wehub-resource-sync
95 lines
3.0 KiB
C++
95 lines
3.0 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/roll_kernel.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/tensor_utils.h"
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#include "paddle/phi/kernels/cast_kernel.h"
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#include "paddle/phi/kernels/cpu/roll_kernel_impl.h"
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namespace phi {
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template <typename T, typename Context>
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void RollKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& shifts,
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const std::vector<int64_t>& axis,
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DenseTensor* out) {
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if (x.numel() == 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 Type =
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typename std::conditional<std::is_same<T, bool>::value, int16_t, T>::type;
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std::vector<Type> out_vec;
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if (std::is_same<T, bool>::value) {
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DenseTensor tmp_int_tensor;
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tmp_int_tensor = Cast<T, Context>(dev_ctx, x, DataType::INT16);
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TensorToVector(tmp_int_tensor, dev_ctx, &out_vec);
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} else {
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TensorToVector(x, dev_ctx, &out_vec);
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}
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auto shifts_data = shifts.GetData();
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size_t nums = shifts_data.size();
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DDim input_dim = x.dims();
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auto dims = axis;
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// axis = none, reshape to 1-D tensor
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if (dims.empty()) {
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dims.push_back(0l);
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input_dim = Dim<1>(out_vec.size());
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}
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for (size_t i = 0; i < nums; i++) {
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PADDLE_ENFORCE_EQ(
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dims[i] < input_dim.size() && dims[i] >= (0 - input_dim.size()),
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true,
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common::errors::OutOfRange(
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"Attr(axis[%d]) is out of range, It's expected "
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"to be in range of [-%d, %d]. But received Attr(axis[%d]) = %d.",
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i,
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input_dim.size(),
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input_dim.size() - 1,
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i,
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dims[i]));
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ShiftAlongDim(out_vec.data(), input_dim, dims[i], shifts_data[i]);
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}
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dev_ctx.template Alloc<T>(out);
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if (std::is_same<T, bool>::value) {
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DenseTensor tmp_bool_tensor;
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TensorFromVector(out_vec, dev_ctx, &tmp_bool_tensor);
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*out = Cast<Type, Context>(dev_ctx, tmp_bool_tensor, DataType::BOOL);
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} else {
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TensorFromVector(out_vec, dev_ctx, out);
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}
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out->Resize(x.dims());
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}
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} // namespace phi
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PD_REGISTER_KERNEL(roll,
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CPU,
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ALL_LAYOUT,
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phi::RollKernel,
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bool,
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float,
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double,
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int,
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int64_t,
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phi::complex64,
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phi::complex128) {}
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