[Clean Fluid] Remove fluid dygrah apis

python/paddle/fluid/reader.py

@@ -1351,9 +1351,9 @@ def __init__(
         self._use_double_buffer = use_double_buffer
         self._capacity = capacity
         if not self._iterable:
-            # Because layers.io.double_buffer is not supported anymore, and only when iterable and use_double_buffer
-            # are both True layers.io.double_buffer will be in use, here if itrable is False, use_double_buffer will be
-            # forcely set False to avoid using layers.io.double_buffer.
+            # Because layers.io.double_buffer is not supported anymore and that iterable is False and use_double_buffer
+            # is True is not spported, here if itrable is False, use_double_buffer will be
+            # forcely set False to avoid unexpected error.
             # TODO: keep use_double_buffer
             self._use_double_buffer = False
             self._init_non_iterable()

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

@@ -293,21 +293,25 @@ def attr_data_format():
 
 class TestGroupNormEager(unittest.TestCase):
     def test_dygraph_api(self):
-        self.dtype = np.float64
+
+        # not supported float64
+        # only support float32
+        self.dtype = np.float32
+
         self.shape = (8, 32, 32)
         input = np.random.random(self.shape).astype(self.dtype)
 
         with fluid.dygraph.guard():
             tensor_1 = fluid.dygraph.to_variable(input)
             tensor_1.stop_gradient = False
-            groupNorm = fluid.dygraph.nn.GroupNorm(channels=32, groups=4)
+            groupNorm = paddle.nn.GroupNorm(num_channels=32, num_groups=4)
             ret1 = groupNorm(tensor_1)
             ret1.backward()
             with _test_eager_guard():
                 tensor_eager_1 = fluid.dygraph.to_variable(input)
                 tensor_eager_1.stop_gradient = False
-                groupNorm_eager = fluid.dygraph.nn.GroupNorm(
-                    channels=32, groups=4
+                groupNorm_eager = paddle.nn.GroupNorm(
+                    num_channels=32, num_groups=4
                 )
                 ret2 = groupNorm_eager(tensor_eager_1)
                 ret2.backward()
@@ -328,16 +332,14 @@ def test_dygraph_api(self):
         with fluid.dygraph.guard():
             tensor_1 = fluid.dygraph.to_variable(input)
             tensor_1.stop_gradient = False
-            groupNorm = fluid.dygraph.nn.GroupNorm(
-                channels=32, groups=4, dtype='float32'
-            )
+            groupNorm = paddle.nn.GroupNorm(num_channels=32, num_groups=4)
             ret1 = groupNorm(tensor_1)
             ret1.backward()
             with _test_eager_guard():
                 tensor_eager_1 = fluid.dygraph.to_variable(input)
                 tensor_eager_1.stop_gradient = False
-                groupNorm_eager = fluid.dygraph.nn.GroupNorm(
-                    channels=32, groups=4
+                groupNorm_eager = paddle.nn.GroupNorm(
+                    num_channels=32, num_groups=4
                 )
                 ret2 = groupNorm_eager(tensor_eager_1)
                 ret2.backward()
@@ -351,23 +353,25 @@ def test_dygraph_api(self):
 
 class TestGroupNormEager_fp16(unittest.TestCase):
     def test_dygraph_api(self):
+
+        # not supported float16
+        # only support float32
         self.dtype = np.float32
+
         self.shape = (8, 32, 32)
         input = np.random.random(self.shape).astype(self.dtype)
 
         with fluid.dygraph.guard():
             tensor_1 = fluid.dygraph.to_variable(input)
             tensor_1.stop_gradient = False
-            groupNorm = fluid.dygraph.nn.GroupNorm(
-                channels=32, groups=4, dtype='float16'
-            )
+            groupNorm = paddle.nn.GroupNorm(num_channels=32, num_groups=4)
             ret1 = groupNorm(tensor_1)
             ret1.backward()
             with _test_eager_guard():
                 tensor_eager_1 = fluid.dygraph.to_variable(input)
                 tensor_eager_1.stop_gradient = False
-                groupNorm_eager = fluid.dygraph.nn.GroupNorm(
-                    channels=32, groups=4
+                groupNorm_eager = paddle.nn.GroupNorm(
+                    num_channels=32, num_groups=4
                 )
                 ret2 = groupNorm_eager(tensor_eager_1)
                 ret2.backward()

python/paddle/fluid/tests/unittests/test_group_norm_op_v2.py

@@ -19,7 +19,6 @@
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
-from paddle.fluid import Program, program_guard
 from paddle.fluid.framework import _test_eager_guard
 
 
@@ -39,106 +38,6 @@ def group_norm_naive_for_general_dimension(x, scale, bias, epsilon, groups):
     return output
 
