[Feature][Performance] Implement NCCL wrapper for communicating NodeEmbeddings...

[Feature][Performance] Implement NCCL wrapper for communicating NodeEmbeddings and sparse gradients. (#2825)

* Split NCCL wrapper from sparse optimizer and sparse embedding

* Add more unit tests for single node nccl

* Fix unit test for tf

* Switch to device histogram

* Fix histgram issues

* Finish migration to histogram

* Handle cases with zero send/recieve data

* Start on partition object

* Get compiling

* Updates

* Add unit tests

* Switch to partition object

* Fix linting issues

* Rename partition file

* Add python doc

* Fix python assert and finish doxygen comments

* Remove stubs for range based partition to satisfy pylint

* Wrap unit test in GPU only

* Wrap explicit cuda call in ifdef

* Merge with partition.py

* update docstrings

* Cleanup partition_op

* Add Workspace object

* Switch to using workspace object

* Move last remainder based function out of nccl_api

* Add error messages

* Update docs with examples

* Fix linting erros
Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>

CMakeLists.txt

@@ -142,6 +142,7 @@ file(GLOB DGL_SRC
   src/geometry/*.cc
   src/geometry/cpu/*.cc
   src/dataloading/*.cc
+  src/partition/*.cc
 )
 
 file(GLOB_RECURSE DGL_SRC_1
@@ -157,6 +158,10 @@ list(APPEND DGL_SRC ${DGL_SRC_1})
 if(USE_CUDA)
   dgl_config_cuda(DGL_CUDA_SRC)
   list(APPEND DGL_SRC ${DGL_CUDA_SRC})
+
+  include(cmake/util/FindNccl.cmake)
+  include_directories(${NCCL_INCLUDE_DIR})
+  list(APPEND DGL_LINKER_LIBS ${NCCL_LIBRARY})
 endif(USE_CUDA)
 
 if(USE_CUDA)

cmake/modules/CUDA.cmake

@@ -235,6 +235,7 @@ macro(dgl_config_cuda out_variable)
     src/array/cuda/*.cu
     src/kernel/cuda/*.cc
     src/kernel/cuda/*.cu
+    src/partition/cuda/*.cu
     src/runtime/cuda/*.cc
     src/runtime/cuda/*.cu
     src/geometry/cuda/*.cu

cmake/util/FindNccl.cmake

+#
+# 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.
+#
+# Tries to find NCCL headers and libraries.
+#
+# Usage of this module as follows:
+#
+#  find_package(NCCL)
+#
+# Variables used by this module, they can change the default behaviour and need
+# to be set before calling find_package:
+#
+#  NCCL_ROOT - When set, this path is inspected instead of standard library
+#              locations as the root of the NCCL installation.
+#              The environment variable NCCL_ROOT overrides this variable.
+#
+# This module defines
+#  Nccl_FOUND, whether nccl has been found
+#  NCCL_INCLUDE_DIR, directory containing header
+#  NCCL_LIBRARY, directory containing nccl library
+#  NCCL_LIB_NAME, nccl library name
+#  USE_NCCL_LIB_PATH, when set, NCCL_LIBRARY path is also inspected for the 
+#                     location of the nccl library. This would disable
+#                     switching between static and shared.
+#
+# This module assumes that the user has already called find_package(CUDA)
+#
+# This file is from https://github.com/dmlc/xgboost, with modifications to
+# check the version.
+
+if (NCCL_LIBRARY)
+  if(NOT USE_NCCL_LIB_PATH)
+    # Don't cache NCCL_LIBRARY to enable switching between static and shared.
+    unset(NCCL_LIBRARY CACHE)
+  endif(NOT USE_NCCL_LIB_PATH)
+endif()
+
+if (BUILD_WITH_SHARED_NCCL)
+  # libnccl.so
+  set(NCCL_LIB_NAME nccl)
+else ()
+  # libnccl_static.a
+  set(NCCL_LIB_NAME nccl_static)
+endif (BUILD_WITH_SHARED_NCCL)
+
+find_path(NCCL_INCLUDE_DIR
+  NAMES nccl.h
+  PATHS $ENV{NCCL_ROOT}/include ${NCCL_ROOT}/include)
+
+# make sure it has point to point support
+file(STRINGS "${NCCL_INCLUDE_DIR}/nccl.h" NCCL_VERSION_CODE REGEX "^#define[ \t]+NCCL_VERSION_CODE[ \t]+[0-9]+.*$" LIMIT_COUNT 1)
+string(REGEX REPLACE "^.*NCCL_VERSION_CODE[ \t]+([0-9]+).*$" "\\1" NCCL_VERSION "${NCCL_VERSION_CODE}")
+
+
+find_library(NCCL_LIBRARY
+  NAMES ${NCCL_LIB_NAME}
+  PATHS $ENV{NCCL_ROOT}/lib/ ${NCCL_ROOT}/lib)
+
+if ("${NCCL_VERSION}" LESS "2700")
+  message(FATAL_ERROR "Require nccl >= 2700, but found ${NCCL_LIBRARY}==${NCCL_VERSION}")
+else()
+  message(STATUS "Using nccl library: ${NCCL_LIBRARY} ${NCCL_VERSION}")
+endif()
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(Nccl DEFAULT_MSG
+                                  NCCL_INCLUDE_DIR NCCL_LIBRARY)
+
+mark_as_advanced(
+  NCCL_INCLUDE_DIR
+  NCCL_LIBRARY
+)

python/dgl/__init__.py

@@ -20,6 +20,7 @@ from . import random
 from . import sampling
 from . import dataloading
 from . import ops
+from . import cuda
 
 from ._ffi.runtime_ctypes import TypeCode
 from ._ffi.function import register_func, get_global_func, list_global_func_names, extract_ext_funcs

