fix memory type

* [jvm-packages] Fix POM for xgboost-jvm metapackage

* Add script for updating the Scala version

.github/workflows/jvm_tests.yml

@@ -51,14 +51,14 @@ jobs:
       id: extract_branch
       if: |
         (github.ref == 'refs/heads/master' || contains(github.ref, 'refs/heads/release_')) &&
-        matrix.os == 'windows-latest'
+        (matrix.os == 'windows-latest' || matrix.os == 'macos-11')
 
     - name: Publish artifact xgboost4j.dll to S3
       run: |
         cd lib/
         Rename-Item -Path xgboost4j.dll -NewName xgboost4j_${{ github.sha }}.dll
         dir
-        python -m awscli s3 cp xgboost4j_${{ github.sha }}.dll s3://xgboost-nightly-builds/${{ steps.extract_branch.outputs.branch }}/ --acl public-read
+        python -m awscli s3 cp xgboost4j_${{ github.sha }}.dll s3://xgboost-nightly-builds/${{ steps.extract_branch.outputs.branch }}/libxgboost4j/ --acl public-read
       if: |
         (github.ref == 'refs/heads/master' || contains(github.ref, 'refs/heads/release_')) &&
         matrix.os == 'windows-latest'
@@ -66,6 +66,19 @@ jobs:
         AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID_IAM_S3_UPLOADER }}
         AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY_IAM_S3_UPLOADER }}
 
+    - name: Publish artifact libxgboost4j.dylib to S3
+      run: |
+        cd lib/
+        mv -v libxgboost4j.dylib libxgboost4j_${{ github.sha }}.dylib
+        ls
+        python -m awscli s3 cp libxgboost4j_${{ github.sha }}.dylib s3://xgboost-nightly-builds/${{ steps.extract_branch.outputs.branch }}/libxgboost4j/ --acl public-read
+      if: |
+        (github.ref == 'refs/heads/master' || contains(github.ref, 'refs/heads/release_')) &&
+        matrix.os == 'macos-11'
+      env:
+        AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID_IAM_S3_UPLOADER }}
+        AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY_IAM_S3_UPLOADER }}
+
 
     - name: Test XGBoost4J (Core, Spark, Examples)
       run: |

.github/workflows/r_tests.yml

@@ -25,7 +25,7 @@ jobs:
       with:
         submodules: 'true'
 
-    - uses: r-lib/actions/setup-r@50d1eae9b8da0bb3f8582c59a5b82225fa2fe7f2 # v2.3.1
+    - uses: r-lib/actions/setup-r@11a22a908006c25fe054c4ef0ac0436b1de3edbe # v2.6.4
       with:
         r-version: ${{ matrix.config.r }}
 
@@ -64,7 +64,7 @@ jobs:
       with:
         submodules: 'true'
 
-    - uses: r-lib/actions/setup-r@50d1eae9b8da0bb3f8582c59a5b82225fa2fe7f2 # v2.3.1
+    - uses: r-lib/actions/setup-r@11a22a908006c25fe054c4ef0ac0436b1de3edbe # v2.6.4
       with:
         r-version: ${{ matrix.config.r }}
 

.gitmodules

@@ -5,3 +5,6 @@
 [submodule "gputreeshap"]
 	path = gputreeshap
 	url = https://github.com/rapidsai/gputreeshap.git
+[submodule "rocgputreeshap"]
+	path = rocgputreeshap
+	url = https://github.com/ROCmSoftwarePlatform/rocgputreeshap

.readthedocs.yaml

@@ -32,4 +32,3 @@ formats:
 python:
   install:
    - requirements: doc/requirements.txt
-  system_packages: true

CMakeLists.txt

@@ -1,5 +1,5 @@
 cmake_minimum_required(VERSION 3.18 FATAL_ERROR)
-project(xgboost LANGUAGES CXX C VERSION 2.0.0)
+project(xgboost LANGUAGES CXX C VERSION 2.0.3)
 include(cmake/Utils.cmake)
 list(APPEND CMAKE_MODULE_PATH "${xgboost_SOURCE_DIR}/cmake/modules")
 cmake_policy(SET CMP0022 NEW)
@@ -58,7 +58,7 @@ option(ENABLE_ALL_WARNINGS "Enable all compiler warnings. Only effective for GCC
 option(LOG_CAPI_INVOCATION "Log all C API invocations for debugging" OFF)
 option(GOOGLE_TEST "Build google tests" OFF)
 option(USE_DMLC_GTEST "Use google tests bundled with dmlc-core submodule" OFF)
-option(USE_DEVICE_DEBUG "Generate CUDA device debug info." OFF)
+option(USE_DEVICE_DEBUG "Generate CUDA/HIP device debug info." OFF)
 option(USE_NVTX "Build with cuda profiling annotations. Developers only." OFF)
 set(NVTX_HEADER_DIR "" CACHE PATH "Path to the stand-alone nvtx header")
 option(RABIT_MOCK "Build rabit with mock" OFF)
@@ -71,6 +71,10 @@ option(USE_NCCL  "Build with NCCL to enable distributed GPU support." OFF)
 option(BUILD_WITH_SHARED_NCCL "Build with shared NCCL library." OFF)
 set(GPU_COMPUTE_VER "" CACHE STRING
   "Semicolon separated list of compute versions to be built against, e.g. '35;61'")
+## HIP
+option(USE_HIP  "Build with GPU acceleration" OFF)
+option(USE_RCCL "Build with RCCL to enable distributed GPU support." OFF)
+option(BUILD_WITH_SHARED_RCCL "Build with shared RCCL library." OFF)
 ## Copied From dmlc
 option(USE_HDFS "Build with HDFS support" OFF)
 option(USE_AZURE "Build with AZURE support" OFF)
@@ -93,6 +97,7 @@ option(ADD_PKGCONFIG "Add xgboost.pc into system." ON)
 if (USE_DEBUG_OUTPUT AND (NOT (CMAKE_BUILD_TYPE MATCHES Debug)))
   message(SEND_ERROR "Do not enable `USE_DEBUG_OUTPUT' with release build.")
 endif (USE_DEBUG_OUTPUT AND (NOT (CMAKE_BUILD_TYPE MATCHES Debug)))
+
 if (USE_NCCL AND NOT (USE_CUDA))
   message(SEND_ERROR "`USE_NCCL` must be enabled with `USE_CUDA` flag.")
 endif (USE_NCCL AND NOT (USE_CUDA))
@@ -102,6 +107,17 @@ endif (USE_DEVICE_DEBUG AND NOT (USE_CUDA))
 if (BUILD_WITH_SHARED_NCCL AND (NOT USE_NCCL))
   message(SEND_ERROR "Build XGBoost with -DUSE_NCCL=ON to enable BUILD_WITH_SHARED_NCCL.")
 endif (BUILD_WITH_SHARED_NCCL AND (NOT USE_NCCL))
+
+if (USE_RCCL AND NOT (USE_HIP))
+  message(SEND_ERROR "`USE_RCCL` must be enabled with `USE_HIP` flag.")
+endif (USE_RCCL AND NOT (USE_HIP))
+if (USE_DEVICE_DEBUG AND NOT (USE_HIP))
+  message(SEND_ERROR "`USE_DEVICE_DEBUG` must be enabled with `USE_HIP` flag.")
+endif (USE_DEVICE_DEBUG AND NOT (USE_HIP))
+if (BUILD_WITH_SHARED_RCCL AND (NOT USE_RCCL))
+  message(SEND_ERROR "Build XGBoost with -DUSE_RCCL=ON to enable BUILD_WITH_SHARED_RCCL.")
+endif (BUILD_WITH_SHARED_RCCL AND (NOT USE_RCCL))
+
 if (JVM_BINDINGS AND R_LIB)
   message(SEND_ERROR "`R_LIB' is not compatible with `JVM_BINDINGS' as they both have customized configurations.")
 endif (JVM_BINDINGS AND R_LIB)
@@ -115,9 +131,15 @@ endif (USE_AVX)
 if (PLUGIN_LZ4)
   message(SEND_ERROR  "The option 'PLUGIN_LZ4' is removed from XGBoost.")
 endif (PLUGIN_LZ4)
+
 if (PLUGIN_RMM AND NOT (USE_CUDA))
   message(SEND_ERROR "`PLUGIN_RMM` must be enabled with `USE_CUDA` flag.")
 endif (PLUGIN_RMM AND NOT (USE_CUDA))
+
+if (PLUGIN_RMM AND NOT (USE_HIP))
+  message(SEND_ERROR "`PLUGIN_RMM` must be enabled with `USE_HIP` flag.")
+endif (PLUGIN_RMM AND NOT (USE_HIP))
+
 if (PLUGIN_RMM AND NOT ((CMAKE_CXX_COMPILER_ID STREQUAL "Clang") OR (CMAKE_CXX_COMPILER_ID STREQUAL "GNU")))
   message(SEND_ERROR "`PLUGIN_RMM` must be used with GCC or Clang compiler.")
 endif (PLUGIN_RMM AND NOT ((CMAKE_CXX_COMPILER_ID STREQUAL "Clang") OR (CMAKE_CXX_COMPILER_ID STREQUAL "GNU")))
@@ -170,6 +192,24 @@ if (USE_CUDA)
   find_package(CUDAToolkit REQUIRED)
 endif (USE_CUDA)
 
+if (USE_HIP)
+  set(USE_OPENMP ON CACHE BOOL "HIP requires OpenMP" FORCE)
+  # `export CXX=' is ignored by CMake HIP.
+  set(CMAKE_HIP_HOST_COMPILER ${CMAKE_CXX_COMPILER})
+  message(STATUS "Configured HIP host compiler: ${CMAKE_HIP_HOST_COMPILER}")
+
+  enable_language(HIP)
+  find_package(hip REQUIRED)
+  find_package(rocthrust REQUIRED)
+  find_package(hipcub REQUIRED)
+
+  set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} -I${HIP_INCLUDE_DIRS} -I${HIP_INCLUDE_DIRS}/hip")
+  set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} -Wunused-result -w")
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D__HIP_PLATFORM_AMD__")
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -I${HIP_INCLUDE_DIRS}")
+  add_subdirectory(${PROJECT_SOURCE_DIR}/rocgputreeshap)
+endif (USE_HIP)
+
 if (FORCE_COLORED_OUTPUT AND (CMAKE_GENERATOR STREQUAL "Ninja") AND
     ((CMAKE_CXX_COMPILER_ID STREQUAL "GNU") OR
       (CMAKE_CXX_COMPILER_ID STREQUAL "Clang")))
@@ -209,6 +249,10 @@ if (USE_NCCL)
   find_package(Nccl REQUIRED)
 endif (USE_NCCL)
 
+if (USE_RCCL)
+  find_package(rccl REQUIRED)
+endif (USE_RCCL)
+
 # dmlc-core
 msvc_use_static_runtime()
 if (FORCE_SHARED_CRT)
@@ -233,6 +277,11 @@ endif (RABIT_BUILD_MPI)
 add_subdirectory(${xgboost_SOURCE_DIR}/src)
 target_link_libraries(objxgboost PUBLIC dmlc)
 
+# Link -lstdc++fs for GCC 8.x
+if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS "9.0")
+  target_link_libraries(objxgboost PUBLIC stdc++fs)
+endif()
+
 # Exports some R specific definitions and objects
 if (R_LIB)
   add_subdirectory(${xgboost_SOURCE_DIR}/R-package)

R-package/DESCRIPTION

@@ -1,8 +1,8 @@
 Package: xgboost
 Type: Package
 Title: Extreme Gradient Boosting
-Version: 2.0.0.1
-Date: 2022-10-18
+Version: 2.0.3.1
+Date: 2023-12-14
 Authors@R: c(
   person("Tianqi", "Chen", role = c("aut"),
          email = "tianqi.tchen@gmail.com"),

R-package/R/callbacks.R

@@ -70,7 +70,7 @@ cb.print.evaluation <- function(period = 1, showsd = TRUE) {
         i == env$begin_iteration ||
         i == env$end_iteration) {
       stdev <- if (showsd) env$bst_evaluation_err else NULL
-      msg <- format.eval.string(i, env$bst_evaluation, stdev)
+      msg <- .format_eval_string(i, env$bst_evaluation, stdev)
       cat(msg, '\n')
     }
   }
@@ -380,7 +380,9 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
     if ((maximize && score > best_score) ||
         (!maximize && score < best_score)) {
 
-      best_msg <<- format.eval.string(i, env$bst_evaluation, env$bst_evaluation_err)
+      best_msg <<- .format_eval_string(
+        i, env$bst_evaluation, env$bst_evaluation_err
+      )
       best_score <<- score
       best_iteration <<- i
       best_ntreelimit <<- best_iteration * env$num_parallel_tree
@@ -754,7 +756,7 @@ xgb.gblinear.history <- function(model, class_index = NULL) {
 #
 
 # Format the evaluation metric string
-format.eval.string <- function(iter, eval_res, eval_err = NULL) {
+.format_eval_string <- function(iter, eval_res, eval_err = NULL) {
   if (length(eval_res) == 0)
     stop('no evaluation results')
   enames <- names(eval_res)

R-package/configure

@@ -1,6 +1,6 @@
 #! /bin/sh
 # Guess values for system-dependent variables and create Makefiles.
-# Generated by GNU Autoconf 2.71 for xgboost 2.0.0.
+# Generated by GNU Autoconf 2.71 for xgboost 2.0.3.
 #
 #
 # Copyright (C) 1992-1996, 1998-2017, 2020-2021 Free Software Foundation,
@@ -607,8 +607,8 @@ MAKEFLAGS=
 # Identity of this package.
 PACKAGE_NAME='xgboost'
 PACKAGE_TARNAME='xgboost'
-PACKAGE_VERSION='2.0.0'
-PACKAGE_STRING='xgboost 2.0.0'
+PACKAGE_VERSION='2.0.3'
+PACKAGE_STRING='xgboost 2.0.3'
 PACKAGE_BUGREPORT=''
 PACKAGE_URL=''
 
@@ -1225,7 +1225,7 @@ if test "$ac_init_help" = "long"; then
   # Omit some internal or obsolete options to make the list less imposing.
   # This message is too long to be a string in the A/UX 3.1 sh.
   cat <<_ACEOF
-\`configure' configures xgboost 2.0.0 to adapt to many kinds of systems.
+\`configure' configures xgboost 2.0.3 to adapt to many kinds of systems.
 
 Usage: $0 [OPTION]... [VAR=VALUE]...
 
@@ -1287,7 +1287,7 @@ fi
 
 if test -n "$ac_init_help"; then
   case $ac_init_help in
-     short | recursive ) echo "Configuration of xgboost 2.0.0:";;
+     short | recursive ) echo "Configuration of xgboost 2.0.3:";;
    esac
   cat <<\_ACEOF
 
@@ -1367,7 +1367,7 @@ fi
 test -n "$ac_init_help" && exit $ac_status
 if $ac_init_version; then
   cat <<\_ACEOF
-xgboost configure 2.0.0
+xgboost configure 2.0.3
 generated by GNU Autoconf 2.71
 
 Copyright (C) 2021 Free Software Foundation, Inc.
@@ -1533,7 +1533,7 @@ cat >config.log <<_ACEOF
 This file contains any messages produced by compilers while
 running configure, to aid debugging if configure makes a mistake.
 
-It was created by xgboost $as_me 2.0.0, which was
+It was created by xgboost $as_me 2.0.3, which was
 generated by GNU Autoconf 2.71.  Invocation command line was
 
   $ $0$ac_configure_args_raw
@@ -3412,7 +3412,7 @@ cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1
 # report actual input values of CONFIG_FILES etc. instead of their
 # values after options handling.
 ac_log="
-This file was extended by xgboost $as_me 2.0.0, which was
+This file was extended by xgboost $as_me 2.0.3, which was
 generated by GNU Autoconf 2.71.  Invocation command line was
 
   CONFIG_FILES    = $CONFIG_FILES
@@ -3467,7 +3467,7 @@ ac_cs_config_escaped=`printf "%s\n" "$ac_cs_config" | sed "s/^ //; s/'/'\\\\\\\\
 cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1
 ac_cs_config='$ac_cs_config_escaped'
 ac_cs_version="\\
-xgboost config.status 2.0.0
+xgboost config.status 2.0.3
 configured by $0, generated by GNU Autoconf 2.71,
   with options \\"\$ac_cs_config\\"
 

R-package/configure.ac

@@ -2,7 +2,7 @@
 
 AC_PREREQ(2.69)
 
-AC_INIT([xgboost],[2.0.0],[],[xgboost],[])
+AC_INIT([xgboost],[2.0.3],[],[xgboost],[])
 
 : ${R_HOME=`R RHOME`}
 if test -z "${R_HOME}"; then

R-package/src/xgboost_R.cc

@@ -120,11 +120,25 @@ XGB_DLL SEXP XGDMatrixCreateFromMat_R(SEXP mat, SEXP missing, SEXP n_threads) {
   ctx.nthread = asInteger(n_threads);
   std::int32_t threads = ctx.Threads();
 
+  if (is_int) {
     xgboost::common::ParallelFor(nrow, threads, [&](xgboost::omp_ulong i) {
       for (size_t j = 0; j < ncol; ++j) {
-      data[i * ncol + j] = is_int ? static_cast<float>(iin[i + nrow * j]) : din[i + nrow * j];
+        auto v = iin[i + nrow * j];
+        if (v == NA_INTEGER) {
+          data[i * ncol + j] = std::numeric_limits<float>::quiet_NaN();
+        } else {
+          data[i * ncol + j] = static_cast<float>(v);
+        }
       }
     });
+  } else {
+    xgboost::common::ParallelFor(nrow, threads, [&](xgboost::omp_ulong i) {
+      for (size_t j = 0; j < ncol; ++j) {
+        data[i * ncol + j] = din[i + nrow * j];
+      }
+    });
+  }
+
   DMatrixHandle handle;
   CHECK_CALL(XGDMatrixCreateFromMat_omp(BeginPtr(data), nrow, ncol,
                                         asReal(missing), &handle, threads));

R-package/src/xgboost_custom.cc

@@ -32,7 +32,7 @@ namespace common {
 bool CheckNAN(double v) {
   return ISNAN(v);
 }
-#if !defined(XGBOOST_USE_CUDA)
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 double LogGamma(double v) {
   return lgammafn(v);
 }

R-package/tests/testthat/test_dmatrix.R

@@ -56,6 +56,42 @@ test_that("xgb.DMatrix: basic construction", {
   expect_equal(raw_fd, raw_dgc)
 })
 
+test_that("xgb.DMatrix: NA", {
+  n_samples <- 3
+  x <- cbind(
+    x1 = sample(x = 4, size = n_samples, replace = TRUE),
+    x2 = sample(x = 4, size = n_samples, replace = TRUE)
+  )
+  x[1, "x1"] <- NA
+
+  m <- xgb.DMatrix(x)
+  xgb.DMatrix.save(m, "int.dmatrix")
+
+  x <- matrix(as.numeric(x), nrow = n_samples, ncol = 2)
+  colnames(x) <- c("x1", "x2")
+  m <- xgb.DMatrix(x)
+
+  xgb.DMatrix.save(m, "float.dmatrix")
+
+  iconn <- file("int.dmatrix", "rb")
+  fconn <- file("float.dmatrix", "rb")
+
+  expect_equal(file.size("int.dmatrix"), file.size("float.dmatrix"))
+
+  bytes <- file.size("int.dmatrix")
+  idmatrix <- readBin(iconn, "raw", n = bytes)
+  fdmatrix <- readBin(fconn, "raw", n = bytes)
+
+  expect_equal(length(idmatrix), length(fdmatrix))
+  expect_equal(idmatrix, fdmatrix)
+
+  close(iconn)
+  close(fconn)
+
+  file.remove("int.dmatrix")
+  file.remove("float.dmatrix")
+})
+
 test_that("xgb.DMatrix: saving, loading", {
   # save to a local file
   dtest1 <- xgb.DMatrix(test_data, label = test_label)

cmake/Utils.cmake

@@ -181,6 +181,23 @@ function(xgboost_set_cuda_flags target)
     CUDA_RUNTIME_LIBRARY Static)
 endfunction(xgboost_set_cuda_flags)
 
+# Set HIP related flags to target.
+function(xgboost_set_hip_flags target)
+  if (USE_DEVICE_DEBUG)
+    target_compile_options(${target} PRIVATE
+      $<$<AND:$<CONFIG:DEBUG>,$<COMPILE_LANGUAGE:HIP>>:-G>)
+  endif (USE_DEVICE_DEBUG)
+
+  target_compile_definitions(${target} PRIVATE -DXGBOOST_USE_HIP=1)
+  target_include_directories(${target} PRIVATE ${xgboost_SOURCE_DIR}/rocgputreeshap)
+  target_include_directories(${target} PRIVATE ${xgboost_SOURCE_DIR}/warp-primitives/include)
+
+  set_target_properties(${target} PROPERTIES
+    HIP_STANDARD 17
+    HIP_STANDARD_REQUIRED ON
+    HIP_SEPARABLE_COMPILATION OFF)
+endfunction(xgboost_set_hip_flags)
+
 macro(xgboost_link_nccl target)
   if (BUILD_STATIC_LIB)
     target_include_directories(${target} PUBLIC ${NCCL_INCLUDE_DIR})
@@ -193,6 +210,20 @@ macro(xgboost_link_nccl target)
   endif (BUILD_STATIC_LIB)
 endmacro(xgboost_link_nccl)
 
