Add bool type for attribute and use it in dropout_op.

paddle/framework/attribute.cc

@@ -21,7 +21,7 @@ namespace framework {
 
 template <>
 AttrType AttrTypeID<bool>() {
-  return BOOL;
+  return BOOLEAN;
 }
 template <>
 AttrType AttrTypeID<int>() {
@@ -37,7 +37,7 @@ AttrType AttrTypeID<std::string>() {
 }
 template <>
 AttrType AttrTypeID<std::vector<bool>>() {
-  return BOOLS;
+  return BOOLEANS;
 }
 template <>
 AttrType AttrTypeID<std::vector<int>>() {
@@ -58,7 +58,7 @@ AttrType AttrTypeID<std::vector<std::pair<int, int>>>() {
 
 Attribute GetAttrValue(const OpDesc::Attr& attr_desc) {
   switch (attr_desc.type()) {
-    case paddle::framework::AttrType::BOOL: {
+    case paddle::framework::AttrType::BOOLEAN: {
       return attr_desc.b();
     }
     case paddle::framework::AttrType::INT: {
@@ -70,7 +70,7 @@ Attribute GetAttrValue(const OpDesc::Attr& attr_desc) {
     case paddle::framework::AttrType::STRING: {
       return attr_desc.s();
     }
-    case paddle::framework::AttrType::BOOLS: {
+    case paddle::framework::AttrType::BOOLEANS: {
       std::vector<bool> val(attr_desc.bools_size());
       for (int i = 0; i < attr_desc.bools_size(); ++i) {
         val[i] = attr_desc.bools(i);

paddle/framework/framework.proto

@@ -23,8 +23,8 @@ enum AttrType {
   FLOATS = 4;
   STRINGS = 5;
   INT_PAIRS = 6;
-  BOOL = 7;
-  BOOLS = 8;
+  BOOLEAN = 7;
+  BOOLEANS = 8;
 }
 
 message IntPair {
@@ -47,7 +47,7 @@ message OpDesc {
     repeated string strings = 8;
     repeated IntPair int_pairs = 9;
     optional bool b = 10;
-    repeated bool bools = 6;
+    repeated bool bools = 11;
   };
 
   message Var {

paddle/operators/dropout_op.cc

@@ -29,13 +29,10 @@ class DropoutOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
     PADDLE_ENFORCE_GE(ctx.Attr<float>("dropout_prob"), 0);
     PADDLE_ENFORCE_LE(ctx.Attr<float>("dropout_prob"), 1);
-    // TODO(xinghai-sun): remove this check after swtiching to bool
-    PADDLE_ENFORCE(ctx.Attr<int>("is_training") == 0 ||
-                   ctx.Attr<int>("is_training") == 1);
 
     auto dims = ctx.Input<Tensor>("X")->dims();
     ctx.Output<LoDTensor>("Out")->Resize(dims);
-    if (ctx.Attr<int>("is_training") == 1) {
+    if (ctx.Attr<bool>("is_training")) {
       ctx.Output<LoDTensor>("Mask")->Resize(dims);
     }
   }
@@ -49,8 +46,7 @@ class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddAttr<AttrType>("dropout_prob", "Probability of setting units to zero.")
         .SetDefault(.5f);
-    // TODO(xinghai-sun): use bool for is_training after bool is supported.
-    AddAttr<int>("is_training", "Whether in training phase.").SetDefault(1);
+    AddAttr<bool>("is_training", "Whether in training phase.").SetDefault(true);
     AddAttr<int>("seed", "Dropout random seed.").SetDefault(0);
     AddInput("X", "The input of dropout op.");
     AddOutput("Out", "The output of dropout op.");
@@ -59,7 +55,7 @@ class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 Dropout Operator.
 
-"Dropout" refers to randomly dropping out units in a nerual network. It is a
+'Dropout' refers to randomly dropping out units in a nerual network. It 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
@@ -75,7 +71,7 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx.Attr<int>("is_training"), 1,
+    PADDLE_ENFORCE(ctx.Attr<bool>("is_training"),
                    "GradOp is only callable when is_training is true");
 
     PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
@@ -85,9 +81,6 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
 
