add dropout attr; test=develop

.gitignore

@@ -28,3 +28,4 @@ third_party/
 build_*
 # clion workspace.
 cmake-build-*
+model_test

paddle/fluid/API.spec

@@ -86,7 +86,7 @@ paddle.fluid.layers.reduce_prod ArgSpec(args=['input', 'dim', 'keep_dim', 'name'
 paddle.fluid.layers.sequence_first_step ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.sequence_last_step ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.sequence_slice ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.dropout ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name'], varargs=None, keywords=None, defaults=(False, None, None))
+paddle.fluid.layers.dropout ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, False))
 paddle.fluid.layers.split ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None))
 paddle.fluid.layers.ctc_greedy_decoder ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.edit_distance ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens'], varargs=None, keywords=None, defaults=(True, None))

paddle/fluid/operators/dropout_op.cc

@@ -57,6 +57,15 @@ class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
                   "will be dropped.")
         .SetDefault(false);
     AddAttr<int>("seed", "Dropout random seed.").SetDefault(0);
+    AddAttr<bool>("dropout_implementation",
+                  "When it's True, In the training, after set some value"
+                  "to 0 (probability is dropout_prob),"
+                  "all the value will divide (1-dropout_prob)"
+                  "By using this way, will do nothing in the inference program"
+                  "The dropout op can be removed in the inference program."
+                  "The inference program will be more efficient"
+                  "When it's False, same as original")
+        .SetDefault(false);
 
     AddComment(R"DOC(
 Dropout Operator.
@@ -104,7 +113,9 @@ REGISTER_OPERATOR(dropout, ops::DropoutOp, ops::DropoutOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(dropout_grad, ops::DropoutOpGrad);
 REGISTER_OP_CPU_KERNEL(
-    dropout, ops::CPUDropoutKernel<paddle::platform::CPUDeviceContext, float>);
+    dropout, ops::CPUDropoutKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::CPUDropoutKernel<paddle::platform::CPUDeviceContext, double>);
 REGISTER_OP_CPU_KERNEL(
     dropout_grad,
-    ops::DropoutGradKernel<paddle::platform::CPUDeviceContext, float>);
+    ops::DropoutGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::DropoutGradKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/dropout_op.cu

@@ -26,7 +26,8 @@ namespace operators {
 template <typename T>
 __global__ void RandomGenerator(const size_t n, const int seed,
                                 const float dropout_prob, const T* src,
-                                T* mask_data, T* dst) {
+                                T* mask_data, T* dst,
+                                bool dropout_implementation) {
   thrust::minstd_rand rng;
   rng.seed(seed);
   thrust::uniform_real_distribution<float> dist(0, 1);
@@ -47,7 +48,11 @@ __global__ void RandomGenerator(const size_t n, const int seed,
     if (dist(rng) < dropout_prob) {
       mask = static_cast<T>(0);
     } else {
-      mask = static_cast<T>(1);
+      if (dropout_implementation) {
+        mask = static_cast<T>(1.0f / (1.0f - dropout_prob));
+      } else {
+        mask = static_cast<T>(1);
+      }
     }
     dest = s * mask;
     mask_data[idx] = mask;
@@ -67,6 +72,7 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
     y->mutable_data<T>(context.GetPlace());
     float dropout_prob = context.Attr<float>("dropout_prob");
 
+    auto dropout_implementation = context.Attr<bool>("dropout_implementation");
     auto& place = *context.template device_context<Place>().eigen_device();
     if (!context.Attr<bool>("is_test")) {
       auto* mask = context.Output<Tensor>("Mask");
@@ -83,11 +89,16 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
       int grid = (x->numel() + threads - 1) / threads;
       RandomGenerator<
           T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
-          size, seed, dropout_prob, x_data, mask_data, y_data);
+          size, seed, dropout_prob, x_data, mask_data, y_data,
+          dropout_implementation);
     } else {
       auto X = EigenMatrix<T>::Reshape(*x, 1);
       auto Y = EigenMatrix<T>::Reshape(*y, 1);
-      Y.device(place) = X * static_cast<T>(1.0f - dropout_prob);
+      if (dropout_implementation) {
+        Y.device(place) = X;
+      } else {
+        Y.device(place) = X * static_cast<T>(1.0f - dropout_prob);
+      }
     }
   }
 };
@@ -99,6 +110,8 @@ namespace ops = paddle::operators;
 namespace plat = paddle::platform;
 REGISTER_OP_CUDA_KERNEL(
     dropout, ops::GPUDropoutKernel<plat::CUDADeviceContext, float>,
-    ops::GPUDropoutKernel<plat::CUDADeviceContext, plat::float16>);
-REGISTER_OP_CUDA_KERNEL(dropout_grad,
-                        ops::DropoutGradKernel<plat::CUDADeviceContext, float>);
+    ops::GPUDropoutKernel<plat::CUDADeviceContext, plat::float16>,
+    ops::GPUDropoutKernel<plat::CUDADeviceContext, double>);
+REGISTER_OP_CUDA_KERNEL(
+    dropout_grad, ops::DropoutGradKernel<plat::CUDADeviceContext, float>,
+    ops::DropoutGradKernel<plat::CUDADeviceContext, double>);