+macro(xgboost_link_rccl target)
+  if(BUILD_STATIC_LIB)
+    target_include_directories(${target} PUBLIC ${RCCL_INCLUDE_DIR}/rccl)
+    target_compile_definitions(${target} PUBLIC -DXGBOOST_USE_RCCL=1)
+    target_link_directories(${target} PUBLIC ${HIP_LIB_INSTALL_DIR})
+    target_link_libraries(${target} PUBLIC ${RCCL_LIBRARY})
+  else()
+    target_include_directories(${target} PRIVATE ${RCCL_INCLUDE_DIR}/rccl)
+    target_compile_definitions(${target} PRIVATE -DXGBOOST_USE_RCCL=1)
+    target_link_directories(${target} PUBLIC ${HIP_LIB_INSTALL_DIR})
+    target_link_libraries(${target} PRIVATE ${RCCL_LIBRARY})
+  endif()
+endmacro()
+
 # compile options
 macro(xgboost_target_properties target)
   set_target_properties(${target} PROPERTIES
@@ -215,6 +246,10 @@ macro(xgboost_target_properties target)
       -Xcompiler=-Wall -Xcompiler=-Wextra -Xcompiler=-Wno-expansion-to-defined,
       -Wall -Wextra -Wno-expansion-to-defined>
     )
+    target_compile_options(${target} PUBLIC
+       $<IF:$<COMPILE_LANGUAGE:HIP>,
+      -Wall -Wextra >
+    )
   endif(ENABLE_ALL_WARNINGS)
 
   target_compile_options(${target}
@@ -283,6 +318,10 @@ macro(xgboost_target_link_libraries target)
     target_link_libraries(${target} PUBLIC CUDA::cudart_static)
   endif (USE_CUDA)
 
+  if (USE_HIP)
+    xgboost_set_hip_flags(${target})
+  endif (USE_HIP)
+
   if (PLUGIN_RMM)
     target_link_libraries(${target} PRIVATE rmm::rmm)
   endif (PLUGIN_RMM)
@@ -291,6 +330,10 @@ macro(xgboost_target_link_libraries target)
     xgboost_link_nccl(${target})
   endif (USE_NCCL)
 
+  if(USE_RCCL)
+    xgboost_link_rccl(${target})
+  endif()
+
   if (USE_NVTX)
     target_link_libraries(${target} PRIVATE CUDA::nvToolsExt)
   endif (USE_NVTX)

cmake/xgboost-config.cmake.in

@@ -3,6 +3,8 @@
 set(USE_OPENMP @USE_OPENMP@)
 set(USE_CUDA @USE_CUDA@)
 set(USE_NCCL @USE_NCCL@)
+set(USE_HIP @USE_HIP@)
+set(USE_RCCL @USE_RCCL@)
 set(XGBOOST_BUILD_STATIC_LIB @BUILD_STATIC_LIB@)
 
 include(CMakeFindDependencyMacro)
@@ -15,6 +17,9 @@ if (XGBOOST_BUILD_STATIC_LIB)
   if(USE_CUDA)
     find_dependency(CUDA)
   endif()
+  if(USE_HIP)
+      find_dependency(HIP)
+  endif()
   # nccl should be linked statically if xgboost is built as static library.
 endif (XGBOOST_BUILD_STATIC_LIB)
 

demo/CLI/regression/runexp.sh

@@ -4,13 +4,13 @@ python mapfeat.py
 # split train and test
 python mknfold.py machine.txt 1
 # training and output the models
-../../xgboost machine.conf
+../../../xgboost machine.conf
 # output predictions of test data
-../../xgboost machine.conf task=pred model_in=0002.model
+../../../xgboost machine.conf task=pred model_in=0002.model
 # print the boosters of 0002.model in dump.raw.txt
-../../xgboost machine.conf task=dump model_in=0002.model name_dump=dump.raw.txt
+../../../xgboost machine.conf task=dump model_in=0002.model name_dump=dump.raw.txt
 # print the boosters of 0002.model in dump.nice.txt with feature map
-../../xgboost machine.conf task=dump model_in=0002.model fmap=featmap.txt name_dump=dump.nice.txt 
+../../../xgboost machine.conf task=dump model_in=0002.model fmap=featmap.txt name_dump=dump.nice.txt 
 
 # cat the result
 cat dump.nice.txt

dev/change_scala_version.py

@@ -0,0 +1,79 @@
+import argparse
+import pathlib
+import re
+import shutil
+
+
+def main(args):
+    if args.scala_version == "2.12":
+        scala_ver = "2.12"
+        scala_patchver = "2.12.18"
+    elif args.scala_version == "2.13":
+        scala_ver = "2.13"
+        scala_patchver = "2.13.11"
+    else:
+        raise ValueError(f"Unsupported Scala version: {args.scala_version}")
+
+    # Clean artifacts
+    if args.purge_artifacts:
+        for target in pathlib.Path("jvm-packages/").glob("**/target"):
+            if target.is_dir():
+                print(f"Removing {target}...")
+                shutil.rmtree(target)
+
+    # Update pom.xml
+    for pom in pathlib.Path("jvm-packages/").glob("**/pom.xml"):
+        print(f"Updating {pom}...")
+        with open(pom, "r", encoding="utf-8") as f:
+            lines = f.readlines()
+        with open(pom, "w", encoding="utf-8") as f:
+            replaced_scalaver = False
+            replaced_scala_binver = False
+            for line in lines:
+                for artifact in [
+                    "xgboost-jvm",
+                    "xgboost4j",
+                    "xgboost4j-gpu",
+                    "xgboost4j-spark",
+                    "xgboost4j-spark-gpu",
+                    "xgboost4j-flink",
+                    "xgboost4j-example",
+                ]:
+                    line = re.sub(
+                        f"<artifactId>{artifact}_[0-9\\.]*",
+                        f"<artifactId>{artifact}_{scala_ver}",
+                        line,
+                    )
+                # Only replace the first occurrence of scala.version
+                if not replaced_scalaver:
+                    line, nsubs = re.subn(
+                        r"<scala.version>[0-9\.]*",
+                        f"<scala.version>{scala_patchver}",
+                        line,
+                    )
+                    if nsubs > 0:
+                        replaced_scalaver = True
+                # Only replace the first occurrence of scala.binary.version
+                if not replaced_scala_binver:
+                    line, nsubs = re.subn(
+                        r"<scala.binary.version>[0-9\.]*",
+                        f"<scala.binary.version>{scala_ver}",
+                        line,
+                    )
+                    if nsubs > 0:
+                        replaced_scala_binver = True
+                f.write(line)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--purge-artifacts", action="store_true")
+    parser.add_argument(
+        "--scala-version",
+        type=str,
+        required=True,
+        help="Version of Scala to use in the JVM packages",
+        choices=["2.12", "2.13"],
+    )
+    parsed_args = parser.parse_args()
+    main(parsed_args)

dev/prepare_jvm_release.py

@@ -2,7 +2,6 @@ import argparse
 import errno
 import glob
 import os
-import platform
 import re
 import shutil
 import subprocess
@@ -21,12 +20,14 @@ def normpath(path):
     else:
         return normalized
 
+
 def cp(source, target):
     source = normpath(source)
     target = normpath(target)
     print("cp {0} {1}".format(source, target))
     shutil.copy(source, target)
 
+
 def maybe_makedirs(path):
     path = normpath(path)
     print("mkdir -p " + path)
@@ -36,6 +37,7 @@ def maybe_makedirs(path):
         if e.errno != errno.EEXIST:
             raise
 
+
 @contextmanager
 def cd(path):
     path = normpath(path)
@@ -47,18 +49,22 @@ def cd(path):
     finally:
         os.chdir(cwd)
 
+
 def run(command, **kwargs):
     print(command)
     subprocess.check_call(command, shell=True, **kwargs)
 
+
 def get_current_git_tag():
     out = subprocess.check_output(["git", "tag", "--points-at", "HEAD"])
     return out.decode().split("\n")[0]
 
+
 def get_current_commit_hash():
     out = subprocess.check_output(["git", "rev-parse", "HEAD"])
     return out.decode().split("\n")[0]
 
+
 def get_current_git_branch():
     out = subprocess.check_output(["git", "log", "-n", "1", "--pretty=%d", "HEAD"])
     m = re.search(r"release_[0-9\.]+", out.decode())
@@ -66,38 +72,49 @@ def get_current_git_branch():
         raise ValueError("Expected branch name of form release_xxx")
     return m.group(0)
 
+
 def retrieve(url, filename=None):
     print(f"{url} -> {filename}")
     return urlretrieve(url, filename)
 
+
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--release-version", type=str, required=True,
-                        help="Version of the release being prepared")
+    parser.add_argument(
+        "--release-version",
+        type=str,
+        required=True,
+        help="Version of the release being prepared",
+    )
     args = parser.parse_args()
-
-    if sys.platform != "darwin" or platform.machine() != "x86_64":
-        raise NotImplementedError("Please run this script using an Intel Mac")
-
     version = args.release_version
     expected_git_tag = "v" + version
     current_git_tag = get_current_git_tag()
     if current_git_tag != expected_git_tag:
         if not current_git_tag:
-            raise ValueError(f"Expected git tag {expected_git_tag} but current HEAD has no tag. "
-                             f"Run: git checkout {expected_git_tag}")
-        raise ValueError(f"Expected git tag {expected_git_tag} but current HEAD is at tag "
-                         f"{current_git_tag}. Run: git checkout {expected_git_tag}")
+            raise ValueError(
+                f"Expected git tag {expected_git_tag} but current HEAD has no tag. "
+                f"Run: git checkout {expected_git_tag}"
+            )
+        raise ValueError(
+            f"Expected git tag {expected_git_tag} but current HEAD is at tag "
+            f"{current_git_tag}. Run: git checkout {expected_git_tag}"
+        )
 
     commit_hash = get_current_commit_hash()
     git_branch = get_current_git_branch()
-    print(f"Using commit {commit_hash} of branch {git_branch}, git tag {current_git_tag}")
+    print(
+        f"Using commit {commit_hash} of branch {git_branch}, git tag {current_git_tag}"
+    )
 
     with cd("jvm-packages/"):
         print("====copying pure-Python tracker====")
         for use_cuda in [True, False]:
             xgboost4j = "xgboost4j-gpu" if use_cuda else "xgboost4j"
-            cp("../python-package/xgboost/tracker.py", f"{xgboost4j}/src/main/resources")
+            cp(
+                "../python-package/xgboost/tracker.py",
+                f"{xgboost4j}/src/main/resources",
+            )
 
         print("====copying resources for testing====")
         with cd("../demo/CLI/regression"):
@@ -115,7 +132,12 @@ def main():
                 cp(file, f"{xgboost4j_spark}/src/test/resources")
 
         print("====Creating directories to hold native binaries====")
-        for os_ident, arch in [("linux", "x86_64"), ("windows", "x86_64"), ("macos", "x86_64")]:
+        for os_ident, arch in [
+            ("linux", "x86_64"),
+            ("windows", "x86_64"),
+            ("macos", "x86_64"),
+            ("macos", "aarch64"),
+        ]:
             output_dir = f"xgboost4j/src/main/resources/lib/{os_ident}/{arch}"
             maybe_makedirs(output_dir)
         for os_ident, arch in [("linux", "x86_64")]:
@@ -123,52 +145,98 @@ def main():
             maybe_makedirs(output_dir)
 
         print("====Downloading native binaries from CI====")
-        nightly_bucket_prefix = "https://s3-us-west-2.amazonaws.com/xgboost-nightly-builds"
-        maven_repo_prefix = "https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/release/ml/dmlc"
+        nightly_bucket_prefix = (
+            "https://s3-us-west-2.amazonaws.com/xgboost-nightly-builds"
+        )
+        maven_repo_prefix = (
+            "https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/release/ml/dmlc"
+        )
 
-        retrieve(url=f"{nightly_bucket_prefix}/{git_branch}/xgboost4j_{commit_hash}.dll",
-                 filename="xgboost4j/src/main/resources/lib/windows/x86_64/xgboost4j.dll")
+        retrieve(
+            url=f"{nightly_bucket_prefix}/{git_branch}/libxgboost4j/xgboost4j_{commit_hash}.dll",
+            filename="xgboost4j/src/main/resources/lib/windows/x86_64/xgboost4j.dll",
+        )
+        retrieve(
+            url=f"{nightly_bucket_prefix}/{git_branch}/libxgboost4j/libxgboost4j_{commit_hash}.dylib",
+            filename="xgboost4j/src/main/resources/lib/macos/x86_64/libxgboost4j.dylib",
+        )
+        retrieve(
+            url=f"{nightly_bucket_prefix}/{git_branch}/libxgboost4j/libxgboost4j_m1_{commit_hash}.dylib",
+            filename="xgboost4j/src/main/resources/lib/macos/aarch64/libxgboost4j.dylib",
+        )
 
         with tempfile.TemporaryDirectory() as tempdir:
             # libxgboost4j.so for Linux x86_64, CPU only
             zip_path = os.path.join(tempdir, "xgboost4j_2.12.jar")
             extract_dir = os.path.join(tempdir, "xgboost4j")
-            retrieve(url=f"{maven_repo_prefix}/xgboost4j_2.12/{version}/"
+            retrieve(
+                url=f"{maven_repo_prefix}/xgboost4j_2.12/{version}/"
                 f"xgboost4j_2.12-{version}.jar",
-                     filename=zip_path)
+                filename=zip_path,
+            )
             os.mkdir(extract_dir)
             with zipfile.ZipFile(zip_path, "r") as t:
                 t.extractall(extract_dir)
-            cp(os.path.join(extract_dir, "lib", "linux", "x86_64", "libxgboost4j.so"),
-               "xgboost4j/src/main/resources/lib/linux/x86_64/libxgboost4j.so")
+            cp(
+                os.path.join(extract_dir, "lib", "linux", "x86_64", "libxgboost4j.so"),
+                "xgboost4j/src/main/resources/lib/linux/x86_64/libxgboost4j.so",
+            )
 
             # libxgboost4j.so for Linux x86_64, GPU support
             zip_path = os.path.join(tempdir, "xgboost4j-gpu_2.12.jar")
             extract_dir = os.path.join(tempdir, "xgboost4j-gpu")
-            retrieve(url=f"{maven_repo_prefix}/xgboost4j-gpu_2.12/{version}/"
+            retrieve(
+                url=f"{maven_repo_prefix}/xgboost4j-gpu_2.12/{version}/"
                 f"xgboost4j-gpu_2.12-{version}.jar",
-                     filename=zip_path)
+                filename=zip_path,
+            )
             os.mkdir(extract_dir)
             with zipfile.ZipFile(zip_path, "r") as t:
                 t.extractall(extract_dir)
-            cp(os.path.join(extract_dir, "lib", "linux", "x86_64", "libxgboost4j.so"),
-               "xgboost4j-gpu/src/main/resources/lib/linux/x86_64/libxgboost4j.so")
-
+            cp(
+                os.path.join(extract_dir, "lib", "linux", "x86_64", "libxgboost4j.so"),
+                "xgboost4j-gpu/src/main/resources/lib/linux/x86_64/libxgboost4j.so",
+            )
 
     print("====Next Steps====")
     print("1. Gain upload right to Maven Central repo.")
     print("1-1. Sign up for a JIRA account at Sonatype: ")
-    print("1-2. File a JIRA ticket: "
+    print(
+        "1-2. File a JIRA ticket: "
         "https://issues.sonatype.org/secure/CreateIssue.jspa?issuetype=21&pid=10134. Example: "
-          "https://issues.sonatype.org/browse/OSSRH-67724")
-    print("2. Store the Sonatype credentials in .m2/settings.xml. See insturctions in "
-          "https://central.sonatype.org/publish/publish-maven/")
-    print("3. Now on a Mac machine, run:")
-    print("   GPG_TTY=$(tty) mvn deploy -Prelease -DskipTests")
-    print("4. Log into https://oss.sonatype.org/. On the left menu panel, click Staging "
-          "Repositories. Visit the URL https://oss.sonatype.org/content/repositories/mldmlc-1085 "
+        "https://issues.sonatype.org/browse/OSSRH-67724"
+    )
+    print(
+        "2. Store the Sonatype credentials in .m2/settings.xml. See insturctions in "
+        "https://central.sonatype.org/publish/publish-maven/"
+    )
+    print(
+        "3. Now on a Linux machine, run the following to build Scala 2.12 artifacts. "
+        "Make sure to use an Internet connection with fast upload speed:"
+    )
+    print(
+        "   # Skip native build, since we have all needed native binaries from CI\n"
+        "   export MAVEN_SKIP_NATIVE_BUILD=1\n"
+        "   GPG_TTY=$(tty) mvn deploy -Prelease -DskipTests"
+    )
+    print(
+        "4. Log into https://oss.sonatype.org/. On the left menu panel, click Staging "
+        "Repositories. Visit the URL https://oss.sonatype.org/content/repositories/mldmlc-xxxx "
         "to inspect the staged JAR files. Finally, press Release button to publish the "
-          "artifacts to the Maven Central repository.")
+        "artifacts to the Maven Central repository. The top-level metapackage should be "
+        "named xgboost-jvm_2.12."
+    )
+    print(
+        "5. Remove the Scala 2.12 artifacts and build Scala 2.13 artifacts:\n"
+        "   export MAVEN_SKIP_NATIVE_BUILD=1\n"
+        "   python dev/change_scala_version.py --scala-version 2.13 --purge-artifacts\n"
+        "   GPG_TTY=$(tty) mvn deploy -Prelease-cpu-only,scala-2.13 -DskipTests"
+    )
+    print(
+        "6. Go to https://oss.sonatype.org/ to release the Scala 2.13 artifacts. "
+        "The top-level metapackage should be named xgboost-jvm_2.13."
+    )
+
 
 if __name__ == "__main__":
     main()

doc/parameter.rst

@@ -329,7 +329,7 @@ Parameters for Linear Booster (``booster=gblinear``)
   - Choice of algorithm to fit linear model
 
     - ``shotgun``: Parallel coordinate descent algorithm based on shotgun algorithm. Uses 'hogwild' parallelism and therefore produces a nondeterministic solution on each run.
-    - ``coord_descent``: Ordinary coordinate descent algorithm. Also multithreaded but still produces a deterministic solution.
+    - ``coord_descent``: Ordinary coordinate descent algorithm. Also multithreaded but still produces a deterministic solution. When the ``device`` parameter is set to ``cuda`` or ``gpu``, a GPU variant would be used.
 