     PADDLE_ENFORCE_GE(ctx.Attr<AttrType>("dropout_prob"), 0);
     PADDLE_ENFORCE_LE(ctx.Attr<AttrType>("dropout_prob"), 1);
-    // TODO(xinghai-sun): remove this check after swtiching to bool
-    PADDLE_ENFORCE(ctx.Attr<int>("is_training") == 0 ||
-                   ctx.Attr<int>("is_training") == 1);
     auto x_dims = ctx.Input<Tensor>("X")->dims();
     auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
     PADDLE_ENFORCE_EQ(x_dims, out_dims,

paddle/operators/dropout_op.cu

@@ -59,7 +59,7 @@ class GPUDropoutKernel : public framework::OpKernel {
     auto Y = EigenMatrix<T>::Reshape(*y, 1);
 
     auto place = context.GetEigenDevice<Place>();
-    if (context.Attr<int>("is_training") == 1) {
+    if (context.Attr<bool>("is_training")) {
       auto* mask = context.Output<Tensor>("Mask");
       auto* mask_data = mask->mutable_data<T>(context.GetPlace());
       int size = framework::product(mask->dims());

paddle/operators/dropout_op.h

@@ -35,7 +35,7 @@ class CPUDropoutKernel : public framework::OpKernel {
     auto* y_data = y->mutable_data<T>(context.GetPlace());
     AttrType dropout_prob = context.Attr<AttrType>("dropout_prob");
 
-    if (context.Attr<int>("is_training") == 1) {
+    if (context.Attr<bool>("is_training")) {
       auto* mask = context.Output<Tensor>("Mask");
       auto* mask_data = mask->mutable_data<T>(context.GetPlace());
       int seed = context.Attr<int>("seed");
@@ -65,7 +65,7 @@ template <typename Place, typename T>
 class DropoutGradKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    PADDLE_ENFORCE_EQ(context.Attr<int>("is_training"), 1,
+    PADDLE_ENFORCE(context.Attr<bool>("is_training"),
                    "GradOp is only callable when is_training is true");
 
     auto* grad_x = context.Output<Tensor>(framework::GradVarName("X"));

python/paddle/v2/framework/op.py

@@ -89,7 +89,7 @@ class OpDescCreationMethod(object):
                     new_attr.f = user_defined_attr
                 elif attr.type == framework_pb2.STRING:
                     new_attr.s = user_defined_attr
-                elif attr.type == framework_pb2.BOOL:
+                elif attr.type == framework_pb2.BOOLEAN:
                     new_attr.b = user_defined_attr
                 elif attr.type == framework_pb2.INTS:
                     new_attr.ints.extend(user_defined_attr)
@@ -97,7 +97,7 @@ class OpDescCreationMethod(object):
                     new_attr.floats.extend(user_defined_attr)
                 elif attr.type == framework_pb2.STRINGS:
                     new_attr.strings.extend(user_defined_attr)
-                elif attr.type == framework_pb2.BOOLS:
+                elif attr.type == framework_pb2.BOOLEANS:
                     new_attr.bools.extend(user_defined_attr)
                 elif attr.type == framework_pb2.INT_PAIRS:
                     for p in user_defined_attr:

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

@@ -7,7 +7,7 @@ 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_training': 1}
+        self.attrs = {'dropout_prob': 0.0, 'is_training': True}
         self.outputs = {'Out': self.inputs['X'], 'Mask': np.ones((32, 64))}
 
     def test_check_output(self):
@@ -21,7 +21,7 @@ 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_training': 1}
+        self.attrs = {'dropout_prob': 1.0, 'is_training': True}
         self.outputs = {'Out': np.zeros((32, 64)), 'Mask': np.zeros((32, 64))}
 
 
@@ -29,7 +29,7 @@ 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_training': 1}
+        self.attrs = {'dropout_prob': 0.0, 'is_training': True}
         self.outputs = {'Out': self.inputs['X'], 'Mask': np.ones((32, 64, 2))}
 
 
@@ -37,7 +37,7 @@ 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_training': 0}
+        self.attrs = {'dropout_prob': 0.35, 'is_training': False}
         self.outputs = {'Out': self.inputs['X'] * self.attrs['dropout_prob']}
 
     def test_check_output(self):
@@ -48,7 +48,7 @@ class TestDropoutOp5(OpTest):
     def setUp(self):
         self.op_type = "dropout"
         self.inputs = {'X': np.random.random((32, 64, 3)).astype("float32")}
-        self.attrs = {'dropout_prob': 0.75, 'is_training': 0}
+        self.attrs = {'dropout_prob': 0.75, 'is_training': False}
         self.outputs = {'Out': self.inputs['X'] * self.attrs['dropout_prob']}
 
     def test_check_output(self):