python/dgl/cuda/__init__.py

+""" CUDA wrappers """
+from . import nccl

python/dgl/cuda/nccl.py

+"""API creating NCCL communicators."""
+
+from .. import backend as F
+from .._ffi.function import _init_api
+
+_COMM_MODES_MAP = {
+    'remainder': 0
+}
+
+class UniqueId(object):
+    """ Class for allowing python code to create and communicate NCCL Unique
+        IDs, needed for creating communicators.
+    """
+    def __init__(self, id_str=None):
+        """ Create an object reference the current NCCL unique id.
+        """
+        if id_str:
+            if isinstance(id_str, bytes):
+                id_str = id_str.decode('utf-8')
+            self._handle = _CAPI_DGLNCCLUniqueIdFromString(id_str)
+        else:
+            self._handle = _CAPI_DGLNCCLGetUniqueId()
+
+    def get(self):
+        """ Get the C-handle for this object.
+        """
+        return self._handle
+
+    def __str__(self):
+        return _CAPI_DGLNCCLUniqueIdToString(self._handle)
+
+    def __repr__(self):
+        return "UniqueId[{}]".format(str(self))
+
+    def __eq__(self, other):
+        return str(self) == str(other)
+
+
+class Communicator(object):
+    """ High-level wrapper for NCCL communication.
+    """
+    def __init__(self, size, rank, unique_id):
+        """ Create a new NCCL communicator.
+
+            Parameters
+            ----------
+            size : int
+                The number of processes in the communicator.
+            rank : int
+                The rank of the current process in the communicator.
+            unique_id : NCCLUniqueId
+                The unique id of the root process (rank=0).
+
+            Examples
+            --------
+
+            >>> from dgl.cuda.nccl import Communicator, UniqueId
+
+            The root process will generate a unique NCCL id and communicate it
+            to the other processes.
+
+            >>> uid = UniqueId()
+            >>> store.set('nccl_root_id', str(uid))
+
+            And all other processes create unique ids from the root processes.
+
+            >>> uid = UniqueId(store.get('nccl_root_id'))
+
+            Then, all processes should create the communicator.
+
+            >>> comm = Communicator(world_size, rank, uid)
+        """
+        assert rank < size, "The rank of a process must be less than the " \
+            "size of the communicator."
+        self._handle = _CAPI_DGLNCCLCreateComm(size, rank, unique_id.get())
+        self._rank = rank
+        self._size = size
+
+    def sparse_all_to_all_push(self, idx, value, partition):
+        """ Perform an all-to-all-v operation, where by all processors send out
+            a set of indices and corresponding values. Indices and values,
+            corresponding to the current process, will copied into the output
+            arrays.
+
+            Parameters
+            ----------
+            idx : tensor
+                The 1D set of indices to send to other processors.
+            value : tensor
+                The multi-dimension set of values to send to other processors.
+                The 0th dimension must match that of `idx`.
+            partition : NDArrayPartition
+                The object containing information for assigning indices to
+                processors.
+
+            Returns
+            -------
+            tensor
+                The 1D tensor of the recieved indices.
+            tensor
+                The set of recieved values.
+
+            Examples
+            --------
+
+            To perform a sparse_all_to_all_push(), a partition object must be
+            provided. A partition of a homgeonous graph, where the vertices are
+            striped across processes can be generated via:
+
+            >>> from dgl.partition import NDArrayPartition
+            >>> part = NDArrayPartition(g.num_nodes(), comm.size(), mode='remainder' )
+
+            With this partition, each processor can send values to be associatd
+            with vertices in the graph. So if we have an array `global_idxs` of all of
+            the neighbors updated during mini-batch processing, and an array
+            `global_values` containing the new values associated with the neighbors,
+            we communicate them to the own processes via:
+
+            >>> my_idxs, my_values = comm.sparse_all_to_all_push(global_idxs, global_values, part)
+
+            This communication pattern is common when communicating gradient
+            updates for node embeddings.
+
+            Indices the current process owns, do not need to treated specially,
+            as internally they will be copied to the output array. If we have a
+            set of indices in process 0 '[0, 3, 8, 9, 10]` and for process 1
+            '[0, 2, 4, 5, 8, 8, 9]'. Using a remainder partition will result
+            indices for processe 0 of '[0, 8, 10, 0, 2, 4, 8, 8]', and for
+            process 1 of '[3, 9, 5, 9]'.
+        """
+        out_idx, out_value = _CAPI_DGLNCCLSparseAllToAllPush(
+            self.get(), F.zerocopy_to_dgl_ndarray(idx),
+            F.zerocopy_to_dgl_ndarray(value),
+            partition.get())
+        return (F.zerocopy_from_dgl_ndarray(out_idx),
+                F.zerocopy_from_dgl_ndarray(out_value))
+
+    def sparse_all_to_all_pull(self, req_idx, value, partition):
+        """ Perform an all-to-all-v operation, where by all processors request
+            the values corresponding to ther set of indices.
+
+            Parameters
+            ----------
+            req_idx : IdArray
+                The set of indices this processor is requesting.
+            value : NDArray
+                The multi-dimension set of values that can be requested from
+                this processor.
+            partition : NDArrayPartition
+                The object containing information for assigning indices to
+                processors.
+
+            Returns
+            -------
+            tensor
+                The set of recieved values, corresponding to `req_idx`.
+
+            Examples
+            --------
+
+            To perform a sparse_all_to_all_pull(), a partition object must be
+            provided. A partition of a homgeonous graph, where the vertices are
+            striped across processes can be generated via:
+
+            >>> from dgl.partition import NDArrayPartition
+            >>> part = NDArrayPartition(g.num_nodes(), comm.size(), mode='remainder' )
+
+            With this partition, each processor can request values/features
+            associated with vertices in the graph. So in the case where we have
+            a set of neighbors 'nbr_idxs' we need features for, and each process
+            has a tensor 'node_feat' storing the features of nodes it owns in
+            the partition, the features can be requested via:
+
+            >>> nbr_values = comm.sparse_all_to_all_pull(nbr_idxs, node_feat, part)
+
+            Then two the arrays 'nbr_idxs' and 'nbr_values' forms the sparse
+            set of features, where 'nbr_idxs[i]' is the global node id, and
+            'nbr_values[i]' is the feature vector for that node. This
+            communication pattern is useful for node features or node
+            embeddings.
+        """
+        out_value = _CAPI_DGLNCCLSparseAllToAllPull(
+            self.get(), F.zerocopy_to_dgl_ndarray(req_idx),
+            F.zerocopy_to_dgl_ndarray(value),
+            partition.get())
+        return F.zerocopy_from_dgl_ndarray(out_value)
+
+    def get(self):
+        """ Get the C-Handle for this object.
+        """
+        return self._handle
+
+    def rank(self):
+        """ Get the rank of this process in this communicator.
+
+            Returns
+            -------
+            int
+                The rank of this process.
+        """
+        return self._rank
+
+    def size(self):
+        """ Get the size of this communicator.
+
+            Returns
+            -------
+            int
+                The number of processes in this communicator.
+        """
+        return self._size
+
+_init_api("dgl.cuda.nccl")

