Add double grad op for sigmoid activation, test=develop (#32971)

jim19930609 · web-flow · commit c711e913bdc6 · 2021-05-26T15:54:00.000+08:00
Sigmoid: Out = Sigmoid(X)
SigmoidGrad: DX = DOut*(1-Out)*Out

[This Patch]
Out
DOut -&gt; SigmoidGradGrad -&gt; DOutNew
DDX                        DDOut

DDOut = (1-Out)*Out*DDX
DOutNew = (1-2*Out)*DOut*DDX
diff --git a/paddle/fluid/operators/activation_op.cc b/paddle/fluid/operators/activation_op.cc
@@ -789,6 +789,27 @@ class ActivationOpDoubleGrad2 : public framework::OperatorWithKernel {
   }
 };
 
+template <typename T>
+class SigmoidDoubleGradMaker
+    : public ::paddle::framework::SingleGradOpMaker<T> {
+ public:
+  using ::paddle::framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> op) const override {
+    op->SetType("sigmoid_grad_grad");
+    // input1: Out
+    op->SetInput("Out", this->Input("Out"));
+    // input2: ddx
+    op->SetInput("DDX", this->OutputGrad(framework::GradVarName("X")));
+    op->SetInput("DOut", this->Input(framework::GradVarName("Out")));
+    op->SetAttrMap(this->Attrs());
+    // output: ddy
+    op->SetOutput("DOutNew", this->InputGrad("Out"));
+    op->SetOutput("DDOut", this->InputGrad(framework::GradVarName("Out")));
+  }
+};
+
 template <typename T>
 class TanhDoubleGradMaker : public ::paddle::framework::SingleGradOpMaker<T> {
  public:
@@ -1068,6 +1089,47 @@ namespace plat = paddle::platform;
 FOR_EACH_ACTIVATION_OP(REGISTER_ACTIVATION_OP);
 FOR_EACH_ACTIVATION_OP(REGISTER_ACTIVATION_CPU_KERNEL);
 
+/* ==========================    sigmoid register  =============================
+ */
+// 1. Register Sigmoid Operator
+REGISTER_OPERATOR(
+    sigmoid, ops::ActivationOp, ops::SigmoidOpMaker,
+    ops::ActivationOpInferVarType,
+    ops::ActivationGradOpMaker<ops::SigmoidGradFunctor<float>::FwdDeps(),
+                               paddle::framework::OpDesc>,
+    ops::ActivationGradOpMaker<ops::SigmoidGradFunctor<float>::FwdDeps(),
+                               paddle::imperative::OpBase>,
+    std::conditional<ops::CanInplaceAct<ops::SigmoidGradFunctor<float>>(),
+                     ops::ActFwdInplaceInferer, void>::type);
+
+// 2. Register Sigmoid Grad Operator
+REGISTER_OPERATOR(sigmoid_grad, ops::ActivationOpGrad,
+                  ops::ActivationGradOpInplaceInferer,
+                  ops::SigmoidDoubleGradMaker<paddle::framework::OpDesc>,
+                  ops::SigmoidDoubleGradMaker<paddle::imperative::OpBase>)
+
+// 3. Register Sigmoid DoubleGrad Operator
+REGISTER_OPERATOR(
+    sigmoid_grad_grad,
+    ops::ActivationOpDoubleGrad<ops::SigmoidGradFunctor<float>::FwdDeps()>,
+    ops::ActivationDoubleGradOpInplaceInferer);
+
+// Register Sigmoid/GradSigmoid Kernels
+REGISTER_ACTIVATION_CPU_KERNEL(sigmoid, Sigmoid, SigmoidFunctor,
+                               SigmoidGradFunctor);
+
+// Register DoubleGrad Kernel
+REGISTER_OP_CPU_KERNEL(
+    sigmoid_grad_grad,
+    ops::SigmoidDoubleGradKernel<plat::CPUDeviceContext,
+                                 ops::SigmoidGradGradFunctor<float>>,
+    ops::SigmoidDoubleGradKernel<plat::CPUDeviceContext,
+                                 ops::SigmoidGradGradFunctor<double>>,
+    ops::SigmoidDoubleGradKernel<plat::CPUDeviceContext,
+                                 ops::SigmoidGradGradFunctor<plat::float16>>);
+
+/* ========================================================================== */
+
 /* ==========================    tanh register  ============================= */
 REGISTER_OPERATOR(
     tanh, ops::ActivationOp, ops::TanhOpMaker, ops::ActivationOpInferVarType,
diff --git a/paddle/fluid/operators/activation_op.cu b/paddle/fluid/operators/activation_op.cu
@@ -1481,6 +1481,21 @@ REGISTER_OP_CUDA_KERNEL(
 #endif
 /* ========================================================================== */
 
