chore: import upstream snapshot with attribution

2026-07-13 12:14:16 +08:00
commit 8a852e4b4e
36502 changed files with 9277225 additions and 0 deletions
@@ -0,0 +1,121 @@
+load("@rules_python//python:proto.bzl", "py_proto_library")
+load("@xla//third_party/rules_python/python:py_binary.bzl", "py_binary")
+load(
+    "//tensorflow/core/platform:build_config.bzl",
+    "tf_proto_library",
+)
+
+package(
+    # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"],
+    default_visibility = ["//tensorflow/tools/proto_splitter:__subpackages__"],
+    licenses = ["notice"],
+)
+
+exports_files([
+    "df-split-tree.cpb",
+    "df-split-tree.pbtxt",
+    "bf-split-tree.cpb",
+    "bf-split-tree.pbtxt",
+    "split-tree.pbtxt",
+    "split-large-nodes.cpb",
+    "split-large-nodes.pb",
+    "split-large-nodes.pbtxt",
+    "split-standard.cpb",
+    "split-standard.pb",
+    "split-standard.pbtxt",
+    "split-large-constant.cpb",
+    "split-large-constant.pbtxt",
+    "split-lots-nodes.cpb",
+    "split-lots-nodes.pb",
+    "split-lots-nodes.pbtxt",
+    "split-large-constant.cpb",
+    "split-large-constant.pb",
+    "split-large-constant.pbtxt",
+    "function-large-nodes.pb",
+    "function-lots-of-nodes.pb",
+    "graph-def-and-function.pb",
+    "many-field.cpb",
+    "many-field.pbtxt",
+])
+
+tf_proto_library(
+    name = "test_message_proto",
+    srcs = ["test_message.proto"],
+    make_default_target_header_only = True,
+    visibility = [
+        "//tensorflow/cc/saved_model:__subpackages__",
+        "//tensorflow/tools/proto_splitter:__subpackages__",
+    ],
+)
+
+# copybara:uncomment_begin(google-only)
+#
+# py_proto_library(
+#     name = "test_message_proto_py_pb2",
+#     deps = [
+#         ":test_message_proto",
+#     ],
+# )
+#
+# py_binary(
+#     name = "split_gen",
+#     srcs = ["split_gen.py"],
+#     strict_deps = True,
+#     deps = [
+#         ":test_message_proto_py_pb2",
+#         "//tensorflow/python/lib/io:file_io",
+#         "//tensorflow/tools/proto_splitter:chunk_proto_py_pb2",
+#         "//tensorflow/tools/proto_splitter:split",
+#         "//tensorflow/tools/proto_splitter:util",
+#         "@absl_py//absl:app",
+#         "@absl_py//absl/flags",
+#         "@absl_py//absl/logging",
+#     ],
+# )
+#
+# py_binary(
+#     name = "split_graph_def_gen",
+#     srcs = ["split_graph_def_gen.py"],
+#     strict_deps = True,
+#     deps = [
+#         "//tensorflow/core:protos_all_py",
+#         "//tensorflow/python/lib/io:file_io",
+#         "//tensorflow/tools/proto_splitter:constants",
+#         "//tensorflow/tools/proto_splitter:split_graph_def",
+#         "//tensorflow/tools/proto_splitter/python:test_util",
+#         "@absl_py//absl:app",
+#         "@absl_py//absl/flags",
+#         "@absl_py//absl/logging",
+#     ],
+# )
+#
+# py_binary(
+#     name = "split_saved_model_gen",
+#     srcs = ["split_saved_model_gen.py"],
+#     strict_deps = True,
+#     deps = [
+#         "//tensorflow/core:protos_all_py",
+#         "//tensorflow/python/lib/io:file_io",
+#         "//tensorflow/tools/proto_splitter:constants",
+#         "//tensorflow/tools/proto_splitter/python:saved_model",
+#         "//tensorflow/tools/proto_splitter/python:test_util",
+#         "@absl_py//absl:app",
+#         "@absl_py//absl/flags",
+#         "@absl_py//absl/logging",
+#     ],
+# )
+#
+# py_binary(
+#     name = "many_field_gen",
+#     srcs = ["many_field_gen.py"],
+#     strict_deps = True,
+#     deps = [
+#         ":test_message_proto_py_pb2",
+#         "//tensorflow/python/lib/io:file_io",
+#         "//tensorflow/tools/proto_splitter:split",
+#         "@absl_py//absl:app",
+#         "@absl_py//absl/flags",
+#     ],
+# )
+#
+# copybara:uncomment_end
@@ -0,0 +1,100 @@
+chunk_index: 0
+chunked_fields {
+  field_tag {
+    field: 2
+  }
+  field_tag {
+    index: 0
+  }
+  message {
+    chunk_index: 1
+    chunked_fields {
+      field_tag {
+        field: 2
+      }
+      field_tag {
+        index: 0
+      }
+      message {
+        chunk_index: 2
+        chunked_fields {
+          field_tag {
+            field: 2
+          }
+          field_tag {
+            index: 0
+          }
+          message {
+            chunk_index: 4
+          }
+        }
+        chunked_fields {
+          field_tag {
+            field: 2
+          }
+          field_tag {
+            index: 1
+          }
+          message {
