text: /* Copyright 2017 The TensorFlow 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.
  ==============================================================================*/
  
  // See docs in ../ops/math_ops.cc.
  
  #define EIGEN_USE_THREADS
  
  #include "tensorflow/core/kernels/bincount_op.h"
  #include "tensorflow/core/framework/op_kernel.h"
  #include "tensorflow/core/framework/register_types.h"
  #include "tensorflow/core/framework/types.h"
  #include "tensorflow/core/lib/core/threadpool.h"
  #include "tensorflow/core/platform/types.h"
  
  namespace tensorflow {
  
  using thread::ThreadPool;
  
  typedef Eigen::ThreadPoolDevice CPUDevice;
  typedef Eigen::GpuDevice GPUDevice;
  
  namespace functor {
  
  template <typename T>
  struct BincountFunctor<CPUDevice, T> {
    static Status Compute(OpKernelContext* context,
                          const typename TTypes<int32, 1>::ConstTensor& arr,
                          const typename TTypes<T, 1>::ConstTensor& weights,
                          typename TTypes<T, 1>::Tensor& output) {
      int size = output.size();
  
      Tensor all_nonneg_t;
      TF_RETURN_IF_ERROR(context->allocate_temp(
          DT_BOOL, TensorShape({}), &all_nonneg_t, AllocatorAttributes()));
      all_nonneg_t.scalar<bool>().device(context->eigen_cpu_device()) =
          (arr >= 0).all();
      if (!all_nonneg_t.scalar<bool>()()) {
        return errors::InvalidArgument("Input arr must be non-negative!");
      }
  
      // Allocate partial output bin sums for each worker thread. Worker ids in
      // ParallelForWithWorkerId range from 0 to NumThreads() inclusive.
      ThreadPool* thread_pool =
          context->device()->tensorflow_cpu_worker_threads()->workers;
      const int64 num_threads = thread_pool->NumThreads() + 1;
      Tensor partial_bins_t;
      TF_RETURN_IF_ERROR(context->allocate_temp(DataTypeToEnum<T>::value,
                                                TensorShape({num_threads, size}),
                                                &partial_bins_t));
      auto partial_bins = partial_bins_t.matrix<T>();
      partial_bins.setZero();
      thread_pool->ParallelForWithWorkerId(
          arr.size(), 8 /* cost */,
          [&](int64 start_ind, int64 limit_ind, int64 worker_id) {
            for (int64 i = start_ind; i < limit_ind; i++) {
              int32 value = arr(i);
              if (value < size) {
                if (weights.size()) {
                  partial_bins(worker_id, value) += weights(i);
                } else {
                  // Complex numbers don't support "++".
                  partial_bins(worker_id, value) += T(1);
                }
              }
            }
          });
  
      // Sum the partial bins along the 0th axis.
      Eigen::array<int, 1> reduce_dims({0});
      output.device(context->eigen_cpu_device()) = partial_bins.sum(reduce_dims);
      return Status::OK();
    }
  };
  
  }  // namespace functor
  
  template <typename Device, typename T>
  class BincountOp : public OpKernel {
   public:
    explicit BincountOp(OpKernelConstruction* ctx) : OpKernel(ctx) {}
  
    void Compute(OpKernelContext* ctx) override {
      const Tensor& arr_t = ctx->input(0);
      const Tensor& size_tensor = ctx->input(1);
      const Tensor& weights_t = ctx->input(2);
  
      int32 size = size_tensor.scalar<int32>()();
      OP_REQUIRES(
          ctx, size >= 0,
          errors::InvalidArgument("size (", size, ") must be non-negative"));
  
      const auto arr = arr_t.flat<int32>();
      const auto weights = weights_t.flat<T>();
      Tensor* output_t;
      OP_REQUIRES_OK(ctx,
                     ctx->allocate_output(0, TensorShape({size}), &output_t));
      auto output = output_t->flat<T>();
      OP_REQUIRES_OK(ctx, functor::BincountFunctor<Device, T>::Compute(
                              ctx, arr, weights, output));
    }
  };
  
  #define REGISTER_KERNELS(type)                                       \
    REGISTER_KERNEL_BUILDER(                                           \
        Name("Bincount").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
        BincountOp<CPUDevice, type>)
  
  TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
  #undef REGISTER_KERNELS
  
  #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
  
  #define REGISTER_KERNELS(type)                            \
    REGISTER_KERNEL_BUILDER(Name("Bincount")                \
                                .Device(DEVICE_GPU)         \
                                .HostMemory("size")         \
                                .TypeConstraint<type>("T"), \
                            BincountOp<GPUDevice, type>)
  
  TF_CALL_int32(REGISTER_KERNELS);
  TF_CALL_float(REGISTER_KERNELS);
  #undef REGISTER_KERNELS
  
  #endif  // GOOGLE_CUDA || TENSORFLOW_USE_ROCM
  
  }  // end namespace tensorflow
  