wehub-resource-sync
71 lines
2.4 KiB
C++
71 lines
2.4 KiB
C++
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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/truncated_gaussian_random_kernel.h"
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#include <limits>
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#include <random>
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#include "paddle/phi/backends/xpu/xpu_context.h"
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#include "paddle/phi/common/memory_utils.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/truncated_normal.h"
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namespace phi {
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template <typename T, typename Context>
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void TruncatedGaussianRandomKernel(const Context& dev_ctx,
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const std::vector<int>& shape,
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float mean,
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float std,
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int seed,
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float a,
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float b,
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DataType dtype,
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DenseTensor* out) {
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T* data = dev_ctx.template Alloc<T>(out);
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std::uniform_real_distribution<T> dist(std::numeric_limits<float>::min(),
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1.0);
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TruncatedNormal<T> truncated_normal(mean, std, a, b);
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int64_t size = out->numel();
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std::unique_ptr<T[]> data_cpu(new T[size]);
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std::shared_ptr<std::mt19937_64> engine;
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if (seed) {
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engine = std::make_shared<std::mt19937_64>();
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engine->seed(seed);
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} else {
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engine = dev_ctx.GetGenerator()->GetCPUEngine();
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}
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for (int64_t i = 0; i < size; ++i) {
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data_cpu[i] = truncated_normal(dist(*engine));
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}
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memory_utils::Copy(dev_ctx.GetPlace(),
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data,
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CPUPlace(),
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reinterpret_cast<void*>(data_cpu.get()),
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size * sizeof(T));
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}
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} // namespace phi
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PD_REGISTER_KERNEL(truncated_gaussian_random,
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XPU,
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ALL_LAYOUT,
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phi::TruncatedGaussianRandomKernel,
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float) {}
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