chore: import upstream snapshot with attribution

tensorflow/lite/profiling/proto/BUILD

@@ -0,0 +1,55 @@
+load("@com_google_protobuf//bazel:proto_library.bzl", "proto_library")
+load("@rules_python//python:proto.bzl", "py_proto_library")
+load("//tensorflow:tensorflow.default.bzl", "get_compatible_with_portable")
+load(
+    "//tensorflow/core/platform:build_config.bzl",
+    "tf_proto_library",
+)
+
+package(
+    # copybara:uncomment default_applicable_licenses = ["//tensorflow:LICENSE"],
+    default_visibility = ["//visibility:public"],
+    licenses = ["notice"],
+)
+
+proto_library(
+    name = "profiling_info_proto",
+    srcs = ["profiling_info.proto"],
+    compatible_with = get_compatible_with_portable(),
+    visibility = ["//visibility:public"],
+)
+
+proto_library(
+    name = "model_runtime_info_proto",
+    srcs = ["model_runtime_info.proto"],
+    compatible_with = get_compatible_with_portable(),
+    visibility = ["//visibility:public"],
+    deps = [":profiling_info_proto"],
+)
+
+tf_proto_library(
+    name = "profiling_info",  # bzl adds _py
+    srcs = ["profiling_info.proto"],
+    visibility = ["//visibility:public"],
+)
+
+tf_proto_library(
+    name = "model_runtime_info",  # bzl adds _py
+    srcs = ["model_runtime_info.proto"],
+    protodeps = [":profiling_info"],
+    visibility = ["//visibility:public"],
+)
+
+# copybara:uncomment_begin(google-only)
+# py_proto_library(
+#     name = "profiling_info_py_pb2",
+#     compatible_with = get_compatible_with_portable(),
+#     deps = [":profiling_info_proto"],
+# )
+#
+# py_proto_library(
+#     name = "model_runtime_info_py_pb2",
+#     compatible_with = get_compatible_with_portable(),
+#     deps = [":model_runtime_info_proto"],
+# )
+# copybara:uncomment_end

tensorflow/lite/profiling/proto/CMakeLists.txt

@@ -0,0 +1,57 @@
+#
+# Copyright 2024 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
+#
+#      https://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.
+find_package(Protobuf REQUIRED)
+
+add_library(profiling_info_proto profiling_info.proto)
+
+list(APPEND profiling_info_generated_files
+    ${CMAKE_BINARY_DIR}/tensorflow/lite/profiling/proto/profiling_info.pb.cc
+    ${CMAKE_BINARY_DIR}/tensorflow/lite/profiling/proto/profiling_info.pb.h)
+
+# Generate profiling_info.pb.cc and profiling_info.pb.h from
+# profiling_info.proto using protoc. Once the protobuf package version is
+# upgraded, we can use protobuf_generate_cpp/protobuf_generate here directly.
+add_custom_command(
+    OUTPUT ${profiling_info_generated_files}
+    COMMAND ${Protobuf_PROTOC_EXECUTABLE}
+    ARGS --cpp_out=${CMAKE_BINARY_DIR} --proto_path=${TENSORFLOW_SOURCE_DIR} ${CMAKE_CURRENT_SOURCE_DIR}/profiling_info.proto
+    DEPENDS ${Protobuf_PROTOC_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/profiling_info.proto
+)
+
+set_source_files_properties(${profiling_info_generated_files} PROPERTIES GENERATED TRUE)
+target_sources(profiling_info_proto PRIVATE ${profiling_info_generated_files})
+target_link_libraries(profiling_info_proto protobuf::libprotobuf)
+target_include_directories(profiling_info_proto PUBLIC ${CMAKE_BINARY_DIR})
+
+add_library(model_runtime_info_proto model_runtime_info.proto)
+list(APPEND model_runtime_info_generated_files
+    ${CMAKE_BINARY_DIR}/tensorflow/lite/profiling/proto/model_runtime_info.pb.cc
+    ${CMAKE_BINARY_DIR}/tensorflow/lite/profiling/proto/model_runtime_info.pb.h
+)
+
+# Generate model_runtime_info.pb.cc and model_runtime_info.pb.h from
+# model_runtime_info.proto using protoc. Once the protobuf package version is
+# upgraded, we can use protobuf_generate_cpp/protobuf_generate here directly.
+add_custom_command(
+    OUTPUT ${model_runtime_info_generated_files}
+    COMMAND ${Protobuf_PROTOC_EXECUTABLE}
+    ARGS --cpp_out=${CMAKE_BINARY_DIR} --proto_path=${TENSORFLOW_SOURCE_DIR} ${CMAKE_CURRENT_SOURCE_DIR}/model_runtime_info.proto
+    DEPENDS ${Protobuf_PROTOC_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/model_runtime_info.proto ${profiling_info_generated_files}
+)
+
+set_source_files_properties(${model_runtime_info_generated_files} PROPERTIES GENERATED TRUE)
+target_sources(model_runtime_info_proto PRIVATE ${model_runtime_info_generated_files})
+target_link_libraries(model_runtime_info_proto protobuf::libprotobuf)
+target_include_directories(model_runtime_info_proto PUBLIC ${CMAKE_BINARY_DIR})

