Add three modes for prelu_op (#12630)

* Add three modes for prelu_op.

paddle/fluid/API.spec

@@ -159,6 +159,7 @@ paddle.fluid.layers.relu ArgSpec(args=['x'], varargs=None, keywords=None, defaul
 paddle.fluid.layers.log ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.prelu ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.layers.flatten ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_recordio_file ArgSpec(args=['filename', 'shapes', 'lod_levels', 'dtypes', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, True))

paddle/fluid/operators/prelu_op.cc

 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
-
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
-
     http://www.apache.org/licenses/LICENSE-2.0
-
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -26,14 +23,40 @@ class PReluOp : public framework::OperatorWithKernel {
       : OperatorWithKernel(type, inputs, outputs, attrs) {}
 
   void InferShape(framework::InferShapeContext *ctx) const override {
+    std::string mode = ctx->Attrs().Get<std::string>("mode");
+
+    auto x_dim = ctx->GetInputDim("X");
     PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
     PADDLE_ENFORCE(ctx->HasInput("Alpha"), "Input(Alpha) should not be null");
-    PADDLE_ENFORCE(product(ctx->GetInputDim("Alpha")) == 1,
-                   "Size of weight Alpha must be one.");
+
     PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null");
-    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
+    if (mode == "all") {
+      PADDLE_ENFORCE(product(ctx->GetInputDim("Alpha")) == 1,
+                     "For mode 'all', size of weight Alpha must be one.");
+    } else if (mode == "channel") {
+      PADDLE_ENFORCE(product(ctx->GetInputDim("Alpha")) == x_dim[1],
+                     "For channel-wise mode, size of weight Alpha must be "
+                     "equal to the number of channels, should be %d",
+                     x_dim[1]);
+    } else if (mode == "element") {
+      PADDLE_ENFORCE(product(ctx->GetInputDim("Alpha")) == product(x_dim),
+                     "For element-wise mode, size of weight Alpha must be "
+                     "equal to the number of input, should be %d",
+                     product(x_dim));
+    } else {
+      PADDLE_THROW("Unkown mode %s", mode);
+    }
+    ctx->SetOutputDim("Out", x_dim);
     ctx->ShareLoD("X", /*->*/ "Out");
   }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
+        platform::CPUPlace());
+  }
 };
 
 class PReluOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -44,9 +67,7 @@ class PReluOpMaker : public framework::OpProtoAndCheckerMaker {
     AddOutput("Out", "The output tensor of prelu operator.");
     AddComment(R"DOC(
 PRelu Operator.
-
 The equation is:
-
 $$
 f(x) =
 \begin{cases}
@@ -54,11 +75,15 @@ f(x) =
 x,         \qquad  \text{if} \ x >= 0
 \end{cases}
 $$
-
 The input `X` can carry the LoD (Level of Details) information,
 or not. And the output shares the LoD information with input `X`.
-
+There are modes: 
+  all: all elements share same weight
+  channel: elements in a channel share same weight
+  element: each element has a weight 
 )DOC");
+    AddAttr<std::string>("mode", "The mode for inputs to share weights.")
+        .SetDefault("all");
   }
 };
 
@@ -71,9 +96,23 @@ class PReluGradOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
                    "Input(Out@GRAD) should not be null");
-    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
-    ctx->SetOutputDim(framework::GradVarName("Alpha"),
-                      ctx->GetInputDim("Alpha"));
+    auto x_grad_name = framework::GradVarName("X");
+    auto alpha_grad_name = framework::GradVarName("Alpha");
+
+    if (ctx->HasOutput(x_grad_name)) {
+      ctx->SetOutputDim(x_grad_name, ctx->GetInputDim("X"));
+    }
+    if (ctx->HasOutput(alpha_grad_name)) {
+      ctx->SetOutputDim(alpha_grad_name, ctx->GetInputDim("Alpha"));
+    }
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
+        platform::CPUPlace());
   }
 };
 