python/dgl/partition.py

@@ -377,4 +377,48 @@ def metis_partition(g, k, extra_cached_hops=0, reshuffle=False,
     # Then we split the original graph into parts based on the METIS partitioning results.
     return partition_graph_with_halo(g, node_part, extra_cached_hops, reshuffle)[0]
 
+
+class NDArrayPartition(object):
+    """ Create a new partition of an NDArray. That is, an object which assigns
+    each row of an NDArray to a specific partition.
+
+    Parameters
+    ----------
+    array_size : int
+        The first dimension of the array being partitioned.
+    num_parts : int
+        The number of parts to divide the array into.
+    mode : String
+        The type of partition. Currently, the only valid value is 'remainder',
+        which assigns rows based on remainder when dividing the row id by the
+        number of parts (e.g., i % num_parts).
+    part_ranges : List
+        Currently unused.
+
+    Examples
+    --------
+
+    A partition of a homgeonous graph `g`, where the vertices are
+    striped across processes can be generated via:
+
+    >>> from dgl.partition import NDArrayPartition
+    >>> part = NDArrayPartition(g.num_nodes(), num_parts, mode='remainder' )
+    """
+    def __init__(self, array_size, num_parts, mode='remainder', part_ranges=None):
+        assert num_parts > 0, 'Invalid "num_parts", must be > 0.'
+        if mode == 'remainder':
+            assert part_ranges is None, 'When using remainder-based ' \
+                    'partitioning, "part_ranges" should not be specified.'
+            self._partition = _CAPI_DGLNDArrayPartitionCreateRemainderBased(
+                array_size, num_parts)
+        else:
+            assert False, 'Unknown partition mode "{}"'.format(mode)
+
+
+    def get(self):
+        """ Get the C-handle for this object.
+        """
+        return self._partition
+
+
 _init_api("dgl.partition")

src/array/array.cc

@@ -114,8 +114,6 @@ NDArray IndexSelect(NDArray array, IdArray index) {
   NDArray ret;
   CHECK_SAME_CONTEXT(array, index);
   CHECK_GE(array->ndim, 1) << "Only support array with at least 1 dimension";
-  CHECK_EQ(array->shape[0], array.NumElements()) << "Only support tensor"
-    << " whose first dimension equals number of elements, e.g. (5,), (5, 1)";
   CHECK_EQ(index->ndim, 1) << "Index array must be an 1D array.";
   ATEN_XPU_SWITCH_CUDA(array->ctx.device_type, XPU, "IndexSelect", {
     ATEN_DTYPE_SWITCH(array->dtype, DType, "values", {

src/array/cpu/array_index_select.cc

@@ -12,6 +12,9 @@ namespace impl {
 
 template<DLDeviceType XPU, typename DType, typename IdType>
 NDArray IndexSelect(NDArray array, IdArray index) {
+  CHECK_EQ(array->shape[0], array.NumElements()) << "Only support tensor"
+    << " whose first dimension equals number of elements, e.g. (5,), (5, 1)";
+
   const DType* array_data = static_cast<DType*>(array->data);
   const IdType* idx_data = static_cast<IdType*>(index->data);
   const int64_t arr_len = array->shape[0];

src/array/cuda/array_index_select.cu

@@ -5,6 +5,7 @@
  */
 #include <dgl/array.h>
 #include "../../runtime/cuda/cuda_common.h"
+#include "./array_index_select.cuh"
 #include "./utils.h"
 
 namespace dgl {
@@ -12,17 +13,6 @@ using runtime::NDArray;
 namespace aten {
 namespace impl {
 
-template <typename DType, typename IdType>
-__global__ void _IndexSelectKernel(const DType* array, const IdType* index,
-                                   int64_t length, DType* out) {
-  int tx = blockIdx.x * blockDim.x + threadIdx.x;
-  int stride_x = gridDim.x * blockDim.x;
-  while (tx < length) {
-    out[tx] = array[index[tx]];
-    tx += stride_x;
-  }
-}
-
 template<DLDeviceType XPU, typename DType, typename IdType>
 NDArray IndexSelect(NDArray array, IdArray index) {
   auto* thr_entry = runtime::CUDAThreadEntry::ThreadLocal();
@@ -30,14 +20,33 @@ NDArray IndexSelect(NDArray array, IdArray index) {
   const IdType* idx_data = static_cast<IdType*>(index->data);
   const int64_t arr_len = array->shape[0];
   const int64_t len = index->shape[0];
-  NDArray ret = NDArray::Empty({len}, array->dtype, array->ctx);
+  int64_t num_feat = 1;
+  std::vector<int64_t> shape{len};
+  for (int d = 1; d < array->ndim; ++d) {
+    num_feat *= array->shape[d];
+    shape.emplace_back(array->shape[d]);
+  }
+
+  NDArray ret = NDArray::Empty(shape, array->dtype, array->ctx);
   if (len == 0)
     return ret;
   DType* ret_data = static_cast<DType*>(ret->data);
-  const int nt = cuda::FindNumThreads(len);
-  const int nb = (len + nt - 1) / nt;
-  CUDA_KERNEL_CALL(_IndexSelectKernel, nb, nt, 0, thr_entry->stream,
-      array_data, idx_data, len, ret_data);
+
+  if (num_feat == 1) {
+      const int nt = cuda::FindNumThreads(len);
+      const int nb = (len + nt - 1) / nt;
+      CUDA_KERNEL_CALL(IndexSelectSingleKernel, nb, nt, 0, thr_entry->stream,
+          array_data, idx_data, len, ret_data);
+  } else {
+      dim3 block(256, 1);
+      while (static_cast<int64_t>(block.x) >= 2*num_feat) {
+          block.x /= 2;
+          block.y *= 2;
+      }
+      const dim3 grid((len+block.y-1)/block.y);
+      CUDA_KERNEL_CALL(IndexSelectMultiKernel, grid, block, 0, thr_entry->stream,
+          array_data, num_feat, idx_data, len, ret_data);
+  }
   return ret;
 }
 

