From cddea179de74cf1c679371517583a608c206d9ee Mon Sep 17 00:00:00 2001
From: Tadayoshi Sato <sato.tadayoshi@gmail.com>
Date: Wed, 20 Nov 2024 19:39:34 +0900
Subject: [PATCH] wip - Initial commit
---
.editorconfig | 16 +
.github/dependabot.yml | 6 +
.github/workflows/test.yml | 37 +
.gitignore | 10 +
.mvn/wrapper/maven-wrapper.properties | 19 +
LICENSE | 201 ++++
README.md | 233 ++++
examples/describe_model.java | 24 +
examples/list_models.java | 22 +
examples/metrics.java | 22 +
examples/mnist.java | 31 +
examples/register_mnist.java | 32 +
examples/set_default.java | 24 +
examples/token_auth.java | 30 +
examples/unregister_mnist.java | 25 +
mvnw | 259 +++++
mvnw.cmd | 149 +++
pom.xml | 166 +++
.../serving/client/Configuration.java | 65 ++
.../serving/client/TensorFlowServingApi.java | 20 +
.../client/TensorFlowServingClient.java | 86 ++
src/main/proto/google/protobuf/any.proto | 162 +++
src/main/proto/google/protobuf/wrappers.proto | 123 ++
.../tensorflow/core/example/example.proto | 301 +++++
.../tensorflow/core/example/feature.proto | 110 ++
.../framework/allocation_description.proto | 29 +
.../core/framework/attr_value.proto | 64 ++
.../core/framework/cost_graph.proto | 89 ++
.../tensorflow/core/framework/full_type.proto | 310 +++++
.../tensorflow/core/framework/function.proto | 136 +++
.../tensorflow/core/framework/graph.proto | 60 +
.../core/framework/graph_debug_info.proto | 61 +
.../tensorflow/core/framework/node_def.proto | 95 ++
.../tensorflow/core/framework/op_def.proto | 193 ++++
.../core/framework/resource_handle.proto | 47 +
.../core/framework/step_stats.proto | 88 ++
.../tensorflow/core/framework/tensor.proto | 101 ++
.../core/framework/tensor_description.proto | 24 +
.../core/framework/tensor_shape.proto | 46 +
.../tensorflow/core/framework/types.proto | 100 ++
.../tensorflow/core/framework/variable.proto | 84 ++
.../tensorflow/core/framework/versions.proto | 33 +
.../tensorflow/core/protobuf/cluster.proto | 87 ++
.../tensorflow/core/protobuf/config.proto | 1015 +++++++++++++++++
.../tensorflow/core/protobuf/debug.proto | 94 ++
.../core/protobuf/error_codes.proto | 11 +
.../tensorflow/core/protobuf/meta_graph.proto | 286 +++++
.../core/protobuf/named_tensor.proto | 25 +
.../core/protobuf/rewriter_config.proto | 241 ++++
.../core/protobuf/rpc_options.proto | 7 +
.../core/protobuf/saved_object_graph.proto | 251 ++++
.../tensorflow/core/protobuf/saver.proto | 48 +
.../tensorflow/core/protobuf/struct.proto | 164 +++
.../protobuf/trackable_object_graph.proto | 80 ++
.../core/protobuf/verifier_config.proto | 27 +
.../apis/classification.proto | 48 +
.../apis/get_model_metadata.proto | 30 +
.../apis/get_model_status.proto | 68 ++
.../tensorflow_serving/apis/inference.proto | 59 +
.../proto/tensorflow_serving/apis/input.proto | 82 ++
.../tensorflow_serving/apis/logging.proto | 20 +
.../proto/tensorflow_serving/apis/model.proto | 36 +
.../apis/model_management.proto | 25 +
.../apis/model_service.proto | 24 +
.../tensorflow_serving/apis/predict.proto | 191 ++++
.../apis/prediction_log.proto | 55 +
.../apis/prediction_service.proto | 31 +
.../tensorflow_serving/apis/regression.proto | 37 +
.../apis/session_service.proto | 56 +
.../tensorflow_serving/apis/status.proto | 17 +
.../file_system_storage_path_source.proto | 83 ++
.../config/log_collector_config.proto | 12 +
.../config/logging_config.proto | 29 +
.../config/model_server_config.proto | 85 ++
.../config/monitoring_config.proto | 19 +
.../config/platform_config.proto | 19 +
.../config/ssl_config.proto | 16 +
.../tsl/protobuf/coordination_config.proto | 78 ++
.../proto/xla/tsl/protobuf/error_codes.proto | 155 +++
.../proto/xla/tsl/protobuf/rpc_options.proto | 41 +
.../serving/client/ConfigurationTest.java | 34 +
.../client/TensorFlowServingClientTest.java | 30 +
.../client/TensorFlowServingTestSupport.java | 31 +
src/test/resources/data/0.png | Bin 0 -> 272 bytes
src/test/resources/data/1.png | Bin 0 -> 181 bytes
src/test/resources/data/kitten.jpg | Bin 0 -> 16016 bytes
src/test/resources/log4j2.properties | 17 +
.../00000123/assets/foo.txt | 1 +
.../00000123/saved_model.pb | Bin 0 -> 12107 bytes
.../variables/variables.data-00000-of-00001 | Bin 0 -> 20 bytes
.../00000123/variables/variables.index | Bin 0 -> 172 bytes
src/test/resources/tfsc4j.properties | 2 +
92 files changed, 7500 insertions(+)
create mode 100644 .editorconfig
create mode 100644 .github/dependabot.yml
create mode 100644 .github/workflows/test.yml
create mode 100644 .gitignore
create mode 100644 .mvn/wrapper/maven-wrapper.properties
create mode 100644 LICENSE
create mode 100644 README.md
create mode 100755 examples/describe_model.java
create mode 100755 examples/list_models.java
create mode 100755 examples/metrics.java
create mode 100755 examples/mnist.java
create mode 100755 examples/register_mnist.java
create mode 100755 examples/set_default.java
create mode 100755 examples/token_auth.java
create mode 100755 examples/unregister_mnist.java
create mode 100755 mvnw
create mode 100644 mvnw.cmd
create mode 100644 pom.xml
create mode 100644 src/main/java/com/github/tadayosi/tensorflow/serving/client/Configuration.java
create mode 100644 src/main/java/com/github/tadayosi/tensorflow/serving/client/TensorFlowServingApi.java
create mode 100644 src/main/java/com/github/tadayosi/tensorflow/serving/client/TensorFlowServingClient.java
create mode 100644 src/main/proto/google/protobuf/any.proto
create mode 100644 src/main/proto/google/protobuf/wrappers.proto
create mode 100644 src/main/proto/tensorflow/core/example/example.proto
create mode 100644 src/main/proto/tensorflow/core/example/feature.proto
create mode 100644 src/main/proto/tensorflow/core/framework/allocation_description.proto
create mode 100644 src/main/proto/tensorflow/core/framework/attr_value.proto
create mode 100644 src/main/proto/tensorflow/core/framework/cost_graph.proto
create mode 100644 src/main/proto/tensorflow/core/framework/full_type.proto
create mode 100644 src/main/proto/tensorflow/core/framework/function.proto
create mode 100644 src/main/proto/tensorflow/core/framework/graph.proto
create mode 100644 src/main/proto/tensorflow/core/framework/graph_debug_info.proto
create mode 100644 src/main/proto/tensorflow/core/framework/node_def.proto
create mode 100644 src/main/proto/tensorflow/core/framework/op_def.proto
create mode 100644 src/main/proto/tensorflow/core/framework/resource_handle.proto
create mode 100644 src/main/proto/tensorflow/core/framework/step_stats.proto
create mode 100644 src/main/proto/tensorflow/core/framework/tensor.proto
create mode 100644 src/main/proto/tensorflow/core/framework/tensor_description.proto
create mode 100644 src/main/proto/tensorflow/core/framework/tensor_shape.proto
create mode 100644 src/main/proto/tensorflow/core/framework/types.proto
create mode 100644 src/main/proto/tensorflow/core/framework/variable.proto
create mode 100644 src/main/proto/tensorflow/core/framework/versions.proto
create mode 100644 src/main/proto/tensorflow/core/protobuf/cluster.proto
create mode 100644 src/main/proto/tensorflow/core/protobuf/config.proto
create mode 100644 src/main/proto/tensorflow/core/protobuf/debug.proto
create mode 100644 src/main/proto/tensorflow/core/protobuf/error_codes.proto
create mode 100644 src/main/proto/tensorflow/core/protobuf/meta_graph.proto
create mode 100644 src/main/proto/tensorflow/core/protobuf/named_tensor.proto
create mode 100644 src/main/proto/tensorflow/core/protobuf/rewriter_config.proto
create mode 100644 src/main/proto/tensorflow/core/protobuf/rpc_options.proto
create mode 100644 src/main/proto/tensorflow/core/protobuf/saved_object_graph.proto
create mode 100644 src/main/proto/tensorflow/core/protobuf/saver.proto
create mode 100644 src/main/proto/tensorflow/core/protobuf/struct.proto
create mode 100644 src/main/proto/tensorflow/core/protobuf/trackable_object_graph.proto
create mode 100644 src/main/proto/tensorflow/core/protobuf/verifier_config.proto
create mode 100644 src/main/proto/tensorflow_serving/apis/classification.proto
create mode 100644 src/main/proto/tensorflow_serving/apis/get_model_metadata.proto
create mode 100644 src/main/proto/tensorflow_serving/apis/get_model_status.proto
create mode 100644 src/main/proto/tensorflow_serving/apis/inference.proto
create mode 100644 src/main/proto/tensorflow_serving/apis/input.proto
create mode 100644 src/main/proto/tensorflow_serving/apis/logging.proto
create mode 100644 src/main/proto/tensorflow_serving/apis/model.proto
create mode 100644 src/main/proto/tensorflow_serving/apis/model_management.proto
create mode 100644 src/main/proto/tensorflow_serving/apis/model_service.proto
create mode 100644 src/main/proto/tensorflow_serving/apis/predict.proto
create mode 100644 src/main/proto/tensorflow_serving/apis/prediction_log.proto
create mode 100644 src/main/proto/tensorflow_serving/apis/prediction_service.proto
create mode 100644 src/main/proto/tensorflow_serving/apis/regression.proto
create mode 100644 src/main/proto/tensorflow_serving/apis/session_service.proto
create mode 100644 src/main/proto/tensorflow_serving/apis/status.proto
create mode 100644 src/main/proto/tensorflow_serving/config/file_system_storage_path_source.proto
create mode 100644 src/main/proto/tensorflow_serving/config/log_collector_config.proto
create mode 100644 src/main/proto/tensorflow_serving/config/logging_config.proto
create mode 100644 src/main/proto/tensorflow_serving/config/model_server_config.proto
create mode 100644 src/main/proto/tensorflow_serving/config/monitoring_config.proto
create mode 100644 src/main/proto/tensorflow_serving/config/platform_config.proto
create mode 100644 src/main/proto/tensorflow_serving/config/ssl_config.proto
create mode 100644 src/main/proto/xla/tsl/protobuf/coordination_config.proto
create mode 100644 src/main/proto/xla/tsl/protobuf/error_codes.proto
create mode 100644 src/main/proto/xla/tsl/protobuf/rpc_options.proto
create mode 100644 src/test/java/com/github/tadayosi/tensorflow/serving/client/ConfigurationTest.java
create mode 100644 src/test/java/com/github/tadayosi/tensorflow/serving/client/TensorFlowServingClientTest.java
create mode 100644 src/test/java/com/github/tadayosi/tensorflow/serving/client/TensorFlowServingTestSupport.java
create mode 100644 src/test/resources/data/0.png
create mode 100644 src/test/resources/data/1.png
create mode 100644 src/test/resources/data/kitten.jpg
create mode 100644 src/test/resources/log4j2.properties
create mode 100644 src/test/resources/testdata/saved_model_half_plus_two_cpu/00000123/assets/foo.txt
create mode 100644 src/test/resources/testdata/saved_model_half_plus_two_cpu/00000123/saved_model.pb
create mode 100644 src/test/resources/testdata/saved_model_half_plus_two_cpu/00000123/variables/variables.data-00000-of-00001
create mode 100644 src/test/resources/testdata/saved_model_half_plus_two_cpu/00000123/variables/variables.index
create mode 100644 src/test/resources/tfsc4j.properties
diff --git a/.editorconfig b/.editorconfig
new file mode 100644
index 0000000..e567d42
--- /dev/null
+++ b/.editorconfig
@@ -0,0 +1,16 @@
+root = true
+
+[*]
+end_of_line = lf
+indent_size = 4
+indent_style = space
+insert_final_newline = true
+
+[*.{yml,yaml}]
+indent_size = 2
+
+[*.json]
+indent_size = 2
+
+[*.md]
+indent_size = 2
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 0000000..b52e6a4
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,6 @@
+version: 2
+updates:
+ - package-ecosystem: maven
+ directory: '/'
+ schedule:
+ interval: monthly
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
new file mode 100644
index 0000000..c53d312
--- /dev/null
+++ b/.github/workflows/test.yml
@@ -0,0 +1,37 @@
+name: Test
+
+on:
+ pull_request:
+ branches:
+ - main
+ paths-ignore:
+ - "**.md"
+ push:
+ branches:
+ - main
+ paths-ignore:
+ - "**.md"
+
+concurrency:
+ group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+ cancel-in-progress: true
+
+jobs:
+ build:
+ runs-on: ubuntu-latest
+ strategy:
+ fail-fast: false
+ matrix:
+ java: [ '17', '21' ]
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v4
+ - name: Set up Java
+ uses: actions/setup-java@v4
+ with:
+ distribution: 'temurin'
+ java-version: ${{ matrix.java }}
+ cache: 'maven'
+ - name: Build & test
+ run: |
+ mvn --batch-mode --no-transfer-progress install
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..2a40a20
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,10 @@
+.*
+!.gitignore
+!.editorconfig
+
+target/
+/data
+
+# IDE
+.idea/
+.vscode/
diff --git a/.mvn/wrapper/maven-wrapper.properties b/.mvn/wrapper/maven-wrapper.properties
new file mode 100644
index 0000000..f95f1ee
--- /dev/null
+++ b/.mvn/wrapper/maven-wrapper.properties
@@ -0,0 +1,19 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you 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.
+wrapperVersion=3.3.2
+distributionType=only-script
+distributionUrl=https://repo.maven.apache.org/maven2/org/apache/maven/apache-maven/3.9.8/apache-maven-3.9.8-bin.zip
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..261eeb9
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000..9f8745c
--- /dev/null
+++ b/README.md
@@ -0,0 +1,233 @@
+# TensorFlow Serving Client for Java
+
+[](<https://jitpack.io/#tadayosi/tensorflow-serving-client-java>)
+[](https://github.com/tadayosi/tensorflow-serving-client-java/actions/workflows/test.yml)
+
+TensorFlow Serving Client for Java (TFSC4J) is a Java client library for [TensorFlow Serving](https://github.com/tensorflow/serving). It supports the following [TensorFlow Serving REST API](https://www.tensorflow.org/tfx/serving/api_rest):
+
+- [Model status API](https://www.tensorflow.org/tfx/serving/api_rest#model_status_api)
+- [Model Metadata API](https://www.tensorflow.org/tfx/serving/api_rest#model_metadata_api)
+- [Classify and Regress API](https://www.tensorflow.org/tfx/serving/api_rest#classify_and_regress_api)
+- [Predict API](https://www.tensorflow.org/tfx/serving/api_rest#predict_api)
+
+## Requirements
+
+- Java 17+
+
+## Install
+
+1. Add the [JitPack](https://jitpack.io) repository to your `pom.xml`:
+
+ ```xml
+ <repositories>
+ <repository>
+ <id>jitpack.io</id>
+ <url>https://jitpack.io</url>
+ </repository>
+ </repositories>
+ ```
+
+2. Add the dependency:
+
+ ```xml
+ <dependency>
+ <groupId>com.github.tadayosi</groupId>
+ <artifactId>tensorflow-serving-client-java</artifactId>
+ <version>v0.3</version>
+ </dependency>
+ ```
+
+## Usage
+
+### Inference
+
+- Prediction:
+
+ ```java
+ TensorFlowServingClient client = TensorFlowServingClient.newInstance();
+
+ byte[] image = Files.readAllBytes(Path.of("0.png"));
+ Object result = client.inference().predictions("mnist_v2", image);
+ System.out.println(result);
+ // => 0
+ ```
+
+- With the inference API endpoint other than <http://localhost:8080>:
+
+ ```java
+ TensorFlowServingClient client = TensorFlowServingClient.builder()
+ .inferenceAddress("http://localhost:12345")
+ .build();
+ ```
+
+- With token authorization:
+
+ ```java
+ TensorFlowServingClient client = TensorFlowServingClient.builder()
+ .inferenceKey("<inference-key>")
+ .build();
+ ```
+
+### Management
+
+- Register a model:
+
+ ```java
+ TensorFlowServingClient client = TensorFlowServingClient.newInstance();
+
+ Response response = client.management().registerModel(
+ "https://torchserve.pytorch.org/mar_files/mnist_v2.mar",
+ RegisterModelOptions.empty());
+ System.out.println(response.getStatus());
+ // => "Model "mnist_v2" Version: 2.0 registered with 0 initial workers. Use scale workers API to add workers for the model."
+ ```
+
+- Scale workers for a model:
+
+ ```java
+ TensorFlowServingClient client = TensorFlowServingClient.newInstance();
+
+ Response response = client.management().setAutoScale(
+ "mnist_v2",
+ SetAutoScaleOptions.builder()
+ .minWorker(1)
+ .maxWorker(2)
+ .build());
+ System.out.println(response.getStatus());
+ // => "Processing worker updates..."
+ ```
+
+- Describe a model:
+
+ ```java
+ TensorFlowServingClient client = TensorFlowServingClient.newInstance();
+
+ List<ModelDetail> model = client.management().describeModel("mnist_v2");
+ System.out.println(model.get(0));
+ // =>
+ // ModelDetail {
+ // modelName: mnist_v2
+ // modelVersion: 2.0
+ // ...
+ ```
+
+- Unregister a model:
+
+ ```java
+ TensorFlowServingClient client = TensorFlowServingClient.newInstance();
+
+ Response response = client.management().unregisterModel(
+ "mnist_v2",
+ UnregisterModelOptions.empty());
+ System.out.println(response.getStatus());
+ // => "Model "mnist_v2" unregistered"
+ ```
+
+- List models:
+
+ ```java
+ TensorFlowServingClient client = TensorFlowServingClient.newInstance();
+
+ ModelList models = client.management().listModels(10, null);
+ System.out.println(models);
+ // =>
+ // ModelList {
+ // nextPageToken: null
+ // models: [Model {
+ // modelName: mnist_v2
+ // modelUrl: https://torchserve.pytorch.org/mar_files/mnist_v2.mar
+ // },
+ // ...
+ ```
+
+- Set default version for a model:
+
+ ```java
+ TensorFlowServingClient client = TensorFlowServingClient.newInstance();
+
+ Response response = client.management().setDefault("mnist_v2", "2.0");
+ System.out.println(response.getStatus());
+ // => "Default version successfully updated for model "mnist_v2" to "2.0""
+ ```
+
+- With the management API endpoint other than <http://localhost:8081>:
+
+ ```java
+ TensorFlowServingClient client = TensorFlowServingClient.builder()
+ .managementAddress("http://localhost:12345")
+ .build();
+ ```
+
+- With token authorization:
+
+ ```java
+ TensorFlowServingClient client = TensorFlowServingClient.builder()
+ .managementKey("<management-key>")
+ .build();
+ ```
+
+### Metrics
+
+- Get metrics in Prometheus format:
+
+ ```java
+ TensorFlowServingClient client = TensorFlowServingClient.newInstance();
+
+ String metrics = client.metrics().metrics();
+ System.out.println(metrics);
+ // =>
+ // # HELP MemoryUsed Torchserve prometheus gauge metric with unit: Megabytes
+ // # TYPE MemoryUsed gauge
+ // MemoryUsed{Level="Host",Hostname="3a9b51d41fbf",} 2075.09765625
+ // ...
+ ```
+
+- With the metrics API endpoint other than <http://localhost:8082>:
+
+ ```java
+ TensorFlowServingClient client = TensorFlowServingClient.builder()
+ .metricsAddress("http://localhost:12345")
+ .build();
+ ```
+
+## Configuration
+
+### tsc4j.properties
+
+```properties
+inference.key = <inference-key>
+inference.address = http://localhost:8080
+# inference.address takes precedence over inference.port if it's defined
+inference.port = 8080
+
+management.key = <management-key>
+management.address = http://localhost:8081
+# management.address takes precedence over management.port if it's defined
+management.port = 8081
+
+metrics.address = http://localhost:8082
+# metrics.address takes precedence over metrics.port if it's defined
+metrics.port = 8082
+```
+
+### System properties
+
+You can configure the TSC4J properties via system properties with prefix `tsc4j.`.
+
+For instance, you can configure `inference.address` with the `tsc4j.inference.address` system property.
+
+### Environment variables
+
+You can also configure the TSC4J properties via environment variables with prefix `TSC4J_`.
+
+For instance, you can configure `inference.address` with the `TSC4J_INFERENCE_ADDRESS` environment variable.
+
+## Examples
+
+See [examples](./examples/).
+
+## Build
+
+```console
+mvn clean install
+```
diff --git a/examples/describe_model.java b/examples/describe_model.java
new file mode 100755
index 0000000..ffc4cb1
--- /dev/null
+++ b/examples/describe_model.java
@@ -0,0 +1,24 @@
+///usr/bin/env jbang "$0" "$@" ; exit $?
+
+//JAVA 17+
+//REPOS mavencentral,jitpack=https://jitpack.io
+//DEPS com.github.tadayosi:torchserve-client-java:main-SNAPSHOT
+//DEPS org.slf4j:slf4j-simple:1.7.36
+
+import com.github.tadayosi.torchserve.client.TorchServeClient;
+import com.github.tadayosi.torchserve.client.model.ApiException;
+
+public class describe_model {
+
+ private static String MNIST_MODEL = "mnist_v2";
+
+ public static void main(String... args) throws Exception {
+ try {
+ var client = TorchServeClient.newInstance();
+ var models = client.management().describeModel(MNIST_MODEL);
+ System.out.println("Model> " + models.get(0));
+ } catch (ApiException e) {
+ e.printStackTrace();
+ }
+ }
+}
diff --git a/examples/list_models.java b/examples/list_models.java
new file mode 100755
index 0000000..be3f06f
--- /dev/null
+++ b/examples/list_models.java
@@ -0,0 +1,22 @@
+///usr/bin/env jbang "$0" "$@" ; exit $?
+
+//JAVA 17+
+//REPOS mavencentral,jitpack=https://jitpack.io
+//DEPS com.github.tadayosi:torchserve-client-java:main-SNAPSHOT
+//DEPS org.slf4j:slf4j-simple:1.7.36
+
+import com.github.tadayosi.torchserve.client.TorchServeClient;
+import com.github.tadayosi.torchserve.client.model.ApiException;
+
+public class list_models {
+
+ public static void main(String... args) throws Exception {
+ try {
+ var client = TorchServeClient.newInstance();
+ var models = client.management().listModels(10, null);
+ System.out.println("Models> " + models);
+ } catch (ApiException e) {
+ e.printStackTrace();
+ }
+ }
+}
diff --git a/examples/metrics.java b/examples/metrics.java
new file mode 100755
index 0000000..e938d9f
--- /dev/null
+++ b/examples/metrics.java
@@ -0,0 +1,22 @@
+///usr/bin/env jbang "$0" "$@" ; exit $?
+
+//JAVA 17+
+//REPOS mavencentral,jitpack=https://jitpack.io
+//DEPS com.github.tadayosi:torchserve-client-java:main-SNAPSHOT
+//DEPS org.slf4j:slf4j-simple:1.7.36
+
+import com.github.tadayosi.torchserve.client.TorchServeClient;
+import com.github.tadayosi.torchserve.client.model.ApiException;
+
+public class metrics {
+
+ public static void main(String... args) throws Exception {
+ try {
+ var client = TorchServeClient.newInstance();
+ var metrics = client.metrics().metrics();
+ System.out.println(metrics);
+ } catch (ApiException e) {
+ e.printStackTrace();
+ }
+ }
+}
diff --git a/examples/mnist.java b/examples/mnist.java
new file mode 100755
index 0000000..de8b40d
--- /dev/null
+++ b/examples/mnist.java
@@ -0,0 +1,31 @@
+///usr/bin/env jbang "$0" "$@" ; exit $?
+
+//JAVA 17+
+//REPOS mavencentral,jitpack=https://jitpack.io
+//DEPS com.github.tadayosi:torchserve-client-java:main-SNAPSHOT
+//DEPS org.slf4j:slf4j-simple:1.7.36
+
+import java.nio.file.Files;
+import java.nio.file.Path;
+
+import com.github.tadayosi.torchserve.client.TorchServeClient;
+import com.github.tadayosi.torchserve.client.model.ApiException;
+
+public class mnist {
+
+ private static String MNIST_MODEL = "mnist_v2";
+
+ public static void main(String... args) throws Exception {
+ var zero = Files.readAllBytes(Path.of("src/test/resources/data/0.png"));
+ var one = Files.readAllBytes(Path.of("src/test/resources/data/1.png"));
+ try {
+ var client = TorchServeClient.newInstance();
+ var result0 = client.inference().predictions(MNIST_MODEL, zero);
+ System.out.println("Answer> " + result0);
+ var result1 = client.inference().predictions(MNIST_MODEL, one);
+ System.out.println("Answer> " + result1);
+ } catch (ApiException e) {
+ e.printStackTrace();
+ }
+ }
+}
diff --git a/examples/register_mnist.java b/examples/register_mnist.java
new file mode 100755
index 0000000..3d6e11c
--- /dev/null
+++ b/examples/register_mnist.java
@@ -0,0 +1,32 @@
+///usr/bin/env jbang "$0" "$@" ; exit $?
+
+//JAVA 17+
+//REPOS mavencentral,jitpack=https://jitpack.io
+//DEPS com.github.tadayosi:torchserve-client-java:main-SNAPSHOT
+//DEPS org.slf4j:slf4j-simple:1.7.36
+
+import com.github.tadayosi.torchserve.client.TorchServeClient;
+import com.github.tadayosi.torchserve.client.model.ApiException;
+import com.github.tadayosi.torchserve.client.model.RegisterModelOptions;
+import com.github.tadayosi.torchserve.client.model.SetAutoScaleOptions;
+
+public class register_mnist {
+
+ private static String MNIST_URL = "https://torchserve.pytorch.org/mar_files/mnist_v2.mar";
+ private static String MNIST_MODEL = "mnist_v2";
+
+ public static void main(String... args) throws Exception {
+ try {
+ var client = TorchServeClient.newInstance();
+ var response = client.management().registerModel(MNIST_URL, RegisterModelOptions.empty());
+ System.out.println("registerModel> " + response.getStatus());
+ response = client.management().setAutoScale(MNIST_MODEL, SetAutoScaleOptions.builder()
+ .minWorker(1)
+ .maxWorker(1)
+ .build());
+ System.out.println("setAutoScale> " + response.getStatus());
+ } catch (ApiException e) {
+ e.printStackTrace();
+ }
+ }
+}
diff --git a/examples/set_default.java b/examples/set_default.java
new file mode 100755
index 0000000..fdb4067
--- /dev/null
+++ b/examples/set_default.java
@@ -0,0 +1,24 @@
+///usr/bin/env jbang "$0" "$@" ; exit $?
+
+//JAVA 17+
+//REPOS mavencentral,jitpack=https://jitpack.io
+//DEPS com.github.tadayosi:torchserve-client-java:main-SNAPSHOT
+//DEPS org.slf4j:slf4j-simple:1.7.36
+
+import com.github.tadayosi.torchserve.client.TorchServeClient;
+import com.github.tadayosi.torchserve.client.model.ApiException;
+
+public class set_default {
+
+ private static String MNIST_MODEL = "mnist_v2";
+
+ public static void main(String... args) throws Exception {
+ try {
+ var client = TorchServeClient.newInstance();
+ var response = client.management().setDefault(MNIST_MODEL, "2.0");
+ System.out.println("Status> " + response.getStatus());
+ } catch (ApiException e) {
+ e.printStackTrace();
+ }
+ }
+}
diff --git a/examples/token_auth.java b/examples/token_auth.java
new file mode 100755
index 0000000..5d7aefd
--- /dev/null
+++ b/examples/token_auth.java
@@ -0,0 +1,30 @@
+///usr/bin/env jbang "$0" "$@" ; exit $?
+
+//JAVA 17+
+//REPOS mavencentral,jitpack=https://jitpack.io
+//DEPS com.github.tadayosi:torchserve-client-java:main-SNAPSHOT
+//DEPS org.slf4j:slf4j-simple:1.7.36
+
+import com.github.tadayosi.torchserve.client.TorchServeClient;
+import com.github.tadayosi.torchserve.client.model.ApiException;
+
+public class token_auth {
+
+ /**
+ * You can find the auth token for the inference API by looking at the
+ * `key_file.json` in the current directory of the TorchServe server.
+ */
+ private static String INFERENCE_AUTH_TOKEN = "<inference-key>";
+
+ public static void main(String... args) throws Exception {
+ try {
+ var client = TorchServeClient.builder()
+ .inferenceKey(INFERENCE_AUTH_TOKEN)
+ .build();
+ var response = client.inference().ping();
+ System.out.println(response);
+ } catch (ApiException e) {
+ e.printStackTrace();
+ }
+ }
+}
diff --git a/examples/unregister_mnist.java b/examples/unregister_mnist.java
new file mode 100755
index 0000000..383efe9
--- /dev/null
+++ b/examples/unregister_mnist.java
@@ -0,0 +1,25 @@
+///usr/bin/env jbang "$0" "$@" ; exit $?
+
+//JAVA 17+
+//REPOS mavencentral,jitpack=https://jitpack.io
+//DEPS com.github.tadayosi:torchserve-client-java:main-SNAPSHOT
+//DEPS org.slf4j:slf4j-simple:1.7.36
+
+import com.github.tadayosi.torchserve.client.TorchServeClient;
+import com.github.tadayosi.torchserve.client.model.ApiException;
+import com.github.tadayosi.torchserve.client.model.UnregisterModelOptions;
+
+public class unregister_mnist {
+
+ private static String MNIST_MODEL = "mnist_v2";
+
+ public static void main(String... args) throws Exception {
+ try {
+ var client = TorchServeClient.newInstance();
+ var response = client.management().unregisterModel(MNIST_MODEL, UnregisterModelOptions.empty());
+ System.out.println("Status> " + response.getStatus());
+ } catch (ApiException e) {
+ e.printStackTrace();
+ }
+ }
+}
diff --git a/mvnw b/mvnw
new file mode 100755
index 0000000..19529dd
--- /dev/null
+++ b/mvnw
@@ -0,0 +1,259 @@
+#!/bin/sh
+# ----------------------------------------------------------------------------
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you 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.
+# ----------------------------------------------------------------------------
+
+# ----------------------------------------------------------------------------
+# Apache Maven Wrapper startup batch script, version 3.3.2
+#
+# Optional ENV vars
+# -----------------
+# JAVA_HOME - location of a JDK home dir, required when download maven via java source
+# MVNW_REPOURL - repo url base for downloading maven distribution
+# MVNW_USERNAME/MVNW_PASSWORD - user and password for downloading maven
+# MVNW_VERBOSE - true: enable verbose log; debug: trace the mvnw script; others: silence the output
+# ----------------------------------------------------------------------------
+
+set -euf
+[ "${MVNW_VERBOSE-}" != debug ] || set -x
+
+# OS specific support.
+native_path() { printf %s\\n "$1"; }
+case "$(uname)" in
+CYGWIN* | MINGW*)
+ [ -z "${JAVA_HOME-}" ] || JAVA_HOME="$(cygpath --unix "$JAVA_HOME")"
+ native_path() { cygpath --path --windows "$1"; }
+ ;;
+esac
+
+# set JAVACMD and JAVACCMD
+set_java_home() {
+ # For Cygwin and MinGW, ensure paths are in Unix format before anything is touched
+ if [ -n "${JAVA_HOME-}" ]; then
+ if [ -x "$JAVA_HOME/jre/sh/java" ]; then
+ # IBM's JDK on AIX uses strange locations for the executables
+ JAVACMD="$JAVA_HOME/jre/sh/java"
+ JAVACCMD="$JAVA_HOME/jre/sh/javac"
+ else
+ JAVACMD="$JAVA_HOME/bin/java"
+ JAVACCMD="$JAVA_HOME/bin/javac"
+
+ if [ ! -x "$JAVACMD" ] || [ ! -x "$JAVACCMD" ]; then
+ echo "The JAVA_HOME environment variable is not defined correctly, so mvnw cannot run." >&2
+ echo "JAVA_HOME is set to \"$JAVA_HOME\", but \"\$JAVA_HOME/bin/java\" or \"\$JAVA_HOME/bin/javac\" does not exist." >&2
+ return 1
+ fi
+ fi
+ else
+ JAVACMD="$(
+ 'set' +e
+ 'unset' -f command 2>/dev/null
+ 'command' -v java
+ )" || :
+ JAVACCMD="$(
+ 'set' +e
+ 'unset' -f command 2>/dev/null
+ 'command' -v javac
+ )" || :
+
+ if [ ! -x "${JAVACMD-}" ] || [ ! -x "${JAVACCMD-}" ]; then
+ echo "The java/javac command does not exist in PATH nor is JAVA_HOME set, so mvnw cannot run." >&2
+ return 1
+ fi
+ fi
+}
+
+# hash string like Java String::hashCode
+hash_string() {
+ str="${1:-}" h=0
+ while [ -n "$str" ]; do
+ char="${str%"${str#?}"}"
+ h=$(((h * 31 + $(LC_CTYPE=C printf %d "'$char")) % 4294967296))
+ str="${str#?}"
+ done
+ printf %x\\n $h
+}
+
+verbose() { :; }
+[ "${MVNW_VERBOSE-}" != true ] || verbose() { printf %s\\n "${1-}"; }
+
+die() {
+ printf %s\\n "$1" >&2
+ exit 1
+}
+
+trim() {
+ # MWRAPPER-139:
+ # Trims trailing and leading whitespace, carriage returns, tabs, and linefeeds.
+ # Needed for removing poorly interpreted newline sequences when running in more
+ # exotic environments such as mingw bash on Windows.