paddle/fluid/operators/prelu_op.cu

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#include "paddle/fluid/operators/prelu_op.h"
-
-REGISTER_OP_CUDA_KERNEL(
-    prelu,
-    paddle::operators::PReluKernel<paddle::platform::CUDADeviceContext, float>);
-REGISTER_OP_CUDA_KERNEL(prelu_grad,
-                        paddle::operators::PReluGradKernel<
-                            paddle::platform::CUDADeviceContext, float>);

paddle/fluid/operators/prelu_op.h

 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
-
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
-
     http://www.apache.org/licenses/LICENSE-2.0
-
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -13,32 +10,16 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <string>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/platform/transform.h"
-
 namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
 using platform::Transform;
 
-template <typename T>
-class PReluFunctor {
- public:
-  explicit PReluFunctor(const T* alpha) : alpha_(alpha) {}
-
-  HOSTDEVICE T operator()(const T& x) const {
-    if (x > 0)
-      return x;
-    else
-      return x * (*alpha_);
-  }
-
- private:
-  const T* alpha_;
-};
-
 template <typename DeviceContext, typename T>
 class PReluKernel : public framework::OpKernel<T> {
  public:
@@ -50,53 +31,93 @@ class PReluKernel : public framework::OpKernel<T> {
     const T* x_ptr = x->data<T>();
     T* o_ptr = out->mutable_data<T>(context.GetPlace());
 
-    auto* alpha_ptr = alpha->data<T>();
+    const T* alpha_ptr = alpha->data<T>();
+    std::string mode = context.Attr<std::string>("mode");
 
     int numel = x->numel();
-
-    Transform<DeviceContext> trans;
-    trans(context.template device_context<DeviceContext>(), x_ptr,
-          x_ptr + numel, o_ptr, PReluFunctor<T>(alpha_ptr));
-  }
-};
-
-template <typename T>
-class PReluGradFunctor {
- public:
-  explicit PReluGradFunctor(const T* alpha) : alpha_(alpha) {}
-
-  HOSTDEVICE T operator()(const T& out, const T& dout) const {
-    if (out > 0)
-      return dout;
-    else
-      return dout * (*alpha_);
+    auto dim = x->dims();
+    int index = 0;
+    int i = 0;
+    int temp = 0;
+    if (mode == "channel") {
+      for (i = 0; i < numel; i++) {
+        temp = numel / (dim[0] * dim[1]);
+        index = (i / temp) % dim[1];
+        o_ptr[i] = x_ptr[i] > 0 ? x_ptr[i] : alpha_ptr[index] * x_ptr[i];
+      }
+    } else if (mode == "element") {
+      for (i = 0; i < numel; i++) {
+        o_ptr[i] = x_ptr[i] > 0 ? x_ptr[i] : alpha_ptr[i] * x_ptr[i];
+      }
+    } else {
+      for (i = 0; i < numel; i++) {
+        o_ptr[i] = x_ptr[i] > 0 ? x_ptr[i] : alpha_ptr[0] * x_ptr[i];
+      }
+    }
   }
-
- private:
-  const T* alpha_;
 };
 