src/array/cuda/array_index_select.cuh

+/*!
+ *  Copyright (c) 2021 by Contributors
+ * \file array/cpu/array_index_select.cuh
+ * \brief Array index select GPU kernel implementation
+ */
+
+#ifndef DGL_ARRAY_CUDA_ARRAY_INDEX_SELECT_CUH_
+#define DGL_ARRAY_CUDA_ARRAY_INDEX_SELECT_CUH_
+
+namespace dgl {
+namespace aten {
+namespace impl {
+
+template <typename DType, typename IdType>
+__global__ void IndexSelectSingleKernel(const DType* array, const IdType* index,
+                                   int64_t length, DType* out) {
+  int tx = blockIdx.x * blockDim.x + threadIdx.x;
+  int stride_x = gridDim.x * blockDim.x;
+  while (tx < length) {
+    out[tx] = array[index[tx]];
+    tx += stride_x;
+  }
+}
+
+template <typename DType, typename IdType>
+__global__ void IndexSelectMultiKernel(
+        const DType* const array, 
+        const int64_t num_feat,
+        const IdType* const index,
+        const int64_t length,
+        DType* const out) {
+  int64_t out_row = blockIdx.x*blockDim.y+threadIdx.y;
+
+  const int64_t stride = blockDim.y*gridDim.x;
+
+  while (out_row < length) {
+    int64_t col = threadIdx.x;
+    const int64_t in_row = index[out_row];
+    while (col < num_feat) {
+      out[out_row*num_feat+col] = array[in_row*num_feat+col];
+      col += blockDim.x;
+    }
+    out_row += stride;
+  }
+}
+
+}  // namespace impl
+}  // namespace aten
+}  // namespace dgl
+
+#endif