+ printf "%s" "${1}" | tr -d '[:space:]'
+}
+
+# parse distributionUrl and optional distributionSha256Sum, requires .mvn/wrapper/maven-wrapper.properties
+while IFS="=" read -r key value; do
+ case "${key-}" in
+ distributionUrl) distributionUrl=$(trim "${value-}") ;;
+ distributionSha256Sum) distributionSha256Sum=$(trim "${value-}") ;;
+ esac
+done <"${0%/*}/.mvn/wrapper/maven-wrapper.properties"
+[ -n "${distributionUrl-}" ] || die "cannot read distributionUrl property in ${0%/*}/.mvn/wrapper/maven-wrapper.properties"
+
+case "${distributionUrl##*/}" in
+maven-mvnd-*bin.*)
+ MVN_CMD=mvnd.sh _MVNW_REPO_PATTERN=/maven/mvnd/
+ case "${PROCESSOR_ARCHITECTURE-}${PROCESSOR_ARCHITEW6432-}:$(uname -a)" in
+ *AMD64:CYGWIN* | *AMD64:MINGW*) distributionPlatform=windows-amd64 ;;
+ :Darwin*x86_64) distributionPlatform=darwin-amd64 ;;
+ :Darwin*arm64) distributionPlatform=darwin-aarch64 ;;
+ :Linux*x86_64*) distributionPlatform=linux-amd64 ;;
+ *)
+ echo "Cannot detect native platform for mvnd on $(uname)-$(uname -m), use pure java version" >&2
+ distributionPlatform=linux-amd64
+ ;;
+ esac
+ distributionUrl="${distributionUrl%-bin.*}-$distributionPlatform.zip"
+ ;;
+maven-mvnd-*) MVN_CMD=mvnd.sh _MVNW_REPO_PATTERN=/maven/mvnd/ ;;
+*) MVN_CMD="mvn${0##*/mvnw}" _MVNW_REPO_PATTERN=/org/apache/maven/ ;;
+esac
+
+# apply MVNW_REPOURL and calculate MAVEN_HOME
+# maven home pattern: ~/.m2/wrapper/dists/{apache-maven-<version>,maven-mvnd-<version>-<platform>}/<hash>
+[ -z "${MVNW_REPOURL-}" ] || distributionUrl="$MVNW_REPOURL$_MVNW_REPO_PATTERN${distributionUrl#*"$_MVNW_REPO_PATTERN"}"
+distributionUrlName="${distributionUrl##*/}"
+distributionUrlNameMain="${distributionUrlName%.*}"
+distributionUrlNameMain="${distributionUrlNameMain%-bin}"
+MAVEN_USER_HOME="${MAVEN_USER_HOME:-${HOME}/.m2}"
+MAVEN_HOME="${MAVEN_USER_HOME}/wrapper/dists/${distributionUrlNameMain-}/$(hash_string "$distributionUrl")"
+
+exec_maven() {
+ unset MVNW_VERBOSE MVNW_USERNAME MVNW_PASSWORD MVNW_REPOURL || :
+ exec "$MAVEN_HOME/bin/$MVN_CMD" "$@" || die "cannot exec $MAVEN_HOME/bin/$MVN_CMD"
+}
+
+if [ -d "$MAVEN_HOME" ]; then
+ verbose "found existing MAVEN_HOME at $MAVEN_HOME"
+ exec_maven "$@"
+fi
+
+case "${distributionUrl-}" in
+*?-bin.zip | *?maven-mvnd-?*-?*.zip) ;;
+*) die "distributionUrl is not valid, must match *-bin.zip or maven-mvnd-*.zip, but found '${distributionUrl-}'" ;;
+esac
+
+# prepare tmp dir
+if TMP_DOWNLOAD_DIR="$(mktemp -d)" && [ -d "$TMP_DOWNLOAD_DIR" ]; then
+ clean() { rm -rf -- "$TMP_DOWNLOAD_DIR"; }
+ trap clean HUP INT TERM EXIT
+else
+ die "cannot create temp dir"
+fi
+
+mkdir -p -- "${MAVEN_HOME%/*}"
+
+# Download and Install Apache Maven
+verbose "Couldn't find MAVEN_HOME, downloading and installing it ..."
+verbose "Downloading from: $distributionUrl"
+verbose "Downloading to: $TMP_DOWNLOAD_DIR/$distributionUrlName"
+
+# select .zip or .tar.gz
+if ! command -v unzip >/dev/null; then
+ distributionUrl="${distributionUrl%.zip}.tar.gz"
+ distributionUrlName="${distributionUrl##*/}"
+fi
+
+# verbose opt
+__MVNW_QUIET_WGET=--quiet __MVNW_QUIET_CURL=--silent __MVNW_QUIET_UNZIP=-q __MVNW_QUIET_TAR=''
+[ "${MVNW_VERBOSE-}" != true ] || __MVNW_QUIET_WGET='' __MVNW_QUIET_CURL='' __MVNW_QUIET_UNZIP='' __MVNW_QUIET_TAR=v
+
+# normalize http auth
+case "${MVNW_PASSWORD:+has-password}" in
+'') MVNW_USERNAME='' MVNW_PASSWORD='' ;;
+has-password) [ -n "${MVNW_USERNAME-}" ] || MVNW_USERNAME='' MVNW_PASSWORD='' ;;
+esac
+
+if [ -z "${MVNW_USERNAME-}" ] && command -v wget >/dev/null; then
+ verbose "Found wget ... using wget"
+ wget ${__MVNW_QUIET_WGET:+"$__MVNW_QUIET_WGET"} "$distributionUrl" -O "$TMP_DOWNLOAD_DIR/$distributionUrlName" || die "wget: Failed to fetch $distributionUrl"
+elif [ -z "${MVNW_USERNAME-}" ] && command -v curl >/dev/null; then
+ verbose "Found curl ... using curl"
+ curl ${__MVNW_QUIET_CURL:+"$__MVNW_QUIET_CURL"} -f -L -o "$TMP_DOWNLOAD_DIR/$distributionUrlName" "$distributionUrl" || die "curl: Failed to fetch $distributionUrl"
+elif set_java_home; then
+ verbose "Falling back to use Java to download"
+ javaSource="$TMP_DOWNLOAD_DIR/Downloader.java"
+ targetZip="$TMP_DOWNLOAD_DIR/$distributionUrlName"
+ cat >"$javaSource" <<-END
+ public class Downloader extends java.net.Authenticator
+ {
+ protected java.net.PasswordAuthentication getPasswordAuthentication()
+ {
+ return new java.net.PasswordAuthentication( System.getenv( "MVNW_USERNAME" ), System.getenv( "MVNW_PASSWORD" ).toCharArray() );
+ }
+ public static void main( String[] args ) throws Exception
+ {
+ setDefault( new Downloader() );
+ java.nio.file.Files.copy( java.net.URI.create( args[0] ).toURL().openStream(), java.nio.file.Paths.get( args[1] ).toAbsolutePath().normalize() );
+ }
+ }
+ END
+ # For Cygwin/MinGW, switch paths to Windows format before running javac and java
+ verbose " - Compiling Downloader.java ..."
+ "$(native_path "$JAVACCMD")" "$(native_path "$javaSource")" || die "Failed to compile Downloader.java"
+ verbose " - Running Downloader.java ..."
+ "$(native_path "$JAVACMD")" -cp "$(native_path "$TMP_DOWNLOAD_DIR")" Downloader "$distributionUrl" "$(native_path "$targetZip")"
+fi
+
+# If specified, validate the SHA-256 sum of the Maven distribution zip file
+if [ -n "${distributionSha256Sum-}" ]; then
+ distributionSha256Result=false
+ if [ "$MVN_CMD" = mvnd.sh ]; then
+ echo "Checksum validation is not supported for maven-mvnd." >&2
+ echo "Please disable validation by removing 'distributionSha256Sum' from your maven-wrapper.properties." >&2
+ exit 1
+ elif command -v sha256sum >/dev/null; then
+ if echo "$distributionSha256Sum $TMP_DOWNLOAD_DIR/$distributionUrlName" | sha256sum -c >/dev/null 2>&1; then
+ distributionSha256Result=true
+ fi
+ elif command -v shasum >/dev/null; then
+ if echo "$distributionSha256Sum $TMP_DOWNLOAD_DIR/$distributionUrlName" | shasum -a 256 -c >/dev/null 2>&1; then
+ distributionSha256Result=true
+ fi
+ else
+ echo "Checksum validation was requested but neither 'sha256sum' or 'shasum' are available." >&2
+ echo "Please install either command, or disable validation by removing 'distributionSha256Sum' from your maven-wrapper.properties." >&2
+ exit 1
+ fi
+ if [ $distributionSha256Result = false ]; then
+ echo "Error: Failed to validate Maven distribution SHA-256, your Maven distribution might be compromised." >&2
+ echo "If you updated your Maven version, you need to update the specified distributionSha256Sum property." >&2
+ exit 1
+ fi
+fi
+
+# unzip and move
+if command -v unzip >/dev/null; then
+ unzip ${__MVNW_QUIET_UNZIP:+"$__MVNW_QUIET_UNZIP"} "$TMP_DOWNLOAD_DIR/$distributionUrlName" -d "$TMP_DOWNLOAD_DIR" || die "failed to unzip"
+else
+ tar xzf${__MVNW_QUIET_TAR:+"$__MVNW_QUIET_TAR"} "$TMP_DOWNLOAD_DIR/$distributionUrlName" -C "$TMP_DOWNLOAD_DIR" || die "failed to untar"
+fi
+printf %s\\n "$distributionUrl" >"$TMP_DOWNLOAD_DIR/$distributionUrlNameMain/mvnw.url"
+mv -- "$TMP_DOWNLOAD_DIR/$distributionUrlNameMain" "$MAVEN_HOME" || [ -d "$MAVEN_HOME" ] || die "fail to move MAVEN_HOME"
+
+clean || :
+exec_maven "$@"
diff --git a/mvnw.cmd b/mvnw.cmd
new file mode 100644
index 0000000..249bdf3
--- /dev/null
+++ b/mvnw.cmd
@@ -0,0 +1,149 @@
+<# : batch portion
+@REM ----------------------------------------------------------------------------
+@REM Licensed to the Apache Software Foundation (ASF) under one
+@REM or more contributor license agreements. See the NOTICE file
+@REM distributed with this work for additional information
+@REM regarding copyright ownership. The ASF licenses this file
+@REM to you under the Apache License, Version 2.0 (the
+@REM "License"); you may not use this file except in compliance
+@REM with the License. You may obtain a copy of the License at
+@REM
+@REM http://www.apache.org/licenses/LICENSE-2.0
+@REM
+@REM Unless required by applicable law or agreed to in writing,
+@REM software distributed under the License is distributed on an
+@REM "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+@REM KIND, either express or implied. See the License for the
+@REM specific language governing permissions and limitations
+@REM under the License.
+@REM ----------------------------------------------------------------------------
+
+@REM ----------------------------------------------------------------------------
+@REM Apache Maven Wrapper startup batch script, version 3.3.2
+@REM
+@REM Optional ENV vars
+@REM MVNW_REPOURL - repo url base for downloading maven distribution
+@REM MVNW_USERNAME/MVNW_PASSWORD - user and password for downloading maven
+@REM MVNW_VERBOSE - true: enable verbose log; others: silence the output
+@REM ----------------------------------------------------------------------------
+
+@IF "%__MVNW_ARG0_NAME__%"=="" (SET __MVNW_ARG0_NAME__=%~nx0)
+@SET __MVNW_CMD__=
+@SET __MVNW_ERROR__=
+@SET __MVNW_PSMODULEP_SAVE=%PSModulePath%
+@SET PSModulePath=
+@FOR /F "usebackq tokens=1* delims==" %%A IN (`powershell -noprofile "& {$scriptDir='%~dp0'; $script='%__MVNW_ARG0_NAME__%'; icm -ScriptBlock ([Scriptblock]::Create((Get-Content -Raw '%~f0'))) -NoNewScope}"`) DO @(
+ IF "%%A"=="MVN_CMD" (set __MVNW_CMD__=%%B) ELSE IF "%%B"=="" (echo %%A) ELSE (echo %%A=%%B)
+)
+@SET PSModulePath=%__MVNW_PSMODULEP_SAVE%
+@SET __MVNW_PSMODULEP_SAVE=
+@SET __MVNW_ARG0_NAME__=
+@SET MVNW_USERNAME=
+@SET MVNW_PASSWORD=
+@IF NOT "%__MVNW_CMD__%"=="" (%__MVNW_CMD__% %*)
+@echo Cannot start maven from wrapper >&2 && exit /b 1
+@GOTO :EOF
+: end batch / begin powershell #>
+
+$ErrorActionPreference = "Stop"
+if ($env:MVNW_VERBOSE -eq "true") {
+ $VerbosePreference = "Continue"
+}
+
+# calculate distributionUrl, requires .mvn/wrapper/maven-wrapper.properties
+$distributionUrl = (Get-Content -Raw "$scriptDir/.mvn/wrapper/maven-wrapper.properties" | ConvertFrom-StringData).distributionUrl
+if (!$distributionUrl) {
+ Write-Error "cannot read distributionUrl property in $scriptDir/.mvn/wrapper/maven-wrapper.properties"
+}
+
+switch -wildcard -casesensitive ( $($distributionUrl -replace '^.*/','') ) {
+ "maven-mvnd-*" {
+ $USE_MVND = $true
+ $distributionUrl = $distributionUrl -replace '-bin\.[^.]*$',"-windows-amd64.zip"
+ $MVN_CMD = "mvnd.cmd"
+ break
+ }
+ default {
+ $USE_MVND = $false
+ $MVN_CMD = $script -replace '^mvnw','mvn'
+ break
+ }
+}
+
+# apply MVNW_REPOURL and calculate MAVEN_HOME
+# maven home pattern: ~/.m2/wrapper/dists/{apache-maven-<version>,maven-mvnd-<version>-<platform>}/<hash>
+if ($env:MVNW_REPOURL) {
+ $MVNW_REPO_PATTERN = if ($USE_MVND) { "/org/apache/maven/" } else { "/maven/mvnd/" }
+ $distributionUrl = "$env:MVNW_REPOURL$MVNW_REPO_PATTERN$($distributionUrl -replace '^.*'+$MVNW_REPO_PATTERN,'')"
+}
+$distributionUrlName = $distributionUrl -replace '^.*/',''
+$distributionUrlNameMain = $distributionUrlName -replace '\.[^.]*$','' -replace '-bin$',''
+$MAVEN_HOME_PARENT = "$HOME/.m2/wrapper/dists/$distributionUrlNameMain"
+if ($env:MAVEN_USER_HOME) {
+ $MAVEN_HOME_PARENT = "$env:MAVEN_USER_HOME/wrapper/dists/$distributionUrlNameMain"
+}
+$MAVEN_HOME_NAME = ([System.Security.Cryptography.MD5]::Create().ComputeHash([byte[]][char[]]$distributionUrl) | ForEach-Object {$_.ToString("x2")}) -join ''
+$MAVEN_HOME = "$MAVEN_HOME_PARENT/$MAVEN_HOME_NAME"
+
+if (Test-Path -Path "$MAVEN_HOME" -PathType Container) {
+ Write-Verbose "found existing MAVEN_HOME at $MAVEN_HOME"
+ Write-Output "MVN_CMD=$MAVEN_HOME/bin/$MVN_CMD"
+ exit $?
+}
+
+if (! $distributionUrlNameMain -or ($distributionUrlName -eq $distributionUrlNameMain)) {
+ Write-Error "distributionUrl is not valid, must end with *-bin.zip, but found $distributionUrl"
+}
+
+# prepare tmp dir
+$TMP_DOWNLOAD_DIR_HOLDER = New-TemporaryFile
+$TMP_DOWNLOAD_DIR = New-Item -Itemtype Directory -Path "$TMP_DOWNLOAD_DIR_HOLDER.dir"
+$TMP_DOWNLOAD_DIR_HOLDER.Delete() | Out-Null
+trap {
+ if ($TMP_DOWNLOAD_DIR.Exists) {
+ try { Remove-Item $TMP_DOWNLOAD_DIR -Recurse -Force | Out-Null }
+ catch { Write-Warning "Cannot remove $TMP_DOWNLOAD_DIR" }
+ }
+}
+
+New-Item -Itemtype Directory -Path "$MAVEN_HOME_PARENT" -Force | Out-Null
+
+# Download and Install Apache Maven
+Write-Verbose "Couldn't find MAVEN_HOME, downloading and installing it ..."
+Write-Verbose "Downloading from: $distributionUrl"
+Write-Verbose "Downloading to: $TMP_DOWNLOAD_DIR/$distributionUrlName"
+
+$webclient = New-Object System.Net.WebClient
+if ($env:MVNW_USERNAME -and $env:MVNW_PASSWORD) {
+ $webclient.Credentials = New-Object System.Net.NetworkCredential($env:MVNW_USERNAME, $env:MVNW_PASSWORD)
+}
+[Net.ServicePointManager]::SecurityProtocol = [Net.SecurityProtocolType]::Tls12
+$webclient.DownloadFile($distributionUrl, "$TMP_DOWNLOAD_DIR/$distributionUrlName") | Out-Null
+
+# If specified, validate the SHA-256 sum of the Maven distribution zip file
+$distributionSha256Sum = (Get-Content -Raw "$scriptDir/.mvn/wrapper/maven-wrapper.properties" | ConvertFrom-StringData).distributionSha256Sum
+if ($distributionSha256Sum) {
+ if ($USE_MVND) {
+ Write-Error "Checksum validation is not supported for maven-mvnd. `nPlease disable validation by removing 'distributionSha256Sum' from your maven-wrapper.properties."
+ }
+ Import-Module $PSHOME\Modules\Microsoft.PowerShell.Utility -Function Get-FileHash
+ if ((Get-FileHash "$TMP_DOWNLOAD_DIR/$distributionUrlName" -Algorithm SHA256).Hash.ToLower() -ne $distributionSha256Sum) {
+ Write-Error "Error: Failed to validate Maven distribution SHA-256, your Maven distribution might be compromised. If you updated your Maven version, you need to update the specified distributionSha256Sum property."
+ }
+}
+
+# unzip and move
+Expand-Archive "$TMP_DOWNLOAD_DIR/$distributionUrlName" -DestinationPath "$TMP_DOWNLOAD_DIR" | Out-Null
+Rename-Item -Path "$TMP_DOWNLOAD_DIR/$distributionUrlNameMain" -NewName $MAVEN_HOME_NAME | Out-Null
+try {
+ Move-Item -Path "$TMP_DOWNLOAD_DIR/$MAVEN_HOME_NAME" -Destination $MAVEN_HOME_PARENT | Out-Null
+} catch {
+ if (! (Test-Path -Path "$MAVEN_HOME" -PathType Container)) {
+ Write-Error "fail to move MAVEN_HOME"
+ }
+} finally {
+ try { Remove-Item $TMP_DOWNLOAD_DIR -Recurse -Force | Out-Null }
+ catch { Write-Warning "Cannot remove $TMP_DOWNLOAD_DIR" }
+}
+
+Write-Output "MVN_CMD=$MAVEN_HOME/bin/$MVN_CMD"
diff --git a/pom.xml b/pom.xml
new file mode 100644
index 0000000..d08fbb8
--- /dev/null
+++ b/pom.xml
@@ -0,0 +1,166 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+ <modelVersion>4.0.0</modelVersion>
+
+ <groupId>com.github.tadayosi.tensorflow</groupId>
+ <artifactId>tensorflow-serving-client</artifactId>
+ <version>0.1-SNAPSHOT</version>
+
+ <name>TensorFlow Serving Client for Java</name>
+ <description>A Java client library for TensorFlow Serving</description>
+ <url>https://github.com/tadayosi/tensorflow-serving-client-java</url>
+
+ <scm>
+ <connection>scm:git:git@github.com:tadayosi/tensorflow-serving-client-java.git</connection>
+ <developerConnection>scm:git:git@github.com:tadayosi/tensorflow-serving-client-java.git</developerConnection>
+ <url>git@github.com:tadayosi/tensorflow-serving-client-java.git</url>
+ <tag>HEAD</tag>
+ </scm>
+
+ <properties>
+ <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+ <maven.compiler.source>17</maven.compiler.source>
+ <maven.compiler.target>17</maven.compiler.target>
+ <maven.compiler.release>17</maven.compiler.release>
+
+ <annotations-api-version>6.0.53</annotations-api-version>
+ <grpc-version>1.68.1</grpc-version>
+ <junit-jupiter-version>5.11.1</junit-jupiter-version>
+ <log4j2-version>2.24.0</log4j2-version>
+ <protobuf-version>4.29.0-RC3</protobuf-version>
+ <slf4j-version>1.7.36</slf4j-version>
+ <testcontainers-version>1.20.2</testcontainers-version>
+
+ <build-helper-maven-plugin-version>3.6.0</build-helper-maven-plugin-version>
+ <maven-failsafe-plugin-version>3.5.0</maven-failsafe-plugin-version>
+ <maven-release-plugin-version>3.1.1</maven-release-plugin-version>
+ <maven-surefire-plugin-version>3.5.0</maven-surefire-plugin-version>
+ <protobuf-maven-plugin-version>2.7.0</protobuf-maven-plugin-version>
+ </properties>
+
+ <dependencyManagement>
+ <dependencies>
+ <dependency>
+ <groupId>org.junit</groupId>
+ <artifactId>junit-bom</artifactId>
+ <version>${junit-jupiter-version}</version>
+ <type>pom</type>
+ <scope>import</scope>
+ </dependency>
+ <dependency>
+ <groupId>org.testcontainers</groupId>
+ <artifactId>testcontainers-bom</artifactId>
+ <version>${testcontainers-version}</version>
+ <type>pom</type>
+ <scope>import</scope>
+ </dependency>
+ </dependencies>
+ </dependencyManagement>
+
+ <dependencies>
+ <dependency>
+ <groupId>com.google.protobuf</groupId>
+ <artifactId>protobuf-java</artifactId>
+ <version>${protobuf-version}</version>
+ <scope>compile</scope>
+ </dependency>
+ <dependency>
+ <groupId>io.grpc</groupId>
+ <artifactId>grpc-netty-shaded</artifactId>
+ <version>${grpc-version}</version>
+ </dependency>
+ <dependency>
+ <groupId>io.grpc</groupId>
+ <artifactId>grpc-protobuf</artifactId>
+ <version>${grpc-version}</version>
+ </dependency>
+ <dependency>
+ <groupId>io.grpc</groupId>
+ <artifactId>grpc-stub</artifactId>
+ <version>${grpc-version}</version>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.tomcat</groupId>
+ <artifactId>annotations-api</artifactId>
+ <version>${annotations-api-version}</version>
+ </dependency>
+
+ <!-- Logging -->
+ <dependency>
+ <groupId>org.slf4j</groupId>
+ <artifactId>slf4j-api</artifactId>
+ <version>${slf4j-version}</version>
+ </dependency>
+
+ <!-- Testing -->
+ <dependency>
+ <groupId>org.junit.jupiter</groupId>
+ <artifactId>junit-jupiter</artifactId>
+ <scope>test</scope>
+ </dependency>
+ <dependency>
+ <groupId>org.testcontainers</groupId>
+ <artifactId>testcontainers</artifactId>
+ <scope>test</scope>
+ </dependency>
+ <dependency>
+ <groupId>org.testcontainers</groupId>
+ <artifactId>junit-jupiter</artifactId>
+ <scope>test</scope>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.logging.log4j</groupId>
+ <artifactId>log4j-slf4j-impl</artifactId>
+ <version>${log4j2-version}</version>
+ <scope>test</scope>
+ </dependency>
+ </dependencies>
+
+ <build>
+ <plugins>
+ <plugin>
+ <groupId>io.github.ascopes</groupId>
+ <artifactId>protobuf-maven-plugin</artifactId>
+ <version>${protobuf-maven-plugin-version}</version>
+ <configuration>
+ <protocVersion>${protobuf-version}</protocVersion>
+ <sourceDirectories>
+ <sourceDirectory>src/main/proto</sourceDirectory>
+ </sourceDirectories>
+ <binaryMavenPlugins>
+ <binaryMavenPlugin>
+ <groupId>io.grpc</groupId>
+ <artifactId>protoc-gen-grpc-java</artifactId>
+ <version>${grpc-version}</version>
+ </binaryMavenPlugin>
+ </binaryMavenPlugins>
+ </configuration>
+ <executions>
+ <execution>
+ <goals>
+ <goal>generate</goal>
+ </goals>
+ </execution>
+ </executions>
+ </plugin>
+ <plugin>
+ <artifactId>maven-surefire-plugin</artifactId>
+ <version>${maven-surefire-plugin-version}</version>
+ </plugin>
+ <plugin>
+ <artifactId>maven-failsafe-plugin</artifactId>
+ <version>${maven-failsafe-plugin-version}</version>
+ </plugin>
+
+ <!-- Releasing -->
+ <plugin>
+ <groupId>org.apache.maven.plugins</groupId>
+ <artifactId>maven-release-plugin</artifactId>
+ <version>${maven-release-plugin-version}</version>
+ <configuration>
+ <tagNameFormat>v@{project.version}</tagNameFormat>
+ </configuration>
+ </plugin>
+ </plugins>
+ </build>
+</project>
diff --git a/src/main/java/com/github/tadayosi/tensorflow/serving/client/Configuration.java b/src/main/java/com/github/tadayosi/tensorflow/serving/client/Configuration.java
new file mode 100644
index 0000000..811b63a
--- /dev/null
+++ b/src/main/java/com/github/tadayosi/tensorflow/serving/client/Configuration.java
@@ -0,0 +1,65 @@
+package com.github.tadayosi.tensorflow.serving.client;
+
+import java.io.InputStream;
+import java.util.Optional;
+import java.util.Properties;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+public class Configuration {
+
+ private static final Logger LOG = LoggerFactory.getLogger(Configuration.class);
+
+ public static final String TFSC4J_PROPERTIES = "tfsc4j.properties";
+ public static final String TFSC4J_PREFIX = "tfsc4j.";
+
+ public static final String TARGET = "target";
+ public static final String CREDENTIALS = "credentials";
+
+ private final Optional<String> target;
+ private final Optional<String> credentials;
+
+ private Configuration() {
+ Properties props = loadProperties();
+
+ this.target = loadProperty(TARGET, props);
+ this.credentials = loadProperty(CREDENTIALS, props);
+ }
+
+ static Properties loadProperties() {
+ Properties properties = new Properties();
+ try {
+ InputStream is = Configuration.class.getClassLoader().getResourceAsStream(TFSC4J_PROPERTIES);
+ properties.load(is);
+ } catch (Exception e) {
+ // Ignore
+ LOG.debug("Failed to load properties file: {}", e.getMessage());
+ }
+ return properties;
+ }
+
+ /**
+ * Order of precedence: System properties > environment variables > properties file
+ */
+ static Optional<String> loadProperty(String key, Properties properties) {
+ String tsc4jKey = TFSC4J_PREFIX + key;
+ Optional<String> value = Optional.ofNullable(System.getProperty(tsc4jKey))
+ .or(() -> Optional.ofNullable(System.getenv(tsc4jKey.toUpperCase().replace(".", "_"))))
+ .or(() -> Optional.ofNullable(properties.getProperty(key)));
+ LOG.debug("Loaded property {}: {}", key, value.orElse(null));
+ return value;
+ }
+
+ public static Configuration load() {
+ return new Configuration();
+ }
+
+ public Optional<String> getTarget() {
+ return target;
+ }
+
+ public Optional<String> getCredentials() {
+ return credentials;
+ }
+}
diff --git a/src/main/java/com/github/tadayosi/tensorflow/serving/client/TensorFlowServingApi.java b/src/main/java/com/github/tadayosi/tensorflow/serving/client/TensorFlowServingApi.java
new file mode 100644
index 0000000..81f32f5
--- /dev/null
+++ b/src/main/java/com/github/tadayosi/tensorflow/serving/client/TensorFlowServingApi.java
@@ -0,0 +1,20 @@
+package com.github.tadayosi.tensorflow.serving.client;
+
+import tensorflow.serving.Classification;
+import tensorflow.serving.GetModelMetadata;
+import tensorflow.serving.GetModelStatus;
+import tensorflow.serving.Predict;
+import tensorflow.serving.RegressionOuterClass;
+
+public interface TensorFlowServingApi {
+
+ GetModelStatus.GetModelStatusResponse getModelStatus(GetModelStatus.GetModelStatusRequest request);
+
+ GetModelMetadata.GetModelMetadataResponse getModelMetadata(GetModelMetadata.GetModelMetadataRequest request);
+
+ Classification.ClassificationResponse classify(Classification.ClassificationRequest request);
+
+ RegressionOuterClass.RegressionResponse regress(RegressionOuterClass.RegressionRequest request);
+
+ Predict.PredictResponse predict(Predict.PredictRequest request);
+}
diff --git a/src/main/java/com/github/tadayosi/tensorflow/serving/client/TensorFlowServingClient.java b/src/main/java/com/github/tadayosi/tensorflow/serving/client/TensorFlowServingClient.java
new file mode 100644
index 0000000..35a3fcc
--- /dev/null
+++ b/src/main/java/com/github/tadayosi/tensorflow/serving/client/TensorFlowServingClient.java
@@ -0,0 +1,86 @@
+package com.github.tadayosi.tensorflow.serving.client;
+
+import java.util.Optional;
+
+import io.grpc.ChannelCredentials;
+import io.grpc.Grpc;
+import io.grpc.InsecureChannelCredentials;
+import io.grpc.ManagedChannel;
+import tensorflow.serving.Classification;
+import tensorflow.serving.GetModelMetadata;
+import tensorflow.serving.GetModelStatus;
+import tensorflow.serving.ModelServiceGrpc;
+import tensorflow.serving.Predict;
+import tensorflow.serving.PredictionServiceGrpc;
+import tensorflow.serving.RegressionOuterClass;
+
+public class TensorFlowServingClient implements TensorFlowServingApi {
+
+ private static final String DEFAULT_TARGET = "localhost:8501";
+
+ private final ModelServiceGrpc.ModelServiceBlockingStub modelService;
+ private final PredictionServiceGrpc.PredictionServiceBlockingStub predictionService;
+
+ private TensorFlowServingClient(String target, ChannelCredentials credentials) {
+ ManagedChannel channel = Grpc.newChannelBuilder(target, credentials).build();
+ this.modelService = ModelServiceGrpc.newBlockingStub(channel);
+ this.predictionService = PredictionServiceGrpc.newBlockingStub(channel);
+ }
+
+ public static TensorFlowServingClient newInstance() {
+ return builder().build();
+ }
+
+ public static Builder builder() {
+ return new Builder();
+ }
+
+ @Override
+ public GetModelStatus.GetModelStatusResponse getModelStatus(GetModelStatus.GetModelStatusRequest request) {
+ return modelService.getModelStatus(request);
+ }
+
+ @Override
+ public GetModelMetadata.GetModelMetadataResponse getModelMetadata(GetModelMetadata.GetModelMetadataRequest request) {
+ return predictionService.getModelMetadata(request);
+ }
+
+ @Override
+ public Classification.ClassificationResponse classify(Classification.ClassificationRequest request) {
+ return predictionService.classify(request);
+ }
+
+ @Override
+ public RegressionOuterClass.RegressionResponse regress(RegressionOuterClass.RegressionRequest request) {
+ return predictionService.regress(request);
+ }
+
+ @Override
+ public Predict.PredictResponse predict(Predict.PredictRequest request) {
+ return predictionService.predict(request);
+ }
+
+ public static class Builder {
+
+ private final Configuration configuration = Configuration.load();
+
+ private Optional<String> target = configuration.getTarget();
+ private Optional<String> credentials = configuration.getCredentials();
+
+ public Builder target(String target) {
+ this.target = Optional.of(target);
+ return this;
+ }
+
+ public Builder credentials(String credentials) {
+ this.credentials = Optional.of(credentials);
+ return this;
+ }
+
+ public TensorFlowServingClient build() {
+ return new TensorFlowServingClient(
+ target.orElse(DEFAULT_TARGET),
+ InsecureChannelCredentials.create());
+ }
+ }
+}
diff --git a/src/main/proto/google/protobuf/any.proto b/src/main/proto/google/protobuf/any.proto
new file mode 100644
index 0000000..eff44e5
--- /dev/null
+++ b/src/main/proto/google/protobuf/any.proto
@@ -0,0 +1,162 @@
+// Protocol Buffers - Google's data interchange format
+// Copyright 2008 Google Inc. All rights reserved.
+// https://developers.google.com/protocol-buffers/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are
+// met:
+//
+// * Redistributions of source code must retain the above copyright
+// notice, this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above
+// copyright notice, this list of conditions and the following disclaimer
+// in the documentation and/or other materials provided with the
+// distribution.
+// * Neither the name of Google Inc. nor the names of its
+// contributors may be used to endorse or promote products derived from
+// this software without specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+syntax = "proto3";
+
+package google.protobuf;
+
+option go_package = "google.golang.org/protobuf/types/known/anypb";
+option java_package = "com.google.protobuf";
+option java_outer_classname = "AnyProto";
+option java_multiple_files = true;
+option objc_class_prefix = "GPB";
+option csharp_namespace = "Google.Protobuf.WellKnownTypes";
+
+// `Any` contains an arbitrary serialized protocol buffer message along with a
+// URL that describes the type of the serialized message.
+//
+// Protobuf library provides support to pack/unpack Any values in the form
+// of utility functions or additional generated methods of the Any type.
+//
+// Example 1: Pack and unpack a message in C++.
+//
+// Foo foo = ...;
+// Any any;
+// any.PackFrom(foo);
+// ...
+// if (any.UnpackTo(&foo)) {
+// ...
+// }
+//
+// Example 2: Pack and unpack a message in Java.
+//
+// Foo foo = ...;
+// Any any = Any.pack(foo);
+// ...
+// if (any.is(Foo.class)) {
+// foo = any.unpack(Foo.class);
+// }
+// // or ...
+// if (any.isSameTypeAs(Foo.getDefaultInstance())) {
+// foo = any.unpack(Foo.getDefaultInstance());
+// }
+//
+// Example 3: Pack and unpack a message in Python.
+//
+// foo = Foo(...)
+// any = Any()
+// any.Pack(foo)
+// ...
+// if any.Is(Foo.DESCRIPTOR):
+// any.Unpack(foo)
+// ...
+//
+// Example 4: Pack and unpack a message in Go
+//
+// foo := &pb.Foo{...}
+// any, err := anypb.New(foo)
+// if err != nil {
+// ...
+// }
+// ...
+// foo := &pb.Foo{}
+// if err := any.UnmarshalTo(foo); err != nil {
+// ...
+// }
+//
+// The pack methods provided by protobuf library will by default use
+// 'type.googleapis.com/full.type.name' as the type URL and the unpack
+// methods only use the fully qualified type name after the last '/'
+// in the type URL, for example "foo.bar.com/x/y.z" will yield type
+// name "y.z".
+//
+// JSON
+// ====
+// The JSON representation of an `Any` value uses the regular
+// representation of the deserialized, embedded message, with an
+// additional field `@type` which contains the type URL. Example:
+//
+// package google.profile;
+// message Person {
+// string first_name = 1;
+// string last_name = 2;
+// }
+//
+// {
+// "@type": "type.googleapis.com/google.profile.Person",
+// "firstName": <string>,
+// "lastName": <string>
+// }
+//
+// If the embedded message type is well-known and has a custom JSON
+// representation, that representation will be embedded adding a field
+// `value` which holds the custom JSON in addition to the `@type`
+// field. Example (for message [google.protobuf.Duration][]):
+//
+// {
+// "@type": "type.googleapis.com/google.protobuf.Duration",
+// "value": "1.212s"
+// }
+//
+message Any {
+ // A URL/resource name that uniquely identifies the type of the serialized
+ // protocol buffer message. This string must contain at least
+ // one "/" character. The last segment of the URL's path must represent
+ // the fully qualified name of the type (as in
+ // `path/google.protobuf.Duration`). The name should be in a canonical form
+ // (e.g., leading "." is not accepted).
+ //
+ // In practice, teams usually precompile into the binary all types that they
+ // expect it to use in the context of Any. However, for URLs which use the
+ // scheme `http`, `https`, or no scheme, one can optionally set up a type
+ // server that maps type URLs to message definitions as follows:
+ //
+ // * If no scheme is provided, `https` is assumed.
+ // * An HTTP GET on the URL must yield a [google.protobuf.Type][]
+ // value in binary format, or produce an error.
+ // * Applications are allowed to cache lookup results based on the
+ // URL, or have them precompiled into a binary to avoid any
+ // lookup. Therefore, binary compatibility needs to be preserved
+ // on changes to types. (Use versioned type names to manage
+ // breaking changes.)
+ //
+ // Note: this functionality is not currently available in the official
+ // protobuf release, and it is not used for type URLs beginning with
+ // type.googleapis.com. As of May 2023, there are no widely used type server
+ // implementations and no plans to implement one.
+ //
+ // Schemes other than `http`, `https` (or the empty scheme) might be
+ // used with implementation specific semantics.
+ //
+ string type_url = 1;
+
+ // Must be a valid serialized protocol buffer of the above specified type.
+ bytes value = 2;
+}
diff --git a/src/main/proto/google/protobuf/wrappers.proto b/src/main/proto/google/protobuf/wrappers.proto
new file mode 100644
index 0000000..1959fa5
--- /dev/null
+++ b/src/main/proto/google/protobuf/wrappers.proto
@@ -0,0 +1,123 @@
+// Protocol Buffers - Google's data interchange format
+// Copyright 2008 Google Inc. All rights reserved.
+// https://developers.google.com/protocol-buffers/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are
+// met:
+//
+// * Redistributions of source code must retain the above copyright
+// notice, this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above
+// copyright notice, this list of conditions and the following disclaimer
+// in the documentation and/or other materials provided with the
+// distribution.