 * ``feature_selector`` [default= ``cyclic``]
 

include/xgboost/base.h

@@ -58,19 +58,19 @@
 /*!
  * \brief Tag function as usable by device
  */
-#if defined (__CUDA__) || defined(__NVCC__)
+#if defined (__CUDA__) || defined(__NVCC__) || defined(__HIPCC__)
 #define XGBOOST_DEVICE __host__ __device__
 #else
 #define XGBOOST_DEVICE
-#endif  // defined (__CUDA__) || defined(__NVCC__)
+#endif  // defined (__CUDA__) || defined(__NVCC__) || defined(__HIPCC__)
 
-#if defined(__CUDA__) || defined(__CUDACC__)
+#if defined(__CUDA__) || defined(__CUDACC__) || defined(__HIPCC__)
 #define XGBOOST_HOST_DEV_INLINE XGBOOST_DEVICE __forceinline__
 #define XGBOOST_DEV_INLINE __device__ __forceinline__
 #else
 #define XGBOOST_HOST_DEV_INLINE
 #define XGBOOST_DEV_INLINE
-#endif  // defined(__CUDA__) || defined(__CUDACC__)
+#endif  // defined(__CUDA__) || defined(__CUDACC__) || defined(__HIPCC__)
 
 // These check are for Makefile.
 #if !defined(XGBOOST_MM_PREFETCH_PRESENT) && !defined(XGBOOST_BUILTIN_PREFETCH_PRESENT)
@@ -274,8 +274,8 @@ class GradientPairInt64 {
   GradientPairInt64(GradientPairInt64 const &g) = default;
   GradientPairInt64 &operator=(GradientPairInt64 const &g) = default;
 
-  XGBOOST_DEVICE [[nodiscard]] T GetQuantisedGrad() const { return grad_; }
-  XGBOOST_DEVICE [[nodiscard]] T GetQuantisedHess() const { return hess_; }
+  [[nodiscard]] XGBOOST_DEVICE T GetQuantisedGrad() const { return grad_; }
+  [[nodiscard]] XGBOOST_DEVICE T GetQuantisedHess() const { return hess_; }
 
   XGBOOST_DEVICE GradientPairInt64 &operator+=(const GradientPairInt64 &rhs) {
     grad_ += rhs.grad_;

include/xgboost/global_config.h

@@ -1,5 +1,5 @@
-/*!
- * Copyright 2020 by Contributors
+/**
+ * Copyright 2020-2023, XGBoost Contributors
  * \file global_config.h
  * \brief Global configuration for XGBoost
  * \author Hyunsu Cho
@@ -7,24 +7,22 @@
 #ifndef XGBOOST_GLOBAL_CONFIG_H_
 #define XGBOOST_GLOBAL_CONFIG_H_
 
-#include <xgboost/parameter.h>
-#include <vector>
-#include <string>
+#include <dmlc/thread_local.h>  // for ThreadLocalStore
+#include <xgboost/parameter.h>  // for XGBoostParameter
+
+#include <cstdint>  // for int32_t
 
 namespace xgboost {
-class Json;
-
 struct GlobalConfiguration : public XGBoostParameter<GlobalConfiguration> {
-  int verbosity { 1 };
-  bool use_rmm { false };
+  std::int32_t verbosity{1};
+  bool use_rmm{false};
   DMLC_DECLARE_PARAMETER(GlobalConfiguration) {
     DMLC_DECLARE_FIELD(verbosity)
         .set_range(0, 3)
         .set_default(1)  // shows only warning
         .describe("Flag to print out detailed breakdown of runtime.");
-    DMLC_DECLARE_FIELD(use_rmm)
-        .set_default(false)
-        .describe("Whether to use RAPIDS Memory Manager to allocate GPU memory in XGBoost");
+    DMLC_DECLARE_FIELD(use_rmm).set_default(false).describe(
+        "Whether to use RAPIDS Memory Manager to allocate GPU memory in XGBoost");
   }
 };
 

include/xgboost/host_device_vector.h

@@ -58,11 +58,11 @@
 
 namespace xgboost {
 
-#ifdef __CUDACC__
+#if defined(__CUDACC__) || defined(__HIPCC__)
 // Sets a function to call instead of cudaSetDevice();
 // only added for testing
 void SetCudaSetDeviceHandler(void (*handler)(int));
-#endif  // __CUDACC__
+#endif  // __CUDACC__ || __HIPCC__
 
 template <typename T> struct HostDeviceVectorImpl;
 

include/xgboost/linalg.h

@@ -30,11 +30,11 @@
 
 // decouple it from xgboost.
 #ifndef LINALG_HD
-#if defined(__CUDA__) || defined(__NVCC__)
+#if defined(__CUDA__) || defined(__NVCC__) || defined(__HIPCC__)
 #define LINALG_HD __host__ __device__
 #else
 #define LINALG_HD
-#endif  // defined (__CUDA__) || defined(__NVCC__)
+#endif  // defined (__CUDA__) || defined(__NVCC__) || defined(__HIPCC__)
 #endif  // LINALG_HD
 
 namespace xgboost::linalg {
@@ -118,7 +118,7 @@ using IndexToTag = std::conditional_t<std::is_integral<RemoveCRType<S>>::value,
 
 template <int32_t n, typename Fn>
 LINALG_HD constexpr auto UnrollLoop(Fn fn) {
-#if defined __CUDA_ARCH__
+#if defined(__CUDA_ARCH__) || defined(__HIPCC__)
 #pragma unroll n
 #endif  // defined __CUDA_ARCH__
   for (int32_t i = 0; i < n; ++i) {
@@ -136,7 +136,7 @@ int32_t NativePopc(T v) {
 inline LINALG_HD int Popc(uint32_t v) {
 #if defined(__CUDA_ARCH__)
   return __popc(v);
-#elif defined(__GNUC__) || defined(__clang__)
+#elif defined(__GNUC__) || defined(__clang__) || defined(__HIPCC__)
   return __builtin_popcount(v);
 #elif defined(_MSC_VER)
   return __popcnt(v);
@@ -148,7 +148,7 @@ inline LINALG_HD int Popc(uint32_t v) {
 inline LINALG_HD int Popc(uint64_t v) {
 #if defined(__CUDA_ARCH__)
   return __popcll(v);
-#elif defined(__GNUC__) || defined(__clang__)
+#elif defined(__GNUC__) || defined(__clang__) || defined(__HIPCC__)
   return __builtin_popcountll(v);
 #elif defined(_MSC_VER) && defined(_M_X64)
   return __popcnt64(v);

include/xgboost/span.h

@@ -41,7 +41,9 @@
 
 #if defined(__CUDACC__)
 #include <cuda_runtime.h>
-#endif  // defined(__CUDACC__)
+#elif defined(__HIPCC__)
+#include <hip/hip_runtime.h>
+#endif
 
 /*!
  * The version number 1910 is picked up from GSL.
@@ -104,7 +106,42 @@ namespace common {
 
 #define SPAN_CHECK KERNEL_CHECK
 
-#else  // ------------------------------ not CUDA ----------------------------
+#elif defined(__HIPCC__)
+// Usual logging facility is not available inside device code.
+
+#if defined(_MSC_VER)
+
+// Windows HIP doesn't have __assert_fail.
+#define HIP_KERNEL_CHECK(cond)           \
+  do {                                    \
+    if (XGBOOST_EXPECT(!(cond), false)) { \
+      __builtin_trap();                           \
+    }                                     \
+  } while (0)
+
+#else  // defined(_MSC_VER)
+
+#define __ASSERT_STR_HELPER(x) #x
+
+#if 0
+#define HIP_KERNEL_CHECK(cond)  \
+  (XGBOOST_EXPECT((cond), true) \
+       ? static_cast<void>(0)   \
+       : __assert_fail(__ASSERT_STR_HELPER((cond)), __FILE__, __LINE__, __PRETTY_FUNCTION__))
+#else
+#define HIP_KERNEL_CHECK(cond)  \
+  (XGBOOST_EXPECT((cond), true) \
+       ? static_cast<void>(0)   \
+       : __builtin_trap())
+#endif
+
+#endif  // defined(_MSC_VER)
+
+#define KERNEL_CHECK HIP_KERNEL_CHECK
+
+#define SPAN_CHECK KERNEL_CHECK
+
+#else  // ------------------------------ not CUDA or HIP ----------------------------
 
 #if defined(XGBOOST_STRICT_R_MODE) && XGBOOST_STRICT_R_MODE == 1
 
@@ -120,7 +157,7 @@ namespace common {
 
 #endif  // defined(XGBOOST_STRICT_R_MODE)
 
-#endif  // __CUDA_ARCH__
+#endif  // __CUDA_ARCH__ || __HIPCC__
 
 #define SPAN_LT(lhs, rhs) SPAN_CHECK((lhs) < (rhs))
 
@@ -317,7 +354,7 @@ struct IsSpanOracle<Span<T, Extent>> : std::true_type {};
 template <class T>
 struct IsSpan : public IsSpanOracle<typename std::remove_cv<T>::type> {};
 
-// Re-implement std algorithms here to adopt CUDA.
+// Re-implement std algorithms here to adopt CUDA/HIP
 template <typename T>
 struct Less {
   XGBOOST_DEVICE constexpr bool operator()(const T& _x, const T& _y) const {

include/xgboost/version_config.h

@@ -6,6 +6,6 @@
 
 #define XGBOOST_VER_MAJOR 2  /* NOLINT */
 #define XGBOOST_VER_MINOR 0  /* NOLINT */
-#define XGBOOST_VER_PATCH 0  /* NOLINT */
+#define XGBOOST_VER_PATCH 3  /* NOLINT */
 
 #endif  // XGBOOST_VERSION_CONFIG_H_

jvm-packages/CMakeLists.txt

@@ -9,6 +9,11 @@ if (USE_CUDA)
     ${PROJECT_SOURCE_DIR}/jvm-packages/xgboost4j-gpu/src/native/xgboost4j-gpu.cu)
 endif (USE_CUDA)
 
+if (USE_HIP)
+  list(APPEND JVM_SOURCES
+    ${PROJECT_SOURCE_DIR}/jvm-packages/xgboost4j-gpu/src/native/xgboost4j-gpu.hip)
+endif (USE_HIP)
+
 add_library(xgboost4j SHARED ${JVM_SOURCES} ${XGBOOST_OBJ_SOURCES})
 
 if (ENABLE_ALL_WARNINGS)
@@ -25,4 +30,3 @@ target_include_directories(xgboost4j
   ${PROJECT_SOURCE_DIR}/rabit/include)
 
 set_output_directory(xgboost4j ${PROJECT_SOURCE_DIR}/lib)
-target_link_libraries(xgboost4j PRIVATE ${JAVA_JVM_LIBRARY})

jvm-packages/create_jni.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python
-import errno
 import argparse
+import errno
 import glob
 import os
 import platform
@@ -19,11 +19,12 @@ CONFIG = {
     "USE_HDFS": "OFF",
     "USE_AZURE": "OFF",
     "USE_S3": "OFF",
-
     "USE_CUDA": "OFF",
     "USE_NCCL": "OFF",
+    "USE_HIP": "OFF",
+    "USE_RCCL": "OFF",
     "JVM_BINDINGS": "ON",
-    "LOG_CAPI_INVOCATION": "OFF"
+    "LOG_CAPI_INVOCATION": "OFF",
 }
 
 
@@ -70,26 +71,22 @@ def normpath(path):
         return normalized
 
 
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--log-capi-invocation', type=str, choices=['ON', 'OFF'], default='OFF')
-    parser.add_argument('--use-cuda', type=str, choices=['ON', 'OFF'], default='OFF')
-    cli_args = parser.parse_args()
-
+def native_build(args):
     if sys.platform == "darwin":
         # Enable of your compiler supports OpenMP.
         CONFIG["USE_OPENMP"] = "OFF"
-        os.environ["JAVA_HOME"] = subprocess.check_output(
-            "/usr/libexec/java_home").strip().decode()
+        os.environ["JAVA_HOME"] = (
+            subprocess.check_output("/usr/libexec/java_home").strip().decode()
+        )
 
     print("building Java wrapper")
     with cd(".."):
-        build_dir = 'build-gpu' if cli_args.use_cuda == 'ON' else 'build'
+        build_dir = "build-gpu" if cli_args.use_cuda == "ON" or cli_args.use_hip == "ON" else "build"
         maybe_makedirs(build_dir)
         with cd(build_dir):
             if sys.platform == "win32":
                 # Force x64 build on Windows.
-                maybe_generator = ' -A x64'
+                maybe_generator = " -A x64"
             else:
                 maybe_generator = ""
             if sys.platform == "linux":
@@ -97,12 +94,15 @@ if __name__ == "__main__":
             else:
                 maybe_parallel_build = ""
 
-            if cli_args.log_capi_invocation == 'ON':
-                CONFIG['LOG_CAPI_INVOCATION'] = 'ON'
+            if cli_args.log_capi_invocation == "ON":
+                CONFIG["LOG_CAPI_INVOCATION"] = "ON"
 
-            if cli_args.use_cuda == 'ON':
-                CONFIG['USE_CUDA'] = 'ON'
-                CONFIG['USE_NCCL'] = 'ON'
+            if cli_args.use_cuda == "ON":
+                CONFIG["USE_CUDA"] = "ON"
+                CONFIG["USE_NCCL"] = "ON"
+            elif cli_args.use_hip == "ON":
+                CONFIG["USE_HIP"] = "ON"
+                CONFIG["USE_RCCL"] = "ON"
 
             args = ["-D{0}:BOOL={1}".format(k, v) for k, v in CONFIG.items()]
 
@@ -115,7 +115,7 @@ if __name__ == "__main__":
             if gpu_arch_flag is not None:
                 args.append("%s" % gpu_arch_flag)
 
-            lib_dir = os.path.join(os.pardir, 'lib')
+            lib_dir = os.path.join(os.pardir, "lib")
             if os.path.exists(lib_dir):
                 shutil.rmtree(lib_dir)
             run("cmake .. " + " ".join(args) + maybe_generator)
@@ -125,8 +125,10 @@ if __name__ == "__main__":
             run(f'"{sys.executable}" mapfeat.py')
             run(f'"{sys.executable}" mknfold.py machine.txt 1')
 
-    xgboost4j = 'xgboost4j-gpu' if cli_args.use_cuda == 'ON' else 'xgboost4j'
-    xgboost4j_spark = 'xgboost4j-spark-gpu' if cli_args.use_cuda == 'ON' else 'xgboost4j-spark'
+    xgboost4j = "xgboost4j-gpu" if cli_args.use_cuda == "ON" or cli_args.use_hip == "ON" else "xgboost4j"
+    xgboost4j_spark = (
+        "xgboost4j-spark-gpu" if cli_args.use_cuda == "ON" or cli_args.use_hip == "ON" else "xgboost4j-spark"
+    )
 
     print("copying native library")
     library_name, os_folder = {
@@ -141,14 +143,19 @@ if __name__ == "__main__":
         "i86pc": "x86_64",  # on Solaris x86_64
         "sun4v": "sparc",  # on Solaris sparc
         "arm64": "aarch64",  # on macOS & Windows ARM 64-bit
-        "aarch64": "aarch64"
+        "aarch64": "aarch64",
     }[platform.machine().lower()]
-    output_folder = "{}/src/main/resources/lib/{}/{}".format(xgboost4j, os_folder, arch_folder)
+    output_folder = "{}/src/main/resources/lib/{}/{}".format(
+        xgboost4j, os_folder, arch_folder
+    )
     maybe_makedirs(output_folder)
     cp("../lib/" + library_name, output_folder)
 
     print("copying pure-Python tracker")
-    cp("../python-package/xgboost/tracker.py", "{}/src/main/resources".format(xgboost4j))
+    cp(
+        "../python-package/xgboost/tracker.py",
+        "{}/src/main/resources".format(xgboost4j),
+    )
 
     print("copying train/test files")
     maybe_makedirs("{}/src/test/resources".format(xgboost4j_spark))
@@ -164,3 +171,18 @@ if __name__ == "__main__":
     maybe_makedirs("{}/src/test/resources".format(xgboost4j))
     for file in glob.glob("../demo/data/agaricus.*"):
         cp(file, "{}/src/test/resources".format(xgboost4j))
+
+
+if __name__ == "__main__":
+    if "MAVEN_SKIP_NATIVE_BUILD" in os.environ:
+        print("MAVEN_SKIP_NATIVE_BUILD is set. Skipping native build...")
+    else:
+        parser = argparse.ArgumentParser()
+        parser.add_argument(
+            "--log-capi-invocation", type=str, choices=["ON", "OFF"], default="OFF"
+        )
+        parser.add_argument(
+            "--use-cuda", type=str, choices=["ON", "OFF"], default="OFF"
+        )
+        cli_args = parser.parse_args()
+        native_build(cli_args)

jvm-packages/pom.xml

@@ -5,8 +5,8 @@
     <modelVersion>4.0.0</modelVersion>
 
     <groupId>ml.dmlc</groupId>
-    <artifactId>xgboost-jvm</artifactId>
-    <version>2.0.0-SNAPSHOT</version>
+    <artifactId>xgboost-jvm_2.12</artifactId>
+    <version>2.0.3</version>
     <packaging>pom</packaging>
     <name>XGBoost JVM Package</name>
     <description>JVM Package for XGBoost</description>
@@ -43,6 +43,7 @@
         <maven.wagon.http.retryHandler.count>5</maven.wagon.http.retryHandler.count>
         <log.capi.invocation>OFF</log.capi.invocation>
         <use.cuda>OFF</use.cuda>
+        <use.hip>OFF</use.hip>
         <cudf.version>23.08.0</cudf.version>
         <spark.rapids.version>23.08.0</spark.rapids.version>
         <cudf.classifier>cuda11</cudf.classifier>
@@ -189,6 +190,93 @@
                 </plugins>
             </build>
         </profile>
+        <profile>
+            <id>release-cpu-only</id>
+            <modules>
+                <module>xgboost4j</module>
+                <module>xgboost4j-example</module>
+                <module>xgboost4j-spark</module>
+                <module>xgboost4j-flink</module>
+            </modules>
+            <build>
+                <plugins>
+                    <plugin>
+                        <groupId>org.apache.maven.plugins</groupId>
+                        <artifactId>maven-jar-plugin</artifactId>
+                        <version>3.3.0</version>
+                        <executions>
+                            <execution>
+                                <id>empty-javadoc-jar</id>
+                                <phase>package</phase>
+                                <goals>
+                                    <goal>jar</goal>
+                                </goals>
+                                <configuration>
+                                    <classifier>javadoc</classifier>
+                                    <classesDirectory>${basedir}/javadoc</classesDirectory>
+                                </configuration>
+                            </execution>
+                        </executions>
+                    </plugin>
+                    <plugin>
+                        <groupId>org.apache.maven.plugins</groupId>
+                        <artifactId>maven-release-plugin</artifactId>
+                        <version>3.0.1</version>
+                        <configuration>
+                            <autoVersionSubmodules>true</autoVersionSubmodules>
+                            <useReleaseProfile>false</useReleaseProfile>
+                            <releaseProfiles>release</releaseProfiles>
+                            <goals>deploy</goals>
+                        </configuration>
+                    </plugin>
+                    <plugin>
+                        <groupId>org.apache.maven.plugins</groupId>
+                        <artifactId>maven-gpg-plugin</artifactId>
+                        <version>3.1.0</version>
+                        <executions>
+                            <execution>
+                                <id>sign-artifacts</id>
+                                <phase>verify</phase>
+                                <goals>
+                                    <goal>sign</goal>
+                                </goals>
+                            </execution>
+                        </executions>
+                    </plugin>
+                    <plugin>
+                        <groupId>org.apache.maven.plugins</groupId>
+                        <artifactId>maven-source-plugin</artifactId>
+                        <version>3.3.0</version>
+                        <executions>
+                            <execution>
+                                <id>attach-sources</id>
+                                <goals>
+                                    <goal>jar-no-fork</goal>
+                                </goals>
+                            </execution>
+                        </executions>
+                    </plugin>
+                    <plugin>
+                        <groupId>org.sonatype.plugins</groupId>
+                        <artifactId>nexus-staging-maven-plugin</artifactId>
+                        <version>1.6.13</version>
+                        <extensions>true</extensions>
+                        <configuration>
+                            <serverId>ossrh</serverId>
+                            <nexusUrl>https://oss.sonatype.org/</nexusUrl>
+                            <autoReleaseAfterClose>false</autoReleaseAfterClose>
+                        </configuration>
+                    </plugin>
+                    <plugin>
+                        <groupId>org.apache.maven.plugins</groupId>
+                        <artifactId>maven-surefire-plugin</artifactId>
+                        <configuration>
+                            <skipTests>true</skipTests>
+                        </configuration>
+                    </plugin>
+                </plugins>
+            </build>
+        </profile>
         <profile>
             <id>assembly</id>
             <build>

jvm-packages/xgboost4j-example/pom.xml

@@ -5,12 +5,12 @@
     <modelVersion>4.0.0</modelVersion>
     <parent>
         <groupId>ml.dmlc</groupId>
-        <artifactId>xgboost-jvm</artifactId>
-        <version>2.0.0-SNAPSHOT</version>
+        <artifactId>xgboost-jvm_2.12</artifactId>
+        <version>2.0.3</version>
     </parent>
     <name>xgboost4j-example</name>
-    <artifactId>xgboost4j-example_${scala.binary.version}</artifactId>
-    <version>2.0.0-SNAPSHOT</version>
+    <artifactId>xgboost4j-example_2.12</artifactId>
+    <version>2.0.3</version>
     <packaging>jar</packaging>
     <build>
         <plugins>
@@ -26,7 +26,7 @@
     <dependencies>
         <dependency>
             <groupId>ml.dmlc</groupId>
-            <artifactId>xgboost4j-spark_${scala.binary.version}</artifactId>
+            <artifactId>xgboost4j-spark_2.12</artifactId>
             <version>${project.version}</version>
         </dependency>
         <dependency>
@@ -37,7 +37,7 @@
         </dependency>
         <dependency>
             <groupId>ml.dmlc</groupId>
-            <artifactId>xgboost4j-flink_${scala.binary.version}</artifactId>
+            <artifactId>xgboost4j-flink_2.12</artifactId>
             <version>${project.version}</version>
         </dependency>
     </dependencies>

jvm-packages/xgboost4j-flink/pom.xml

@@ -5,13 +5,13 @@
     <modelVersion>4.0.0</modelVersion>
     <parent>
         <groupId>ml.dmlc</groupId>
-        <artifactId>xgboost-jvm</artifactId>
-        <version>2.0.0-SNAPSHOT</version>
+        <artifactId>xgboost-jvm_2.12</artifactId>
+        <version>2.0.3</version>
     </parent>
 
     <name>xgboost4j-flink</name>
-    <artifactId>xgboost4j-flink_${scala.binary.version}</artifactId>
-    <version>2.0.0-SNAPSHOT</version>
+    <artifactId>xgboost4j-flink_2.12</artifactId>
+    <version>2.0.3</version>
     <properties>
       <flink-ml.version>2.2.0</flink-ml.version>
     </properties>
@@ -30,7 +30,7 @@
     <dependencies>
         <dependency>
             <groupId>ml.dmlc</groupId>
-            <artifactId>xgboost4j_${scala.binary.version}</artifactId>
+            <artifactId>xgboost4j_2.12</artifactId>
             <version>${project.version}</version>
         </dependency>
         <dependency>

jvm-packages/xgboost4j-gpu/pom.xml

@@ -5,12 +5,12 @@
     <modelVersion>4.0.0</modelVersion>
     <parent>
         <groupId>ml.dmlc</groupId>
-        <artifactId>xgboost-jvm</artifactId>
-        <version>2.0.0-SNAPSHOT</version>
+        <artifactId>xgboost-jvm_2.12</artifactId>
+        <version>2.0.3</version>
     </parent>
-    <artifactId>xgboost4j-gpu_${scala.binary.version}</artifactId>
+    <artifactId>xgboost4j-gpu_2.12</artifactId>
     <name>xgboost4j-gpu</name>
-    <version>2.0.0-SNAPSHOT</version>
+    <version>2.0.3</version>
     <packaging>jar</packaging>
 