 
-class TestDygraphGroupNormv2(unittest.TestCase):
-    def test_dygraph(self):
-        places = [fluid.CPUPlace()]
-        if core.is_compiled_with_cuda() and core.op_support_gpu("group_norm"):
-            places.append(fluid.CUDAPlace(0))
-        shapes = [
-            [2, 2, 2, 2],
-            [2, 2, 4],
-            [4, 2],
-            [4, 2, 6, 6, 2],
-            [2, 2, 2, 2, 2, 2],
-        ]
-        for p in places:
-
-            def compute_v1(x):
-                with fluid.dygraph.guard(p):
-                    gn = fluid.dygraph.GroupNorm(channels=2, groups=2)
-                    y = gn(fluid.dygraph.to_variable(x))
-                return y.numpy()
-
-            def compute_v2(x):
-                with fluid.dygraph.guard(p):
-                    gn = paddle.nn.GroupNorm(num_channels=2, num_groups=2)
-                    y = gn(fluid.dygraph.to_variable(x))
-                return y.numpy()
-
-            def test_weight_bias_false():
-                with fluid.dygraph.guard(p):
-                    gn = paddle.nn.GroupNorm(
-                        num_channels=2,
-                        num_groups=2,
-                        weight_attr=False,
-                        bias_attr=False,
-                    )
-
-            def test_nn_exception():
-                with fluid.dygraph.guard(p):
-
-                    def attr_data_format():
-                        out = paddle.nn.GroupNorm(
-                            num_groups=2, num_channels=2, data_format="CNHW"
-                        )
-
-                    self.assertRaises(ValueError, attr_data_format)
-
-            for shape in shapes:
-                x = np.random.randn(*shape).astype("float32")
-                y1 = compute_v1(x)
-                y2 = compute_v2(x)
-                result = np.allclose(y1, y2, atol=1e-5)
-                if not result:
-                    print("y1:", y1, "\ty2:", y2)
-                self.assertTrue(result)
-                test_weight_bias_false()
-                test_nn_exception()
-
-    def test_static(self):
-        paddle.enable_static()
-        places = [fluid.CPUPlace()]
-        if core.is_compiled_with_cuda() and core.op_support_gpu("group_norm"):
-            places.append(fluid.CUDAPlace(0))
-        shapes = [
-            [2, 6, 2, 2],
-            [2, 6, 4],
-            [4, 6],
-            [4, 6, 6, 6, 2],
-            [4, 6, 2, 2, 2, 2],
-        ]
-        for p in places:
-            exe = fluid.Executor(p)
-
-            def compute_v1(x_np):
-                with program_guard(Program(), Program()):
-                    gn = fluid.dygraph.GroupNorm(channels=6, groups=2)
-                    x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
-                    y = gn(x)
-                    exe.run(fluid.default_startup_program())
-                    r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
-                return r
-
-            def compute_v2(x_np):
-                with program_guard(Program(), Program()):
-                    gn = paddle.nn.GroupNorm(num_channels=6, num_groups=2)
-                    x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
-                    y = gn(x)
-                    exe.run(fluid.default_startup_program())
-                    r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
-                return r
-
-            for shape in shapes:
-                x = np.random.randn(*shape).astype("float32")
-                y1 = compute_v1(x)
-                y2 = compute_v2(x)
-                np.testing.assert_allclose(y1, y2, rtol=1e-05, atol=1e-05)
-
-    def test_eager_api(self):
-        with _test_eager_guard():
-            self.test_dygraph()
-
-
 class TestGroupNormAPIV2_With_General_Dimensions(unittest.TestCase):
     def test_numerical_accuracy(self):
         paddle.disable_static()

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

@@ -21,7 +21,7 @@
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.framework as framework
-from paddle.fluid.dygraph.nn import BatchNorm, Embedding, GroupNorm
+from paddle.fluid.dygraph.nn import BatchNorm, Embedding
 from paddle.nn import Linear
 
 
@@ -122,10 +122,10 @@ def testLoadStaticModel(self):
             name='groupnorm_in', shape=[None, 8, 32, 32], dtype='float32'
         )
         groupnorm_out1 = paddle.static.nn.group_norm(
-            input=groupnorm_in, groups=4
+            input=groupnorm_in, groups=4, param_attr=True, bias_attr=True
         )
         groupnorm_out2 = paddle.static.nn.group_norm(
-            input=groupnorm_in, groups=4
+            input=groupnorm_in, groups=4, param_attr=True, bias_attr=True
         )
         '''
         spec_norm = fluid.data(name='spec_norm', shape=[2, 8, 32, 32], dtype='float32')
@@ -212,8 +212,8 @@ def __init__(self):
                     self.layer_norm_1 = paddle.nn.LayerNorm([10])
                     self.layer_norm_2 = paddle.nn.LayerNorm(10)
 
-                    self.group_norm1 = GroupNorm(8, 4)
-                    self.gourp_norm2 = GroupNorm(8, 4)
+                    self.group_norm1 = paddle.nn.GroupNorm(4, 8)
+                    self.gourp_norm2 = paddle.nn.GroupNorm(4, 8)
 
                     self.w_1 = self.create_parameter(
                         [100, 100], dtype='float32', attr="weight_test_1"