+// * Neither the name of Google Inc. nor the names of its
+// contributors may be used to endorse or promote products derived from
+// this software without specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+//
+// Wrappers for primitive (non-message) types. These types are useful
+// for embedding primitives in the `google.protobuf.Any` type and for places
+// where we need to distinguish between the absence of a primitive
+// typed field and its default value.
+//
+// These wrappers have no meaningful use within repeated fields as they lack
+// the ability to detect presence on individual elements.
+// These wrappers have no meaningful use within a map or a oneof since
+// individual entries of a map or fields of a oneof can already detect presence.
+
+syntax = "proto3";
+
+package google.protobuf;
+
+option cc_enable_arenas = true;
+option go_package = "google.golang.org/protobuf/types/known/wrapperspb";
+option java_package = "com.google.protobuf";
+option java_outer_classname = "WrappersProto";
+option java_multiple_files = true;
+option objc_class_prefix = "GPB";
+option csharp_namespace = "Google.Protobuf.WellKnownTypes";
+
+// Wrapper message for `double`.
+//
+// The JSON representation for `DoubleValue` is JSON number.
+message DoubleValue {
+ // The double value.
+ double value = 1;
+}
+
+// Wrapper message for `float`.
+//
+// The JSON representation for `FloatValue` is JSON number.
+message FloatValue {
+ // The float value.
+ float value = 1;
+}
+
+// Wrapper message for `int64`.
+//
+// The JSON representation for `Int64Value` is JSON string.
+message Int64Value {
+ // The int64 value.
+ int64 value = 1;
+}
+
+// Wrapper message for `uint64`.
+//
+// The JSON representation for `UInt64Value` is JSON string.
+message UInt64Value {
+ // The uint64 value.
+ uint64 value = 1;
+}
+
+// Wrapper message for `int32`.
+//
+// The JSON representation for `Int32Value` is JSON number.
+message Int32Value {
+ // The int32 value.
+ int32 value = 1;
+}
+
+// Wrapper message for `uint32`.
+//
+// The JSON representation for `UInt32Value` is JSON number.
+message UInt32Value {
+ // The uint32 value.
+ uint32 value = 1;
+}
+
+// Wrapper message for `bool`.
+//
+// The JSON representation for `BoolValue` is JSON `true` and `false`.
+message BoolValue {
+ // The bool value.
+ bool value = 1;
+}
+
+// Wrapper message for `string`.
+//
+// The JSON representation for `StringValue` is JSON string.
+message StringValue {
+ // The string value.
+ string value = 1;
+}
+
+// Wrapper message for `bytes`.
+//
+// The JSON representation for `BytesValue` is JSON string.
+message BytesValue {
+ // The bytes value.
+ bytes value = 1;
+}
diff --git a/src/main/proto/tensorflow/core/example/example.proto b/src/main/proto/tensorflow/core/example/example.proto
new file mode 100644
index 0000000..9f762fb
--- /dev/null
+++ b/src/main/proto/tensorflow/core/example/example.proto
@@ -0,0 +1,301 @@
+// Protocol messages for describing input data Examples for machine learning
+// model training or inference.
+syntax = "proto3";
+
+package tensorflow;
+
+import "tensorflow/core/example/feature.proto";
+
+option cc_enable_arenas = true;
+option java_outer_classname = "ExampleProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.example";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/example/example_protos_go_proto";
+
+// An Example is a mostly-normalized data format for storing data for
+// training and inference. It contains a key-value store (features); where
+// each key (string) maps to a Feature message (which is oneof packed BytesList,
+// FloatList, or Int64List). This flexible and compact format allows the
+// storage of large amounts of typed data, but requires that the data shape
+// and use be determined by the configuration files and parsers that are used to
+// read and write this format. That is, the Example is mostly *not* a
+// self-describing format. In TensorFlow, Examples are read in row-major
+// format, so any configuration that describes data with rank-2 or above
+// should keep this in mind. If you flatten a matrix into a FloatList it should
+// be stored as [ row 0 ... row 1 ... row M-1 ]
+//
+// An Example for a movie recommendation application:
+// features {
+// feature {
+// key: "age"
+// value { float_list {
+// value: 29.0
+// }}
+// }
+// feature {
+// key: "movie"
+// value { bytes_list {
+// value: "The Shawshank Redemption"
+// value: "Fight Club"
+// }}
+// }
+// feature {
+// key: "movie_ratings"
+// value { float_list {
+// value: 9.0
+// value: 9.7
+// }}
+// }
+// feature {
+// key: "suggestion"
+// value { bytes_list {
+// value: "Inception"
+// }}
+// }
+// # Note that this feature exists to be used as a label in training.
+// # E.g., if training a logistic regression model to predict purchase
+// # probability in our learning tool we would set the label feature to
+// # "suggestion_purchased".
+// feature {
+// key: "suggestion_purchased"
+// value { float_list {
+// value: 1.0
+// }}
+// }
+// # Similar to "suggestion_purchased" above this feature exists to be used
+// # as a label in training.
+// # E.g., if training a linear regression model to predict purchase
+// # price in our learning tool we would set the label feature to
+// # "purchase_price".
+// feature {
+// key: "purchase_price"
+// value { float_list {
+// value: 9.99
+// }}
+// }
+// }
+//
+// A conformant Example data set obeys the following conventions:
+// - If a Feature K exists in one example with data type T, it must be of
+// type T in all other examples when present. It may be omitted.
+// - The number of instances of Feature K list data may vary across examples,
+// depending on the requirements of the model.
+// - If a Feature K doesn't exist in an example, a K-specific default will be
+// used, if configured.
+// - If a Feature K exists in an example but contains no items, the intent
+// is considered to be an empty tensor and no default will be used.
+
+message Example {
+ Features features = 1;
+}
+
+// A SequenceExample is an Example representing one or more sequences, and
+// some context. The context contains features which apply to the entire
+// example. The feature_lists contain a key, value map where each key is
+// associated with a repeated set of Features (a FeatureList).
+// A FeatureList thus represents the values of a feature identified by its key
+// over time / frames.
+//
+// Below is a SequenceExample for a movie recommendation application recording a
+// sequence of ratings by a user. The time-independent features ("locale",
+// "age", "favorites") describing the user are part of the context. The sequence
+// of movies the user rated are part of the feature_lists. For each movie in the
+// sequence we have information on its name and actors and the user's rating.
+// This information is recorded in three separate feature_list(s).
+// In the example below there are only two movies. All three feature_list(s),
+// namely "movie_ratings", "movie_names", and "actors" have a feature value for
+// both movies. Note, that "actors" is itself a bytes_list with multiple
+// strings per movie.
+//
+// context: {
+// feature: {
+// key : "locale"
+// value: {
+// bytes_list: {
+// value: [ "pt_BR" ]
+// }
+// }
+// }
+// feature: {
+// key : "age"
+// value: {
+// float_list: {
+// value: [ 19.0 ]
+// }
+// }
+// }
+// feature: {
+// key : "favorites"
+// value: {
+// bytes_list: {
+// value: [ "Majesty Rose", "Savannah Outen", "One Direction" ]
+// }
+// }
+// }
+// }
+// feature_lists: {
+// feature_list: {
+// key : "movie_ratings"
+// value: {
+// feature: {
+// float_list: {
+// value: [ 4.5 ]
+// }
+// }
+// feature: {
+// float_list: {
+// value: [ 5.0 ]
+// }
+// }
+// }
+// }
+// feature_list: {
+// key : "movie_names"
+// value: {
+// feature: {
+// bytes_list: {
+// value: [ "The Shawshank Redemption" ]
+// }
+// }
+// feature: {
+// bytes_list: {
+// value: [ "Fight Club" ]
+// }
+// }
+// }
+// }
+// feature_list: {
+// key : "actors"
+// value: {
+// feature: {
+// bytes_list: {
+// value: [ "Tim Robbins", "Morgan Freeman" ]
+// }
+// }
+// feature: {
+// bytes_list: {
+// value: [ "Brad Pitt", "Edward Norton", "Helena Bonham Carter" ]
+// }
+// }
+// }
+// }
+// }
+//
+// A conformant SequenceExample data set obeys the following conventions:
+//
+// Context:
+// - All conformant context features K must obey the same conventions as
+// a conformant Example's features (see above).
+// Feature lists:
+// - A FeatureList L may be missing in an example; it is up to the
+// parser configuration to determine if this is allowed or considered
+// an empty list (zero length).
+// - If a FeatureList L exists, it may be empty (zero length).
+// - If a FeatureList L is non-empty, all features within the FeatureList
+// must have the same data type T. Even across SequenceExamples, the type T
+// of the FeatureList identified by the same key must be the same. An entry
+// without any values may serve as an empty feature.
+// - If a FeatureList L is non-empty, it is up to the parser configuration
+// to determine if all features within the FeatureList must
+// have the same size. The same holds for this FeatureList across multiple
+// examples.
+// - For sequence modeling, e.g.:
+// http://colah.github.io/posts/2015-08-Understanding-LSTMs/
+// https://github.com/tensorflow/nmt
+// the feature lists represent a sequence of frames.
+// In this scenario, all FeatureLists in a SequenceExample have the same
+// number of Feature messages, so that the ith element in each FeatureList
+// is part of the ith frame (or time step).
+// Examples of conformant and non-conformant examples' FeatureLists:
+//
+// Conformant FeatureLists:
+// feature_lists: { feature_list: {
+// key: "movie_ratings"
+// value: { feature: { float_list: { value: [ 4.5 ] } }
+// feature: { float_list: { value: [ 5.0 ] } } }
+// } }
+//
+// Non-conformant FeatureLists (mismatched types):
+// feature_lists: { feature_list: {
+// key: "movie_ratings"
+// value: { feature: { float_list: { value: [ 4.5 ] } }
+// feature: { int64_list: { value: [ 5 ] } } }
+// } }
+//
+// Conditionally conformant FeatureLists, the parser configuration determines
+// if the feature sizes must match:
+// feature_lists: { feature_list: {
+// key: "movie_ratings"
+// value: { feature: { float_list: { value: [ 4.5 ] } }
+// feature: { float_list: { value: [ 5.0, 6.0 ] } } }
+// } }
+//
+// Conformant pair of SequenceExample
+// feature_lists: { feature_list: {
+// key: "movie_ratings"
+// value: { feature: { float_list: { value: [ 4.5 ] } }
+// feature: { float_list: { value: [ 5.0 ] } } }
+// } }
+// and:
+// feature_lists: { feature_list: {
+// key: "movie_ratings"
+// value: { feature: { float_list: { value: [ 4.5 ] } }
+// feature: { float_list: { value: [ 5.0 ] } }
+// feature: { float_list: { value: [ 2.0 ] } } }
+// } }
+//
+// Conformant pair of SequenceExample
+// feature_lists: { feature_list: {
+// key: "movie_ratings"
+// value: { feature: { float_list: { value: [ 4.5 ] } }
+// feature: { float_list: { value: [ 5.0 ] } } }
+// } }
+// and:
+// feature_lists: { feature_list: {
+// key: "movie_ratings"
+// value: { }
+// } }
+//
+// Conditionally conformant pair of SequenceExample, the parser configuration
+// determines if the second feature_lists is consistent (zero-length) or
+// invalid (missing "movie_ratings"):
+// feature_lists: { feature_list: {
+// key: "movie_ratings"
+// value: { feature: { float_list: { value: [ 4.5 ] } }
+// feature: { float_list: { value: [ 5.0 ] } } }
+// } }
+// and:
+// feature_lists: { }
+//
+// Non-conformant pair of SequenceExample (mismatched types)
+// feature_lists: { feature_list: {
+// key: "movie_ratings"
+// value: { feature: { float_list: { value: [ 4.5 ] } }
+// feature: { float_list: { value: [ 5.0 ] } } }
+// } }
+// and:
+// feature_lists: { feature_list: {
+// key: "movie_ratings"
+// value: { feature: { int64_list: { value: [ 4 ] } }
+// feature: { int64_list: { value: [ 5 ] } }
+// feature: { int64_list: { value: [ 2 ] } } }
+// } }
+//
+// Conditionally conformant pair of SequenceExample; the parser configuration
+// determines if the feature sizes must match:
+// feature_lists: { feature_list: {
+// key: "movie_ratings"
+// value: { feature: { float_list: { value: [ 4.5 ] } }
+// feature: { float_list: { value: [ 5.0 ] } } }
+// } }
+// and:
+// feature_lists: { feature_list: {
+// key: "movie_ratings"
+// value: { feature: { float_list: { value: [ 4.0 ] } }
+// feature: { float_list: { value: [ 5.0, 3.0 ] } }
+// } }
+
+message SequenceExample {
+ Features context = 1;
+ FeatureLists feature_lists = 2;
+}
diff --git a/src/main/proto/tensorflow/core/example/feature.proto b/src/main/proto/tensorflow/core/example/feature.proto
new file mode 100644
index 0000000..7f9fad9
--- /dev/null
+++ b/src/main/proto/tensorflow/core/example/feature.proto
@@ -0,0 +1,110 @@
+// Protocol messages for describing features for machine learning model
+// training or inference.
+//
+// There are three base Feature types:
+// - bytes
+// - float
+// - int64
+//
+// A Feature contains Lists which may hold zero or more values. These
+// lists are the base values BytesList, FloatList, Int64List.
+//
+// Features are organized into categories by name. The Features message
+// contains the mapping from name to Feature.
+//
+// Example Features for a movie recommendation application:
+// feature {
+// key: "age"
+// value { float_list {
+// value: 29.0
+// }}
+// }
+// feature {
+// key: "movie"
+// value { bytes_list {
+// value: "The Shawshank Redemption"
+// value: "Fight Club"
+// }}
+// }
+// feature {
+// key: "movie_ratings"
+// value { float_list {
+// value: 9.0
+// value: 9.7
+// }}
+// }
+// feature {
+// key: "suggestion"
+// value { bytes_list {
+// value: "Inception"
+// }}
+// }
+// feature {
+// key: "suggestion_purchased"
+// value { int64_list {
+// value: 1
+// }}
+// }
+// feature {
+// key: "purchase_price"
+// value { float_list {
+// value: 9.99
+// }}
+// }
+//
+
+syntax = "proto3";
+
+package tensorflow;
+
+option cc_enable_arenas = true;
+option java_outer_classname = "FeatureProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.example";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/example/example_protos_go_proto";
+
+// LINT.IfChange
+// Containers to hold repeated fundamental values.
+message BytesList {
+ repeated bytes value = 1;
+}
+message FloatList {
+ repeated float value = 1 [packed = true];
+}
+message Int64List {
+ repeated int64 value = 1 [packed = true];
+}
+
+// Containers for non-sequential data.
+message Feature {
+ // Each feature can be exactly one kind.
+ oneof kind {
+ BytesList bytes_list = 1;
+ FloatList float_list = 2;
+ Int64List int64_list = 3;
+ }
+}
+
+message Features {
+ // Map from feature name to feature.
+ map<string, Feature> feature = 1;
+}
+
+// Containers for sequential data.
+//
+// A FeatureList contains lists of Features. These may hold zero or more
+// Feature values.
+//
+// FeatureLists are organized into categories by name. The FeatureLists message
+// contains the mapping from name to FeatureList.
+//
+message FeatureList {
+ repeated Feature feature = 1;
+}
+
+message FeatureLists {
+ // Map from feature name to feature list.
+ map<string, FeatureList> feature_list = 1;
+}
+// LINT.ThenChange(
+// https://www.tensorflow.org/code/tensorflow/python/training/training.py)
diff --git a/src/main/proto/tensorflow/core/framework/allocation_description.proto b/src/main/proto/tensorflow/core/framework/allocation_description.proto
new file mode 100644
index 0000000..f18caa4
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/allocation_description.proto
@@ -0,0 +1,29 @@
+syntax = "proto3";
+
+package tensorflow;
+
+option cc_enable_arenas = true;
+option java_outer_classname = "AllocationDescriptionProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/allocation_description_go_proto";
+
+message AllocationDescription {
+ // Total number of bytes requested
+ int64 requested_bytes = 1;
+
+ // Total number of bytes allocated if known
+ int64 allocated_bytes = 2;
+
+ // Name of the allocator used
+ string allocator_name = 3;
+
+ // Identifier of the allocated buffer if known
+ int64 allocation_id = 4;
+
+ // Set if this tensor only has one remaining reference
+ bool has_single_reference = 5;
+
+ // Address of the allocation.
+ uint64 ptr = 6;
+}
diff --git a/src/main/proto/tensorflow/core/framework/attr_value.proto b/src/main/proto/tensorflow/core/framework/attr_value.proto
new file mode 100644
index 0000000..2bd5b55
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/attr_value.proto
@@ -0,0 +1,64 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "tensorflow/core/framework/tensor.proto";
+import "tensorflow/core/framework/tensor_shape.proto";
+import "tensorflow/core/framework/types.proto";
+
+option cc_enable_arenas = true;
+option java_outer_classname = "AttrValueProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/attr_value_go_proto";
+
+// Protocol buffer representing the value for an attr used to configure an Op.
+// Comment indicates the corresponding attr type. Only the field matching the
+// attr type may be filled.
+message AttrValue {
+ // LINT.IfChange
+ message ListValue {
+ repeated bytes s = 2; // "list(string)"
+ repeated int64 i = 3 [packed = true]; // "list(int)"
+ repeated float f = 4 [packed = true]; // "list(float)"
+ repeated bool b = 5 [packed = true]; // "list(bool)"
+ repeated DataType type = 6 [packed = true]; // "list(type)"
+ repeated TensorShapeProto shape = 7; // "list(shape)"
+ repeated TensorProto tensor = 8; // "list(tensor)"
+ repeated NameAttrList func = 9; // "list(attr)"
+ }
+ // LINT.ThenChange(//tensorflow/c/c_api.cc)
+
+ oneof value {
+ bytes s = 2; // "string"
+ int64 i = 3; // "int"
+ float f = 4; // "float"
+ bool b = 5; // "bool"
+ DataType type = 6; // "type"
+ TensorShapeProto shape = 7; // "shape"
+ TensorProto tensor = 8; // "tensor"
+ ListValue list = 1; // any "list(...)"
+
+ // "func" represents a function. func.name is a function's name or
+ // a primitive op's name. func.attr.first is the name of an attr
+ // defined for that function. func.attr.second is the value for
+ // that attr in the instantiation.
+ NameAttrList func = 10;
+
+ // This is a placeholder only used in nodes defined inside a
+ // function. It indicates the attr value will be supplied when
+ // the function is instantiated. For example, let us suppose a
+ // node "N" in function "FN". "N" has an attr "A" with value
+ // placeholder = "foo". When FN is instantiated with attr "foo"
+ // set to "bar", the instantiated node N's attr A will have been
+ // given the value "bar".
+ string placeholder = 9;
+ }
+}
+
+// A list of attr names and their values. The whole list is attached
+// with a string name. E.g., MatMul[T=float].
+message NameAttrList {
+ string name = 1;
+ map<string, AttrValue> attr = 2;
+}
diff --git a/src/main/proto/tensorflow/core/framework/cost_graph.proto b/src/main/proto/tensorflow/core/framework/cost_graph.proto
new file mode 100644
index 0000000..42c9e23
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/cost_graph.proto
@@ -0,0 +1,89 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "tensorflow/core/framework/tensor_shape.proto";
+import "tensorflow/core/framework/types.proto";
+
+option cc_enable_arenas = true;
+option java_outer_classname = "CostGraphProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/cost_graph_go_proto";
+
+message CostGraphDef {
+ message Node {
+ // The name of the node. Names are globally unique.
+ string name = 1;
+
+ // The device of the node. Can be empty if the node is mapped to the
+ // default partition or partitioning hasn't been run yet.
+ string device = 2;
+
+ // The id of the node. Node ids are only unique inside a partition.
+ int32 id = 3;
+
+ // Inputs of this node. They must be executed before this node can be
+ // executed. An input is a particular output of another node, specified
+ // by the node id and the output index.
+ message InputInfo {
+ int32 preceding_node = 1;
+ int32 preceding_port = 2;
+ }
+ repeated InputInfo input_info = 4;
+
+ // Outputs of this node.
+ message OutputInfo {
+ int64 size = 1;
+ // If >= 0, the output is an alias of an input. Note that an alias input
+ // may itself be an alias. The algorithm will therefore need to follow
+ // those pointers.
+ int64 alias_input_port = 2;
+ TensorShapeProto shape = 3;
+ DataType dtype = 4;
+ }
+ repeated OutputInfo output_info = 5;
+
+ // Temporary memory used by this node.
+ int64 temporary_memory_size = 6;
+
+ // Persistent memory used by this node.
+ int64 persistent_memory_size = 12;
+
+ int64 host_temp_memory_size = 10 [deprecated = true];
+ int64 device_temp_memory_size = 11 [deprecated = true];
+ int64 device_persistent_memory_size = 16 [deprecated = true];
+
+ // Estimate of the computational cost of this node, in microseconds.
+ int64 compute_cost = 9;
+
+ // Analytical estimate of the computational cost of this node, in
+ // microseconds.
+ int64 compute_time = 14;
+
+ // Analytical estimate of the memory access cost of this node, in
+ // microseconds.
+ int64 memory_time = 15;
+
+ // If true, the output is permanent: it can't be discarded, because this
+ // node is part of the "final output". Nodes may depend on final nodes.
+ bool is_final = 7;
+
+ // Ids of the control inputs for this node.
+ repeated int32 control_input = 8;
+
+ // Are the costs inaccurate?
+ bool inaccurate = 17;
+ }
+ repeated Node node = 1;
+
+ // Total cost of this graph, typically used for balancing decisions.
+ message AggregatedCost {
+ // Aggregated cost value.
+ float cost = 1;
+
+ // Aggregated cost dimension (e.g. 'memory', 'compute', 'network').
+ string dimension = 2;
+ }
+ repeated AggregatedCost cost = 2;
+}
diff --git a/src/main/proto/tensorflow/core/framework/full_type.proto b/src/main/proto/tensorflow/core/framework/full_type.proto
new file mode 100644
index 0000000..19e8da5
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/full_type.proto
@@ -0,0 +1,310 @@
+syntax = "proto3";
+
+package tensorflow;
+
+option cc_enable_arenas = true;
+option java_outer_classname = "FullTypeProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/full_type_go_proto";
+
+// LINT.IfChange
+// Experimental. Represents the complete type information of a TensorFlow value.
+enum FullTypeId {
+ // The default represents an uninitialized values.
+ TFT_UNSET = 0;
+
+ // Type symbols. Used to construct more complex type expressions like
+ // algebraic data types.
+
+ // Type variables may serve as placeholder for any other type ID in type
+ // templates.
+ //
+ // Examples:
+ // TFT_DATASET[TFT_VAR["T"]] is a Dataset returning a type indicated by "T".
+ // TFT_TENSOR[TFT_VAR["T"]] is a Tensor of n element type indicated by "T".
+ // TFT_TENSOR[TFT_VAR["T"]], TFT_TENSOR[TFT_VAR["T"]] are two tensors of
+ // identical element types.
+ // TFT_TENSOR[TFT_VAR["P"]], TFT_TENSOR[TFT_VAR["Q"]] are two tensors of
+ // independent element types.
+ //
+ TFT_VAR = 1;
+
+ // Wildcard type. Describes a parameter of unknown type. In TensorFlow, that
+ // can mean either a "Top" type (accepts any type), or a dynamically typed
+ // object whose type is unknown in context.
+ // Important: "unknown" does not necessarily mean undeterminable!
+ TFT_ANY = 2;
+
+ // The algebraic product type. This is an algebraic type that may be used just
+ // for logical grouping. Not to confused with TFT_TUPLE which describes a
+ // concrete object of several elements.
+ //
+ // Example:
+ // TFT_DATASET[TFT_PRODUCT[TFT_TENSOR[TFT_INT32], TFT_TENSOR[TFT_FLOAT64]]]
+ // is a Dataset producing two tensors, an integer one and a float one.
+ //
+ TFT_PRODUCT = 3;
+
+ // Represents a named field, with the name stored in the attribute.
+ //
+ // Parametrization:
+ // TFT_NAMED[<type>]{<name>}
+ // * <type> is the type of the field
+ // * <name> is the field name, as string (thpugh can theoretically be an int
+ // as well)
+ //
+ // Example:
+ // TFT_RECORD[
+ // TFT_NAMED[TFT_TENSOR[TFT_INT32]]{'foo'},
+ // TFT_NAMED[TFT_TENSOR[TFT_FLOAT32]]{'bar'},
+ // ]
+ // is a structure with two fields, an int tensor "foo" and a float tensor
+ // "bar".
+ TFT_NAMED = 4;
+
+ // Template definition. Expands the variables by repeating a template as
+ // arguments of container.
+ //
+ // Parametrization:
+ // TFT_FOR_EACH[<container_type>, <template>, <expansions>]
+ // * <container_type> is the type of the container that the template will be
+ // expanded into
+ // * <template> is any type definition that potentially contains type
+ // variables
+ // * <expansions> is a TFT_VAR and may include more types in the future
+ //
+ // Example:
+ // TFT_FOR_EACH[
+ // TFT_PRODUCT,
+ // TFT_TENSOR[TFT_VAR["t"]],
+ // TFT_VAR["t"]
+ // ]
+ // will substitute a T = TFT_INT32 to TFT_PRODUCT[TFT_TENSOR[TFT_INT32]]
+ // and a T = (TFT_INT32, TFT_INT64) to
+ // TFT_PRODUCT[TFT_TENSOR[TFT_INT32], TFT_TENSOR[TFT_INT64]].
+ TFT_FOR_EACH = 20;
+
+ // Callable types describe functions and ops.
+ //
+ // Parametrization:
+ // TFT_CALLABLE[<arg type>, <return type>]
+ // * <arg type> is the type of the arguments; TFT_PRODUCT represents
+ // multiple
+ // arguments.
+ // * <return type> is the return type; TFT_PRODUCT represents multiple
+ // return values (that means that callables returning multiple things
+ // don't necessarily return a single tuple).
+ //
+ // Example:
+ // TFT_CALLABLE[
+ // TFT_ANY,
+ // TFT_PRODUCT[TFT_TENSOR[TFT_INT32], TFT_TENSOR[TFT_FLOAT64]],
+ // ]
+ // is a callable with unspecified (for now) input arguments, and
+ // two return values of type tensor.
+ //
+ TFT_CALLABLE = 100;
+
+ // Concrete type IDs, representing "proper" data types that can describe
+ // runtime TensorFlow objects.
+
+ // The usual Tensor. This is a parametric type.
+ //
+ // Parametrization:
+ // TFT_TENSOR[<element type>, <shape type>]
+ // * <element type> is currently limited to one of the element types
+ // defined below.
+ // * <shape type> is not yet defined, and may only be TFT_UNKNOWN for now.
+ //
+ // A TFT_SHAPE type will be defined in the future.
+ //
+ // Example:
+ // TFT_TENSOR[TFT_INT32, TFT_UNKNOWN]
+ // is a Tensor of int32 element type and unknown shape.
+ //
+ // TODO(mdan): Define TFT_SHAPE and add more examples.
+ TFT_TENSOR = 1000;
+
+ // Array (or tensorflow::TensorList in the variant type registry).
+ // Note: this is not to be confused with the deprecated `TensorArray*` ops
+ // which are not supported by FullType.
+ // This type represents a random-access list whose elements can be
+ // described by a single type. Although immutable, Array is expected to
+ // support efficient mutation semantics (i.e. element update) in the
+ // user-facing API.
+ // The element type may be generic or even TFT_ANY for a heterogenous list.
+ //
+ // Parametrization:
+ // TFT_ARRAY[<element type>]
+ // * <element type> may be any concrete type.
+ //
+ // Examples:
+ // TFT_ARRAY[TFT_TENSOR[TFT_INT32]] is a TensorArray holding int32 Tensors
+ // of any shape.
+ // TFT_ARRAY[TFT_TENSOR[TFT_UNKNOWN]] is a TensorArray holding Tensors of
+ // mixed element types.
+ // TFT_ARRAY[TFT_UNKNOWN] is a TensorArray holding any element type.
+ // TFT_ARRAY[] is equivalent to TFT_ARRAY[TFT_UNKNOWN].
+ // TFT_ARRAY[TFT_ARRAY[]] is an array or arrays (of unknown types).
+ TFT_ARRAY = 1001;
+
+ // Optional (or tensorflow::OptionalVariant in the variant type registry).
+ // This type represents a value that may either hold an element of a single
+ // specified type, or nothing at all.
+ //
+ // Parametrization:
+ // TFT_OPTIONAL[<element type>]
+ // * <element type> may be any concrete type.
+ //
+ // Examples:
+ // TFT_OPTIONAL[TFT_TENSOR[TFT_INT32]] is an Optional holding an int32
+ // Tensor of any shape.
+ TFT_OPTIONAL = 1002;
+
+ // Literal types describe compile-time constant values.
+ // Literal types may also participate in dependent types.
+ //
+ // Parametrization:
+ // TFT_LITERAL[<value type>]{<value>}
+ // * <value type> may be any concrete type compatible that can hold <value>
+ // * <value> is the type's attribute, and holds the actual literal value
+ //
+ // Examples:
+ // TFT_LITERAL[TFT_INT32]{1} is the compile-time constant 1.
+ TFT_LITERAL = 1003;
+
+ // Encoding types describe a value of a certain type, encoded as a different
+ // type.
+ //
+ // Parametrization:
+ // TFT_ENCODED[<encoded type>, <encoding type>]
+ // * <encoded type> may be any type
+ // * <encoding type> may be any type
+ //
+ // Examples:
+ // TFT_ENCODING[TFT_INT32, TFT_STRING] is an integer encoded as string.
+ TFT_ENCODED = 1004;
+
+ // The type of "shape tensors" where the runtime value is the shape of
+ // some tensor(s), i.e. the output of tf.shape.
+ // Shape tensors have special, host-only placement, in contrast to
+ // TFT_TENSOR[TFT_INT32] which is the type of a normal numeric tensor
+ // with no special placement.
+ //
+ // Examples:
+ // TFT_SHAPE_TENSOR[TFT_INT32] is the most common
+ // TFT_SHAPE_TENSOR[TFT_INT64] is also allowed
+ TFT_SHAPE_TENSOR = 1005;
+
+ // Type attributes. These always appear in the parametrization of a type,
+ // never alone. For example, there is no such thing as a "bool" TensorFlow
+ // object (for now).
+
+ // The bool element type.
+ // TODO(mdan): Quantized types, legacy representations (e.g. ref)
+ TFT_BOOL = 200;
+ // Integer element types.
+ TFT_UINT8 = 201;
+ TFT_UINT16 = 202;
+ TFT_UINT32 = 203;
+ TFT_UINT64 = 204;
+ TFT_INT8 = 205;
+ TFT_INT16 = 206;
+ TFT_INT32 = 207;
+ TFT_INT64 = 208;
+ // Floating-point element types.
+ TFT_HALF = 209;
+ TFT_FLOAT = 210;
+ TFT_DOUBLE = 211;
+ TFT_BFLOAT16 = 215;
+ // Complex element types.
+ // TODO(mdan): Represent as TFT_COMPLEX[TFT_DOUBLE] instead?
+ TFT_COMPLEX64 = 212;
+ TFT_COMPLEX128 = 213;
+ // The string element type.
+ TFT_STRING = 214;
+
+ // Other types that we don't know yet whether they will become part of the
+ // core type system or be consisdered third-party (and consequently moved to
+ // user-defined type mechanisms). Presently, they are effectively in the core
+ // type system, because key compilation passes like Placer account for their
+ // existence.
+
+ // Datasets created by tf.data ops and APIs. Datasets have generator/iterable
+ // semantics, that is, one can construct an iterator from them. Like
+ // Array, they are considered to return elements that can be described
+ // by a single type. Unlike Array, they do not support random access or
+ // mutation, and can potentially produce an infinite number of elements.
+ // A datasets can produce logical structures (e.g. multiple elements). This
+ // is expressed using TFT_PRODUCT.
+ //
+ //
+ // Parametrization: TFT_DATASET[<element type>].
+ // * <element type> may be a concrete type or a type symbol. It represents
+ // the data type of the elements produced by the dataset.
+ //
+ // Examples:
+ // TFT_DATSET[TFT_TENSOR[TFT_INT32]] is a Dataset producing single int32
+ // Tensors of unknown shape.
+ // TFT_DATSET[TFT_PRODUCT[TFT_TENSOR[TFT_INT32], TFT_TENSOR[TFT_FLOAT32]] is
+ // a Dataset producing pairs of Tensors, one integer and one float.
+ // Note: The high ID number is to prepare for the eventuality that Datasets
+ // will be supported by user types in the future.
+ TFT_DATASET = 10102;
+
+ // A ragged tensor created by tf.ragged ops and APIs.
+ //
+ // Parametrization: TFT_RAGGED[<element_type>].
+ TFT_RAGGED = 10103;
+
+ // Iterators created by tf.data ops and APIs. Very similar to Datasets, except
+ // they are mutable.
+ //
+ //
+ // Parametrization: TFT_ITERATOR[<element type>].
+ // * <element type> may be a concrete type or a type symbol. It represents
+ // the data type of the elements produced by the dataset.
+ TFT_ITERATOR = 10104;
+
+ // A mutex lock tensor, produced by tf.raw_ops.MutexLock.
+ // Unlike strict execution models, where ownership of a lock is denoted by
+ // "running after the lock has been acquired", in non-strict mode, lock
+ // ownership is in the true sense: "the op argument representing the lock is
+ // available".
+ // Mutex locks are the dynamic counterpart of control dependencies.
+ // TODO(mdan): Properly document this thing.
+ //
+ // Parametrization: TFT_MUTEX_LOCK[].
+ TFT_MUTEX_LOCK = 10202;
+
+ // The equivalent of a Tensor with DT_VARIANT dtype, kept here to simplify
+ // translation. This type should not normally appear after type inference.
+ // Note that LEGACY_VARIANT != ANY: TENSOR[INT32] is a subtype of ANY, but is
+ // not a subtype of LEGACY_VARIANT.
+ TFT_LEGACY_VARIANT = 10203;
+}
+
+// Highly experimental and very likely to change.
+// This encoding uses tags instead of dedicated messages for regularity. In
+// particular the encoding imposes no restrictions on what the parameters of any
+// type should be, which in particular needs to be true for type symbols.
+message FullTypeDef {
+ // The principal type represented by this object. This may be a concrete type
+ // (Tensor, Dataset) a type variable (used for dependent types) a type
+ // symbol (Any, Union). See FullTypeId for details.
+ FullTypeId type_id = 1;
+
+ repeated FullTypeDef args = 2;
+
+ // Literal values of this type object, if the type admits one.
+ // For example, a type variable admits a string attribute - its name.
+ // Shape-related types may admit int attributes - their static shape values.
+ // Fields for more data types to be added as needed.
+ oneof attr {
+ string s = 3;
+ int64 i = 4;
+ // TODO(mdan): list/tensor, map? Need to reconcile with TFT_RECORD, etc.
+ }
+}
+// LINT.ThenChange(../ir/types/attributes.td)
diff --git a/src/main/proto/tensorflow/core/framework/function.proto b/src/main/proto/tensorflow/core/framework/function.proto
new file mode 100644
index 0000000..6e59df7
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/function.proto
@@ -0,0 +1,136 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "tensorflow/core/framework/attr_value.proto";
+import "tensorflow/core/framework/node_def.proto";
+import "tensorflow/core/framework/op_def.proto";
+
+option cc_enable_arenas = true;
+option java_outer_classname = "FunctionProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/function_go_proto";
+
+// A library is a set of named functions.
+message FunctionDefLibrary {
+ repeated FunctionDef function = 1;
+ repeated GradientDef gradient = 2;
+ repeated RegisteredGradient registered_gradients = 3;
+}
+
+// A function can be instantiated when the runtime can bind every attr
+// with a value. When a GraphDef has a call to a function, it must
+// have binding for every attr defined in the signature.
+//
+// TODO(zhifengc):
+// * device spec, etc.
+message FunctionDef {
+ // The definition of the function's name, arguments, return values,
+ // attrs etc.
+ OpDef signature = 1;
+
+ // Attributes specific to this function definition.
