leaky_relu and LeakyReLU: alpha->negative_slope

paddle/fluid/operators/activation_op.cc

@@ -781,8 +781,8 @@ class ReluDoubleGradMaker : public ::paddle::framework::SingleGradOpMaker<T> {
   }
 };
 
-// leaky_relu Grad: dx=dy if y>=0 else alpha * dy
-// leaky_relu GradGrad: ddy=ddx if y>=0 else alpha * ddx
+// leaky_relu Grad: dx=dy if x>=0 else alpha * dy
+// leaky_relu GradGrad: ddy=ddx if x>=0 else alpha * ddx
 template <typename T>
 class LeakyReluDoubleGradMaker
     : public ::paddle::framework::SingleGradOpMaker<T> {
@@ -792,8 +792,8 @@ class LeakyReluDoubleGradMaker
  protected:
   void Apply(GradOpPtr<T> op) const override {
     op->SetType("leaky_relu_grad_grad");
-    // input1: Out
-    op->SetInput("Out", this->Input("Out"));
+    // input1: X
+    op->SetInput("X", this->Input("X"));
     // X@GRAD@GRAD: ddx
     op->SetInput("DDX", this->OutputGrad(framework::GradVarName("X")));
     op->SetAttrMap(this->Attrs());

paddle/fluid/operators/activation_op.h

@@ -1084,7 +1084,11 @@ struct LeakyReluFunctor : public BaseActivationFunctor<T> {
 
   template <typename Device, typename X, typename Out>
   void operator()(Device d, X x, Out out) const {
-    out.device(d) = x.cwiseMax(static_cast<T>(alpha) * x);
+    if (alpha < 1.f) {
+      out.device(d) = x.cwiseMax(static_cast<T>(alpha) * x);
+    } else {
+      out.device(d) = x.cwiseMin(static_cast<T>(alpha) * x);
+    }
   }
 };
 
@@ -1098,12 +1102,12 @@ struct LeakyReluGradFunctor : public BaseActivationFunctor<T> {
             typename dX>
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     auto temp1 =
-        static_cast<T>(alpha) * (out <= static_cast<T>(0)).template cast<T>();
-    auto temp2 = (out > static_cast<T>(0)).template cast<T>();
+        static_cast<T>(alpha) * (x < static_cast<T>(0)).template cast<T>();
+    auto temp2 = (x >= static_cast<T>(0)).template cast<T>();
     dx.device(d) = dout * (temp1 + temp2).template cast<T>();
   }
 
-  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 template <typename T>
@@ -1451,18 +1455,18 @@ struct LeakyReluGradGradFunctor : public BaseActivationFunctor<T> {
       auto* d = dev.eigen_device();
       auto ddx = framework::EigenVector<T>::Flatten(
           GET_DATA_SAFELY(ddX, "Input", "DDX", "LeakyReluGradGrad"));
-      auto out = framework::EigenVector<T>::Flatten(
-          GET_DATA_SAFELY(Out, "Output", "Out", "LeakyReluGradGrad"));
+      auto x = framework::EigenVector<T>::Flatten(
+          GET_DATA_SAFELY(X, "Input", "X", "LeakyReluGradGrad"));
       auto ddout = framework::EigenVector<T>::Flatten(
           GET_DATA_SAFELY(ddOut, "Output", "DOut", "LeakyReluGradGrad"));
-      ddout.device(*d) = ddx *
-                         ((out > static_cast<T>(0)).template cast<T>() +
-                          static_cast<T>(alpha) *
-                              (out <= static_cast<T>(0)).template cast<T>())
-                             .template cast<T>();
+      ddout.device(*d) =
+          ddx *
+          ((x > static_cast<T>(0)).template cast<T>() +
+           static_cast<T>(alpha) * (x <= static_cast<T>(0)).template cast<T>())
+              .template cast<T>();
     }
   }
-  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 template <typename T>

paddle/fluid/operators/test_leaky_relu_grad_grad_functor.h

@@ -41,20 +41,20 @@ static void InitRandom(framework::Tensor *tensor,
 
 template <typename T>
 struct LeakyReluGradGradEachElementFunctor {
-  LeakyReluGradGradEachElementFunctor(const T *ddx, const T *out, T alpha,
+  LeakyReluGradGradEachElementFunctor(const T *ddx, const T *x, T alpha,
                                       T *ddout)
-      : ddx_(ddx), out_(out), alpha_(alpha), ddout_(ddout) {}
+      : ddx_(ddx), x_(x), alpha_(alpha), ddout_(ddout) {}
 
   HOSTDEVICE void operator()(int idx) {
-    if (out_[idx] > 0) {
+    if (x_[idx] >= 0) {
       ddout_[idx] = ddx_[idx];
     } else {
       ddout_[idx] = ddx_[idx] * alpha_;
     }
   }
 
   const T *ddx_;
-  const T *out_;
+  const T *x_;
   T alpha_;
   T *ddout_;
 };
@@ -66,13 +66,13 @@ static bool TestLeakyReluGradGradMain(const framework::DDim &dim,
   LeakyReluGradGradFunctor<T> functor;
   functor.alpha = alpha;
   auto &dev_ctx = *platform::DeviceContextPool::Instance().Get(place);
-  framework::Tensor *x = nullptr;
+  framework::Tensor *out = nullptr;
   framework::Tensor *dout = nullptr;
   framework::Tensor *dx = nullptr;
 
