[Zero-Dim] support input 0D Tensor for maximum,minimum,allclose,sigmoid_focal_loss (#49616)

* [Zero-Dim] support input 0D Tensor for maximum,minimum,allclose,sigmoid_focal_loss

* [Zero-Dim] add backward test for sigmoid_focal_loss with 0-D input Tensor

paddle/phi/infermeta/binary.cc

@@ -86,7 +86,13 @@ void AllValueCompareInferMeta(const MetaTensor& x,
                               MetaConfig config) {
   detail::BinarySameInputDimsCheck(x, y, config);
 
-  out->set_dims(phi::make_ddim({1}));
+  auto x_dims = x.dims();
+  auto y_dims = y.dims();
+  if (x_dims.size() == 0 && y_dims.size() == 0) {
+    out->set_dims(phi::make_ddim({}));
+  } else {
+    out->set_dims(phi::make_ddim({1}));
+  }
   out->set_dtype(DataType::BOOL);
 }
 

python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py

@@ -283,6 +283,8 @@ binary_api_list = [
     paddle.logical_and,
     paddle.logical_or,
     paddle.logical_xor,
+    paddle.maximum,
+    paddle.minimum,
 ]
 
 binary_int_api_list = [
@@ -994,6 +996,35 @@ class TestSundryAPI(unittest.TestCase):
             # check grad shape with 1D repeats
             self.assertEqual(x.grad.shape, [])
 
+    def test_sigmoid_focal_loss(self):
+        logit = paddle.to_tensor(
+            [[0.97, 0.91, 0.03], [0.55, 0.43, 0.71]],
+            dtype='float32',
+            stop_gradient=False,
+        )
+        label = paddle.to_tensor(
+            [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]], dtype='float32'
+        )
+        fg_num_0 = paddle.full([], 2.0)
+        fg_num_1 = paddle.full([1], 2.0)
+
+        out0 = F.sigmoid_focal_loss(logit, label, normalizer=fg_num_0)
+        out1 = F.sigmoid_focal_loss(logit, label, normalizer=fg_num_1)
+
+        np.testing.assert_array_equal(
+            out0.numpy(),
+            out1.numpy(),
+        )
+
+        out0.backward()
+        self.assertEqual(out0.grad.shape, [1])
+        self.assertEqual(logit.grad.shape, [2, 3])
+
+    def test_allclose(self):
+        x = paddle.full([], 0.5)
+        y = paddle.full([], 0.6)
+        self.assertFalse(paddle.allclose(x, y))
+
 
 class TestSundryAPIStatic(unittest.TestCase):
     def setUp(self):

python/paddle/fluid/tests/unittests/xpu/test_zero_dim_tensor_xpu.py

@@ -178,6 +178,8 @@ binary_api_list = [
     paddle.logical_and,
     paddle.logical_or,
     paddle.logical_xor,
+    paddle.maximum,
+    paddle.minimum,
 ]
 
 binary_int_api_list = [
@@ -703,6 +705,35 @@ class TestSundryAPI(unittest.TestCase):
         self.assertEqual(x1.grad.numpy(), 0)
         self.assertEqual(x2.grad.numpy(), 0)
 
+    def test_sigmoid_focal_loss(self):
+        logit = paddle.to_tensor(
+            [[0.97, 0.91, 0.03], [0.55, 0.43, 0.71]],
+            dtype='float32',
+            stop_gradient=False,
+        )
+        label = paddle.to_tensor(
+            [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]], dtype='float32'
+        )
+        fg_num_0 = paddle.full([], 2.0)
+        fg_num_1 = paddle.full([1], 2.0)
+
+        out0 = F.sigmoid_focal_loss(logit, label, normalizer=fg_num_0)
+        out1 = F.sigmoid_focal_loss(logit, label, normalizer=fg_num_1)
+
+        np.testing.assert_array_equal(
+            out0.numpy(),
+            out1.numpy(),
+        )
+
+        out0.backward()
+        self.assertEqual(out0.grad.shape, [1])
+        self.assertEqual(logit.grad.shape, [2, 3])
+
+    def test_allclose(self):
+        x = paddle.full([], 0.5)
+        y = paddle.full([], 0.6)
+        self.assertFalse(paddle.allclose(x, y))
+
 
 # Use to test API whose zero-dim input tensors don't have grad and not need to test backward in OpTest.
 class TestNoBackwardAPI(unittest.TestCase):

python/paddle/nn/functional/loss.py

@@ -2882,8 +2882,8 @@ def sigmoid_focal_loss(
             ``logit``. The target label whose value should be numbers between 0 and 1.
             Available dtype is float32, float64.
         normalizer (Tensor, optional): The number normalizes the focal loss. It has to be
-            a 1-D Tensor whose shape is `[1, ]`. The data type is float32, float64.
-            For object detection task, it is the number of positive samples.
+            a 1-D Tensor with shape `[1, ]` or 0-D Tensor with shape `[]`. The data type
+            is float32, float64. For object detection task, it is the number of positive samples.
             If set to None, the focal loss will not be normalized. Default is None.
         alpha(int|float, optional): Hyper-parameter to balance the positive and negative example,
             it should be between 0 and 1.  Default value is set to 0.25.
@@ -2934,7 +2934,7 @@ def sigmoid_focal_loss(
         normalizer_dims = len(normalizer_shape)
         if normalizer_dims > 1:
             raise ValueError(
-                "Expected one dimension of normalizer in sigmoid_focal_loss but got {}.".format(
+                "Expected zero or one dimension of normalizer in sigmoid_focal_loss but got {}.".format(
                     normalizer_dims
                 )
             )