+            chunk_index: 5
+          }
+        }
+        chunked_fields {
+          field_tag {
+            field: 2
+          }
+          field_tag {
+            index: 2
+          }
+          message {
+            chunk_index: 6
+          }
+        }
+      }
+    }
+    chunked_fields {
+      field_tag {
+        field: 2
+      }
+      field_tag {
+        index: 1
+      }
+      message {
+        chunk_index: 3
+        chunked_fields {
+          field_tag {
+            field: 2
+          }
+          field_tag {
+            index: 0
+          }
+          message {
+            chunk_index: 7
+          }
+        }
+        chunked_fields {
+          field_tag {
+            field: 2
+          }
+          field_tag {
+            index: 1
+          }
+          message {
+            chunk_index: 8
+          }
+        }
+        chunked_fields {
+          field_tag {
+            field: 2
+          }
+          field_tag {
+            index: 2
+          }
+          message {
+            chunk_index: 9
+          }
+        }
+      }
+    }
+  }
+}
@@ -0,0 +1,100 @@
+chunk_index: 0
+chunked_fields {
+  field_tag {
+    field: 2
+  }
+  field_tag {
+    index: 0
+  }
+  message {
+    chunk_index: 1
+    chunked_fields {
+      field_tag {
+        field: 2
+      }
+      field_tag {
+        index: 0
+      }
+      message {
+        chunk_index: 2
+        chunked_fields {
+          field_tag {
+            field: 2
+          }
+          field_tag {
+            index: 0
+          }
+          message {
+            chunk_index: 3
+          }
+        }
+        chunked_fields {
+          field_tag {
+            field: 2
+          }
+          field_tag {
+            index: 1
+          }
+          message {
+            chunk_index: 4
+          }
+        }
+        chunked_fields {
+          field_tag {
+            field: 2
+          }
+          field_tag {
+            index: 2
+          }
+          message {
+            chunk_index: 5
+          }
+        }
+      }
+    }
+    chunked_fields {
+      field_tag {
+        field: 2
+      }
+      field_tag {
+        index: 1
+      }
+      message {
+        chunk_index: 6
+        chunked_fields {
+          field_tag {
+            field: 2
+          }
+          field_tag {
+            index: 0
+          }
+          message {
+            chunk_index: 7
+          }
+        }
+        chunked_fields {
+          field_tag {
+            field: 2
+          }
+          field_tag {
+            index: 1
+          }
+          message {
+            chunk_index: 8
+          }
+        }
+        chunked_fields {
+          field_tag {
+            field: 2
+          }
+          field_tag {
+            index: 2
+          }
+          message {
+            chunk_index: 9
+          }
+        }
+      }
+    }
+  }
+}
@@ -0,0 +1,17 @@
+÷
+ô
+
+fn3
+Const_0Const*!
+valueB"C3D?7�	>ÿt²>_
+Const_1Const*M
+valueDBB"8>�h>ñÞ²>wd>|(˜<8Ú>¨éL?}\°=¾Ãu>»œà>Ûm€>D•”>]sE?1,?
+ü>_
+Const_2Const*M
+valueDBB"8”–×>R\â>7Ö<OwE>cý	?KÐÖ=¥?î�?f\?ˆð8?ˆ&°<õZ²>&èö>×õ¤>_
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@@ -0,0 +1,31 @@
+field_one {
+  repeated_field {
+  }
+  repeated_field {
+    string_field: "inner_inner_string"
+    map_field_uint32 {
+      key: 324
+      value: "map_value_324"
+    }
+    map_field_uint32 {
+      key: 543
+      value: "map_value_543"
+    }
+  }
+}
+map_field_int64 {
+  key: -1345
+  value: "map_value_-1345"
+}
+nested_map_bool {
+  key: false
+  value {
+    string_field: "string_false"
+  }
+}
+nested_map_bool {
+  key: true
+  value {
+    string_field: "string_true"
+  }
+}
@@ -0,0 +1,102 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+r"""Generates ManyField test data for Merger.
+
+Constructs chunked proto test data containing various field types for
+Merger::Read and Merger::Merge.
+
+Usage: bazel run tensorflow/tools/proto_splitter/testdata:many_field_gen -- \
+    --path=/tmp/many_field
+"""
+
+from collections.abc import Sequence
+import os
+
+from absl import app
+from absl import flags
+
+from tensorflow.python.lib.io import file_io
+from tensorflow.tools.proto_splitter import split
+from tensorflow.tools.proto_splitter.testdata import test_message_pb2
+
+# Example path: /tmp/many_field
+SPLITTER_TESTDATA_PATH = flags.DEFINE_string(
+    "path", None, help="Path to testdata directory."
