Merge pull request #7979 from lcy-seso/fix_dropout

fix the bug that dropout always uses a fixed seed.

paddle/operators/dropout_op.cc

@@ -51,6 +51,13 @@ class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
                          "'dropout_prob' must be between 0.0 and 1.0.");
         });
     AddAttr<bool>("is_test", "True if in test phase.").SetDefault(false);
+    AddAttr<bool>("fix_seed",
+                  "A flag indicating whether to use a fixed seed to generate "
+                  "random mask. NOTE: DO NOT set this flag to true in "
+                  "training. Setting this flag to true is only useful in "
+                  "unittest or for debug that always the same output units "
+                  "will be dropped.")
+        .SetDefault(false);
     AddAttr<int>("seed", "Dropout random seed.").SetDefault(0);
 
     AddComment(R"DOC(

paddle/operators/dropout_op.cu

@@ -62,7 +62,11 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
       auto* mask = context.Output<Tensor>("Mask");
       auto* mask_data = mask->mutable_data<T>(context.GetPlace());
       int size = framework::product(mask->dims());
-      int seed = context.Attr<int>("seed");
+
+      std::random_device rnd;
+      int seed =
+          context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : rnd();
+
       thrust::counting_iterator<unsigned int> index_sequence_begin(0);
       thrust::transform(index_sequence_begin, index_sequence_begin + size,
                         thrust::device_ptr<T>(mask_data),

paddle/operators/dropout_op.h

@@ -38,9 +38,15 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
     if (!context.Attr<bool>("is_test")) {
       auto* mask = context.Output<Tensor>("Mask");
       auto* mask_data = mask->mutable_data<T>(context.GetPlace());
-      int seed = context.Attr<int>("seed");
+
+      // NOTE: fixed seed should only be used in unittest or for debug.
+      // Guarantee to use random seed in training.
+      std::random_device rnd;
       std::minstd_rand engine;
+      int seed =
+          context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : rnd();
       engine.seed(seed);
+
       std::uniform_real_distribution<float> dist(0, 1);
       size_t size = framework::product(mask->dims());
       for (size_t i = 0; i < size; ++i) {

python/paddle/v2/fluid/layers/nn.py

@@ -847,7 +847,35 @@ def cos_sim(X, Y, **kwargs):
     return out
 
 
-def dropout(x, dropout_prob, is_test=False, seed=0, **kwargs):
+def dropout(x, dropout_prob, is_test=False, seed=None, **kwargs):
+    """
+    Computes dropout.
+
+    Drop or keep each element of `x` independently. Dropout is a regularization
+    technique for reducing overfitting by preventing neuron co-adaption during
+    training. The dropout operator randomly set (according to the given dropout
+    probability) the outputs of some units to zero, while others are remain
+    unchanged.
+
+    Args:
+       x(variable): The input tensor.
+       dropout_prob(float): Probability of setting units to zero.
+       is_test(bool): A flag indicating whether it is in test phrase or not.
+       seed(int): A Python integer used to create random seeds. If this
+                  parameter is set to None, a random seed is used.
+                  NOTE: If an integer seed is given, always the same output
+                  units will be dropped. DO NOT use a fixed seed in training.
+
+    Returns:
+        Variable: A tensor variable.
+
+    Examples:
+        .. code-block:: python
+
+          x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+          droped = fluid.layers.dropout(input=x, dropout_rate=0.5)
+    """
+
     helper = LayerHelper('dropout', **kwargs)
     out = helper.create_tmp_variable(dtype=x.dtype)
     mask = helper.create_tmp_variable(dtype=x.dtype, stop_gradient=True)
@@ -856,9 +884,12 @@ def dropout(x, dropout_prob, is_test=False, seed=0, **kwargs):
         inputs={'X': [x]},
         outputs={'Out': [out],
                  'Mask': [mask]},
-        attrs={'dropout_prob': dropout_prob,
-               'is_test': is_test,
-               'seed': seed})
+        attrs={
+            'dropout_prob': dropout_prob,
+            'is_test': is_test,
+            'fix_seed': seed is not None,
+            'seed': seed if seed is not None else 0
+        })
     return out
 
 

python/paddle/v2/fluid/tests/test_dropout_op.py

@@ -21,7 +21,7 @@ class TestDropoutOp(OpTest):
     def setUp(self):
         self.op_type = "dropout"
         self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
-        self.attrs = {'dropout_prob': 0.0, 'is_test': False}
+        self.attrs = {'dropout_prob': 0.0, 'fix_seed': True, 'is_test': False}
         self.outputs = {
             'Out': self.inputs['X'],
             'Mask': np.ones((32, 64)).astype('float32')
@@ -38,7 +38,7 @@ class TestDropoutOp2(TestDropoutOp):
     def setUp(self):
         self.op_type = "dropout"
         self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
-        self.attrs = {'dropout_prob': 1.0, 'is_test': False}
+        self.attrs = {'dropout_prob': 1.0, 'fix_seed': True, 'is_test': False}
         self.outputs = {
             'Out': np.zeros((32, 64)).astype('float32'),
             'Mask': np.zeros((32, 64)).astype('float32')
@@ -49,7 +49,7 @@ class TestDropoutOp3(TestDropoutOp):
     def setUp(self):
         self.op_type = "dropout"
         self.inputs = {'X': np.random.random((32, 64, 2)).astype("float32")}
-        self.attrs = {'dropout_prob': 0.0, 'is_test': False}
+        self.attrs = {'dropout_prob': 0.0, 'fix_seed': True, 'is_test': False}
         self.outputs = {
             'Out': self.inputs['X'],
             'Mask': np.ones((32, 64, 2)).astype('float32')
@@ -60,7 +60,7 @@ class TestDropoutOp4(OpTest):
     def setUp(self):
         self.op_type = "dropout"
         self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
-        self.attrs = {'dropout_prob': 0.35, 'is_test': True}
+        self.attrs = {'dropout_prob': 0.35, 'fix_seed': True, 'is_test': True}
         self.outputs = {
             'Out': self.inputs['X'] * (1.0 - self.attrs['dropout_prob'])
         }