From 5ea61b9e324704ebc2dc9ba8f27e6f9b2e428a3e Mon Sep 17 00:00:00 2001
From: Zonglin Peng <zonglinpeng@meta.com>
Date: Thu, 4 Sep 2025 14:19:15 -0700
Subject: [PATCH] fix facto contraints to avoid char,byte in full (#13966)

Summary:
Pull Request resolved: https://github.com/pytorch/executorch/pull/13966

div, mul, add, sub, neg, slice decomp with full. only generate int32 and float32 testcases for these ops

Differential Revision: D81705758
---
 backends/cadence/utils/facto_util.py | 64 ++++++++++++++++++++--------
 1 file changed, 47 insertions(+), 17 deletions(-)

diff --git a/backends/cadence/utils/facto_util.py b/backends/cadence/utils/facto_util.py
index 7a7afbac128..fd056cd08cc 100644
--- a/backends/cadence/utils/facto_util.py
+++ b/backends/cadence/utils/facto_util.py
@@ -23,23 +23,49 @@
 
 
 def apply_tensor_contraints(op_name: str, index: int) -> list[object]:
-    tensor_constraints = [
-        cp.Dtype.In(
-            lambda deps: [
-                torch.int8,
-                torch.int16,
-                torch.uint8,
-                torch.uint16,
-                torch.float32,
-            ]
-        ),
-        cp.Value.Ge(lambda deps, dtype, struct: -(2**4)),
-        cp.Value.Le(lambda deps, dtype, struct: 2**4),
-        cp.Rank.Ge(lambda deps: 1),
-        cp.Size.Ge(lambda deps, r, d: 1),
-        cp.Size.Le(lambda deps, r, d: 2**9),
-        cp.Rank.Le(lambda deps: 2**3),
-    ]
+    tensor_constraints = (
+        [
+            cp.Dtype.In(
+                lambda deps: [
+                    torch.int8,
+                    torch.int16,
+                    torch.uint8,
+                    torch.uint16,
+                    torch.int32,
+                    torch.float32,
+                ]
+            ),
+            cp.Value.Ge(lambda deps, dtype, struct: -(2**4)),
+            cp.Value.Le(lambda deps, dtype, struct: 2**4),
+            cp.Rank.Ge(lambda deps: 1),
+            cp.Size.Ge(lambda deps, r, d: 1),
+            cp.Size.Le(lambda deps, r, d: 2**9),
+            cp.Rank.Le(lambda deps: 2**3),
+        ]
+        if op_name
+        not in (
+            "slice_copy.Tensor",
+            "add.Scalar",
+            "sub.Scalar",
+            "mul.Scalar",
+            "div.Tensor",
+            "neg.default",
+        )
+        else [
+            cp.Dtype.In(
+                lambda deps: [
+                    torch.int32,
+                    torch.float32,
+                ]
+            ),
+            cp.Value.Ge(lambda deps, dtype, struct: -(2**4)),
+            cp.Value.Le(lambda deps, dtype, struct: 2**4),
+            cp.Rank.Ge(lambda deps: 1),
+            cp.Size.Ge(lambda deps, r, d: 1),
+            cp.Size.Le(lambda deps, r, d: 2**9),
+            cp.Rank.Le(lambda deps: 2**3),
+        ]
+    )
 
     match op_name:
         case "where.self":
@@ -60,6 +86,7 @@ def apply_tensor_contraints(op_name: str, index: int) -> list[object]:
                             torch.int16,
                             torch.uint8,
                             torch.uint16,
+                            torch.int32,
                             torch.float32,
                         ]
                     ),
@@ -143,6 +170,9 @@ def apply_tensor_contraints(op_name: str, index: int) -> list[object]:
             tensor_constraints.extend(
                 [
                     cp.Value.Ne(lambda deps, dtype, struct: 0),
+                    cp.Value.Le(lambda deps, dtype, struct: 2**3),
+                    cp.Size.Le(lambda deps, r, d: 2**3),
+                    cp.Rank.Le(lambda deps: 2**2),
                 ]
             )
         case "div.Tensor_mode" | "minimum.default":