/* Copyright 2016 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.
==============================================================================*/

#ifndef TENSORFLOW_CONTRIB_MPI_H_
#define TENSORFLOW_CONTRIB_MPI_H_

#ifdef TENSORFLOW_USE_MPI

#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/shape_inference.h"

#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/tensor_types.h"

#if GOOGLE_CUDA
#include "cuda_runtime.h"
#endif

// Needed to avoid header issues with C++-supporting MPI implementations
#define OMPI_SKIP_MPICXX
#include "third_party/mpi/mpi.h"

#define TAG_TENSOR 12

namespace tensorflow {
namespace contrib {
namespace mpi_collectives {

using CPUDevice = Eigen::ThreadPoolDevice;
using GPUDevice = Eigen::GpuDevice;

// Convert from templated types to values we can pass to MPI.
template <typename T>
MPI_Datatype MPIType();

// Convert from templated types to TensorFlow data types.
template <typename T>
DataType TensorFlowDataType();

#define MPI_REQUIRES_OK(MPI_STATUS)                               \
  if ((MPI_STATUS) != MPI_SUCCESS) {                              \
    return errors::Unknown("MPI operation failed unexpectedly."); \
  }

// Copy data from one tensor to another tensor.
// This uses a custom CUDA stream on GPU, which is necessary to overlay the
// backpropagation computations with the allreduce.
template <typename Device>
void CopyTensorData(void* destination, void* source, size_t size);

// Add a tensor into another tensor, accumulating in place.
// This uses a custom CUDA stream on GPU, which is necessary to overlay the
// backpropagation computations with the allreduce.
template <typename Device, typename T>
void AccumulateTensorData(T* destination, T* source, size_t size);

// We need to get the right stream for doing CUDA memory transfers and
// operations, which is possibly different from the standard TensorFlow stream.
#if GOOGLE_CUDA
cudaStream_t CudaStreamForMPI();
#endif

/* Perform a ring allreduce on the data. Allocate the necessary output tensor
 * and store it in the output parameter.
 *
 * Assumes that all MPI processes are doing an allreduce of the same tensor,
 * with the same dimensions.
 *
 * A ring allreduce is a bandwidth-optimal way to do an allreduce. To do the
 * allreduce, the nodes involved are arranged in a ring:
 *
 *                   .--0--.
 *                  /       \
 *                 3         1
 *                  \       /
 *                   *--2--*
 *
 *  Each node always sends to the next clockwise node in the ring, and receives
 *  from the previous one.
 *
 *  The allreduce is done in two parts: a scatter-reduce and an allgather. In
 *  the scatter reduce, a reduction is done, so that each node ends up with a
 *  chunk of the final output tensor which has contributions from all other
 *  nodes.  In the allgather, those chunks are distributed among all the nodes,
 *  so that all nodes have the entire output tensor.
 *
 *  Both of these operations are done by dividing the input tensor into N
 *  evenly sized chunks (where N is the number of nodes in the ring).
 *
 *  The scatter-reduce is done in N-1 steps. In the ith step, node j will send
 *  the (j - i)th chunk and receive the (j - i - 1)th chunk, adding it in to
 *  its existing data for that chunk. For example, in the first iteration with
 *  the ring depicted above, you will have the following transfers:
 *
 *      Segment 0:  Node 0 --> Node 1
 *      Segment 1:  Node 1 --> Node 2
 *      Segment 2:  Node 2 --> Node 3
 *      Segment 3:  Node 3 --> Node 0
 *
 *  In the second iteration, you'll have the following transfers:
 *
 *      Segment 0:  Node 1 --> Node 2
 *      Segment 1:  Node 2 --> Node 3
 *      Segment 2:  Node 3 --> Node 0
 *      Segment 3:  Node 0 --> Node 1
 *
 *  After this iteration, Node 2 has 3 of the four contributions to Segment 0.
 *  The last iteration has the following transfers:
 *
 *      Segment 0:  Node 2 --> Node 3
 *      Segment 1:  Node 3 --> Node 0
 *      Segment 2:  Node 0 --> Node 1
 *      Segment 3:  Node 1 --> Node 2
 *
 *  After this iteration, Node 3 has the fully accumulated Segment 0; Node 0
 *  has the fully accumulated Segment 1; and so on. The scatter-reduce is
 * complete.
 *
 *  Next, the allgather distributes these fully accumululated chunks across all
 * nodes. Communication proceeds in the same ring, once again in N-1 steps. At
 * the ith step, node j will send chunk (j - i + 1) and receive chunk (j - i).
 * For example, at the first iteration, the following transfers will occur:
 *
 *      Segment 0:  Node 3 --> Node 0
 *      Segment 1:  Node 0 --> Node 1
 *      Segment 2:  Node 1 --> Node 2
 *      Segment 3:  Node 2 --> Node 3
 *
 * After the first iteration, Node 0 will have a fully accumulated Segment 0
 * (from Node 3) and Segment 1. In the next iteration, Node 0 will send its
 * just-received Segment 0 onward to Node 1, and receive Segment 3 from Node 3.
 * After this has continued for N - 1 iterations, all nodes will have a the
 * fully accumulated tensor.
 *
 * Each node will do (N-1) sends for the scatter-reduce and (N-1) sends for the
 * allgather. Each send will contain K / N bytes, if there are K bytes in the
 * original tensor on every node. Thus, each node sends and receives 2K(N - 1)/N
 * bytes of data, and the performance of the allreduce (assuming no latency in
 * connections) is constrained by the slowest interconnect between the nodes.
 *
 */
template <typename Device, typename T>
Status RingAllreduce(OpKernelContext* context, const Tensor* input,
                     Tensor* temp, Tensor* output) {
  // Acquire MPI size and rank
  int n, r;
  MPI_REQUIRES_OK(MPI_Comm_size(MPI_COMM_WORLD, &n));
  MPI_REQUIRES_OK(MPI_Comm_rank(MPI_COMM_WORLD, &r));

