API Compatibility Edit by AI Agent

paconvert/global_var.py

@@ -34,10 +34,6 @@ class GlobalManager:
     with open(json_file, "r") as file:
         ALIAS_MAPPING = json.load(file)
 
-    json_file = os.path.dirname(__file__) + "/api_alias_mapping.json"
-    with open(json_file, "r") as file:
-        ALIAS_MAPPING = json.load(file)
-
     # used to replace import (means replace api by all)
     IMPORT_PACKAGE_MAPPING = {
         "audiotools": "paddlespeech.audiotools",
@@ -78,6 +74,23 @@ class GlobalManager:
 
     # 完全对齐的Pytorch API名单
     NO_NEED_CONVERT_LIST = [
+        # Edit by AI Agent
+        "torch.fmax",
+        "torch.fmin",
+        "torch.bincount",
+        "torch.diag",
+        "torch.atan",
+        "torch.tan",
+        "torch.bitwise_and",
+        "torch.bitwise_not",
+        "torch.bitwise_xor",
+        "torch.nextafter",
+        "torch.angle",
+        "torch.heaviside",
+        "torch.asinh",
+        "torch.reciprocal",
+        "torch.square",
+
         # Manfredss
         "torch.asin",
 
@@ -978,7 +991,6 @@ class GlobalManager:
         # algorithm1832
         "torch.cosh",
         "torch.frac",
-        "torch.diag",
         "torch.Tensor.diag",
 
         # lijialin

tests/test_angle.py

@@ -74,3 +74,63 @@ def test_case_5():
         """
     )
     obj.run(pytorch_code, ["result", "out"])
+
+
+def test_case_6():
+    """2D复数张量"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1+1j, 2+2j], [3+3j, 4+4j]])
+        result = torch.angle(x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_7():
+    """3D复数张量"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[[1+1j, 2+2j], [3+3j, 4+4j]], [[5+5j, 6+6j], [7+7j, 8+8j]]])
+        result = torch.angle(x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_8():
+    """实数输入"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1.0, 2.0, 3.0])
+        result = torch.angle(x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_9():
+    """零值复数"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([0+0j, 1+0j, 0+1j])
+        result = torch.angle(x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_10():
+    """不同数据类型 - float64"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1+1j, 2+2j, 3+3j], dtype=torch.complex128)
+        result = torch.angle(x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_asinh.py

@@ -75,3 +75,65 @@ def test_case_5():
         """
     )
     obj.run(pytorch_code, ["out", "result"])
+
+
+def test_case_6():
+    """2D张量"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([[0.1606, -1.4267], [-1.0899, -1.0250]])
+        result = torch.asinh(a)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_7():
+    """3D张量"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([[[0.1, -1.0], [-1.5, 2.0]], [[3.0, -4.0], [5.0, -6.0]]])
+        result = torch.asinh(a)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_8():
+    """边界值 - 零值和大值"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([0.0, 1.0, -1.0, 10.0, -10.0])
+        result = torch.asinh(a)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_9():
+    """不同数据类型 - float64"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([0.1606, -1.4267, -1.0899], dtype=torch.float64)
+        result = torch.asinh(a)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_10():
+    """梯度计算测试"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([0.5, 1.0, 2.0], requires_grad=True)
+        y = torch.asinh(a)
+        y.sum().backward()
+        a_grad = a.grad
+        """
+    )
+    obj.run(pytorch_code, ["y", "a_grad"], check_stop_gradient=False)

tests/test_atan.py

@@ -75,3 +75,65 @@ def test_case_5():
         """
     )
     obj.run(pytorch_code, ["out", "result"])
+
+
+def test_case_6():
+    """2D张量"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([[0.2341, 0.2539], [-0.6256, -0.6448]])
+        result = torch.atan(a)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_7():
+    """3D张量"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([[[0.1, 0.2], [0.3, 0.4]], [[0.5, 0.6], [0.7, 0.8]]])
+        result = torch.atan(a)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_8():
+    """边界值 - 零值和无穷大"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([0.0, 1.0, -1.0, 100.0, -100.0])
+        result = torch.atan(a)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_9():
+    """不同数据类型 - float64"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([0.2341, 0.2539, -0.6256], dtype=torch.float64)
+        result = torch.atan(a)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_10():
+    """梯度计算测试"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([0.5, 1.0, 2.0], requires_grad=True)
+        y = torch.atan(a)
+        y.sum().backward()
+        a_grad = a.grad
+        """
+    )
+    obj.run(pytorch_code, ["y", "a_grad"], check_stop_gradient=False)

tests/test_bincount.py

@@ -104,3 +104,52 @@ def test_case_7():
         """
     )
     obj.run(pytorch_code, ["result"])
+
+
+def test_case_8():
+    """边界值 - 空输入"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.tensor([], dtype=torch.int64)
+        result = torch.bincount(input)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_9():
+    """边界值 - 所有索引相同"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.tensor([2, 2, 2, 2])
+        result = torch.bincount(input)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_10():
+    """不同数据类型 - float64"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.tensor([0, 1, 2, 1, 0])
+        weights = torch.tensor([1.5, 2.5, 3.5, 4.5, 5.5], dtype=torch.float64)
+        result = torch.bincount(input, weights=weights)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_11():
+    """不同数据类型 - int64输入"""
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.tensor([0, 1, 2, 1, 0], dtype=torch.int64)
+        result = torch.bincount(input)
+        """
+    )
+    obj.run(pytorch_code, ["result"])