elu support alpha < 0

paddle/fluid/operators/activation_op.cc

@@ -584,6 +584,22 @@ Applies the following element-wise computation on the input according to
   }
 };
 
+template <typename T>
+class ELUGradOpMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> op) const override {
+    op->SetType("elu_grad");
+    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
+    op->SetInput("Out", this->Output("Out"));
+    op->SetInput("X", this->Input("X"));
+    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
+    op->SetAttrMap(this->Attrs());
+  }
+};
+
 class CELUOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
@@ -1443,13 +1459,11 @@ REGISTER_OP_CPU_KERNEL(
 /* ========================================================================== */
 
 /* ========================    elu  register     ============================ */
-REGISTER_OPERATOR(
-    elu, ops::ActivationOp, ops::ELUOpMaker, ops::ActivationOpInferVarType,
-    ops::ActivationGradOpMaker<ops::ELUGradFunctor<float>::FwdDeps(),
-                               paddle::framework::OpDesc>,
-    ops::ActivationGradOpMaker<ops::ELUGradFunctor<float>::FwdDeps(),
-                               paddle::imperative::OpBase>,
-    ops::ActFwdInplaceInferer);
+REGISTER_OPERATOR(elu, ops::ActivationOp, ops::ELUOpMaker,
+                  ops::ActivationOpInferVarType,
+                  ops::ELUGradOpMaker<paddle::framework::OpDesc>,
+                  ops::ELUGradOpMaker<paddle::imperative::OpBase>,
+                  ops::ActFwdInplaceInferer);
 REGISTER_OPERATOR(elu_grad, ops::ActivationOpGrad,
                   ops::ActivationGradOpInplaceInferer,
                   ops::ELUDoubleGradMaker<paddle::framework::OpDesc>,
@@ -1459,7 +1473,14 @@ REGISTER_OPERATOR(
     ops::ActivationOpDoubleGrad<ops::ELUGradFunctor<float>::FwdDeps()>,
     ops::ActivationDoubleGradOpInplaceInferer);
 
-REGISTER_ACTIVATION_CPU_KERNEL(elu, ELU, ELUFunctor, ELUGradFunctor);
+REGISTER_OP_CPU_KERNEL(elu,
+                       ops::ActivationKernel<paddle::platform::CPUDeviceContext,
+                                             ops::ELUFunctor<float>>,
+                       ops::ActivationKernel<paddle::platform::CPUDeviceContext,
+                                             ops::ELUFunctor<double>>);
+REGISTER_OP_CPU_KERNEL(
+    elu_grad, ops::ELUGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::ELUGradKernel<paddle::platform::CPUDeviceContext, double>);
 REGISTER_OP_CPU_KERNEL(
     elu_grad_grad, ops::ELUDoubleGradKernel<plat::CPUDeviceContext,
                                             ops::ELUGradGradFunctor<float>>,

paddle/fluid/operators/activation_op.cu

@@ -1161,11 +1161,12 @@ struct CudaELUFunctor : public BaseActivationFunctor<T> {
     return {{"alpha", &alpha}};
   }
 
-  // elu(x) = max(0, x) + min(0, alpha * (exp(x) - 1))
+  // elu(x) = x, if x > 0
+  // elu(x) = alpha * (e^x - 1), if x <= 0
   __device__ __forceinline__ T operator()(const T& arg_x) const {
     CT x = static_cast<CT>(arg_x);
     CT temp = static_cast<CT>(alpha) * (exp(x) - one);
-    CT res = (x > zero ? x : zero) + (temp > zero ? zero : temp);
+    CT res = x > zero ? x : temp;
     return static_cast<T>(res);
   }
 };
@@ -1174,34 +1175,84 @@ template <typename T>
 struct CudaELUGradFunctor : public BaseActivationFunctor<T> {
   using MPType = typename details::MPTypeTrait<T>::Type;
   MPType zero = static_cast<MPType>(0.0f);
-  MPType one = static_cast<MPType>(1.0f);
   float alpha;
 
   typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
     return {{"alpha", &alpha}};
   }
 