  T* buffer = (T*)output->tensor_data().data();

  CopyTensorData<Device>((void*)buffer, (void*)input->tensor_data().data(),
                         output->tensor_data().size());

  // Calculate segment sizes and segment ends
  const size_t elements_to_reduce = input->NumElements();
  const size_t segment_size = elements_to_reduce / n;
  std::vector<size_t> segment_sizes(n, segment_size);

  const size_t residual = elements_to_reduce % n;
  for (size_t i = 0; i < residual; ++i) {
    segment_sizes[i]++;
  }

  std::vector<size_t> segment_starts(n);
  segment_starts[0] = 0;
  for (size_t i = 1; i < segment_starts.size(); ++i) {
    segment_starts[i] = segment_starts[i - 1] + segment_sizes[i - 1];
  }

  assert(segment_starts[n - 1] + segment_sizes[n - 1] == elements_to_reduce);

  T* segment_recv = (T*)temp->tensor_data().data();

  // Receive from your left neighbor with wrap-around
  const size_t recv_from = ((r - 1) + n) % n;

  // Send to your right neighbor with wrap-around
  const size_t send_to = (r + 1) % n;

  MPI_Status recv_status;
  MPI_Request recv_req;

  // Now start ring. At every step, for every rank, we iterate through
  // segments with wraparound and send and recv from our neighbors and reduce
  // locally. At the i'th iteration, rank r, sends segment (r-i) and receives
  // segment (r-i-1).
  for (int i = 0; i < n - 1; i++) {
    const size_t send_seg_id = ((r - i) + n) % n;
    const size_t recv_seg_id = ((r - i - 1) + n) % n;

    T* segment_send = &(buffer[segment_starts[send_seg_id]]);

    MPI_REQUIRES_OK(MPI_Irecv(segment_recv, segment_sizes[recv_seg_id],
                              MPIType<T>(), recv_from, TAG_TENSOR,
                              MPI_COMM_WORLD, &recv_req));

    MPI_REQUIRES_OK(MPI_Send(segment_send, segment_sizes[send_seg_id],
                             MPIType<T>(), send_to, TAG_TENSOR,
                             MPI_COMM_WORLD));

    T* segment_update = &(buffer[segment_starts[recv_seg_id]]);

    // Wait for recv to complete before reduction
    MPI_REQUIRES_OK(MPI_Wait(&recv_req, &recv_status));

    const size_t recv_seg_size = segment_sizes[recv_seg_id];
    AccumulateTensorData<Device, T>(segment_update, segment_recv,
                                    recv_seg_size);
  }

