[API Compatibility] Add conv_transpose decorator and alias, test conv2d_transpose only -part (PaddlePaddle#78475)

* Add conv_transpose decorator and alias, test conv2d_transpose only

Split from PaddlePaddle#78222 to isolate Windows-inference CI crash (0xc0000409).
This PR tests only conv2d_transpose compatibility.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* add conv2d_transpose compatibility test

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix code style

* reduce conv2d_transpose test tensor sizes to avoid Windows-Inference crash

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

Author: Manfredss

python/paddle/nn/functional/__init__.py

@@ -175,6 +175,9 @@
 )
 
 logsigmoid = log_sigmoid
+conv_transpose1d = conv1d_transpose
+conv_transpose2d = conv2d_transpose
+conv_transpose3d = conv3d_transpose
 __all__ = [
     'celu',
     'conv1d',
@@ -183,6 +186,9 @@
     'conv2d_transpose',
     'conv3d',
     'conv3d_transpose',
+    'conv_transpose1d',
+    'conv_transpose2d',
+    'conv_transpose3d',
     'pairwise_distance',
     'elu',
     'elu_',

python/paddle/nn/functional/conv.py

@@ -954,6 +954,7 @@ def conv2d(
     )
 
 
+@param_one_alias(["x", "input"])
 def conv1d_transpose(
     x: Tensor,
     weight: Tensor,
@@ -1222,6 +1223,7 @@ def conv1d_transpose(
     return out
 
 
+@param_one_alias(["x", "input"])
 def conv2d_transpose(
     x: Tensor,
     weight: Tensor,
@@ -1725,6 +1727,7 @@ def conv3d(
     )
 
 
+@param_one_alias(["x", "input"])
 def conv3d_transpose(
     x: Tensor,
     weight: Tensor,

test/legacy_test/test_api_compatibility_part3.py

@@ -1336,5 +1336,86 @@ def test_static_Compatibility(self):
         )
 
 
+# Test conv2d_transpose / conv_transpose2d compatibility
+class TestConv2dTransposeAPI(unittest.TestCase):
+    def setUp(self):
+        np.random.seed(2025)
+        self.dtype = 'float32'
+        self.np_x = np.random.rand(1, 2, 4, 4).astype(self.dtype)
+        self.np_weight = np.random.rand(2, 2, 3, 3).astype(self.dtype)
+        self.np_bias = np.random.rand(2).astype(self.dtype)
+
+    def test_dygraph_Compatibility(self):
+        paddle.disable_static()
+        x = paddle.to_tensor(self.np_x)
+        weight = paddle.to_tensor(self.np_weight)
+        bias = paddle.to_tensor(self.np_bias)
+
+        # 1. Paddle Positional arguments
+        out1 = paddle.nn.functional.conv2d_transpose(x, weight)
+        # 2. Paddle keyword arguments
+        out2 = paddle.nn.functional.conv2d_transpose(x=x, weight=weight)
+        # 3. PyTorch keyword arguments (alias: input)
+        out3 = paddle.nn.functional.conv2d_transpose(input=x, weight=weight)
+        # 4. PyTorch function name alias
+        out4 = paddle.nn.functional.conv_transpose2d(x, weight)
+        # 5. PyTorch function name alias + PyTorch keyword
+        out5 = paddle.nn.functional.conv_transpose2d(input=x, weight=weight)
+        # 6. Mixed arguments (positional + keyword)
+        out6 = paddle.nn.functional.conv2d_transpose(
+            x, weight, bias=bias, stride=1, padding=0
+        )
+        # 7. Positional arguments with bias
+        out7 = paddle.nn.functional.conv2d_transpose(x, weight, bias)
+
+        # Verify outputs without bias
+        ref = out1.numpy()
+        for out in [out2, out3, out4, out5]:
+            np.testing.assert_allclose(out.numpy(), ref, rtol=1e-5)
+
+        # Verify outputs with bias
+        ref_bias = out6.numpy()
+        np.testing.assert_allclose(out7.numpy(), ref_bias, rtol=1e-5)
+
+        paddle.enable_static()
+
+    def test_static_Compatibility(self):
+        paddle.enable_static()
+        main = paddle.static.Program()
+        startup = paddle.static.Program()
+        with paddle.static.program_guard(main, startup):
+            x = paddle.static.data(
+                name="x", shape=[1, 2, 4, 4], dtype=self.dtype
+            )
+            weight = paddle.static.data(
+                name="weight", shape=[2, 2, 3, 3], dtype=self.dtype
+            )
+
+            # 1. Paddle Positional arguments
+            out1 = paddle.nn.functional.conv2d_transpose(x, weight)
+            # 2. Paddle keyword arguments
+            out2 = paddle.nn.functional.conv2d_transpose(x=x, weight=weight)
+            # 3. PyTorch keyword arguments (alias: input)
+            out3 = paddle.nn.functional.conv2d_transpose(input=x, weight=weight)
+            # 4. PyTorch function name alias
+            out4 = paddle.nn.functional.conv_transpose2d(x, weight)
+            # 5. PyTorch function name alias + PyTorch keyword
+            out5 = paddle.nn.functional.conv_transpose2d(input=x, weight=weight)
+
+            exe = paddle.static.Executor()
+            fetches = exe.run(
+                main,
+                feed={
+                    "x": self.np_x,
+                    "weight": self.np_weight,
+                },
+                fetch_list=[out1, out2, out3, out4, out5],
+            )
+
+            # Verify all outputs
+            for i in range(1, len(fetches)):
+                np.testing.assert_allclose(fetches[0], fetches[i], rtol=1e-5)
+
+
 if __name__ == "__main__":
     unittest.main()