-  // dx = dout, if alpha > 0 and x > 0
-  // dx = dout * alpha * x.exp(), if alpha > 0 and x <= 0
-  // dx = dout * (1 + alpha * x.exp()), if alpha <= 0 and x > 0
-  // dx = 0, if alpha <= 0 and x <=0
+  // case 1: alpha >= 0
+  // dx = dout, if out > 0
+  // dx = dout * (out + alpha), if out <= 0
   __device__ __forceinline__ T operator()(const T& arg_dout,
+                                          const T& arg_out) const {
+    MPType dout = static_cast<MPType>(arg_dout);
+    MPType out = static_cast<MPType>(arg_out);
+    MPType a = static_cast<MPType>(alpha);
+    MPType out_pos = static_cast<MPType>(out > zero);
+    MPType out_neg = static_cast<MPType>(out <= zero);
+    return static_cast<T>(dout * (out_pos + out_neg * (out + a)));
+  }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
+};
+
+template <typename T>
+struct CudaELUGradNegativeAlphaFunctor : public BaseActivationFunctor<T> {
+  using MPType = typename details::MPTypeTrait<T>::Type;
+  MPType zero = static_cast<MPType>(0.0f);
+  float alpha;
+
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"alpha", &alpha}};
+  }
+
+  // case 2: alpha < 0
+  // dx = dout, if x > 0
+  // dx = dout * (out + alpha), if x <=0
+  __device__ __forceinline__ T operator()(const T& arg_dout, const T& arg_out,
                                           const T& arg_x) const {
     MPType dout = static_cast<MPType>(arg_dout);
+    MPType out = static_cast<MPType>(arg_out);
     MPType x = static_cast<MPType>(arg_x);
     MPType a = static_cast<MPType>(alpha);
-    MPType temp_a_pos = static_cast<MPType>(alpha > 0.0f);
-    MPType temp_a_neg = static_cast<MPType>(alpha <= 0.0f);
-    MPType temp_x_pos = static_cast<MPType>(x > zero);
-    MPType temp_x_neg = static_cast<MPType>(x <= zero);
-    return static_cast<T>(
-        dout * (temp_a_pos * temp_x_pos + temp_a_pos * temp_x_neg * a * exp(x) +
-                temp_a_neg * temp_x_pos * (one + a * exp(x))));
+    MPType x_pos = static_cast<MPType>(x > zero);
+    MPType x_neg = static_cast<MPType>(x <= zero);
+    return static_cast<T>(dout * (x_pos + x_neg * (out + a)));
   }
 
   static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
+template <typename DeviceContext, typename T>
+class ELUGradCudaKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* out = ctx.Input<framework::Tensor>("Out");
+    auto* x = ctx.Input<framework::Tensor>("X");
+    auto* d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+    d_x->mutable_data<T>(ctx.GetPlace());
+    const float alpha = ctx.Attr<float>("alpha");
+
+    auto& dev_ctx = ctx.device_context<DeviceContext>();
+    std::vector<const framework::Tensor*> ins = {d_out, out};
+    std::vector<framework::Tensor*> outs = {d_x};
+    if (alpha > 0) {
+      CudaELUGradFunctor<T> functor;
+      functor.alpha = alpha;
+      LaunchSameDimsElementwiseCudaKernel<ElementwiseType::kBinary, T, T>(
+          dev_ctx, ins, &outs, functor);
+    } else {
+      CudaELUGradNegativeAlphaFunctor<T> functor;
+      functor.alpha = alpha;
+      ins.push_back(x);
+      LaunchSameDimsElementwiseCudaKernel<ElementwiseType::kBinary, T, T>(
+          dev_ctx, ins, &outs, functor);
+    }
+  }
+};
+
 template <typename T>
 struct CudaCELUFunctor : public BaseActivationFunctor<T> {
   using CT = typename details::MPTypeTrait<T>::Type;
@@ -1383,7 +1434,17 @@ REGISTER_OP_CUDA_KERNEL(
 /* ========================================================================== */
 
