fix fp16 dtype checking for clip op (#50878)

* fix fp16 dtype checking for clip op

* modify the name

* fix type error

* fix check error

* Update test_clip_op.py

fix test error

* Update test_clip_op.py

fix code style

---------
Co-authored-by: NZhang Ting <Douyaer2020@qq.com>

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

@@ -299,6 +299,33 @@ class TestClipAPI(unittest.TestCase):
         paddle.disable_static()
 
 
+class TestClipOpFp16(unittest.TestCase):
+    def test_fp16(self):
+        paddle.enable_static()
+        data_shape = [1, 9, 9, 4]
+        data = np.random.random(data_shape).astype('float16')
+
+        with paddle.static.program_guard(paddle.static.Program()):
+            images = paddle.static.data(
+                name='image1', shape=data_shape, dtype='float16'
+            )
+            min = paddle.static.data(name='min1', shape=[1], dtype='float16')
+            max = paddle.static.data(name='max1', shape=[1], dtype='float16')
+            out = paddle.clip(images, min, max)
+            if fluid.core.is_compiled_with_cuda():
+                place = paddle.CUDAPlace(0)
+                exe = paddle.static.Executor(place)
+                res1 = exe.run(
+                    feed={
+                        "image1": data,
+                        "min1": np.array([0.2]).astype('float16'),
+                        "max1": np.array([0.8]).astype('float16'),
+                    },
+                    fetch_list=[out],
+                )
+        paddle.disable_static()
+
+
 class TestInplaceClipAPI(TestClipAPI):
     def _executed_api(self, x, min=None, max=None):
         return x.clip_(min, max)

python/paddle/tensor/math.py

@@ -2770,11 +2770,11 @@ def clip(x, min=None, max=None, name=None):
         Out = MIN(MAX(x, min), max)
 
     Args:
-        x (Tensor): An N-D Tensor with data type float32, float64, int32 or int64.
+        x (Tensor): An N-D Tensor with data type float16, float32, float64, int32 or int64.
         min (float|int|Tensor, optional): The lower bound with type ``float`` , ``int`` or a ``Tensor``
-            with shape [1] and type ``int32``, ``float32``, ``float64``.
+            with shape [1] and type ``int32``, ``float16``, ``float32``, ``float64``.
         max (float|int|Tensor, optional): The upper bound with type ``float``, ``int`` or a ``Tensor``
-            with shape [1] and type ``int32``, ``float32``, ``float64``.
+            with shape [1] and type ``int32``, ``float16``, ``float32``, ``float64``.
         name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
@@ -2803,6 +2803,9 @@ def clip(x, min=None, max=None, name=None):
     elif x_dtype == 'paddle.int64':
         min_ = np.iinfo(np.int64).min
         max_ = np.iinfo(np.int64).max - 2**39
+    elif x_dtype == 'paddle.float16':
+        min_ = float(np.finfo(np.float16).min)
+        max_ = float(np.finfo(np.float16).max)
     else:
         min_ = float(np.finfo(np.float32).min)
         max_ = float(np.finfo(np.float32).max)
@@ -2822,7 +2825,7 @@ def clip(x, min=None, max=None, name=None):
                 check_dtype(
                     min.dtype,
                     'min',
-                    ['float32', 'float64', 'int32'],
+                    ['float16', 'float32', 'float64', 'int32'],
                     'clip',
                     '(When the type of min in clip is Variable.)',
                 )
@@ -2832,13 +2835,13 @@ def clip(x, min=None, max=None, name=None):
                 check_dtype(
                     max.dtype,
                     'max',
-                    ['float32', 'float64', 'int32'],
+                    ['float16', 'float32', 'float64', 'int32'],
                     'clip',
                     '(When the type of max in clip is Variable.)',
                 )
 
         check_variable_and_dtype(
-            x, 'x', ['float32', 'float64', 'int32', 'int64'], 'clip'
+            x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], 'clip'
         )
 
         inputs = {'X': x}