accuracy_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/accuracy_kernel.h"

#include <thrust/execution_policy.h>
#include <thrust/reduce.h>
#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/backends/gpu/gpu_info.h"
#include "paddle/phi/common/float16.h"
#include "paddle/phi/core/kernel_registry.h"

namespace phi {
using paddle::platform::PADDLE_CUDA_NUM_THREADS;

template <int BlockSize>
__global__ void AccuracyCudaKernel(const int N,
                                   const int D,
                                   const int64_t* Xdata,
                                   const int64_t* labeldata,
                                   int* correct_data,
                                   float* accuracy,
                                   int* total_data) {
  int count = 0;
  __shared__ int total[BlockSize];

  // support only 1 block
  for (int i = threadIdx.x; i < (N); i += BlockSize) {
    for (int j = 0; j < D; ++j) {
      if (Xdata[i * D + j] == labeldata[i]) {
        ++count;
        break;
      }
    }
  }
  total[threadIdx.x] = count;
  __syncthreads();

// reduce the count with init value 0, and output accuracy.
#ifdef PADDLE_WITH_CUDA
  int result = thrust::reduce(thrust::device, total, total + BlockSize, 0);
#else
  // HIP thrust::reduce not support __device__
  for (int s = BlockSize / 2; s > 0; s >>= 1) {
    if (threadIdx.x < s) {
      total[threadIdx.x] += total[threadIdx.x + s];
    }
    __syncthreads();
  }
  int result = total[0];
#endif
  if (threadIdx.x == 0) {
    *correct_data = result;
    *accuracy = static_cast<float>(result) / static_cast<float>(N);
    *total_data = N;
  }
}

template <typename T, typename Context>
void AccuracyRawKernel(const Context& dev_ctx,
                       const DenseTensor& inference,
                       const DenseTensor& indices,
                       const DenseTensor& label,
                       DenseTensor* accuracy,
                       DenseTensor* correct,
                       DenseTensor* total) {
  // FIXME(typhoonzero): only support indices currently
  // if add support for output values, how to detect the data type?
  const int64_t* indices_data = indices.data<int64_t>();
  const int64_t* label_data = label.data<int64_t>();

  int* correct_data = dev_ctx.template Alloc<int>(correct);
  int* total_data = dev_ctx.template Alloc<int>(total);
  float* accuracy_data = dev_ctx.template Alloc<float>(accuracy);

  int num_samples = static_cast<int>(inference.dims()[0]);
  size_t infer_width = inference.dims()[1];
  auto stream = dev_ctx.stream();
  phi::backends::gpu::GpuMemsetAsync(accuracy_data, 0, sizeof(float), stream);

  if (num_samples == 0) {
    return;
  }

  AccuracyCudaKernel<
      PADDLE_CUDA_NUM_THREADS><<<1, PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
      num_samples,
      infer_width,
      indices_data,
      label_data,
      correct_data,
      accuracy_data,
      total_data);
}
}  // namespace phi

// FIXME(typhoonzero): types of T is for inference data.
// label data is always int64
PD_REGISTER_KERNEL(accuracy,
                   GPU,
                   ALL_LAYOUT,
                   phi::AccuracyRawKernel,
                   phi::dtype::float16,
                   float,
                   double) {}