+)
+
+
+class ManyFieldSplitter(split.ComposableSplitter):
+  """Splitter for ManyField proto."""
+
+  def build_chunks(self):
+    self.add_chunk(
+        self._proto.field_one,
+        [
+            test_message_pb2.ManyFields.DESCRIPTOR.fields_by_name[
+                "field_one"
+            ].number
+        ],
+    )
+    self._proto.ClearField("field_one")
+    for map_key, map_value in self._proto.nested_map_bool.items():
+      self.add_chunk(
+          map_value,
+          [
+              test_message_pb2.ManyFields.DESCRIPTOR.fields_by_name[
+                  "nested_map_bool"
+              ].number,
+              map_key,
+          ],
+      )
+    self._proto.ClearField("nested_map_bool")
+
+
+def main(argv: Sequence[str]) -> None:
+  if len(argv) > 1:
+    raise app.UsageError("Too many command-line arguments.")
+
+  proto = test_message_pb2.ManyFields(
+      field_one=test_message_pb2.ManyFields(
+          repeated_field=[
+              test_message_pb2.ManyFields(),
+              test_message_pb2.ManyFields(
+                  string_field="inner_inner_string",
+                  map_field_uint32={
+                      324: "map_value_324",
+                      543: "map_value_543",
+                  },
+              ),
+          ]
+      ),
+      map_field_int64={
+          -1345: "map_value_-1345",
+      },
+      nested_map_bool={
+          True: test_message_pb2.ManyFields(string_field="string_true"),
+          False: test_message_pb2.ManyFields(string_field="string_false"),
+      },
+  )
+  file_io.write_string_to_file(
+      os.path.join(SPLITTER_TESTDATA_PATH.value, "many-field.pbtxt"), str(proto)
+  )
+
+  ManyFieldSplitter(proto).write(
+      os.path.join(SPLITTER_TESTDATA_PATH.value, "many-field")
+  )
+
+
+if __name__ == "__main__":
+  app.run(main)
@@ -0,0 +1,90 @@
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+  name: "Const_0"
+  op: "Const"
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 3
+          }
+        }
+        tensor_content: "/O5?\342\020\271>\215\326{?"
+      }
+    }
+  }
+}
+node {
+  name: "Const_1"
+  op: "Const"
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
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+        }
+        tensor_content: "\327\2177>\024)G?\007\027\224>"
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+}
+node {
+  name: "Const_2"
+  op: "Const"
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+    key: "value"
+    value {
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+        dtype: DT_FLOAT
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+      }
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+node {
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+  op: "Const"
+  attr {
+    key: "value"
+    value {
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+        tensor_shape {
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+        tensor_content: "\355,W?y\232{?\234G\234>"
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+  }
+}
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+  op: "Const"
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
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+            tensor_content: "\206\226v?4\211F?c\250\353>\'G\t?:\241%?\272y|=\020o\n>NN\265>9\3301>\313\377\325>&\302\013>3\035%?x\217\017?\037\255\345>P(\334>g\263<?"
+          }
+        }
+      }
+    }
+    node {
+      name: "Const_8"
+      op: "Const"
+      attr {
+        key: "value"
+        value {
+          tensor {
+            dtype: DT_FLOAT
+            tensor_shape {
+              dim {
+                size: 16
+              }
+            }
+            tensor_content: ".\356\201>\t\246\371>\234\014p?D\3626?j;\013=V\232h>\032\327w>I\000B?\320\336Z?(\364\234>\020$\223=~q\211>v\345&?a\257\000?\030\221\360>\350pX?"
+          }
+        }
+      }
+    }
+    library {
+      function {
+        signature {
+          name: "fn1"
+        }
+        node_def {
+          name: "Const_9"
+          op: "Const"
+          attr {
+            key: "value"
+            value {
+              tensor {
+                dtype: DT_FLOAT
+                tensor_shape {
+                  dim {
+                    size: 16
+                  }
+                }
+                tensor_content: "\021\"/?\032\335\354;\210\352Q>!\344\341>\243[\204>\245\227h>\020\177\006?^\357\203;\273\305T?\257\213\370=p\352\230>\261\n\220>\256\375m=KO\267>\\|w>\241\324q?"
+              }
+            }
+          }
+        }
+        node_def {
+          name: "Const_10"
+          op: "Const"
+          attr {
+            key: "value"
+            value {
+              tensor {
+                dtype: DT_FLOAT
+                tensor_shape {
+                  dim {
+                    size: 15
+                  }
+                }
+                tensor_content: "CT8?\254\211Q?HHd?\240\210\351>\352BJ?\013\253\265=\0305\357=&\225\016>\362\267\211>?G\332>*\3031?\356\315\255;Z\344$?\023$X=\344\272\271>"
+              }
+            }
+          }
+        }
+        node_def {
+          name: "Const_11"
+          op: "Const"
+          attr {
+            key: "value"
+            value {
+              tensor {
+                dtype: DT_FLOAT
+                tensor_shape {
+                  dim {
+                    size: 15
+                  }
+                }
+                tensor_content: "\317\350l?A\030\357=b\313\246>\304@*>]~\205=t6\010>\251]\000>\205ms=\236\252\006>/\343\236;T\226\364=/b\273=v|E?\374\375\014?\303\317\027?"
