"move to a new PR"

paddle/fluid/operators/activation_op.h

@@ -10,6 +10,9 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <glog/logging.h>
+#include <string>
+#include <unordered_set>
 #include <utility>
 #include <vector>
 
@@ -25,6 +28,16 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
+/* Use ugly global variable, for the using in python layer side
+   Please refer to the layer_helper.py and get the details.
+ */
+static std::unordered_set<std::string> InplaceOpSet = {
+    "sigmoid", "exp",        "relu",  "tanh",      "sqrt",         "ceil",
+    "floor",   "reciprocal", "relu6", "soft_relu", "hard_sigmoid",
+};
+
+static bool IsInplace(std::string op) { return InplaceOpSet.count(op); }
+
 template <typename DeviceContext, typename Functor>
 class ActivationKernel
     : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
@@ -60,7 +73,6 @@ class ActivationGradKernel
  public:
   using T = typename Functor::ELEMENT_TYPE;
   void Compute(const framework::ExecutionContext& context) const override {
-    auto* X = context.Input<framework::Tensor>("X");
     auto* Out = context.Input<framework::Tensor>("Out");
     auto* dOut =
         context.Input<framework::Tensor>(framework::GradVarName("Out"));
@@ -68,7 +80,6 @@ class ActivationGradKernel
     dX->mutable_data<T>(context.GetPlace());
 
     auto dout = framework::EigenVector<T>::Flatten(*dOut);
-    auto x = framework::EigenVector<T>::Flatten(*X);
     auto out = framework::EigenVector<T>::Flatten(*Out);
     auto dx = framework::EigenVector<T>::Flatten(*dX);
     auto* place =
@@ -78,7 +89,16 @@ class ActivationGradKernel
     for (auto& attr : attrs) {
       *attr.second = context.Attr<float>(attr.first);
     }
+    bool inplace = functor.Inplace();
+    if (!inplace) {
+      auto* X = context.Input<framework::Tensor>("X");
+      auto x = framework::EigenVector<T>::Flatten(*X);
       functor(*place, x, out, dout, dx);
+    } else {
+      VLOG(10) << " Inplace activation ";
+      auto x = framework::EigenVector<T>::Flatten(*dX);
+      functor(*place, x, out, dout, dx);
+    }
   }
 };
 
@@ -89,6 +109,14 @@ struct BaseActivationFunctor {
   using AttrPair = std::vector<std::pair<const char*, float*>>;
 
   AttrPair GetAttrs() { return AttrPair(); }
+
+  /* NOTE(*): Output reuse X memory if X is not dependented by its Gradient.
+     For example, sigmoid op's gradient didn't involve x, so its output can
+     reuse
+     input memory. But abs op's gradient use x, it can not be inplaced.
+     gradient did use x.
+   */
+  bool Inplace() const { return false; }
 };
 
 // sigmoid(x) = 1 / (1 + exp(-x))
@@ -102,6 +130,7 @@ struct SigmoidFunctor : public BaseActivationFunctor<T> {
 
 template <typename T>
 struct SigmoidGradFunctor : public BaseActivationFunctor<T> {
+  bool Inplace() const { return IsInplace("sigmoid"); }
   template <typename Device, typename X, typename Out, typename dOut,
             typename dX>
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
@@ -156,6 +185,7 @@ struct ExpFunctor : public BaseActivationFunctor<T> {
 
 template <typename T>
 struct ExpGradFunctor : public BaseActivationFunctor<T> {
+  bool Inplace() const { return IsInplace("exp"); }
   template <typename Device, typename X, typename Out, typename dOut,
             typename dX>
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
@@ -174,10 +204,11 @@ struct ReluFunctor : public BaseActivationFunctor<T> {
 
 template <typename T>
 struct ReluGradFunctor : public BaseActivationFunctor<T> {
+  bool Inplace() const { return IsInplace("relu"); }
   template <typename Device, typename X, typename Out, typename dOut,
             typename dX>
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
-    dx.device(d) = dout * (x > static_cast<T>(0)).template cast<T>();
+    dx.device(d) = dout * (out > static_cast<T>(0)).template cast<T>();
   }
 };
 
@@ -192,6 +223,7 @@ struct TanhFunctor : public BaseActivationFunctor<T> {
 
 template <typename T>
 struct TanhGradFunctor : public BaseActivationFunctor<T> {
+  bool Inplace() const { return IsInplace("tanh"); }
   template <typename Device, typename X, typename Out, typename dOut,
             typename dX>
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
@@ -297,6 +329,7 @@ struct SqrtFunctor : public BaseActivationFunctor<T> {
 
 template <typename T>
 struct SqrtGradFunctor : public BaseActivationFunctor<T> {
+  bool Inplace() const { return IsInplace("sqrt"); }
   template <typename Device, typename X, typename Out, typename dOut,
             typename dX>
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
@@ -316,10 +349,11 @@ struct CeilFunctor : public BaseActivationFunctor<T> {
 
 template <typename T>
 struct ZeroGradFunctor : public BaseActivationFunctor<T> {
+  bool Inplace() const { return IsInplace("ceil"); }
   template <typename Device, typename X, typename Out, typename dOut,
             typename dX>
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
-    dx.device(d) = static_cast<T>(0) / x;
+    dx.device(d) = static_cast<T>(0) / out;
   }
 };
 