src/partition/cuda/partition_op.cu

+/*!
+ *  Copyright (c) 2021 by Contributors
+ * \file ndarray_partition.h
+ * \brief Operations on partition implemented in CUDA.
+ */
+
+#include "../partition_op.h"
+
+#include <dgl/runtime/device_api.h>
+
+#include "../../array/cuda/dgl_cub.cuh"
+#include "../../runtime/cuda/cuda_common.h"
+#include "../../runtime/workspace.h"
+
+using namespace dgl::runtime;
+
+namespace dgl {
+namespace partition {
+namespace impl {
+
+template<typename IdType> __global__ void _MapProcByRemainder(
+    const IdType * const index,
+    const int64_t num_index,
+    const int64_t num_proc,
+    IdType * const proc_id) {
+  const int64_t idx = blockDim.x*static_cast<int64_t>(blockIdx.x)+threadIdx.x;
+
+  if (idx < num_index) {
+    proc_id[idx] = index[idx] % num_proc;
+  }
+}
+
+template<typename IdType>
+__global__ void _MapProcByMaskRemainder(
+    const IdType * const index,
+    const int64_t num_index,
+    const IdType mask,
+    IdType * const proc_id) {
+  const int64_t idx = blockDim.x*static_cast<int64_t>(blockIdx.x)+threadIdx.x;
+
+  if (idx < num_index) {
+    proc_id[idx] = index[idx] & mask;
+  }
+}
+
+template<typename IdType>
+__global__ void _MapLocalIndexByRemainder(
+    const IdType * const in,
+    IdType * const out,
+    const int64_t num_items,
+    const int comm_size) {
+  const int64_t idx = threadIdx.x+blockDim.x*blockIdx.x;
+
+  if (idx < num_items) {
+    out[idx] = in[idx] / comm_size;
+  }
+}
+
+template <DLDeviceType XPU, typename IdType>
+std::pair<IdArray, NDArray>
+GeneratePermutationFromRemainder(
+        int64_t array_size,
+        int num_parts,
+        IdArray in_idx) {
+  std::pair<IdArray, NDArray> result;
+
+  const auto& ctx = in_idx->ctx;
+  auto device = DeviceAPI::Get(ctx);
+  cudaStream_t stream = CUDAThreadEntry::ThreadLocal()->stream;
+
+  const int64_t num_in = in_idx->shape[0];
+
+  CHECK_GE(num_parts, 1) << "The number of partitions (" << num_parts <<
+      ") must be at least 1.";
+  if (num_parts == 1) {
+    // no permutation
+    result.first = aten::Range(0, num_in, sizeof(IdType)*8, ctx);
+    result.second = aten::Full(num_in, num_parts, sizeof(int64_t)*8, ctx);
+
+    return result;
+  }
+
+  result.second = aten::Full(0, num_parts, sizeof(int64_t)*8, ctx);
+  int64_t * out_counts = static_cast<int64_t*>(result.second->data);
+  if (num_in == 0) {
+    // now that we've zero'd out_counts, nothing left to do for an empty
+    // mapping
+    return result;
+  }
+
+  const int64_t part_bits =
+      static_cast<int64_t>(std::ceil(std::log2(num_parts)));
+
+  // First, generate a mapping of indexes to processors
+  Workspace<IdType> proc_id_in(device, ctx, num_in);
+  {
+    const dim3 block(256);
+    const dim3 grid((num_in+block.x-1)/block.x);
+
+    if (num_parts < (1 << part_bits)) {
+      // num_parts is not a power of 2
+      CUDA_KERNEL_CALL(_MapProcByRemainder, grid, block, 0, stream,
+          static_cast<const IdType*>(in_idx->data),
+          num_in,
+          num_parts,
+          proc_id_in.get());
+    } else {
+      // num_parts is a power of 2
+      CUDA_KERNEL_CALL(_MapProcByMaskRemainder, grid, block, 0, stream,
+          static_cast<const IdType*>(in_idx->data),
+          num_in,
+          static_cast<IdType>(num_parts-1),  // bit mask
+          proc_id_in.get());
+    }
+  }
+
+  // then create a permutation array that groups processors together by
+  // performing a radix sort
+  Workspace<IdType> proc_id_out(device, ctx, num_in);
+  result.first = aten::NewIdArray(num_in, ctx, sizeof(IdType)*8);
+  IdType * perm_out = static_cast<IdType*>(result.first->data);
+  {
+    IdArray perm_in = aten::Range(0, num_in, sizeof(IdType)*8, ctx);
+
+    size_t sort_workspace_size;
+    CUDA_CALL(cub::DeviceRadixSort::SortPairs(nullptr, sort_workspace_size,
+        proc_id_in.get(), proc_id_out.get(), static_cast<IdType*>(perm_in->data), perm_out,
+        num_in, 0, part_bits, stream));
+
+    Workspace<void> sort_workspace(device, ctx, sort_workspace_size);
+    CUDA_CALL(cub::DeviceRadixSort::SortPairs(sort_workspace.get(), sort_workspace_size,
+        proc_id_in.get(), proc_id_out.get(), static_cast<IdType*>(perm_in->data), perm_out,
+        num_in, 0, part_bits, stream));
+  }
+  // explicitly free so workspace can be re-used
+  proc_id_in.free();
+
+  // perform a histogram and then prefixsum on the sorted proc_id vector
+
+  // Count the number of values to be sent to each processor
+  {
+    using AtomicCount = unsigned long long; // NOLINT
+    static_assert(sizeof(AtomicCount) == sizeof(*out_counts),
+        "AtomicCount must be the same width as int64_t for atomicAdd "
+        "in cub::DeviceHistogram::HistogramEven() to work");
+
+    // TODO(dlasalle): Once https://github.com/NVIDIA/cub/pull/287 is merged,
+    // add a compile time check against the cub version to allow
+    // num_in > (2 << 31).
+    CHECK(num_in < static_cast<int64_t>(std::numeric_limits<int>::max())) <<
+        "number of values to insert into histogram must be less than max "
+        "value of int.";
+
+    size_t hist_workspace_size;
+    CUDA_CALL(cub::DeviceHistogram::HistogramEven(
+        nullptr,
+        hist_workspace_size,
+        proc_id_out.get(),
+        reinterpret_cast<AtomicCount*>(out_counts),
+        num_parts+1,
+        static_cast<IdType>(0),
+        static_cast<IdType>(num_parts+1),
+        static_cast<int>(num_in),
+        stream));
+
+    Workspace<void> hist_workspace(device, ctx, hist_workspace_size);
+    CUDA_CALL(cub::DeviceHistogram::HistogramEven(
+        hist_workspace.get(),
+        hist_workspace_size,
+        proc_id_out.get(),
+        reinterpret_cast<AtomicCount*>(out_counts),
+        num_parts+1,
+        static_cast<IdType>(0),
+        static_cast<IdType>(num_parts+1),
+        static_cast<int>(num_in),
+        stream));
+  }
+
+  return result;
+}
+
+
+template std::pair<IdArray, IdArray>
+GeneratePermutationFromRemainder<kDLGPU, int32_t>(
+        int64_t array_size,
+        int num_parts,
+        IdArray in_idx);
+template std::pair<IdArray, IdArray>
+GeneratePermutationFromRemainder<kDLGPU, int64_t>(
+        int64_t array_size,
+        int num_parts,
+        IdArray in_idx);
+
+
+template <DLDeviceType XPU, typename IdType>
+IdArray MapToLocalFromRemainder(
+    const int num_parts,
+    IdArray global_idx) {
+  const auto& ctx = global_idx->ctx;
+  cudaStream_t stream = CUDAThreadEntry::ThreadLocal()->stream;
+
+  IdArray local_idx = aten::NewIdArray(global_idx->shape[0], ctx,
+      sizeof(IdType)*8);
+
+  const dim3 block(128);
+  const dim3 grid((global_idx->shape[0] +block.x-1)/block.x);
+
+  CUDA_KERNEL_CALL(
+      _MapLocalIndexByRemainder,
+      grid,
+      block,
+      0,
+      stream,
+      static_cast<const IdType*>(global_idx->data),
+      static_cast<IdType*>(local_idx->data),
+      global_idx->shape[0],
+      num_parts);
+
+  return local_idx;
+}
+
+template IdArray
+MapToLocalFromRemainder<kDLGPU, int32_t>(
+        int num_parts,
+        IdArray in_idx);
+template IdArray
+MapToLocalFromRemainder<kDLGPU, int64_t>(
+        int num_parts,
+        IdArray in_idx);
+
+
+}  // namespace impl
+}  // namespace partition
+}  // namespace dgl