+              }
+            }
+          }
+        }
+      }
+      function {
+        signature {
+          name: "fn2"
+        }
+        node_def {
+          name: "Const_12"
+          op: "Const"
+          attr {
+            key: "value"
+            value {
+              tensor {
+                dtype: DT_FLOAT
+                tensor_shape {
+                  dim {
+                    size: 15
+                  }
+                }
+                tensor_content: "\205\324l?\274|H?)\235!?\242[??\3551\307>\355\2040?%\310\342>\034\025E?\302j\'?\237Q\350>*\r^=a\317;?p\271=?\272MA?\267&\346>"
+              }
+            }
+          }
+        }
+        node_def {
+          name: "Const_13"
+          op: "Const"
+          attr {
+            key: "value"
+            value {
+              tensor {
+                dtype: DT_FLOAT
+                tensor_shape {
+                  dim {
+                    size: 115
+                  }
+                }
+                tensor_content: "N\265\276>.X\304>\352%k?\225!\245>\305\304\235>\346\211{?\3272*?R\321f?\320\300i?Iy@>\271x\203>\002\322\274=ioI?\t\225\372>\362Bp?\2031W<\022\337\002>E3\361>\366\214\274<\2274\036?\337\220\371>,B\336>*\361C?\250[\263>\236\t\020?\346Y\261>\336\232U>v=\325>f\277f<\207C2?h#=?_\215h?\314\211\215>\032rn?\214CH>\'\232\342=\204\376r=\374\237\'?x\310\265>\005\223\315>\260\270\016?\2179\255>\370\t\025>\224RE?ay9?\004\372=?\237\024G?!\034\351=\0231e?M\327\234>`\357f?\375\355t?\365\274\177?n\220E?sN\267>\253tQ?\303\277K?h\351\272>\031\277w?\007\341.>\240\024S=\217\177y?\271z\010?\323\231\356>\306k\034?\031@\264>\024\371\031>\036\211\035>W\276K?\001\272K>\223\'v?\213\212\376>\372F$?6\276R?\257vb?V\341\202>\004\023\032?=P\371>W\010F?\007\222\376>]\232\321>\231\252\370>\'7.>,\233k?`_\376>I\337d?\363\220b?hS]?\203\377u?\177Y\007?\023\177\306=\234\2534?\322\257\262>\225R\010<\371 m>4\334P?\337\241\345>E\001\271>\352\226O?*|@?\203\352a??#\010?\277\243\005?]\324\361>3\007;?\"\3753>\3427\000?ZW\022>\0037G?\350\220\273>XZ\277<q\252m?\342\315\311>xJd?\n\343\035?"
+              }
+            }
+          }
+        }
+      }
+      function {
+        signature {
+          name: "fn3"
+        }
+        node_def {
+          name: "Const_14"
+          op: "Const"
+          attr {
+            key: "value"
+            value {
+              tensor {
+                dtype: DT_FLOAT
+                tensor_shape {
+                  dim {
+                    size: 15
+                  }
+                }
+                tensor_content: "\367\356)?\020y\310=o\2048?\243 >?\312\203T?a\273W>n\212m?\232C6?\370\331/?/\374\253>\372R ?\257|\017?\230\256X=\204\330\"?\0007\366>"
+              }
+            }
+          }
+        }
+      }
+      function {
+        signature {
+          name: "fn4"
+        }
+        node_def {
+          name: "Const_15"
+          op: "Const"
+          attr {
+            key: "value"
+            value {
+              tensor {
+                dtype: DT_FLOAT
+                tensor_shape {
+                  dim {
+                    size: 15
+                  }
+                }
+                tensor_content: "&\036\000?\271\260b?\375\307h?\277u\371=Vl[?rP\321>:\306\010?\274\001\002?\267\\k?\374e\006?\347\355;>RlC?\017\370\330>\362\002\276=\336p\357>"
+              }
+            }
+          }
+        }
+        node_def {
+          name: "Const_16"
+          op: "Const"
+          attr {
+            key: "value"
+            value {
+              tensor {
+                dtype: DT_FLOAT
+                tensor_shape {
+                  dim {
+                    size: 15
+                  }
+                }
+                tensor_content: "J\241F>\232\"i>H(z?b\227\274>t\331\233>\224\361O>&\210\210>au\316>\231\210V?\224\241j>\377P&>T>\303=C\371 ?O\232;>\216\016|>"
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
@@ -0,0 +1,28 @@
+val: "0"
+child_nodes {
+  val: "010"
+  child_nodes {
+    val: "01020"
+    child_nodes {
+      val: "0102030"
+    }
+    child_nodes {
+      val: "0102031"
+    }
+    child_nodes {
+      val: "0102032"
+    }
+  }
+  child_nodes {
+    val: "01021"
+    child_nodes {
+      val: "0102130"
+    }
+    child_nodes {
+      val: "0102131"
+    }
+    child_nodes {
+      val: "0102132"
+    }
+  }
+}
@@ -0,0 +1,179 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Generates test data for Merger.