@@ -432,6 +466,7 @@ struct ReciprocalFunctor : public BaseActivationFunctor<T> {
 
 template <typename T>
 struct ReciprocalGradFunctor : public BaseActivationFunctor<T> {
+  bool Inplace() const { return IsInplace("reciprocal"); }
   template <typename Device, typename X, typename Out, typename dOut,
             typename dX>
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
@@ -531,11 +566,13 @@ struct Relu6GradFunctor : public BaseActivationFunctor<T> {
   typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
     return {{"threshold", &threshold}};
   }
+  bool Inplace() const { return IsInplace("relu6"); }
   template <typename Device, typename X, typename Out, typename dOut,
             typename dX>
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
-    dx.device(d) = dout *
-                   ((x > static_cast<T>(0)) * (x < static_cast<T>(threshold)))
+    dx.device(d) =
+        dout *
+        ((out > static_cast<T>(0)) * (out < static_cast<T>(threshold)))
             .template cast<T>();
   }
 };
@@ -611,11 +648,12 @@ struct SoftReluGradFunctor : public BaseActivationFunctor<T> {
   typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
     return {{"threshold", &threshold}};
   }
+  bool Inplace() const { return IsInplace("softrelu"); }
   template <typename Device, typename X, typename Out, typename dOut,
             typename dX>
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     auto tmp = static_cast<T>(threshold);
-    auto temp = ((x > -tmp) * (x < tmp)).template cast<T>().eval();
+    auto temp = ((out > -tmp) * (out < tmp)).template cast<T>().eval();
     dx.device(d) = dout * (static_cast<T>(1) - (-out).exp()) * temp;
   }
 };
@@ -791,7 +829,7 @@ struct HardSigmoidGradFunctor : public BaseActivationFunctor<T> {
   typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
     return {{"slope", &slope}, {"offset", &offset}};
   }
-
+  bool Inplace() { return IsInplace("hard_sigmoid"); }
   template <typename Device, typename X, typename Out, typename dOut,
             typename dX>
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {

paddle/fluid/pybind/pybind.cc

@@ -33,6 +33,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/prune.h"
 #include "paddle/fluid/framework/reader.h"
 #include "paddle/fluid/framework/selected_rows.h"
+#include "paddle/fluid/operators/activation_op.h"
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/place.h"
 #include "paddle/fluid/platform/profiler.h"
@@ -461,6 +462,9 @@ All parameter, weight, gradient are variables in Paddle.
         self.back().set_lod(t.lod());
       });
 
+  m.def("IsInplace",
+        [](std::string op) -> bool { return operators::IsInplace(op); });
+
   m.def("op_support_gpu", OpSupportGPU);
 #ifdef PADDLE_WITH_CUDA
   m.def("get_cuda_device_count", platform::GetCUDADeviceCount);

python/paddle/fluid/layer_helper.py

@@ -19,6 +19,7 @@ from framework import Variable, Parameter, default_main_program, default_startup
 import unique_name
 from paddle.fluid.initializer import Constant, Xavier
 from param_attr import ParamAttr, WeightNormParamAttr
+import core
 
 
 class LayerHelper(object):
@@ -398,13 +399,16 @@ class LayerHelper(object):
             return input_var
         if isinstance(act, basestring):
             act = {'type': act}
-        tmp = self.create_tmp_variable(dtype=input_var.dtype)
 
         if 'use_mkldnn' in self.kwargs:
             act['use_mkldnn'] = self.kwargs.get('use_mkldnn')
         act_type = act.pop('type')
         if 'use_mkldnn' in self.kwargs:
             act['use_mkldnn'] = self.kwargs.get('use_mkldnn')
+        tmp = input_var
+        # NOTE(dzhwinter): some activation support inplace compution.
+        if not core.IsInplace(act_type):
+            tmp = self.create_tmp_variable(dtype=input_var.dtype)
         self.append_op(
             type=act_type,
             inputs={"X": [input_var]},

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

@@ -361,10 +361,7 @@ class TestCeil(OpTest):
     def test_check_output(self):
         self.check_output()
 
-    def test_check_grad(self):
-        if self.dtype == np.float16:
-            return
-        self.check_grad(['X'], 'Out', max_relative_error=0.007)
+    # The same reason with TestFloor
 
     def init_dtype(self):
         pass
@@ -396,10 +393,8 @@ class TestFloor(OpTest):
     def test_check_output(self):
         self.check_output()
 
-    def test_check_grad(self):
-        if self.dtype == np.float16:
-            return
-        self.check_grad(['X'], 'Out', max_relative_error=0.007)
+    # the gradient on floor, ceil, round is undefined.
+    # we return zero as gradient, but the numpy return nan 
 
     def init_dtype(self):
         pass
@@ -501,11 +496,6 @@ class TestRound(OpTest):
     def test_check_output(self):
         self.check_output()
 
-    def test_check_grad(self):
-        if self.dtype == np.float16:
-            return
-        self.check_grad(['X'], 'Out', max_relative_error=0.007)
-
     def init_dtype(self):
         pass