tensorflow/lite/profiling/proto/model_runtime_info.proto

@@ -0,0 +1,159 @@
+/* Copyright 2024 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 optional 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 = "proto2";
+
+package tflite.profiling;
+
+import "tensorflow/lite/profiling/proto/profiling_info.proto";
+
+option java_package = "tflite.profiling";
+option java_multiple_files = true;
+
+// Corresponds to a TFLite Model.
+message ModelRuntimeDetails {
+  optional string model_name = 1;
+  repeated RuntimeSubgraph subgraphs = 2;
+}
+
+message RuntimeSubgraph {
+  enum SubgraphType {
+    UNKNOWN_SUBGRAPH = 0;
+
+    // Set if the RuntimeSubgraph is from the TFLite runtime.
+    TFLITE_SUBGRAPH = 1;
+
+    // Set if the RuntimeSubgraph is from a delegate's runtime.
+    DELEGATE_SUBGRAPH = 2;
+  }
+
+  optional int32 subgraph_id = 1;
+  repeated Edge edges = 2;  // All the edges that are part of this subgraph.
+
+  repeated Node nodes = 3;  // All the nodes that are part of this subgraph.
+
+  // List of node ids in the execution plan
+  repeated int32 execution_plan = 4 [packed = true];
+
+  optional SubgraphType subgraph_type = 5;
+  // The name of the subgraph.
+  optional string name = 6;
+}
+
+message Node {
+  optional int32 id = 1;
+  optional string name = 2;
+  optional string type = 3;
+
+  // Inputs and outputs are ids of the edges specified in the corresponding
+  // subgraph.
+  repeated int32 inputs = 4 [packed = true];
+
+  repeated int32 outputs = 5 [packed = true];
+
+  // Intermediate tensors to this node expressed as indices into the
+  // subgraph's tensors.
+  repeated int32 intermediates = 6 [packed = true];
+
+  // Indices of temporary tensors used during the computations. This usually
+  // contains no tensors, but ops are allowed to change that if they need
+  // scratch space of any sort.
+  repeated int32 temporaries = 7 [packed = true];
+
+  optional OpProfileData op_profile_data = 10;
+
+  oneof node_info {
+    // If this node is a delegate node, metadata about it.
+    DelegateNodeDetails delegate_node_details = 8;
+
+    // If this node is a tflite node and delegated, the delegate node
+    // id.
+    int32 delegated_to_node_id = 9;
+  }
+}
+
+message DelegateNodeDetails {
+  // Delegate name, e.g., TfLiteXNNPackDelegate, TfLiteGpuDelegateV2, etc.
+  // This comes from the custom_name field in the TfLiteRegistration struct.
+  optional string delegate_name = 1;
+
+  // These node ids correspond to the nodes saved in the upper-level
+  // RunTimeSubgraph.
+  repeated int32 tflite_node_ids_replaced = 2 [packed = true];
+}
+
+message Edge {
+  // LINT.IfChange(EdgeDataType)
+  enum DataType {
+    // Similar to TFLiteType.
+    UNKNOWN_TYPE = 0;
+    FLOAT32 = 1;
+    INT32 = 2;
+    UINT8 = 3;
+    INT64 = 4;
+    STRING = 5;
+    BOOL = 6;
+    INT16 = 7;
+    COMPLEX64 = 8;
+    INT8 = 9;
+    FLOAT16 = 10;
+    FLOAT64 = 11;
+    COMPLEX128 = 12;
+    UINT64 = 13;
+    RESOURCE = 14;
+    VARIANT = 15;
+    UINT32 = 16;
+    UINT16 = 17;
+    INT4 = 18;
+    BFLOAT16 = 19;
+    INT2 = 20;
+    UINT4 = 21;
+    FLOAT8_E4M3FN = 22;
+    FLOAT8_E5M2 = 23;
+  }
+  // LINT.ThenChange(//tensorflow/lite/profiling/model_runtime_info.cc:EdgeDataTypeTransform)
+
+  enum LayoutType {
+    UNKNOWN = 0;
+    SCALAR = 1;
+    LINEAR = 2;
+    HW = 3;
+    CHW = 4;
+    HWC = 5;
+    OIHW = 6;
+    OHWI = 7;
+    IHWO = 8;
+    IOHW = 9;
+    BHWC = 10;
+    HWDC = 11;
+    BHWDC = 12;
+    HWD = 13;
+    OHWDI = 14;
+    HWIO = 15;
+  }
+
+  optional int32 id = 1;
+  optional string name = 2;
+  optional DataType data_type = 3;
+  repeated int32 shape = 4 [packed = true];
+
+  // This corresponds to the memory allocation type of the tensor in the TFLite
+  // runtime. Maps to the TfLiteAllocationType enum.
+  optional string allocation_type = 5;
+  // What layout this tensor is stored in.
+  optional LayoutType layout_type = 6;
+
+  optional int32 size = 7;
+}