+
+Constructs depth- and breadth-first tree-like chunked protos test data for
+Merger::Read and Merger::Merge.
+"""
+
+from collections.abc import Sequence
+import os
+from typing import Optional, Union
+
+from absl import app
+from absl import flags
+from absl import logging
+
+from google.protobuf import message
+from tensorflow.python.lib.io import file_io
+from tensorflow.tools.proto_splitter import chunk_pb2
+from tensorflow.tools.proto_splitter import split
+from tensorflow.tools.proto_splitter import util
+from tensorflow.tools.proto_splitter.testdata import test_message_pb2
+
+SPLITTER_TESTDATA_PATH = flags.DEFINE_string(
+    "path", None, help="Path to testdata directory.")
+
+_CHILD_NODES_FIELD_TAG = (
+    test_message_pb2.StringNode.DESCRIPTOR.fields_by_name[
+        "child_nodes"
+    ].number
+)
+
+
+class StringNodeSplitter(split.ComposableSplitter):
+  """Splits a StringNode proto with N strings into a tree with depth N."""
+
+  def __init__(self, proto: test_message_pb2.StringNode,
+               chunked_message: Optional[chunk_pb2.ChunkedMessage] = None,
+               **kwargs):
+    super().__init__(proto, **kwargs)
+    self._chunked_message = self._chunked_message or chunked_message
+
+  def add_chunk(
+      self, chunk: Union[message.Message, bytes], field_tags: util.FieldTypes
+  ) -> None:
+    """Adds a new chunk and updates the ChunkedMessage proto."""
+    assert self._chunked_message is not None
+    field = self._chunked_message.chunked_fields.add(
+        field_tag=util.get_field_tag(self._proto, field_tags)
+    )
+    field.message.chunk_index = self.total_chunks_len()
+    self.add_root_chunk(chunk)
+
+  def total_chunks_len(self) -> int:
+    """Returns length of chunks stored in root splitter."""
+    if self._parent_splitter is not None:
+      return self._parent_splitter.total_chunks_len()
+    return len(self._chunks)
+
+  def add_root_chunk(self, chunk: Union[message.Message, bytes]) -> None:
+    """Adds chunk to root splitter chunks."""
+    if self._parent_splitter is None:
+      assert self._chunks is not None
+      self._chunks.append(chunk)
+    else:
+      self._parent_splitter.add_root_chunk(chunk)
+
+
+class DFStringNodeSplitter(StringNodeSplitter):
+  """Depth-first string node splitter."""
+
+  def build_chunks(self) -> Sequence[Union[message.Message, bytes]]:
+    if not isinstance(self._proto, test_message_pb2.StringNode):
+      raise TypeError("Can only split TreeString type protos")
+
+    if not self._proto.child_nodes:
+      return
+    for i, node in enumerate(self._proto.child_nodes):
+      self.add_chunk(node, [_CHILD_NODES_FIELD_TAG, i])
+      DFStringNodeSplitter(
+          proto=node,
+          parent_splitter=self,
+          fields_in_parent=[_CHILD_NODES_FIELD_TAG],
+          chunked_message=self._chunked_message.chunked_fields[i].message
+      ).build_chunks()
+
+    self._proto.ClearField("child_nodes")
+    if self._parent_splitter is None:
+      self._chunks.append(self._chunked_message)
+      file_io.write_string_to_file(
+          os.path.join(SPLITTER_TESTDATA_PATH.value, "df-split-tree.pbtxt"),
+          str(self._chunked_message))
+
+    return self._chunks
+
+
+class BFStringNodeSplitter(StringNodeSplitter):
+  """Breadth-first string node splitter."""
+
+  def build_chunks(self) -> Sequence[Union[message.Message, bytes]]:
+    if not isinstance(self._proto, test_message_pb2.StringNode):
+      raise TypeError("Can only split TreeString type protos")
+
+    if not self._proto.child_nodes:
+      return
+    for i, node in enumerate(self._proto.child_nodes):
+      self.add_chunk(node, [_CHILD_NODES_FIELD_TAG, i])
+    for i, node in enumerate(self._proto.child_nodes):
+      BFStringNodeSplitter(
+          proto=node,
+          parent_splitter=self,
+          fields_in_parent=[_CHILD_NODES_FIELD_TAG],
+          chunked_message=self._chunked_message.chunked_fields[i].message
+      ).build_chunks()
+
+    self._proto.ClearField("child_nodes")
+    if self._parent_splitter is None:
+      self._chunks.append(self._chunked_message)
+      file_io.write_string_to_file(
+          os.path.join(SPLITTER_TESTDATA_PATH.value, "bf-split-tree.pbtxt"),
+          str(self._chunked_message))
+
+    return self._chunks
+
+
+def main(argv: Sequence[str]) -> None:
+  if len(argv) > 1:
+    raise app.UsageError("Too many command-line arguments.")
