Add api of constant in paddle.nn.initializer (#27786)

* Add api of constant in paddle.nn.initializer

* Add api of constant in paddle.nn.initializer

* Add api of constant in paddle.nn.initializer

* Add api of constant in paddle.nn.initializer

* Add api of constant in paddle.nn.initializer

* Add api of constant in paddle.nn.initializer

* Add api of constant in paddle.nn.initializer

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

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import numpy as np
+import unittest
+
+import paddle
+import paddle.nn as nn
+import paddle.fluid as fluid
+import paddle.fluid.framework as framework
+import paddle.nn.initializer as initializer
+from paddle.fluid.core import VarDesc
+
+DELTA = 0.00001
+
+
+def check_cast_op(op):
+    return op.type == 'cast' and \
+           op.attr('in_dtype') == VarDesc.VarType.FP32 and \
+           op.attr('out_dtype') == VarDesc.VarType.FP16
+
+
+class TestConstantInitializer(unittest.TestCase):
+    def static_test_constant_initializer_common(self,
+                                                init_inst,
+                                                dtype="float32",
+                                                value_target=0.0):
+        paddle.enable_static()
+        program = framework.Program()
+        block = program.global_block()
+        for _ in range(2):
+            block.create_parameter(
+                dtype=dtype,
+                shape=[5, 10],
+                lod_level=0,
+                name="param",
+                initializer=init_inst)
+        num_ops = 2 if dtype == "float16" else 1
+        self.assertEqual(len(block.ops), num_ops)
+        init_op = block.ops[0]
+        self.assertEqual(init_op.type, 'fill_constant')
+        self.assertAlmostEqual(init_op.attr('value'), value_target, delta=DELTA)
+        paddle.disable_static()
+        return block
+
+    def test_constant_initializer_default_value_static(self, dtype="float32"):
+        """Test the constant initializer with default value in static graph
+        """
+        block = self.static_test_constant_initializer_common(
+            init_inst=initializer.Constant(), dtype=dtype, value_target=0.0)
+        return block
+
+    def test_constant_initializer_default_value_dygraph(self, dtype="float32"):
+        """Test constant initializer with supplied value in dygraph
+        """
+        with fluid.dygraph.guard():
+            linear = nn.Linear(2, 4, weight_attr=nn.initializer.Constant())
+            mat_target = np.ones((2, 4), dtype=dtype) * 0.0
+            mat_linear = linear.weight.numpy()
+            mismatch = np.sum(
+                (mat_target - mat_linear) * (mat_target - mat_linear))
+            self.assertAlmostEqual(mismatch, 0.0, delta=DELTA)
+
+    def test_constant_initializer_static(self, dtype="float32"):
+        """Test constant initializer with supplied value in static graph
+        """
+        block = self.static_test_constant_initializer_common(
+            init_inst=initializer.Constant(2.3), dtype=dtype, value_target=2.3)
+        return block
+
+    def test_constant_initializer_dygraph(self, dtype="float32"):
+        """Test constant initializer with supplied value in dygraph
+        """
+        with fluid.dygraph.guard():
+            linear = nn.Linear(
+                2, 4, weight_attr=nn.initializer.Constant(value=2.0))
+            mat_target = np.ones((2, 4), dtype=dtype) * 2.0
+            mat_linear = linear.weight.numpy()
+            mismatch = np.sum(
+                (mat_target - mat_linear) * (mat_target - mat_linear))
+            self.assertAlmostEqual(mismatch, 0.0, delta=DELTA)
+
+    def test_constant_initializer_fp16(self):
+        """Test constant initializer with float16
+        """
+        block = self.test_constant_initializer_default_value_static("float16")
+        self.assertTrue(check_cast_op(block.ops[1]))
+        block = self.test_constant_initializer_static("float16")
+        self.assertTrue(check_cast_op(block.ops[1]))
+        self.test_constant_initializer_default_value_dygraph("float16")
+        self.test_constant_initializer_dygraph("float16")
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/nn/initializer/__init__.py

@@ -13,21 +13,23 @@
 # limitations under the License.
 
 # TODO: define the initializers to create a Parameter in neural network
-
 from ...fluid.initializer import Bilinear  #DEFINE_ALIAS
-from ...fluid.initializer import Constant  #DEFINE_ALIAS
 from ...fluid.initializer import MSRA  #DEFINE_ALIAS
 from ...fluid.initializer import Normal  #DEFINE_ALIAS
 from ...fluid.initializer import TruncatedNormal  #DEFINE_ALIAS
 from ...fluid.initializer import Uniform  #DEFINE_ALIAS
 from ...fluid.initializer import Xavier  #DEFINE_ALIAS
 
+from . import constant
+from .constant import Constant  #DEFINE_ALIAS
+
 __all__ = [
     'Bilinear',
-    'Constant',
     'MSRA',
     'Normal',
     'TruncatedNormal',
     'Uniform',
     'Xavier',
 ]
+
+__all__ += constant.__all__

python/paddle/nn/initializer/constant.py

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# TODO: define the initializers of Constant in neural network
+from ...fluid.initializer import ConstantInitializer
+
+__all__ = ['Constant']
+
+
+class Constant(ConstantInitializer):
+    """Implement the constant initializer.
+
+    Args:
+        value (float32): constant value to initialize the parameter 
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import paddle.nn as nn
+
+            data = paddle.rand([30, 10, 2], dtype='float32')
+            linear = nn.Linear(2,
+                               4,
+                               weight_attr=nn.initializer.Constant(value=2.0))
+            res = linear(data)
+            print(linear.weight.numpy())
+            #result is [[2. 2. 2. 2.],[2. 2. 2. 2.]]
+
+    """
+
+    def __init__(self, value=0.0):
+        if value is None:
+            raise ValueError("value must not be none.")
+        super(Constant, self).__init__(value=value, force_cpu=False)