From 0afe6ec0b76db4faa8b230684abc349c9d2e600d Mon Sep 17 00:00:00 2001
From: cangtianhuang <1903374751@qq.com>
Date: Thu, 18 Jun 2026 11:00:09 +0800
Subject: [PATCH] =?UTF-8?q?=F0=9F=90=9B=20Update=20CopsFull=5FRule?=
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---
 tester/paddle_to_torch/rules.py | 46 +++++++++++++++++++++++++++++++--
 1 file changed, 44 insertions(+), 2 deletions(-)

diff --git a/tester/paddle_to_torch/rules.py b/tester/paddle_to_torch/rules.py
index c8ff49ce..c090296c 100644
--- a/tester/paddle_to_torch/rules.py
+++ b/tester/paddle_to_torch/rules.py
@@ -7138,10 +7138,52 @@ class CopsFull_Rule(BaseRule):
 
     def apply(self, paddle_api: str) -> ConvertResult:
         core = """
+shape = locals().get('shape')
+fill_value = locals().get("value", 0.0)
+dtype = locals().get('dtype')
 x     = locals().get("x")
-value = locals().get("value", 0.0)
+
+# handle shape
+def convert_to_list(shape):
+    if isinstance(shape, torch.Tensor):
+        return shape.tolist()
+    elif isinstance(shape, (list, tuple)):
+        shape_list = []
+        for item in shape:
+            if isinstance(item, torch.Tensor):
+                if item.shape == torch.Size([]):
+                    shape_list.append(item.item())
+                else:
+                    shape_list.extend(item.tolist())
+            else:
+                shape_list.append(item)
+        return shape_list
+    elif isinstance(shape, int):
+        return [shape]
+    else:
+        return shape
+
+# handle fill_value
+def convert_to_scalar(fill_value):
+    if isinstance(fill_value, torch.Tensor):
+        return fill_value.item()
+    # example: "-inf", "3.5"
+    elif isinstance(fill_value, str):
+        return float(fill_value)
+    else:
+        return fill_value
+
+shape = convert_to_list(shape)
+fill_value = convert_to_scalar(fill_value)
+
+if dtype is None and not isinstance(fill_value, bool):
+    if isinstance(fill_value, complex):
+        dtype = torch.complex128
+    else:
+        dtype = torch.float32
+tmp = torch.full(size=shape, fill_value=fill_value, dtype=dtype)
 with torch.no_grad():
-    x.fill_(float(value))
+    x.set_(tmp)
 result = x
 """
         code = Code(core=core.splitlines())