src/partition/ndarray_partition.cc

+/*!
+ *  Copyright (c) 2021 by Contributors
+ * \file ndarray_partition.cc
+ * \brief DGL utilities for working with the partitioned NDArrays
+ */
+
+#include "ndarray_partition.h"
+
+#include <dgl/runtime/registry.h>
+#include <dgl/runtime/packed_func.h>
+#include <utility>
+#include <memory>
+
+#include "partition_op.h"
+
+using namespace dgl::runtime;
+
+namespace dgl {
+namespace partition {
+
+NDArrayPartition::NDArrayPartition(
+    const int64_t array_size, const int num_parts) :
+  array_size_(array_size),
+  num_parts_(num_parts) {
+}
+
+int64_t NDArrayPartition::ArraySize() const {
+  return array_size_;
+}
+
+int NDArrayPartition::NumParts() const {
+  return num_parts_;
+}
+
+
+class RemainderPartition : public NDArrayPartition {
+ public:
+  RemainderPartition(
+      const int64_t array_size, const int num_parts) :
+    NDArrayPartition(array_size, num_parts) {
+    // do nothing
+  }
+
+  std::pair<IdArray, NDArray>
+  GeneratePermutation(
+      IdArray in_idx) const override {
+    auto ctx = in_idx->ctx;
+
+#ifdef DGL_USE_CUDA
+    if (ctx.device_type == kDLGPU) {
+      ATEN_ID_TYPE_SWITCH(in_idx->dtype, IdType, {
+        return impl::GeneratePermutationFromRemainder<kDLGPU, IdType>(
+            ArraySize(), NumParts(), in_idx);
+      });
+    }
+#endif
+
+    LOG(FATAL) << "Remainder based partitioning for the CPU is not yet "
+        "implemented.";
+    // should be unreachable
+    return std::pair<IdArray, NDArray>{};
+  }
+
+  IdArray MapToLocal(
+      IdArray in_idx) const override {
+    auto ctx = in_idx->ctx;
+#ifdef DGL_USE_CUDA
+    if (ctx.device_type == kDLGPU) {
+      ATEN_ID_TYPE_SWITCH(in_idx->dtype, IdType, {
+        return impl::MapToLocalFromRemainder<kDLGPU, IdType>(
+            NumParts(), in_idx);
+      });
+    }
+#endif
+
+    LOG(FATAL) << "Remainder based partitioning for the CPU is not yet "
+        "implemented.";
+    // should be unreachable
+    return IdArray{};
+  }
+};
+
+NDArrayPartitionRef CreatePartitionRemainderBased(
+    const int64_t array_size,
+    const int num_parts) {
+  return NDArrayPartitionRef(std::make_shared<RemainderPartition>(
+          array_size, num_parts));
+}
+
+DGL_REGISTER_GLOBAL("partition._CAPI_DGLNDArrayPartitionCreateRemainderBased")
+.set_body([] (DGLArgs args, DGLRetValue* rv) {
+  int64_t array_size = args[0];
+  int num_parts = args[1];
+
+  *rv = CreatePartitionRemainderBased(array_size, num_parts);
+});
+
+}  // namespace partition
+}  // namespace dgl

src/partition/ndarray_partition.h

+/*!
+ *  Copyright (c) 2021 by Contributors
+ * \file ndarray_partition.h 
+ * \brief DGL utilities for working with the partitioned NDArrays 
+ */
+
+
+#ifndef DGL_PARTITION_NDARRAY_PARTITION_H_
+#define DGL_PARTITION_NDARRAY_PARTITION_H_
+
+#include <dgl/runtime/object.h>
+#include <dgl/array.h>
+#include <utility>
+
+namespace dgl {
+namespace partition {
+
+/**
+ * @brief The top-level partition class. Specific types of partitions should be
+ * sub-classes of this.
+ */
+class NDArrayPartition : public runtime::Object {
+ public:
+  /**
+   * @brief Create a new partition.
+   *
+   * @param array_size The first dimension of the partitioned array.
+   * @param num_parts The number parts to the array is split into.
+   */
+  NDArrayPartition(
+      int64_t array_size,
+      int num_parts);
+
+  virtual ~NDArrayPartition() = default;
+
+  static constexpr const char* _type_key = "partition.NDArrayPartition";
+
+  DGL_DECLARE_OBJECT_TYPE_INFO(NDArrayPartition, Object);
+
+  /**
+   * @brief Create a mapping for the given indices to different partitions,
+   * and a count of the number of indices per part.
+   *
+   * A prefix-sum of the counts, can be used to select the continuous sets of
+   * indices destined for each part.
+   *
+   * @param in_idx The input indices to map.
+   *
+   * @return A pair containing 0) the permutation to re-order the indices by
+   * partition, 1) the number of indices per partition (int64_t).
+   */
+  virtual std::pair<IdArray, NDArray>
+  GeneratePermutation(
+      IdArray in_idx) const = 0;
+
+  /**
+   * @brief Generate the local indices (the numbering within each processor)
+   * from a set of global indices.
+   *
+   * @param in_idx The global indices.
+   *
+   * @return The local indices.
+   */
+  virtual IdArray MapToLocal(
+      IdArray in_idx) const = 0;
+
+  /**
+   * @brief Get the first dimension of the partitioned array.
+   *
+   * @return The size.
+   */
+  int64_t ArraySize() const;
+
+  /**
+   * @brief Get the number of parts in this partition.
+   *
+   * @return The number of parts.
+   */
+  int NumParts() const;
+
+ private:
+  int64_t array_size_;
+  int num_parts_;
+};
+
+DGL_DEFINE_OBJECT_REF(NDArrayPartitionRef, NDArrayPartition);
+
+/**
+ * @brief Create a new partition object, using the remainder of the row id
+ * divided by the number of parts, to assign rows to parts.
+ *
+ * @param array_size The first dimension of the array.
+ * @param num_parts The number of parts.
+ *
+ * @return The partition object.
+ */
+NDArrayPartitionRef CreatePartitionRemainderBased(
+    int64_t array_size,
+    int num_parts);
+
+}  // namespace partition
+}  // namespace dgl
+
+#endif  // DGL_PARTITION_NDARRAY_PARTITION_H_

