review(min_max_to_clip): check bounds only for Max(Min) pattern

Author: AyoubMDL

onnxscript/rewriter/rules/common/_min_max_to_clip.py

@@ -14,7 +14,9 @@
         * All constant inputs must be scalars.
         * The effective lower bound is the maximum of all lower-bound constants.
         * The effective upper bound is the minimum of all upper-bound constants.
-        * The rule applies only if lower_bound ≤ upper_bound.
+
+        For the case of Max(Min(X, upper_bound), lower_bound):
+            * The rule applies only if lower_bound ≤ upper_bound.
 
 General constraints:
     - The first input may be any tensor.
@@ -38,9 +40,11 @@ class _FuseMinMaxBase(RewriteRuleClassBase, abc.ABC):
     Constraints:
         - All inputs except the first must be constants (from Constant nodes or initializers).
         - If ``need_scalars`` is True (Clip fusion), all constants must be scalars.
+        - If ``check_bounds`` is True (Clip fusion in the pattern Max(Min(X, upper_bound), lower_bound)), lower_bound ≤ upper_bound.
     """
 
     need_scalars: ClassVar = False
+    check_bounds: ClassVar = False
 
     @abc.abstractmethod
     def compute_constants(
@@ -75,7 +79,8 @@ def check(self, context, out1, out2, **_):
             - These inputs (except the first) must be constant values (from Constant nodes or initializers).
             - In the case of Min(Max) and Max(Min) patterns:
                 * All inputs must be scalars (as Clip requires scalars).
-                * The lower bound must be less than or equal to the upper bound.
+                For Max(Min) pattern:
+                    * The lower bound must be less than or equal to the upper bound.
 
         Returns:
             MatchResult:
@@ -96,8 +101,8 @@ def check(self, context, out1, out2, **_):
             if self.need_scalars and not self._is_scalar(input_.const_value.numpy()):
                 return check_result.fail(f"{input_.name} is not a scalar.")
 
-        if self.need_scalars:
-            # For Clip fusion: check that lower_bound <= upper_bound
+        if self.need_scalars and self.check_bounds:
+            # For Clip fusion in the case of Max(Min(X, upper_bound), lower_bound): check that lower_bound <= upper_bound
             lower_bound, upper_bound = self.compute_constants(first_node, second_node)
             if lower_bound[0].numpy() > upper_bound[0].numpy():
                 return check_result.fail(
@@ -170,7 +175,6 @@ class FuseMaxMinToClip(_FuseMinMaxBase):
         - All constant inputs must be scalars.
         - The effective lower bound is ``max(lb1, lb2, ...)``.
         - The effective upper bound is ``min(ub1, ub2, ...)``.
-        - Requires ``lower_bound <= upper_bound``.
     """
 
     op_type: ClassVar = "Clip"
@@ -210,6 +214,7 @@ class FuseMinMaxToClip(_FuseMinMaxBase):
 
     op_type: ClassVar = "Clip"
     need_scalars: ClassVar = True
+    check_bounds: ClassVar = True
 
     def compute_constants(
         self,

onnxscript/rewriter/rules/common/_min_max_to_clip_test.py

@@ -311,8 +311,7 @@ def test_successful_max_min_to_clip_graph_inputs_as_constants(self):
             """)
         self.run_test(base_model, expected_op_types=["Clip"])
 
-    def test_failure_max_min_to_clip_invalid_bounds(self):
-        """Min node should have the max value and Max node should have the min value."""
+    def test_successful_max_min_to_clip_check_bounds(self):
         base_model = ir.from_onnx_text("""
             < ir_version: 10, opset_import: ["" : 20] >
             test_model (float[N, 32, 14, 17] X) => (float [N, ?, ?, ?] Y)
@@ -322,9 +321,7 @@ def test_failure_max_min_to_clip_invalid_bounds(self):
                 Y = Min(x1, min)
             }
         """)
-        self.run_failed_condition_test(
-            base_model, fuse_successive_max_min_rule, "Invalid bounds:"
-        )
+        self.run_test(base_model, expected_op_types=["Clip"])
 
     def test_failure_fuse_max_min_to_clip_non_constant(self):
         model = ir.from_onnx_text("""