tensorflow/lite/profiling/proto/profiling_info.proto

@@ -0,0 +1,72 @@
+/* Copyright 2024 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 optional 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 = "proto2";
+
+package tflite.profiling;
+
+option java_package = "tflite.profiling";
+option java_multiple_files = true;
+
+message BenchmarkProfilingData {
+  optional string model_name = 1;
+  optional ModelProfilingData init_profile = 2;
+  optional ModelProfilingData runtime_profile = 3;
+}
+
+// LINT.IfChange(ModelProfilingData)
+message ModelProfilingData {
+  repeated SubGraphProfilingData subgraph_profiles = 1;
+  repeated DelegateProfilingData delegate_profiles = 2;
+}
+// LINT.ThenChange(//tensorflow/lite/profiling/profile_summary_formatter_test.cc:ModelProfilingDataComparator)
+
+// LINT.IfChange(SubGraphProfilingData)
+message SubGraphProfilingData {
+  optional string subgraph_name = 1;
+  optional int32 subgraph_index = 2;
+  repeated OpProfileData per_op_profiles = 3;
+}
+// LINT.ThenChange(//tensorflow/lite/profiling/profile_summary_formatter_test.cc:SubGraphProfilingDataComparator)
+
+message DelegateProfilingData {
+  optional string delegate_name = 1;
+  repeated OpProfileData per_op_profiles = 2;
+}
+
+// LINT.IfChange(OpProfilingStat)
+message OpProfilingStat {
+  optional int64 first = 1;
+  optional int64 last = 2;
+  optional int64 avg = 3;
+  optional float stddev = 4;
+  optional float variance = 5;
+  optional int64 min = 6;
+  optional int64 max = 7;
+  optional int64 sum = 8;
+  optional int64 count = 9;
+}
+// LINT.ThenChange(//tensorflow/lite/profiling/profile_summary_formatter_test.cc:OpProfilingStatComparator)
+
+// LINT.IfChange(OpProfileData)
+message OpProfileData {
+  optional string node_type = 1;
+  optional OpProfilingStat inference_microseconds = 2;
+  optional OpProfilingStat mem_kb = 3;
+  optional int64 times_called = 4;
+  optional string name = 5;
+  optional int64 run_order = 6;
+}
+// LINT.ThenChange(//tensorflow/lite/profiling/profile_summary_formatter_test.cc:OpProfileDataComparator)