wehub-resource-sync
58 lines
1.8 KiB
C++
58 lines
1.8 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/kernels/bernoulli_kernel.h"
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#include <random>
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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template <typename T>
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inline T BernoulliFunctor(T p, T rand) {
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PADDLE_ENFORCE_LE(p,
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1.0,
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common::errors::OutOfRange(
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"The probability should be <= 1, but got %f", p));
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PADDLE_ENFORCE_GE(p,
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0.0,
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common::errors::OutOfRange(
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"The probability should be >= 0, but got %f", p));
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return static_cast<T>(rand < p);
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}
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template <typename T, typename Context>
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void BernoulliKernel(const Context& dev_ctx,
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const DenseTensor& x,
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DenseTensor* out) {
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auto numel = x.numel();
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auto* x_data = x.data<T>();
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T* out_data = dev_ctx.template Alloc<T>(out);
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std::uniform_real_distribution<T> dist(0.0, 1.0);
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auto gen_ptr = dev_ctx.GetGenerator();
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auto engine = gen_ptr->GetCPUEngine();
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for (int64_t i = 0; i < numel; ++i) {
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out_data[i] = BernoulliFunctor(x_data[i], dist(*engine));
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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bernoulli, CPU, ALL_LAYOUT, phi::BernoulliKernel, float, double) {}
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