     <dependencies>
@@ -104,6 +104,8 @@
                                 <argument>${log.capi.invocation}</argument>
                                 <argument>--use-cuda</argument>
                                 <argument>${use.cuda}</argument>
+                                <argument>--use-hip</argument>
+                                <argument>${use.hip}</argument>
                             </arguments>
                             <workingDirectory>${user.dir}</workingDirectory>
                         </configuration>

jvm-packages/xgboost4j-gpu/src/native/xgboost4j-gpu.cpp

@@ -2,7 +2,7 @@
 // Created by bobwang on 2021/9/8.
 //
 
-#ifndef XGBOOST_USE_CUDA
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 
 #include <jni.h>
 

jvm-packages/xgboost4j-gpu/src/native/xgboost4j-gpu.cu

@@ -1,6 +1,10 @@
 #include <jni.h>
 
+#if defined(XGBOOST_USE_CUDA)
 #include "../../../../src/common/device_helpers.cuh"
+#elif defined(XGBOOST_USE_HIP)
+#include "../../../../src/common/device_helpers.hip.h"
+#endif
 #include "../../../../src/common/cuda_pinned_allocator.h"
 #include "../../../../src/data/array_interface.h"
 #include "jvm_utils.h"

jvm-packages/xgboost4j-gpu/src/native/xgboost4j-gpu.hip

@@ -0,0 +1,4 @@
+
+#if defined(XGBOOST_USE_HIP)
+#include "xgboost4j-gpu.cu"
+#endif

jvm-packages/xgboost4j-spark-gpu/pom.xml

@@ -5,11 +5,11 @@
     <modelVersion>4.0.0</modelVersion>
     <parent>
         <groupId>ml.dmlc</groupId>
-        <artifactId>xgboost-jvm</artifactId>
-        <version>2.0.0-SNAPSHOT</version>
+        <artifactId>xgboost-jvm_2.12</artifactId>
+        <version>2.0.3</version>
     </parent>
     <name>xgboost4j-spark-gpu</name>
-    <artifactId>xgboost4j-spark-gpu_${scala.binary.version}</artifactId>
+    <artifactId>xgboost4j-spark-gpu_2.12</artifactId>
     <build>
         <plugins>
             <plugin>
@@ -24,7 +24,7 @@
     <dependencies>
         <dependency>
             <groupId>ml.dmlc</groupId>
-            <artifactId>xgboost4j-gpu_${scala.binary.version}</artifactId>
+            <artifactId>xgboost4j-gpu_2.12</artifactId>
             <version>${project.version}</version>
         </dependency>
         <dependency>

jvm-packages/xgboost4j-spark/pom.xml

@@ -5,11 +5,11 @@
     <modelVersion>4.0.0</modelVersion>
     <parent>
         <groupId>ml.dmlc</groupId>
-        <artifactId>xgboost-jvm</artifactId>
-        <version>2.0.0-SNAPSHOT</version>
+        <artifactId>xgboost-jvm_2.12</artifactId>
+        <version>2.0.3</version>
     </parent>
     <name>xgboost4j-spark</name>
-    <artifactId>xgboost4j-spark_${scala.binary.version}</artifactId>
+    <artifactId>xgboost4j-spark_2.12</artifactId>
     <build>
         <plugins>
             <plugin>
@@ -24,7 +24,7 @@
     <dependencies>
         <dependency>
             <groupId>ml.dmlc</groupId>
-            <artifactId>xgboost4j_${scala.binary.version}</artifactId>
+            <artifactId>xgboost4j_2.12</artifactId>
             <version>${project.version}</version>
         </dependency>
         <dependency>

jvm-packages/xgboost4j/pom.xml

@@ -5,12 +5,12 @@
     <modelVersion>4.0.0</modelVersion>
     <parent>
         <groupId>ml.dmlc</groupId>
-        <artifactId>xgboost-jvm</artifactId>
-        <version>2.0.0-SNAPSHOT</version>
+        <artifactId>xgboost-jvm_2.12</artifactId>
+        <version>2.0.3</version>
     </parent>
     <name>xgboost4j</name>
-    <artifactId>xgboost4j_${scala.binary.version}</artifactId>
-    <version>2.0.0-SNAPSHOT</version>
+    <artifactId>xgboost4j_2.12</artifactId>
+    <version>2.0.3</version>
     <packaging>jar</packaging>
 
     <dependencies>

python-package/packager/build_config.py

@@ -15,6 +15,10 @@ class BuildConfiguration:  # pylint: disable=R0902
     use_cuda: bool = False
     # Whether to enable NCCL
     use_nccl: bool = False
+    # Whether to enablea HIP 
+    use_hip: bool = False
+    # Whether to enable RCCL
+    use_rccl: bool = False
     # Whether to enable HDFS
     use_hdfs: bool = False
     # Whether to enable Azure Storage

python-package/packager/nativelib.py

@@ -132,17 +132,29 @@ def locate_or_build_libxgboost(
 
     if build_config.use_system_libxgboost:
         # Find libxgboost from system prefix
-        sys_base_prefix = pathlib.Path(sys.base_prefix).absolute().resolve()
-        libxgboost_sys = sys_base_prefix / "lib" / _lib_name()
-        if not libxgboost_sys.exists():
+        sys_prefix = pathlib.Path(sys.base_prefix)
+        sys_prefix_candidates = [
+            sys_prefix / "lib",
+            # Paths possibly used on Windows
+            sys_prefix / "bin",
+            sys_prefix / "Library",
+            sys_prefix / "Library" / "bin",
+            sys_prefix / "Library" / "lib",
+        ]
+        sys_prefix_candidates = [
+            p.expanduser().resolve() for p in sys_prefix_candidates
+        ]
+        for candidate_dir in sys_prefix_candidates:
+            libtreelite_sys = candidate_dir / _lib_name()
+            if libtreelite_sys.exists():
+                logger.info("Using system XGBoost: %s", str(libtreelite_sys))
+                return libtreelite_sys
         raise RuntimeError(
             f"use_system_libxgboost was specified but {_lib_name()} is "
-                f"not found in {libxgboost_sys.parent}"
+            f"not found. Paths searched (in order): \n"
+            + "\n".join([f"* {str(p)}" for p in sys_prefix_candidates])
         )
 
-        logger.info("Using system XGBoost: %s", str(libxgboost_sys))
-        return libxgboost_sys
-
     libxgboost = locate_local_libxgboost(toplevel_dir, logger=logger)
     if libxgboost is not None:
         return libxgboost

python-package/pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "packager.pep517"
 
 [project]
 name = "xgboost"
-version = "2.0.0-dev"
+version = "2.0.3"
 authors = [
     { name = "Hyunsu Cho", email = "chohyu01@cs.washington.edu" },
     { name = "Jiaming Yuan", email = "jm.yuan@outlook.com" }

python-package/xgboost/VERSION

@@ -1 +1 @@
-2.0.0-dev
+2.0.3

python-package/xgboost/compat.py

@@ -88,6 +88,18 @@ def is_cudf_available() -> bool:
         return False
 
 
+def is_cupy_available() -> bool:
+    """Check cupy package available or not"""
+    if importlib.util.find_spec("cupy") is None:
+        return False
+    try:
+        import cupy
+
+        return True
+    except ImportError:
+        return False
+
+
 try:
     import scipy.sparse as scipy_sparse
     from scipy.sparse import csr_matrix as scipy_csr

python-package/xgboost/core.py

@@ -206,6 +206,7 @@ def _load_lib() -> ctypes.CDLL:
             lib = ctypes.cdll.LoadLibrary(lib_path)
             setattr(lib, "path", os.path.normpath(lib_path))
             lib_success = True
+            break
         except OSError as e:
             os_error_list.append(str(e))
             continue
@@ -2399,6 +2400,7 @@ class Booster:
             _is_cudf_df,
             _is_cupy_array,
             _is_list,
+            _is_np_array_like,
             _is_pandas_df,
             _is_pandas_series,
             _is_tuple,
@@ -2428,7 +2430,7 @@ class Booster:
                     f"got {data.shape[1]}"
                 )
 
-        if isinstance(data, np.ndarray):
+        if _is_np_array_like(data):
             from .data import _ensure_np_dtype
 
             data, _ = _ensure_np_dtype(data, data.dtype)

python-package/xgboost/dask.py

@@ -78,7 +78,6 @@ from .data import _is_cudf_ser, _is_cupy_array
 from .sklearn import (
     XGBClassifier,
     XGBClassifierBase,
-    XGBClassifierMixIn,
     XGBModel,
     XGBRanker,
     XGBRankerMixIn,
@@ -1854,7 +1853,7 @@ class DaskXGBRegressor(DaskScikitLearnBase, XGBRegressorBase):
     "Implementation of the scikit-learn API for XGBoost classification.",
     ["estimators", "model"],
 )
-class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierMixIn, XGBClassifierBase):
+class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
     # pylint: disable=missing-class-docstring
     async def _fit_async(
         self,
@@ -2036,10 +2035,6 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierMixIn, XGBClassifierBa
             preds = da.map_blocks(_argmax, pred_probs, drop_axis=1)
         return preds
 
-    def load_model(self, fname: ModelIn) -> None:
-        super().load_model(fname)
-        self._load_model_attributes(self.get_booster())
-
 
 @xgboost_model_doc(
     """Implementation of the Scikit-Learn API for XGBoost Ranking.

python-package/xgboost/data.py

@@ -164,8 +164,8 @@ def _is_scipy_coo(data: DataType) -> bool:
     return isinstance(data, scipy.sparse.coo_matrix)
 
 
-def _is_numpy_array(data: DataType) -> bool:
-    return isinstance(data, (np.ndarray, np.matrix))
+def _is_np_array_like(data: DataType) -> bool:
+    return hasattr(data, "__array_interface__")
 
 
 def _ensure_np_dtype(
@@ -317,7 +317,6 @@ def pandas_feature_info(
 ) -> Tuple[Optional[FeatureNames], Optional[FeatureTypes]]:
     """Handle feature info for pandas dataframe."""
     import pandas as pd
-    from pandas.api.types import is_categorical_dtype, is_sparse
 
     # handle feature names
     if feature_names is None and meta is None:
@@ -332,10 +331,10 @@ def pandas_feature_info(
     if feature_types is None and meta is None:
         feature_types = []
         for dtype in data.dtypes:
-            if is_sparse(dtype):
+            if is_pd_sparse_dtype(dtype):
                 feature_types.append(_pandas_dtype_mapper[dtype.subtype.name])
             elif (
-                is_categorical_dtype(dtype) or is_pa_ext_categorical_dtype(dtype)
+                is_pd_cat_dtype(dtype) or is_pa_ext_categorical_dtype(dtype)
             ) and enable_categorical:
                 feature_types.append(CAT_T)
             else:
@@ -345,18 +344,13 @@ def pandas_feature_info(
 
 def is_nullable_dtype(dtype: PandasDType) -> bool:
     """Whether dtype is a pandas nullable type."""
-    from pandas.api.types import (
-        is_bool_dtype,
-        is_categorical_dtype,
-        is_float_dtype,
-        is_integer_dtype,
-    )
+    from pandas.api.types import is_bool_dtype, is_float_dtype, is_integer_dtype
 
     is_int = is_integer_dtype(dtype) and dtype.name in pandas_nullable_mapper
     # np.bool has alias `bool`, while pd.BooleanDtype has `boolean`.
     is_bool = is_bool_dtype(dtype) and dtype.name == "boolean"
     is_float = is_float_dtype(dtype) and dtype.name in pandas_nullable_mapper
-    return is_int or is_bool or is_float or is_categorical_dtype(dtype)
+    return is_int or is_bool or is_float or is_pd_cat_dtype(dtype)
 
 
 def is_pa_ext_dtype(dtype: Any) -> bool:
@@ -371,17 +365,48 @@ def is_pa_ext_categorical_dtype(dtype: Any) -> bool:
     )
 
 
+def is_pd_cat_dtype(dtype: PandasDType) -> bool:
+    """Wrapper for testing pandas category type."""
+    import pandas as pd
+
+    if hasattr(pd.util, "version") and hasattr(pd.util.version, "Version"):
+        Version = pd.util.version.Version
+        if Version(pd.__version__) >= Version("2.1.0"):
+            from pandas import CategoricalDtype
+
+            return isinstance(dtype, CategoricalDtype)
+
+    from pandas.api.types import is_categorical_dtype
+
+    return is_categorical_dtype(dtype)
+
+
+def is_pd_sparse_dtype(dtype: PandasDType) -> bool:
+    """Wrapper for testing pandas sparse type."""
+    import pandas as pd
+
+    if hasattr(pd.util, "version") and hasattr(pd.util.version, "Version"):
+        Version = pd.util.version.Version
+        if Version(pd.__version__) >= Version("2.1.0"):
+            from pandas import SparseDtype
+
+            return isinstance(dtype, SparseDtype)
+
+    from pandas.api.types import is_sparse
+
+    return is_sparse(dtype)
+
+
 def pandas_cat_null(data: DataFrame) -> DataFrame:
     """Handle categorical dtype and nullable extension types from pandas."""
     import pandas as pd
-    from pandas.api.types import is_categorical_dtype
 
     # handle category codes and nullable.
     cat_columns = []
     nul_columns = []
     # avoid an unnecessary conversion if possible
     for col, dtype in zip(data.columns, data.dtypes):
-        if is_categorical_dtype(dtype):
+        if is_pd_cat_dtype(dtype):
             cat_columns.append(col)
         elif is_pa_ext_categorical_dtype(dtype):
             raise ValueError(
@@ -398,7 +423,7 @@ def pandas_cat_null(data: DataFrame) -> DataFrame:
         transformed = data
 
     def cat_codes(ser: pd.Series) -> pd.Series:
-        if is_categorical_dtype(ser.dtype):
+        if is_pd_cat_dtype(ser.dtype):
             return ser.cat.codes
         assert is_pa_ext_categorical_dtype(ser.dtype)
         # Not yet supported, the index is not ordered for some reason. Alternately:
@@ -454,14 +479,12 @@ def _transform_pandas_df(
     meta: Optional[str] = None,
     meta_type: Optional[NumpyDType] = None,
 ) -> Tuple[np.ndarray, Optional[FeatureNames], Optional[FeatureTypes]]:
-    from pandas.api.types import is_categorical_dtype, is_sparse
-
     pyarrow_extension = False
     for dtype in data.dtypes:
         if not (
             (dtype.name in _pandas_dtype_mapper)
-            or is_sparse(dtype)
-            or (is_categorical_dtype(dtype) and enable_categorical)
+            or is_pd_sparse_dtype(dtype)
+            or (is_pd_cat_dtype(dtype) and enable_categorical)
             or is_pa_ext_dtype(dtype)
         ):
             _invalid_dataframe_dtype(data)
@@ -515,9 +538,8 @@ def _meta_from_pandas_series(
 ) -> None:
     """Help transform pandas series for meta data like labels"""
     data = data.values.astype("float")
-    from pandas.api.types import is_sparse
 
-    if is_sparse(data):
+    if is_pd_sparse_dtype(getattr(data, "dtype", data)):
         data = data.to_dense()  # type: ignore
     assert len(data.shape) == 1 or data.shape[1] == 0 or data.shape[1] == 1
     _meta_from_numpy(data, name, dtype, handle)
@@ -539,13 +561,11 @@ def _from_pandas_series(
     feature_names: Optional[FeatureNames],
     feature_types: Optional[FeatureTypes],
 ) -> DispatchedDataBackendReturnType:
-    from pandas.api.types import is_categorical_dtype
-
     if (data.dtype.name not in _pandas_dtype_mapper) and not (
-        is_categorical_dtype(data.dtype) and enable_categorical
+        is_pd_cat_dtype(data.dtype) and enable_categorical
     ):
         _invalid_dataframe_dtype(data)
-    if enable_categorical and is_categorical_dtype(data.dtype):
+    if enable_categorical and is_pd_cat_dtype(data.dtype):
         data = data.cat.codes
     return _from_numpy_array(
         data.values.reshape(data.shape[0], 1).astype("float"),
@@ -1051,7 +1071,7 @@ def dispatch_data_backend(
         return _from_scipy_csr(
             data.tocsr(), missing, threads, feature_names, feature_types
         )
-    if _is_numpy_array(data):
+    if _is_np_array_like(data):
         return _from_numpy_array(
             data, missing, threads, feature_names, feature_types, data_split_mode
         )
@@ -1194,7 +1214,7 @@ def dispatch_meta_backend(
     if _is_tuple(data):
         _meta_from_tuple(data, name, dtype, handle)
         return
-    if _is_numpy_array(data):
+    if _is_np_array_like(data):
         _meta_from_numpy(data, name, dtype, handle)
         return
     if _is_pandas_df(data):
@@ -1281,7 +1301,7 @@ def _proxy_transform(
         return _transform_dlpack(data), None, feature_names, feature_types
     if _is_list(data) or _is_tuple(data):
         data = np.array(data)
-    if _is_numpy_array(data):
+    if _is_np_array_like(data):
         data, _ = _ensure_np_dtype(data, data.dtype)
         return data, None, feature_names, feature_types
     if _is_scipy_csr(data):
@@ -1331,7 +1351,7 @@ def dispatch_proxy_set_data(
     if not allow_host:
         raise err
 
-    if _is_numpy_array(data):
+    if _is_np_array_like(data):
         _check_data_shape(data)
         proxy._set_data_from_array(data)  # pylint: disable=W0212
         return

python-package/xgboost/libpath.py

@@ -31,16 +31,15 @@ def find_lib_path() -> List[str]:
     ]
 
     if sys.platform == "win32":
-        if platform.architecture()[0] == "64bit":
-            dll_path.append(os.path.join(curr_path, "../../windows/x64/Release/"))
-            # hack for pip installation when copy all parent source
-            # directory here
-            dll_path.append(os.path.join(curr_path, "./windows/x64/Release/"))
-        else:
-            dll_path.append(os.path.join(curr_path, "../../windows/Release/"))
-            # hack for pip installation when copy all parent source
-            # directory here
-            dll_path.append(os.path.join(curr_path, "./windows/Release/"))
+        # On Windows, Conda may install libs in different paths
+        dll_path.extend(
+            [
+                os.path.join(sys.base_prefix, "bin"),
+                os.path.join(sys.base_prefix, "Library"),
+                os.path.join(sys.base_prefix, "Library", "bin"),
+                os.path.join(sys.base_prefix, "Library", "lib"),
+            ]
+        )
         dll_path = [os.path.join(p, "xgboost.dll") for p in dll_path]
     elif sys.platform.startswith(("linux", "freebsd", "emscripten")):
         dll_path = [os.path.join(p, "libxgboost.so") for p in dll_path]

python-package/xgboost/sklearn.py

@@ -43,19 +43,6 @@ from .data import _is_cudf_df, _is_cudf_ser, _is_cupy_array, _is_pandas_df
 from .training import train
 
 
-class XGBClassifierMixIn:  # pylint: disable=too-few-public-methods
-    """MixIn for classification."""
-
-    def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(*args, **kwargs)
-
-    def _load_model_attributes(self, booster: Booster) -> None:
-        config = json.loads(booster.save_config())
-        self.n_classes_ = int(config["learner"]["learner_model_param"]["num_class"])
-        # binary classification is treated as regression in XGBoost.
-        self.n_classes_ = 2 if self.n_classes_ < 2 else self.n_classes_
-
-
 class XGBRankerMixIn:  # pylint: disable=too-few-public-methods
     """MixIn for ranking, defines the _estimator_type usually defined in scikit-learn
     base classes.
@@ -845,9 +832,7 @@ class XGBModel(XGBModelBase):
         self.get_booster().load_model(fname)
 
         meta_str = self.get_booster().attr("scikit_learn")
-        if meta_str is None:
-            return
-
+        if meta_str is not None:
             meta = json.loads(meta_str)
             t = meta.get("_estimator_type", None)
             if t is not None and t != self._get_type():
@@ -855,11 +840,30 @@ class XGBModel(XGBModelBase):
                     "Loading an estimator with different type. Expecting: "
                     f"{self._get_type()}, got: {t}"
                 )
+
         self.feature_types = self.get_booster().feature_types
         self.get_booster().set_attr(scikit_learn=None)
+        config = json.loads(self.get_booster().save_config())
+        self._load_model_attributes(config)
 
     load_model.__doc__ = f"""{Booster.load_model.__doc__}"""
 