src/partition/partition_op.h

+/*!
+ *  Copyright (c) 2021 by Contributors
+ * \file ndarray_partition.h
+ * \brief DGL utilities for working with the partitioned NDArrays
+ */
+
+
+#ifndef DGL_PARTITION_PARTITION_OP_H_
+#define DGL_PARTITION_PARTITION_OP_H_
+
+#include <dgl/array.h>
+#include <utility>
+
+namespace dgl {
+namespace partition {
+namespace impl {
+
+/**
+ * @brief Create a permutation that groups indices by the part id.
+ *
+ * @tparam XPU The type of device to run on.
+ * @tparam IdType The type of the index.
+ * @param array_size The total size of the partitioned array.
+ * @param num_parts The number parts the array id divided into.
+ * @param in_idx The array of indices to group by part id.
+ *
+ * @return The permutation to group the indices by part id, and the number of
+ * indices in each part.
+ */
+template <DLDeviceType XPU, typename IdType>
+std::pair<IdArray, IdArray>
+GeneratePermutationFromRemainder(
+        int64_t array_size,
+        int num_parts,
+        IdArray in_idx);
+
+/**
+ * @brief Generate the set of local indices from the global indices, using
+ * remainder. That is, for each index `i` in `global_idx`, the local index
+ * is computed as `global_idx[i] / num_parts`.
+ *
+ * @tparam XPU The type of device to run on.
+ * @tparam IdType The type of the index.
+ * @param num_parts The number parts the array id divided into.
+ * @param global_idx The array of global indices to map.
+ *
+ * @return The array of local indices.
+ */
+template <DLDeviceType XPU, typename IdType>
+IdArray MapToLocalFromRemainder(
+    int num_parts,
+    IdArray global_idx);
+
+
+}  // namespace impl
+}  // namespace partition
+}  // namespace dgl
+
+#endif  // DGL_PARTITION_PARTITION_OP_H_

src/runtime/cuda/cuda_device_api.cc

@@ -190,10 +190,10 @@ class CUDADeviceAPI final : public DeviceAPI {
                       size_t size,
                       cudaMemcpyKind kind,
                       cudaStream_t stream) {
-    if (stream != 0) {
-      CUDA_CALL(cudaMemcpyAsync(to, from, size, kind, stream));
-    } else {
-      CUDA_CALL(cudaMemcpy(to, from, size, kind));
+    CUDA_CALL(cudaMemcpyAsync(to, from, size, kind, stream));
+    if (stream == 0 && kind == cudaMemcpyDeviceToHost) {
+      // only wait for the copy, when it's on the default stream, and it's to host memory
+      CUDA_CALL(cudaStreamSynchronize(stream));
     }
   }
 };

src/runtime/cuda/nccl_api.h

+/*!
+ *  Copyright (c) 2021 by Contributors
+ * \file nccl_api.h
+ * \brief Wrapper around NCCL routines. 
+ */
+
+
+#ifndef DGL_RUNTIME_CUDA_NCCL_API_H_
+#define DGL_RUNTIME_CUDA_NCCL_API_H_
+
+#include "nccl.h"
+
+#include <dgl/runtime/object.h>
+#include <string>
+
+namespace dgl {
+namespace runtime {
+namespace cuda {
+
+class NCCLUniqueId : public runtime::Object {
+ public:
+  NCCLUniqueId();
+
+  static constexpr const char* _type_key = "cuda.NCCLUniqueId";
+  DGL_DECLARE_OBJECT_TYPE_INFO(NCCLUniqueId, Object);
+
+  ncclUniqueId Get() const;
+
+  std::string ToString() const;
+
+  void FromString(const std::string& str);
+
+ private:
+  ncclUniqueId id_;
+};
+
+DGL_DEFINE_OBJECT_REF(NCCLUniqueIdRef, NCCLUniqueId);
+
+class NCCLCommunicator : public runtime::Object {
+ public:
+  NCCLCommunicator(
+      int size,
+      int rank,
+      ncclUniqueId id);
+
+  ~NCCLCommunicator();
+
+  // disable copying
+  NCCLCommunicator(const NCCLCommunicator& other) = delete;
+  NCCLCommunicator& operator=(
+      const NCCLCommunicator& other);
+
+  ncclComm_t Get();
+
+  /**
+   * @brief Perform an all-to-all communication.
+   *
+   * @param send The continous array of data to send.
+   * @param recv The continous array of data to recieve.
+   * @param count The size of data to send to each rank.
+   * @param stream The stream to operate on.
+   */
+  template<typename IdType>
+  void AllToAll(
+      const IdType * send,
+      IdType * recv,
+      int64_t count,
+      cudaStream_t stream);
+
+  /**
+   * @brief Perform an all-to-all variable sized communication.
+   *
+   * @tparam DType The type of value to send.
+   * @param send The arrays of data to send.
+   * @param send_prefix The prefix of each array to send.
+   * @param recv The arrays of data to recieve.
+   * @param recv_prefix The prefix of each array to recieve.
+   * @param type The type of data to send.
+   * @param stream The stream to operate on.
+   */
+  template<typename DType>
+  void AllToAllV(
+      const DType * const send,
+      const int64_t * send_prefix,
+      DType * const recv,
+      const int64_t * recv_prefix,
+      cudaStream_t stream);
+
+  /**
+   * @brief Perform an all-to-all with sparse data (idx and value pairs). By
+   * necessity, the sizes of each message are variable.
+   *
+   * @tparam IdType The type of index.
+   * @tparam DType The type of value.
+   * @param send_idx The set of indexes to send on the device.
+   * @param send_value The set of values to send on the device.
+   * @param num_feat The number of values per index.
+   * @param send_prefix The exclusive prefix sum of elements to send on the
+   * host.
+   * @param recv_idx The set of indexes to recieve on the device.
+   * @param recv_value The set of values to recieve on the device.
+   * @param recv_prefix The exclusive prefix sum of the number of elements to
+   * recieve on the host.
+   * @param stream The stream to communicate on.
+   */
+  template<typename IdType, typename DType>
+  void SparseAllToAll(
+          const IdType * send_idx,
+          const DType * send_value,
+          const int64_t num_feat,
+          const int64_t * send_prefix,
+          IdType * recv_idx,
+          DType * recv_value,
+          const int64_t * recv_prefix,
+          cudaStream_t stream);
+
+  int size() const;
+
+  int rank() const;
+
+  static constexpr const char* _type_key = "cuda.NCCLCommunicator";
+  DGL_DECLARE_OBJECT_TYPE_INFO(NCCLCommunicator, Object);
+
+ private:
+  ncclComm_t comm_;
+  int size_;
+  int rank_;
+};
+
+DGL_DEFINE_OBJECT_REF(NCCLCommunicatorRef, NCCLCommunicator);
+
+}  // namespace cuda
+}  // namespace runtime
+}  // namespace dgl
+
+#endif  // DGL_RUNTIME_CUDA_NCCL_API_H_