-  framework::Tensor out;
-  out.Resize(dim);
-  InitRandom<T>(&out, place);
+  framework::Tensor x;
+  x.Resize(dim);
+  InitRandom<T>(&x, place);
 
   framework::Tensor ddx;
   ddx.Resize(dim);
@@ -85,22 +85,22 @@ static bool TestLeakyReluGradGradMain(const framework::DDim &dim,
   framework::Tensor ddout_actual;
   ddout_actual.mutable_data<T>(dim, place);
   LeakyReluGradGradEachElementFunctor<T> actual_functor(
-      ddx.data<T>(), out.data<T>(), static_cast<T>(alpha),
+      ddx.data<T>(), x.data<T>(), static_cast<T>(alpha),
       ddout_actual.data<T>());
 
-  int64_t limit = out.numel();
+  int64_t limit = x.numel();
 
 #ifdef __NVCC__
   if (platform::is_gpu_place(place)) {
     auto &cuda_dev_ctx = dynamic_cast<platform::CUDADeviceContext &>(dev_ctx);
-    functor(cuda_dev_ctx, x, &out, &ddx, &ddout, dout, dx);
+    functor(cuda_dev_ctx, &x, out, &ddx, &ddout, dout, dx);
     platform::ForRange<platform::CUDADeviceContext> for_range(cuda_dev_ctx,
                                                               limit);
     for_range(actual_functor);
   } else {
 #endif
     auto &cpu_dev_ctx = dynamic_cast<platform::CPUDeviceContext &>(dev_ctx);
-    functor(cpu_dev_ctx, x, &out, &ddx, &ddout, dout, dx);
+    functor(cpu_dev_ctx, &x, out, &ddx, &ddout, dout, dx);
     platform::ForRange<platform::CPUDeviceContext> for_range(cpu_dev_ctx,
                                                              limit);
     for_range(actual_functor);

python/paddle/fluid/layers/nn.py

@@ -9715,13 +9715,10 @@ def brelu(x, t_min=0.0, t_max=24.0, name=None):
     return out
 
 
+@deprecated(since="2.0.0", update_to="paddle.nn.functional.leaky_relu")
 @templatedoc()
 def leaky_relu(x, alpha=0.02, name=None):
     """
-    :alias_main: paddle.nn.functional.leaky_relu
-	:alias: paddle.nn.functional.leaky_relu,paddle.nn.functional.activation.leaky_relu
-	:old_api: paddle.fluid.layers.leaky_relu
-
     ${comment}
     Args:
         x(${x_type}): ${x_comment}
@@ -9750,19 +9747,7 @@ def leaky_relu(x, alpha=0.02, name=None):
             res_val, = exe.run(fluid.default_main_program(), feed={'x':x_i}, fetch_list=[res])
             print(res_val) # [[-0.1, 2], [3, -0.4]]
     """
-    if in_dygraph_mode():
-        return core.ops.leaky_relu(x, 'alpha', alpha)
-
-    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
-                             'leaky_relu')
-
-    inputs = {'X': [x]}
-    attrs = {'alpha': alpha}
-    helper = LayerHelper('leaky_relu', **locals())
-    out = helper.create_variable_for_type_inference(dtype=x.dtype)
-    helper.append_op(
-        type='leaky_relu', inputs=inputs, outputs={'Out': out}, attrs=attrs)
-    return out
+    return paddle.nn.functional.leaky_relu(x, alpha, name)
 
 
 def soft_relu(x, threshold=40.0, name=None):

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

@@ -903,37 +903,109 @@ def test_errors(self):
             F.relu(x_fp16)
 
 
+def ref_leaky_relu(x, alpha=0.01):
+    out = np.copy(x)
+    out[out < 0] *= alpha
+    return out
+
+
 class TestLeakyRelu(TestActivation):
+    def get_alpha(self):
+        return 0.02
+
     def setUp(self):
         self.op_type = "leaky_relu"
         self.init_dtype()
+        alpha = self.get_alpha()
 
+        np.random.seed(10)
         x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
         # The same reason with TestAbs
-        x[np.abs(x) < 0.005] = 0.02
-        out = np.maximum(x, 0.02 * x)
+        x[np.abs(x) < 0.005] = 0.05
+        out = ref_leaky_relu(x, alpha)
 
-        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
+        self.inputs = {'X': x}
         self.outputs = {'Out': out}
+        self.attrs = {'alpha': alpha}
 
     def test_check_grad(self):
         if self.dtype == np.float16:
             return
         self.check_grad(['X'], 'Out')
 