+    def _load_model_attributes(self, config: dict) -> None:
+        """Load model attributes without hyper-parameters."""
+        from sklearn.base import is_classifier
+
+        booster = self.get_booster()
+
+        self.objective = config["learner"]["objective"]["name"]
+        self.booster = config["learner"]["gradient_booster"]["name"]
+        self.base_score = config["learner"]["learner_model_param"]["base_score"]
+        self.feature_types = booster.feature_types
+
+        if is_classifier(self):
+            self.n_classes_ = int(config["learner"]["learner_model_param"]["num_class"])
+            # binary classification is treated as regression in XGBoost.
+            self.n_classes_ = 2 if self.n_classes_ < 2 else self.n_classes_
+
     # pylint: disable=too-many-branches
     def _configure_fit(
         self,
@@ -1409,7 +1413,7 @@ def _cls_predict_proba(n_classes: int, prediction: PredtT, vstack: Callable) ->
         Number of boosting rounds.
 """,
 )
-class XGBClassifier(XGBModel, XGBClassifierMixIn, XGBClassifierBase):
+class XGBClassifier(XGBModel, XGBClassifierBase):
     # pylint: disable=missing-docstring,invalid-name,too-many-instance-attributes
     @_deprecate_positional_args
     def __init__(
@@ -1637,10 +1641,6 @@ class XGBClassifier(XGBModel, XGBClassifierMixIn, XGBClassifierBase):
     def classes_(self) -> np.ndarray:
         return np.arange(self.n_classes_)
 
-    def load_model(self, fname: ModelIn) -> None:
-        super().load_model(fname)
-        self._load_model_attributes(self.get_booster())
-
 
 @xgboost_model_doc(
     "scikit-learn API for XGBoost random forest classification.",
@@ -2093,7 +2093,17 @@ class XGBRanker(XGBModel, XGBRankerMixIn):
 
         """
         X, qid = _get_qid(X, None)
-        Xyq = DMatrix(X, y, qid=qid)
+        # fixme(jiamingy): base margin and group weight is not yet supported. We might
+        # need to make extra special fields in the dataframe.
+        Xyq = DMatrix(
+            X,
+            y,
+            qid=qid,
+            missing=self.missing,
+            enable_categorical=self.enable_categorical,
+            nthread=self.n_jobs,
+            feature_types=self.feature_types,
+        )
         if callable(self.eval_metric):
             metric = ltr_metric_decorator(self.eval_metric, self.n_jobs)
             result_str = self.get_booster().eval_set([(Xyq, "eval")], feval=metric)

python-package/xgboost/spark/core.py

@@ -22,7 +22,7 @@ from typing import (
 
 import numpy as np
 import pandas as pd
-from pyspark import SparkContext, cloudpickle
+from pyspark import RDD, SparkContext, cloudpickle
 from pyspark.ml import Estimator, Model
 from pyspark.ml.functions import array_to_vector, vector_to_array
 from pyspark.ml.linalg import VectorUDT
@@ -44,6 +44,7 @@ from pyspark.ml.util import (
     MLWritable,
     MLWriter,
 )
+from pyspark.resource import ResourceProfileBuilder, TaskResourceRequests
 from pyspark.sql import Column, DataFrame
 from pyspark.sql.functions import col, countDistinct, pandas_udf, rand, struct
 from pyspark.sql.types import (
@@ -59,11 +60,12 @@ from scipy.special import expit, softmax  # pylint: disable=no-name-in-module
 
 import xgboost
 from xgboost import XGBClassifier
-from xgboost.compat import is_cudf_available
+from xgboost.compat import is_cudf_available, is_cupy_available
 from xgboost.core import Booster, _check_distributed_params
 from xgboost.sklearn import DEFAULT_N_ESTIMATORS, XGBModel, _can_use_qdm
 from xgboost.training import train as worker_train
 
+from .._typing import ArrayLike
 from .data import (
     _read_csr_matrix_from_unwrapped_spark_vec,
     alias,
@@ -87,6 +89,7 @@ from .utils import (
     _get_rabit_args,
     _get_spark_session,
     _is_local,
+    _is_standalone_or_localcluster,
     deserialize_booster,
     deserialize_xgb_model,
     get_class_name,
@@ -241,6 +244,13 @@ class _SparkXGBParams(
         TypeConverters.toList,
     )
 
+    def set_device(self, value: str) -> "_SparkXGBParams":
+        """Set device, optional value: cpu, cuda, gpu"""
+        _check_distributed_params({"device": value})
+        assert value in ("cpu", "cuda", "gpu")
+        self.set(self.device, value)
+        return self
+
     @classmethod
     def _xgb_cls(cls) -> Type[XGBModel]:
         """
@@ -334,6 +344,54 @@ class _SparkXGBParams(
                 predict_params[param.name] = self.getOrDefault(param)
         return predict_params
 
+    def _validate_gpu_params(self) -> None:
+        """Validate the gpu parameters and gpu configurations"""
+
+        if use_cuda(self.getOrDefault(self.device)) or self.getOrDefault(self.use_gpu):
+            ss = _get_spark_session()
+            sc = ss.sparkContext
+
+            if _is_local(sc):
+                # Support GPU training in Spark local mode is just for debugging
+                # purposes, so it's okay for printing the below warning instead of
+                # checking the real gpu numbers and raising the exception.
+                get_logger(self.__class__.__name__).warning(
+                    "You have enabled GPU in spark local mode. Please make sure your"
+                    " local node has at least %d GPUs",
+                    self.getOrDefault(self.num_workers),
+                )
+            else:
+                executor_gpus = sc.getConf().get("spark.executor.resource.gpu.amount")
+                if executor_gpus is None:
+                    raise ValueError(
+                        "The `spark.executor.resource.gpu.amount` is required for training"
+                        " on GPU."
+                    )
+
+                if not (ss.version >= "3.4.0" and _is_standalone_or_localcluster(sc)):
+                    # We will enable stage-level scheduling in spark 3.4.0+ which doesn't
+                    # require spark.task.resource.gpu.amount to be set explicitly
+                    gpu_per_task = sc.getConf().get("spark.task.resource.gpu.amount")
+                    if gpu_per_task is not None:
+                        if float(gpu_per_task) < 1.0:
+                            raise ValueError(
+                                "XGBoost doesn't support GPU fractional configurations. "
+                                "Please set `spark.task.resource.gpu.amount=spark.executor"
+                                ".resource.gpu.amount`"
+                            )
+
+                        if float(gpu_per_task) > 1.0:
+                            get_logger(self.__class__.__name__).warning(
+                                "%s GPUs for each Spark task is configured, but each "
+                                "XGBoost training task uses only 1 GPU.",
+                                gpu_per_task,
+                            )
+                    else:
+                        raise ValueError(
+                            "The `spark.task.resource.gpu.amount` is required for training"
+                            " on GPU."
+                        )
+
     def _validate_params(self) -> None:
         # pylint: disable=too-many-branches
         init_model = self.getOrDefault("xgb_model")
@@ -413,53 +471,7 @@ class _SparkXGBParams(
                     "`pyspark.ml.linalg.Vector` type."
                 )
 
-        if use_cuda(self.getOrDefault(self.device)) or self.getOrDefault(self.use_gpu):
-            gpu_per_task = (
-                _get_spark_session()
-                .sparkContext.getConf()
-                .get("spark.task.resource.gpu.amount")
-            )
-
-            is_local = _is_local(_get_spark_session().sparkContext)
-
-            if is_local:
-                # checking spark local mode.
-                if gpu_per_task is not None:
-                    raise RuntimeError(
-                        "The spark local mode does not support gpu configuration."
-                        "Please remove spark.executor.resource.gpu.amount and "
-                        "spark.task.resource.gpu.amount"
-                    )
-
-                # Support GPU training in Spark local mode is just for debugging
-                # purposes, so it's okay for printing the below warning instead of
-                # checking the real gpu numbers and raising the exception.
-                get_logger(self.__class__.__name__).warning(
-                    "You have enabled GPU in spark local mode. Please make sure your"
-                    " local node has at least %d GPUs",
-                    self.getOrDefault(self.num_workers),
-                )
-            else:
-                # checking spark non-local mode.
-                if gpu_per_task is not None:
-                    if float(gpu_per_task) < 1.0:
-                        raise ValueError(
-                            "XGBoost doesn't support GPU fractional configurations. "
-                            "Please set `spark.task.resource.gpu.amount=spark.executor"
-                            ".resource.gpu.amount`"
-                        )
-
-                    if float(gpu_per_task) > 1.0:
-                        get_logger(self.__class__.__name__).warning(
-                            "%s GPUs for each Spark task is configured, but each "
-                            "XGBoost training task uses only 1 GPU.",
-                            gpu_per_task,
-                        )
-                else:
-                    raise ValueError(
-                        "The `spark.task.resource.gpu.amount` is required for training"
-                        " on GPU."
-                    )
+        self._validate_gpu_params()
 
 
 def _validate_and_convert_feature_col_as_float_col_list(
@@ -584,6 +596,8 @@ class _SparkXGBEstimator(Estimator, _SparkXGBParams, MLReadable, MLWritable):
             arbitrary_params_dict={},
         )
 
+        self.logger = get_logger(self.__class__.__name__)
+
     def setParams(self, **kwargs: Any) -> None:  # pylint: disable=invalid-name
         """
         Set params for the estimator.
@@ -886,6 +900,116 @@ class _SparkXGBEstimator(Estimator, _SparkXGBParams, MLReadable, MLWritable):
 
         return booster_params, train_call_kwargs_params, dmatrix_kwargs
 
+    def _skip_stage_level_scheduling(self) -> bool:
+        # pylint: disable=too-many-return-statements
+        """Check if stage-level scheduling is not needed,
+        return true to skip stage-level scheduling"""
+
+        if use_cuda(self.getOrDefault(self.device)) or self.getOrDefault(self.use_gpu):
+            ss = _get_spark_session()
+            sc = ss.sparkContext
+
+            if ss.version < "3.4.0":
+                self.logger.info(
+                    "Stage-level scheduling in xgboost requires spark version 3.4.0+"
+                )
+                return True
+
+            if not _is_standalone_or_localcluster(sc):
+                self.logger.info(
+                    "Stage-level scheduling in xgboost requires spark standalone or "
+                    "local-cluster mode"
+                )
+                return True
+
+            executor_cores = sc.getConf().get("spark.executor.cores")
+            executor_gpus = sc.getConf().get("spark.executor.resource.gpu.amount")
+            if executor_cores is None or executor_gpus is None:
+                self.logger.info(
+                    "Stage-level scheduling in xgboost requires spark.executor.cores, "
+                    "spark.executor.resource.gpu.amount to be set."
+                )
+                return True
+
+            if int(executor_cores) == 1:
+                # there will be only 1 task running at any time.
+                self.logger.info(
+                    "Stage-level scheduling in xgboost requires spark.executor.cores > 1 "
+                )
+                return True
+
+            if int(executor_gpus) > 1:
+                # For spark.executor.resource.gpu.amount > 1, we suppose user knows how to configure
+                # to make xgboost run successfully.
+                #
+                self.logger.info(
+                    "Stage-level scheduling in xgboost will not work "
+                    "when spark.executor.resource.gpu.amount>1"
+                )
+                return True
+
+            task_gpu_amount = sc.getConf().get("spark.task.resource.gpu.amount")
+
+            if task_gpu_amount is None:
+                # The ETL tasks will not grab a gpu when spark.task.resource.gpu.amount is not set,
+                # but with stage-level scheduling, we can make training task grab the gpu.
+                return False
+
+            if float(task_gpu_amount) == float(executor_gpus):
+                # spark.executor.resource.gpu.amount=spark.task.resource.gpu.amount "
+                # results in only 1 task running at a time, which may cause perf issue.
+                return True
+
+            # We can enable stage-level scheduling
+            return False
+
+        # CPU training doesn't require stage-level scheduling
+        return True
+
+    def _try_stage_level_scheduling(self, rdd: RDD) -> RDD:
+        """Try to enable stage-level scheduling"""
+
+        if self._skip_stage_level_scheduling():
+            return rdd
+
+        ss = _get_spark_session()
+
+        # executor_cores will not be None
+        executor_cores = ss.sparkContext.getConf().get("spark.executor.cores")
+        assert executor_cores is not None
+
+        # Spark-rapids is a project to leverage GPUs to accelerate spark SQL.
+        # If spark-rapids is enabled, to avoid GPU OOM, we don't allow other
+        # ETL gpu tasks running alongside training tasks.
+        spark_plugins = ss.conf.get("spark.plugins", " ")
+        assert spark_plugins is not None
+        spark_rapids_sql_enabled = ss.conf.get("spark.rapids.sql.enabled", "true")
+        assert spark_rapids_sql_enabled is not None
+
+        task_cores = (
+            int(executor_cores)
+            if "com.nvidia.spark.SQLPlugin" in spark_plugins
+            and "true" == spark_rapids_sql_enabled.lower()
+            else (int(executor_cores) // 2) + 1
+        )
+
+        # Each training task requires cpu cores > total executor cores//2 + 1 which can
+        # make sure the tasks be sent to different executors.
+        #
+        # Please note that we can't use GPU to limit the concurrent tasks because of
+        # https://issues.apache.org/jira/browse/SPARK-45527.
+
+        task_gpus = 1.0
+        treqs = TaskResourceRequests().cpus(task_cores).resource("gpu", task_gpus)
+        rp = ResourceProfileBuilder().require(treqs).build
+
+        self.logger.info(
+            "XGBoost training tasks require the resource(cores=%s, gpu=%s).",
+            task_cores,
+            task_gpus,
+        )
+        return rdd.withResources(rp)
+
     def _fit(self, dataset: DataFrame) -> "_SparkXGBModel":
         # pylint: disable=too-many-statements, too-many-locals
         self._validate_params()
@@ -986,14 +1110,16 @@ class _SparkXGBEstimator(Estimator, _SparkXGBParams, MLReadable, MLWritable):
                 )
 
         def _run_job() -> Tuple[str, str]:
-            ret = (
+            rdd = (
                 dataset.mapInPandas(
-                    _train_booster, schema="config string, booster string"  # type: ignore
+                    _train_booster,  # type: ignore
+                    schema="config string, booster string",
                 )
                 .rdd.barrier()
                 .mapPartitions(lambda x: x)
-                .collect()[0]
             )
+            rdd_with_resource = self._try_stage_level_scheduling(rdd)
+            ret = rdd_with_resource.collect()[0]
             return ret[0], ret[1]
 
         get_logger("XGBoost-PySpark").info(
@@ -1117,12 +1243,111 @@ class _SparkXGBModel(Model, _SparkXGBParams, MLReadable, MLWritable):
             )
         return features_col, feature_col_names
 
+    def _get_pred_contrib_col_name(self) -> Optional[str]:
+        """Return the pred_contrib_col col name"""
+        pred_contrib_col_name = None
+        if (
+            self.isDefined(self.pred_contrib_col)
+            and self.getOrDefault(self.pred_contrib_col) != ""
+        ):
+            pred_contrib_col_name = self.getOrDefault(self.pred_contrib_col)
+
+        return pred_contrib_col_name
+
+    def _out_schema(self) -> Tuple[bool, str]:
+        """Return the bool to indicate if it's a single prediction, true is single prediction,
+        and the returned type of the user-defined function. The value must
+        be a DDL-formatted type string."""
+
+        if self._get_pred_contrib_col_name() is not None:
+            return False, f"{pred.prediction} double, {pred.pred_contrib} array<double>"
+
+        return True, "double"
+
+    def _get_predict_func(self) -> Callable:
+        """Return the true prediction function which will be running on the executor side"""
+
+        predict_params = self._gen_predict_params_dict()
+        pred_contrib_col_name = self._get_pred_contrib_col_name()
+
+        def _predict(
+            model: XGBModel, X: ArrayLike, base_margin: Optional[ArrayLike]
+        ) -> Union[pd.DataFrame, pd.Series]:
+            data = {}
+            preds = model.predict(
+                X,
+                base_margin=base_margin,
+                validate_features=False,
+                **predict_params,
+            )
+            data[pred.prediction] = pd.Series(preds)
+
+            if pred_contrib_col_name is not None:
+                contribs = pred_contribs(model, X, base_margin)
+                data[pred.pred_contrib] = pd.Series(list(contribs))
+                return pd.DataFrame(data=data)
+
+            return data[pred.prediction]
+
+        return _predict
+
+    def _post_transform(self, dataset: DataFrame, pred_col: Column) -> DataFrame:
+        """Post process of transform"""
+        prediction_col_name = self.getOrDefault(self.predictionCol)
+        single_pred, _ = self._out_schema()
+
+        if single_pred:
+            if prediction_col_name:
+                dataset = dataset.withColumn(prediction_col_name, pred_col)
+        else:
+            pred_struct_col = "_prediction_struct"
+            dataset = dataset.withColumn(pred_struct_col, pred_col)
+
+            if prediction_col_name:
+                dataset = dataset.withColumn(
+                    prediction_col_name, getattr(col(pred_struct_col), pred.prediction)
+                )
+
+            pred_contrib_col_name = self._get_pred_contrib_col_name()
+            if pred_contrib_col_name is not None:
+                dataset = dataset.withColumn(
+                    pred_contrib_col_name,
+                    array_to_vector(getattr(col(pred_struct_col), pred.pred_contrib)),
+                )
+
+            dataset = dataset.drop(pred_struct_col)
+        return dataset
+
+    def _gpu_transform(self) -> bool:
+        """If gpu is used to do the prediction, true to gpu prediction"""
+
+        if _is_local(_get_spark_session().sparkContext):
+            # if it's local model, we just use the internal "device"
+            return use_cuda(self.getOrDefault(self.device))
+
+        gpu_per_task = (
+            _get_spark_session()
+            .sparkContext.getConf()
+            .get("spark.task.resource.gpu.amount")
+        )
+
+        # User don't set gpu configurations, just use cpu
+        if gpu_per_task is None:
+            if use_cuda(self.getOrDefault(self.device)):
+                get_logger("XGBoost-PySpark").warning(
+                    "Do the prediction on the CPUs since "
+                    "no gpu configurations are set"
+                )
+            return False
+
+        # User already sets the gpu configurations, we just use the internal "device".
+        return use_cuda(self.getOrDefault(self.device))
+
     def _transform(self, dataset: DataFrame) -> DataFrame:
         # pylint: disable=too-many-statements, too-many-locals
         # Save xgb_sklearn_model and predict_params to be local variable
         # to avoid the `self` object to be pickled to remote.
         xgb_sklearn_model = self._xgb_sklearn_model
-        predict_params = self._gen_predict_params_dict()
 
         has_base_margin = False
         if (
@@ -1137,79 +1362,92 @@ class _SparkXGBModel(Model, _SparkXGBParams, MLReadable, MLWritable):
         features_col, feature_col_names = self._get_feature_col(dataset)
         enable_sparse_data_optim = self.getOrDefault(self.enable_sparse_data_optim)
 
-        pred_contrib_col_name = None
-        if (
-            self.isDefined(self.pred_contrib_col)
-            and self.getOrDefault(self.pred_contrib_col) != ""
-        ):
-            pred_contrib_col_name = self.getOrDefault(self.pred_contrib_col)
+        predict_func = self._get_predict_func()
 
-        single_pred = True
-        schema = "double"
-        if pred_contrib_col_name:
-            single_pred = False
-            schema = f"{pred.prediction} double, {pred.pred_contrib} array<double>"
+        _, schema = self._out_schema()
+
+        is_local = _is_local(_get_spark_session().sparkContext)
+        run_on_gpu = self._gpu_transform()
 
         @pandas_udf(schema)  # type: ignore
         def predict_udf(iterator: Iterator[pd.DataFrame]) -> Iterator[pd.Series]:
             assert xgb_sklearn_model is not None
             model = xgb_sklearn_model
+
+            from pyspark import TaskContext
+
+            context = TaskContext.get()
+            assert context is not None
+
+            dev_ordinal = -1
+
+            if is_cudf_available():
+                if is_local:
+                    if run_on_gpu and is_cupy_available():
+                        import cupy as cp  # pylint: disable=import-error
+
+                        total_gpus = cp.cuda.runtime.getDeviceCount()
+                        if total_gpus > 0:
+                            partition_id = context.partitionId()
+                            # For transform local mode, default the dev_ordinal to
+                            # (partition id) % gpus.
+                            dev_ordinal = partition_id % total_gpus
+                elif run_on_gpu:
+                    dev_ordinal = _get_gpu_id(context)
+
+                if dev_ordinal >= 0:
+                    device = "cuda:" + str(dev_ordinal)
+                    get_logger("XGBoost-PySpark").info(
+                        "Do the inference with device: %s", device
+                    )
+                    model.set_params(device=device)
+                else:
+                    get_logger("XGBoost-PySpark").info("Do the inference on the CPUs")
+            else:
+                msg = (
+                    "CUDF is unavailable, fallback the inference on the CPUs"
+                    if run_on_gpu
+                    else "Do the inference on the CPUs"
+                )
+                get_logger("XGBoost-PySpark").info(msg)
+
+            def to_gpu_if_possible(data: ArrayLike) -> ArrayLike:
+                """Move the data to gpu if possible"""
+                if dev_ordinal >= 0:
+                    import cudf  # pylint: disable=import-error
+                    import cupy as cp  # pylint: disable=import-error
+
+                    # We must set the device after import cudf, which will change the device id to 0
+                    # See https://github.com/rapidsai/cudf/issues/11386
+                    cp.cuda.runtime.setDevice(dev_ordinal)  # pylint: disable=I1101
+                    df = cudf.DataFrame(data)
+                    del data
+                    return df
+                return data
+
             for data in iterator:
                 if enable_sparse_data_optim:
                     X = _read_csr_matrix_from_unwrapped_spark_vec(data)
                 else:
                     if feature_col_names is not None:
-                        X = data[feature_col_names]
+                        tmp = data[feature_col_names]
                     else:
-                        X = stack_series(data[alias.data])
+                        tmp = stack_series(data[alias.data])
+                    X = to_gpu_if_possible(tmp)
 
                 if has_base_margin:
-                    base_margin = data[alias.margin].to_numpy()
+                    base_margin = to_gpu_if_possible(data[alias.margin])
                 else:
                     base_margin = None
 