src/runtime/workspace.h

+/*!
+ *  Copyright (c) 2021 by Contributors
+ * \file ndarray_partition.h
+ * \brief Operations on partition implemented in CUDA.
+ */
+
+
+#ifndef DGL_RUNTIME_WORKSPACE_H_
+#define DGL_RUNTIME_WORKSPACE_H_
+
+#include <dgl/runtime/device_api.h>
+#include <cassert>
+
+namespace dgl {
+namespace runtime {
+
+template<typename T>
+class Workspace {
+ public:
+  Workspace(DeviceAPI* device, DGLContext ctx, const size_t size) :
+      device_(device),
+      ctx_(ctx),
+      ptr_(static_cast<T*>(device_->AllocWorkspace(ctx_, sizeof(T)*size))) {
+  }
+
+  ~Workspace() {
+    if (*this) {
+      free();
+    }
+  }
+
+  operator bool() const {
+    return ptr_ != nullptr;
+  }
+
+  T * get() {
+    assert(*this);
+    return ptr_;
+  }
+
+  T const * get() const {
+    assert(*this);
+    return ptr_;
+  }
+
+  void free() {
+    assert(*this);
+    device_->FreeWorkspace(ctx_, ptr_);
+    ptr_ = nullptr;
+  }
+
+ private:
+  DeviceAPI* device_;
+  DGLContext ctx_;
+  T * ptr_;
+};
+
+template<>
+class Workspace<void> {
+ public:
+  Workspace(DeviceAPI* device, DGLContext ctx, const size_t size) :
+      device_(device),
+      ctx_(ctx),
+      ptr_(static_cast<void*>(device_->AllocWorkspace(ctx_, size))) {
+  }
+
+  ~Workspace() {
+    if (*this) {
+      free();
+    }
+  }
+
+  operator bool() const {
+    return ptr_ != nullptr;
+  }
+
+  void * get() {
+    assert(*this);
+    return ptr_;
+  }
+
+  void const * get() const {
+    assert(*this);
+    return ptr_;
+  }
+
+  void free() {
+    assert(*this);
+    device_->FreeWorkspace(ctx_, ptr_);
+    ptr_ = nullptr;
+  }
+
+ private:
+  DeviceAPI* device_;
+  DGLContext ctx_;
+  void * ptr_;
+};
+
+}  // namespace runtime
+}  // namespace dgl
+
+#endif  // DGL_RUNTIME_WORKSPACE_H_

tests/compute/test_nccl.py

+from dgl.cuda import nccl
+from dgl.partition import NDArrayPartition
+import unittest
+import backend as F
+
+
+def gen_test_id():
+    return '{:0256x}'.format(78236728318467363)
+
+@unittest.skipIf(F._default_context_str == 'cpu', reason="NCCL only runs on GPU.")
+def test_nccl_id():
+    nccl_id = nccl.UniqueId()
+    text = str(nccl_id)
+    nccl_id2 = nccl.UniqueId(id_str=text)
+
+    assert nccl_id == nccl_id2
+
+    nccl_id2 = nccl.UniqueId(gen_test_id())
+
+    assert nccl_id2 != nccl_id
+
+    nccl_id3 = nccl.UniqueId(str(nccl_id2))
+
+    assert nccl_id2 == nccl_id3
+
+
+@unittest.skipIf(F._default_context_str == 'cpu', reason="NCCL only runs on GPU.")
+def test_nccl_sparse_push_single():
+    nccl_id = nccl.UniqueId()
+    comm = nccl.Communicator(1, 0, nccl_id)
+
+    index = F.randint([10000], F.int32, F.ctx(), 0, 10000)
+    value = F.uniform([10000, 100], F.float32, F.ctx(), -1.0, 1.0)
+
+    part = NDArrayPartition(10000, 1, 'remainder')
+
+    ri, rv = comm.sparse_all_to_all_push(index, value, part)
+    assert F.array_equal(ri, index)
+    assert F.array_equal(rv, value)
+
+@unittest.skipIf(F._default_context_str == 'cpu', reason="NCCL only runs on GPU.")
+def test_nccl_sparse_pull_single():
+    nccl_id = nccl.UniqueId()
+    comm = nccl.Communicator(1, 0, nccl_id)
+
+    req_index = F.randint([10000], F.int64, F.ctx(), 0, 100000)
+    value = F.uniform([100000, 100], F.float32, F.ctx(), -1.0, 1.0)
+
+    part = NDArrayPartition(100000, 1, 'remainder')
+
+    rv = comm.sparse_all_to_all_pull(req_index, value, part)
+    exp_rv = F.gather_row(value, req_index)
+    assert F.array_equal(rv, exp_rv)
+
+
+if __name__ == '__main__':
+    test_nccl_id()
+    test_nccl_sparse_push_single()
+    test_nccl_sparse_pull_single()