 template <typename DeviceContext, typename T>
 class PReluGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
+    auto* x = context.Input<Tensor>("X");
     auto* dx = context.Output<Tensor>(framework::GradVarName("X"));
     auto* dout = context.Input<Tensor>(framework::GradVarName("Out"));
-
+    auto* dalpha = context.Output<Tensor>(framework::GradVarName("Alpha"));
     auto* out = context.Input<Tensor>("Out");
     auto* alpha = context.Input<Tensor>("Alpha");
-    auto* alpha_ptr = alpha->data<T>();
-
-    T* dx_ptr = dx->mutable_data<T>(context.GetPlace());
+    const T* alpha_ptr = alpha->data<T>();
+    const T* x_ptr = x->data<T>();
     const T* dout_ptr = dout->data<T>();
     const T* out_ptr = out->data<T>();
-    int numel = dx->numel();
-
-    Transform<DeviceContext> trans;
-    trans(context.template device_context<DeviceContext>(), out_ptr,
-          out_ptr + numel, dout_ptr, dx_ptr, PReluGradFunctor<T>(alpha_ptr));
-
-    // TODO(Zhuoyuan): add dalpha upgrade when GPU kernels ready
+    std::string mode = context.Attr<std::string>("mode");
+    int numel = x->numel();
+    auto dim = x->dims();
+    int index = 0;
+    int i = 0;
+    int temp = 0;
+    if (dx) {
+      T* dx_ptr = dx->mutable_data<T>(context.GetPlace());
+      if (mode == "channel") {
+        for (i = 0; i < numel; i++) {
+          temp = numel / (dim[0] * dim[1]);
+          index = (i / temp) % dim[1];
+          dx_ptr[i] =
+              out_ptr[i] > 0 ? dout_ptr[i] : alpha_ptr[index] * dout_ptr[i];
+        }
+      } else if (mode == "element") {
+        for (i = 0; i < numel; i++) {
+          dx_ptr[i] = out_ptr[i] > 0 ? dout_ptr[i] : alpha_ptr[i] * dout_ptr[i];
+        }
+      } else {
+        for (i = 0; i < numel; i++) {
+          dx_ptr[i] = out_ptr[i] > 0 ? dout_ptr[i] : alpha_ptr[0] * dout_ptr[i];
+        }
+      }
+    }
+
+    index = 0;
+    if (dalpha) {
+      T* dalpha_ptr = dalpha->mutable_data<T>(context.GetPlace());
+      if (mode == "channel") {
+        for (i = 0; i < numel; i++) {
+          temp = numel / (dim[0] * dim[1]);
+          index = (i / temp) % dim[1];
+          dalpha_ptr[index] += out_ptr[i] > 0 ? 0 : x_ptr[i] * dout_ptr[i];
+        }
+      } else if (mode == "element") {
+        for (i = 0; i < numel; i++) {
+          dalpha_ptr[i] += out_ptr[i] > 0 ? 0 : x_ptr[i] * dout_ptr[i];
+        }
+      } else {
+        for (i = 0; i < numel; i++) {
+          dalpha_ptr[0] += out_ptr[i] > 0 ? 0 : x_ptr[i] * dout_ptr[i];
+        }
+      }
+    }
+
+    // TODO(Guanzhong): add GPU kernels
   }
 };
 

python/paddle/fluid/layers/nn.py

@@ -112,6 +112,7 @@ __all__ = [
     'log',
     'crop',
     'rank_loss',
+    'prelu',
     'flatten',
 ]
 
@@ -5364,6 +5365,59 @@ def rank_loss(label, left, right, name=None):
     return out
 
 
+def prelu(x, mode, param_attr=None, name=None):
+    """
+    Equation:
+
+        y = \max(0, x) + alpha \min(0, x)
+
+    Args:
+        x (Variable): The input tensor.
+	  param_attr(ParamAttr|None): The parameter attribute for the learnable
+                                    weight (alpha).
+        mode (string): The mode for weight sharing
+		       all: all elements share same weight
+ 		       channel:elements in a channel share same weight
+ 		       element:each element has a weight
+	  name(str|None): A name for this layer(optional). If set None, the layer
+                        will be named automatically. 
+
+    Returns:
+        Variable: The output tensor with the same shape as input.
+
+    Examples:
+
+        .. code-block:: python
+
+         x = fluid.layers.data(name="x", shape=[10,10], dtype="float32")
+            mode = 'channel'
+            output = fluid.layers.prelu(x,mode)
+    """
+    helper = LayerHelper('prelu', **locals())
+    if mode not in ['all', 'channel', 'element']:
+        raise ValueError('mode should be one of all, channel, element.')
+    alpha_shape = [1]
+    if mode == 'channel':
+        alpha_shape = [1, x.shape[1], 1, 1]
+    elif mode == 'element':
+        alpha_shape = x.shape
+    dtype = helper.input_dtype(input_param_name='x')
+    alpha = helper.create_parameter(
+        attr=param_attr,
+        shape=alpha_shape,
+        dtype='float32',
+        is_bias=False,
+        default_initializer=Constant(1.0))
+    out = helper.create_tmp_variable(dtype)
+    helper.append_op(
+        type="prelu",
+        inputs={"X": x,
+                'Alpha': alpha},
+        attrs={"mode": mode},
+        outputs={"Out": out})
+    return out
+
+
 def flatten(x, axis=1, name=None):
     """
     **Flatten layer**