-                data = {}
-                preds = model.predict(
-                    X,
-                    base_margin=base_margin,
-                    validate_features=False,
-                    **predict_params,
-                )
-                data[pred.prediction] = pd.Series(preds)
-
-                if pred_contrib_col_name:
-                    contribs = pred_contribs(model, X, base_margin)
-                    data[pred.pred_contrib] = pd.Series(list(contribs))
-                    yield pd.DataFrame(data=data)
-                else:
-                    yield data[pred.prediction]
+                yield predict_func(model, X, base_margin)
 
         if has_base_margin:
             pred_col = predict_udf(struct(*features_col, base_margin_col))
         else:
             pred_col = predict_udf(struct(*features_col))
 
-        prediction_col_name = self.getOrDefault(self.predictionCol)
-
-        if single_pred:
-            dataset = dataset.withColumn(prediction_col_name, pred_col)
-        else:
-            pred_struct_col = "_prediction_struct"
-            dataset = dataset.withColumn(pred_struct_col, pred_col)
-
-            dataset = dataset.withColumn(
-                prediction_col_name, getattr(col(pred_struct_col), pred.prediction)
-            )
-
-            if pred_contrib_col_name:
-                dataset = dataset.withColumn(
-                    pred_contrib_col_name,
-                    array_to_vector(getattr(col(pred_struct_col), pred.pred_contrib)),
-                )
-
-            dataset = dataset.drop(pred_struct_col)
-
-        return dataset
+        return self._post_transform(dataset, pred_col)
 
 
 class _ClassificationModel(  # pylint: disable=abstract-method
@@ -1221,22 +1459,21 @@ class _ClassificationModel(  # pylint: disable=abstract-method
     .. Note:: This API is experimental.
     """
 
-    def _transform(self, dataset: DataFrame) -> DataFrame:
-        # pylint: disable=too-many-statements, too-many-locals
-        # Save xgb_sklearn_model and predict_params to be local variable
-        # to avoid the `self` object to be pickled to remote.
-        xgb_sklearn_model = self._xgb_sklearn_model
-        predict_params = self._gen_predict_params_dict()
-
-        has_base_margin = False
-        if (
-            self.isDefined(self.base_margin_col)
-            and self.getOrDefault(self.base_margin_col) != ""
-        ):
-            has_base_margin = True
-            base_margin_col = col(self.getOrDefault(self.base_margin_col)).alias(
-                alias.margin
+    def _out_schema(self) -> Tuple[bool, str]:
+        schema = (
+            f"{pred.raw_prediction} array<double>, {pred.prediction} double,"
+            f" {pred.probability} array<double>"
         )
+        if self._get_pred_contrib_col_name() is not None:
+            # We will force setting strict_shape to True when predicting contribs,
+            # So, it will also output 3-D shape result.
+            schema = f"{schema}, {pred.pred_contrib} array<array<double>>"
+
+        return False, schema
+
+    def _get_predict_func(self) -> Callable:
+        predict_params = self._gen_predict_params_dict()
+        pred_contrib_col_name = self._get_pred_contrib_col_name()
 
         def transform_margin(margins: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
             if margins.ndim == 1:
@@ -1251,45 +1488,9 @@ class _ClassificationModel(  # pylint: disable=abstract-method
                 class_probs = softmax(raw_preds, axis=1)
             return raw_preds, class_probs
 
-        features_col, feature_col_names = self._get_feature_col(dataset)
-        enable_sparse_data_optim = self.getOrDefault(self.enable_sparse_data_optim)
-
-        pred_contrib_col_name = None
-        if (
-            self.isDefined(self.pred_contrib_col)
-            and self.getOrDefault(self.pred_contrib_col) != ""
-        ):
-            pred_contrib_col_name = self.getOrDefault(self.pred_contrib_col)
-
-        schema = (
-            f"{pred.raw_prediction} array<double>, {pred.prediction} double,"
-            f" {pred.probability} array<double>"
-        )
-        if pred_contrib_col_name:
-            # We will force setting strict_shape to True when predicting contribs,
-            # So, it will also output 3-D shape result.
-            schema = f"{schema}, {pred.pred_contrib} array<array<double>>"
-
-        @pandas_udf(schema)  # type: ignore
-        def predict_udf(
-            iterator: Iterator[Tuple[pd.Series, ...]]
-        ) -> Iterator[pd.DataFrame]:
-            assert xgb_sklearn_model is not None
-            model = xgb_sklearn_model
-            for data in iterator:
-                if enable_sparse_data_optim:
-                    X = _read_csr_matrix_from_unwrapped_spark_vec(data)
-                else:
-                    if feature_col_names is not None:
-                        X = data[feature_col_names]  # type: ignore
-                    else:
-                        X = stack_series(data[alias.data])
-
-                if has_base_margin:
-                    base_margin = stack_series(data[alias.margin])
-                else:
-                    base_margin = None
-
+        def _predict(
+            model: XGBModel, X: ArrayLike, base_margin: Optional[np.ndarray]
+        ) -> Union[pd.DataFrame, pd.Series]:
             margins = model.predict(
                 X,
                 base_margin=base_margin,
@@ -1308,19 +1509,17 @@ class _ClassificationModel(  # pylint: disable=abstract-method
                 pred.probability: pd.Series(list(class_probs)),
             }
 
-                if pred_contrib_col_name:
+            if pred_contrib_col_name is not None:
                 contribs = pred_contribs(model, X, base_margin, strict_shape=True)
                 result[pred.pred_contrib] = pd.Series(list(contribs.tolist()))
 
-                yield pd.DataFrame(data=result)
+            return pd.DataFrame(data=result)
 
-        if has_base_margin:
-            pred_struct = predict_udf(struct(*features_col, base_margin_col))
-        else:
-            pred_struct = predict_udf(struct(*features_col))
+        return _predict
 
+    def _post_transform(self, dataset: DataFrame, pred_col: Column) -> DataFrame:
         pred_struct_col = "_prediction_struct"
-        dataset = dataset.withColumn(pred_struct_col, pred_struct)
+        dataset = dataset.withColumn(pred_struct_col, pred_col)
 
         raw_prediction_col_name = self.getOrDefault(self.rawPredictionCol)
         if raw_prediction_col_name:
@@ -1342,7 +1541,8 @@ class _ClassificationModel(  # pylint: disable=abstract-method
                 array_to_vector(getattr(col(pred_struct_col), pred.probability)),
             )
 
-        if pred_contrib_col_name:
+        pred_contrib_col_name = self._get_pred_contrib_col_name()
+        if pred_contrib_col_name is not None:
             dataset = dataset.withColumn(
                 pred_contrib_col_name,
                 getattr(col(pred_struct_col), pred.pred_contrib),

python-package/xgboost/spark/utils.py

@@ -10,7 +10,7 @@ from threading import Thread
 from typing import Any, Callable, Dict, Optional, Set, Type
 
 import pyspark
-from pyspark import BarrierTaskContext, SparkContext, SparkFiles
+from pyspark import BarrierTaskContext, SparkContext, SparkFiles, TaskContext
 from pyspark.sql.session import SparkSession
 
 from xgboost import Booster, XGBModel, collective
@@ -129,7 +129,14 @@ def _is_local(spark_context: SparkContext) -> bool:
     return spark_context._jsc.sc().isLocal()
 
 
-def _get_gpu_id(task_context: BarrierTaskContext) -> int:
+def _is_standalone_or_localcluster(spark_context: SparkContext) -> bool:
+    master = spark_context.getConf().get("spark.master")
+    return master is not None and (
+        master.startswith("spark://") or master.startswith("local-cluster")
+    )
+
+
+def _get_gpu_id(task_context: TaskContext) -> int:
     """Get the gpu id from the task resources"""
     if task_context is None:
         # This is a safety check.

python-package/xgboost/testing/ranking.py

@@ -75,3 +75,28 @@ def run_ranking_qid_df(impl: ModuleType, tree_method: str) -> None:
 
     with pytest.raises(ValueError, match="Either `group` or `qid`."):
         ranker.fit(df, y, eval_set=[(X, y)])
+
+
+def run_ranking_categorical(device: str) -> None:
+    """Test LTR with categorical features."""
+    from sklearn.model_selection import cross_val_score
+
+    X, y = tm.make_categorical(
+        n_samples=512, n_features=10, n_categories=3, onehot=False
+    )
+    rng = np.random.default_rng(1994)
+    qid = rng.choice(3, size=y.shape[0])
+    qid = np.sort(qid)
+    X["qid"] = qid
+
+    ltr = xgb.XGBRanker(enable_categorical=True, device=device)
+    ltr.fit(X, y)
+    score = ltr.score(X, y)
+    assert score > 0.9
+
+    ltr = xgb.XGBRanker(enable_categorical=True, device=device)
+
+    # test using the score function inside sklearn.
+    scores = cross_val_score(ltr, X, y)
+    for s in scores:
+        assert s > 0.7

rocgputreeshap

@@ -0,0 +1 @@
+Subproject commit 2fea6734e83cf147c1bbe580ac4713cd50abcad5

src/CMakeLists.txt

@@ -16,6 +16,11 @@ if (USE_CUDA)
   target_sources(objxgboost PRIVATE ${CUDA_SOURCES})
 endif (USE_CUDA)
 
+if (USE_HIP)
+  file(GLOB_RECURSE HIP_SOURCES *.hip *.hip.h)
+  target_sources(objxgboost PRIVATE ${HIP_SOURCES})
+endif (USE_HIP)
+
 target_include_directories(objxgboost
   PRIVATE
   ${xgboost_SOURCE_DIR}/include
@@ -33,6 +38,7 @@ msvc_use_static_runtime()
 
 # This grouping organises source files nicely in visual studio
 auto_source_group("${CUDA_SOURCES}")
+auto_source_group("${HIP_SOURCES}")
 auto_source_group("${CPU_SOURCES}")
 
 #-- End object library

src/c_api/c_api.cc

@@ -70,12 +70,14 @@ XGB_DLL void XGBoostVersion(int* major, int* minor, int* patch) {
 
 using GlobalConfigAPIThreadLocalStore = dmlc::ThreadLocalStore<XGBAPIThreadLocalEntry>;
 
-#if !defined(XGBOOST_USE_CUDA)
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 namespace xgboost {
 void XGBBuildInfoDevice(Json *p_info) {
   auto &info = *p_info;
   info["USE_CUDA"] = Boolean{false};
   info["USE_NCCL"] = Boolean{false};
+  info["USE_HIP"] = Boolean{false};
+  info["USE_RCCL"] = Boolean{false};
   info["USE_RMM"] = Boolean{false};
 }
 }  // namespace xgboost
@@ -279,7 +281,7 @@ XGB_DLL int XGDMatrixCreateFromDataIter(
   API_END();
 }
 
-#ifndef XGBOOST_USE_CUDA
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 XGB_DLL int XGDMatrixCreateFromCudaColumnar(char const *, char const *, DMatrixHandle *) {
   API_BEGIN();
   common::AssertGPUSupport();
@@ -1189,7 +1191,7 @@ XGB_DLL int XGBoosterPredictFromCSR(BoosterHandle handle, char const *indptr, ch
   API_END();
 }
 
-#if !defined(XGBOOST_USE_CUDA)
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 XGB_DLL int XGBoosterPredictFromCUDAArray(BoosterHandle handle, char const *, char const *,
                                           DMatrixHandle, xgboost::bst_ulong const **,
                                           xgboost::bst_ulong *, const float **) {

src/c_api/c_api.cu

@@ -17,7 +17,11 @@ namespace xgboost {
 void XGBBuildInfoDevice(Json *p_info) {
   auto &info = *p_info;
 
+#if defined(XGBOOST_USE_CUDA)
   info["USE_CUDA"] = true;
+#elif defined(XGBOOST_USE_HIP)
+  info["USE_HIP"] = true;
+#endif
 
   std::vector<Json> v{Json{Integer{THRUST_MAJOR_VERSION}}, Json{Integer{THRUST_MINOR_VERSION}},
                       Json{Integer{THRUST_SUBMINOR_VERSION}}};
@@ -30,8 +34,13 @@ void XGBBuildInfoDevice(Json *p_info) {
   info["USE_NCCL"] = Boolean{true};
   v = {Json{Integer{NCCL_MAJOR}}, Json{Integer{NCCL_MINOR}}, Json{Integer{NCCL_PATCH}}};
   info["NCCL_VERSION"] = v;
+#elif defined(XGBOOST_USE_RCCL)
+  info["USE_RCCL"] = Boolean{true};
+  v = {Json{Integer{NCCL_MAJOR}}, Json{Integer{NCCL_MINOR}}, Json{Integer{NCCL_PATCH}}};
+  info["RCCL_VERSION"] = v;
 #else
   info["USE_NCCL"] = Boolean{false};
+  info["USE_RCCL"] = Boolean{false};
 #endif
 
 #if defined(XGBOOST_USE_RMM)

src/c_api/c_api.hip

@@ -0,0 +1,4 @@
+
+#if defined(XGBOOST_USE_HIP)
+#include "c_api.cu"
+#endif

src/c_api/c_api_utils.h

@@ -175,7 +175,7 @@ inline float GetMissing(Json const &config) {
 
 // Safe guard some global variables from being changed by XGBoost.
 class XGBoostAPIGuard {
-#if defined(XGBOOST_USE_CUDA)
+#if defined(XGBOOST_USE_CUDA) || defined(XGBOOST_USE_HIP)
   int32_t device_id_ {0};
 
   void SetGPUAttribute();

src/collective/communicator.cc

@@ -51,7 +51,7 @@ void Communicator::Init(Json const& config) {
   }
 }
 
-#ifndef XGBOOST_USE_CUDA
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 void Communicator::Finalize() {
   communicator_->Shutdown();
   communicator_.reset(new NoOpCommunicator());

src/collective/communicator.cu

@@ -5,7 +5,7 @@
 #include "device_communicator.cuh"
 #include "device_communicator_adapter.cuh"
 #include "noop_communicator.h"
-#ifdef XGBOOST_USE_NCCL
+#if defined(XGBOOST_USE_NCCL) || defined(XGBOOST_USE_RCCL)
 #include "nccl_device_communicator.cuh"
 #endif
 
@@ -28,7 +28,7 @@ DeviceCommunicator* Communicator::GetDevice(int device_ordinal) {
       communicator_->GetWorldSize() != old_world_size) {
     old_device_ordinal = device_ordinal;
     old_world_size = communicator_->GetWorldSize();
-#ifdef XGBOOST_USE_NCCL
+#if defined(XGBOOST_USE_NCCL) || defined(XGBOOST_USE_RCCL)
     switch (type_) {
       case CommunicatorType::kRabit:
         device_communicator_.reset(new NcclDeviceCommunicator(device_ordinal, false));

src/collective/communicator.h

@@ -98,7 +98,7 @@ class Communicator {
   /** @brief Get the communicator instance. */
   static Communicator *Get() { return communicator_.get(); }
 
-#if defined(XGBOOST_USE_CUDA)
+#if defined(XGBOOST_USE_CUDA) || defined(XGBOOST_USE_HIP)
   /**
    * @brief Get the device communicator.
    *
@@ -230,7 +230,7 @@ class Communicator {
 
   static thread_local std::unique_ptr<Communicator> communicator_;
   static thread_local CommunicatorType type_;
-#if defined(XGBOOST_USE_CUDA)
+#if defined(XGBOOST_USE_CUDA) || defined(XGBOOST_USE_HIP)
   static thread_local std::unique_ptr<DeviceCommunicator> device_communicator_;
 #endif
 

src/collective/communicator.hip

@@ -0,0 +1,4 @@
+
+#if defined(XGBOOST_USE_HIP)
+#include "communicator.cu"
+#endif

src/collective/device_communicator_adapter.cuh

@@ -41,7 +41,6 @@ class DeviceCommunicatorAdapter : public DeviceCommunicator {
     }
 
     dh::safe_cuda(cudaSetDevice(device_ordinal_));
-
     segments->clear();
     segments->resize(world_size_, 0);
     segments->at(rank_) = length_bytes;
@@ -60,6 +59,7 @@ class DeviceCommunicatorAdapter : public DeviceCommunicator {
       Broadcast(host_buffer_.data() + offset, as_bytes, i);
       offset += as_bytes;
     }
+
     dh::safe_cuda(cudaMemcpy(receive_buffer->data().get(), host_buffer_.data(), total_bytes,
                              cudaMemcpyDefault));
   }

src/collective/nccl_device_communicator.cu

@@ -1,7 +1,7 @@
 /*!
  * Copyright 2023 XGBoost contributors
  */
-#if defined(XGBOOST_USE_NCCL)
+#if defined(XGBOOST_USE_NCCL) || defined(XGBOOST_USE_RCCL)
 #include "nccl_device_communicator.cuh"
 
 namespace xgboost {

src/collective/nccl_device_communicator.cuh

@@ -35,12 +35,22 @@ class NcclDeviceCommunicator : public DeviceCommunicator {
 
  private:
   static constexpr std::size_t kUuidLength =
+#if defined(XGBOOST_USE_CUDA)
       sizeof(std::declval<cudaDeviceProp>().uuid) / sizeof(uint64_t);
+#elif defined(XGBOOST_USE_HIP)
+      sizeof(hipUUID) / sizeof(uint64_t);
+#endif
 
   void GetCudaUUID(xgboost::common::Span<uint64_t, kUuidLength> const &uuid) const {
+#if defined(XGBOOST_USE_CUDA)
     cudaDeviceProp prob{};
     dh::safe_cuda(cudaGetDeviceProperties(&prob, device_ordinal_));
     std::memcpy(uuid.data(), static_cast<void *>(&(prob.uuid)), sizeof(prob.uuid));
+#elif defined(XGBOOST_USE_HIP)
+    hipUUID id;
+    hipDeviceGetUuid(&id, device_ordinal_);
+    std::memcpy(uuid.data(), static_cast<void *>(&id), sizeof(id));
+#endif
   }
 
   static std::string PrintUUID(xgboost::common::Span<uint64_t, kUuidLength> const &uuid) {

src/collective/nccl_device_communicator.hip

@@ -0,0 +1,4 @@
+
+#if defined(XGBOOST_USE_HIP)
+#include "nccl_device_communicator.cu"
+#endif

src/common/algorithm.cuh

@@ -10,7 +10,13 @@
 
 #include <cstddef>             // size_t
 #include <cstdint>             // int32_t
+
+#if defined(XGBOOST_USE_HIP)
+#include <hipcub/hipcub.hpp>
+#elif defined(XGBOOST_USE_CUDA)
 #include <cub/cub.cuh>         // DispatchSegmentedRadixSort,NullType,DoubleBuffer
+#endif
+
 #include <iterator>            // distance
 #include <limits>              // numeric_limits
 #include <type_traits>         // conditional_t,remove_const_t
@@ -39,6 +45,7 @@ static void DeviceSegmentedRadixSortKeys(CUDAContext const *ctx, void *d_temp_st
   using OffsetT = int;
 
   // Null value type
+#if defined(XGBOOST_USE_CUDA)
   cub::DoubleBuffer<KeyT> d_keys(const_cast<KeyT *>(d_keys_in), d_keys_out);
   cub::DoubleBuffer<cub::NullType> d_values;
 
@@ -47,6 +54,20 @@ static void DeviceSegmentedRadixSortKeys(CUDAContext const *ctx, void *d_temp_st
                  OffsetT>::Dispatch(d_temp_storage, temp_storage_bytes, d_keys, d_values, num_items,
                                     num_segments, d_begin_offsets, d_end_offsets, begin_bit,
                                     end_bit, false, ctx->Stream(), debug_synchronous)));
+#elif defined(XGBOOST_USE_HIP)
+  if (IS_DESCENDING) {
+      rocprim::segmented_radix_sort_pairs_desc<KeyT, hipcub::NullType, BeginOffsetIteratorT>(d_temp_storage,
+              temp_storage_bytes, d_keys_in, d_keys_out, nullptr, nullptr, num_items,
+              num_segments, d_begin_offsets, d_end_offsets,
+              begin_bit, end_bit, ctx->Stream(), debug_synchronous);
+  }
+  else {
+      rocprim::segmented_radix_sort_pairs<KeyT, hipcub::NullType, BeginOffsetIteratorT>(d_temp_storage,
+              temp_storage_bytes, d_keys_in, d_keys_out, nullptr, nullptr, num_items,
+              num_segments, d_begin_offsets, d_end_offsets,
+              begin_bit, end_bit, ctx->Stream(), debug_synchronous);
+  }
+#endif
 }
 
