wehub-resource-sync
66 lines
2.0 KiB
C++
66 lines
2.0 KiB
C++
// Copyright (c) 2023 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/randperm_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/backends/xpu/xpu_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, typename Context>
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void RandpermKernel(const Context& dev_ctx,
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int n,
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DataType dtype,
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DenseTensor* out) {
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std::shared_ptr<std::mt19937_64> engine;
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int seed = 0;
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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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if (dev_ctx.GetPlace().GetType() == AllocationType::CPU) {
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T* out_data = dev_ctx.template HostAlloc<T>(out);
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for (int i = 0; i < n; ++i) {
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out_data[i] = static_cast<T>(i);
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}
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std::shuffle(out_data, out_data + n, *engine);
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} else {
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dev_ctx.template Alloc<T>(out);
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DenseTensor tmp_tensor;
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tmp_tensor.Resize({n});
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T* tmp_data = dev_ctx.template HostAlloc<T>(&tmp_tensor);
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for (int i = 0; i < n; ++i) {
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tmp_data[i] = static_cast<T>(i);
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}
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std::shuffle(tmp_data, tmp_data + n, *engine);
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Copy(dev_ctx, tmp_tensor, dev_ctx.GetPlace(), true, out);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(randperm,
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XPU,
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ALL_LAYOUT,
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phi::RandpermKernel,
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int,
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int64_t,
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float,
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double) {}
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