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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
// | ||
// Licensed under the Apache License, Version 2.0 (the "License"); | ||
// you may not use this file except in compliance with the License. | ||
// You may obtain a copy of the License at | ||
// | ||
// http://www.apache.org/licenses/LICENSE-2.0 | ||
// | ||
// Unless required by applicable law or agreed to in writing, software | ||
// distributed under the License is distributed on an "AS IS" BASIS, | ||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
// See the License for the specific language governing permissions and | ||
// limitations under the License. | ||
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#include "paddle/phi/kernels/flip_kernel.h" | ||
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#include <bitset> | ||
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#include "paddle/phi/backends/cpu/cpu_context.h" | ||
#include "paddle/phi/core/kernel_registry.h" | ||
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namespace phi { | ||
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constexpr size_t dim_bitset_size = 64; | ||
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template <typename T, typename Context> | ||
void FlipKernel(const Context& dev_ctx, | ||
const DenseTensor& x, | ||
const std::vector<int>& axis, | ||
DenseTensor* out) { | ||
auto x_dims = x.dims(); | ||
const int total_dims = x_dims.size(); | ||
std::bitset<dim_bitset_size> dim_bitset; | ||
for (size_t i = 0; i < axis.size(); ++i) { | ||
int dim = axis[i]; | ||
if (axis[i] < 0) { | ||
dim += total_dims; | ||
} | ||
dim_bitset[dim] = true; | ||
} | ||
auto x_strides = phi::stride(x_dims); | ||
auto numel = x.numel(); | ||
const T* x_data = x.data<T>(); | ||
T* out_data = dev_ctx.template Alloc<T>(out); | ||
#ifdef PADDLE_WITH_MKLML | ||
#pragma omp parallel for | ||
#endif | ||
for (int64_t i = 0; i < numel; ++i) { | ||
int64_t cur_indices = i; | ||
int64_t rem = 0; | ||
int64_t dst_offset = 0; | ||
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for (int d = 0; d < total_dims; ++d) { | ||
int64_t temp = cur_indices; | ||
cur_indices = cur_indices / x_strides[d]; | ||
rem = temp - cur_indices * x_strides[d]; | ||
dst_offset += dim_bitset[d] ? (x_dims[d] - 1 - cur_indices) * x_strides[d] | ||
: cur_indices * x_strides[d]; | ||
cur_indices = rem; | ||
} | ||
out_data[i] = x_data[dst_offset]; | ||
} | ||
} | ||
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} // namespace phi | ||
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PD_REGISTER_KERNEL(flip, | ||
CPU, | ||
ALL_LAYOUT, | ||
phi::FlipKernel, | ||
float, | ||
double, | ||
int32_t, | ||
int64_t, | ||
bool, | ||
phi::dtype::complex<float>, | ||
phi::dtype::complex<double>) {} |
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
// | ||
// Licensed under the Apache License, Version 2.0 (the "License"); | ||
// you may not use this file except in compliance with the License. | ||
// You may obtain a copy of the License at | ||
// | ||
// http://www.apache.org/licenses/LICENSE-2.0 | ||
// | ||
// Unless required by applicable law or agreed to in writing, software | ||
// distributed under the License is distributed on an "AS IS" BASIS, | ||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
// See the License for the specific language governing permissions and | ||
// limitations under the License. | ||
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#pragma once | ||
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#include <vector> | ||
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#include "paddle/phi/core/dense_tensor.h" | ||
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namespace phi { | ||
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template <typename T, typename Context> | ||
void FlipKernel(const Context& dev_ctx, | ||
const DenseTensor& x, | ||
const std::vector<int>& axis, | ||
DenseTensor* out); | ||
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} // namespace phi |
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✅Congratulation! Your pull request passed all required CI. You could ask reviewer(s) to approve and merge. 🎉