paddlepaddle--paddle/paddle/phi/kernels/gpu/cross_kernel.cu

// 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.

#include "paddle/phi/kernels/cross_kernel.h"

#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/backends/gpu/gpu_launch_config.h"
#include "paddle/phi/common/amp_type_traits.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/index_calculator.h"

namespace phi {

template <typename T>
__global__ void Cross(const T* x,
                      const T* y,
                      T* out,
                      const int64_t stride,
                      const int64_t N,
                      funcs::IndexCalculator<int64_t> index_calculator) {
  CUDA_KERNEL_LOOP_TYPE(i, N, int64_t) {
    int64_t offset = index_calculator(i);

    int64_t pos0 = offset + 0 * stride;
    int64_t pos1 = offset + 1 * stride;
    int64_t pos2 = offset + 2 * stride;

    using MT = typename MPTypeTrait<T>::Type;

    MT x_pos0_mp = static_cast<MT>(x[pos0]);
    MT x_pos1_mp = static_cast<MT>(x[pos1]);
    MT x_pos2_mp = static_cast<MT>(x[pos2]);
    MT y_pos0_mp = static_cast<MT>(y[pos0]);
    MT y_pos1_mp = static_cast<MT>(y[pos1]);
    MT y_pos2_mp = static_cast<MT>(y[pos2]);

    out[pos0] = static_cast<T>(x_pos1_mp * y_pos2_mp - x_pos2_mp * y_pos1_mp);
    out[pos1] = static_cast<T>(x_pos2_mp * y_pos0_mp - x_pos0_mp * y_pos2_mp);
    out[pos2] = static_cast<T>(x_pos0_mp * y_pos1_mp - x_pos1_mp * y_pos0_mp);
  }
}

template <typename T, typename Context>
void CrossKernel(const Context& dev_ctx,
                 const DenseTensor& x,
                 const DenseTensor& y,
                 int axis,
                 DenseTensor* out) {
  auto& input_x = x;
  auto& input_y = y;
  auto* output = out;
  int dim = axis;

  auto input_x_dims = input_x.dims();
  if (dim != DDim::kMaxRank) {
    PADDLE_ENFORCE_EQ(
        dim < input_x_dims.size() && dim >= (0 - input_x_dims.size()),
        true,
        common::errors::OutOfRange(
            "Attr(dim) is out of range, It's expected "
            "to be in range of [-%d, %d]. But received Attr(dim) = %d.",
            input_x_dims.size(),
            input_x_dims.size() - 1,
            dim));
    if (dim < 0) {
      dim += input_x_dims.size();
    }

    PADDLE_ENFORCE_EQ(
        input_x_dims[dim] == 3,
        true,
        common::errors::InvalidArgument(
            "Input(X/Y).dims[dim] must be equal to 3. But received: "
            "Input(X/Y).dims[dim] = [%d].",
            input_x_dims[dim]));
  } else {
    for (auto i = 0; i < input_x_dims.size(); i++) {
      if (input_x_dims[i] == 3) {
        dim = i;
        break;
      }
    }
    PADDLE_ENFORCE_EQ(dim == DDim::kMaxRank,
                      false,
                      common::errors::InvalidArgument(
                          "There must be at least one dimension 'd' so that "
                          "Input(X/Y).dims()[d] is equal to 3. "
                          "But received: Input(X/Y).dims() == [%s].",
                          input_x_dims));
  }

  if (input_x.numel() == 0 || input_y.numel() == 0) {
    output->Resize(input_x.dims());
    dev_ctx.template Alloc<T>(output);
    return;
  }
  std::vector<int64_t> cal_dims;
  std::vector<int64_t> left_strides;
  std::vector<int64_t> full_strides;
  std::vector<int64_t> merged_dims;

  for (int i = 0; i < dim; i++) {
    if (i == 0) {
      merged_dims.push_back(input_x_dims[i]);
    } else {
      merged_dims[0] *= input_x_dims[i];
    }
  }
  int merge_axis = merged_dims.size();
  merged_dims.push_back(input_x_dims[dim]);
  for (int i = dim + 1; i < input_x_dims.size(); i++) {
    if (i == dim + 1) {
      merged_dims.push_back(input_x_dims[i]);
    } else {
      merged_dims[merge_axis + 1] *= input_x_dims[i];
    }
  }

  int64_t full_dim = 1;
  for (int i = 0; i < merged_dims.size(); i++) {
    full_strides.insert(full_strides.begin(), full_dim);
    full_dim *= merged_dims[merged_dims.size() - i - 1];
    if (i == merge_axis) {
      continue;
    }
    cal_dims.push_back(i);
  }
  int64_t left_dim = 1;
  for (int i = merged_dims.size() - 1; i >= 0; i--) {
    if (i == merge_axis) {
      continue;
    }
    left_strides.insert(left_strides.begin(), left_dim);
    left_dim *= merged_dims[i];
  }

  const auto* input_x_data = input_x.data<T>();
  const auto* input_y_data = input_y.data<T>();
  auto* out_data = dev_ctx.template Alloc<T>(out);

  int64_t numel = x.numel();
  backends::gpu::GpuLaunchConfig config =
      backends::gpu::GetGpuLaunchConfig1D(dev_ctx, numel / 3);

  auto index_calculator = funcs::IndexCalculator<int64_t>(
      merged_dims.size() - 1, cal_dims, left_strides, full_strides);
  Cross<<<config.block_per_grid,
          config.thread_per_block,
          0,
          dev_ctx.stream()>>>(input_x_data,
                              input_y_data,
                              out_data,
                              full_strides[merge_axis],
                              static_cast<int64_t>(numel / 3),
                              index_calculator);
}
}  // namespace phi

PD_REGISTER_KERNEL(cross,
                   GPU,
                   ALL_LAYOUT,
                   phi::CrossKernel,
                   phi::float16,
                   phi::bfloat16,
                   float,
                   double,
                   int,
                   int64_t,
                   phi::complex64,
                   phi::complex128) {}