wehub-resource-sync
123 lines
3.9 KiB
C++
123 lines
3.9 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/cross_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.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/kernels/funcs/common_shape.h"
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namespace phi {
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template <typename T, typename Context>
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void CrossKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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int axis,
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DenseTensor* out) {
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auto& input_x = x;
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auto& input_y = y;
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auto* output = out;
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int dim = axis;
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auto input_x_dims = input_x.dims();
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if (dim != DDim::kMaxRank) {
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PADDLE_ENFORCE_EQ(
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dim < input_x_dims.size() && dim >= (0 - input_x_dims.size()),
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true,
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common::errors::OutOfRange(
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"Attr(dim) is out of range, It's expected "
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"to be in range of [-%d, %d]. But received Attr(dim) = %d.",
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input_x_dims.size(),
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input_x_dims.size() - 1,
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dim));
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if (dim < 0) {
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dim += input_x_dims.size();
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}
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PADDLE_ENFORCE_EQ(
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input_x_dims[dim] == 3,
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true,
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common::errors::InvalidArgument(
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"Input(X/Y).dims[dim] must be equal to 3. But received: "
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"Input(X/Y).dims[dim] = [%d].",
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input_x_dims[dim]));
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} else {
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for (auto i = 0; i < input_x_dims.size(); i++) {
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if (input_x_dims[i] == 3) {
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dim = i;
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break;
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}
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}
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PADDLE_ENFORCE_EQ(dim == DDim::kMaxRank,
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false,
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common::errors::InvalidArgument(
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"There must be at least one dimension 'd' so that "
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"Input(X/Y).dims()[d] is equal to 3. "
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"But received: Input(X/Y).dims() == [%s].",
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input_x_dims));
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}
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if (input_x.numel() == 0 || input_y.numel() == 0) {
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output->Resize(input_x.dims());
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dev_ctx.template Alloc<T>(output);
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return;
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}
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int64_t outer_loops = 1;
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for (auto i = 0; i < dim; i++) {
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outer_loops *= input_x_dims[i];
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}
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int64_t slice_size = 1;
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for (auto i = dim + 1; i < input_x_dims.size(); i++) {
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slice_size *= input_x_dims[i];
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}
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std::vector<T> input_x_vec, input_y_vec;
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TensorToVector(input_x, dev_ctx, &input_x_vec);
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TensorToVector(input_y, dev_ctx, &input_y_vec);
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std::vector<T> out_vec(output->numel());
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dev_ctx.template Alloc<T>(output);
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for (int64_t i = 0; i < outer_loops; i++) {
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for (int64_t j = 0; j < 3; j++) {
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int64_t dst_pos = (3 * i + j) * slice_size;
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int64_t in_pos1 = (3 * i + ((j + 1) % 3)) * slice_size;
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int64_t in_pos2 = (3 * i + ((j + 2) % 3)) * slice_size;
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for (int64_t k = 0; k < slice_size; k++) {
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out_vec[dst_pos + k] =
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input_x_vec[in_pos1 + k] * input_y_vec[in_pos2 + k] -
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input_x_vec[in_pos2 + k] * input_y_vec[in_pos1 + k];
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}
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}
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}
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TensorFromVector(out_vec, dev_ctx, output);
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output->Resize(input_x_dims);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(cross,
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CPU,
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ALL_LAYOUT,
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phi::CrossKernel,
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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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