Drop support for loading remote files. (#9504)

CMakeLists.txt

@@ -72,10 +72,6 @@ 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'")
-## Copied From dmlc
-option(USE_HDFS "Build with HDFS support" OFF)
-option(USE_AZURE "Build with AZURE support" OFF)
-option(USE_S3 "Build with S3 support" OFF)
 ## Sanitizers
 option(USE_SANITIZER "Use santizer flags" OFF)
 option(SANITIZER_PATH "Path to sanitizes.")

doc/jvm/xgboost4j_spark_tutorial.rst

@@ -390,39 +390,6 @@ Then we can load this model with single node Python XGBoost:
   bst = xgb.Booster({'nthread': 4})
   bst.load_model(nativeModelPath)
 
-.. note:: Using HDFS and S3 for exporting the models with nativeBooster.saveModel()
-
-  When interacting with other language bindings, XGBoost also supports saving-models-to and loading-models-from file systems other than the local one. You can use HDFS and S3 by prefixing the path with ``hdfs://`` and ``s3://`` respectively. However, for this capability, you must do **one** of the following:
-
-  1. Build XGBoost4J-Spark with the steps described in :ref:`here <install_jvm_packages>`, but turning `USE_HDFS <https://github.com/dmlc/xgboost/blob/e939192978a0c152ad7b49b744630e99d54cffa8/jvm-packages/create_jni.py#L18>`_ (or USE_S3, etc. in the same place) switch on. With this approach, you can reuse the above code example by replacing "nativeModelPath" with a HDFS path.
-
-     - However, if you build with USE_HDFS, etc. you have to ensure that the involved shared object file, e.g. libhdfs.so, is put in the LIBRARY_PATH of your cluster. To avoid the complicated cluster environment configuration, choose the other option.
-
-  2. Use bindings of HDFS, S3, etc. to pass model files around. Here are the steps (taking HDFS as an example):
-
-     - Create a new file with
-
-       .. code-block:: scala
-
-         val outputStream = fs.create("hdfs_path")
-
-       where "fs" is an instance of `org.apache.hadoop.fs.FileSystem <https://hadoop.apache.org/docs/stable/api/org/apache/hadoop/fs/FileSystem.html>`_ class in Hadoop.
-
-     - Pass the returned OutputStream in the first step to nativeBooster.saveModel():
-
-       .. code-block:: scala
-
-         xgbClassificationModel.nativeBooster.saveModel(outputStream)
-
-     - Download file in other languages from HDFS and load with the pre-built (without the requirement of libhdfs.so) version of XGBoost. (The function "download_from_hdfs" is a helper function to be implemented by the user)
-
-       .. code-block:: python
-
-         import xgboost as xgb
-         bst = xgb.Booster({'nthread': 4})
-         local_path = download_from_hdfs("hdfs_path")
-         bst.load_model(local_path)
-
 .. note:: Consistency issue between XGBoost4J-Spark and other bindings
 
   There is a consistency issue between XGBoost4J-Spark and other language bindings of XGBoost.

python-package/xgboost/core.py

@@ -505,8 +505,7 @@ class DataIter(ABC):  # pylint: disable=too-many-instance-attributes
     Parameters
     ----------
     cache_prefix :
-        Prefix to the cache files, only used in external memory.  It can be either an
-        URI or a file path.
+        Prefix to the cache files, only used in external memory.
     release_data :
         Whether the iterator should release the data during reset. Set it to True if the
         data transformation (converting data to np.float32 type) is expensive.
@@ -2558,8 +2557,7 @@ def save_raw(self, raw_format: str = "deprecated") -> bytearray:
         return ctypes2buffer(cptr, length.value)
 
     def load_model(self, fname: ModelIn) -> None:
-        """Load the model from a file or bytearray. Path to file can be local
-        or as an URI.
+        """Load the model from a file or a bytearray.
 
         The model is loaded from XGBoost format which is universal among the various
         XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as

src/c_api/c_api.cc

@@ -1220,12 +1220,12 @@ XGB_DLL int XGBoosterLoadModel(BoosterHandle handle, const char* fname) {
     return str;
   };
   if (common::FileExtension(fname) == "json") {
-    auto str = read_file();
-    Json in{Json::Load(StringView{str})};
+    auto buffer = read_file();
+    Json in{Json::Load(StringView{buffer.data(), buffer.size()})};
     static_cast<Learner*>(handle)->LoadModel(in);
   } else if (common::FileExtension(fname) == "ubj") {
-    auto str = read_file();
-    Json in = Json::Load(StringView{str}, std::ios::binary);
+    auto buffer = read_file();
+    Json in = Json::Load(StringView{buffer.data(), buffer.size()}, std::ios::binary);
     static_cast<Learner *>(handle)->LoadModel(in);
   } else {
     std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname, "r"));