+
+  if SPLITTER_TESTDATA_PATH.value is None:
+    raise app.UsageError("'path' flag not specified.")
+
+  levels = 4
+  def make_string_tree(
+      string_tree: test_message_pb2.StringNode, level: int = 0, label: str = "0"
+  ) -> test_message_pb2.StringNode:
+    string_tree.val = label
+    if level >= levels-1:
+      return string_tree
+    for i in range(level+1):
+      make_string_tree(string_tree.child_nodes.add(),
+                       level+1, label+str(level+1)+str(i))
+    return string_tree
+
+  def copy_string_tree(string_tree: test_message_pb2.StringNode):
+    new_tree = test_message_pb2.StringNode()
+    new_tree.CopyFrom(string_tree)
+    return new_tree
+
+  string_tree = make_string_tree(test_message_pb2.StringNode())
+  logging.info("StringNode tree generated:\n%s", string_tree)
+  file_io.write_string_to_file(
+      os.path.join(SPLITTER_TESTDATA_PATH.value, "split-tree.pbtxt"),
+      str(string_tree))
+
+  # depth-first chunk ordering
+  DFStringNodeSplitter(copy_string_tree(string_tree)).write(
+      os.path.join(SPLITTER_TESTDATA_PATH.value, "df-split-tree"))
+
+  # breadth-first
+  BFStringNodeSplitter(copy_string_tree(string_tree)).write(
+      os.path.join(SPLITTER_TESTDATA_PATH.value, "bf-split-tree"))
+
+
+if __name__ == "__main__":
+  app.run(main)
@@ -0,0 +1,198 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+r"""Generates GraphDef test data for Merger.
+
+Constructs chunked protos test data containing GraphDefs with lots of nodes and
+large nodes for Merger::Read and Merger::Merge.
+
+Example command:
+
+bazel run tensorflow/tools/proto_splitter/testdata:split_graph_def_gen -- \
+  --path /tmp \
+  --graph_type=split-lots-nodes,split-large-nodes,split-large-constant \
+  --export=pb,cpb
+"""
+
+from collections.abc import Sequence
+import os
+
+from absl import app
+from absl import flags
+from absl import logging
+
+from tensorflow.core.framework import graph_pb2
+from tensorflow.python.lib.io import file_io
+from tensorflow.tools.proto_splitter import constants
+from tensorflow.tools.proto_splitter import split_graph_def
+from tensorflow.tools.proto_splitter.python import test_util
+
+
+LOTS_NODES_SIZES = [95] * 15
+LARGE_NODES_SIZES = [50, 95, 95, 95, 50, 95]
+LARGE_CONSTANT_SIZES = [50, 50, 1000, 50, 1000]
+
+
+def _split_and_write(
+    path: str,
+    graph_def: graph_pb2.GraphDef,
+    max_size: int,
+    export_files: Sequence[str],
+):
+  """Writes the .pb, .pbtxt and .cpb files for a GraphDef."""
+  constants.debug_set_max_size(max_size)
+
+  if "pbtxt" in export_files:
+    output_path = f"{path}.pbtxt"
+    file_io.write_string_to_file(output_path, str(graph_def))
+    logging.info("  %s written", output_path)
+  if "pb" in export_files:
+    output_path = f"{path}.pb"
+    file_io.write_string_to_file(output_path, graph_def.SerializeToString())
+    logging.info("  %s written", output_path)
+  if "cpb" in export_files:
+    splitter = split_graph_def.GraphDefSplitter(graph_def)
+    splitter.write(path)
+    chunks, _ = splitter.split()
+    if len(chunks) > 1:
+      logging.info("  %s.cpb written", path)
+    else:
+      raise RuntimeError(
+          "For some reason this graph was not chunked, so a .cpb file was not"
+          " produced. Raising an error since this should not be the case."
+      )
+
+
+def split_lots_nodes(path: str, export_files: Sequence[str]):
+  """GraphDef with lots of nodes."""
+  # The actual sizes in the generated graph has a slight deviation, but are
+  # between [90, 100] (tested in testMakeGraphDef with atol=5).
+  #    Expected Chunks (Max Size = 500)
+  #    -----------------------------
+  #       Chunk #: Contents
+  #    -----------------------------
+  #       0: GraphDef  # (nodes [0:5])
+  #    -----------------------------
+  #       1: GraphDef  # (nodes [5:10])
+  #    -----------------------------
+  #       2: GraphDef  # (nodes [10:15])
+  #    -----------------------------
+  #       3: ChunkedMessage
+  #    -----------------------------
+  graph_def = test_util.make_graph_def_with_constant_nodes(LOTS_NODES_SIZES)
+  _split_and_write(path, graph_def, 500, export_files)
+
+
+def split_large_nodes(path: str, export_files: Sequence[str]):
+  """GraphDef with large nodes."""