+ map<string, AttrValue> attr = 5;
+
+ // Attributes for function arguments. These attributes are the same set of
+ // valid attributes as to _Arg nodes.
+ message ArgAttrs {
+ map<string, AttrValue> attr = 1;
+ }
+ map<uint32, ArgAttrs> arg_attr = 7;
+
+ // Unique IDs for each resource argument, used to track aliasing resources. If
+ // Argument A and Argument B alias each other, then
+ // resource_arg_unique_ids[A.index] == resource_arg_unique_ids[B.index].
+ //
+ // If this field is empty, none of the arguments could alias; otherwise, every
+ // resource argument should have an entry in this field.
+ //
+ // When instantiated, the unique IDs will be attached to the _Arg nodes'
+ // "_resource_arg_unique_id" attribute.
+ map<uint32, uint32> resource_arg_unique_id = 8;
+
+ // NOTE: field id 2 deleted on Jan 11, 2017, GraphDef version 21.
+ reserved 2;
+
+ // In both of the following fields, there is the need to specify an
+ // output that is used as either the input to another node (in
+ // `node_def`) or as a return value of the function (in `ret`).
+ // Unlike the NodeDefs in GraphDef, we need to be able to specify a
+ // list in some cases (instead of just single outputs). Also, we
+ // need to be able to deal with lists of unknown length (so the
+ // output index may not be known at function definition time). So
+ // we use the following format instead:
+ // * "fun_in" where "fun_in" is the name of a function input arg in
+ // the `signature` field above. This represents that input, whether
+ // it is a single tensor or a list.
+ // * "fun_in:0" gives the first element of a function input arg (a
+ // non-list input is considered a list of length 1 for these
+ // purposes).
+ // * "node:out" where "node" is the name of a node in `node_def` and
+ // "out" is the name one of its op's output arguments (the name
+ // comes from the OpDef of the node's op). This represents that
+ // node's output, whether it is a single tensor or a list.
+ // Note: We enforce that an op's output arguments are never
+ // renamed in the backwards-compatibility test.
+ // * "node:out:0" gives the first element of a node output arg (a
+ // non-list output is considered a list of length 1 for these
+ // purposes).
+ //
+ // NOT CURRENTLY SUPPORTED (but may be in the future):
+ // * "node:out:-1" gives last element in a node output list
+ // * "node:out:1:" gives a list with all but the first element in a
+ // node output list
+ // * "node:out::-1" gives a list with all but the last element in a
+ // node output list
+
+ // The body of the function. Unlike the NodeDefs in a GraphDef, attrs
+ // may have values of type `placeholder` and the `input` field uses
+ // the "output" format above.
+
+ // By convention, "op" in node_def is resolved by consulting with a
+ // user-defined library first. If not resolved, "func" is assumed to
+ // be a builtin op.
+ repeated NodeDef node_def = 3;
+
+ // A mapping from the output arg names from `signature` to the
+ // outputs from `node_def` that should be returned by the function.
+ map<string, string> ret = 4;
+
+ // A mapping from control output names from `signature` to node names in
+ // `node_def` which should be control outputs of this function.
+ map<string, string> control_ret = 6;
+}
+
+// GradientDef defines the gradient function of a function defined in
+// a function library.
+//
+// A gradient function g (specified by gradient_func) for a function f
+// (specified by function_name) must follow the following:
+//
+// The function 'f' must be a numerical function which takes N inputs
+// and produces M outputs. Its gradient function 'g', which is a
+// function taking N + M inputs and produces N outputs.
+//
+// I.e. if we have
+// (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
+// then, g is
+// (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
+// dL/dy1, dL/dy2, ..., dL/dy_M),
+// where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
+// loss function). dL/dx_i is the partial derivative of L with respect
+// to x_i.
+message GradientDef {
+ string function_name = 1; // The function name.
+ string gradient_func = 2; // The gradient function's name.
+}
+
+// RegisteredGradient stores a gradient function that is registered in the
+// gradients library and used in the ops of a function in the function library.
+// Unlike GradientDef, these gradients are identified by op type, and not
+// directly linked to any function.
+message RegisteredGradient {
+ string gradient_func = 1; // The gradient function's name.
+ string registered_op_type = 2; // The gradient function's registered op type.
+}
diff --git a/src/main/proto/tensorflow/core/framework/graph.proto b/src/main/proto/tensorflow/core/framework/graph.proto
new file mode 100644
index 0000000..8523f13
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/graph.proto
@@ -0,0 +1,60 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "tensorflow/core/framework/function.proto";
+import "tensorflow/core/framework/graph_debug_info.proto";
+import "tensorflow/core/framework/node_def.proto";
+import "tensorflow/core/framework/versions.proto";
+
+option cc_enable_arenas = true;
+option java_outer_classname = "GraphProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/graph_go_proto";
+
+// Represents the graph of operations
+message GraphDef {
+ repeated NodeDef node = 1;
+
+ // Compatibility versions of the graph. See core/public/version.h for version
+ // history. The GraphDef version is distinct from the TensorFlow version, and
+ // each release of TensorFlow will support a range of GraphDef versions.
+ VersionDef versions = 4;
+
+ // Deprecated single version field; use versions above instead. Since all
+ // GraphDef changes before "versions" was introduced were forward
+ // compatible, this field is entirely ignored.
+ int32 version = 3 [deprecated = true];
+
+ // "library" provides user-defined functions.
+ //
+ // Naming:
+ // * library.function.name are in a flat namespace.
+ // NOTE: We may need to change it to be hierarchical to support
+ // different orgs. E.g.,
+ // { "/google/nn", { ... }},
+ // { "/google/vision", { ... }}
+ // { "/org_foo/module_bar", { ... }}
+ // map<string, FunctionDefLib> named_lib;
+ // * If node[i].op is the name of one function in "library",
+ // node[i] is deemed as a function call. Otherwise, node[i].op
+ // must be a primitive operation supported by the runtime.
+ //
+ //
+ // Function call semantics:
+ //
+ // * The callee may start execution as soon as some of its inputs
+ // are ready. The caller may want to use Tuple() mechanism to
+ // ensure all inputs are ready in the same time.
+ //
+ // * The consumer of return values may start executing as soon as
+ // the return values the consumer depends on are ready. The
+ // consumer may want to use Tuple() mechanism to ensure the
+ // consumer does not start until all return values of the callee
+ // function are ready.
+ FunctionDefLibrary library = 2;
+
+ // Stack traces for the nodes in this graph.
+ GraphDebugInfo debug_info = 5;
+}
diff --git a/src/main/proto/tensorflow/core/framework/graph_debug_info.proto b/src/main/proto/tensorflow/core/framework/graph_debug_info.proto
new file mode 100644
index 0000000..9a49344
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/graph_debug_info.proto
@@ -0,0 +1,61 @@
+syntax = "proto2";
+
+package tensorflow;
+
+option cc_enable_arenas = true;
+option java_outer_classname = "GraphDebugInfoProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";
+
+message GraphDebugInfo {
+ // This represents a file/line location in the source code.
+ message FileLineCol {
+ // File name index, which can be used to retrieve the file name string from
+ // `files`. The value should be between 0 and (len(files)-1)
+ optional int32 file_index = 1;
+
+ // Line number in the file.
+ optional int32 line = 2;
+
+ // Col number in the file line.
+ optional int32 col = 3;
+
+ // Name of function contains the file line.
+ optional string func = 4;
+
+ // Source code contained in this file line.
+ optional string code = 5;
+ }
+
+ // This represents a stack trace which is a ordered list of `FileLineCol`.
+ message StackTrace {
+ repeated FileLineCol file_line_cols = 1; // Deprecated.
+ repeated fixed64 frame_id = 2 [packed = true];
+ }
+
+ // This stores all the source code file names and can be indexed by the
+ // `file_index`.
+ repeated string files = 1;
+
+ // Stack traces and frames are uniqueified during construction. These maps
+ // index from the unique id for a frame/trace to the value.
+ map<fixed64, FileLineCol> frames_by_id = 4;
+ map<fixed64, StackTrace> traces_by_id = 6;
+
+ map<string, StackTrace> traces = 2; // Deprecated.
+
+ // This maps a node name to a trace id contained in `traces_by_id`.
+ //
+ // The map key is a mangling of the containing function and op name with
+ // syntax:
+ // op.name '@' func_name
+ // For ops in the top-level graph, the func_name is the empty string and hence
+ // the `@` may be ommitted.
+ // Note that op names are restricted to a small number of characters which
+ // exclude '@', making it impossible to collide keys of this form. Function
+ // names accept a much wider set of characters.
+ // It would be preferable to avoid mangling and use a tuple key of (op.name,
+ // func_name), but this is not supported with protocol buffers.
+ map<string, fixed64> name_to_trace_id = 5;
+}
diff --git a/src/main/proto/tensorflow/core/framework/node_def.proto b/src/main/proto/tensorflow/core/framework/node_def.proto
new file mode 100644
index 0000000..705e90a
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/node_def.proto
@@ -0,0 +1,95 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "tensorflow/core/framework/attr_value.proto";
+import "tensorflow/core/framework/full_type.proto";
+
+option cc_enable_arenas = true;
+option java_outer_classname = "NodeProto";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/node_def_go_proto";
+
+message NodeDef {
+ // The name given to this operator. Used for naming inputs,
+ // logging, visualization, etc. Unique within a single GraphDef.
+ // Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_>./]*".
+ string name = 1;
+
+ // The operation name. There may be custom parameters in attrs.
+ // Op names starting with an underscore are reserved for internal use.
+ string op = 2;
+
+ // Each input is "node:src_output" with "node" being a string name and
+ // "src_output" indicating which output tensor to use from "node". If
+ // "src_output" is 0 the ":0" suffix can be omitted. Regular inputs
+ // may optionally be followed by control inputs that have the format
+ // "^node".
+ repeated string input = 3;
+
+ // A (possibly partial) specification for the device on which this
+ // node should be placed.
+ // The expected syntax for this string is as follows:
+ //
+ // DEVICE_SPEC ::= PARTIAL_SPEC
+ //
+ // PARTIAL_SPEC ::= ("/" CONSTRAINT) *
+ // CONSTRAINT ::= ("job:" JOB_NAME)
+ // | ("replica:" [1-9][0-9]*)
+ // | ("task:" [1-9][0-9]*)
+ // | ("device:" [A-Za-z]* ":" ([1-9][0-9]* | "*") )
+ //
+ // Valid values for this string include:
+ // * "/job:worker/replica:0/task:1/device:GPU:3" (full specification)
+ // * "/job:worker/device:GPU:3" (partial specification)
+ // * "" (no specification)
+ //
+ // If the constraints do not resolve to a single device (or if this
+ // field is empty or not present), the runtime will attempt to
+ // choose a device automatically.
+ string device = 4;
+
+ // Operation-specific graph-construction-time configuration.
+ // Note that this should include all attrs defined in the
+ // corresponding OpDef, including those with a value matching
+ // the default -- this allows the default to change and makes
+ // NodeDefs easier to interpret on their own. However, if
+ // an attr with a default is not specified in this list, the
+ // default will be used.
+ // The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
+ // one of the names from the corresponding OpDef's attr field).
+ // The values must have a type matching the corresponding OpDef
+ // attr's type field.
+ // TODO(josh11b): Add some examples here showing best practices.
+ map<string, AttrValue> attr = 5;
+
+ message ExperimentalDebugInfo {
+ // Opaque string inserted into error messages created by the runtime.
+ //
+ // This is intended to store the list of names of the nodes from the
+ // original graph that this node was derived. For example if this node, say
+ // C, was result of a fusion of 2 nodes A and B, then 'original_node' would
+ // be {A, B}. This information can be used to map errors originating at the
+ // current node to some top level source code.
+ repeated string original_node_names = 1;
+
+ // This is intended to store the list of names of the functions from the
+ // original graph that this node was derived. For example if this node, say
+ // C, was result of a fusion of node A in function FA and node B in function
+ // FB, then `original_funcs` would be {FA, FB}. If the node is in the top
+ // level graph, the `original_func` is empty. This information, with the
+ // `original_node_names` can be used to map errors originating at the
+ // current ndoe to some top level source code.
+ repeated string original_func_names = 2;
+ }
+
+ // This stores debug information associated with the node.
+ ExperimentalDebugInfo experimental_debug_info = 6;
+
+ // The complete type of this node. Experimental and subject to change.
+ // Currently, the field only contains the return types of the node. That will
+ // extend in the future to contain the entire signature of the node, as a
+ // function type.
+ FullTypeDef experimental_type = 7;
+}
diff --git a/src/main/proto/tensorflow/core/framework/op_def.proto b/src/main/proto/tensorflow/core/framework/op_def.proto
new file mode 100644
index 0000000..b71f5ce
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/op_def.proto
@@ -0,0 +1,193 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "tensorflow/core/framework/attr_value.proto";
+import "tensorflow/core/framework/full_type.proto";
+import "tensorflow/core/framework/resource_handle.proto";
+import "tensorflow/core/framework/types.proto";
+
+option cc_enable_arenas = true;
+option java_outer_classname = "OpDefProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/op_def_go_proto";
+
+// Defines an operation. A NodeDef in a GraphDef specifies an Op by
+// using the "op" field which should match the name of a OpDef.
+// LINT.IfChange
+message OpDef {
+ // Op names starting with an underscore are reserved for internal use.
+ // Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9>_]*".
+ string name = 1;
+
+ // For describing inputs and outputs.
+ message ArgDef {
+ // Name for the input/output. Should match the regexp "[a-z][a-z0-9_]*".
+ string name = 1;
+
+ // Human readable description.
+ string description = 2;
+
+ // Describes the type of one or more tensors that are accepted/produced
+ // by this input/output arg. The only legal combinations are:
+ // * For a single tensor: either the "type" field is set or the
+ // "type_attr" field is set to the name of an attr with type "type".
+ // * For a sequence of tensors with the same type: the "number_attr"
+ // field will be set to the name of an attr with type "int", and
+ // either the "type" or "type_attr" field will be set as for
+ // single tensors.
+ // * For a sequence of tensors, the "type_list_attr" field will be set
+ // to the name of an attr with type "list(type)".
+ DataType type = 3;
+ string type_attr = 4; // if specified, attr must have type "type"
+ string number_attr = 5; // if specified, attr must have type "int"
+ // If specified, attr must have type "list(type)", and none of
+ // type, type_attr, and number_attr may be specified.
+ string type_list_attr = 6;
+
+ // The handle data for resource inputs.
+ repeated ResourceHandleProto.DtypeAndShape handle_data = 7;
+
+ // For inputs: if true, the inputs are required to be refs.
+ // By default, inputs can be either refs or non-refs.
+ // For outputs: if true, outputs are refs, otherwise they are not.
+ bool is_ref = 16;
+
+ // Experimental. Full type declaration for this argument.
+ // The full type specification combines type, type_attr, type_list_attr,
+ // etc. into a unified representation.
+ // This declaration may contain non-concrete types (for example,
+ // Tensor<TypeVar<'T'>> is a valid type declaration.
+ //
+ // Note: this is a transient field. The long-term aim is to represent the
+ // entire OpDef as a single type: a callable. In that context, this field is
+ // just the type of a single argument.
+ FullTypeDef experimental_full_type = 17;
+ }
+
+ // Description of the input(s).
+ repeated ArgDef input_arg = 2;
+
+ // Description of the output(s).
+ repeated ArgDef output_arg = 3;
+
+ // Named control outputs for this operation. Useful only for composite
+ // operations (i.e. functions) which want to name different control outputs.
+ repeated string control_output = 20;
+
+ // Description of the graph-construction-time configuration of this
+ // Op. That is to say, this describes the attr fields that will
+ // be specified in the NodeDef.
+ message AttrDef {
+ // A descriptive name for the argument. May be used, e.g. by the
+ // Python client, as a keyword argument name, and so should match
+ // the regexp "[a-z][a-z0-9_]+".
+ string name = 1;
+
+ // One of the type names from attr_value.proto ("string", "list(string)",
+ // "int", etc.).
+ string type = 2;
+
+ // A reasonable default for this attribute if the user does not supply
+ // a value. If not specified, the user must supply a value.
+ AttrValue default_value = 3;
+
+ // Human-readable description.
+ string description = 4;
+
+ // TODO(josh11b): bool is_optional?
+
+ // --- Constraints ---
+ // These constraints are only in effect if specified. Default is no
+ // constraints.
+
+ // For type == "int", this is a minimum value. For "list(___)"
+ // types, this is the minimum length.
+ bool has_minimum = 5;
+ int64 minimum = 6;
+
+ // The set of allowed values. Has type that is the "list" version
+ // of the "type" field above (uses the "list" field of AttrValue).
+ // If type == "type" or "list(type)" above, then the "type" field
+ // of "allowed_values.list" has the set of allowed DataTypes.
+ // If type == "string" or "list(string)", then the "s" field of
+ // "allowed_values.list" has the set of allowed strings.
+ AttrValue allowed_values = 7;
+ }
+ repeated AttrDef attr = 4;
+
+ // Optional deprecation based on GraphDef versions.
+ OpDeprecation deprecation = 8;
+
+ // One-line human-readable description of what the Op does.
+ string summary = 5;
+
+ // Additional, longer human-readable description of what the Op does.
+ string description = 6;
+
+ // -------------------------------------------------------------------------
+ // Which optimizations this operation can participate in.
+
+ // True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
+ bool is_commutative = 18;
+
+ // If is_aggregate is true, then this operation accepts N >= 2
+ // inputs and produces 1 output all of the same type. Should be
+ // associative and commutative, and produce output with the same
+ // shape as the input. The optimizer may replace an aggregate op
+ // taking input from multiple devices with a tree of aggregate ops
+ // that aggregate locally within each device (and possibly within
+ // groups of nearby devices) before communicating.
+ // TODO(josh11b): Implement that optimization.
+ bool is_aggregate = 16; // for things like add
+
+ // Other optimizations go here, like
+ // can_alias_input, rewrite_when_output_unused, partitioning_strategy, etc.
+
+ // -------------------------------------------------------------------------
+ // Optimization constraints.
+
+ // Ops are marked as stateful if their behavior depends on some state beyond
+ // their input tensors (e.g. variable reading op) or if they have
+ // a side-effect (e.g. printing or asserting ops). Equivalently, stateless ops
+ // must always produce the same output for the same input and have
+ // no side-effects.
+ //
+ // By default Ops may be moved between devices. Stateful ops should
+ // either not be moved, or should only be moved if that state can also
+ // be moved (e.g. via some sort of save / restore).
+ // Stateful ops are guaranteed to never be optimized away by Common
+ // Subexpression Elimination (CSE).
+ bool is_stateful = 17; // for things like variables, queue
+
+ // -------------------------------------------------------------------------
+ // Non-standard options.
+
+ // By default, all inputs to an Op must be initialized Tensors. Ops
+ // that may initialize tensors for the first time should set this
+ // field to true, to allow the Op to take an uninitialized Tensor as
+ // input.
+ bool allows_uninitialized_input = 19; // for Assign, etc.
+
+ // Indicates whether the op implementation uses distributed communication.
+ // If True, the op is allowed to return errors for network disconnection and
+ // trigger TF network failure handling logics.
+ bool is_distributed_communication = 21;
+}
+// LINT.ThenChange(
+// https://www.tensorflow.org/code/tensorflow/core/framework/op_def_util.cc)
+
+// Information about version-dependent deprecation of an op
+message OpDeprecation {
+ // First GraphDef version at which the op is disallowed.
+ int32 version = 1;
+
+ // Explanation of why it was deprecated and what to use instead.
+ string explanation = 2;
+}
+
+// A collection of OpDefs
+message OpList {
+ repeated OpDef op = 1;
+}
diff --git a/src/main/proto/tensorflow/core/framework/resource_handle.proto b/src/main/proto/tensorflow/core/framework/resource_handle.proto
new file mode 100644
index 0000000..aa53f7f
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/resource_handle.proto
@@ -0,0 +1,47 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "tensorflow/core/framework/tensor_shape.proto";
+import "tensorflow/core/framework/types.proto";
+
+option cc_enable_arenas = true;
+option java_outer_classname = "ResourceHandle";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/resource_handle_go_proto";
+
+// Protocol buffer representing a handle to a tensorflow resource. Handles are
+// not valid across executions, but can be serialized back and forth from within
+// a single run.
+message ResourceHandleProto {
+ // Unique name for the device containing the resource.
+ string device = 1;
+
+ // Container in which this resource is placed.
+ string container = 2;
+
+ // Unique name of this resource.
+ string name = 3;
+
+ // Hash code for the type of the resource. Is only valid in the same device
+ // and in the same execution.
+ uint64 hash_code = 4;
+
+ // For debug-only, the name of the type pointed to by this handle, if
+ // available.
+ string maybe_type_name = 5;
+
+ // Protocol buffer representing a pair of (data type, tensor shape).
+ message DtypeAndShape {
+ // Data type of the tensor.
+ DataType dtype = 1;
+ // Shape of the tensor.
+ TensorShapeProto shape = 2;
+ }
+
+ // Data types and shapes for the underlying resource.
+ repeated DtypeAndShape dtypes_and_shapes = 6;
+
+ reserved 7;
+}
diff --git a/src/main/proto/tensorflow/core/framework/step_stats.proto b/src/main/proto/tensorflow/core/framework/step_stats.proto
new file mode 100644
index 0000000..762487f
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/step_stats.proto
@@ -0,0 +1,88 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "tensorflow/core/framework/allocation_description.proto";
+import "tensorflow/core/framework/tensor_description.proto";
+
+option cc_enable_arenas = true;
+option java_outer_classname = "StepStatsProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/step_stats_go_proto";
+
+// An allocation/de-allocation operation performed by the allocator.
+message AllocationRecord {
+ // The timestamp of the operation.
+ int64 alloc_micros = 1;
+ // Number of bytes allocated, or de-allocated if negative.
+ int64 alloc_bytes = 2;
+}
+
+message AllocatorMemoryUsed {
+ string allocator_name = 1;
+ // These are per-node allocator memory stats.
+ int64 total_bytes = 2;
+ int64 peak_bytes = 3;
+ // The bytes that are not deallocated.
+ int64 live_bytes = 4;
+ // The allocation and deallocation timeline.
+ repeated AllocationRecord allocation_records = 6;
+
+ // These are snapshots of the overall allocator memory stats.
+ // The number of live bytes currently allocated by the allocator.
+ int64 allocator_bytes_in_use = 5;
+}
+
+// Output sizes recorded for a single execution of a graph node.
+message NodeOutput {
+ int32 slot = 1;
+ TensorDescription tensor_description = 3;
+}
+
+// For memory tracking.
+message MemoryStats {
+ int64 temp_memory_size = 1;
+ int64 persistent_memory_size = 3;
+ repeated int64 persistent_tensor_alloc_ids = 5;
+
+ int64 device_temp_memory_size = 2 [deprecated = true];
+ int64 device_persistent_memory_size = 4 [deprecated = true];
+ repeated int64 device_persistent_tensor_alloc_ids = 6 [deprecated = true];
+}
+
+// Time/size stats recorded for a single execution of a graph node.
+message NodeExecStats {
+ // TODO(tucker): Use some more compact form of node identity than
+ // the full string name. Either all processes should agree on a
+ // global id (cost_id?) for each node, or we should use a hash of
+ // the name.
+ string node_name = 1;
+ int64 all_start_micros = 2;
+ int64 op_start_rel_micros = 3;
+ int64 op_end_rel_micros = 4;
+ int64 all_end_rel_micros = 5;
+ repeated AllocatorMemoryUsed memory = 6;
+ repeated NodeOutput output = 7;
+ string timeline_label = 8;
+ int64 scheduled_micros = 9;
+ uint32 thread_id = 10;
+ repeated AllocationDescription referenced_tensor = 11;
+ MemoryStats memory_stats = 12;
+ int64 all_start_nanos = 13;
+ int64 op_start_rel_nanos = 14;
+ int64 op_end_rel_nanos = 15;
+ int64 all_end_rel_nanos = 16;
+ int64 scheduled_nanos = 17;
+}
+
+message DeviceStepStats {
+ string device = 1;
+ repeated NodeExecStats node_stats = 2;
+ // Its key is thread id.
+ map<uint32, string> thread_names = 3;
+}
+
+message StepStats {
+ repeated DeviceStepStats dev_stats = 1;
+}
diff --git a/src/main/proto/tensorflow/core/framework/tensor.proto b/src/main/proto/tensorflow/core/framework/tensor.proto
new file mode 100644
index 0000000..9f5a29a
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/tensor.proto
@@ -0,0 +1,101 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "tensorflow/core/framework/resource_handle.proto";
+import "tensorflow/core/framework/tensor_shape.proto";
+import "tensorflow/core/framework/types.proto";
+
+option cc_enable_arenas = true;
+option java_outer_classname = "TensorProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_go_proto";
+
+// Protocol buffer representing a tensor.
+message TensorProto {
+ // Data type of the tensor.
+ DataType dtype = 1;
+
+ // Shape of the tensor. TODO(touts): sort out the 0-rank issues.
+ TensorShapeProto tensor_shape = 2;
+
+ // Only one of the representations below is set, one of "tensor_contents" and
+ // the "xxx_val" attributes. We are not using oneof because as oneofs cannot
+ // contain repeated fields it would require another extra set of messages.
+
+ // Version number.
+ //
+ // In version 0, if the "repeated xxx" representations contain only one
+ // element, that element is repeated to fill the shape. This makes it easy
+ // to represent a constant Tensor with a single value.
+ int32 version_number = 3;
+
+ // Serialized raw tensor content from either Tensor::AsProtoTensorContent or
+ // memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation
+ // can be used for all tensor types. The purpose of this representation is to
+ // reduce serialization overhead during RPC call by avoiding serialization of
+ // many repeated small items.
+ bytes tensor_content = 4;
+
+ // Type specific representations that make it easy to create tensor protos in
+ // all languages. Only the representation corresponding to "dtype" can
+ // be set. The values hold the flattened representation of the tensor in
+ // row major order.
+
+ // DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll
+ // have some pointless zero padding for each value here.
+ repeated int32 half_val = 13 [packed = true];
+
+ // DT_FLOAT.
+ repeated float float_val = 5 [packed = true];
+
+ // DT_DOUBLE.
+ repeated double double_val = 6 [packed = true];
+
+ // DT_INT32, DT_INT16, DT_UINT16, DT_INT8, DT_UINT8.
+ repeated int32 int_val = 7 [packed = true];
+
+ // DT_STRING
+ repeated bytes string_val = 8;
+
+ // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
+ // and imaginary parts of i-th single precision complex.
+ repeated float scomplex_val = 9 [packed = true];
+
+ // DT_INT64
+ repeated int64 int64_val = 10 [packed = true];
+
+ // DT_BOOL
+ repeated bool bool_val = 11 [packed = true];
+
+ // DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
+ // and imaginary parts of i-th double precision complex.
+ repeated double dcomplex_val = 12 [packed = true];
+
+ // DT_RESOURCE
+ repeated ResourceHandleProto resource_handle_val = 14;
+
+ // DT_VARIANT
+ repeated VariantTensorDataProto variant_val = 15;
+
+ // DT_UINT32
+ repeated uint32 uint32_val = 16 [packed = true];
+
+ // DT_UINT64
+ repeated uint64 uint64_val = 17 [packed = true];
+
+ // DT_FLOAT8_*, use variable-sized set of bytes
+ // (i.e. the equivalent of repeated uint8, if such a thing existed).
+ bytes float8_val = 18;
+}
+
+// Protocol buffer representing the serialization format of DT_VARIANT tensors.
+message VariantTensorDataProto {
+ // Name of the type of objects being serialized.
+ string type_name = 1;
+ // Portions of the object that are not Tensors.
+ bytes metadata = 2;
+ // Tensors contained within objects being serialized.
+ repeated TensorProto tensors = 3;
+}
diff --git a/src/main/proto/tensorflow/core/framework/tensor_description.proto b/src/main/proto/tensorflow/core/framework/tensor_description.proto
new file mode 100644
index 0000000..8462c63
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/tensor_description.proto
@@ -0,0 +1,24 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "tensorflow/core/framework/allocation_description.proto";
+import "tensorflow/core/framework/tensor_shape.proto";
+import "tensorflow/core/framework/types.proto";
+
+option cc_enable_arenas = true;
+option java_outer_classname = "TensorDescriptionProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_description_go_proto";
+
+message TensorDescription {
+ // Data type of tensor elements
+ DataType dtype = 1;
+
+ // Shape of the tensor.
+ TensorShapeProto shape = 2;
+
+ // Information about the size and allocator used for the data
+ AllocationDescription allocation_description = 4;
+}
diff --git a/src/main/proto/tensorflow/core/framework/tensor_shape.proto b/src/main/proto/tensorflow/core/framework/tensor_shape.proto
new file mode 100644
index 0000000..45d5b78
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/tensor_shape.proto
@@ -0,0 +1,46 @@
+// Protocol buffer representing the shape of tensors.
+
+syntax = "proto3";
+option cc_enable_arenas = true;
+option java_outer_classname = "TensorShapeProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto";
+
+package tensorflow;
+
+// Dimensions of a tensor.
+message TensorShapeProto {
+ // One dimension of the tensor.
+ message Dim {
+ // Size of the tensor in that dimension.
+ // This value must be >= -1, but values of -1 are reserved for "unknown"
+ // shapes (values of -1 mean "unknown" dimension). Certain wrappers
+ // that work with TensorShapeProto may fail at runtime when deserializing
+ // a TensorShapeProto containing a dim value of -1.
+ int64 size = 1;
+
+ // Optional name of the tensor dimension.
+ string name = 2;
+ };
+
+ // Dimensions of the tensor, such as {"input", 30}, {"output", 40}
+ // for a 30 x 40 2D tensor. If an entry has size -1, this
+ // corresponds to a dimension of unknown size. The names are
+ // optional.
+ //
+ // The order of entries in "dim" matters: It indicates the layout of the
+ // values in the tensor in-memory representation.
+ //
+ // The first entry in "dim" is the outermost dimension used to layout the
+ // values, the last entry is the innermost dimension. This matches the
+ // in-memory layout of RowMajor Eigen tensors.
+ //
+ // If "dim.size()" > 0, "unknown_rank" must be false.
+ repeated Dim dim = 2;
+
+ // If true, the number of dimensions in the shape is unknown.
+ //
+ // If true, "dim.size()" must be 0.
+ bool unknown_rank = 3;
+};
diff --git a/src/main/proto/tensorflow/core/framework/types.proto b/src/main/proto/tensorflow/core/framework/types.proto
new file mode 100644
index 0000000..d0a9738
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/types.proto
@@ -0,0 +1,100 @@
+syntax = "proto3";
+
+package tensorflow;
+
+option cc_enable_arenas = true;
+option java_outer_classname = "TypesProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto";
+
+// (== suppress_warning documentation-presence ==)
+// LINT.IfChange
+enum DataType {
+ // Not a legal value for DataType. Used to indicate a DataType field
+ // has not been set.
+ DT_INVALID = 0;
+
+ // Data types that all computation devices are expected to be
+ // capable to support.
+ DT_FLOAT = 1;
+ DT_DOUBLE = 2;
+ DT_INT32 = 3;
+ DT_UINT8 = 4;
+ DT_INT16 = 5;
+ DT_INT8 = 6;
+ DT_STRING = 7;
+ DT_COMPLEX64 = 8; // Single-precision complex
+ DT_INT64 = 9;
+ DT_BOOL = 10;
+ DT_QINT8 = 11; // Quantized int8
+ DT_QUINT8 = 12; // Quantized uint8
+ DT_QINT32 = 13; // Quantized int32
+ DT_BFLOAT16 = 14; // Float32 truncated to 16 bits.
+ DT_QINT16 = 15; // Quantized int16
+ DT_QUINT16 = 16; // Quantized uint16
+ DT_UINT16 = 17;
+ DT_COMPLEX128 = 18; // Double-precision complex
+ DT_HALF = 19;
+ DT_RESOURCE = 20;
+ DT_VARIANT = 21; // Arbitrary C++ data types
+ DT_UINT32 = 22;
+ DT_UINT64 = 23;
+ DT_FLOAT8_E5M2 = 24; // 5 exponent bits, 2 mantissa bits.
+ DT_FLOAT8_E4M3FN = 25; // 4 exponent bits, 3 mantissa bits, finite-only, with
+ // 2 NaNs (0bS1111111).
+ // TODO - b/299182407: Leaving room for remaining float8 types.
+ // DT_FLOAT8_E4M3FNUZ = 26;
+ // DT_FLOAT8_E4M3B11FNUZ = 27;
+ // DT_FLOAT8_E5M2FNUZ = 28;
+ DT_INT4 = 29;
+ DT_UINT4 = 30;
+
+ // Do not use! These are only for TF1's obsolete reference Variables.
+ // Every enum above should have a corresponding value below (verified by
+ // types_test).
+ DT_FLOAT_REF = 101;
+ DT_DOUBLE_REF = 102;
+ DT_INT32_REF = 103;
+ DT_UINT8_REF = 104;
+ DT_INT16_REF = 105;
+ DT_INT8_REF = 106;
+ DT_STRING_REF = 107;
+ DT_COMPLEX64_REF = 108;
+ DT_INT64_REF = 109;
+ DT_BOOL_REF = 110;
+ DT_QINT8_REF = 111;
+ DT_QUINT8_REF = 112;
+ DT_QINT32_REF = 113;
+ DT_BFLOAT16_REF = 114;
+ DT_QINT16_REF = 115;
+ DT_QUINT16_REF = 116;
+ DT_UINT16_REF = 117;
+ DT_COMPLEX128_REF = 118;
+ DT_HALF_REF = 119;
+ DT_RESOURCE_REF = 120;
+ DT_VARIANT_REF = 121;
+ DT_UINT32_REF = 122;
+ DT_UINT64_REF = 123;
+ DT_FLOAT8_E5M2_REF = 124;
+ DT_FLOAT8_E4M3FN_REF = 125;
+ // TODO - b/299182407: Leaving room for remaining float8 types.
+ // DT_FLOAT8_E4M3FNUZ_REF = 126;
+ // DT_FLOAT8_E4M3B11FNUZ_REF = 127;
+ // DT_FLOAT8_E5M2FNUZ_REF = 128;
+ DT_INT4_REF = 129;
+ DT_UINT4_REF = 130;
+}
+// LINT.ThenChange(
+// https://www.tensorflow.org/code/tensorflow/c/tf_datatype.h,
+// https://www.tensorflow.org/code/tensorflow/go/tensor.go,
+// https://www.tensorflow.org/code/tensorflow/core/framework/tensor.cc,
+// https://www.tensorflow.org/code/tensorflow/core/framework/types.h,
+// https://www.tensorflow.org/code/tensorflow/core/framework/types.cc,
+// https://www.tensorflow.org/code/tensorflow/python/framework/dtypes.py,
+// https://www.tensorflow.org/code/tensorflow/python/framework/function.py)
+
+// Represents a serialized tf.dtypes.Dtype
+message SerializedDType {
+ DataType datatype = 1;
+}
diff --git a/src/main/proto/tensorflow/core/framework/variable.proto b/src/main/proto/tensorflow/core/framework/variable.proto
new file mode 100644
index 0000000..09d7fb3
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/variable.proto
@@ -0,0 +1,84 @@
+syntax = "proto3";
+
+package tensorflow;
+
+option cc_enable_arenas = true;
+option java_outer_classname = "VariableProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/variable_go_proto";
+
+// Indicates when a distributed variable will be synced.
+enum VariableSynchronization {
+ // `AUTO`: Indicates that the synchronization will be determined by the
+ // current `DistributionStrategy` (eg. With `MirroredStrategy` this would be
+ // `ON_WRITE`).
+ VARIABLE_SYNCHRONIZATION_AUTO = 0;
+ // `NONE`: Indicates that there will only be one copy of the variable, so
+ // there is no need to sync.
+ VARIABLE_SYNCHRONIZATION_NONE = 1;
+ // `ON_WRITE`: Indicates that the variable will be updated across devices
+ // every time it is written.