 /* ======================== elu register  ============================ */
-REGISTER_ACTIVATION_CUDA_KERNEL(elu, ELU, CudaELUFunctor, CudaELUGradFunctor);
+REGISTER_OP_CUDA_KERNEL(
+    elu, ops::ActivationCudaKernel<paddle::platform::CUDADeviceContext,
+                                   ops::CudaELUFunctor<float>>,
+    ops::ActivationCudaKernel<paddle::platform::CUDADeviceContext,
+                              ops::CudaELUFunctor<double>>,
+    ops::ActivationCudaKernel<plat::CUDADeviceContext,
+                              ops::CudaELUFunctor<plat::float16>>);
+REGISTER_OP_CUDA_KERNEL(
+    elu_grad, ops::ELUGradCudaKernel<plat::CUDADeviceContext, float>,
+    ops::ELUGradCudaKernel<plat::CUDADeviceContext, double>,
+    ops::ELUGradCudaKernel<plat::CUDADeviceContext, plat::float16>);
 
 REGISTER_OP_CUDA_KERNEL(
     elu_grad_grad, ops::ELUDoubleGradKernel<plat::CUDADeviceContext,

paddle/fluid/operators/activation_op.h

@@ -1425,25 +1425,70 @@ struct ELUGradFunctor : public BaseActivationFunctor<T> {
   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 temp_a_pos = static_cast<T>(alpha > 0);
-    auto temp_a_neg = static_cast<T>(alpha <= 0);
-    auto temp_x_pos = (x > static_cast<T>(0)).template cast<T>();
-    auto temp_x_neg = (x <= static_cast<T>(0)).template cast<T>();
+    // case 1: alpha >= 0
+    // dx = dout, if out > 0
+    // dx = dout * (out + alpha), if out <= 0
+    dx.device(d) = (out > static_cast<T>(0))
+                       .select(dout, dout * (out + static_cast<T>(alpha)));
+  }
 
-    // dx = dout, if alpha > 0 and x > 0
-    // dx = dout * alpha * x.exp(), if alpha > 0 and x <= 0
-    // dx = dout * (1 + alpha * x.exp()), if alpha <= 0 and x > 0
-    // dx = 0, if alpha <= 0 and x <=0
-    dx.device(d) =
-        dout * temp_a_pos * temp_x_pos +
-        dout * static_cast<T>(alpha) * x.exp() * temp_a_pos * temp_x_neg +
-        dout * (static_cast<T>(1) + static_cast<T>(alpha) * x.exp()) *
-            temp_a_neg * temp_x_pos;
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
+};
+
+template <typename T>
+struct ELUGradNegativeAlphaFunctor : public BaseActivationFunctor<T> {
+  float alpha;
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"alpha", &alpha}};
+  }
+  template <typename Device, typename X, typename Out, typename dOut,
+            typename dX>
+  void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
+    // case 2: alpha < 0
+    // dx = dout, if x > 0
+    // dx = dout * (out + alpha), if x <=0
+    dx.device(d) = (x > static_cast<T>(0))
+                       .select(dout, dout * static_cast<T>(alpha) * x.exp());
   }
 
   static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
+template <typename DeviceContext, typename T>
+class ELUGradKernel : public framework::OpKernel<T> {
+ public:
+  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"));
+    auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
+    const float alpha = context.Attr<float>("alpha");
+    dX->mutable_data<T>(context.GetPlace());
+
+    auto x = framework::EigenVector<T>::Flatten(
+        GET_DATA_SAFELY(X, "Input", "X", "elu_grad"));
+    auto out = framework::EigenVector<T>::Flatten(
+        GET_DATA_SAFELY(Out, "Input", "Out", "elu_grad"));
+    auto dout = framework::EigenVector<T>::Flatten(
+        GET_DATA_SAFELY(dOut, "Input", "dOut", "elu_grad"));
+    auto dx = framework::EigenVector<T>::Flatten(
+        GET_DATA_SAFELY(dX, "Output", "dX", "elu_grad"));
+    auto* place =
+        context.template device_context<DeviceContext>().eigen_device();
+
+    if (alpha > 0) {
+      ELUGradFunctor<T> functor;
+      functor.alpha = alpha;
+      functor(*place, x, out, dout, dx);
+    } else {
+      ELUGradNegativeAlphaFunctor<T> functor;
+      functor.alpha = alpha;
+      functor(*place, x, out, dout, dx);
+    }
+  }
+};
+
 template <typename T>
 struct CELUFunctor : public BaseActivationFunctor<T> {
   float alpha;

paddle/fluid/operators/inplace_abn_op.h

@@ -104,7 +104,7 @@ class InplaceABNActivation {
       auto temp2 = (y * temp / static_cast<T>(alpha) + static_cast<T>(1)).log();
       x.device(d) = (y * temp1 + temp2).template cast<T>();
 