 // Wrapper around cub sort for easier `descending` sort.
@@ -60,14 +81,18 @@ void DeviceSegmentedRadixSortPair(void *d_temp_storage,
                                   BeginOffsetIteratorT d_begin_offsets,
                                   EndOffsetIteratorT d_end_offsets, dh::CUDAStreamView stream,
                                   int begin_bit = 0, int end_bit = sizeof(KeyT) * 8) {
+#if defined(XGBOOST_USE_CUDA)
   cub::DoubleBuffer<KeyT> d_keys(const_cast<KeyT *>(d_keys_in), d_keys_out);
   cub::DoubleBuffer<ValueT> d_values(const_cast<ValueT *>(d_values_in), d_values_out);
+#endif
+
   // In old version of cub, num_items in dispatch is also int32_t, no way to change.
   using OffsetT = std::conditional_t<dh::BuildWithCUDACub() && dh::HasThrustMinorVer<13>(),
                                      std::size_t, std::int32_t>;
   CHECK_LE(num_items, std::numeric_limits<OffsetT>::max());
   // For Thrust >= 1.12 or CUDA >= 11.4, we require system cub installation
 
+#if defined(XGBOOST_USE_CUDA)
 #if THRUST_MAJOR_VERSION >= 2
   dh::safe_cuda((cub::DispatchSegmentedRadixSort<
                  descending, KeyT, ValueT, BeginOffsetIteratorT, EndOffsetIteratorT,
@@ -88,6 +113,18 @@ void DeviceSegmentedRadixSortPair(void *d_temp_storage,
                                                           d_begin_offsets, d_end_offsets, begin_bit,
                                                           end_bit, false, stream, false)));
 #endif
+#elif defined(XGBOOST_USE_HIP)
+  if (descending) {
+      rocprim::segmented_radix_sort_pairs_desc(d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out,
+              d_values_in, d_values_out, num_items, num_segments,
+              d_begin_offsets, d_end_offsets, begin_bit, end_bit, stream, false);
+  }
+  else {
+      rocprim::segmented_radix_sort_pairs(d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out,
+              d_values_in, d_values_out, num_items, num_segments, d_begin_offsets, d_end_offsets,
+              begin_bit, end_bit, stream, false);
+  }
+#endif
 }
 }  // namespace detail
 

src/common/bitfield.h

@@ -17,14 +17,18 @@
 #include <thrust/copy.h>
 #include <thrust/device_ptr.h>
 #include "device_helpers.cuh"
-#endif  // defined(__CUDACC__)
+#elif defined(__HIPCC__)
+#include <thrust/copy.h>
+#include <thrust/device_ptr.h>
+#include "device_helpers.hip.h"
+#endif  // defined(__CUDACC__) || defined(__HIPCC__)
 
 #include "xgboost/span.h"
 #include "common.h"
 
 namespace xgboost {
 
-#if defined(__CUDACC__)
+#if defined(__CUDACC__) || defined(__HIPCC__)
 using BitFieldAtomicType = unsigned long long;  // NOLINT
 
 __forceinline__ __device__ BitFieldAtomicType AtomicOr(BitFieldAtomicType* address,
@@ -48,7 +52,7 @@ __forceinline__ __device__ BitFieldAtomicType AtomicAnd(BitFieldAtomicType* addr
 
   return old;
 }
-#endif  // defined(__CUDACC__)
+#endif  // defined(__CUDACC__) || defined(__HIPCC__)
 
 /**
  * @brief A non-owning type with auxiliary methods defined for manipulating bits.
@@ -106,7 +110,7 @@ struct BitFieldContainer {
   XGBOOST_DEVICE static size_t ComputeStorageSize(index_type size) {
     return common::DivRoundUp(size, kValueSize);
   }
-#if defined(__CUDA_ARCH__)
+#if defined(__CUDA_ARCH__) || defined(__HIPCC__)
   __device__ BitFieldContainer& operator|=(BitFieldContainer const& rhs) {
     auto tid = blockIdx.x * blockDim.x + threadIdx.x;
     size_t min_size = min(NumValues(), rhs.NumValues());
@@ -123,9 +127,9 @@ struct BitFieldContainer {
     }
     return *this;
   }
-#endif  // #if defined(__CUDA_ARCH__)
+#endif  // #if defined(__CUDA_ARCH__) || defined(__HIPCC__)
 
-#if defined(__CUDA_ARCH__)
+#if defined(__CUDA_ARCH__) || defined(__HIPCC__)
   __device__ BitFieldContainer& operator&=(BitFieldContainer const& rhs) {
     size_t min_size = min(NumValues(), rhs.NumValues());
     auto tid = blockIdx.x * blockDim.x + threadIdx.x;
@@ -144,7 +148,7 @@ struct BitFieldContainer {
   }
 #endif  // defined(__CUDA_ARCH__)
 
-#if defined(__CUDA_ARCH__)
+#if defined(__CUDA_ARCH__) || defined(__HIPCC__)
   __device__ auto Set(index_type pos) noexcept(true) {
     Pos pos_v = Direction::Shift(ToBitPos(pos));
     value_type& value = Data()[pos_v.int_pos];
@@ -159,6 +163,14 @@ struct BitFieldContainer {
     using Type = typename dh::detail::AtomicDispatcher<sizeof(value_type)>::Type;
     atomicAnd(reinterpret_cast<Type *>(&value), clear_bit);
   }
+#ifdef __HIPCC__
+  void Clear(index_type pos) noexcept(true) {
+    Pos pos_v = Direction::Shift(ToBitPos(pos));
+    value_type& value = Data()[pos_v.int_pos];
+    value_type clear_bit = ~(kOne << pos_v.bit_pos);
+    value &= clear_bit;
+  }
+#endif
 #else
   void Set(index_type pos) noexcept(true) {
     Pos pos_v = Direction::Shift(ToBitPos(pos));
@@ -172,7 +184,7 @@ struct BitFieldContainer {
     value_type clear_bit = ~(kOne << pos_v.bit_pos);
     value &= clear_bit;
   }
-#endif  // defined(__CUDA_ARCH__)
+#endif  // defined(__CUDA_ARCH__) || defined(__HIPCC__)
 
   XGBOOST_DEVICE bool Check(Pos pos_v) const noexcept(true) {
     pos_v = Direction::Shift(pos_v);

src/common/categorical.h

@@ -52,7 +52,7 @@ inline XGBOOST_DEVICE bool InvalidCat(float cat) {
  *
  *   Go to left if it's NOT the matching category, which matches one-hot encoding.
  */
-inline XGBOOST_DEVICE bool Decision(common::Span<uint32_t const> cats, float cat) {
+inline XGBOOST_DEVICE bool Decision(common::Span<CatBitField::value_type const> cats, float cat) {
   KCatBitField const s_cats(cats);
   if (XGBOOST_EXPECT(InvalidCat(cat), false)) {
     return true;

src/common/common.cc

@@ -55,7 +55,7 @@ void EscapeU8(std::string const &string, std::string *p_buffer) {
   }
 }
 
-#if !defined(XGBOOST_USE_CUDA)
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 int AllVisibleGPUs() { return 0; }
 #endif  // !defined(XGBOOST_USE_CUDA)
 

src/common/common.cu

@@ -2,6 +2,7 @@
  * Copyright 2018-2022 XGBoost contributors
  */
 #include "common.h"
+#include "cuda_to_hip.h"
 
 namespace xgboost {
 namespace common {

src/common/common.h

@@ -26,6 +26,12 @@
 
 #define WITH_CUDA() true
 
+#elif defined(__HIPCC__)
+#include <thrust/system/hip/error.h>
+#include <thrust/system_error.h>
+
+#define WITH_CUDA() true
+
 #else
 
 #define WITH_CUDA() false
@@ -39,8 +45,8 @@ namespace dh {
  */
 #define safe_cuda(ans) ThrowOnCudaError((ans), __FILE__, __LINE__)
 
-inline cudaError_t ThrowOnCudaError(cudaError_t code, const char *file,
-                                    int line) {
+inline cudaError_t ThrowOnCudaError(cudaError_t code, const char *file, int line)
+{
   if (code != cudaSuccess) {
     LOG(FATAL) << thrust::system_error(code, thrust::cuda_category(),
                                        std::string{file} + ": " +  // NOLINT
@@ -48,7 +54,23 @@ inline cudaError_t ThrowOnCudaError(cudaError_t code, const char *file,
   }
   return code;
 }
-#endif  // defined(__CUDACC__)
+
+#elif defined(__HIPCC__)
+/*
+ * Error handling  functions
+ */
+#define safe_cuda(ans) ThrowOnCudaError((ans), __FILE__, __LINE__)
+
+inline hipError_t ThrowOnCudaError(hipError_t code, const char *file, int line)
+{
+  if (code != hipSuccess) {
+    LOG(FATAL) << thrust::system_error(code, thrust::hip_category(),
+                                       std::string{file} + ": " +  // NOLINT
+                                       std::to_string(line)).what();
+  }
+  return code;
+}
+#endif
 }  // namespace dh
 
 namespace xgboost::common {
@@ -159,9 +181,9 @@ class Range {
 int AllVisibleGPUs();
 
 inline void AssertGPUSupport() {
-#ifndef XGBOOST_USE_CUDA
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
     LOG(FATAL) << "XGBoost version not compiled with GPU support.";
-#endif  // XGBOOST_USE_CUDA
+#endif  // XGBOOST_USE_CUDA && XGBOOST_USE_HIP
 }
 
 inline void AssertOneAPISupport() {
@@ -172,7 +194,7 @@ inline void AssertOneAPISupport() {
 
 void SetDevice(std::int32_t device);
 
-#if !defined(XGBOOST_USE_CUDA)
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 inline void SetDevice(std::int32_t device) {
   if (device >= 0) {
     AssertGPUSupport();

src/common/common.hip

@@ -0,0 +1,4 @@
+
+#if defined(XGBOOST_USE_HIP)
+#include "common.cu"
+#endif

src/common/compressed_iterator.h

@@ -11,9 +11,11 @@
 
 #include "common.h"
 
-#ifdef __CUDACC__
+#if defined(__CUDACC__)
 #include "device_helpers.cuh"
-#endif  // __CUDACC__
+#elif defined(__HIPCC__)
+#include "device_helpers.hip.h"
+#endif  // __CUDACC__ || __HIPCC__
 
 namespace xgboost {
 namespace common {
@@ -105,7 +107,7 @@ class CompressedBufferWriter {
     }
   }
 
-#ifdef __CUDACC__
+#if defined(__CUDACC__) || defined(__HIPCC__)
   __device__ void AtomicWriteSymbol
     (CompressedByteT* buffer, uint64_t symbol, size_t offset) {
     size_t ibit_start = offset * symbol_bits_;
@@ -119,7 +121,7 @@ class CompressedBufferWriter {
       symbol >>= 8;
     }
   }
-#endif  // __CUDACC__
+#endif  // __CUDACC__ || __HIPCC__
 
   template <typename IterT>
   void Write(CompressedByteT *buffer, IterT input_begin, IterT input_end) {

src/common/cuda_context.cuh

@@ -4,7 +4,6 @@
 #ifndef XGBOOST_COMMON_CUDA_CONTEXT_CUH_
 #define XGBOOST_COMMON_CUDA_CONTEXT_CUH_
 #include <thrust/execution_policy.h>
-
 #include "device_helpers.cuh"
 
 namespace xgboost {

src/common/cuda_pinned_allocator.h

@@ -72,11 +72,23 @@ class pinned_allocator {
     if (cnt > this->max_size()) { throw std::bad_alloc(); }  // end if
 
     pointer result(nullptr);
+
+#if defined(XGBOOST_USE_HIP)
+    dh::safe_cuda(hipHostMalloc(reinterpret_cast<void**>(&result), cnt * sizeof(value_type)));
+#else
     dh::safe_cuda(cudaMallocHost(reinterpret_cast<void**>(&result), cnt * sizeof(value_type)));
+#endif
+
     return result;
   }
 
-  inline void deallocate(pointer p, size_type) { dh::safe_cuda(cudaFreeHost(p)); } // NOLINT
+  inline void deallocate(pointer p, size_type) {
+#if defined(XGBOOST_USE_HIP)
+      dh::safe_cuda(hipHostFree(p));
+#else
+      dh::safe_cuda(cudaFreeHost(p));
+#endif
+  } // NOLINT
 
   inline size_type max_size() const { return (std::numeric_limits<size_type>::max)() / sizeof(T); } // NOLINT
 

src/common/cuda_to_hip.h

@@ -0,0 +1,71 @@
+/**
+ * Copyright 2017-2023 XGBoost contributors
+ */
+#pragma once
+
+#if defined(XGBOOST_USE_HIP)
+
+#define cudaSuccess                  hipSuccess
+#define cudaGetLastError             hipGetLastError
+
+#define cudaStream_t                 hipStream_t
+#define cudaStreamCreate             hipStreamCreate
+#define cudaStreamCreateWithFlags    hipStreamCreateWithFlags
+#define cudaStreamDestroy            hipStreamDestroy
+#define cudaStreamWaitEvent          hipStreamWaitEvent
+#define cudaStreamSynchronize        hipStreamSynchronize
+#define cudaStreamPerThread          hipStreamPerThread
+#define cudaStreamLegacy             hipStreamLegacy
+
+#define cudaEvent_t                  hipEvent_t
+#define cudaEventCreate              hipEventCreate
+#define cudaEventCreateWithFlags     hipEventCreateWithFlags
+#define cudaEventDestroy             hipEventDestroy
+
+#define cudaGetDevice                hipGetDevice
+#define cudaSetDevice                hipSetDevice
+#define cudaGetDeviceCount           hipGetDeviceCount
+#define cudaDeviceSynchronize        hipDeviceSynchronize
+
+#define cudaGetDeviceProperties      hipGetDeviceProperties
+#define cudaDeviceGetAttribute       hipDeviceGetAttribute
+
+#define cudaMallocHost               hipMallocHost
+#define cudaFreeHost                 hipFreeHost
+#define cudaMalloc                   hipMalloc
+#define cudaFree                     hipFree
+
+#define cudaMemcpy                   hipMemcpy
+#define cudaMemcpyAsync              hipMemcpyAsync
+#define cudaMemcpyDefault            hipMemcpyDefault
+#define cudaMemcpyHostToDevice       hipMemcpyHostToDevice
+#define cudaMemcpyHostToHost         hipMemcpyHostToHost
+#define cudaMemcpyDeviceToHost       hipMemcpyDeviceToHost
+#define cudaMemcpyDeviceToDevice     hipMemcpyDeviceToDevice
+#define cudaMemsetAsync              hipMemsetAsync
+#define cudaMemset                   hipMemset
+
+#define cudaPointerAttributes        hipPointerAttribute_t 
+#define cudaPointerGetAttributes     hipPointerGetAttributes
+
+#define cudaMemGetInfo               hipMemGetInfo
+#define cudaFuncSetAttribute         hipFuncSetAttribute
+
+#define cudaDevAttrMultiProcessorCount                hipDeviceAttributeMultiprocessorCount
+#define cudaOccupancyMaxActiveBlocksPerMultiprocessor hipOccupancyMaxActiveBlocksPerMultiprocessor
+
+namespace thrust {
+    namespace hip {
+    }
+}
+
+namespace thrust {
+    namespace cuda = thrust::hip;
+}
+
+namespace hipcub {
+}
+
+namespace cub = hipcub;
+
+#endif

src/common/device_helpers.cuh

@@ -2,6 +2,8 @@
  * Copyright 2017-2023 XGBoost contributors
  */
 #pragma once
+
+#if defined(XGBOOST_USE_CUDA)
 #include <thrust/binary_search.h>  // thrust::upper_bound
 #include <thrust/device_malloc_allocator.h>
 #include <thrust/device_ptr.h>
@@ -1218,3 +1220,7 @@ class LDGIterator {
   }
 };
 }  // namespace dh
+
+#elif defined(XGBOOST_USE_HIP)
+#include "device_helpers.hip.h"
+#endif

src/common/error_msg.cc

@@ -3,9 +3,11 @@
  */
 #include "error_msg.h"
 
+#include <mutex>    // for call_once, once_flag
 #include <sstream>  // for stringstream
 
 #include "../collective/communicator-inl.h"  // for GetRank
+#include "xgboost/context.h"                 // for Context
 #include "xgboost/logging.h"
 
 namespace xgboost::error {
@@ -26,34 +28,43 @@ void WarnDeprecatedGPUHist() {
 }
 
 void WarnManualUpdater() {
-  bool static thread_local logged{false};
-  if (logged) {
-    return;
-  }
+  static std::once_flag flag;
+  std::call_once(flag, [] {
     LOG(WARNING)
         << "You have manually specified the `updater` parameter. The `tree_method` parameter "
            "will be ignored. Incorrect sequence of updaters will produce undefined "
            "behavior. For common uses, we recommend using `tree_method` parameter instead.";
-  logged = true;
+  });
 }
 
 void WarnDeprecatedGPUId() {
-  static thread_local bool logged{false};
-  if (logged) {
-    return;
-  }
+  static std::once_flag flag;
+  std::call_once(flag, [] {
     auto msg = DeprecatedFunc("gpu_id", "2.0.0", "device");
     msg += " E.g. device=cpu/cuda/cuda:0";
     LOG(WARNING) << msg;
-  logged = true;
+  });
 }
 
 void WarnEmptyDataset() {
-  static thread_local bool logged{false};
-  if (logged) {
-    return;
-  }
-  LOG(WARNING) << "Empty dataset at worker: " << collective::GetRank();
-  logged = true;
+  static std::once_flag flag;
+  std::call_once(flag,
+                 [] { LOG(WARNING) << "Empty dataset at worker: " << collective::GetRank(); });
+}
+
+void MismatchedDevices(Context const* booster, Context const* data) {
+  static std::once_flag flag;
+  std::call_once(flag, [&] {
+    LOG(WARNING)
+        << "Falling back to prediction using DMatrix due to mismatched devices. This might "
+           "lead to higher memory usage and slower performance. XGBoost is running on: "
+        << booster->DeviceName() << ", while the input data is on: " << data->DeviceName() << ".\n"
+        << R"(Potential solutions:
+- Use a data structure that matches the device ordinal in the booster.
+- Set the device for booster before call to inplace_predict.
+
+This warning will only be shown once.
+)";
+  });
 }
 }  // namespace xgboost::error

src/common/error_msg.h

@@ -11,6 +11,7 @@
 #include <string>     // for string
 
 #include "xgboost/base.h"     // for bst_feature_t
+#include "xgboost/context.h"  // for Context
 #include "xgboost/logging.h"
 #include "xgboost/string_view.h"  // for StringView
 
@@ -94,5 +95,7 @@ constexpr StringView InvalidCUDAOrdinal() {
   return "Invalid device. `device` is required to be CUDA and there must be at least one GPU "
          "available for using GPU.";
 }
+
+void MismatchedDevices(Context const* booster, Context const* data);
 }  // namespace xgboost::error
 #endif  // XGBOOST_COMMON_ERROR_MSG_H_

src/common/hist_util.cuh

@@ -84,10 +84,19 @@ __global__ void GetColumnSizeSharedMemKernel(IterSpan<BatchIt> batch_iter,
 template <std::uint32_t kBlockThreads, typename Kernel>
 std::uint32_t EstimateGridSize(std::int32_t device, Kernel kernel, std::size_t shared_mem) {
   int n_mps = 0;
+#if defined(XGBOOST_USE_CUDA)
   dh::safe_cuda(cudaDeviceGetAttribute(&n_mps, cudaDevAttrMultiProcessorCount, device));
+#elif defined(XGBOOST_USE_HIP)
+  dh::safe_cuda(hipDeviceGetAttribute(&n_mps, hipDeviceAttributeMultiprocessorCount, device));
+#endif
   int n_blocks_per_mp = 0;
+#if defined(XGBOOST_USE_CUDA)
   dh::safe_cuda(cudaOccupancyMaxActiveBlocksPerMultiprocessor(&n_blocks_per_mp, kernel,
                                                               kBlockThreads, shared_mem));
+#elif defined(XGBOOST_USE_HIP)
+  dh::safe_cuda(hipOccupancyMaxActiveBlocksPerMultiprocessor(&n_blocks_per_mp, kernel,
+                                                              kBlockThreads, shared_mem));
+#endif
   std::uint32_t grid_size = n_blocks_per_mp * n_mps;
   return grid_size;
 }
@@ -170,6 +179,7 @@ void GetColumnSizesScan(int device, size_t num_columns, std::size_t num_cuts_per
       column_sizes_scan->begin(), [=] __device__(size_t column_size) {
         return thrust::min(num_cuts_per_feature, column_size);
       });
+
   thrust::exclusive_scan(thrust::cuda::par(alloc), cut_ptr_it,
                          cut_ptr_it + column_sizes_scan->size(), cuts_ptr->DevicePointer());
   thrust::exclusive_scan(thrust::cuda::par(alloc), column_sizes_scan->begin(),
@@ -294,6 +304,7 @@ void ProcessSlidingWindow(AdapterBatch const &batch, MetaInfo const &info,
                                  &column_sizes_scan,
                                  &sorted_entries);
   dh::XGBDeviceAllocator<char> alloc;
+
   thrust::sort(thrust::cuda::par(alloc), sorted_entries.begin(),
                sorted_entries.end(), detail::EntryCompareOp());
 