+ VARIABLE_SYNCHRONIZATION_ON_WRITE = 2;
+ // `ON_READ`: Indicates that the variable will be aggregated across devices
+ // when it is read (eg. when checkpointing or when evaluating an op that uses
+ // the variable).
+ VARIABLE_SYNCHRONIZATION_ON_READ = 3;
+}
+
+// Indicates how a distributed variable will be aggregated.
+enum VariableAggregation {
+ // `NONE`: This is the default, giving an error if you use a
+ // variable-update operation with multiple replicas.
+ VARIABLE_AGGREGATION_NONE = 0;
+ // `SUM`: Add the updates across replicas.
+ VARIABLE_AGGREGATION_SUM = 1;
+ // `MEAN`: Take the arithmetic mean ("average") of the updates across
+ // replicas.
+ VARIABLE_AGGREGATION_MEAN = 2;
+ // `ONLY_FIRST_REPLICA`: This is for when every replica is performing the same
+ // update, but we only want to perform the update once. Used, e.g., for the
+ // global step counter.
+ VARIABLE_AGGREGATION_ONLY_FIRST_REPLICA = 3;
+}
+
+// Protocol buffer representing a Variable.
+message VariableDef {
+ // Name of the variable tensor.
+ string variable_name = 1;
+
+ // Name of the tensor holding the variable's initial value.
+ string initial_value_name = 6;
+
+ // Name of the initializer op.
+ string initializer_name = 2;
+
+ // Name of the snapshot tensor.
+ string snapshot_name = 3;
+
+ // Support for saving variables as slices of a larger variable.
+ SaveSliceInfoDef save_slice_info_def = 4;
+
+ // Whether to represent this as a ResourceVariable.
+ bool is_resource = 5;
+
+ // Whether this variable should be trained.
+ bool trainable = 7;
+
+ // Indicates when a distributed variable will be synced.
+ VariableSynchronization synchronization = 8;
+
+ // Indicates how a distributed variable will be aggregated.
+ VariableAggregation aggregation = 9;
+}
+
+message SaveSliceInfoDef {
+ // Name of the full variable of which this is a slice.
+ string full_name = 1;
+ // Shape of the full variable.
+ repeated int64 full_shape = 2;
+ // Offset of this variable into the full variable.
+ repeated int64 var_offset = 3;
+ // Shape of this variable.
+ repeated int64 var_shape = 4;
+}
diff --git a/src/main/proto/tensorflow/core/framework/versions.proto b/src/main/proto/tensorflow/core/framework/versions.proto
new file mode 100644
index 0000000..2cca6e3
--- /dev/null
+++ b/src/main/proto/tensorflow/core/framework/versions.proto
@@ -0,0 +1,33 @@
+syntax = "proto3";
+
+package tensorflow;
+
+option cc_enable_arenas = true;
+option java_outer_classname = "VersionsProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/versions_go_proto";
+
+// Version information for a piece of serialized data
+//
+// There are different types of versions for each type of data
+// (GraphDef, etc.), but they all have the same common shape
+// described here.
+//
+// Each consumer has "consumer" and "min_producer" versions (specified
+// elsewhere). A consumer is allowed to consume this data if
+//
+// producer >= min_producer
+// consumer >= min_consumer
+// consumer not in bad_consumers
+//
+message VersionDef {
+ // The version of the code that produced this data.
+ int32 producer = 1;
+
+ // Any consumer below this version is not allowed to consume this data.
+ int32 min_consumer = 2;
+
+ // Specific consumer versions which are disallowed (e.g. due to bugs).
+ repeated int32 bad_consumers = 3;
+}
diff --git a/src/main/proto/tensorflow/core/protobuf/cluster.proto b/src/main/proto/tensorflow/core/protobuf/cluster.proto
new file mode 100644
index 0000000..4065eb4
--- /dev/null
+++ b/src/main/proto/tensorflow/core/protobuf/cluster.proto
@@ -0,0 +1,87 @@
+/* Copyright 2016 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;
+
+option cc_enable_arenas = true;
+option java_outer_classname = "ClusterProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.distruntime";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";
+
+// This file contains protos to be used when defining a TensorFlow
+// cluster.
+//
+// EXAMPLES
+// --------
+//
+// 1. A single-process cluster, containing "/job:local/task:0".
+//
+// Cluster:
+// job { name: 'local' tasks { key: 0 value: 'localhost:2222' } }
+//
+// Server:
+// cluster { $CLUSTER } job_name: 'local' task_index: 0
+//
+// 2. A two-process cluster, containing "/job:local/task:{0,1}".
+//
+// Cluster:
+// job { name: 'local' tasks { key: 0 value: 'localhost:2222' }
+// tasks { key: 1 value: 'localhost:2223' } }
+//
+// Servers:
+// cluster { $CLUSTER } job_name: 'local' task_index: 0
+// cluster { $CLUSTER } job_name: 'local' task_index: 1
+//
+// 3. A two-job cluster, containing "/job:worker/task:{0,1,2}" and
+// "/job:ps/task:{0,1}".
+//
+// Cluster:
+// job { name: 'worker' tasks { key: 0 value: 'worker1:2222' }
+// tasks { key: 1 value: 'worker2:2222' }
+// tasks { key: 2 value: 'worker3:2222' } }
+// job { name: 'ps' tasks { key: 0 value: 'ps0:2222' }
+// tasks { key: 1 value: 'ps1:2222' } }
+//
+// Servers:
+// cluster { $CLUSTER } job_name: 'worker' task_index: 0
+// cluster { $CLUSTER } job_name: 'worker' task_index: 1
+// cluster { $CLUSTER } job_name: 'worker' task_index: 2
+// cluster { $CLUSTER } job_name: 'ps' task_index: 0
+// cluster { $CLUSTER } job_name: 'ps' task_index: 1
+
+// Defines a single job in a TensorFlow cluster.
+message JobDef {
+ // The name of this job.
+ string name = 1;
+
+ // Mapping from task ID to "hostname:port" string.
+ //
+ // If the `name` field contains "worker", and the `tasks` map contains a
+ // mapping from 7 to "example.org:2222", then the device prefix
+ // "/job:worker/task:7" will be assigned to "example.org:2222".
+ //
+ // If a job has multiple replicas, host-ports will be comma-delimited, with
+ // one entry for each replica.
+ map<int32, string> tasks = 2;
+}
+
+// Defines a TensorFlow cluster as a set of jobs.
+message ClusterDef {
+ // The jobs that comprise the cluster.
+ repeated JobDef job = 1;
+}
diff --git a/src/main/proto/tensorflow/core/protobuf/config.proto b/src/main/proto/tensorflow/core/protobuf/config.proto
new file mode 100644
index 0000000..8e6b814
--- /dev/null
+++ b/src/main/proto/tensorflow/core/protobuf/config.proto
@@ -0,0 +1,1015 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "xla/tsl/protobuf/coordination_config.proto";
+import "tensorflow/core/framework/cost_graph.proto";
+import "tensorflow/core/framework/graph.proto";
+import "tensorflow/core/framework/step_stats.proto";
+import "tensorflow/core/protobuf/cluster.proto";
+import "tensorflow/core/protobuf/debug.proto";
+import "tensorflow/core/protobuf/rewriter_config.proto";
+import "tensorflow/core/protobuf/rpc_options.proto";
+
+option cc_enable_arenas = true;
+option java_outer_classname = "ConfigProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";
+
+message GPUOptions {
+ // Fraction of the total GPU memory to allocate for each process.
+ // 1 means to allocate all of the GPU memory, 0.5 means the process
+ // allocates up to ~50% of the total GPU memory.
+ //
+ // GPU memory is pre-allocated unless the allow_growth option is enabled.
+ //
+ // If greater than 1.0, uses CUDA unified memory to potentially oversubscribe
+ // the amount of memory available on the GPU device by using host memory as a
+ // swap space. Accessing memory not available on the device will be
+ // significantly slower as that would require memory transfer between the host
+ // and the device. Options to reduce the memory requirement should be
+ // considered before enabling this option as this may come with a negative
+ // performance impact. Oversubscription using the unified memory requires
+ // Pascal class or newer GPUs and it is currently only supported on the Linux
+ // operating system. See
+ // https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#um-requirements
+ // for the detailed requirements.
+ double per_process_gpu_memory_fraction = 1;
+
+ // If true, the allocator does not pre-allocate the entire specified
+ // GPU memory region, instead starting small and growing as needed.
+ bool allow_growth = 4;
+
+ // The type of GPU allocation strategy to use.
+ //
+ // Allowed values:
+ // "": The empty string (default) uses a system-chosen default
+ // which may change over time.
+ //
+ // "BFC": A "Best-fit with coalescing" algorithm, simplified from a
+ // version of dlmalloc.
+ string allocator_type = 2;
+
+ // Delay deletion of up to this many bytes to reduce the number of
+ // interactions with gpu driver code. If 0, the system chooses
+ // a reasonable default (several MBs).
+ int64 deferred_deletion_bytes = 3;
+
+ // A comma-separated list of GPU ids that determines the 'visible'
+ // to 'virtual' mapping of GPU devices. For example, if TensorFlow
+ // can see 8 GPU devices in the process, and one wanted to map
+ // visible GPU devices 5 and 3 as "/device:GPU:0", and "/device:GPU:1",
+ // then one would specify this field as "5,3". This field is similar in
+ // spirit to the CUDA_VISIBLE_DEVICES environment variable, except
+ // it applies to the visible GPU devices in the process.
+ //
+ // NOTE:
+ // 1. The GPU driver provides the process with the visible GPUs
+ // in an order which is not guaranteed to have any correlation to
+ // the *physical* GPU id in the machine. This field is used for
+ // remapping "visible" to "virtual", which means this operates only
+ // after the process starts. Users are required to use vendor
+ // specific mechanisms (e.g., CUDA_VISIBLE_DEVICES) to control the
+ // physical to visible device mapping prior to invoking TensorFlow.
+ // 2. In the code, the ids in this list are also called "platform GPU id"s,
+ // and the 'virtual' ids of GPU devices (i.e. the ids in the device
+ // name "/device:GPU:<id>") are also called "TF GPU id"s. Please
+ // refer to third_party/tensorflow/core/common_runtime/gpu/gpu_id.h
+ // for more information.
+ // 3. The visible_device_list is also used for PluggableDevice. And
+ // different types of PluggableDevices share this field. In that case,
+ // the pluggable_device_type is used to distinguish them, making the
+ // visible_device_list a list of <pluggable_device_type>:<device_index>,
+ // e.g. "PluggableDeviceA:0,PluggableDeviceA:1,PluggableDeviceB:0".
+ string visible_device_list = 5;
+
+ // In the event polling loop sleep this many microseconds between
+ // PollEvents calls, when the queue is not empty. If value is not
+ // set or set to 0, gets set to a non-zero default.
+ int32 polling_active_delay_usecs = 6;
+
+ // This field is deprecated and ignored.
+ int32 polling_inactive_delay_msecs = 7;
+
+ // Force all tensors to be gpu_compatible. On a GPU-enabled TensorFlow,
+ // enabling this option forces all CPU tensors to be allocated with Cuda
+ // pinned memory. Normally, TensorFlow will infer which tensors should be
+ // allocated as the pinned memory. But in case where the inference is
+ // incomplete, this option can significantly speed up the cross-device memory
+ // copy performance as long as it fits the memory.
+ // Note that this option is not something that should be
+ // enabled by default for unknown or very large models, since all Cuda pinned
+ // memory is unpageable, having too much pinned memory might negatively impact
+ // the overall host system performance.
+ bool force_gpu_compatible = 8;
+
+ message Experimental {
+ // Configuration for breaking down a visible GPU into multiple "virtual"
+ // devices.
+ message VirtualDevices {
+ // Per "virtual" device memory limit, in MB. The number of elements in
+ // the list is the number of virtual devices to create on the
+ // corresponding visible GPU (see "virtual_devices" below).
+ // If empty and `num_virtual_devices_per_gpu` is not set, it will create
+ // single virtual device taking all available memory from the device.
+ //
+ // For the concept of "visible" and "virtual" GPU, see the comments for
+ // "visible_device_list" above for more information.
+ repeated float memory_limit_mb = 1;
+
+ // Priority values to use with the virtual devices. Use the cuda function
+ // cudaDeviceGetStreamPriorityRange to query for valid range of values for
+ // priority.
+ //
+ // On a P4000 GPU with cuda 10.1, the priority range reported was 0 for
+ // least priority and -1 for greatest priority.
+ //
+ // If this field is not specified, then the virtual devices will be
+ // created with the default. If this field has values set, then the size
+ // of this must match with the above memory_limit_mb.
+ repeated int32 priority = 2;
+
+ // Virtual Device ordinal number determines the device ID of the device.
+ // A Virtual device with a lower ordinal number always receives the a
+ // smaller device id. The phyiscal device id and location in the
+ // virtual device list is used to break ties.
+ repeated int32 device_ordinal = 3;
+ }
+
+ // The multi virtual device settings. If empty (not set), it will create
+ // single virtual device on each visible GPU, according to the settings
+ // in "visible_device_list" above. Otherwise, the number of elements in the
+ // list must be the same as the number of visible GPUs (after
+ // "visible_device_list" filtering if it is set), and the string represented
+ // device names (e.g. /device:GPU:<id>) will refer to the virtual
+ // devices and have the <id> field assigned sequentially starting from 0,
+ // according to the order of the virtual devices determined by
+ // device_ordinal and the location in the virtual device list.
+ //
+ // For example,
+ // visible_device_list = "1,0"
+ // virtual_devices { memory_limit: 1GB memory_limit: 2GB }
+ // virtual_devices { memory_limit: 3GB memory_limit: 4GB }
+ // will create 4 virtual devices as:
+ // /device:GPU:0 -> visible GPU 1 with 1GB memory
+ // /device:GPU:1 -> visible GPU 1 with 2GB memory
+ // /device:GPU:2 -> visible GPU 0 with 3GB memory
+ // /device:GPU:3 -> visible GPU 0 with 4GB memory
+ //
+ // but
+ // visible_device_list = "1,0"
+ // virtual_devices { memory_limit: 1GB memory_limit: 2GB
+ // device_ordinal: 10 device_ordinal: 20}
+ // virtual_devices { memory_limit: 3GB memory_limit: 4GB
+ // device_ordinal: 10 device_ordinal: 20}
+ // will create 4 virtual devices as:
+ // /device:GPU:0 -> visible GPU 1 with 1GB memory (ordinal 10)
+ // /device:GPU:1 -> visible GPU 0 with 3GB memory (ordinal 10)
+ // /device:GPU:2 -> visible GPU 1 with 2GB memory (ordinal 20)
+ // /device:GPU:3 -> visible GPU 0 with 4GB memory (ordinal 20)
+ //
+ // NOTE:
+ // 1. It's invalid to set both this and "per_process_gpu_memory_fraction"
+ // at the same time.
+ // 2. Currently this setting is per-process, not per-session. Using
+ // different settings in different sessions within same process will
+ // result in undefined behavior.
+ repeated VirtualDevices virtual_devices = 1;
+
+ // The number of virtual devices to create on each visible GPU. The
+ // available memory will be split equally among all virtual devices. If the
+ // field `memory_limit_mb` in `VirtualDevices` is not empty, this field will
+ // be ignored.
+ int32 num_virtual_devices_per_gpu = 15;
+
+ // If true, uses CUDA unified memory for memory allocations. If
+ // per_process_gpu_memory_fraction option is greater than 1.0, then unified
+ // memory is used regardless of the value for this field. See comments for
+ // per_process_gpu_memory_fraction field for more details and requirements
+ // of the unified memory. This option is useful to oversubscribe memory if
+ // multiple processes are sharing a single GPU while individually using less
+ // than 1.0 per process memory fraction.
+ bool use_unified_memory = 2;
+
+ // If > 1, the number of device-to-device copy streams to create
+ // for each GPUDevice. Default value is 0, which is automatically
+ // converted to 1.
+ int32 num_dev_to_dev_copy_streams = 3;
+
+ // If non-empty, defines a good GPU ring order on a single worker based on
+ // device interconnect. This assumes that all workers have the same GPU
+ // topology. Specify as a comma-separated string, e.g. "3,2,1,0,7,6,5,4".
+ // This ring order is used by the RingReducer implementation of
+ // CollectiveReduce, and serves as an override to automatic ring order
+ // generation in OrderTaskDeviceMap() during CollectiveParam resolution.
+ string collective_ring_order = 4;
+
+ // If true then extra work is done by GPUDevice and GPUBFCAllocator to
+ // keep track of when GPU memory is freed and when kernels actually
+ // complete so that we can know when a nominally free memory chunk
+ // is really not subject to pending use.
+ bool timestamped_allocator = 5;
+
+ // reserved id: 6
+
+ // Parameters for GPUKernelTracker. By default no kernel tracking is done.
+ // Note that timestamped_allocator is only effective if some tracking is
+ // specified.
+ //
+ // If kernel_tracker_max_interval = n > 0, then a tracking event
+ // is inserted after every n kernels without an event.
+ int32 kernel_tracker_max_interval = 7;
+ // If kernel_tracker_max_bytes = n > 0, then a tracking event is
+ // inserted after every series of kernels allocating a sum of
+ // memory >= n. If one kernel allocates b * n bytes, then one
+ // event will be inserted after it, but it will count as b against
+ // the pending limit.
+ int32 kernel_tracker_max_bytes = 8;
+ // If kernel_tracker_max_pending > 0 then no more than this many
+ // tracking events can be outstanding at a time. An attempt to
+ // launch an additional kernel will stall until an event
+ // completes.
+ int32 kernel_tracker_max_pending = 9;
+
+ // BFC Allocator can return an allocated chunk of memory upto 2x the
+ // requested size. For virtual devices with tight memory constraints, and
+ // proportionately large allocation requests, this can lead to a significant
+ // reduction in available memory. The threshold below controls when a chunk
+ // should be split if the chunk size exceeds requested memory size. It is
+ // expressed as a fraction of total available memory for the tf device. For
+ // example setting it to 0.05 would imply a chunk needs to be split if its
+ // size exceeds the requested memory by 5% of the total virtual device/gpu
+ // memory size.
+ double internal_fragmentation_fraction = 10;
+
+ // When true, use CUDA cudaMallocAsync API instead of TF gpu allocator.
+ bool use_cuda_malloc_async = 11;
+
+ // By default, BFCAllocator may sleep when it runs out of memory, in the
+ // hopes that another thread will free up memory in the meantime. Setting
+ // this to true disables the sleep; instead we'll OOM immediately.
+ bool disallow_retry_on_allocation_failure = 12;
+
+ // Memory limit for "GPU host allocator", aka pinned memory allocator. This
+ // can also be set via the envvar TF_GPU_HOST_MEM_LIMIT_IN_MB.
+ float gpu_host_mem_limit_in_mb = 13;
+
+ // If true, then the host allocator allocates its max memory all upfront and
+ // never grows. This can be useful for latency-sensitive systems, because
+ // growing the GPU host memory pool can be expensive.
+ //
+ // You probably only want to use this in combination with
+ // gpu_host_mem_limit_in_mb, because the default GPU host memory limit is
+ // quite high.
+ bool gpu_host_mem_disallow_growth = 14;
+
+ // Memory limit for gpu system. This can also be set by
+ // TF_DEVICE_MIN_SYS_MEMORY_IN_MB, which takes precedence over
+ // gpu_system_memory_size_in_mb. With this, user can configure the gpu
+ // system memory size for better resource estimation of multi-tenancy(one
+ // gpu with multiple model) use case.
+ int32 gpu_system_memory_size_in_mb = 16;
+
+ // If true, save information needed for created a PjRt GPU client for
+ // creating a client with remote devices.
+ bool populate_pjrt_gpu_client_creation_info = 17;
+
+ // node_id for use when creating a PjRt GPU client with remote devices,
+ // which enumerates jobs*tasks from a ServerDef.
+ int32 node_id = 18;
+
+ // Whether to merge data transfer streams into the compute stream in the
+ // same stream group. Stream merging helps reduce the overhead caused by
+ // stream synchronization, especially when data transfers are frequent. For
+ // example, setting "merge_host_to_device_stream = true" will make the
+ // compute stream responsible for both computation and host to device memory
+ // copy.
+ message StreamMergeOptions {
+ // If true, the compute stream will be used for host_to_device copy as
+ // well. It's no longer necessary to record an event before the copy to
+ // let the copy stream wait for the compute stream to finish. There is
+ // also no need to wait for the copy to complete before executing the
+ // callback function.
+ bool merge_host_to_device_stream = 1;
+
+ // If true, the compute stream will be used for device_to_host copy as
+ // well. It's no longer necessary to record an event before the copy to
+ // let the copy stream wait for the compute stream to finish.
+ bool merge_device_to_host_stream = 2;
+
+ // If true, the compute stream will be used for device_to_device copy as
+ // well. It's no longer necessary to record an event before the copy to
+ // let the copy stream wait for the compute stream of the sending device
+ // to finish. There is also no need to wait for the compute stream of the
+ // receiving device to finish if the copy is within the same device.
+ bool merge_device_to_device_stream = 3;
+ }
+
+ StreamMergeOptions stream_merge_options = 19;
+ }
+
+ // Everything inside experimental is subject to change and is not subject
+ // to API stability guarantees in
+ // https://www.tensorflow.org/guide/version_compat.
+ Experimental experimental = 9;
+}
+
+// Options passed to the graph optimizer
+message OptimizerOptions {
+ // If true, optimize the graph using common subexpression elimination.
+ // Note: the optimization Level L1 will override this setting to true. So in
+ // order to disable common subexpression elimination the opt_level has to be
+ // set to L0.
+ bool do_common_subexpression_elimination = 1;
+
+ // If true, perform constant folding optimization on the graph.
+ // Note: the optimization Level L1 will override this setting to true. So in
+ // order to disable constant folding the opt_level has to be set to L0.
+ bool do_constant_folding = 2;
+
+ // Constant folding optimization replaces tensors whose values can be
+ // predetermined, with constant nodes. To avoid inserting too large constants,
+ // the size of each constant created can be limited. If this value is zero, a
+ // default limit of 10 MiB will be applied. If constant folding optimization
+ // is disabled, this value is ignored.
+ int64 max_folded_constant_in_bytes = 6;
+
+ // If true, perform function inlining on the graph.
+ bool do_function_inlining = 4;
+
+ // Optimization level
+ enum Level {
+ // L1 is the default level.
+ // Optimization performed at L1 :
+ // 1. Common subexpression elimination
+ // 2. Constant folding
+ L1 = 0;
+
+ // No optimizations
+ L0 = -1;
+ }
+
+ // Overall optimization level. The actual optimizations applied will be the
+ // logical OR of the flags that this level implies and any flags already set.
+ Level opt_level = 3;
+
+ // Control the use of the compiler/jit. Experimental.
+ enum GlobalJitLevel {
+ DEFAULT = 0; // Default setting ("off" now, but later expected to be "on")
+ OFF = -1;
+ // The following settings turn on compilation, with higher values being
+ // more aggressive. Higher values may reduce opportunities for parallelism
+ // and may use more memory. (At present, there is no distinction, but this
+ // is expected to change.)
+ ON_1 = 1;
+ ON_2 = 2;
+ }
+ GlobalJitLevel global_jit_level = 5;
+
+ // CPU code will be autoclustered only if global_jit_level >= ON_1 and either:
+ // - this flag is true, or
+ // - TF_XLA_FLAGS contains --tf_xla_cpu_global_jit=true.
+ bool cpu_global_jit = 7;
+}
+
+message GraphOptions {
+ // Removed, use optimizer_options below.
+ reserved "skip_common_subexpression_elimination";
+ reserved 1;
+
+ // If true, use control flow to schedule the activation of Recv nodes.
+ // (Currently ignored.)
+ bool enable_recv_scheduling = 2;
+
+ // Options controlling how graph is optimized.
+ OptimizerOptions optimizer_options = 3;
+
+ // The number of steps to run before returning a cost model detailing
+ // the memory usage and performance of each node of the graph. 0 means
+ // no cost model.
+ int64 build_cost_model = 4;
+
+ // The number of steps to skip before collecting statistics for the
+ // cost model.
+ int64 build_cost_model_after = 9;
+
+ // Annotate each Node with Op output shape data, to the extent it can
+ // be statically inferred.
+ bool infer_shapes = 5;
+
+ // Only place the subgraphs that are run, rather than the entire graph.
+ //
+ // This is useful for interactive graph building, where one might
+ // produce graphs that cannot be placed during the debugging
+ // process. In particular, it allows the client to continue work in
+ // a session after adding a node to a graph whose placement
+ // constraints are unsatisfiable.
+ bool place_pruned_graph = 6;
+
+ // If true, transfer float values between processes as bfloat16.
+ bool enable_bfloat16_sendrecv = 7;
+
+ // If > 0, record a timeline every this many steps.
+ // EXPERIMENTAL: This currently has no effect in MasterSession.
+ int32 timeline_step = 8;
+
+ // Options that control the type and amount of graph rewriting.
+ // Not currently configurable via the public Python API (i.e. there is no API
+ // stability guarantee if you import RewriterConfig explicitly).
+ RewriterConfig rewrite_options = 10;
+}
+
+message ThreadPoolOptionProto {
+ // The number of threads in the pool.
+ //
+ // 0 means the system picks a value based on where this option proto is used
+ // (see the declaration of the specific field for more info).
+ int32 num_threads = 1;
+
+ // The global name of the threadpool.
+ //
+ // If empty, then the threadpool is made and used according to the scope it's
+ // in - e.g., for a session threadpool, it is used by that session only.
+ //
+ // If non-empty, then:
+ // - a global threadpool associated with this name is looked
+ // up or created. This allows, for example, sharing one threadpool across
+ // many sessions (e.g., like the default behavior, if
+ // inter_op_parallelism_threads is not configured), but still partitioning
+ // into a large and small pool.
+ // - if the threadpool for this global_name already exists, then it is an
+ // error if the existing pool was created using a different num_threads
+ // value as is specified on this call.
+ // - threadpools created this way are never garbage collected.
+ string global_name = 2;
+}
+
+// Metadata about the session.
+//
+// This can be used by the runtime and the Ops for debugging, monitoring, etc.
+//
+// The (name, version) tuple is expected to be a unique identifier for
+// sessions within the same process.
+//
+// NOTE: This is currently used and propagated only by the direct session.
+message SessionMetadata {
+ string name = 1;
+
+ // The version is optional. If set, needs to be >= 0.
+ int64 version = 2;
+}
+
+// Session configuration parameters.
+// The system picks appropriate values for fields that are not set.
+message ConfigProto {
+ // Map from device type name (e.g., "CPU" or "GPU" ) to maximum
+ // number of devices of that type to use. If a particular device
+ // type is not found in the map, the system picks an appropriate
+ // number.
+ map<string, int32> device_count = 1;
+
+ // The execution of an individual op (for some op types) can be
+ // parallelized on a pool of intra_op_parallelism_threads.
+ // 0 means the system picks an appropriate number.
+ //
+ // If you create an ordinary session, e.g., from Python or C++,
+ // then there is exactly one intra op thread pool per process.
+ // The first session created determines the number of threads in this pool.
+ // All subsequent sessions reuse/share this one global pool.
+ //
+ // There are notable exceptions to the default behavior described above:
+ // 1. There is an environment variable for overriding this thread pool,
+ // named TF_OVERRIDE_GLOBAL_THREADPOOL.
+ // 2. When connecting to a server, such as a remote `tf.train.Server`
+ // instance, then this option will be ignored altogether.
+ int32 intra_op_parallelism_threads = 2;
+
+ // Nodes that perform blocking operations are enqueued on a pool of
+ // inter_op_parallelism_threads available in each process.
+ //
+ // 0 means the system picks an appropriate number.
+ // Negative means all operations are performed in caller's thread.
+ //
+ // Note that the first Session created in the process sets the
+ // number of threads for all future sessions unless use_per_session_threads is
+ // true or session_inter_op_thread_pool is configured.
+ int32 inter_op_parallelism_threads = 5;
+
+ // If true, use a new set of threads for this session rather than the global
+ // pool of threads. Only supported by direct sessions.
+ //
+ // If false, use the global threads created by the first session, or the
+ // per-session thread pools configured by session_inter_op_thread_pool.
+ //
+ // This option is deprecated. The same effect can be achieved by setting
+ // session_inter_op_thread_pool to have one element, whose num_threads equals
+ // inter_op_parallelism_threads.
+ bool use_per_session_threads = 9;
+
+ // This option is experimental - it may be replaced with a different mechanism
+ // in the future.
+ //
+ // Configures session thread pools. If this is configured, then RunOptions for
+ // a Run call can select the thread pool to use.
+ //
+ // The intended use is for when some session invocations need to run in a
+ // background pool limited to a small number of threads:
+ // - For example, a session may be configured to have one large pool (for
+ // regular compute) and one small pool (for periodic, low priority work);
+ // using the small pool is currently the mechanism for limiting the inter-op
+ // parallelism of the low priority work. Note that it does not limit the
+ // parallelism of work spawned by a single op kernel implementation.
+ // - Using this setting is normally not needed in training, but may help some
+ // serving use cases.
+ // - It is also generally recommended to set the global_name field of this
+ // proto, to avoid creating multiple large pools. It is typically better to
+ // run the non-low-priority work, even across sessions, in a single large
+ // pool.
+ repeated ThreadPoolOptionProto session_inter_op_thread_pool = 12;
+
+ // Assignment of Nodes to Devices is recomputed every placement_period
+ // steps until the system warms up (at which point the recomputation
+ // typically slows down automatically).
+ int32 placement_period = 3;
+
+ // When any filters are present sessions will ignore all devices which do not
+ // match the filters. Each filter can be partially specified, e.g. "/job:ps"
+ // "/job:worker/replica:3", etc.
+ repeated string device_filters = 4;
+
+ // Options that apply to all GPUs.
+ GPUOptions gpu_options = 6;
+
+ // Options that apply to pluggable devices.
+ GPUOptions pluggable_device_options = 18;
+
+ // Whether soft placement is allowed. If allow_soft_placement is true,
+ // an op will be placed on CPU if
+ // 1. there's no GPU implementation for the OP
+ // or
+ // 2. no GPU devices are known or registered
+ // or
+ // 3. need to co-locate with reftype input(s) which are from CPU.
+ bool allow_soft_placement = 7;
+
+ // Whether device placements should be logged.
+ bool log_device_placement = 8;
+
+ // Options that apply to all graphs.
+ GraphOptions graph_options = 10;
+
+ // Global timeout for all blocking operations in this session. If non-zero,
+ // and not overridden on a per-operation basis, this value will be used as the
+ // deadline for all blocking operations.
+ int64 operation_timeout_in_ms = 11;
+
+ // Options that apply when this session uses the distributed runtime.
+ RPCOptions rpc_options = 13;
+
+ // Optional list of all workers to use in this session.
+ ClusterDef cluster_def = 14;
+
+ // If true, any resources such as Variables used in the session will not be
+ // shared with other sessions. However, when clusterspec propagation is
+ // enabled, this field is ignored and sessions are always isolated.
+ bool isolate_session_state = 15;
+
+ // When true, WorkerSessions are created with device attributes from the
+ // full cluster.
+ // This is helpful when a worker wants to partition a graph
+ // (for example during a PartitionedCallOp).
+ bool share_cluster_devices_in_session = 17;
+
+ // Everything inside Experimental is subject to change and is not subject
+ // to API stability guarantees in
+ // https://www.tensorflow.org/guide/version_compat.
+ message Experimental {
+ // Task name for group resolution.
+ string collective_group_leader = 1;
+
+ // We removed the flag client_handles_error_formatting. Marking the tag
+ // number as reserved.
+ // TODO(shikharagarwal): Should we just remove this tag so that it can be
+ // used in future for other purpose?
+ reserved 2;
+
+ // Which executor to use, the default executor will be used
+ // if it is an empty string or "DEFAULT"
+ string executor_type = 3;
+
+ // Guidance to formatting of large RecvBuf fields for transfer.
+ // Any positive value sets the max chunk size. 0 defaults to 4096.
+ // Any negative value indicates no max, i.e. one chunk only.
+ int32 recv_buf_max_chunk = 4;
+
+ // If true, and supported by the platform, the runtime will attempt to
+ // use NUMA affinity where applicable. One consequence will be the
+ // existence of as many CPU devices as there are available NUMA nodes.
+ bool use_numa_affinity = 5;
+
+ // If true, make collective op execution order sequential and deterministic
+ // for potentially concurrent collective instances.
+ bool collective_deterministic_sequential_execution = 6;
+
+ // If true, use NCCL for CollectiveOps. This feature is highly
+ // experimental.
+ bool collective_nccl = 7;
+
+ // In the following, session state means the value of a variable, elements
+ // in a hash table, or any other resource, accessible by worker sessions
+ // held by a TF server.
+ //
+ // When ClusterSpec propagation is enabled, the value of
+ // isolate_session_state is ignored when deciding whether to share session
+ // states in a TF server (for backwards compatibility reasons).
+ // - If share_session_state_in_clusterspec_propagation is true, the session
+ // states are shared.
+ // - If share_session_state_in_clusterspec_propagation is false, session
+ // states are isolated.
+ //
+ // When clusterspec propagation is not used, the value of
+ // share_session_state_in_clusterspec_propagation is ignored when deciding
+ // whether to share session states in a TF server.
+ // - If isolate_session_state is true, session states are isolated.
+ // - If isolate_session_state is false, session states are shared.
+ //
+ // TODO(b/129330037): Add a single API that consistently treats
+ // isolate_session_state and ClusterSpec propagation.
+ bool share_session_state_in_clusterspec_propagation = 8;
+
+ // If using a direct session, disable spinning while waiting for work in
+ // the thread pool. This may result in higher latency for completing ops,
+ // but in the case where there is a lot of spinning may result in lower
+ // CPU usage.
+ bool disable_thread_spinning = 9;
+
+ // This was promoted to a non-experimental API. Please use
+ // ConfigProto.share_cluster_devices_in_session instead.
+ bool share_cluster_devices_in_session = 10;
+
+ // Metadata about the session.
+ //
+ // If set, this can be used by the runtime and the Ops for debugging,
+ // monitoring, etc.
+ //
+ // NOTE: This is currently used and propagated only by the direct session
+ // and EagerContext.
+ SessionMetadata session_metadata = 11;
+
+ // If true, the session may treat the graph as being static for optimization
+ // purposes.
+ //
+ // If this option is set to true when a session is created, the full
+ // GraphDef must be passed in a single call to Session::Create(), and
+ // Session::Extend() may not be supported.
+ bool optimize_for_static_graph = 12;
+
+ // Whether to enable the MLIR-based TF->XLA bridge. This is only used if set
+ // to true. Default value or false is ignored. Use mlir_bridge_rollout for
+ // finer control.
+ //
+ // If this option is set to true when a session is created, MLIR is used to
+ // perform the set of graph transformations to put the graph in a form that
+ // can be executed with delegation of some computations to an accelerator.
+ // This builds on the model of XLA where a subset of the graph is
+ // encapsulated and attached to a "compile" operation, whose result is fed
+ // to an "execute" operation. The kernel for these operations is responsible
+ // to lower the encapsulated graph to a particular device.
+ bool enable_mlir_bridge = 13;
+
+ // An enum that describes the state of the MLIR bridge rollout.
+ enum MlirBridgeRollout {
+ // If this field is left unspecified, the MLIR bridge may be selectively
+ // enabled on a per graph basis.
+ MLIR_BRIDGE_ROLLOUT_UNSPECIFIED = 0;
+ // Enabling the MLIR bridge enables it for all graphs in this session.
+ MLIR_BRIDGE_ROLLOUT_ENABLED = 1;
+ // Disabling the MLIR bridge disables it for all graphs in this session.
+ MLIR_BRIDGE_ROLLOUT_DISABLED = 2;
+ reserved 3, 4;
+ reserved "MLIR_BRIDGE_ROLLOUT_SAFE_MODE_ENABLED",
+ "MLIR_BRIDGE_ROLLOUT_SAFE_MODE_FALLBACK_ENABLED";
+ }
+ // Whether to enable the MLIR-based TF->XLA bridge.
+ MlirBridgeRollout mlir_bridge_rollout = 17;
+
+ // Whether to enable the MLIR-based Graph optimizations.
