wehub-resource-sync
85 lines
2.7 KiB
C++
85 lines
2.7 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/linspace_kernel.h"
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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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#include "paddle/phi/kernels/funcs/data_type_transform.h"
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namespace phi {
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template <typename T, typename Context>
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void LinspaceKernel(const Context& dev_ctx,
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const DenseTensor& start,
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const DenseTensor& stop,
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const DenseTensor& number,
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DataType dtype,
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DenseTensor* out) {
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int64_t num = 0;
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if (number.dtype() == DataType::INT64) {
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num = number.data<int64_t>()[0];
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} else if (number.dtype() == DataType::INT32) {
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num = number.data<int32_t>()[0];
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}
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PADDLE_ENFORCE_GE(num,
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0,
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common::errors::InvalidArgument(
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"The num of linspace op should be larger "
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"than or equal to 0, but received num is %d",
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num));
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if (num == 0) {
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out->Resize({0});
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dev_ctx.template Alloc<T>(out);
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return;
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}
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using StepT = std::conditional_t<std::is_integral_v<T>, double, T>;
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auto start_t = funcs::TransDataType(dev_ctx, start, dtype);
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auto stop_t = funcs::TransDataType(dev_ctx, stop, dtype);
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T start_data = start_t.template data<T>()[0];
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T stop_data = stop_t.template data<T>()[0];
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out->Resize({num});
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T* out_data = dev_ctx.template Alloc<T>(out);
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if (num > 1) {
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// step should be of StepT type
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StepT step =
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(static_cast<StepT>(stop_data) - static_cast<StepT>(start_data)) /
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(num - 1);
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int half_num = num / 2;
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for (int i = 0; i < num; ++i) {
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if (i < half_num) {
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out_data[i] = static_cast<T>(start_data + step * i);
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} else {
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out_data[i] = static_cast<T>(stop_data - step * (num - i - 1));
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}
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}
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} else {
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out_data[0] = static_cast<T>(start_data);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(linspace,
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CPU,
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ALL_LAYOUT,
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phi::LinspaceKernel,
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float,
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int32_t,
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int64_t,
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double) {}
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