"), w: R.Tensor((32, 32), "float32")) -> R.Tuple:
         cls = Module
         with R.dataflow():
             lv0 = R.call_tir(cls.tir_matmul, (x, w), R.Tensor((32, 32), dtype="float32"))
             lv1 = R.call_tir(cls.tir_relu, (lv0), R.Tensor((32, 32), dtype="float32"))
-            R.output(lv1)
-        return lv1
+            lv2 = R.call_tir(
+                cls.tir_zeros, (lv1), R.Tensor((32,), dtype="float32"), tir_vars=R.ShapeExpr([32])
+            )
+            gv = (lv1, lv2)
+            R.output(gv)
+        return gv
 
 
 main_fn = Module["main"]
@@ -293,10 +306,12 @@ def test_match_call_attr():
 
 def test_is_call_tir():
     lv1_val = bindings[1].value
+    lv2_val = bindings[2].value
     var2val = get_var2val(Module["main"])
     assert is_call_tir("tir_relu").match(lv1_val)
     assert is_call_tir("tir_relu", [is_call_tir("tir_matmul")]).match(lv1_val, var2val=var2val)
     assert not is_call_tir("tir_relu", [is_call_tir("tir_relu")]).match(lv1_val, var2val=var2val)
+    assert is_call_tir("tir_zeros", wildcard(), wildcard()).match(lv2_val, var2val=var2val)
 
 
 @R.function