+/* ===========================    sigmoid register  ============================
+ */
+REGISTER_ACTIVATION_CUDA_KERNEL(sigmoid, Sigmoid, CudaSigmoidFunctor,
+                                CudaSigmoidGradFunctor);
+
+REGISTER_OP_CUDA_KERNEL(
+    sigmoid_grad_grad,
+    ops::SigmoidDoubleGradKernel<paddle::platform::CUDADeviceContext,
+                                 ops::SigmoidGradGradFunctor<float>>,
+    ops::SigmoidDoubleGradKernel<paddle::platform::CUDADeviceContext,
+                                 ops::SigmoidGradGradFunctor<double>>,
+    ops::SigmoidDoubleGradKernel<plat::CUDADeviceContext,
+                                 ops::SigmoidGradGradFunctor<plat::float16>>);
+/* ========================================================================== */
+
 /* ===========================    tanh register  ============================ */
 REGISTER_ACTIVATION_CUDA_KERNEL(tanh, Tanh, CudaTanhFunctor,
                                 CudaTanhGradFunctor);
@@ -1595,7 +1610,6 @@ REGISTER_OP_CUDA_KERNEL(
 /* ========================================================================== */
 
 #define FOR_EACH_ACTIVATION_CUDA_OP(__macro)                                  \
-  __macro(sigmoid, Sigmoid, CudaSigmoidFunctor, CudaSigmoidGradFunctor);      \
   __macro(silu, Silu, CudaSiluFunctor, CudaSiluGradFunctor);                  \
   __macro(logsigmoid, LogSigmoid, CudaLogSigmoidFunctor,                      \
           CudaLogSigmoidGradFunctor);                                         \
diff --git a/paddle/fluid/operators/activation_op.h b/paddle/fluid/operators/activation_op.h
@@ -258,6 +258,43 @@ struct SigmoidGradFunctor : public BaseActivationFunctor<T> {
   static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
 };
 
+/*
+    Out
+    DOut -> SigmoidGradGrad -> DOutNew
+    DDX                        DDOut
+
+    DDOut = (1-Out)*Out*DDX
+    DOutNew = (1-2*Out)*DOut*DDX
+*/
+template <typename T>
+struct SigmoidGradGradFunctor : public BaseActivationFunctor<T> {
+  template <typename Device>
+  void operator()(const Device& dev, const framework::Tensor* Out,
+                  const framework::Tensor* ddX, const framework::Tensor* dOut,
+                  framework::Tensor* dOutNew, framework::Tensor* ddOut) const {
+    auto* d = dev.eigen_device();
+    auto ddx = framework::EigenVector<T>::Flatten(
+        GET_DATA_SAFELY(ddX, "Input", "DDX", "SigmoidGradGrad"));
+    auto out = framework::EigenVector<T>::Flatten(
+        GET_DATA_SAFELY(Out, "Input", "Out", "SigmoidGradGrad"));
+
+    if (dOutNew) {
+      auto dout = framework::EigenVector<T>::Flatten(
+          GET_DATA_SAFELY(dOut, "Input", "DOut", "SigmoidGradGrad"));
+      auto dout_new = framework::EigenVector<T>::Flatten(
+          GET_DATA_SAFELY(dOutNew, "Output", "DOutNew", "SquareGradGrad"));
+      dout_new.device(*d) =
+          (static_cast<T>(1) - static_cast<T>(2) * out) * dout * ddx;
+    }
+    if (ddOut) {
+      auto ddout = framework::EigenVector<T>::Flatten(
+          GET_DATA_SAFELY(ddOut, "Output", "DDOut", "SquareGradGrad"));
+      ddout.device(*d) = (static_cast<T>(1) - out) * out * ddx;
+    }
+  }
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
+};
+
 // silu(x) = x / (1 + exp(-x))
 template <typename T>
 struct SiluFunctor : public BaseActivationFunctor<T> {
@@ -1789,6 +1826,50 @@ inline void ExtractDoubleGradTensorWithInputDOut(
   }
 }
 