 
-class TestLeakyReluOpError(unittest.TestCase):
+class TestLeakyReluAlpha1(TestLeakyRelu):
+    def get_alpha(self):
+        return 2
+
+
+class TestLeakyReluAlpha2(TestLeakyRelu):
+    def get_alpha(self):
+        return -0.01
+
+
+class TestLeakyReluAlpha3(TestLeakyRelu):
+    def get_alpha(self):
+        return -2.0
+
+
+class TestLeakyReluAPI(unittest.TestCase):
+    # test paddle.nn.LeakyReLU, paddle.nn.functional.leaky_relu,
+    # fluid.layers.leaky_relu
+    def setUp(self):
+        self.x_np = np.random.uniform(-1, 1, [10, 12]).astype('float32')
+        self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
+            else paddle.CPUPlace()
+
+    def test_static_api(self):
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.data('X', [10, 12])
+            out1 = F.leaky_relu(x)
+            m = paddle.nn.LeakyReLU()
+            out2 = m(x)
+            exe = paddle.static.Executor(self.place)
+            res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
+        out_ref = ref_leaky_relu(self.x_np)
+        for r in res:
+            self.assertEqual(np.allclose(out_ref, r), True)
+
+    def test_dygraph_api(self):
+        paddle.disable_static(self.place)
+        x = paddle.to_variable(self.x_np)
+        out1 = F.leaky_relu(x)
+        m = paddle.nn.LeakyReLU()
+        out2 = m(x)
+        out_ref = ref_leaky_relu(self.x_np)
+        for r in [out1, out2]:
+            self.assertEqual(np.allclose(out_ref, r.numpy()), True)
+
+        out1 = F.leaky_relu(x, 0.6)
+        m = paddle.nn.LeakyReLU(0.6)
+        out2 = m(x)
+        out_ref = ref_leaky_relu(self.x_np, 0.6)
+        for r in [out1, out2]:
+            self.assertEqual(np.allclose(out_ref, r.numpy()), True)
+        paddle.enable_static()
+
+    def test_fluid_api(self):
+        with fluid.program_guard(fluid.Program()):
+            x = fluid.data('X', [10, 12])
+            out = fluid.layers.leaky_relu(x, 0.01)
+            exe = fluid.Executor(self.place)
+            res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
+        out_ref = ref_leaky_relu(self.x_np)
+        self.assertEqual(np.allclose(out_ref, res[0]), True)
+
     def test_errors(self):
-        with program_guard(Program()):
+        with paddle.static.program_guard(paddle.static.Program()):
             # The input type must be Variable.
-            self.assertRaises(TypeError, fluid.layers.leaky_relu, 1)
+            self.assertRaises(TypeError, F.leaky_relu, 1)
             # The input dtype must be float16, float32, float64.
-            x_int32 = fluid.data(name='x_int32', shape=[12, 10], dtype='int32')
-            self.assertRaises(TypeError, fluid.layers.leaky_relu, x_int32)
-            # support the input dtype is float32
-            x_fp16 = fluid.layers.data(
-                name='x_fp16', shape=[12, 10], dtype='float32')
-            fluid.layers.leaky_relu(x_fp16)
+            x_int32 = paddle.data(name='x_int32', shape=[12, 10], dtype='int32')
+            self.assertRaises(TypeError, F.leaky_relu, x_int32)
+            # support the input dtype is float16
+            x_fp16 = paddle.data(name='x_fp16', shape=[12, 10], dtype='float16')
+            F.leaky_relu(x_fp16)
 
 
 def gelu(x, approximate):

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

@@ -316,21 +316,6 @@ def test_relu(self):
 
         self.assertTrue(np.allclose(static_ret, dy_ret_value))
 
-    def test_leakyrelu(self):
-        inputs = np.random.uniform(-1, 1, (10, 10)).astype('float32')
-        with self.static_graph():
-            t = layers.data(name='t', shape=[10, 10], dtype='float32')
-            ret = layers.leaky_relu(t, alpha=0.01)
-            static_ret = self.get_static_graph_result(
-                feed={'t': inputs}, fetch_list=[ret])[0]
-
-        with self.dynamic_graph():
-            lrelu = paddle.nn.LeakyReLU(alpha=0.01)
-            dy_ret = lrelu(base.to_variable(inputs))
-            dy_ret_value = dy_ret.numpy()
-
-        self.assertTrue(np.allclose(static_ret, dy_ret_value))
-
     def test_pad2d(self):
         with self.static_graph():
             t = layers.data(name='t', shape=[-1, 3, 5, 5], dtype='float32')
@@ -2678,13 +2663,6 @@ def make_brelu(self):
             out = layers.brelu(input, t_min=1.0, t_max=20.0, name='brelu')
             return (out)
 
-    def make_leaky_relu(self):
-        with program_guard(fluid.default_main_program(),
-                           fluid.default_startup_program()):
-            input = self._get_data(name="input", shape=[16], dtype="float32")
-            out = layers.leaky_relu(input, alpha=0.1, name='leaky_relu')
-            return (out)
-
     def make_soft_relu(self):
         with program_guard(fluid.default_main_program(),
                            fluid.default_startup_program()):