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rrelu_kernel.cc
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rrelu_kernel.cc
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// Copyright (c) 2022 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/phi/kernels/rrelu_kernel.h"
#include "paddle/fluid/framework/generator.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
template <typename T, typename Context>
void RReluKernel(const Context& dev_ctx,
const DenseTensor& x,
const float lower,
const float upper,
bool is_test,
DenseTensor* out,
DenseTensor* noise) {
const T* x_ptr = x.data<T>();
T* o_ptr = dev_ctx.template Alloc<T>(out);
T* n_ptr = dev_ctx.template Alloc<T>(noise);
T zero = static_cast<T>(0);
int numel = x.numel();
int i = 0;
if (is_test) {
T mid_val = static_cast<T>((lower + upper) / 2.0);
for (i = 0; i < numel; i++) {
if (x_ptr[i] < zero) {
o_ptr[i] = mid_val * x_ptr[i];
n_ptr[i] = mid_val;
} else {
o_ptr[i] = x_ptr[i];
n_ptr[i] = 1.0;
}
}
return;
}
auto engine = paddle::framework::GetCPURandomEngine(0);
std::uniform_real_distribution<float> dist(lower, upper);
for (i = 0; i < numel; i++) {
if (x_ptr[i] < zero) {
T scale = static_cast<T>(dist(*engine));
o_ptr[i] = scale * x_ptr[i];
n_ptr[i] = scale;
} else {
o_ptr[i] = x_ptr[i];
n_ptr[i] = 1.0;
}
}
}
} // namespace phi
PD_REGISTER_KERNEL(rrelu,
CPU,
ALL_LAYOUT,
phi::RReluKernel,
float,
phi::dtype::float16,
double) {}