+ //
+ // This will become a part of standard Tensorflow graph optimization
+ // pipeline, currently this is only used for gradual migration and testing
+ // new passes that are replacing existing optimizations in Grappler.
+ bool enable_mlir_graph_optimization = 16;
+
+ // If true, the session will not store an additional copy of the graph for
+ // each subgraph.
+ //
+ // If this option is set to true when a session is created, the
+ // `RunOptions.output_partition_graphs` options must not be set.
+ bool disable_output_partition_graphs = 14;
+
+ // Minimum number of batches run through the XLA graph before XLA fusion
+ // autotuner is enabled. Default value of zero disables the autotuner.
+ //
+ // The XLA fusion autotuner can improve performance by executing a heuristic
+ // search on the compiler parameters.
+ int64 xla_fusion_autotuner_thresh = 15;
+
+ // Whether runtime execution uses TFRT.
+ bool use_tfrt = 18;
+
+ // If true, use Pathways with TFRT API for multi host support.
+ bool enable_multi_host = 27;
+
+ // If true, use ifrt as the backend for TFRT. This is only used when
+ // `use_tfrt` is true.
+ bool tfrt_use_ifrt = 32;
+
+ // Port for the Pathways server. Ignored if enable_multi_host=false.
+ int32 backend_server_port = 28;
+
+ // If true, TFRT will use TPU specific compiler passes and perform TPU
+ // specific initialization.
+ bool target_tpu = 29;
+
+ // If true, TFRT will use GPU specific compiler passes and perform GPU
+ // specific initialization.
+ bool target_gpu = 30;
+
+ // The threshold to merge small streams in TFRT. The stream with cost
+ // smaller than the threshold will be merged. Setting it to value 1
+ // disables all merges.
+ int32 stream_merge_threshold = 31;
+
+ // The field "coordination_service was previously specified as a string;
+ // this has been replaced with a message below.
+ reserved 19;
+
+ // We removed the flag fetch_remote_devices_in_multi_client. Marking the tag
+ // number as reserved.
+ reserved 20;
+
+ // Whether functional control flow op lowering should be disabled. This is
+ // useful when executing within a portable runtime where control flow op
+ // kernels may not be loaded due to selective registration.
+ bool disable_functional_ops_lowering = 21;
+
+ // Provides a hint to XLA auto clustering to prefer forming a single large
+ // cluster that encompases most of the graph.
+ bool xla_prefer_single_graph_cluster = 22;
+
+ // Distributed coordination service configurations.
+ CoordinationServiceConfig coordination_config = 23;
+
+ // If true, the session will treat the graph as being non-static for
+ // optimization purposes.
+ //
+ // If this option is set to true when a session is created, the full
+ // GraphDef will be retained to enable calls to Session::Extend().
+ // Calling Extend() without setting this flag will result in errors.
+ //
+ // This option is meant to replace `optimize_for_static_graph` and it
+ // aims to negate its value.
+ bool disable_optimize_for_static_graph = 24;
+
+ // Whether eager remote execution will stream all the function calls or
+ // allow them to happen in parallel. When true, streaming execution is
+ // disabled, and parallel execution is allowed.
+ bool disable_eager_executor_streaming_enqueue = 26;
+
+ reserved 25;
+
+ // Next: 33
+ }
+
+ Experimental experimental = 16;
+
+ // Next: 19
+}
+
+// Options for a single Run() call.
+message RunOptions {
+ // TODO(pbar) Turn this into a TraceOptions proto which allows
+ // tracing to be controlled in a more orthogonal manner?
+ enum TraceLevel {
+ NO_TRACE = 0;
+ SOFTWARE_TRACE = 1;
+ HARDWARE_TRACE = 2;
+ FULL_TRACE = 3;
+ }
+ TraceLevel trace_level = 1;
+
+ // Time to wait for operation to complete in milliseconds.
+ int64 timeout_in_ms = 2;
+
+ // The thread pool to use, if session_inter_op_thread_pool is configured.
+ // To use the caller thread set this to -1 - this uses the caller thread
+ // to execute Session::Run() and thus avoids a context switch. Using the
+ // caller thread to execute Session::Run() should be done ONLY for simple
+ // graphs, where the overhead of an additional context switch is
+ // comparable with the overhead of Session::Run().
+ int32 inter_op_thread_pool = 3;
+
+ // Whether the partition graph(s) executed by the executor(s) should be
+ // outputted via RunMetadata.
+ bool output_partition_graphs = 5;
+
+ // EXPERIMENTAL. Options used to initialize DebuggerState, if enabled.
+ DebugOptions debug_options = 6;
+
+ // When enabled, causes tensor allocation information to be included in
+ // the error message when the Run() call fails because the allocator ran
+ // out of memory (OOM).
+ //
+ // Enabling this option can slow down the Run() call.
+ bool report_tensor_allocations_upon_oom = 7;
+
+ // Everything inside Experimental is subject to change and is not subject
+ // to API stability guarantees in
+ // https://www.tensorflow.org/guide/version_compat.
+ message Experimental {
+ // If non-zero, declares that this graph is going to use collective
+ // ops and must synchronize step_ids with any other graph with this
+ // same group_key value (in a distributed computation where tasks
+ // run disjoint graphs).
+ int64 collective_graph_key = 1;
+ // If true, then operations (using the inter-op pool) across all
+ // session::run() calls will be centrally scheduled, optimizing for (median
+ // and tail) latency.
+ // Consider using this option for CPU-bound workloads like inference.
+ bool use_run_handler_pool = 2;
+ // Options for run handler thread pool.
+ message RunHandlerPoolOptions {
+ // Priority of the request. The run handler thread pool will schedule ops
+ // based on the priority number. The larger number means higher priority.
+ int64 priority = 1;
+ }
+ RunHandlerPoolOptions run_handler_pool_options = 3;
+ }
+
+ Experimental experimental = 8;
+
+ reserved 4;
+}
+
+// Metadata output (i.e., non-Tensor) for a single Run() call.
+message RunMetadata {
+ // Statistics traced for this step. Populated if tracing is turned on via the
+ // "RunOptions" proto.
+ // EXPERIMENTAL: The format and set of events may change in future versions.
+ StepStats step_stats = 1;
+
+ // The cost graph for the computation defined by the run call.
+ CostGraphDef cost_graph = 2;
+
+ // Graphs of the partitions executed by executors.
+ repeated GraphDef partition_graphs = 3;
+
+ message FunctionGraphs {
+ // TODO(nareshmodi): Include some sort of function/cache-key identifier?
+ repeated GraphDef partition_graphs = 1;
+
+ GraphDef pre_optimization_graph = 2;
+ GraphDef post_optimization_graph = 3;
+ }
+ // This is only populated for graphs that are run as functions in TensorFlow
+ // V2. There will be an entry below for each function that is traced.
+ // The main use cases of the post_optimization_graph and the partition_graphs
+ // is to give the caller insight into the graphs that were actually run by the
+ // runtime. Additional information (such as those in step_stats) will match
+ // these graphs.
+ // We also include the pre_optimization_graph since it is usually easier to
+ // read, and is helpful in situations where the caller wants to get a high
+ // level idea of what the built graph looks like (since the various graph
+ // optimization passes might change the structure of the graph significantly).
+ repeated FunctionGraphs function_graphs = 4;
+
+ // Metadata about the session.
+ SessionMetadata session_metadata = 5;
+}
+
+// Defines a connection between two tensors in a `GraphDef`.
+message TensorConnection {
+ // A tensor name. The value of this tensor will be substituted for
+ // the tensor named in `to_tensor`.
+ string from_tensor = 1;
+
+ // A tensor name. The value of this tensor will be bound to the
+ // value of the tensor named in `from_tensor`.
+ string to_tensor = 2;
+}
+
+// Defines a subgraph in another `GraphDef` as a set of feed points and nodes
+// to be fetched or executed.
+//
+// Compare with the arguments to `Session::Run()`.
+message CallableOptions {
+ // Tensors to be fed in the callable. Each feed is the name of a tensor.
+ repeated string feed = 1;
+
+ // Fetches. A list of tensor names. The caller of the callable expects a
+ // tensor to be returned for each fetch[i] (see RunStepResponse.tensor). The
+ // order of specified fetches does not change the execution order.
+ repeated string fetch = 2;
+
+ // Target Nodes. A list of node names. The named nodes will be run by the
+ // callable but their outputs will not be returned.
+ repeated string target = 3;
+
+ // Options that will be applied to each run.
+ RunOptions run_options = 4;
+
+ // Tensors to be connected in the callable. Each TensorConnection denotes
+ // a pair of tensors in the graph, between which an edge will be created
+ // in the callable.
+ repeated TensorConnection tensor_connection = 5;
+
+ // The Tensor objects fed in the callable and fetched from the callable
+ // are expected to be backed by host (CPU) memory by default.
+ //
+ // The options below allow changing that - feeding tensors backed by
+ // device memory, or returning tensors that are backed by device memory.
+ //
+ // The maps below map the name of a feed/fetch tensor (which appears in
+ // 'feed' or 'fetch' fields above), to the fully qualified name of the device
+ // owning the memory backing the contents of the tensor.
+ //
+ // For example, creating a callable with the following options:
+ //
+ // CallableOptions {
+ // feed: "a:0"
+ // feed: "b:0"
+ //
+ // fetch: "x:0"
+ // fetch: "y:0"
+ //
+ // feed_devices: {
+ // "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
+ // }
+ //
+ // fetch_devices: {
+ // "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
+ // }
+ // }
+ //
+ // means that the Callable expects:
+ // - The first argument ("a:0") is a Tensor backed by GPU memory.
+ // - The second argument ("b:0") is a Tensor backed by host memory.
+ // and of its return values:
+ // - The first output ("x:0") will be backed by host memory.
+ // - The second output ("y:0") will be backed by GPU memory.
+ //
+ // FEEDS:
+ // It is the responsibility of the caller to ensure that the memory of the fed
+ // tensors will be correctly initialized and synchronized before it is
+ // accessed by operations executed during the call to Session::RunCallable().
+ //
+ // This is typically ensured by using the TensorFlow memory allocators
+ // (Device::GetAllocator()) to create the Tensor to be fed.
+ //
+ // Alternatively, for CUDA-enabled GPU devices, this typically means that the
+ // operation that produced the contents of the tensor has completed, i.e., the
+ // CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
+ // cuStreamSynchronize()).
+ map<string, string> feed_devices = 6;
+ map<string, string> fetch_devices = 7;
+
+ // By default, RunCallable() will synchronize the GPU stream before returning
+ // fetched tensors on a GPU device, to ensure that the values in those tensors
+ // have been produced. This simplifies interacting with the tensors, but
+ // potentially incurs a performance hit.
+ //
+ // If this options is set to true, the caller is responsible for ensuring
+ // that the values in the fetched tensors have been produced before they are
+ // used. The caller can do this by invoking `Device::Sync()` on the underlying
+ // device(s), or by feeding the tensors back to the same Session using
+ // `feed_devices` with the same corresponding device name.
+ bool fetch_skip_sync = 8;
+
+ // Next: 9
+}
+
+message BatchingOptions {
+ // Number of scheduling threads for processing batches of work. Determines
+ // the number of batches processed in parallel. This should be roughly in line
+ // with the number of TPU cores available.
+ int32 num_batch_threads = 1;
+
+ // The maximum allowed batch size. Can be larger than allowed_batch_sizes to
+ // utilize large batch splitting.
+ int32 max_batch_size = 2;
+
+ // Maximum number of microseconds to wait before outputting an incomplete
+ // batch.
+ int32 batch_timeout_micros = 3;
+
+ // Optional list of allowed batch sizes. If left empty, does nothing.
+ // Otherwise, supplies a list of batch sizes, causing the op to pad batches up
+ // to one of those sizes. The entries must increase monotonically, and the
+ // final entry must be equal or less than the max_batch_size.
+ repeated int32 allowed_batch_sizes = 4;
+
+ // Maximum number of batches enqueued for processing before requests are
+ // failed fast.
+ int32 max_enqueued_batches = 5;
+}
diff --git a/src/main/proto/tensorflow/core/protobuf/debug.proto b/src/main/proto/tensorflow/core/protobuf/debug.proto
new file mode 100644
index 0000000..2fabd03
--- /dev/null
+++ b/src/main/proto/tensorflow/core/protobuf/debug.proto
@@ -0,0 +1,94 @@
+syntax = "proto3";
+
+package tensorflow;
+
+option cc_enable_arenas = true;
+option java_outer_classname = "DebugProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";
+
+// Option for watching a node in TensorFlow Debugger (tfdbg).
+message DebugTensorWatch {
+ // Name of the node to watch.
+ // Use "*" for wildcard. But note: currently, regex is not supported in
+ // general.
+ string node_name = 1;
+
+ // Output slot to watch.
+ // The semantics of output_slot == -1 is that all outputs of the node
+ // will be watched (i.e., a wildcard).
+ // Other negative values of output_slot are invalid and will lead to
+ // errors currently.
+ int32 output_slot = 2;
+
+ // Name(s) of the debugging op(s).
+ // One or more than one probes on a tensor.
+ // e.g., {"DebugIdentity", "DebugNanCount"}
+ repeated string debug_ops = 3;
+
+ // URL(s) for debug targets(s).
+ //
+ // Supported URL formats are:
+ // - file:///foo/tfdbg_dump: Writes out Event content to file
+ // /foo/tfdbg_dump. Assumes all directories can be created if they don't
+ // already exist.
+ // - grpc://localhost:11011: Sends an RPC request to an EventListener
+ // service running at localhost:11011 with the event.
+ // - memcbk:///event_key: Routes tensors to clients using the
+ // callback registered with the DebugCallbackRegistry for event_key.
+ //
+ // Each debug op listed in debug_ops will publish its output tensor (debug
+ // signal) to all URLs in debug_urls.
+ //
+ // N.B. Session::Run() supports concurrent invocations of the same inputs
+ // (feed keys), outputs and target nodes. If such concurrent invocations
+ // are to be debugged, the callers of Session::Run() must use distinct
+ // debug_urls to make sure that the streamed or dumped events do not overlap
+ // among the invocations.
+ // TODO(cais): More visible documentation of this in g3docs.
+ repeated string debug_urls = 4;
+
+ // Do not error out if debug op creation fails (e.g., due to dtype
+ // incompatibility). Instead, just log the failure.
+ bool tolerate_debug_op_creation_failures = 5;
+}
+
+// Options for initializing DebuggerState in TensorFlow Debugger (tfdbg).
+message DebugOptions {
+ // Debugging options
+ repeated DebugTensorWatch debug_tensor_watch_opts = 4;
+
+ // Caller-specified global step count.
+ // Note that this is distinct from the session run count and the executor
+ // step count.
+ int64 global_step = 10;
+
+ // Whether the total disk usage of tfdbg is to be reset to zero
+ // in this Session.run call. This is used by wrappers and hooks
+ // such as the local CLI ones to indicate that the dumped tensors
+ // are cleaned up from the disk after each Session.run.
+ bool reset_disk_byte_usage = 11;
+}
+
+message DebuggedSourceFile {
+ // The host name on which a source code file is located.
+ string host = 1;
+
+ // Path to the source code file.
+ string file_path = 2;
+
+ // The timestamp at which the source code file is last modified.
+ int64 last_modified = 3;
+
+ // Byte size of the file.
+ int64 bytes = 4;
+
+ // Line-by-line content of the source code file.
+ repeated string lines = 5;
+}
+
+message DebuggedSourceFiles {
+ // A collection of source code files.
+ repeated DebuggedSourceFile source_files = 1;
+}
diff --git a/src/main/proto/tensorflow/core/protobuf/error_codes.proto b/src/main/proto/tensorflow/core/protobuf/error_codes.proto
new file mode 100644
index 0000000..6842c69
--- /dev/null
+++ b/src/main/proto/tensorflow/core/protobuf/error_codes.proto
@@ -0,0 +1,11 @@
+syntax = "proto3";
+
+// Add a dummy package name. Having no package, like
+// core/lib/core/error_codes.proto, or having tensorflow.error, like
+// tsl/protobuf/error_codes.proto, results in name collision errors in generated
+// code for some users that use JS through J2CL.
+package tensorflow.error.dummy;
+
+import public "xla/tsl/protobuf/error_codes.proto";
+
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";
diff --git a/src/main/proto/tensorflow/core/protobuf/meta_graph.proto b/src/main/proto/tensorflow/core/protobuf/meta_graph.proto
new file mode 100644
index 0000000..b525c52
--- /dev/null
+++ b/src/main/proto/tensorflow/core/protobuf/meta_graph.proto
@@ -0,0 +1,286 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "google/protobuf/any.proto";
+import "tensorflow/core/framework/graph.proto";
+import "tensorflow/core/framework/op_def.proto";
+import "tensorflow/core/framework/tensor.proto";
+import "tensorflow/core/framework/tensor_shape.proto";
+import "tensorflow/core/framework/types.proto";
+import "tensorflow/core/protobuf/saved_object_graph.proto";
+import "tensorflow/core/protobuf/saver.proto";
+import "tensorflow/core/protobuf/struct.proto";
+
+option cc_enable_arenas = true;
+option java_outer_classname = "MetaGraphProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";
+
+// Protocol buffer containing the following which are necessary to restart
+// training, run inference. It can be used to serialize/de-serialize memory
+// objects necessary for running computation in a graph when crossing the
+// process boundary. It can be used for long term storage of graphs,
+// cross-language execution of graphs, etc.
+// MetaInfoDef
+// GraphDef
+// SaverDef
+// CollectionDef
+// TensorInfo
+// SignatureDef
+message MetaGraphDef {
+ // Meta information regarding the graph to be exported. To be used by users
+ // of this protocol buffer to encode information regarding their meta graph.
+ message MetaInfoDef {
+ // User specified Version string. Can be the name of the model and revision,
+ // steps this model has been trained to, etc.
+ string meta_graph_version = 1;
+
+ // A copy of the OpDefs used by the producer of this graph_def.
+ // Descriptions and Ops not used in graph_def are stripped out.
+ OpList stripped_op_list = 2;
+
+ // A serialized protobuf. Can be the time this meta graph is created, or
+ // modified, or name of the model.
+ google.protobuf.Any any_info = 3;
+
+ // User supplied tag(s) on the meta_graph and included graph_def.
+ //
+ // MetaGraphDefs should be tagged with their capabilities or use-cases.
+ // Examples: "train", "serve", "gpu", "tpu", etc.
+ // These tags enable loaders to access the MetaGraph(s) appropriate for a
+ // specific use-case or runtime environment.
+ repeated string tags = 4;
+
+ // The __version__ string of the tensorflow build used to write this graph.
+ // This will be populated by the framework, which will overwrite any user
+ // supplied value.
+ string tensorflow_version = 5;
+
+ // The __git_version__ string of the tensorflow build used to write this
+ // graph. This will be populated by the framework, which will overwrite any
+ // user supplied value.
+ string tensorflow_git_version = 6;
+
+ // A flag to denote whether default-valued attrs have been stripped from
+ // the nodes in this graph_def.
+ bool stripped_default_attrs = 7;
+
+ // FunctionDef name to aliases mapping.
+ map<string, string> function_aliases = 8;
+ }
+ MetaInfoDef meta_info_def = 1;
+
+ // GraphDef.
+ GraphDef graph_def = 2;
+
+ // SaverDef.
+ SaverDef saver_def = 3;
+
+ // collection_def: Map from collection name to collections.
+ // See CollectionDef section for details.
+ map<string, CollectionDef> collection_def = 4;
+
+ // signature_def: Map from user supplied key for a signature to a single
+ // SignatureDef.
+ map<string, SignatureDef> signature_def = 5;
+
+ // Asset file def to be used with the defined graph.
+ repeated AssetFileDef asset_file_def = 6;
+
+ // Extra information about the structure of functions and stateful objects.
+ SavedObjectGraph object_graph_def = 7;
+}
+
+// CollectionDef should cover most collections.
+// To add a user-defined collection, do one of the following:
+// 1. For simple data types, such as string, int, float:
+// tf.add_to_collection("your_collection_name", your_simple_value)
+// strings will be stored as bytes_list.
+//
+// 2. For Protobuf types, there are three ways to add them:
+// 1) tf.add_to_collection("your_collection_name",
+// your_proto.SerializeToString())
+//
+// collection_def {
+// key: "user_defined_bytes_collection"
+// value {
+// bytes_list {
+// value: "queue_name: \"test_queue\"\n"
+// }
+// }
+// }
+//
+// or
+//
+// 2) tf.add_to_collection("your_collection_name", str(your_proto))
+//
+// collection_def {
+// key: "user_defined_string_collection"
+// value {
+// bytes_list {
+// value: "\n\ntest_queue"
+// }
+// }
+// }
+//
+// or
+//
+// 3) any_buf = any_pb2.Any()
+// tf.add_to_collection("your_collection_name",
+// any_buf.Pack(your_proto))
+//
+// collection_def {
+// key: "user_defined_any_collection"
+// value {
+// any_list {
+// value {
+// type_url: "type.googleapis.com/tensorflow.QueueRunnerDef"
+// value: "\n\ntest_queue"
+// }
+// }
+// }
+// }
+//
+// 3. For Python objects, implement to_proto() and from_proto(), and register
+// them in the following manner:
+// ops.register_proto_function("your_collection_name",
+// proto_type,
+// to_proto=YourPythonObject.to_proto,
+// from_proto=YourPythonObject.from_proto)
+// These functions will be invoked to serialize and de-serialize the
+// collection. For example,
+// ops.register_proto_function(ops.GraphKeys.GLOBAL_VARIABLES,
+// proto_type=variable_pb2.VariableDef,
+// to_proto=Variable.to_proto,
+// from_proto=Variable.from_proto)
+message CollectionDef {
+ // NodeList is used for collecting nodes in graph. For example
+ // collection_def {
+ // key: "summaries"
+ // value {
+ // node_list {
+ // value: "input_producer/ScalarSummary:0"
+ // value: "shuffle_batch/ScalarSummary:0"
+ // value: "ImageSummary:0"
+ // }
+ // }
+ message NodeList {
+ repeated string value = 1;
+ }
+
+ // BytesList is used for collecting strings and serialized protobufs. For
+ // example:
+ // collection_def {
+ // key: "trainable_variables"
+ // value {
+ // bytes_list {
+ // value: "\n\017conv1/weights:0\022\024conv1/weights/Assign
+ // \032\024conv1/weights/read:0"
+ // value: "\n\016conv1/biases:0\022\023conv1/biases/Assign\032
+ // \023conv1/biases/read:0"
+ // }
+ // }
+ // }
+ message BytesList {
+ repeated bytes value = 1;
+ }
+
+ // Int64List is used for collecting int, int64 and long values.
+ message Int64List {
+ repeated int64 value = 1 [packed = true];
+ }
+
+ // FloatList is used for collecting float values.
+ message FloatList {
+ repeated float value = 1 [packed = true];
+ }
+
+ // AnyList is used for collecting Any protos.
+ message AnyList {
+ repeated google.protobuf.Any value = 1;
+ }
+
+ oneof kind {
+ NodeList node_list = 1;
+ BytesList bytes_list = 2;
+ Int64List int64_list = 3;
+ FloatList float_list = 4;
+ AnyList any_list = 5;
+ }
+}
+
+// Information about a Tensor necessary for feeding or retrieval.
+message TensorInfo {
+ // For sparse tensors, The COO encoding stores a triple of values, indices,
+ // and shape.
+ message CooSparse {
+ // The shape of the values Tensor is [?]. Its dtype must be the dtype of
+ // the SparseTensor as a whole, given in the enclosing TensorInfo.
+ string values_tensor_name = 1;
+
+ // The indices Tensor must have dtype int64 and shape [?, ?].
+ string indices_tensor_name = 2;
+
+ // The dynamic logical shape represented by the SparseTensor is recorded in
+ // the Tensor referenced here. It must have dtype int64 and shape [?].
+ string dense_shape_tensor_name = 3;
+ }
+
+ // Generic encoding for composite tensors.
+ message CompositeTensor {
+ // The serialized TypeSpec for the composite tensor.
+ TypeSpecProto type_spec = 1;
+
+ // A TensorInfo for each flattened component tensor.
+ repeated TensorInfo components = 2;
+ }
+
+ oneof encoding {
+ // For dense `Tensor`s, the name of the tensor in the graph.
+ string name = 1;
+ // There are many possible encodings of sparse matrices
+ // (https://en.wikipedia.org/wiki/Sparse_matrix). Currently, TensorFlow
+ // uses only the COO encoding. This is supported and documented in the
+ // SparseTensor Python class.
+ CooSparse coo_sparse = 4;
+ // Generic encoding for CompositeTensors.
+ CompositeTensor composite_tensor = 5;
+ }
+ DataType dtype = 2;
+ // The static shape should be recorded here, to the extent that it can
+ // be known in advance. In the case of a SparseTensor, this field describes
+ // the logical shape of the represented tensor (aka dense_shape).
+ TensorShapeProto tensor_shape = 3;
+}
+
+// SignatureDef defines the signature of a computation supported by a TensorFlow
+// graph.
+message SignatureDef {
+ // Named input parameters.
+ map<string, TensorInfo> inputs = 1;
+ // Named output parameters.
+ map<string, TensorInfo> outputs = 2;
+ // Deprecated: TensorFlow 2 always sets this to a fixed value;
+ // open-source TF Serving stopped checking by default since release 2.4.
+ //
+ // In TensorFlow 1, the method_name enabled users to mark a SignatureDef as
+ // supporting a particular method. Multiple SignatureDefs in a single
+ // MetaGraphDef could have the same method_name (e.g., to support multi-headed
+ // computation).
+ string method_name = 3;
+ // Named input to corresponding default values if any.
+ map<string, TensorProto> defaults = 4;
+}
+
+// An asset file def for a single file or a set of sharded files with the same
+// name.
+message AssetFileDef {
+ // The tensor to bind the asset filename to.
+ TensorInfo tensor_info = 1;
+ // The filename within an assets directory. Note: does not include the path
+ // prefix, i.e. directories. For an asset at /tmp/path/vocab.txt, the filename
+ // would be "vocab.txt".
+ string filename = 2;
+}
diff --git a/src/main/proto/tensorflow/core/protobuf/named_tensor.proto b/src/main/proto/tensorflow/core/protobuf/named_tensor.proto
new file mode 100644
index 0000000..8d401a0
--- /dev/null
+++ b/src/main/proto/tensorflow/core/protobuf/named_tensor.proto
@@ -0,0 +1,25 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "tensorflow/core/framework/tensor.proto";
+
+option cc_enable_arenas = true;
+option java_outer_classname = "NamedTensorProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";
+
+// A pair of tensor name and tensor values.
+message NamedTensorProto {
+ // Name of the tensor.
+ string name = 1;
+
+ // The client can populate a TensorProto using a tensorflow::Tensor`, or
+ // directly using the protobuf field accessors.
+ //
+ // The client specifies whether the returned tensor values should be
+ // filled tensor fields (float_val, int_val, etc.) or encoded in a
+ // compact form in tensor.tensor_content.
+ TensorProto tensor = 2;
+}
diff --git a/src/main/proto/tensorflow/core/protobuf/rewriter_config.proto b/src/main/proto/tensorflow/core/protobuf/rewriter_config.proto
new file mode 100644
index 0000000..f98d192
--- /dev/null
+++ b/src/main/proto/tensorflow/core/protobuf/rewriter_config.proto
@@ -0,0 +1,241 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "tensorflow/core/framework/attr_value.proto";
+import "tensorflow/core/protobuf/verifier_config.proto";
+
+option cc_enable_arenas = true;
+option java_outer_classname = "RewriterConfigProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";
+
+message AutoParallelOptions {
+ bool enable = 1;
+ int32 num_replicas = 2;
+}
+
+message ScopedAllocatorOptions {
+ // If present, only perform optimization for these ops.
+ repeated string enable_op = 1;
+}
+
+message RewriterConfig {
+ // Graph rewriting is experimental and subject to change, not covered by any
+ // API stability guarantees.
+
+ // Configuration options for the meta-optimizer. Unless otherwise noted, these
+ // configuration options do not apply to explicitly triggered optimization
+ // passes in the optimizers field.
+
+ enum Toggle {
+ DEFAULT = 0;
+ ON = 1;
+ OFF = 2;
+ // Enable some aggressive optimizations that use assumptions that TF graphs
+ // may break. For example, assume the shape of a placeholder matches its
+ // actual feed.
+ AGGRESSIVE = 3;
+ // Run MLIR pass if there's one implemented in TFG, do nothing otherwise.
+ // I.e., if there's no corresponding TFG pass, it's an OFF. This is supposed
+ // to be mapped with `ON` and there's no `AGGRESSIVE` in MLIR pass now.
+ EXPERIMENTAL_MLIR = 4;
+ // Run both MLIR and Grappler passes consecutively and MLIR pass will come
+ // first.
+ EXPERIMENTAL_BOTH = 5;
+ }
+
+ // Enum for layout conversion between NCHW and NHWC on CPU. Default is OFF.
+ enum CpuLayout {
+ NO_CONVERSION_ON_CPU = 0;
+ NCHW_TO_NHWC = 1;
+ NHWC_TO_NCHW = 2;
+ }
+
+ // Enum controlling the number of times to run optimizers. The default is to
+ // run them twice.
+ enum NumIterationsType {
+ DEFAULT_NUM_ITERS = 0;
+ ONE = 1;
+ TWO = 2;
+ }
+
+ // CPU Conversion settings between NHCW and NCHW.
+ CpuLayout cpu_layout_conversion = 50;
+
+ // Optimize tensor layouts (default is ON)
+ // e.g. This will try to use NCHW layout on GPU which is faster.
+ Toggle layout_optimizer = 1;
+ // Fold constants (default is ON)
+ // Statically infer the value of tensors when possible, and materialize the
+ // result using constants.
+ Toggle constant_folding = 3;
+ // Shape optimizations (default is ON)
+ // Simplify computations made on shapes.
+ Toggle shape_optimization = 13;
+ // Remapping (default is ON)
+ // Remap subgraphs onto more efficient implementations.
+ Toggle remapping = 14;
+ // Common subgraph elimination (default is ON)
+ // e.g. Simplify arithmetic ops; merge ops with same value (like constants).
+ Toggle common_subgraph_elimination = 24;
+ // Arithmetic optimizations (default is ON)
+ // e.g. Simplify arithmetic ops; merge ops with same value (like constants).
+ Toggle arithmetic_optimization = 7;
+ // Control dependency optimizations (default is ON).
+ // Remove redundant control dependencies, which may enable other optimization.
+ Toggle dependency_optimization = 8;
+ // Loop optimizations (default is ON).
+ Toggle loop_optimization = 9;
+ // Function optimizations (default is ON).
+ Toggle function_optimization = 10;
+ // Strips debug-related nodes from the graph (off by default).
+ Toggle debug_stripper = 11;
+ // If true, don't remove unnecessary ops from the graph
+ bool disable_model_pruning = 2;
+ // Try to allocate some independent Op outputs contiguously in order to
+ // merge or eliminate downstream Ops (off by default).
+ Toggle scoped_allocator_optimization = 15;
+ // Force small ops onto the CPU (default is OFF).
+ Toggle pin_to_host_optimization = 18;
+ // Enable the swap of kernel implementations based on the device placement
+ // (default is ON).
+ Toggle implementation_selector = 22;
+ // Optimize data types for CUDA/oneDNN (default is OFF).
+ // This will try to use float16 on GPU/CPU which is faster.
+ // Note that this can change the numerical stability of the graph and may
+ // require the use of loss scaling to maintain model convergence.
+ Toggle auto_mixed_precision = 23;
+ // Optimize data types for oneDNN (default is OFF).
+ // This will try to use bfloat16 on CPUs, which is faster.
+ // Note that this can change the numerical stability of the graph.
+ // Note: this is deprecated.
+ // It is replaced by auto_mixed_precision_onednn_bfloat16
+ Toggle auto_mixed_precision_mkl = 25;
+ // Optimize data types for oneDNN (default is OFF).
+ // This will try to use bfloat16 on CPUs, which is faster.
+ // Note that this can change the numerical stability of the graph.
+ // Note: this is equivalent to the deprecated option auto_mixed_precision_mkl
+ Toggle auto_mixed_precision_onednn_bfloat16 = 31;
+ // Emulate a model using data type float16 on CPU (default is OFF).
+ // This will try to emulate the float16 inputs and outputs of an operator
+ // on CPU to have better correlation with float16 on GPU; however the
+ // computation in the operator is based on float32.
+ // Note that this can change the numerical stability of the graph.
+ Toggle auto_mixed_precision_cpu = 29;
+ // Disable the entire meta optimizer (off by default).
+ bool disable_meta_optimizer = 19;
+ // Disable the TFG optimizer (off by default).
+ bool disable_tfg_optimizer = 32;
+ // Optimizers registered by plugin (default is ON)
+ Toggle use_plugin_optimizers = 28;
+ // Conditional code motion (default is ON).
+ Toggle experimental_conditional_code_motion = 30;
+
+ // Controls how many times we run the optimizers in meta optimizer (default
+ // is once).
+ NumIterationsType meta_optimizer_iterations = 12;
+
+ // The minimum number of nodes in a graph to optimizer. For smaller graphs,
+ // optimization is skipped.
+ // 0 means the system picks an appropriate number.
+ // < 0 means do not skip optimization.
+ int32 min_graph_nodes = 17;
+
+ // Disable optimizations that assume compressed tensors. Note that this flag
+ // is experimental and may be removed in the future.
+ bool experimental_disable_compressed_tensor_optimization = 26;
+
+ // Disable folding quantization emulation ops such as FakeQuantWithMinMax* and
+ // QuantizeAndDequantize*. Some compilers (e.g. the TF-to-tflite converter)
+ // have to extract quantization configs (e.g. min/max range, number of bits,
+ // and per-channel) from the quantization emulation ops. Note that this flag
+ // is experimental and may be removed in the future. See b/174138564 for more
+ // details.
+ bool experimental_disable_folding_quantization_emulation = 27;
+
+ enum MemOptType {
+ // The default setting (SCHEDULING and SWAPPING HEURISTICS only)
+ DEFAULT_MEM_OPT = 0;
+ // Disabled in the meta-optimizer.
+ NO_MEM_OPT = 1;
+ // Driven by manual op-level annotations.
+ MANUAL = 2;
+
+ // Driven by heuristics. The behavior of these heuristics is subject to
+ // change. Currently includes an experimental recomputation and swapping
+ // heuristics. Manual annotations are respected, but additional nodes are
+ // selected automatically.
+
+ // Swapping heuristic will move a tensor from the GPU to the CPU and move
+ // it back when needed to reduce peak memory usage.
+ SWAPPING_HEURISTICS = 4;
+ // Recomputation heuristics will recompute ops (such as Relu activation)
+ // during backprop instead of storing them, reducing peak memory usage.
+ RECOMPUTATION_HEURISTICS = 5;
+ // Scheduling will split big ops such as AddN and try to enforce a schedule
+ // of the new computations that decreases peak memory usage.
+ SCHEDULING_HEURISTICS = 6;
+ // Use any combination of swapping and recomputation heuristics.
+ HEURISTICS = 3;
+ }
+ // Configures memory optimization passes through the meta-optimizer. Has no
+ // effect on manually requested memory optimization passes in the optimizers
+ // field.
+ MemOptType memory_optimization = 4;
+ // A node name scope for node names which are valid outputs of recomputations.
+ // Inputs to nodes that match this scope may be recomputed (subject either to
+ // manual annotation of those input nodes or to manual annotation and
+ // heuristics depending on memory_optimization), but the nodes themselves will
+ // not be recomputed. This matches any sub-scopes as well, meaning the scope
+ // can appear not just as a top-level scope. For example, if the value is
+ // "gradients/", the default, it will match node name "gradients/foo",
+ // "foo/gradients/bar", but not "foo_gradients/"
+ string memory_optimizer_target_node_name_scope = 6;
+ // Maximum number of milliseconds to spend optimizing a single graph before
+ // timing out. If less than or equal to 0 (default value) the optimizer will
+ // never time out.
+ int64 meta_optimizer_timeout_ms = 20;
+
+ // Configures AutoParallel optimization passes either through the
+ // meta-optimizer or when manually specified through the optimizers field.