+  # Large nodes are greedily split from the original proto if they are
+  # larger than max_size / 3.
+  # This should create 6 chunks:
+  #   [parent GraphDef, node[1], node[2], node[3], node[5], ChunkedMessage]
+  graph_def = test_util.make_graph_def_with_constant_nodes(LARGE_NODES_SIZES)
+  _split_and_write(path, graph_def, 200, export_files)
+
+
+def split_large_constant(path: str, export_files: Sequence[str]):
+  """GraphDef with large constant nodes."""
+  #    Expected Chunks (Max Size = 500)
+  #    -----------------------------
+  #       Chunk #: Contents
+  #    -----------------------------
+  #       0: GraphDef
+  #    -----------------------------
+  #       1: GraphDef.nodes[2].attr["value"].tensor.tensor_content
+  #    -----------------------------
+  #       2: GraphDef.nodes[4].attr["value"].tensor.tensor_content
+  #    -----------------------------
+  graph_def = test_util.make_graph_def_with_constant_nodes(LARGE_CONSTANT_SIZES)
+  _split_and_write(path, graph_def, 500, export_files)
+
+
+def function_lots_of_nodes(path: str, export_files: Sequence[str]):
+  """Generates a proto of GraphDef with a FunctionDef that have many nodes."""
+  graph_def = test_util.make_graph_def_with_constant_nodes(
+      [], fn=LOTS_NODES_SIZES
+  )
+  _split_and_write(path, graph_def, 500, export_files)
+
+
+def function_large_nodes(path: str, export_files: Sequence[str]):
+  graph_def = test_util.make_graph_def_with_constant_nodes(
+      [], fn=LARGE_NODES_SIZES
+  )
+  _split_and_write(path, graph_def, 200, export_files)
+
+
+def graph_def_and_function(path: str, export_files: Sequence[str]):
+  graph_def = test_util.make_graph_def_with_constant_nodes(
+      [50, 50, 50, 50, 50, 50], fn1=[50, 50, 50], fn2=[50], fn3=[50], fn4=[50]
+  )
+  _split_and_write(path, graph_def, 200, export_files)
+
+
+VALID_GRAPH_TYPES = {
+    "split-lots-nodes": split_lots_nodes,
+    "split-large-nodes": split_large_nodes,
+    "split-large-constant": split_large_constant,
+    "function-lots-of-nodes": function_lots_of_nodes,
+    "function-large-nodes": function_large_nodes,
+    "graph-def-and-function": graph_def_and_function,
+}
+ALL_GRAPH_TYPES = ", ".join(VALID_GRAPH_TYPES.keys())
+
+SPLITTER_TESTDATA_PATH = flags.DEFINE_string(
+    "path", None, help="Path to testdata directory."
+)
+GRAPH_TYPES = flags.DEFINE_multi_string(
+    "graph_type",
+    "all",
+    help=f"Type(s) of graph to export. Valid types: all, {ALL_GRAPH_TYPES}",
+)
+EXPORT_FILES = flags.DEFINE_multi_string(
+    "export",
+    "all",
+    help="List of files to export. Valid options: all, pb, pbtxt, cpb",
+)
+
+
+def main(argv: Sequence[str]) -> None:
+  if len(argv) > 1:
+    raise app.UsageError("Too many command-line arguments.")
+
+  if "all" in EXPORT_FILES.value:
+    export_files = ["pb", "pbtxt", "cpb"]
+  else:
+    export_files = EXPORT_FILES.value
+
+  if "all" in GRAPH_TYPES.value:
+    graph_types = VALID_GRAPH_TYPES.keys()
+  else:
+    graph_types = GRAPH_TYPES.value
+
+  for v in graph_types:
+    if v not in VALID_GRAPH_TYPES:
+      raise ValueError(
+          f"Invalid flag passed to `graph_type`: {v}\nValid graph types:"
+          f" {ALL_GRAPH_TYPES}"
+      )
+
+    logging.info("Generating graph %s", v)
+    f = VALID_GRAPH_TYPES[v]
+    f(os.path.join(SPLITTER_TESTDATA_PATH.value, v), export_files)
+
+
+if __name__ == "__main__":
+  app.run(main)
@@ -0,0 +1,145 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+r"""Generates SavedModel test data for Merger.
+
+Constructs chunked proto test data containing a SavedModel.