-      ELUGradFunctor<T> functor;
+      ELUGradNegativeAlphaFunctor<T> functor;
       compute(ctx, &functor, d, x, y, dy, dx);
     } else {
       PADDLE_THROW(

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

@@ -1742,7 +1742,7 @@ def test_errors(self):
 
 
 def elu(x, alpha):
-    out_ref = np.maximum(0, x) + np.minimum(0, alpha * (np.exp(x) - 1))
+    out_ref = np.where(x > 0, x, alpha * (np.exp(x) - 1))
     return out_ref.astype(x.dtype)
 
 
@@ -1753,7 +1753,7 @@ def setUp(self):
 
         np.random.seed(1024)
         x = np.random.uniform(-3, 3, [10, 12]).astype(self.dtype)
-        alpha = 1.
+        alpha = self.get_alpha()
         out = elu(x, alpha)
         # Note: unlike other Relu extensions, point 0 on standard ELU function (i.e. alpha = 1)
         # is differentiable, so we can skip modifications like x[np.abs(x) < 0.005] = 0.02 here
@@ -1766,6 +1766,14 @@ def test_check_grad(self):
             return
         self.check_grad(['X'], 'Out')
 
+    def get_alpha(self):
+        return 1.
+
+
+class TestELUAlpha(TestELU):
+    def get_alpha(self):
+        return -0.2
+
 
 class TestELUAPI(unittest.TestCase):
     # test paddle.nn.ELU, paddle.nn.functional.elu
@@ -1827,6 +1835,18 @@ def test_errors(self):
             self.elu(x_fp16)
 
 
+class TestELUInplaceAPI(TestELUAPI):
+    # test paddle.nn.functional.elu_
+    def executed_api(self):
+        self.elu = F.elu_
+
+    def test_alpha_error(self):
+        paddle.disable_static(self.place)
+        x = paddle.to_tensor(self.x_np)
+        self.assertRaises(Exception, F.elu_, x, -0.2)
+        paddle.enable_static()
+
+
 def celu(x, alpha):
     out_ref = np.maximum(0, x) + np.minimum(0, alpha * (np.exp(x / alpha) - 1))
     return out_ref.astype(x.dtype)
@@ -1915,12 +1935,6 @@ def test_errors(self):
             self.celu(x_fp16)
 
 
-class TestELUInplaceAPI(TestELUAPI):
-    # test paddle.nn.functional.elu_
-    def executed_api(self):
-        self.elu = F.elu_
-
-
 class TestReciprocal(TestActivation):
     def setUp(self):
         self.op_type = "reciprocal"

python/paddle/nn/functional/activation.py

@@ -81,7 +81,13 @@ def elu(x, alpha=1.0, name=None):
 
     .. math::
 
-        elu(x) = max(0, x) + min(0, \alpha * (e^{x}-1))
+        elu(x)=
+            \left\{
+                \begin{array}{lcl}
+                x,& &\text{if } \ x > 0 \\
+                alpha * (e^{x} - 1),& &\text{if } \ x <= 0
+                \end{array}
+            \right.
 
     Parameters:
         x (Tensor): The input Tensor with data type float32, float64.
@@ -124,6 +130,7 @@ def elu_(x, alpha=1.0, name=None):
     Inplace version of ``elu`` API, the output Tensor will be inplaced with input ``x``.
     Please refer to :ref:`api_nn_cn_elu`.
     """
+    assert alpha >= 0., "elu_ only support alpha >= 0, please use elu instead."
     return _C_ops.elu_(x, 'alpha', alpha)
 
 

python/paddle/nn/layer/activation.py

@@ -73,7 +73,13 @@ class ELU(Layer):
 
     .. math::
 
-        ELU(x) = max(0, x) + min(0, \alpha * (e^{x}-1))
+        ELU(x)=
+            \left\{
+                \begin{array}{lcl}
+                x,& &\text{if } \ x > 0 \\
+                alpha * (e^{x} - 1),& &\text{if } \ x <= 0
+                \end{array}
+            \right.
 
     Parameters:
         alpha (float, optional): The 'alpha' value of the ELU formulation. Default is 1.0.