+ AutoParallelOptions auto_parallel = 5;
+
+ // If true, any optimization pass failing will cause the MetaOptimizer to
+ // stop with an error. By default - or when set to false, failing passes are
+ // skipped silently.
+ bool fail_on_optimizer_errors = 21;
+
+ ScopedAllocatorOptions scoped_allocator_opts = 16;
+
+ // If non-empty, will use this as an alternative way to specify a list of
+ // optimizations to turn on and the order of the optimizations (replacing the
+ // meta-optimizer).
+ //
+ // Of the RewriterConfig options, only the AutoParallel configuration options
+ // (the auto_parallel field) apply to manually requested optimization passes
+ // ("autoparallel"). Memory optimization passes ("memory") invoked here are
+ // not configurable (in contrast to memory optimization passes through the
+ // meta-optimizer) and act only on manual op annotations.
+ //
+ // Custom optimizers (see custom_optimizers) that are not part of this
+ // schedule will be run after - in the order that they were specified.
+ repeated string optimizers = 100;
+
+ // Message to describe custom graph optimizer and its parameters
+ message CustomGraphOptimizer {
+ string name = 1;
+ map<string, AttrValue> parameter_map = 2;
+ }
+
+ // list of CustomGraphOptimizers to apply.
+ repeated CustomGraphOptimizer custom_optimizers = 200;
+
+ // VerifierConfig specifying the verifiers to be run after every optimizer.
+ VerifierConfig inter_optimizer_verifier_config = 300;
+
+ // VerifierConfig specifying the verifiers to be run at the end, after all
+ // optimizers have run.
+ VerifierConfig post_optimization_verifier_config = 301;
+}
diff --git a/src/main/proto/tensorflow/core/protobuf/rpc_options.proto b/src/main/proto/tensorflow/core/protobuf/rpc_options.proto
new file mode 100644
index 0000000..03593a6
--- /dev/null
+++ b/src/main/proto/tensorflow/core/protobuf/rpc_options.proto
@@ -0,0 +1,7 @@
+syntax = "proto3";
+
+package tensorflow.dummy;
+
+import public "xla/tsl/protobuf/rpc_options.proto";
+
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";
diff --git a/src/main/proto/tensorflow/core/protobuf/saved_object_graph.proto b/src/main/proto/tensorflow/core/protobuf/saved_object_graph.proto
new file mode 100644
index 0000000..a59ad0e
--- /dev/null
+++ b/src/main/proto/tensorflow/core/protobuf/saved_object_graph.proto
@@ -0,0 +1,251 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "google/protobuf/any.proto";
+import "tensorflow/core/framework/tensor_shape.proto";
+import "tensorflow/core/framework/types.proto";
+import "tensorflow/core/framework/variable.proto";
+import "tensorflow/core/framework/versions.proto";
+import "tensorflow/core/protobuf/struct.proto";
+import "tensorflow/core/protobuf/trackable_object_graph.proto";
+
+option cc_enable_arenas = true;
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";
+
+// A SavedObjectGraph is part of object-based SavedModels in TF 2.0. It
+// describes the directed graph of Python objects (or equivalent in other
+// languages) that make up a model, with nodes[0] at the root.
+
+// SavedObjectGraph shares some structure with TrackableObjectGraph, but
+// SavedObjectGraph belongs to the MetaGraph and contains pointers to functions
+// and type information, while TrackableObjectGraph lives in the checkpoint
+// and contains pointers only to variable values.
+
+message SavedObjectGraph {
+ // Flattened list of objects in the object graph.
+ //
+ // The position of the object in this list indicates its id.
+ // Nodes[0] is considered the root node.
+ repeated SavedObject nodes = 1;
+
+ // Information about captures and output structures in concrete functions.
+ // Referenced from SavedBareConcreteFunction and SavedFunction.
+ map<string, SavedConcreteFunction> concrete_functions = 2;
+}
+
+message SavedObject {
+ // Objects which this object depends on: named edges in the dependency
+ // graph.
+ //
+ // Note: All kinds of SavedObject may have children, except
+ // "constant" and "captured_tensor".
+ repeated TrackableObjectGraph.TrackableObject.ObjectReference children = 1;
+
+ // Ordered list of dependencies that must be loaded before this object.
+ // SavedModel loads with the bottom-up approach, by first creating all objects
+ // (in the order defined by the dependencies), then connecting the edges.
+ repeated TrackableObjectGraph.TrackableObject.ObjectReference dependencies =
+ 15;
+
+ // Removed when forking SavedObject from TrackableObjectGraph.
+ reserved "attributes";
+ reserved 2;
+
+ // Slot variables owned by this object. This describes the three-way
+ // (optimizer, variable, slot variable) relationship; none of the three
+ // depend on the others directly.
+ //
+ // Note: currently only valid if kind == "user_object".
+ repeated TrackableObjectGraph.TrackableObject.SlotVariableReference
+ slot_variables = 3;
+
+ oneof kind {
+ SavedUserObject user_object = 4;
+ SavedAsset asset = 5;
+ SavedFunction function = 6;
+ SavedVariable variable = 7;
+ SavedBareConcreteFunction bare_concrete_function = 8;
+ SavedConstant constant = 9;
+ SavedResource resource = 10;
+ CapturedTensor captured_tensor = 12;
+ }
+
+ // Stores the functions used to save and restore this object. At most one of
+ // `saveable_objects` or `registered_saver` is defined for each SavedObject.
+ // See the comment below for the difference between SaveableObject and
+ // registered savers.
+ map<string, SaveableObject> saveable_objects = 11;
+
+ // The fields below are filled when the user serializes a registered Trackable
+ // class or an object with a registered saver function.
+ //
+ // Registered classes may save additional metadata and supersede the
+ // default loading process where nodes are recreated from the proto.
+ // If the registered class cannot be found, then the object will load as one
+ // one of the default trackable objects: Autotrackable (a class similar to
+ // tf.Module), tf.function, or tf.Variable.
+ //
+ // Unlike SaveableObjects, which store the functions for saving and restoring
+ // from tensors, registered savers allow Trackables to write checkpoint shards
+ // directly (e.g. for performance or coordination reasons).
+ // *All registered savers must be available when loading the SavedModel.*
+
+ // The name of the registered class of the form "{package}.{class_name}".
+ // This field is used to search for the registered class at loading time.
+ string registered_name = 13;
+ // The user-generated proto storing metadata for this object, to be passed to
+ // the registered classes's _deserialize_from_proto method when this object is
+ // loaded from the SavedModel.
+ google.protobuf.Any serialized_user_proto = 14;
+
+ // String name of the registered saver. At most one of `saveable_objects` or
+ // `registered_saver` is defined for each SavedObject.
+ string registered_saver = 16;
+}
+
+// A SavedUserObject is an object (in the object-oriented language of the
+// TensorFlow program) of some user- or framework-defined class other than
+// those handled specifically by the other kinds of SavedObjects.
+//
+// This object cannot be evaluated as a tensor, and therefore cannot be bound
+// to an input of a function.
+message SavedUserObject {
+ // Corresponds to a registration of the type to use in the loading program.
+ string identifier = 1;
+ // Version information from the producer of this SavedUserObject.
+ VersionDef version = 2;
+ // Metadata for deserializing this object.
+ //
+ // Deprecated! At the time of deprecation, Keras was the only user of this
+ // field, and its saving and loading code will be updated shortly.
+ // Please save your application-specific metadata to a separate file.
+ string metadata = 3 [deprecated = true];
+}
+
+// A SavedAsset points to an asset in the MetaGraph.
+//
+// When bound to a function this object evaluates to a tensor with the absolute
+// filename. Users should not depend on a particular part of the filename to
+// remain stable (e.g. basename could be changed).
+message SavedAsset {
+ // Index into `MetaGraphDef.asset_file_def[]` that describes the Asset.
+ //
+ // Only the field `AssetFileDef.filename` is used. Other fields, such as
+ // `AssetFileDef.tensor_info`, MUST be ignored.
+ int32 asset_file_def_index = 1;
+}
+
+// A function with multiple signatures, possibly with non-Tensor arguments.
+message SavedFunction {
+ repeated string concrete_functions = 1;
+ FunctionSpec function_spec = 2;
+}
+
+message CapturedTensor {
+ // Name of captured tensor
+ string name = 1;
+
+ // Name of concrete function which contains the computed graph tensor.
+ string concrete_function = 2;
+}
+
+// Stores low-level information about a concrete function. Referenced in either
+// a SavedFunction or a SavedBareConcreteFunction.
+message SavedConcreteFunction {
+ repeated int32 bound_inputs = 2;
+
+ // Input in canonicalized form that was received to create this concrete
+ // function.
+ StructuredValue canonicalized_input_signature = 3;
+ // Output that was the return value of this function after replacing all
+ // Tensors with TensorSpecs. This can be an arbitrary nested function and will
+ // be used to reconstruct the full structure from pure tensors.
+ StructuredValue output_signature = 4;
+}
+
+message SavedBareConcreteFunction {
+ // Identifies a SavedConcreteFunction.
+ string concrete_function_name = 1;
+
+ // A sequence of unique strings, one per Tensor argument.
+ repeated string argument_keywords = 2;
+ // The prefix of `argument_keywords` which may be identified by position.
+ int64 allowed_positional_arguments = 3;
+ // The spec of the function that this ConcreteFunction is traced from. This
+ // allows the ConcreteFunction to be called with nest structure inputs. This
+ // field may not be populated. If this field is absent, the concrete function
+ // can only be called with flat inputs.
+ // TODO(b/169361281): support calling saved ConcreteFunction with structured
+ // inputs in C++ SavedModel API.
+ FunctionSpec function_spec = 4;
+}
+
+message SavedConstant {
+ // An Operation name for a ConstantOp in this SavedObjectGraph's MetaGraph.
+ string operation = 1;
+}
+
+// Represents a Variable that is initialized by loading the contents from the
+// checkpoint.
+message SavedVariable {
+ DataType dtype = 1;
+ TensorShapeProto shape = 2;
+ bool trainable = 3;
+ VariableSynchronization synchronization = 4;
+ VariableAggregation aggregation = 5;
+ string name = 6;
+ string device = 7;
+ // List of component variables for a distributed variable.
+ //
+ // When this field is non-empty, the SavedVariable will be assumed
+ // to be a distributed variable defined by the components listed here.
+ //
+ // This is only supported by experimental loaders at the moment.
+ repeated SavedVariable experimental_distributed_variable_components = 8;
+}
+
+// Represents `FunctionSpec` used in `Function`. This represents a
+// function that has been wrapped as a TensorFlow `Function`.
+message FunctionSpec {
+ // Full arg spec from inspect.getfullargspec().
+ StructuredValue fullargspec = 1;
+ // Whether this represents a class method.
+ bool is_method = 2;
+ // The input signature, if specified.
+ StructuredValue input_signature = 5;
+
+ // Whether the function should be compiled by XLA.
+ //
+ // The public interface to `tf.function` uses an optional boolean to
+ // represent three distinct states for this field. Unfortunately, proto3
+ // removes the ability to explicitly check for the presence or absence of a
+ // field, so we instead map to an enum.
+ //
+ // See `tf.function` for details.
+ enum JitCompile {
+ DEFAULT = 0;
+ ON = 1;
+ OFF = 2;
+ }
+ JitCompile jit_compile = 6;
+
+ reserved 3, 4;
+}
+
+// A SavedResource represents a TF object that holds state during its lifetime.
+// An object of this type can have a reference to a:
+// create_resource() and an initialize() function.
+message SavedResource {
+ // A device specification indicating a required placement for the resource
+ // creation function, e.g. "CPU". An empty string allows the user to select a
+ // device.
+ string device = 1;
+}
+
+message SaveableObject {
+ // Node ids of concrete functions for saving and loading from a checkpoint.
+ // These functions save and restore directly from tensors.
+ int32 save_function = 2;
+ int32 restore_function = 3;
+}
diff --git a/src/main/proto/tensorflow/core/protobuf/saver.proto b/src/main/proto/tensorflow/core/protobuf/saver.proto
new file mode 100644
index 0000000..208468b
--- /dev/null
+++ b/src/main/proto/tensorflow/core/protobuf/saver.proto
@@ -0,0 +1,48 @@
+syntax = "proto3";
+
+package tensorflow;
+
+option cc_enable_arenas = true;
+option java_outer_classname = "SaverProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.util";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";
+
+// Protocol buffer representing the configuration of a Saver.
+message SaverDef {
+ // The name of the tensor in which to specify the filename when saving or
+ // restoring a model checkpoint.
+ string filename_tensor_name = 1;
+
+ // The operation to run when saving a model checkpoint.
+ string save_tensor_name = 2;
+
+ // The operation to run when restoring a model checkpoint.
+ string restore_op_name = 3;
+
+ // Maximum number of checkpoints to keep. If 0, no checkpoints are deleted.
+ int32 max_to_keep = 4;
+
+ // Shard the save files, one per device that has Variable nodes.
+ bool sharded = 5;
+
+ // How often to keep an additional checkpoint. If not specified, only the last
+ // "max_to_keep" checkpoints are kept; if specified, in addition to keeping
+ // the last "max_to_keep" checkpoints, an additional checkpoint will be kept
+ // for every n hours of training.
+ float keep_checkpoint_every_n_hours = 6;
+
+ // A version number that identifies a different on-disk checkpoint format.
+ // Usually, each subclass of BaseSaverBuilder works with a particular
+ // version/format. However, it is possible that the same builder may be
+ // upgraded to support a newer checkpoint format in the future.
+ enum CheckpointFormatVersion {
+ // Internal legacy format.
+ LEGACY = 0;
+ // Deprecated format: tf.Saver() which works with tensorflow::table::Table.
+ V1 = 1;
+ // Current format: more efficient.
+ V2 = 2;
+ }
+ CheckpointFormatVersion version = 7;
+}
diff --git a/src/main/proto/tensorflow/core/protobuf/struct.proto b/src/main/proto/tensorflow/core/protobuf/struct.proto
new file mode 100644
index 0000000..019382b
--- /dev/null
+++ b/src/main/proto/tensorflow/core/protobuf/struct.proto
@@ -0,0 +1,164 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "tensorflow/core/framework/tensor.proto";
+import "tensorflow/core/framework/tensor_shape.proto";
+import "tensorflow/core/framework/types.proto";
+
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";
+
+// `StructuredValue` represents a dynamically typed value representing various
+// data structures that are inspired by Python data structures typically used in
+// TensorFlow functions as inputs and outputs.
+//
+// For example when saving a Layer there may be a `training` argument. If the
+// user passes a boolean True/False, that switches between two concrete
+// TensorFlow functions. In order to switch between them in the same way after
+// loading the SavedModel, we need to represent "True" and "False".
+//
+// A more advanced example might be a function which takes a list of
+// dictionaries mapping from strings to Tensors. In order to map from
+// user-specified arguments `[{"a": tf.constant(1.)}, {"q": tf.constant(3.)}]`
+// after load to the right saved TensorFlow function, we need to represent the
+// nested structure and the strings, recording that we have a trace for anything
+// matching `[{"a": tf.TensorSpec(None, tf.float32)}, {"q": tf.TensorSpec([],
+// tf.float64)}]` as an example.
+//
+// Likewise functions may return nested structures of Tensors, for example
+// returning a dictionary mapping from strings to Tensors. In order for the
+// loaded function to return the same structure we need to serialize it.
+//
+// This is an ergonomic aid for working with loaded SavedModels, not a promise
+// to serialize all possible function signatures. For example we do not expect
+// to pickle generic Python objects, and ideally we'd stay language-agnostic.
+message StructuredValue {
+ // The kind of value.
+ oneof kind {
+ // Represents None.
+ NoneValue none_value = 1;
+
+ // Represents a double-precision floating-point value (a Python `float`).
+ double float64_value = 11;
+ // Represents a signed integer value, limited to 64 bits.
+ // Larger values from Python's arbitrary-precision integers are unsupported.
+ sint64 int64_value = 12;
+ // Represents a string of Unicode characters stored in a Python `str`.
+ // In Python 3, this is exactly what type `str` is.
+ // In Python 2, this is the UTF-8 encoding of the characters.
+ // For strings with ASCII characters only (as often used in TensorFlow code)
+ // there is effectively no difference between the language versions.
+ // The obsolescent `unicode` type of Python 2 is not supported here.
+ string string_value = 13;
+ // Represents a boolean value.
+ bool bool_value = 14;
+
+ // Represents a TensorShape.
+ tensorflow.TensorShapeProto tensor_shape_value = 31;
+ // Represents an enum value for dtype.
+ tensorflow.DataType tensor_dtype_value = 32;
+ // Represents a value for tf.TensorSpec.
+ TensorSpecProto tensor_spec_value = 33;
+ // Represents a value for tf.TypeSpec.
+ TypeSpecProto type_spec_value = 34;
+ // Represents a value for tf.BoundedTensorSpec.
+ BoundedTensorSpecProto bounded_tensor_spec_value = 35;
+
+ // Represents a list of `Value`.
+ ListValue list_value = 51;
+ // Represents a tuple of `Value`.
+ TupleValue tuple_value = 52;
+ // Represents a dict `Value`.
+ DictValue dict_value = 53;
+ // Represents Python's namedtuple.
+ NamedTupleValue named_tuple_value = 54;
+ // Represents a value for tf.Tensor.
+ tensorflow.TensorProto tensor_value = 55;
+ // Represents a value for np.ndarray.
+ tensorflow.TensorProto numpy_value = 56;
+ }
+}
+
+// Represents None.
+message NoneValue {}
+
+// Represents a Python list.
+message ListValue {
+ repeated StructuredValue values = 1;
+}
+
+// Represents a Python tuple.
+message TupleValue {
+ repeated StructuredValue values = 1;
+}
+
+// Represents a Python dict keyed by `str`.
+// The comment on Unicode from Value.string_value applies analogously.
+message DictValue {
+ map<string, StructuredValue> fields = 1;
+}
+
+// Represents a (key, value) pair.
+message PairValue {
+ string key = 1;
+ StructuredValue value = 2;
+}
+
+// Represents Python's namedtuple.
+message NamedTupleValue {
+ string name = 1;
+ repeated PairValue values = 2;
+}
+
+// A protobuf to represent tf.TensorSpec.
+message TensorSpecProto {
+ string name = 1;
+ tensorflow.TensorShapeProto shape = 2;
+ tensorflow.DataType dtype = 3;
+}
+
+// A protobuf to represent tf.BoundedTensorSpec.
+message BoundedTensorSpecProto {
+ string name = 1;
+ tensorflow.TensorShapeProto shape = 2;
+ tensorflow.DataType dtype = 3;
+ tensorflow.TensorProto minimum = 4;
+ tensorflow.TensorProto maximum = 5;
+}
+
+// Represents a tf.TypeSpec
+message TypeSpecProto {
+ enum TypeSpecClass {
+ UNKNOWN = 0;
+ SPARSE_TENSOR_SPEC = 1; // tf.SparseTensorSpec
+ INDEXED_SLICES_SPEC = 2; // tf.IndexedSlicesSpec
+ RAGGED_TENSOR_SPEC = 3; // tf.RaggedTensorSpec
+ TENSOR_ARRAY_SPEC = 4; // tf.TensorArraySpec
+ DATA_DATASET_SPEC = 5; // tf.data.DatasetSpec
+ DATA_ITERATOR_SPEC = 6; // IteratorSpec from data/ops/iterator_ops.py
+ OPTIONAL_SPEC = 7; // tf.OptionalSpec
+ PER_REPLICA_SPEC = 8; // PerReplicaSpec from distribute/values.py
+ VARIABLE_SPEC = 9; // tf.VariableSpec
+ ROW_PARTITION_SPEC = 10; // RowPartitionSpec from ragged/row_partition.py
+ reserved 11;
+ REGISTERED_TYPE_SPEC = 12; // The type registered as type_spec_class_name.
+ EXTENSION_TYPE_SPEC = 13; // Subclasses of tf.ExtensionType
+ }
+ TypeSpecClass type_spec_class = 1;
+
+ // The value returned by TypeSpec._serialize().
+ StructuredValue type_state = 2;
+
+ // The name of the TypeSpec class.
+ // * If type_spec_class == REGISTERED_TYPE_SPEC, the TypeSpec class is
+ // the one registered under this name. For types registered outside
+ // core TensorFlow by an add-on library, that library must be loaded
+ // before this value can be deserialized by nested_structure_coder.
+ // * If type_spec_class specifies a particular TypeSpec class, this field is
+ // redundant with the type_spec_class enum, and is only used for error
+ // reporting in older binaries that do not know the tupe_spec_class enum.
+ string type_spec_class_name = 3;
+
+ // The number of flat tensor components required by this TypeSpec.
+ int32 num_flat_components = 4;
+}
diff --git a/src/main/proto/tensorflow/core/protobuf/trackable_object_graph.proto b/src/main/proto/tensorflow/core/protobuf/trackable_object_graph.proto
new file mode 100644
index 0000000..2fe4fad
--- /dev/null
+++ b/src/main/proto/tensorflow/core/protobuf/trackable_object_graph.proto
@@ -0,0 +1,80 @@
+syntax = "proto3";
+
+package tensorflow;
+
+import "google/protobuf/wrappers.proto";
+
+option cc_enable_arenas = true;
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";
+
+// A TensorBundle addition which saves extra information about the objects which
+// own variables, allowing for more robust checkpoint loading into modified
+// programs.
+
+message TrackableObjectGraph {
+ message TrackableObject {
+ message ObjectReference {
+ // An index into `TrackableObjectGraph.nodes`, indicating the object
+ // being referenced.
+ int32 node_id = 1;
+ // A user-provided name for the edge.
+ string local_name = 2;
+ }
+
+ message SerializedTensor {
+ // A name for the Tensor. Simple variables have only one
+ // `SerializedTensor` named "VARIABLE_VALUE" by convention. This value may
+ // be restored on object creation as an optimization.
+ string name = 1;
+ // The full name of the variable/tensor, if applicable. Used to allow
+ // name-based loading of checkpoints which were saved using an
+ // object-based API. Should match the checkpoint key which would have been
+ // assigned by tf.train.Saver.
+ string full_name = 2;
+ // The generated name of the Tensor in the checkpoint.
+ string checkpoint_key = 3;
+ // Deprecated bool field for optional restore. This field has never been
+ // set to True.
+ reserved "optional_restore";
+ reserved 4;
+ }
+
+ message SlotVariableReference {
+ // An index into `TrackableObjectGraph.nodes`, indicating the
+ // variable object this slot was created for.
+ int32 original_variable_node_id = 1;
+ // The name of the slot (e.g. "m"/"v").
+ string slot_name = 2;
+ // An index into `TrackableObjectGraph.nodes`, indicating the
+ // `Object` with the value of the slot variable.
+ int32 slot_variable_node_id = 3;
+ }
+
+ // Objects which this object depends on.
+ repeated ObjectReference children = 1;
+ // Serialized data specific to this object.
+ repeated SerializedTensor attributes = 2;
+ // Slot variables owned by this object.
+ repeated SlotVariableReference slot_variables = 3;
+
+ // The registered saver used to save this object. If this saver is not
+ // present when loading the checkpoint, then loading will fail.
+ RegisteredSaver registered_saver = 4;
+
+ // Whether this object has checkpoint values or descendants with checkpoint
+ // values. This is computed at save time to avoid traversing the entire
+ // object graph proto when restoring (which also has to traverse the live
+ // object graph).
+ google.protobuf.BoolValue has_checkpoint_values = 5;
+ }
+
+ repeated TrackableObject nodes = 1;
+}
+
+message RegisteredSaver {
+ // The name of the registered saver/restore function.
+ string name = 1;
+
+ // Unique auto-generated name of the object.
+ string object_name = 2;
+}
diff --git a/src/main/proto/tensorflow/core/protobuf/verifier_config.proto b/src/main/proto/tensorflow/core/protobuf/verifier_config.proto
new file mode 100644
index 0000000..21885ff
--- /dev/null
+++ b/src/main/proto/tensorflow/core/protobuf/verifier_config.proto
@@ -0,0 +1,27 @@
+syntax = "proto3";
+
+package tensorflow;
+
+option cc_enable_arenas = true;
+option java_outer_classname = "VerifierConfigProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";
+
+// The config for graph verifiers.
+message VerifierConfig {
+ enum Toggle {
+ DEFAULT = 0;
+ ON = 1;
+ OFF = 2;
+ }
+
+ // Deadline for completion of all verification i.e. all the Toggle ON
+ // verifiers must complete execution within this time.
+ int64 verification_timeout_in_ms = 1;
+
+ // Perform structural validation on a tensorflow graph. Default is OFF.
+ Toggle structure_verifier = 2;
+
+ // Next tag: 3
+}
diff --git a/src/main/proto/tensorflow_serving/apis/classification.proto b/src/main/proto/tensorflow_serving/apis/classification.proto
new file mode 100644
index 0000000..4de0b7d
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/apis/classification.proto
@@ -0,0 +1,48 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+
+import "tensorflow_serving/apis/input.proto";
+import "tensorflow_serving/apis/model.proto";
+
+option cc_enable_arenas = true;
+
+// A single class.
+message Class {
+ // Label or name of the class.
+ string label = 1;
+ // Score for this class (e.g., the probability the item belongs to this
+ // class). As per the proto3 default-value semantics, if the score is missing,
+ // it should be treated as 0.
+ float score = 2;
+}
+
+// List of classes for a single item (tensorflow.Example).
+message Classifications {
+ repeated Class classes = 1;
+}
+
+// Contains one result per input example, in the same order as the input in
+// ClassificationRequest.
+message ClassificationResult {
+ repeated Classifications classifications = 1;
+}
+
+// RPC Interfaces
+
+message ClassificationRequest {
+ // Model Specification. If version is not specified, will use the latest
+ // (numerical) version.
+ ModelSpec model_spec = 1;
+
+ // Input data.
+ tensorflow.serving.Input input = 2;
+}
+
+message ClassificationResponse {
+ // Effective Model Specification used for classification.
+ ModelSpec model_spec = 2;
+
+ // Result of the classification.
+ ClassificationResult result = 1;
+}
diff --git a/src/main/proto/tensorflow_serving/apis/get_model_metadata.proto b/src/main/proto/tensorflow_serving/apis/get_model_metadata.proto
new file mode 100644
index 0000000..5d765d8
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/apis/get_model_metadata.proto
@@ -0,0 +1,30 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+option cc_enable_arenas = true;
+
+import "google/protobuf/any.proto";
+import "tensorflow/core/protobuf/meta_graph.proto";
+import "tensorflow_serving/apis/model.proto";
+
+// Message returned for "signature_def" field.
+message SignatureDefMap {
+ map<string, SignatureDef> signature_def = 1;
+};
+
+message GetModelMetadataRequest {
+ // Model Specification indicating which model we are querying for metadata.
+ // If version is not specified, will use the latest (numerical) version.
+ ModelSpec model_spec = 1;
+ // Metadata fields to get. Currently supported: "signature_def".
+ repeated string metadata_field = 2;
+}
+
+message GetModelMetadataResponse {
+ // Model Specification indicating which model this metadata belongs to.
+ ModelSpec model_spec = 1;
+ // Map of metadata field name to metadata field. The options for metadata
+ // field name are listed in GetModelMetadataRequest. Currently supported:
+ // "signature_def".
+ map<string, google.protobuf.Any> metadata = 2;
+}
diff --git a/src/main/proto/tensorflow_serving/apis/get_model_status.proto b/src/main/proto/tensorflow_serving/apis/get_model_status.proto
new file mode 100644
index 0000000..8881247
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/apis/get_model_status.proto
@@ -0,0 +1,68 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+
+import "tensorflow_serving/apis/model.proto";
+import "tensorflow_serving/apis/status.proto";
+
+option cc_enable_arenas = true;
+
+// GetModelStatusRequest contains a ModelSpec indicating the model for which
+// to get status.
+message GetModelStatusRequest {
+ // Model Specification. If version is not specified, information about all
+ // versions of the model will be returned. If a version is specified, the
+ // status of only that version will be returned.
+ ModelSpec model_spec = 1;
+}
+
+// Version number, state, and status for a single version of a model.
+message ModelVersionStatus {
+ // Model version.
+ int64 version = 1;
+
+ // States that map to ManagerState enum in
+ // tensorflow_serving/core/servable_state.h
+ enum State {
+ // Default value.
+ UNKNOWN = 0;
+
+ // The manager is tracking this servable, but has not initiated any action
+ // pertaining to it.
+ START = 10;
+
+ // The manager has decided to load this servable. In particular, checks
+ // around resource availability and other aspects have passed, and the
+ // manager is about to invoke the loader's Load() method.
+ LOADING = 20;
+
+ // The manager has successfully loaded this servable and made it available
+ // for serving (i.e. GetServableHandle(id) will succeed). To avoid races,
+ // this state is not reported until *after* the servable is made
+ // available.
+ AVAILABLE = 30;
+
+ // The manager has decided to make this servable unavailable, and unload
+ // it. To avoid races, this state is reported *before* the servable is
+ // made unavailable.
+ UNLOADING = 40;
+
+ // This servable has reached the end of its journey in the manager. Either
+ // it loaded and ultimately unloaded successfully, or it hit an error at
+ // some point in its lifecycle.
+ END = 50;
+ }
+
+ // Model state.
+ State state = 2;
+
+ // Model status.
+ StatusProto status = 3;
+}
+
+// Response for ModelStatusRequest on successful run.
+message GetModelStatusResponse {
+ // Version number and status information for applicable model version(s).
+ repeated ModelVersionStatus model_version_status = 1
+ [json_name = "model_version_status"];
+}
diff --git a/src/main/proto/tensorflow_serving/apis/inference.proto b/src/main/proto/tensorflow_serving/apis/inference.proto
new file mode 100644
index 0000000..16e85ce
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/apis/inference.proto
@@ -0,0 +1,59 @@
+// This file contains messages for various machine learning inferences
+// such as regression and classification.
+//
+// In many applications more than one type of inference is desired for a single
+// input. For example, given meteorologic data an application may want to
+// perform a classification to determine if we should expect rain, snow or sun
+// and also perform a regression to predict the temperature.
+// Sharing the single input data between two inference tasks can be accomplished
+// using MultiInferenceRequest and MultiInferenceResponse.
+
+syntax = "proto3";
+
+option cc_enable_arenas = true;
+
+import "tensorflow_serving/apis/classification.proto";
+import "tensorflow_serving/apis/input.proto";
+import "tensorflow_serving/apis/model.proto";
+import "tensorflow_serving/apis/regression.proto";
+
+package tensorflow.serving;
+
+// Inference request such as classification, regression, etc...
+message InferenceTask {
+ // Model Specification. If version is not specified, will use the latest
+ // (numerical) version.
+ // All ModelSpecs in a MultiInferenceRequest must access the same model name.
+ ModelSpec model_spec = 1;
+
+ // Signature's method_name. Should be one of the method names defined in
+ // third_party/tensorflow/python/saved_model/signature_constants.py.
+ // e.g. "tensorflow/serving/classify".
+ string method_name = 2;
+}
+
+// Inference result, matches the type of request or is an error.
+message InferenceResult {
+ ModelSpec model_spec = 1;
+
+ oneof result {
+ ClassificationResult classification_result = 2;
+ RegressionResult regression_result = 3;
+ }
+}
+
+// Inference request containing one or more requests.
+message MultiInferenceRequest {
+ // Inference tasks.
+ repeated InferenceTask tasks = 1;
+
+ // Input data.
+ Input input = 2;
+}
+
+// Inference request containing one or more responses.
+message MultiInferenceResponse {
+ // List of results; one for each InferenceTask in the request, returned in the
+ // same order as the request.
+ repeated InferenceResult results = 1;
+}
diff --git a/src/main/proto/tensorflow_serving/apis/input.proto b/src/main/proto/tensorflow_serving/apis/input.proto
new file mode 100644
index 0000000..e47ff43
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/apis/input.proto
@@ -0,0 +1,82 @@
+// Input used in serving APIs. Based on the tensorflow.Example family of
+// feature representations.
+
+syntax = "proto3";
+
+option cc_enable_arenas = true;
+
+import "tensorflow/core/example/example.proto";
+
+package tensorflow.serving;
+
+// Specifies one or more fully independent input Examples.
+// See examples at:
+// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto
+message ExampleList {
+ repeated tensorflow.Example examples = 1;
+}
+
+// Specifies one or more independent input Examples, with a common context
+// Example.
+//
+// The common use case for context is to cleanly and optimally specify some
+// features that are common across multiple examples.
+//
+// See example below with a search query as the context and multiple restaurants
+// to perform some inference on.
+//
+// context: {
+// features: {
+// feature: {
+// key : "query"
+// value: {
+// bytes_list: {
+// value: [ "pizza" ]
+// }
+// }
+// }
+// }
+// }
+// examples: {
+// features: {
+// feature: {
+// key : "cuisine"
+// value: {
+// bytes_list: {
+// value: [ "Pizzeria" ]
+// }
+// }
+// }
+// }
+// }
+// examples: {
+// features: {
+// feature: {
+// key : "cuisine"
+// value: {
+// bytes_list: {
+// value: [ "Taqueria" ]
+// }
+// }
+// }
+// }
+// }
+//
+// Implementations of ExampleListWithContext merge the context Example into each
+// of the Examples. Note that feature keys must not be duplicated between the
+// Examples and context Example, or the behavior is undefined.
+//
+// See also:
+// tensorflow/core/example/example.proto
+// https://developers.google.com/protocol-buffers/docs/proto3#maps
+message ExampleListWithContext {
+ repeated tensorflow.Example examples = 1;
+ tensorflow.Example context = 2;
+}
+
+message Input {
+ oneof kind {
+ ExampleList example_list = 1 [lazy = true];
+ ExampleListWithContext example_list_with_context = 2 [lazy = true];
+ }
+}
diff --git a/src/main/proto/tensorflow_serving/apis/logging.proto b/src/main/proto/tensorflow_serving/apis/logging.proto
new file mode 100644
index 0000000..0f9e876
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/apis/logging.proto
@@ -0,0 +1,20 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+
+import "tensorflow_serving/apis/model.proto";
+import "tensorflow_serving/config/logging_config.proto";
+
+option cc_enable_arenas = true;
+
+// Metadata logged along with the request logs.
+message LogMetadata {
+ ModelSpec model_spec = 1;
+ SamplingConfig sampling_config = 2;
+ // List of tags used to load the relevant MetaGraphDef from SavedModel.
+ repeated string saved_model_tags = 3;
+ int64 timestamp_secs = 4; // Seconds since epoch.
+ string dc = 5; // Datacenter where the request was logged.
+ string request_origin = 6; // Request origin identifier.
+ string request_criticality = 7; // Request QoS.
+}
diff --git a/src/main/proto/tensorflow_serving/apis/model.proto b/src/main/proto/tensorflow_serving/apis/model.proto
new file mode 100644
index 0000000..afd9590
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/apis/model.proto
@@ -0,0 +1,36 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+
+import "google/protobuf/wrappers.proto";
+
+option cc_enable_arenas = true;
+
+// Metadata for an inference request such as the model name and version.
+message ModelSpec {
+ // Required servable name.
+ string name = 1;
+
+ // Optional choice of which version of the model to use.
+ //
+ // Expected to be left unset in the common case. Should be specified when
+ // there is a strong version consistency requirement (e.g. when the model
+ // signature changes across versions and requests need to be
+ // version-specific).
+ //
+ // When left unspecified, the system will serve the best available version.
+ // This is typically the latest version, though during version transitions,
+ // notably when serving on a fleet of instances, may be either the previous or
+ // new version.
+ oneof version_choice {
+ // Use this specific version number.
+ google.protobuf.Int64Value version = 2;
+
+ // Use the version associated with the given label.
+ string version_label = 4;
+ }
+
+ // A named signature to evaluate. If unspecified, the default signature will
+ // be used.