  // Now start pipelined ring allgather. At every step, for every rank, we
  // iterate through segments with wraparound and send and recv from our
  // neighbors. At the i'th iteration, rank r, sends segment (r-i+1) and
  // receives segment (r-i).
  for (size_t i = 0; i < n - 1; ++i) {
    const size_t send_seg_id = ((r - i + 1) + n) % n;
    const size_t recv_seg_id = ((r - i) + n) % n;

    // Segment to send - at every iteration we send segment (r-i+1)
    T* segment_send = &(buffer[segment_starts[send_seg_id]]);

    // Segment to recv - at every iteration we receive segment (r-i)
    T* segment_recv = &(buffer[segment_starts[recv_seg_id]]);

    MPI_REQUIRES_OK(MPI_Sendrecv(
        segment_send, segment_sizes[send_seg_id], MPIType<T>(), send_to,
        TAG_TENSOR, segment_recv, segment_sizes[recv_seg_id], MPIType<T>(),
        recv_from, TAG_TENSOR, MPI_COMM_WORLD, &recv_status));
  }

  return Status::OK();
}

// Perform a ring allgather on a Tensor. Other ranks may allgather with a
// tensor which differs in the first dimension only; all other dimensions must
// be the same.
//
// For more information on the ring allgather, read the documentation for the
// ring allreduce, which includes a ring allgather.
template <typename Device, typename T>
Status RingAllgather(OpKernelContext* context, const Tensor* input,
                     const std::vector<size_t>& sizes, Tensor* output) {
  // Acquire MPI size and rank
  int n, r;
  MPI_REQUIRES_OK(MPI_Comm_size(MPI_COMM_WORLD, &n));
  MPI_REQUIRES_OK(MPI_Comm_rank(MPI_COMM_WORLD, &r));

  assert(sizes.size() == n);
  assert(input->dim_size(0) == sizes[r]);

  // Compute number of elements in every "row". We can't compute number of
  // elements in every chunks, because those chunks are variable length.
  size_t elements_per_row = 1;
  for (int i = 1; i < input->shape().dims(); i++) {
    elements_per_row *= input->dim_size(i);
  }

  // Copy data from input tensor to correct place in output tensor.
  std::vector<size_t> segment_starts(n);
  segment_starts[0] = 0;
  for (int i = 1; i < n; i++) {
    segment_starts[i] = segment_starts[i - 1] + elements_per_row * sizes[i - 1];
  }
  size_t offset = segment_starts[r];

  // Copy data to the right offset for this rank.
  T* buffer = (T*)output->tensor_data().data();
  CopyTensorData<Device>((void*)(buffer + offset),
                         (void*)input->tensor_data().data(),
                         elements_per_row * sizes[r] * sizeof(T));

  // Receive from your left neighbor with wrap-around
  const size_t recv_from = ((r - 1) + n) % n;

  // Send to your right neighbor with wrap-around
  const size_t send_to = (r + 1) % n;

  // Perform a ring allgather. At every step, for every rank, we iterate
  // through segments with wraparound and send and recv from our neighbors.
  // At the i'th iteration, rank r, sends segment (r-i) and receives segment
  // (r-1-i).
  MPI_Status recv_status;
  for (size_t i = 0; i < n - 1; ++i) {
    const size_t send_seg_id = ((r - i) + n) % n;
    const size_t recv_seg_id = ((r - i - 1) + n) % n;

    // Segment to send - at every iteration we send segment (r-i)
    size_t offset_send = segment_starts[send_seg_id];
    size_t rows_send = sizes[send_seg_id];
    T* segment_send = &(buffer[offset_send]);

    // Segment to recv - at every iteration we receive segment (r-1-i)
    size_t offset_recv = segment_starts[recv_seg_id];
    size_t rows_recv = sizes[recv_seg_id];
    T* segment_recv = &(buffer[offset_recv]);

    MPI_REQUIRES_OK(MPI_Sendrecv(
        segment_send, elements_per_row * rows_send, MPIType<T>(), send_to,
        TAG_TENSOR, segment_recv, elements_per_row * rows_recv, MPIType<T>(),
        recv_from, TAG_TENSOR, MPI_COMM_WORLD, &recv_status));
  }

  return Status::OK();
}

}  // namespace mpi_collectives
}  // namespace contrib
}  // namespace tensorflow

#endif  // TENSORFLOW_USE_MPI

#undef TENSORFLOW_CONTRIB_MPI_H_
#endif  // TENSORFLOW_CONTRIB_MPI_H_
