[0-size Tensor support] fix std in some 0-size case (#73214)

DanielSun11 · web-flow · commit b7b57f00b83f · 2025-06-12T19:07:39.000+08:00
* fix std 0-dim error

* fix
diff --git a/python/paddle/tensor/stat.py b/python/paddle/tensor/stat.py
@@ -217,6 +217,8 @@ def _replace_nan(out):
 
     if 0 in x.shape:
         out = _replace_nan(out)
+    if len(x.shape) == 0 and not unbiased:
+        out = paddle.to_tensor(0, stop_gradient=out.stop_gradient)
     if out.dtype != x.dtype:
         return out.astype(x.dtype)
     return out
diff --git a/test/legacy_test/test_std_layer.py b/test/legacy_test/test_std_layer.py
@@ -146,5 +146,53 @@ def test_fp16_with_gpu(self):
                 )
 
 
+class TestStdAPI_ZeroSize1(unittest.TestCase):
+    def init_data(self):
+        self.x_shape = []
+        # x = torch.tensor([])
+        # res= torch.std(x)     Here, res is nan
+        self.expact_out = np.nan
+
+    def test_zerosize(self):
+        self.init_data()
+        paddle.disable_static()
+        x = paddle.to_tensor(np.random.random(self.x_shape))
+        out1 = paddle.std(x).numpy()
+        np.testing.assert_allclose(out1, self.expact_out, equal_nan=True)
+        paddle.enable_static()
+
+
+class TestStdAPI_UnBiased1(unittest.TestCase):
+    def init_data(self):
+        self.x_shape = [1]
+        # x = torch.randn([1])
+        # res= torch.std(x,correction=0)     Here, res is 0.
+        self.expact_out = 0.0
+
+    def test_api(self):
+        self.init_data()
+        paddle.disable_static()
+        x = paddle.to_tensor(np.random.random(self.x_shape))
+        out1 = paddle.std(x, unbiased=False).numpy()
+        np.testing.assert_allclose(out1, self.expact_out, equal_nan=True)
+        paddle.enable_static()
+
+
+class TestStdAPI_UnBiased2(unittest.TestCase):
+    def init_data(self):
+        self.x_shape = [1]
+        # x = torch.randn([1])
+        # res= torch.std(x,correction=1)     Here, res is 0.
+        self.expact_out = np.nan
+
+    def test_api(self):
+        self.init_data()
+        paddle.disable_static()
+        x = paddle.to_tensor(np.random.random(self.x_shape))
+        out1 = paddle.std(x, unbiased=True).numpy()
+        np.testing.assert_allclose(out1, self.expact_out, equal_nan=True)
+        paddle.enable_static()
+
+
 if __name__ == '__main__':
     unittest.main()
diff --git a/test/legacy_test/test_variance_layer.py b/test/legacy_test/test_variance_layer.py
@@ -127,14 +127,66 @@ def test_error(self):
 
 
 class TestVarAPI_ZeroSize(unittest.TestCase):
+    def init_data(self):
+        self.x_shape = [10, 0]
+
     def test_zerosize(self):
+        self.init_data()
         paddle.disable_static()
-        x = paddle.to_tensor(np.random.random([10, 0]))
+        x = paddle.to_tensor(np.random.random(self.x_shape))
         out1 = paddle.var(x).numpy()
         out2 = np.var(x.numpy())
         np.testing.assert_allclose(out1, out2, equal_nan=True)
         paddle.enable_static()
 
 
+class TestVarAPI_ZeroSize1(unittest.TestCase):
+    def init_data(self):
+        self.x_shape = []
+        # x = torch.tensor([])
+        # res= torch.var(x)     Here, res is nan
+        self.expact_out = np.nan
+
+    def test_zerosize(self):
+        self.init_data()
+        paddle.disable_static()
+        x = paddle.to_tensor(np.random.random(self.x_shape))
+        out1 = paddle.var(x).numpy()
+        np.testing.assert_allclose(out1, self.expact_out, equal_nan=True)
+        paddle.enable_static()
+
+
+class TestVarAPI_UnBiased1(unittest.TestCase):
+    def init_data(self):
+        self.x_shape = [1]
+        # x = torch.randn([1])
+        # res= torch.var(x,correction=0)     Here, res is 0.
+        self.expact_out = 0.0
+
+    def test_api(self):
+        self.init_data()
+        paddle.disable_static()
+        x = paddle.to_tensor(np.random.random(self.x_shape))
+        out1 = paddle.var(x, unbiased=False).numpy()
+        np.testing.assert_allclose(out1, self.expact_out, equal_nan=True)
+        paddle.enable_static()
+
+
+class TestVarAPI_UnBiased2(unittest.TestCase):
+    def init_data(self):
+        self.x_shape = [1]
+        # x = torch.randn([1])
+        # res= torch.var(x,correction=1)     Here, res is 0.
+        self.expact_out = np.nan
+
+    def test_api(self):
+        self.init_data()
+        paddle.disable_static()
+        x = paddle.to_tensor(np.random.random(self.x_shape))
+        out1 = paddle.var(x, unbiased=True).numpy()
+        np.testing.assert_allclose(out1, self.expact_out, equal_nan=True)
+        paddle.enable_static()
+
+
 if __name__ == '__main__':
     unittest.main()
