【Hackathon4 No58】empty_like fp16&bf16 API test (#52668)

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

@@ -15,6 +15,7 @@
 import unittest
 
 import numpy as np
+from eager_op_test import convert_uint16_to_float
 
 import paddle
 from paddle.fluid import core
@@ -38,7 +39,7 @@ class TestEmptyLikeAPICommon(unittest.TestCase):
             f'shape should be {self.dst_shape}, but get {shape}',
         )
 
-        if data_type in ['float32', 'float64', 'int32', 'int64']:
+        if data_type in ['float16', 'float32', 'float64', 'int32', 'int64']:
             max_value = np.nanmax(out)
             min_value = np.nanmin(out)
             always_non_full_zero = max_value >= min_value
@@ -47,6 +48,16 @@ class TestEmptyLikeAPICommon(unittest.TestCase):
                 always_full_zero or always_non_full_zero,
                 'always_full_zero or always_non_full_zero.',
             )
+        elif data_type in ['uint16']:
+            uout = convert_uint16_to_float(out)
+            max_value = np.nanmax(uout)
+            min_value = np.nanmin(uout)
+            always_non_full_zero = max_value >= min_value
+            always_full_zero = max_value == 0.0 and min_value == 0.0
+            self.assertTrue(
+                always_full_zero or always_non_full_zero,
+                'always_full_zero or always_non_full_zero.',
+            )
         elif data_type in ['bool']:
             total_num = out.size
             true_num = np.sum(out)
@@ -154,16 +165,13 @@ class TestEmptyLikeAPI_Static(TestEmptyLikeAPICommon):
 
     def test_static_graph(self):
         paddle.enable_static()
-
-        dtype = 'float32'
-
         train_program = Program()
         startup_program = Program()
 
         with program_guard(train_program, startup_program):
-            x = np.random.random(self.x_shape).astype(dtype)
+            x = np.random.random(self.x_shape).astype(self.dtype)
             data_x = paddle.static.data(
-                'x', shape=self.data_x_shape, dtype=dtype
+                'x', shape=self.data_x_shape, dtype=self.dtype
             )
 
             out = paddle.empty_like(data_x)
@@ -176,7 +184,7 @@ class TestEmptyLikeAPI_Static(TestEmptyLikeAPICommon):
         exe = paddle.static.Executor(place)
         res = exe.run(train_program, feed={'x': x}, fetch_list=[out])
 
-        self.dst_dtype = dtype
+        self.dst_dtype = self.dtype
         self.dst_shape = x.shape
         self.__check_out__(res[0])
 
@@ -185,12 +193,80 @@ class TestEmptyLikeAPI_Static(TestEmptyLikeAPICommon):
     def init_config(self):
         self.x_shape = (200, 3)
         self.data_x_shape = [200, 3]
+        self.dtype = 'float32'
 
 
 class TestEmptyLikeAPI_Static2(TestEmptyLikeAPI_Static):
     def init_config(self):
         self.x_shape = (3, 200, 3)
         self.data_x_shape = [-1, 200, 3]
+        self.dtype = 'float32'
+
+
+class TestEmptyLikeAPI_StaticForFP16Op(TestEmptyLikeAPICommon):
+    def setUp(self):
+        self.init_config()
+
+    def init_config(self):
+        self.x_shape = (200, 3)
+        self.data_x_shape = [200, 3]
+        self.dtype = 'float16'
+
+    def test_static_graph(self):
+        paddle.enable_static()
+        if paddle.fluid.core.is_compiled_with_cuda():
+            place = paddle.CUDAPlace(0)
+            with paddle.static.program_guard(
+                paddle.static.Program(), paddle.static.Program()
+            ):
+                x = np.random.random([200, 3]).astype(self.dtype)
+                data_x = paddle.static.data(
+                    name="x", shape=[200, 3], dtype=self.dtype
+                )
+                out = paddle.empty_like(data_x)
+                exe = paddle.static.Executor(place)
+                res = exe.run(
+                    paddle.static.default_main_program(),
+                    feed={'x': x},
+                    fetch_list=[out],
+                )
+
+            self.dst_dtype = self.dtype
+            self.dst_shape = x.shape
+            self.__check_out__(res[0])
+
+
+class TestEmptyLikeAPI_StaticForBF16Op(TestEmptyLikeAPICommon):
+    def setUp(self):
+        self.init_config()
+
+    def init_config(self):
+        self.x_shape = (200, 3)
+        self.data_x_shape = [200, 3]
+        self.dtype = 'uint16'
+
+    def test_static_graph(self):
+        paddle.enable_static()
+        if paddle.fluid.core.is_compiled_with_cuda():
+            place = paddle.CUDAPlace(0)
+            with paddle.static.program_guard(
+                paddle.static.Program(), paddle.static.Program()
+            ):
+                x = np.random.random([200, 3]).astype(np.uint16)
+                data_x = paddle.static.data(
+                    name="x", shape=[200, 3], dtype=np.uint16
+                )
+                out = paddle.empty_like(data_x)
+                exe = paddle.static.Executor(place)
+                res = exe.run(
+                    paddle.static.default_main_program(),
+                    feed={'x': x},
+                    fetch_list=[out],
+                )
+
+            self.dst_dtype = self.dtype
+            self.dst_shape = x.shape
+            self.__check_out__(res[0])
 
 
 class TestEmptyError(unittest.TestCase):

python/paddle/tensor/attribute.py

@@ -120,6 +120,7 @@ def shape(input):
                 'int64',
                 'complex64',
                 'complex128',
+                'uint16',
             ],
             'shape',
         )

python/paddle/tensor/creation.py

@@ -1954,13 +1954,29 @@ def empty_like(x, dtype=None, name=None):
         check_variable_and_dtype(
             x,
             'x',
-            ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
+            [
+                'bool',
+                'float16',
+                'float32',
+                'float64',
+                'int32',
+                'int64',
+                'uint16',
+            ],
             'empty_like',
         )
         check_dtype(
             dtype,
             'dtype',
-            ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
+            [
+                'bool',
+                'float16',
+                'float32',
+                'float64',
+                'int32',
+                'int64',
+                'uint16',
+            ],
             'empty_like',
         )
         out = helper.create_variable_for_type_inference(dtype=dtype)