paddle/fluid/operators/dropout_op.h

@@ -36,6 +36,7 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
     auto* y_data = y->mutable_data<T>(context.GetPlace());
     float dropout_prob = context.Attr<float>("dropout_prob");
 
+    auto dropout_implementation = context.Attr<bool>("dropout_implementation");
     if (!context.Attr<bool>("is_test")) {
       auto* mask = context.Output<Tensor>("Mask");
       auto* mask_data = mask->mutable_data<T>(context.GetPlace());
@@ -49,14 +50,20 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
       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) {
         if (dist(engine) < dropout_prob) {
           mask_data[i] = 0;
           y_data[i] = 0;
         } else {
-          mask_data[i] = 1;
-          y_data[i] = x_data[i];
+          if (dropout_implementation) {
+            mask_data[i] = 1.0f / static_cast<T>(1.0f - dropout_prob);
+            y_data[i] = x_data[i] / static_cast<T>(1.0f - dropout_prob);
+          } else {
+            mask_data[i] = 1;
+            y_data[i] = x_data[i];
+          }
         }
       }
     } else {
@@ -64,7 +71,11 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
       auto Y = EigenMatrix<T>::Reshape(*y, 1);
       auto& place =
           *context.template device_context<DeviceContext>().eigen_device();
-      Y.device(place) = X * (1.0f - dropout_prob);
+      if (dropout_implementation) {
+        Y.device(place) = X;
+      } else {
+        Y.device(place) = X * static_cast<T>(1.0f - dropout_prob);
+      }
     }
   }
 };

paddle/fluid/operators/softmax_cudnn_op.cu.cc

@@ -76,6 +76,8 @@ namespace ops = paddle::operators;
 namespace plat = paddle::platform;
 REGISTER_OP_KERNEL(softmax, CUDNN, plat::CUDAPlace,
                    ops::SoftmaxCUDNNKernel<float>,
+                   ops::SoftmaxCUDNNKernel<double>,
                    ops::SoftmaxCUDNNKernel<plat::float16>);
 REGISTER_OP_KERNEL(softmax_grad, CUDNN, plat::CUDAPlace,
-                   ops::SoftmaxGradCUDNNKernel<float>);
+                   ops::SoftmaxGradCUDNNKernel<float>,
+                   ops::SoftmaxGradCUDNNKernel<double>);

paddle/fluid/operators/transpose_op.cc

@@ -210,18 +210,21 @@ REGISTER_OPERATOR(transpose, ops::TransposeOp, ops::TransposeOpMaker,
 REGISTER_OPERATOR(transpose_grad, ops::TransposeOpGrad);
 
 REGISTER_OP_CPU_KERNEL(
-    transpose, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>);
+    transpose, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::TransposeKernel<paddle::platform::CPUDeviceContext, double>);
 REGISTER_OP_CPU_KERNEL(
     transpose_grad,
-    ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>);
+    ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, double>);
 
 REGISTER_OPERATOR(transpose2, ops::Transpose2Op, ops::Transpose2OpMaker,
                   ops::Transpose2GradMaker);
 REGISTER_OPERATOR(transpose2_grad, ops::Transpose2OpGrad);
 
 REGISTER_OP_CPU_KERNEL(
-    transpose2,
-    ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>);
+    transpose2, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::TransposeKernel<paddle::platform::CPUDeviceContext, double>);
 REGISTER_OP_CPU_KERNEL(
     transpose2_grad,
-    ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>);
+    ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/transpose_op.cu.cc