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

@@ -21,6 +21,7 @@ import paddle.fluid.nets as nets
 from paddle.fluid.framework import Program, program_guard, default_main_program
 from paddle.fluid.param_attr import ParamAttr
 import decorators
+from paddle.fluid.initializer import Constant
 
 
 class TestBook(unittest.TestCase):
@@ -485,6 +486,20 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(out)
         print(str(program))
 
+    def test_prelu(self):
+        program = Program()
+        with program_guard(program):
+            input = layers.data(
+                name="input", shape=[5, 200, 100, 100], dtype="float32")
+            mode = 'channel'
+            out = layers.prelu(
+                input,
+                mode,
+                param_attr=ParamAttr(initializer=Constant(1.0)),
+                name='prelu')
+            self.assertIsNotNone(out)
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -20,30 +20,58 @@ from op_test import OpTest
 class PReluTest(OpTest):
     def setUp(self):
         self.op_type = "prelu"
-        x_np = np.random.normal(size=(10, 10)).astype("float32")
-
-        for pos, val in np.ndenumerate(x_np):
-            # Since zero point in prelu is not differentiable, avoid randomize
-            # zero.
-            while abs(val) < 1e-3:
-                x_np[pos] = np.random.normal()
-                val = x_np[pos]
-
-        x_np_sign = np.sign(x_np)
-        x_np = x_np_sign * np.maximum(x_np, .005)
-        alpha_np = np.array([.1], dtype="float32")
-        self.inputs = {'X': x_np, 'Alpha': alpha_np}
+        self.initTestCase()
+        x_np = np.random.normal(size=(3, 5, 5, 10)).astype("float32")
+
+        # Since zero point in prelu is not differentiable, avoid randomize
+        # zero.
+        x_np[np.abs(x_np) < 0.005] = 0.02
+
+        if self.attrs == {'mode': "all"}:
+            alpha_np = np.random.rand(1).astype("float32")
+            self.inputs = {'X': x_np, 'Alpha': alpha_np}
+        elif self.attrs == {'mode': "channel"}:
+            alpha_np = np.random.rand(1, x_np.shape[1], 1, 1).astype("float32")
+            self.inputs = {'X': x_np, 'Alpha': alpha_np}
+        else:
+            alpha_np = np.random.rand(*x_np.shape).astype("float32")
+            self.inputs = {'X': x_np, 'Alpha': alpha_np}
+
         out_np = np.maximum(self.inputs['X'], 0.)
         out_np = out_np + np.minimum(self.inputs['X'],
                                      0.) * self.inputs['Alpha']
         assert out_np is not self.inputs['X']
         self.outputs = {'Out': out_np}
 
+    def initTestCase(self):
+        self.attrs = {'mode': "channel"}
+
     def test_check_output(self):
         self.check_output()
 
     def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
+        self.check_grad(['X', 'Alpha'], 'Out')
+
+    def test_check_grad_ignore_x(self):
+        self.check_grad(['Alpha'], 'Out', no_grad_set=set('X'))
+
+    def test_check_grad_ignore_alpha(self):
+        self.check_grad(['X'], 'Out', no_grad_set=set('Alpha'))
+
+
+class TestCase1(PReluTest):
+    def initTestCase(self):
+        self.attrs = {'mode': "all"}
+
+
+class TestCase2(PReluTest):
+    def initTestCase(self):
+        self.attrs = {'mode': "channel"}
+
+
+class TestCase3(PReluTest):
+    def initTestCase(self):
+        self.attrs = {'mode': "element"}
 
 
 if __name__ == "__main__":