src/cli_main.cc

@@ -345,10 +345,10 @@ class CLI {
 
   void LoadModel(std::string const& path, Learner* learner) const {
     if (common::FileExtension(path) == "json") {
-      auto str = common::LoadSequentialFile(path);
-      CHECK_GT(str.size(), 2);
-      CHECK_EQ(str[0], '{');
-      Json in{Json::Load({str.c_str(), str.size()})};
+      auto buffer = common::LoadSequentialFile(path);
+      CHECK_GT(buffer.size(), 2);
+      CHECK_EQ(buffer[0], '{');
+      Json in{Json::Load({buffer.data(), buffer.size()})};
       learner->LoadModel(in);
     } else {
       std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(path.c_str(), "r"));

src/common/io.cc

@@ -139,7 +139,7 @@ auto SystemErrorMsg() {
 }
 }  // anonymous namespace
 
-std::string LoadSequentialFile(std::string uri, bool stream) {
+std::vector<char> LoadSequentialFile(std::string uri) {
   auto OpenErr = [&uri]() {
     std::string msg;
     msg = "Opening " + uri + " failed: ";
@@ -148,44 +148,20 @@ std::string LoadSequentialFile(std::string uri, bool stream) {
   };
 
   auto parsed = dmlc::io::URI(uri.c_str());
+  CHECK((parsed.protocol == "file://" || parsed.protocol.length() == 0))
+      << "Only local file is supported.";
   // Read from file.
-  if ((parsed.protocol == "file://" || parsed.protocol.length() == 0) && !stream) {
-    std::string buffer;
-    // Open in binary mode so that correct file size can be computed with
-    // seekg(). This accommodates Windows platform:
-    // https://docs.microsoft.com/en-us/cpp/standard-library/basic-istream-class?view=vs-2019#seekg
-    auto path = std::filesystem::weakly_canonical(std::filesystem::u8path(uri));
-    std::ifstream ifs(path, std::ios_base::binary | std::ios_base::in);
-    if (!ifs) {
-      // https://stackoverflow.com/a/17338934
-      OpenErr();
-    }
-
-    ifs.seekg(0, std::ios_base::end);
-    const size_t file_size = static_cast<size_t>(ifs.tellg());
-    ifs.seekg(0, std::ios_base::beg);
-    buffer.resize(file_size + 1);
-    ifs.read(&buffer[0], file_size);
-    buffer.back() = '\0';
-
-    return buffer;
+  auto path = std::filesystem::weakly_canonical(std::filesystem::u8path(uri));
+  std::ifstream ifs(path, std::ios_base::binary | std::ios_base::in);
+  if (!ifs) {
+    // https://stackoverflow.com/a/17338934
+    OpenErr();
   }
 
-  // Read from remote.
-  std::unique_ptr<dmlc::Stream> fs{dmlc::Stream::Create(uri.c_str(), "r")};
-  std::string buffer;
-  size_t constexpr kInitialSize = 4096;
-  size_t size {kInitialSize}, total {0};
-  while (true) {
-    buffer.resize(total + size);
-    size_t read = fs->Read(&buffer[total], size);
-    total += read;
-    if (read < size) {
-      break;
-    }
-    size *= 2;
-  }
-  buffer.resize(total);
+  auto file_size = std::filesystem::file_size(path);
+  std::vector<char> buffer(file_size);
+  ifs.read(&buffer[0], file_size);
+
   return buffer;
 }
 

src/common/io.h

@@ -84,16 +84,14 @@ class FixedSizeStream : public PeekableInStream {
   std::string buffer_;
 };
 
-/*!
- * \brief Helper function for loading consecutive file to avoid dmlc Stream when possible.
+/**
+ * @brief Helper function for loading consecutive file.
  *
- * \param uri    URI or file name to file.
- * \param stream Use dmlc Stream unconditionally if set to true.  Used for running test
- *               without remote filesystem.
+ * @param uri    URI or file name to file.
  *
- * \return File content.
+ * @return File content.
  */
-std::string LoadSequentialFile(std::string uri, bool stream = false);
+std::vector<char> LoadSequentialFile(std::string uri);
 
 /**
  * \brief Get file extension from file name.

tests/cpp/c_api/test_c_api.cc

@@ -216,8 +216,8 @@ TEST(CAPI, JsonModelIO) {
 
   std::string buffer;
   Json::Dump(Json::Load(l, std::ios::binary), &buffer);
-  ASSERT_EQ(model_str_0.size() - 1, buffer.size());
-  ASSERT_EQ(model_str_0.back(), '\0');
+  ASSERT_EQ(model_str_0.size(), buffer.size());
+  ASSERT_EQ(model_str_0.back(), '}');
   ASSERT_TRUE(std::equal(model_str_0.begin(), model_str_0.end() - 1, buffer.begin()));
 