@@ -353,11 +364,13 @@ void ProcessWeightedSlidingWindow(Batch batch, MetaInfo const& info,
           bst_group_t group_idx = dh::SegmentId(d_group_ptr, ridx);
           return weights[group_idx];
         });
+
     auto retit = thrust::copy_if(thrust::cuda::par(alloc),
                                  weight_iter + begin, weight_iter + end,
                                  batch_iter + begin,
                                  d_temp_weights.data(),  // output
                                  is_valid);
+
     CHECK_EQ(retit - d_temp_weights.data(), d_temp_weights.size());
   } else {
     CHECK_EQ(batch.NumRows(), weights.size());
@@ -366,11 +379,13 @@ void ProcessWeightedSlidingWindow(Batch batch, MetaInfo const& info,
         [=]__device__(size_t idx) -> float {
           return weights[batch.GetElement(idx).row_idx];
         });
+
     auto retit = thrust::copy_if(thrust::cuda::par(alloc),
                                  weight_iter + begin, weight_iter + end,
                                  batch_iter + begin,
                                  d_temp_weights.data(),  // output
                                  is_valid);
+
     CHECK_EQ(retit - d_temp_weights.data(), d_temp_weights.size());
   }
 

src/common/hist_util.hip

@@ -0,0 +1,4 @@
+
+#if defined(XGBOOST_USE_HIP)
+#include "hist_util.cu"
+#endif

src/common/host_device_vector.cc

@@ -1,7 +1,7 @@
 /**
  * Copyright 2017-2023 by XGBoost contributors
  */
-#ifndef XGBOOST_USE_CUDA
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 
 // dummy implementation of HostDeviceVector in case CUDA is not used
 
@@ -199,4 +199,4 @@ template class HostDeviceVector<std::size_t>;
 
 }  // namespace xgboost
 
-#endif  // XGBOOST_USE_CUDA
+#endif  // XGBOOST_USE_CUDA && !defined(XGBOOST_USE_HIP)

src/common/host_device_vector.cu

@@ -139,6 +139,7 @@ class HostDeviceVectorImpl {
       auto ptr = other->ConstDevicePointer();
       SetDevice();
       CHECK_EQ(this->DeviceIdx(), other->DeviceIdx());
+
       dh::safe_cuda(cudaMemcpyAsync(this->DevicePointer() + ori_size,
                                     ptr,
                                     other->Size() * sizeof(T),
@@ -195,6 +196,7 @@ class HostDeviceVectorImpl {
     gpu_access_ = access;
     if (data_h_.size() != data_d_->size()) { data_h_.resize(data_d_->size()); }
     SetDevice();
+
     dh::safe_cuda(cudaMemcpy(data_h_.data(),
                              data_d_->data().get(),
                              data_d_->size() * sizeof(T),
@@ -211,6 +213,7 @@ class HostDeviceVectorImpl {
     // data is on the host
     LazyResizeDevice(data_h_.size());
     SetDevice();
+
     dh::safe_cuda(cudaMemcpyAsync(data_d_->data().get(),
                                   data_h_.data(),
                                   data_d_->size() * sizeof(T),
@@ -239,6 +242,7 @@ class HostDeviceVectorImpl {
       LazyResizeDevice(Size());
       gpu_access_ = GPUAccess::kWrite;
       SetDevice();
+
       dh::safe_cuda(cudaMemcpyAsync(data_d_->data().get(), other->data_d_->data().get(),
                                     data_d_->size() * sizeof(T), cudaMemcpyDefault));
     }
@@ -248,6 +252,7 @@ class HostDeviceVectorImpl {
     LazyResizeDevice(Size());
     gpu_access_ = GPUAccess::kWrite;
     SetDevice();
+
     dh::safe_cuda(cudaMemcpyAsync(data_d_->data().get(), begin,
                                   data_d_->size() * sizeof(T), cudaMemcpyDefault));
   }

src/common/host_device_vector.hip

@@ -0,0 +1,4 @@
+
+#if defined(XGBOOST_USE_HIP)
+#include "host_device_vector.cu"
+#endif

src/common/io.h

@@ -384,7 +384,8 @@ class PrivateMmapConstStream : public AlignedResourceReadStream {
    * @param length    See the `length` parameter of `mmap` for details.
    */
   explicit PrivateMmapConstStream(std::string path, std::size_t offset, std::size_t length)
-      : AlignedResourceReadStream{std::make_shared<MmapResource>(path, offset, length)} {}
+      : AlignedResourceReadStream{std::shared_ptr<MmapResource>{  // NOLINT
+            new MmapResource{std::move(path), offset, length}}} {}
   ~PrivateMmapConstStream() noexcept(false) override;
 };
 

src/common/linalg_op.cuh

@@ -12,7 +12,8 @@
 namespace xgboost {
 namespace linalg {
 template <typename T, int32_t D, typename Fn>
-void ElementWiseKernelDevice(linalg::TensorView<T, D> t, Fn&& fn, cudaStream_t s = nullptr) {
+void ElementWiseKernelDevice(linalg::TensorView<T, D> t, Fn&& fn, cudaStream_t s = nullptr)
+{
   dh::safe_cuda(cudaSetDevice(t.DeviceIdx()));
   static_assert(std::is_void<std::result_of_t<Fn(size_t, T&)>>::value,
                 "For function with return, use transform instead.");
@@ -28,7 +29,8 @@ void ElementWiseKernelDevice(linalg::TensorView<T, D> t, Fn&& fn, cudaStream_t s
 }
 
 template <typename T, int32_t D, typename Fn>
-void ElementWiseTransformDevice(linalg::TensorView<T, D> t, Fn&& fn, cudaStream_t s = nullptr) {
+void ElementWiseTransformDevice(linalg::TensorView<T, D> t, Fn&& fn, cudaStream_t s = nullptr)
+{
   if (t.Contiguous()) {
     auto ptr = t.Values().data();
     dh::LaunchN(t.Size(), s, [=] __device__(size_t i) { ptr[i] = fn(i, ptr[i]); });

src/common/linalg_op.h

@@ -42,7 +42,7 @@ void ElementWiseKernelHost(linalg::TensorView<T, D> t, int32_t n_threads, Fn&& f
   }
 }
 
-#if !defined(XGBOOST_USE_CUDA)
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 template <typename T, int32_t D, typename Fn>
 void ElementWiseKernelDevice(linalg::TensorView<T, D>, Fn&&, void* = nullptr) {
   common::AssertGPUSupport();
@@ -60,7 +60,7 @@ void ElementWiseKernel(Context const* ctx, linalg::TensorView<T, D> t, Fn&& fn)
   }
   ElementWiseKernelHost(t, ctx->Threads(), fn);
 }
-#endif  // !defined(XGBOOST_USE_CUDA)
+#endif  // !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 
 template <typename T, std::int32_t kDim>
 auto cbegin(TensorView<T, kDim> const& v) {  // NOLINT

src/common/math.h

@@ -143,7 +143,7 @@ CheckNAN(T) {
   return false;
 }
 
-#if XGBOOST_STRICT_R_MODE && !defined(__CUDA_ARCH__)
+#if XGBOOST_STRICT_R_MODE && !defined(__CUDA_ARCH__) && !defined(__HIPCC__)
 
 bool CheckNAN(double v);
 
@@ -152,23 +152,27 @@ bool CheckNAN(double v);
 XGBOOST_DEVICE bool inline CheckNAN(float x) {
 #if defined(__CUDA_ARCH__)
   return isnan(x);
+#elif defined(__HIPCC__)
+  return __builtin_isnan(x);
 #else
   return std::isnan(x);
-#endif  // defined(__CUDA_ARCH__)
+#endif  // defined(__CUDA_ARCH__) || defined(__HIPCC__)
 }
 
 XGBOOST_DEVICE bool inline CheckNAN(double x) {
 #if defined(__CUDA_ARCH__)
   return isnan(x);
+#elif defined(__HIPCC__)
+  return __builtin_isnan(x);
 #else
   return std::isnan(x);
-#endif  // defined(__CUDA_ARCH__)
+#endif  // defined(__CUDA_ARCH__) || defined(__HIPCC__)
 }
 
 #endif  // XGBOOST_STRICT_R_MODE && !defined(__CUDA_ARCH__)
 // GPU version is not uploaded in CRAN anyway.
 // Specialize only when using R with CPU.
-#if XGBOOST_STRICT_R_MODE && !defined(XGBOOST_USE_CUDA)
+#if XGBOOST_STRICT_R_MODE && !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 double LogGamma(double v);
 
 #else  // Not R or R with GPU.
@@ -191,7 +195,7 @@ XGBOOST_DEVICE inline T LogGamma(T v) {
 #endif  // _MSC_VER
 }
 
-#endif  // XGBOOST_STRICT_R_MODE && !defined(XGBOOST_USE_CUDA)
+#endif  // XGBOOST_STRICT_R_MODE && !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 
 }  // namespace common
 }  // namespace xgboost

src/common/numeric.cu

@@ -15,6 +15,7 @@ double Reduce(Context const* ctx, HostDeviceVector<float> const& values) {
   values.SetDevice(ctx->gpu_id);
   auto const d_values = values.ConstDeviceSpan();
   dh::XGBCachingDeviceAllocator<char> alloc;
+
   return dh::Reduce(thrust::cuda::par(alloc), dh::tcbegin(d_values), dh::tcend(d_values), 0.0,
                     thrust::plus<float>{});
 }

src/common/numeric.h

@@ -99,12 +99,12 @@ void PartialSum(int32_t n_threads, InIt begin, InIt end, T init, OutIt out_it) {
 
 namespace cuda_impl {
 double Reduce(Context const* ctx, HostDeviceVector<float> const& values);
-#if !defined(XGBOOST_USE_CUDA)
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 inline double Reduce(Context const*, HostDeviceVector<float> const&) {
   AssertGPUSupport();
   return 0;
 }
-#endif  // !defined(XGBOOST_USE_CUDA)
+#endif  // !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 }  // namespace cuda_impl
 
 /**

src/common/numeric.hip

@@ -0,0 +1,4 @@
+
+#if defined(XGBOOST_USE_HIP)
+#include "numeric.cu"
+#endif

src/common/quantile.cu

@@ -109,6 +109,7 @@ template <typename T, typename U>
 void CopyTo(Span<T> out, Span<U> src) {
   CHECK_EQ(out.size(), src.size());
   static_assert(std::is_same<std::remove_cv_t<T>, std::remove_cv_t<T>>::value);
+
   dh::safe_cuda(cudaMemcpyAsync(out.data(), src.data(),
                                 out.size_bytes(),
                                 cudaMemcpyDefault));
@@ -162,6 +163,7 @@ common::Span<thrust::tuple<uint64_t, uint64_t>> MergePath(
   // Compute output ptr
   auto transform_it =
       thrust::make_zip_iterator(thrust::make_tuple(x_ptr.data(), y_ptr.data()));
+
   thrust::transform(
       thrust::cuda::par(alloc), transform_it, transform_it + x_ptr.size(),
       out_ptr.data(),
@@ -211,6 +213,7 @@ void MergeImpl(int32_t device, Span<SketchEntry const> const &d_x,
                Span<bst_row_t const> const &x_ptr, Span<SketchEntry const> const &d_y,
                Span<bst_row_t const> const &y_ptr, Span<SketchEntry> out, Span<bst_row_t> out_ptr) {
   dh::safe_cuda(cudaSetDevice(device));
+
   CHECK_EQ(d_x.size() + d_y.size(), out.size());
   CHECK_EQ(x_ptr.size(), out_ptr.size());
   CHECK_EQ(y_ptr.size(), out_ptr.size());
@@ -309,6 +312,7 @@ void SketchContainer::Push(Span<Entry const> entries, Span<size_t> columns_ptr,
                            common::Span<OffsetT> cuts_ptr,
                            size_t total_cuts, Span<float> weights) {
   dh::safe_cuda(cudaSetDevice(device_));
+
   Span<SketchEntry> out;
   dh::device_vector<SketchEntry> cuts;
   bool first_window = this->Current().empty();
@@ -378,6 +382,7 @@ size_t SketchContainer::ScanInput(Span<SketchEntry> entries, Span<OffsetT> d_col
       });
   // Reverse scan to accumulate weights into first duplicated element on left.
   auto val_it = thrust::make_reverse_iterator(dh::tend(entries));
+
   thrust::inclusive_scan_by_key(
       thrust::cuda::par(alloc), key_it, key_it + entries.size(),
       val_it, val_it,
@@ -443,6 +448,7 @@ void SketchContainer::Prune(size_t to) {
 void SketchContainer::Merge(Span<OffsetT const> d_that_columns_ptr,
                             Span<SketchEntry const> that) {
   dh::safe_cuda(cudaSetDevice(device_));
+
   timer_.Start(__func__);
   if (this->Current().size() == 0) {
     CHECK_EQ(this->columns_ptr_.HostVector().back(), 0);
@@ -478,6 +484,7 @@ void SketchContainer::Merge(Span<OffsetT const> d_that_columns_ptr,
 
 void SketchContainer::FixError() {
   dh::safe_cuda(cudaSetDevice(device_));
+
   auto d_columns_ptr = this->columns_ptr_.ConstDeviceSpan();
   auto in = dh::ToSpan(this->Current());
   dh::LaunchN(in.size(), [=] __device__(size_t idx) {
@@ -635,10 +642,12 @@ void SketchContainer::MakeCuts(HistogramCuts* p_cuts, bool is_column_split) {
     CHECK_EQ(num_columns_, d_in_columns_ptr.size() - 1);
     max_values.resize(d_in_columns_ptr.size() - 1);
     dh::caching_device_vector<SketchEntry> d_max_values(d_in_columns_ptr.size() - 1);
+
     thrust::reduce_by_key(thrust::cuda::par(alloc), key_it, key_it + in_cut_values.size(), val_it,
                           thrust::make_discard_iterator(), d_max_values.begin(),
                           thrust::equal_to<bst_feature_t>{},
                           [] __device__(auto l, auto r) { return l.value > r.value ? l : r; });
+
     dh::CopyDeviceSpanToVector(&max_values, dh::ToSpan(d_max_values));
     auto max_it = MakeIndexTransformIter([&](auto i) {
       if (IsCat(h_feature_types, i)) {

src/common/quantile.cuh

@@ -175,6 +175,7 @@ class SketchContainer {
   template <typename KeyComp = thrust::equal_to<size_t>>
   size_t Unique(KeyComp key_comp = thrust::equal_to<size_t>{}) {
     timer_.Start(__func__);
+
     dh::safe_cuda(cudaSetDevice(device_));
     this->columns_ptr_.SetDevice(device_);
     Span<OffsetT> d_column_scan = this->columns_ptr_.DeviceSpan();
@@ -186,11 +187,13 @@ class SketchContainer {
     dh::XGBCachingDeviceAllocator<char> alloc;
 
     d_column_scan = this->columns_ptr_.DeviceSpan();
+
     size_t n_uniques = dh::SegmentedUnique(
         thrust::cuda::par(alloc), d_column_scan.data(),
         d_column_scan.data() + d_column_scan.size(), entries.data(),
         entries.data() + entries.size(), scan_out.DevicePointer(),
         entries.data(), detail::SketchUnique{}, key_comp);
+
     this->columns_ptr_.Copy(scan_out);
     CHECK(!this->columns_ptr_.HostCanRead());
 

src/common/quantile.hip

@@ -0,0 +1,4 @@
+
+#if defined(XGBOOST_USE_HIP)
+#include "quantile.cu"
+#endif

src/common/ranking_utils.cc

@@ -62,7 +62,7 @@ common::Span<std::size_t const> RankingCache::MakeRankOnCPU(Context const* ctx,
   return rank;
 }
 
-#if !defined(XGBOOST_USE_CUDA)
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 void RankingCache::InitOnCUDA(Context const*, MetaInfo const&) { common::AssertGPUSupport(); }
 common::Span<std::size_t const> RankingCache::MakeRankOnCUDA(Context const*,
                                                              common::Span<float const>) {
@@ -108,9 +108,9 @@ void NDCGCache::InitOnCPU(Context const* ctx, MetaInfo const& info) {
   });
 }
 
-#if !defined(XGBOOST_USE_CUDA)
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 void NDCGCache::InitOnCUDA(Context const*, MetaInfo const&) { common::AssertGPUSupport(); }
-#endif  // !defined(XGBOOST_USE_CUDA)
+#endif  // !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 
 DMLC_REGISTER_PARAMETER(LambdaRankParam);
 
@@ -120,7 +120,7 @@ void PreCache::InitOnCPU(Context const*, MetaInfo const& info) {
                  [](auto beg, auto end, auto op) { return std::all_of(beg, end, op); });
 }
 
-#if !defined(XGBOOST_USE_CUDA)
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 void PreCache::InitOnCUDA(Context const*, MetaInfo const&) { common::AssertGPUSupport(); }
 #endif  // !defined(XGBOOST_USE_CUDA)
 
@@ -130,9 +130,9 @@ void MAPCache::InitOnCPU(Context const*, MetaInfo const& info) {
                  [](auto beg, auto end, auto op) { return std::all_of(beg, end, op); });
 }
 
-#if !defined(XGBOOST_USE_CUDA)
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 void MAPCache::InitOnCUDA(Context const*, MetaInfo const&) { common::AssertGPUSupport(); }
-#endif  // !defined(XGBOOST_USE_CUDA)
+#endif  // !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 
 std::string ParseMetricName(StringView name, StringView param, position_t* topn, bool* minus) {
   std::string out_name;

src/common/ranking_utils.cu

@@ -23,6 +23,10 @@
 #include "xgboost/logging.h"                    // for CHECK
 #include "xgboost/span.h"                       // for Span
 
+#if defined(XGBOOST_USE_HIP)
+#include <hipcub/hipcub.hpp>
+#endif
+
 namespace xgboost::ltr {
 namespace cuda_impl {
 void CalcQueriesDCG(Context const* ctx, linalg::VectorView<float const> d_labels,

src/common/ranking_utils.hip

@@ -0,0 +1,4 @@
+
+#if defined(XGBOOST_USE_HIP)
+#include "ranking_utils.cu"
+#endif

src/common/ref_resource_view.h

@@ -76,7 +76,7 @@ class RefResourceView {
 
   [[nodiscard]] size_type size() const { return size_; }  // NOLINT
   [[nodiscard]] size_type size_bytes() const {            // NOLINT
-    return Span{data(), size()}.size_bytes();
+    return Span<const value_type>{data(), size()}.size_bytes();
   }
   [[nodiscard]] value_type* data() { return ptr_; };              // NOLINT
   [[nodiscard]] value_type const* data() const { return ptr_; };  // NOLINT

src/common/stats.cu

@@ -18,6 +18,7 @@
 namespace xgboost {
 namespace common {
 namespace cuda_impl {
+
 void Median(Context const* ctx, linalg::TensorView<float const, 2> t,
             common::OptionalWeights weights, linalg::Tensor<float, 1>* out) {
   CHECK_GE(t.Shape(1), 1);

src/common/stats.cuh

@@ -216,6 +216,7 @@ void SegmentedWeightedQuantile(Context const* ctx, AlphaIt alpha_it, SegIt seg_b
                                                          detail::SegOp<SegIt>{seg_beg, seg_end});
   auto scan_val = dh::MakeTransformIterator<float>(thrust::make_counting_iterator(0ul),
                                                    detail::WeightOp<WIter>{w_begin, d_sorted_idx});
+
   thrust::inclusive_scan_by_key(thrust::cuda::par(caching), scan_key, scan_key + n_weights,
                                 scan_val, weights_cdf.begin());
 

src/common/stats.h

@@ -112,7 +112,7 @@ void Median(Context const* ctx, linalg::TensorView<float const, 2> t, OptionalWe
 
 void Mean(Context const* ctx, linalg::VectorView<float const> v, linalg::VectorView<float> out);
 
-#if !defined(XGBOOST_USE_CUDA)
+#if !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 inline void Median(Context const*, linalg::TensorView<float const, 2>, OptionalWeights,
                    linalg::Tensor<float, 1>*) {
   common::AssertGPUSupport();
@@ -120,7 +120,7 @@ inline void Median(Context const*, linalg::TensorView<float const, 2>, OptionalW
 inline void Mean(Context const*, linalg::VectorView<float const>, linalg::VectorView<float>) {
   common::AssertGPUSupport();
 }
-#endif  // !defined(XGBOOST_USE_CUDA)
+#endif  // !defined(XGBOOST_USE_CUDA) && !defined(XGBOOST_USE_HIP)
 }  // namespace cuda_impl
 
 /**