add weight_norm & remove_weight_norm

python/paddle/fluid/param_attr.py

@@ -216,6 +216,8 @@ class WeightNormParamAttr(ParamAttr):
         It is recommended to use ``minimize(loss, grad_clip=clip)`` to clip gradient. 
         There are three clipping strategies: :ref:`api_fluid_clip_GradientClipByGlobalNorm` , 
         :ref:`api_fluid_clip_GradientClipByNorm` , :ref:`api_fluid_clip_GradientClipByValue` .
+
+        Please use 'paddle.nn.utils.weight_norm' in dygraph mode.
 
     Args:
         dim(int): Dimension over which to compute the norm. Dim is a non-negative

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

@@ -0,0 +1,183 @@
+#   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 unittest
+import numpy
+import collections
+from functools import reduce
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.nn.utils import weight_norm, remove_weight_norm
+
+
+class TestDygraphWeightNorm(unittest.TestCase):
+    def setUp(self):
+        self.init_test_case()
+        self.set_data()
+
+    def init_test_case(self):
+        self.batch_size = 3
+        self.data_desc = (['x', [2, 3, 3]], )
+        self.dim = None
+
+    def set_data(self):
+        self.data = collections.OrderedDict()
+        for desc in self.data_desc:
+            data_name = desc[0]
+            data_shape = desc[1]
+            data_value = numpy.random.random(
+                size=[self.batch_size] + data_shape).astype('float32')
+            self.data[data_name] = data_value
+
+    def norm_except_dim(self, w, dim=None):
+        shape = w.shape
+        ndims = len(shape)
+        shape_numel = reduce(lambda x, y: x * y, shape)
+        if dim == -1:
+            return numpy.linalg.norm(w, axis=None, keepdims=True)
+        elif dim == 0:
+            tile_shape = list(w.shape)
+            tile_shape[0] = 1
+            w_matrix = numpy.reshape(w, (shape[0], shape_numel // shape[0]))
+            return numpy.linalg.norm(w_matrix, axis=1, keepdims=True)
+        elif dim == (ndims - 1):
+            w_matrix = numpy.reshape(w, (shape_numel // shape[-1], shape[-1]))
+            return numpy.linalg.norm(w_matrix, axis=0, keepdims=True)
+        else:
+            perm = list(range(ndims))
+            perm_ori = list(range(ndims))
+            perm[0] = dim
+            perm[dim] = 0
+            p_transposed = numpy.transpose(w, perm)
+            return self.norm_except_dim(p_transposed, 0)
+
+    def weight_normalize(self, w, dim=None):
+        shape = w.shape
+        ndims = len(shape)
+        shape_numel = reduce(lambda x, y: x * y, shape)
+        v = w
+        g = self.norm_except_dim(w, dim)
+        g_mul = g
+
+        if dim == -1:
+            v_norm = v / (numpy.linalg.norm(v, axis=None, keepdims=True))
+        elif dim == 0:
+            w_matrix = numpy.reshape(w, (shape[0], shape_numel // shape[0]))
+            v_norm = v / numpy.linalg.norm(w_matrix, axis=1)
+            v_norm = numpy.reshape(v_norm, shape)
+            g = numpy.squeeze(g, axis=1)
+        elif dim == (ndims - 1):
+            w_matrix = numpy.reshape(w, (shape_numel // shape[-1], shape[-1]))
+            v_norm = v / numpy.linalg.norm(w_matrix, axis=0, keepdims=True)
+            v_norm = numpy.reshape(v_norm, shape)
+        else:
+            perm = list(range(ndims))
+            perm[0] = dim
+            perm[dim] = 0
+            p_transposed = numpy.transpose(v, perm)
+            transposed_shape = p_transposed.shape
+            transposed_shape_numel = reduce(lambda x, y: x * y,
+                                            transposed_shape)
+            p_matrix = numpy.reshape(
+                p_transposed, (p_transposed.shape[0],
+                               transposed_shape_numel // p_transposed.shape[0]))
+            v_norm = v / numpy.expand_dims(
+                numpy.expand_dims(
+                    numpy.linalg.norm(
+                        p_matrix, axis=1, keepdims=True), axis=0),
+                axis=(ndims - 1))
+            v_norm = numpy.reshape(v_norm, transposed_shape)
+            v_norm = numpy.transpose(v_norm, perm)
+            g = numpy.squeeze(g, axis=1)
+            if dim == 1:
+                eaxis = 2
+            elif dim == 2:
+                eaxis = 1
+            g_mul = numpy.expand_dims(
+                numpy.expand_dims(
+                    numpy.expand_dims(
+                        g, axis=0), axis=eaxis),
+                axis=(ndims - 1))
+        w = g_mul * v_norm
+        return g, v
+
+    def test_check_output(self):
+        fluid.enable_imperative()
+        linear = paddle.nn.Conv2D(2, 3, 3)
+        before_weight = linear.weight.numpy()
+        if self.dim == None:
+            self.dim = -1
+        wn = weight_norm(linear, dim=self.dim)
+        outputs = []
+        for name, data in self.data.items():
+            output = linear(fluid.dygraph.to_variable(data))
+            outputs.append(output.numpy())
+        after_weight = linear.weight
+        self.actual_outputs = [linear.weight_g.numpy(), linear.weight_v.numpy()]
+
+        expect_output = self.weight_normalize(before_weight, self.dim)
+
+        for expect, actual in zip(expect_output, self.actual_outputs):
+            self.assertTrue(
+                numpy.allclose(
+                    numpy.array(actual), expect, atol=0.001))
+
+
+class TestDygraphWeightNormCase1(TestDygraphWeightNorm):
+    def init_test_case(self):
+        self.batch_size = 3
+        self.data_desc = (['x', [2, 3, 3]], )
+        self.dim = 0
+
+
+class TestDygraphWeightNormCase2(TestDygraphWeightNorm):
+    def init_test_case(self):
+        self.batch_size = 3
+        self.data_desc = (['x', [2, 3, 3]], )
+        self.dim = 1
+
+
+class TestDygraphWeightNormCase3(TestDygraphWeightNorm):
+    def init_test_case(self):
+        self.batch_size = 3
+        self.data_desc = (['x', [2, 3, 3]], )
+        self.dim = 3
+
+
+class TestDygraphRemoveWeightNorm(unittest.TestCase):
+    def setUp(self):
+        self.init_test_case()
+
+    def init_test_case(self):
+        self.batch_size = 3
+        self.data_desc = (['x', [2, 3, 3]], )
+        self.dim = None
+
+    def test_check_output(self):
+        fluid.enable_imperative()
+        linear = paddle.nn.Conv2D(2, 3, 3)
+        before_weight = linear.weight
+        wn = weight_norm(linear, dim=self.dim)
+        rwn = remove_weight_norm(linear)
+        after_weight = linear.weight
+        self.assertTrue(
+            numpy.allclose(
+                before_weight.numpy(), after_weight.numpy(), atol=0.001))
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/nn/__init__.py

@@ -18,13 +18,15 @@
 from .layer import norm
 from .functional import extension
 from .layer import common
+from .utils import weight_norm_hook
 
 from . import initializer
 
 __all__ = []
 __all__ += norm.__all__
 __all__ += extension.__all__
 __all__ += common.__all__
+__all__ += weight_norm_hook.__all__
 
 # TODO: define alias in nn directory
 # from .clip import ErrorClipByValue        #DEFINE_ALIAS

python/paddle/nn/utils/__init__.py

@@ -0,0 +1,16 @@
+#   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 . import weight_norm_hook
+from .weight_norm_hook import weight_norm, remove_weight_norm