   ASSERT_EQ(XGBoosterSaveModelToBuffer(handle, R"({})", &len, &data), -1);

tests/cpp/common/test_io.cc

@@ -63,31 +63,27 @@ TEST(IO, LoadSequentialFile) {
 
   // Generate a JSON file.
   size_t constexpr kRows = 1000, kCols = 100;
-  std::shared_ptr<DMatrix> p_dmat{
-    RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true)};
-  std::unique_ptr<Learner> learner { Learner::Create({p_dmat}) };
+  std::shared_ptr<DMatrix> p_dmat{RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true)};
+  std::unique_ptr<Learner> learner{Learner::Create({p_dmat})};
   learner->SetParam("tree_method", "hist");
   learner->Configure();
 
   for (int32_t iter = 0; iter < 10; ++iter) {
     learner->UpdateOneIter(iter, p_dmat);
   }
-  Json out { Object() };
+  Json out{Object()};
   learner->SaveModel(&out);
-  std::string str;
+  std::vector<char> str;
   Json::Dump(out, &str);
 
   std::string tmpfile = tempdir.path + "/model.json";
   {
-    std::unique_ptr<dmlc::Stream> fo(
-        dmlc::Stream::Create(tmpfile.c_str(), "w"));
-    fo->Write(str.c_str(), str.size());
+    std::unique_ptr<dmlc::Stream> fo(dmlc::Stream::Create(tmpfile.c_str(), "w"));
+    fo->Write(str.data(), str.size());
   }
 
-  auto loaded = LoadSequentialFile(tmpfile, true);
+  auto loaded = LoadSequentialFile(tmpfile);
   ASSERT_EQ(loaded, str);
-
-  ASSERT_THROW(LoadSequentialFile("non-exist", true), dmlc::Error);
 }
 
 TEST(IO, Resource) {

tests/cpp/common/test_json.cc

@@ -418,7 +418,7 @@ TEST(Json, AssigningString) {
 
 TEST(Json, LoadDump) {
   std::string ori_buffer = GetModelStr();
-  Json origin {Json::Load(StringView{ori_buffer.c_str(), ori_buffer.size()})};
+  Json origin{Json::Load(StringView{ori_buffer.c_str(), ori_buffer.size()})};
 
   dmlc::TemporaryDirectory tempdir;
   auto const& path = tempdir.path + "test_model_dump";
@@ -430,9 +430,9 @@ TEST(Json, LoadDump) {
   ASSERT_TRUE(fout);
   fout << out << std::flush;
 
-  std::string new_buffer = common::LoadSequentialFile(path);
+  std::vector<char> new_buffer = common::LoadSequentialFile(path);
 
-  Json load_back {Json::Load(StringView(new_buffer.c_str(), new_buffer.size()))};
+  Json load_back{Json::Load(StringView(new_buffer.data(), new_buffer.size()))};
   ASSERT_EQ(load_back, origin);
 }
 
@@ -651,7 +651,7 @@ TEST(UBJson, Basic) {
     }
 
     auto data = common::LoadSequentialFile("test.ubj");
-    UBJReader reader{StringView{data}};
+    UBJReader reader{StringView{data.data(), data.size()}};
     json = reader.Load();
     return json;
   };

tests/cpp/data/test_sparse_page_dmatrix.cc

@@ -250,15 +250,15 @@ auto TestSparsePageDMatrixDeterminism(int32_t threads) {
 
   auto cache_name =
       data::MakeId(filename, dynamic_cast<data::SparsePageDMatrix *>(sparse.get())) + ".row.page";
-  std::string cache = common::LoadSequentialFile(cache_name);
+  auto cache = common::LoadSequentialFile(cache_name);
   return cache;
 }
 
 TEST(SparsePageDMatrix, Determinism) {
 #if defined(_MSC_VER)
   return;
 #endif  // defined(_MSC_VER)
-  std::vector<std::string> caches;
+  std::vector<std::vector<char>> caches;
   for (size_t i = 1; i < 18; i += 2) {
     caches.emplace_back(TestSparsePageDMatrixDeterminism(i));
   }

tests/cpp/test_learner.cc

@@ -184,7 +184,7 @@ TEST(Learner, JsonModelIO) {
     fout.close();
 
     auto loaded_str = common::LoadSequentialFile(tmpdir.path + "/model.json");
-    Json loaded = Json::Load(StringView{loaded_str.c_str(), loaded_str.size()});
+    Json loaded = Json::Load(StringView{loaded_str.data(), loaded_str.size()});
 
     learner->LoadModel(loaded);
     learner->Configure();