@@ -16,15 +16,18 @@ limitations under the License. */
 
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
-    transpose,
-    ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>);
+    transpose, ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::TransposeKernel<paddle::platform::CUDADeviceContext, double>);
 REGISTER_OP_CUDA_KERNEL(
     transpose_grad,
-    ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>);
+    ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, double>);
 
 REGISTER_OP_CUDA_KERNEL(
     transpose2,
-    ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>);
+    ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::TransposeKernel<paddle::platform::CUDADeviceContext, double>);
 REGISTER_OP_CUDA_KERNEL(
     transpose2_grad,
-    ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>);
+    ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, double>);

python/paddle/fluid/clip.py

@@ -272,7 +272,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
                 )
 
         square = grad * grad
-        local_norm_var = layers.cast(layers.reduce_sum(input=square), 'float64')
+        local_norm_var = layers.reduce_sum(input=square)
         context[self.group_name].append(local_norm_var)
 
         self.context = context
@@ -282,7 +282,6 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
         if group_scale_name not in self.context:
             group_norm_var = layers.sums(input=self.context[self.group_name])
             group_norm_var = layers.sqrt(x=group_norm_var)
-            group_norm_var = layers.cast(group_norm_var, 'float32')
             clip_var = self.context[self.group_name + "_clip"]
             group_scale_var = layers.elementwise_div(
                 x=clip_var,

python/paddle/fluid/layers/nn.py

@@ -974,7 +974,12 @@ def cos_sim(X, Y):
     return out
 
 
-def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
+def dropout(x,
+            dropout_prob,
+            is_test=False,
+            seed=None,
+            name=None,
+            dropout_implementation=False):
     """
     Computes dropout.
 
@@ -994,6 +999,14 @@ def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
                     units will be dropped. DO NOT use a fixed seed in training.
         name (str|None): A name for this layer(optional). If set None, the layer
                          will be named automatically.
+        dropout_implementation(bool): A Flag indicating whether divide (1-dropout_prob). 
+                                      When it's True, all the units will divide (1-dropout_prob)
+                                      after set some units to zero in the train program.
+                                      And do nothing in the inference program.
+                                      The dropout op can be removed in the inference program.
+                                      The inference program will be more efficient
+                                      When it's False, same as original
+
 
     Returns:
         Variable: A tensor variable is the shape with `x`.
@@ -1022,7 +1035,8 @@ def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
             'dropout_prob': dropout_prob,
             'is_test': is_test,
             'fix_seed': seed is not None,
-            'seed': seed if seed is not None else 0
+            'seed': seed if seed is not None else 0,
+            'dropout_implementation': dropout_implementation,
         })
     return out
 

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

@@ -85,6 +85,69 @@ class TestDropoutOp5(OpTest):
         self.check_output()
 
 
+class TestDropoutOp6(TestDropoutOp):
+    def setUp(self):
+        self.op_type = "dropout"
+        self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
+        self.attrs = {
+            'dropout_prob': 1.0,
+            'fix_seed': True,
+            'is_test': False,
+            'div_prob_in_train': True
+        }
+        self.outputs = {
+            'Out': np.zeros((32, 64)).astype('float32'),
+            'Mask': np.zeros((32, 64)).astype('float32')
+        }
+
+
+class TestDropoutOp7(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,
+            'fix_seed': True,
+            'is_test': False,
+            'div_prob_in_train': True
+        }
+        self.outputs = {
+            'Out': self.inputs['X'],
+            'Mask': np.ones((32, 64, 2)).astype('float32')
+        }
+
+
+class TestDropoutOp8(OpTest):
+    def setUp(self):
+        self.op_type = "dropout"
+        self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
+        self.attrs = {
+            'dropout_prob': 0.35,
+            'fix_seed': True,
+            'is_test': True,
+            'div_prob_in_train': True
+        }
+        self.outputs = {'Out': self.inputs['X']}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestDropoutOp9(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_test': True,
+            'div_prob_in_train': True
+        }
+        self.outputs = {'Out': self.inputs['X']}
+
+    def test_check_output(self):
+        self.check_output()
+
+
 class TestFP16DropoutOp(OpTest):
     def setUp(self):
         self.op_type = "dropout"