+template <typename DeviceContext, typename Functor>
+class SigmoidDoubleGradKernel
+    : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
+ public:
+  using T = typename Functor::ELEMENT_TYPE;
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    const framework::Tensor *Out, *ddX, *dOut;
+    framework::Tensor *dOutNew, *ddOut;
+    Out = ddX = dOut = nullptr;
+    dOutNew = ddOut = nullptr;
+
+    // extract ddx(input) and out(input)
+    ddX = ctx.Input<framework::Tensor>("DDX");
+    Out = ctx.Input<framework::Tensor>("Out");
+    PADDLE_ENFORCE_NOT_NULL(
+        ddX, platform::errors::NotFound(
+                 "Cannot get input Variable ddX, variable name = %s",
+                 ctx.InputName("DDX")));
+    PADDLE_ENFORCE_NOT_NULL(
+        Out, platform::errors::NotFound(
+                 "Cannot get input Variable Out, variable name = %s",
+                 ctx.InputName("Out")));
+
+    // set output ddout
+    ddOut = ctx.Output<framework::Tensor>("DDOut");
+
+    // extract dOut(intput)
+    dOut = ctx.Input<framework::Tensor>("DOut");
+    PADDLE_ENFORCE_NOT_NULL(
+        dOut, platform::errors::NotFound(
+                  "Cannot get input Variable dOut, variable name = %s",
+                  ctx.InputName("DOut")));
+
+    // set output dout_new
+    dOutNew = ctx.Output<framework::Tensor>("DOutNew");
+
+    if (dOutNew) dOutNew->mutable_data<T>(Out->dims(), ctx.GetPlace());
+    if (ddOut) ddOut->mutable_data<T>(Out->dims(), ctx.GetPlace());
+    auto& place = ctx.template device_context<DeviceContext>();
+    Functor functor;
+    functor(place, Out, ddX, dOut, dOutNew, ddOut);
+  }
+};
+
 template <typename DeviceContext, typename Functor>
 class TanhDoubleGradKernel
     : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
@@ -2153,7 +2234,6 @@ struct LogGradGradFunctor : public BaseActivationFunctor<T> {
 }  // namespace paddle
 
 #define FOR_EACH_ACTIVATION_OP(__macro)                                       \
-  __macro(sigmoid, Sigmoid, SigmoidFunctor, SigmoidGradFunctor);              \
   __macro(silu, Silu, SiluFunctor, SiluGradFunctor);                          \
   __macro(logsigmoid, LogSigmoid, LogSigmoidFunctor, LogSigmoidGradFunctor);  \
   __macro(atan, Atan, AtanFunctor, AtanGradFunctor);                          \
diff --git a/python/paddle/fluid/tests/unittests/test_activation_nn_grad.py b/python/paddle/fluid/tests/unittests/test_activation_nn_grad.py
@@ -26,6 +26,28 @@
 from decorator_helper import prog_scope
 
 
+class TestSigmoidDoubleGradCheck(unittest.TestCase):
+    @prog_scope()
+    def func(self, place):
+        shape = [2, 3, 7, 9]
+        eps = 0.0005
+        dtype = np.float64
+        x = layers.data('x', shape, False, dtype=dtype)
+        x.persistable = True
+        y = layers.sigmoid(x)
+        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
+        x_arr[np.abs(x_arr) < 0.005] = 0.002
+        gradient_checker.double_grad_check(
+            [x], y, x_init=x_arr, place=place, eps=eps)
+
+    def test_grad(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+        for p in places:
+            self.func(p)
+
+
 class TestTanhDoubleGradCheck(unittest.TestCase):
     @prog_scope()
     def func(self, place):