+
+Example command:
+
+bazel run tensorflow/tools/proto_splitter/testdata:split_saved_model_gen -- \
+  --path /tmp \
+  --saved_model_type=split-standard \
+  --export=pb,cpb
+"""
+
+from collections.abc import Sequence
+import os
+
+from absl import app
+from absl import flags
+from absl import logging
+
+from tensorflow.core.protobuf import saved_model_pb2
+from tensorflow.python.lib.io import file_io
+from tensorflow.tools.proto_splitter import constants
+from tensorflow.tools.proto_splitter.python import saved_model as split_saved_model
+from tensorflow.tools.proto_splitter.python import test_util
+
+
+STANDARD_SIZES = [100, 100, 1000, 100, 1000, 500, 100, 100, 100]
+
+
+def _split_and_write(
+    path: str,
+    saved_model: saved_model_pb2.SavedModel,
+    max_size: int,
+    export_files: Sequence[str],
+):
+  """Writes the .pb, .pbtxt and .cpb files for a SavedModel."""
+  constants.debug_set_max_size(max_size)
+
+  if "pbtxt" in export_files:
+    output_path = f"{path}.pbtxt"
+    file_io.write_string_to_file(output_path, str(saved_model))
+    logging.info("  %s written", output_path)
+  if "pb" in export_files:
+    output_path = f"{path}.pb"
+    file_io.write_string_to_file(output_path, saved_model.SerializeToString())
+    logging.info("  %s written", output_path)
+  if "cpb" in export_files:
+    splitter = split_saved_model.SavedModelSplitter(saved_model)
+    splitter.write(path)
+    chunks, _ = splitter.split()
+    if len(chunks) > 1:
+      logging.info("  %s.cpb written", path)
+    else:
+      raise RuntimeError(
+          "For some reason this graph was not chunked, so a .cpb file was not"
+          " produced. Raising an error since this should not be the case."
+      )
+
+
+def split_standard(path: str, export_files: Sequence[str]):
+  """Splits a standard SavedModel."""
+  fn1 = [100, 100, 100]
+  fn2 = [100, 500]
+  fn3 = [100]
+  fn4 = [100, 100]
+
+  max_size = 500
+  constants.debug_set_max_size(max_size)
+
+  graph_def = test_util.make_graph_def_with_constant_nodes(
+      STANDARD_SIZES, fn1=fn1, fn2=fn2, fn3=fn3, fn4=fn4
+  )
+  proto = saved_model_pb2.SavedModel()
+  proto.meta_graphs.add().graph_def.CopyFrom(graph_def)
+
+  _split_and_write(path, proto, max_size, export_files)
+
+
+VALID_SAVED_MODEL_TYPES = {
+    "split-standard": split_standard,
+}
+ALL_SAVED_MODEL_TYPES = ", ".join(VALID_SAVED_MODEL_TYPES.keys())
+
+SPLITTER_TESTDATA_PATH = flags.DEFINE_string(
+    "path", None, help="Path to testdata directory."
+)
+SAVED_MODEL_TYPES = flags.DEFINE_multi_string(
+    "saved_model_type",
+    "all",
+    help=(
+        "Type(s) of saved model to export. Valid types: all, "
+        f"{ALL_SAVED_MODEL_TYPES}"
+    ),
+)
+EXPORT_FILES = flags.DEFINE_multi_string(
+    "export",
+    "all",
+    help="List of files to export. Valid options: all, pb, pbtxt, cpb",
+)
+
+
+def main(argv: Sequence[str]) -> None:
+  if len(argv) > 1:
+    raise app.UsageError("Too many command-line arguments.")
+
+  if "all" in EXPORT_FILES.value:
+    export_files = ["pb", "pbtxt", "cpb"]
+  else:
+    export_files = EXPORT_FILES.value
+
+  if "all" in SAVED_MODEL_TYPES.value:
+    saved_model_types = VALID_SAVED_MODEL_TYPES.keys()
+  else:
+    saved_model_types = SAVED_MODEL_TYPES.value
+
+  for v in saved_model_types:
+    if v not in VALID_SAVED_MODEL_TYPES:
+      raise ValueError(
+          "Invalid flag passed to `saved_model_type`: "
+          f"{v}\nValid saved model types:"
+          f" {ALL_SAVED_MODEL_TYPES}"
+      )
+
+    logging.info("Generating saved model %s", v)
+    f = VALID_SAVED_MODEL_TYPES[v]
+    f(os.path.join(SPLITTER_TESTDATA_PATH.value, v), export_files)
+
+
+if __name__ == "__main__":
+  app.run(main)
@@ -0,0 +1,42 @@
+// Copyright 2026 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// ==============================================================================
+
+syntax = "proto3";
+
+package tensorflow.proto_splitter_testdata;
+
+message RepeatedString {
+  repeated string strings = 1;
+}
+
+message RepeatedRepeatedString {
+  int32 filler_field = 1;
+  repeated RepeatedString rs = 2;
+}
+
+message ManyFields {
+  ManyFields field_one = 1;
+  repeated ManyFields repeated_field = 2;
+  string string_field = 3;
+  repeated string repeated_string_field = 4;
+  map<uint32, string> map_field_uint32 = 5;
+  map<int64, string> map_field_int64 = 6;
+  map<bool, ManyFields> nested_map_bool = 7;
+}
+
+message StringNode {
+  string val = 1;
+  repeated StringNode child_nodes = 2;
+}