+ string signature_name = 3;
+}
diff --git a/src/main/proto/tensorflow_serving/apis/model_management.proto b/src/main/proto/tensorflow_serving/apis/model_management.proto
new file mode 100644
index 0000000..2eba09b
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/apis/model_management.proto
@@ -0,0 +1,25 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+
+import "tensorflow_serving/apis/status.proto";
+import "tensorflow_serving/config/model_server_config.proto";
+
+option cc_enable_arenas = true;
+
+message ReloadConfigRequest {
+ ModelServerConfig config = 1;
+ repeated string metric_names = 2;
+}
+
+message ReloadConfigResponse {
+ StatusProto status = 1;
+ repeated Metric metric = 2;
+}
+
+message Metric {
+ string name = 1;
+ oneof value_increase {
+ int64 int64_value_increase = 2;
+ }
+}
diff --git a/src/main/proto/tensorflow_serving/apis/model_service.proto b/src/main/proto/tensorflow_serving/apis/model_service.proto
new file mode 100644
index 0000000..29a3b07
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/apis/model_service.proto
@@ -0,0 +1,24 @@
+syntax = "proto3";
+
+option cc_enable_arenas = true;
+
+import "tensorflow_serving/apis/get_model_status.proto";
+import "tensorflow_serving/apis/model_management.proto";
+
+package tensorflow.serving;
+
+// ModelService provides methods to query and update the state of the server,
+// e.g. which models/versions are being served.
+service ModelService {
+ // Gets status of model. If the ModelSpec in the request does not specify
+ // version, information about all versions of the model will be returned. If
+ // the ModelSpec in the request does specify a version, the status of only
+ // that version will be returned.
+ rpc GetModelStatus(GetModelStatusRequest) returns (GetModelStatusResponse);
+
+ // Reloads the set of served models. The new config supersedes the old one,
+ // so if a model is omitted from the new config it will be unloaded and no
+ // longer served.
+ rpc HandleReloadConfigRequest(ReloadConfigRequest)
+ returns (ReloadConfigResponse);
+}
diff --git a/src/main/proto/tensorflow_serving/apis/predict.proto b/src/main/proto/tensorflow_serving/apis/predict.proto
new file mode 100644
index 0000000..831785a
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/apis/predict.proto
@@ -0,0 +1,191 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+
+import "tensorflow/core/framework/tensor.proto";
+import "tensorflow_serving/apis/model.proto";
+
+option cc_enable_arenas = true;
+
+// PredictRequest specifies which TensorFlow model to run, as well as
+// how inputs are mapped to tensors and how outputs are filtered before
+// returning to user.
+message PredictRequest {
+ // Model Specification. If version is not specified, will use the latest
+ // (numerical) version.
+ ModelSpec model_spec = 1;
+
+ // Input tensors.
+ // Names of input tensor are alias names. The mapping from aliases to real
+ // input tensor names is stored in the SavedModel export as a prediction
+ // SignatureDef under the 'inputs' field.
+ map<string, TensorProto> inputs = 2;
+
+ // Output filter.
+ // Names specified are alias names. The mapping from aliases to real output
+ // tensor names is stored in the SavedModel export as a prediction
+ // SignatureDef under the 'outputs' field.
+ // Only tensors specified here will be run/fetched and returned, with the
+ // exception that when none is specified, all tensors specified in the
+ // named signature will be run/fetched and returned.
+ repeated string output_filter = 3;
+
+ // Reserved field 4.
+ reserved 4;
+
+ // Options for streaming requests to control how multiple requests/responses
+ // are handled within a single stream.
+ PredictStreamedOptions predict_streamed_options = 5;
+
+ // Client identifier to group requests belonging to a specific entity.
+ // Example entities can be product ids, service names, user ids etc.
+ // Servers can use this to optimize placement, caching and colocation.
+ // TODO(b/329897437): Migrate to client_id in RequestOptions.
+ optional bytes client_id = 6;
+
+ // Options for PredictRequest.
+ message RequestOptions {
+ // Client identifier to group requests belonging to a specific entity.
+ // Example entities can be product ids, service names, user ids etc.
+ // Servers can use this to optimize placement, caching and colocation.
+ optional bytes client_id = 1;
+
+ // Deterministic mode for the request. When specified, model servers will
+ // reduce numeric instability based on different mode selections.
+ enum DeterministicMode {
+ DETERMINISTIC_MODE_UNSPECIFIED = 0;
+
+ // Only supported in disaggregated serving. When set, the request will be
+ // pinned to a fixed decoder slot index that's deterministic across
+ // processes.
+ FIXED_DECODER_SLOT = 1;
+ }
+
+ optional DeterministicMode deterministic_mode = 2;
+
+ // Only supported in disaggregated serving. When set, additional arrays from
+ // prefill will be returned if available.
+ optional bool return_additional_arrays_from_prefill = 3;
+
+ // Returns these stop tokens in response if the model stops at them. The
+ // model may stop at other tokens, but will not return them in the response.
+ repeated int64 return_stoptokens = 4;
+ }
+
+ optional RequestOptions request_options = 7;
+}
+
+// Options only used for streaming requests that control how inputs/ouputs are
+// handled in the stream.
+message PredictStreamedOptions {
+ // Request state used to handle splitting of requests. NONE is the
+ // default when the stream request is not split and used for a single-turn,
+ // single request.
+ //
+ // SPLIT is used when multiple streamed requests are used to generate a
+ // logical request. END_SPLIT should be called for the last split of the
+ // multi-turn request to start the processing of the current turn. NONE can
+ // not be interspersed with SPLIT and END_SPLIT messages.
+ // If another request is sent on the same stream after END_SPLIT, it can be
+ // either SPLIT or END_SPLIT to start accumulating input or trigger the next
+ // model turn respectively.
+ //
+ // Some examples with a mix of request states and the logical request.
+ //
+ // Example 1:
+ // NONE
+ //
+ // Single turn, single request.
+ //
+ // Example 2 :
+ // END_SPLIT
+ //
+ // Will be treated as a single logical input request for a single turn,
+ // similar to Example 1.
+ //
+ // Example 3:
+ // SPLIT
+ // SPLIT
+ // END_SPLIT
+ //
+ // Will be treated as a single logical input request for a single turn,
+ // similar to Example 1.
+ //
+ // Example 4:
+ // END_SPLIT
+ // END_SPLIT
+ //
+ // Will be treated as two logical turn requests (1. END_SPLIT 2. END_SPLIT)
+ //
+ // Example 5:
+ // SPLIT
+ // END_SPLIT
+ // SPLIT
+ // SPLIT
+ // END_SPLIT
+ //
+ // Will be treated as two logical turn requests (1. SPLIT, END_SPLIT 2. SPLIT,
+ // SPLIT, END_SPLIT)
+ //
+ // Incorrect Example 1:
+ // NONE
+ // END_SPLIT
+ //
+ // Invalid because NONE and END_SPLIT are interspersed.
+ //
+ // Incorrect Example 2:
+ // SPLIT
+ // SPLIT
+ //
+ // Invalid because END_SPLIT is never called.
+ //
+ // Incorrect Example 3:
+ // SPLIT
+ // NONE
+ // SPLIT
+ // END_SPLIT
+ //
+ // Invalid because NONE is interspersed with SPLIT/END_SPLIT.
+
+ enum RequestState {
+ NONE = 0;
+ SPLIT = 1;
+ END_SPLIT = 2;
+ }
+
+ // Request state used to handle segmentation of requests.
+ RequestState request_state = 1;
+
+ // Input tensors split dimensions.
+ // Defines the dimension used to split input tensors specified
+ // in PredictRequest.inputs. The dimension will be used
+ // for concatenation of multiple SPLIT requests.
+ //
+ // For input tensor in PredictRequest.inputs that are not contained in this
+ // map, the tensors from the first SPLIT request will be used.
+ //
+ // For example, with an original input tensor of [[1, 2, 3, 4], [5, 6, 7, 8]].
+ //
+ // For a split dimension of 0 and two requests (SPLIT and END_SPLIT), the
+ // input tensors for request 1 should be [1, 2, 3, 4] and request 2 should be
+ // be [5, 6, 7, 8].
+ //
+ // For a split dimension of 1 and two requests (SPLIT and END_SPLIT), the
+ // input tensors for request 1 should be [[1, 2], [5, 6]] and request 2 should
+ // be [[3, 4], [7, 8]].
+ map<string, int32> split_dimensions = 2;
+
+ // If true, there will be a single PredictResponse output.
+ // If false, output can be split into 1 or more PredictResponses.
+ // Value of this field should be the same for all requests in the stream.
+ bool return_single_response = 3;
+}
+
+// Response for PredictRequest on successful run.
+message PredictResponse {
+ // Effective Model Specification used to process PredictRequest.
+ ModelSpec model_spec = 2;
+
+ // Output tensors.
+ map<string, TensorProto> outputs = 1;
+}
diff --git a/src/main/proto/tensorflow_serving/apis/prediction_log.proto b/src/main/proto/tensorflow_serving/apis/prediction_log.proto
new file mode 100644
index 0000000..9deebc7
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/apis/prediction_log.proto
@@ -0,0 +1,55 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+
+import "tensorflow_serving/apis/classification.proto";
+import "tensorflow_serving/apis/inference.proto";
+import "tensorflow_serving/apis/logging.proto";
+import "tensorflow_serving/apis/predict.proto";
+import "tensorflow_serving/apis/regression.proto";
+import "tensorflow_serving/apis/session_service.proto";
+
+option cc_enable_arenas = true;
+
+message ClassifyLog {
+ ClassificationRequest request = 1;
+ ClassificationResponse response = 2;
+}
+
+message RegressLog {
+ RegressionRequest request = 1;
+ RegressionResponse response = 2;
+}
+
+message PredictLog {
+ PredictRequest request = 1;
+ PredictResponse response = 2;
+}
+
+message PredictStreamedLog {
+ repeated PredictRequest request = 1;
+ repeated PredictResponse response = 2;
+}
+
+message MultiInferenceLog {
+ MultiInferenceRequest request = 1;
+ MultiInferenceResponse response = 2;
+}
+
+message SessionRunLog {
+ SessionRunRequest request = 1;
+ SessionRunResponse response = 2;
+}
+
+// Logged model inference request.
+message PredictionLog {
+ LogMetadata log_metadata = 1;
+ oneof log_type {
+ ClassifyLog classify_log = 2;
+ RegressLog regress_log = 3;
+ PredictLog predict_log = 6;
+ PredictStreamedLog predict_streamed_log = 7;
+ MultiInferenceLog multi_inference_log = 4;
+ SessionRunLog session_run_log = 5;
+ }
+}
diff --git a/src/main/proto/tensorflow_serving/apis/prediction_service.proto b/src/main/proto/tensorflow_serving/apis/prediction_service.proto
new file mode 100644
index 0000000..44e6554
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/apis/prediction_service.proto
@@ -0,0 +1,31 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+option cc_enable_arenas = true;
+
+import "tensorflow_serving/apis/classification.proto";
+import "tensorflow_serving/apis/get_model_metadata.proto";
+import "tensorflow_serving/apis/inference.proto";
+import "tensorflow_serving/apis/predict.proto";
+import "tensorflow_serving/apis/regression.proto";
+
+// open source marker; do not remove
+// PredictionService provides access to machine-learned models loaded by
+// model_servers.
+service PredictionService {
+ // Classify.
+ rpc Classify(ClassificationRequest) returns (ClassificationResponse);
+
+ // Regress.
+ rpc Regress(RegressionRequest) returns (RegressionResponse);
+
+ // Predict -- provides access to loaded TensorFlow model.
+ rpc Predict(PredictRequest) returns (PredictResponse);
+
+ // MultiInference API for multi-headed models.
+ rpc MultiInference(MultiInferenceRequest) returns (MultiInferenceResponse);
+
+ // GetModelMetadata - provides access to metadata for loaded models.
+ rpc GetModelMetadata(GetModelMetadataRequest)
+ returns (GetModelMetadataResponse);
+}
diff --git a/src/main/proto/tensorflow_serving/apis/regression.proto b/src/main/proto/tensorflow_serving/apis/regression.proto
new file mode 100644
index 0000000..5fb79ba
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/apis/regression.proto
@@ -0,0 +1,37 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+
+import "tensorflow_serving/apis/input.proto";
+import "tensorflow_serving/apis/model.proto";
+
+option cc_enable_arenas = true;
+
+// Regression result for a single item (tensorflow.Example).
+message Regression {
+ float value = 1;
+}
+
+// Contains one result per input example, in the same order as the input in
+// RegressionRequest.
+message RegressionResult {
+ repeated Regression regressions = 1;
+}
+
+// RPC interfaces.
+
+message RegressionRequest {
+ // Model Specification. If version is not specified, will use the latest
+ // (numerical) version.
+ ModelSpec model_spec = 1;
+
+ // Input data.
+ tensorflow.serving.Input input = 2;
+}
+
+message RegressionResponse {
+ // Effective Model Specification used for regression.
+ ModelSpec model_spec = 2;
+
+ RegressionResult result = 1;
+}
diff --git a/src/main/proto/tensorflow_serving/apis/session_service.proto b/src/main/proto/tensorflow_serving/apis/session_service.proto
new file mode 100644
index 0000000..cf53c54
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/apis/session_service.proto
@@ -0,0 +1,56 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+
+import "tensorflow/core/protobuf/config.proto";
+import "tensorflow/core/protobuf/named_tensor.proto";
+import "tensorflow_serving/apis/model.proto";
+
+option cc_enable_arenas = true;
+
+message SessionRunRequest {
+ // Model Specification. If version is not specified, will use the latest
+ // (numerical) version.
+ ModelSpec model_spec = 1;
+
+ // Tensors to be fed in the step. Each feed is a named tensor.
+ repeated NamedTensorProto feed = 2;
+
+ // Fetches. A list of tensor names. The caller expects a tensor to
+ // be returned for each fetch[i] (see RunResponse.tensor). The
+ // order of specified fetches does not change the execution order.
+ repeated string fetch = 3;
+
+ // Target Nodes. A list of node names. The named nodes will be run
+ // to but their outputs will not be fetched.
+ repeated string target = 4;
+
+ // If true, treat names in feed/fetch/target as alias names than actual tensor
+ // names (that appear in the TF graph). Alias names are resolved to actual
+ // names using `SignatureDef` in SavedModel associated with the model.
+ bool tensor_name_is_alias = 6;
+
+ // Options for the run call. **Currently ignored.**
+ RunOptions options = 5;
+}
+
+message SessionRunResponse {
+ // Effective Model Specification used for session run.
+ ModelSpec model_spec = 3;
+
+ // NOTE: The order of the returned tensors may or may not match
+ // the fetch order specified in RunRequest.
+ repeated NamedTensorProto tensor = 1;
+
+ // Returned metadata if requested in the options.
+ RunMetadata metadata = 2;
+}
+
+// SessionService defines a service with which a client can interact to execute
+// Tensorflow model inference. The SessionService::SessionRun method is similar
+// to MasterService::RunStep of Tensorflow, except that all sessions are ready
+// to run, and you request a specific model/session with ModelSpec.
+service SessionService {
+ // Runs inference of a given model.
+ rpc SessionRun(SessionRunRequest) returns (SessionRunResponse);
+}
diff --git a/src/main/proto/tensorflow_serving/apis/status.proto b/src/main/proto/tensorflow_serving/apis/status.proto
new file mode 100644
index 0000000..332d1b7
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/apis/status.proto
@@ -0,0 +1,17 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+
+import "tensorflow/core/protobuf/error_codes.proto";
+
+option cc_enable_arenas = true;
+
+// Status that corresponds to Status in
+// third_party/tensorflow/core/lib/core/status.h.
+message StatusProto {
+ // Error code.
+ error.Code error_code = 1 [json_name = "error_code"];
+
+ // Error message. Will only be set if an error was encountered.
+ string error_message = 2 [json_name = "error_message"];
+}
diff --git a/src/main/proto/tensorflow_serving/config/file_system_storage_path_source.proto b/src/main/proto/tensorflow_serving/config/file_system_storage_path_source.proto
new file mode 100644
index 0000000..6924fe4
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/config/file_system_storage_path_source.proto
@@ -0,0 +1,83 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+
+// Config proto for FileSystemStoragePathSource.
+message FileSystemStoragePathSourceConfig {
+ // A policy that dictates which version(s) of a servable should be served.
+ message ServableVersionPolicy {
+ // Serve the latest versions (i.e. the ones with the highest version
+ // numbers), among those found on disk.
+ //
+ // This is the default policy, with the default number of versions as 1.
+ message Latest {
+ // Number of latest versions to serve. (The default is 1.)
+ uint32 num_versions = 1;
+ }
+
+ // Serve all versions found on disk.
+ message All {}
+
+ // Serve a specific version (or set of versions).
+ //
+ // This policy is useful for rolling back to a specific version, or for
+ // canarying a specific version while still serving a separate stable
+ // version.
+ message Specific {
+ // The version numbers to serve.
+ repeated int64 versions = 1;
+ }
+
+ oneof policy_choice {
+ Latest latest = 100;
+ All all = 101;
+ Specific specific = 102;
+ }
+ }
+
+ // A servable name and base path to look for versions of the servable.
+ message ServableToMonitor {
+ // The servable name to supply in aspired-versions callback calls. Child
+ // paths of 'base_path' are considered to be versions of this servable.
+ string servable_name = 1;
+
+ // The path to monitor, i.e. look for child paths of the form base_path/123.
+ string base_path = 2;
+
+ // The policy to determines the number of versions of the servable to be
+ // served at the same time.
+ tensorflow.serving.FileSystemStoragePathSourceConfig.ServableVersionPolicy
+ servable_version_policy = 4;
+
+ reserved 3; // Legacy version_policy definition.
+ }
+
+ // The servables to monitor for new versions, and aspire.
+ repeated ServableToMonitor servables = 5;
+
+ // How long to wait between file-system polling to look for children of
+ // 'base_path', in seconds.
+ //
+ // If set to zero, filesystem will be polled exactly once. If set to a
+ // negative value (for testing use only), polling will be entirely disabled.
+ int64 file_system_poll_wait_seconds = 3;
+
+ // If true, then FileSystemStoragePathSource::Create() and ::UpdateConfig()
+ // fail if, for any configured servables, the file system doesn't currently
+ // contain at least one version under the base path.
+ // (Otherwise, it will emit a warning and keep pinging the file system to
+ // check for a version to appear later.)
+ // DEPRECATED: Use 'servable_versions_always_present' instead, which includes
+ // this behavior.
+ // TODO(b/30898016): Remove 2019-10-31 or later.
+ bool fail_if_zero_versions_at_startup = 4 [deprecated = true];
+
+ // If true, the servable is always expected to exist on the underlying
+ // filesystem. FileSystemStoragePathSource::Create() and ::UpdateConfig() will
+ // fail if, for any configured servables, the file system doesn't currently
+ // contain at least one version under the base path. In addition, if a polling
+ // loop find the base path empty, it will not unload existing servables.
+ bool servable_versions_always_present = 6;
+
+ reserved 1, 2;
+}
diff --git a/src/main/proto/tensorflow_serving/config/log_collector_config.proto b/src/main/proto/tensorflow_serving/config/log_collector_config.proto
new file mode 100644
index 0000000..4ce01d3
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/config/log_collector_config.proto
@@ -0,0 +1,12 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+option cc_enable_arenas = true;
+
+message LogCollectorConfig {
+ // Identifies the type of the LogCollector we will use to collect these logs.
+ string type = 1;
+
+ // The prefix to use for the filenames of the logs.
+ string filename_prefix = 2;
+}
diff --git a/src/main/proto/tensorflow_serving/config/logging_config.proto b/src/main/proto/tensorflow_serving/config/logging_config.proto
new file mode 100644
index 0000000..31fbe94
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/config/logging_config.proto
@@ -0,0 +1,29 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+
+import "tensorflow_serving/config/log_collector_config.proto";
+
+option cc_enable_arenas = true;
+
+message SamplingConfig {
+ // Requests will be logged uniformly at random with this probability.
+ // Valid range: [0, 1.0].
+ double sampling_rate = 1;
+
+ // Attributes of requests that can be optionally sampled.
+ // Note: Enabling more attributes will increase logging storage requirements.
+ enum Attributes {
+ ATTR_DEFAULT = 0x0;
+ ATTR_REQUEST_ORIGIN = 0x1;
+ ATTR_REQUEST_CRITICALITY = 0x2;
+ }
+ // Bitwise OR of above attributes
+ int32 attributes = 2;
+}
+
+// Configuration for logging query/responses.
+message LoggingConfig {
+ LogCollectorConfig log_collector_config = 1;
+ SamplingConfig sampling_config = 2;
+}
diff --git a/src/main/proto/tensorflow_serving/config/model_server_config.proto b/src/main/proto/tensorflow_serving/config/model_server_config.proto
new file mode 100644
index 0000000..cadc2b6
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/config/model_server_config.proto
@@ -0,0 +1,85 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+
+import "google/protobuf/any.proto";
+import "tensorflow_serving/config/file_system_storage_path_source.proto";
+import "tensorflow_serving/config/logging_config.proto";
+
+option cc_enable_arenas = true;
+
+// The type of model.
+// TODO(b/31336131): DEPRECATED.
+enum ModelType {
+ MODEL_TYPE_UNSPECIFIED = 0 [deprecated = true];
+ TENSORFLOW = 1 [deprecated = true];
+ OTHER = 2 [deprecated = true];
+}
+
+// Common configuration for loading a model being served.
+message ModelConfig {
+ // Name of the model.
+ string name = 1;
+
+ // Base path to the model, excluding the version directory.
+ // E.g> for a model at /foo/bar/my_model/123, where 123 is the version, the
+ // base path is /foo/bar/my_model.
+ //
+ // (This can be changed once a model is in serving, *if* the underlying data
+ // remains the same. Otherwise there are no guarantees about whether the old
+ // or new data will be used for model versions currently loaded.)
+ string base_path = 2;
+
+ // Type of model.
+ // TODO(b/31336131): DEPRECATED. Please use 'model_platform' instead.
+ ModelType model_type = 3 [deprecated = true];
+
+ // Type of model (e.g. "tensorflow").
+ //
+ // (This cannot be changed once a model is in serving.)
+ string model_platform = 4;
+
+ reserved 5, 9;
+
+ // Version policy for the model indicating which version(s) of the model to
+ // load and make available for serving simultaneously.
+ // The default option is to serve only the latest version of the model.
+ //
+ // (This can be changed once a model is in serving.)
+ FileSystemStoragePathSourceConfig.ServableVersionPolicy model_version_policy =
+ 7;
+
+ // String labels to associate with versions of the model, allowing inference
+ // queries to refer to versions by label instead of number. Multiple labels
+ // can map to the same version, but not vice-versa.
+ //
+ // An envisioned use-case for these labels is canarying tentative versions.
+ // For example, one can assign labels "stable" and "canary" to two specific
+ // versions. Perhaps initially "stable" is assigned to version 0 and "canary"
+ // to version 1. Once version 1 passes canary, one can shift the "stable"
+ // label to refer to version 1 (at that point both labels map to the same
+ // version -- version 1 -- which is fine). Later once version 2 is ready to
+ // canary one can move the "canary" label to version 2. And so on.
+ map<string, int64> version_labels = 8;
+
+ // Configures logging requests and responses, to the model.
+ //
+ // (This can be changed once a model is in serving.)
+ LoggingConfig logging_config = 6;
+}
+
+// Static list of models to be loaded for serving.
+message ModelConfigList {
+ repeated ModelConfig config = 1;
+}
+
+// ModelServer config.
+message ModelServerConfig {
+ // ModelServer takes either a static file-based model config list or an Any
+ // proto representing custom model config that is fetched dynamically at
+ // runtime (through network RPC, custom service, etc.).
+ oneof config {
+ ModelConfigList model_config_list = 1;
+ google.protobuf.Any custom_model_config = 2;
+ }
+}
diff --git a/src/main/proto/tensorflow_serving/config/monitoring_config.proto b/src/main/proto/tensorflow_serving/config/monitoring_config.proto
new file mode 100644
index 0000000..9da3700
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/config/monitoring_config.proto
@@ -0,0 +1,19 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+option cc_enable_arenas = true;
+
+// Configuration for Prometheus monitoring.
+message PrometheusConfig {
+ // Whether to expose Prometheus metrics.
+ bool enable = 1;
+
+ // The endpoint to expose Prometheus metrics.
+ // If not specified, PrometheusExporter::kPrometheusPath value is used.
+ string path = 2;
+}
+
+// Configuration for monitoring.
+message MonitoringConfig {
+ PrometheusConfig prometheus_config = 1;
+}
diff --git a/src/main/proto/tensorflow_serving/config/platform_config.proto b/src/main/proto/tensorflow_serving/config/platform_config.proto
new file mode 100644
index 0000000..4e506b3
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/config/platform_config.proto
@@ -0,0 +1,19 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+option cc_enable_arenas = true;
+
+import "google/protobuf/any.proto";
+
+// Configuration for a servable platform e.g. tensorflow or other ML systems.
+message PlatformConfig {
+ // The config proto for a SourceAdapter in the StoragePathSourceAdapter
+ // registry.
+ google.protobuf.Any source_adapter_config = 1;
+};
+
+message PlatformConfigMap {
+ // A map from a platform name to a platform config. The platform name is used
+ // in ModelConfig.model_platform.
+ map<string, PlatformConfig> platform_configs = 1;
+};
diff --git a/src/main/proto/tensorflow_serving/config/ssl_config.proto b/src/main/proto/tensorflow_serving/config/ssl_config.proto
new file mode 100644
index 0000000..0e51cd6
--- /dev/null
+++ b/src/main/proto/tensorflow_serving/config/ssl_config.proto
@@ -0,0 +1,16 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+option cc_enable_arenas = true;
+
+// Configuration for a secure gRPC channel
+message SSLConfig {
+ // private server key for SSL
+ string server_key = 1;
+ // public server certificate
+ string server_cert = 2;
+ // custom certificate authority
+ string custom_ca = 3;
+ // valid client certificate required ?
+ bool client_verify = 4;
+};
diff --git a/src/main/proto/xla/tsl/protobuf/coordination_config.proto b/src/main/proto/xla/tsl/protobuf/coordination_config.proto
new file mode 100644
index 0000000..645c992
--- /dev/null
+++ b/src/main/proto/xla/tsl/protobuf/coordination_config.proto
@@ -0,0 +1,78 @@
+syntax = "proto3";
+
+package tensorflow;
+
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";
+
+// Represents a job type and the number of tasks under this job.
+// For example, ("worker", 20) implies that there will be 20 worker tasks.
+message CoordinatedJob {
+ string name = 1;
+ int32 num_tasks = 2;
+}
+
+// Coordination service configuration parameters.
+// The system picks appropriate values for fields that are not set.
+message CoordinationServiceConfig {
+ // Type of coordination service implementation to enable.
+ // For example, setting the service type as "standalone" starts a service
+ // instance on the leader task to provide the coordination services such as
+ // heartbeats and consistent key-value store.
+ string service_type = 1;
+
+ // Address where the coordination service instance is hosted.
+ string service_leader = 2;
+
+ // Whether to enable the health check mechanism.
+ bool enable_health_check = 3;
+
+ // Maximum wait time for all members in the cluster to be registered.
+ int64 cluster_register_timeout_in_ms = 4;
+
+ // Denotes if we should synchronize the agents' register attempts by blocking
+ // on a barrier. This is useful for synchronized restarts.
+ bool cluster_register_with_barrier = 14;
+
+ // Heartbeat timeout, if a task does not record heartbeat in this time
+ // window, it will be considered disconnected.
+ // Note: This is also used as a grace period to accept any heartbeats after
+ // the agent has disconnected, to account for the lag time between the service
+ // recording the state change and the agent stopping heartbeats.
+ int64 heartbeat_timeout_in_ms = 5;
+
+ // The list of `CoordinatedJob`s that will register in coordination service.
+ reserved 6;
+ repeated CoordinatedJob coordinated_job_list = 10;
+
+ // Denotes how long to wait for all coordination agents to reach the barriers
+ // (after the first shutdown request) before disconnecting together. If
+ // set to 0, no barrier is imposed upon shutdown and each worker can
+ // disconnect individually.
+ int64 shutdown_barrier_timeout_in_ms = 7;
+
+ // If set, agents do not make an explicit Shutdown() call. Service will only
+ // find out about the disconnecte agent via stale heartbeats. Used for
+ // testing.
+ bool agent_destruction_without_shutdown = 8;
+
+ // The list of jobs which are recoverable. If a task in this list fails,
+ // it will not propagate error to other tasks.
+ // If empty, no jobs will be recoverable and every task failure will cause
+ // error propagation to other tasks.
+ repeated string recoverable_jobs = 9;
+
+ // If a task restarts with a new incarnation, we may allow it to reconnect
+ // silently. This is useful when we know that a task can immediately resume
+ // work upon re-connecting to the service.
+ bool allow_new_incarnation_to_reconnect = 11;
+
+ // Disables coordination service.
+ // Some libraries enable coordination service by default even if the user did
+ // not specify any config. This field allows users to explicitly disable
+ // coordination service under all situations.
+ bool force_disable = 12;
+
+ // Use long polling to get error from coordination service as the error
+ // propagation mechanism.
+ bool poll_for_error_from_service_at_startup = 13;
+}
diff --git a/src/main/proto/xla/tsl/protobuf/error_codes.proto b/src/main/proto/xla/tsl/protobuf/error_codes.proto
new file mode 100644
index 0000000..c873d55
--- /dev/null
+++ b/src/main/proto/xla/tsl/protobuf/error_codes.proto
@@ -0,0 +1,155 @@
+syntax = "proto3";
+
+// TODO(b/247876220): Change package and java_package once we figure out how to
+// migrate.
+
+package tensorflow.error;
+
+option cc_enable_arenas = true;
+option java_outer_classname = "ErrorCodesProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/google/tsl/tsl/go/protobuf/for_core_protos_go_proto";
+
+// The canonical error codes for TensorFlow APIs.
+//
+// Warnings:
+//
+// - Do not change any numeric assignments.
+// - Changes to this list should only be made if there is a compelling
+// need that can't be satisfied in another way. Such changes
+// must be approved by at least two OWNERS.
+// - These error codes must match gRPC and protobuf error codes (except for
+// DO_NOT_USE_RESERVED_FOR_FUTURE_EXPANSION_USE_DEFAULT_IN_SWITCH_INSTEAD_).
+//
+// Sometimes multiple error codes may apply. Services should return
+// the most specific error code that applies. For example, prefer
+// OUT_OF_RANGE over FAILED_PRECONDITION if both codes apply.
+// Similarly prefer NOT_FOUND or ALREADY_EXISTS over FAILED_PRECONDITION.
+enum Code {
+ // Not an error; returned on success
+ OK = 0;
+
+ // The operation was cancelled (typically by the caller).
+ CANCELLED = 1;
+
+ // Unknown error. An example of where this error may be returned is
+ // if a Status value received from another address space belongs to
+ // an error-space that is not known in this address space. Also
+ // errors raised by APIs that do not return enough error information
+ // may be converted to this error.
+ UNKNOWN = 2;
+
+ // Client specified an invalid argument. Note that this differs
+ // from FAILED_PRECONDITION. INVALID_ARGUMENT indicates arguments
+ // that are problematic regardless of the state of the system
+ // (e.g., a malformed file name).
+ INVALID_ARGUMENT = 3;
+
+ // Deadline expired before operation could complete. For operations
+ // that change the state of the system, this error may be returned
+ // even if the operation has completed successfully. For example, a
+ // successful response from a server could have been delayed long
+ // enough for the deadline to expire.
+ DEADLINE_EXCEEDED = 4;
+
+ // Some requested entity (e.g., file or directory) was not found.
+ // For privacy reasons, this code *may* be returned when the client
+ // does not have the access right to the entity.
+ NOT_FOUND = 5;
+
+ // Some entity that we attempted to create (e.g., file or directory)
+ // already exists.
+ ALREADY_EXISTS = 6;
+
+ // The caller does not have permission to execute the specified
+ // operation. PERMISSION_DENIED must not be used for rejections
+ // caused by exhausting some resource (use RESOURCE_EXHAUSTED
+ // instead for those errors). PERMISSION_DENIED must not be
+ // used if the caller can not be identified (use UNAUTHENTICATED
+ // instead for those errors).
+ PERMISSION_DENIED = 7;
+
+ // The request does not have valid authentication credentials for the
+ // operation.
+ UNAUTHENTICATED = 16;
+
+ // Some resource has been exhausted, perhaps a per-user quota, or
+ // perhaps the entire file system is out of space.
+ RESOURCE_EXHAUSTED = 8;
+
+ // Operation was rejected because the system is not in a state
+ // required for the operation's execution. For example, directory
+ // to be deleted may be non-empty, an rmdir operation is applied to
+ // a non-directory, etc.
+ //
+ // A litmus test that may help a service implementor in deciding
+ // between FAILED_PRECONDITION, ABORTED, and UNAVAILABLE:
+ // (a) Use UNAVAILABLE if the client can retry just the failing call.
+ // (b) Use ABORTED if the client should retry at a higher-level
+ // (e.g., restarting a read-modify-write sequence).
+ // (c) Use FAILED_PRECONDITION if the client should not retry until
+ // the system state has been explicitly fixed. E.g., if an "rmdir"
+ // fails because the directory is non-empty, FAILED_PRECONDITION
+ // should be returned since the client should not retry unless
+ // they have first fixed up the directory by deleting files from it.
+ // (d) Use FAILED_PRECONDITION if the client performs conditional
+ // REST Get/Update/Delete on a resource and the resource on the
+ // server does not match the condition. E.g., conflicting
+ // read-modify-write on the same resource.
+ FAILED_PRECONDITION = 9;
+
+ // The operation was aborted, typically due to a concurrency issue
+ // like sequencer check failures, transaction aborts, etc.
+ //
+ // See litmus test above for deciding between FAILED_PRECONDITION,
+ // ABORTED, and UNAVAILABLE.
+ ABORTED = 10;
+
+ // Operation tried to iterate past the valid input range. E.g., seeking or
+ // reading past end of file.
+ //
+ // Unlike INVALID_ARGUMENT, this error indicates a problem that may
+ // be fixed if the system state changes. For example, a 32-bit file
+ // system will generate INVALID_ARGUMENT if asked to read at an
+ // offset that is not in the range [0,2^32-1], but it will generate
+ // OUT_OF_RANGE if asked to read from an offset past the current
+ // file size.
+ //
+ // There is a fair bit of overlap between FAILED_PRECONDITION and
+ // OUT_OF_RANGE. We recommend using OUT_OF_RANGE (the more specific
+ // error) when it applies so that callers who are iterating through
+ // a space can easily look for an OUT_OF_RANGE error to detect when
+ // they are done.
+ OUT_OF_RANGE = 11;
+
+ // Operation is not implemented or not supported/enabled in this service.
+ UNIMPLEMENTED = 12;
+
+ // Internal errors. Means some invariant expected by the underlying
+ // system has been broken. If you see one of these errors,
+ // something is very broken.
+ INTERNAL = 13;
+
+ // The service is currently unavailable. This is a most likely a
+ // transient condition and may be corrected by retrying with
+ // a backoff.
+ //
+ // See litmus test above for deciding between FAILED_PRECONDITION,
+ // ABORTED, and UNAVAILABLE.
+ UNAVAILABLE = 14;
+
+ // Unrecoverable data loss or corruption.
+ DATA_LOSS = 15;
+
+ // An extra enum entry to prevent people from writing code that
+ // fails to compile when a new code is added.
+ //
+ // Nobody should ever reference this enumeration entry. In particular,
+ // if you write C++ code that switches on this enumeration, add a default:
+ // case instead of a case that mentions this enumeration entry.
+ //
+ // Nobody should rely on the value (currently 20) listed here. It
+ // may change in the future.
+ DO_NOT_USE_RESERVED_FOR_FUTURE_EXPANSION_USE_DEFAULT_IN_SWITCH_INSTEAD_ = 20;
+}
diff --git a/src/main/proto/xla/tsl/protobuf/rpc_options.proto b/src/main/proto/xla/tsl/protobuf/rpc_options.proto
new file mode 100644
index 0000000..35c5dbe
--- /dev/null
+++ b/src/main/proto/xla/tsl/protobuf/rpc_options.proto
@@ -0,0 +1,41 @@
+syntax = "proto3";
+
+package tensorflow;
+
+option go_package = "github.com/google/tsl/tsl/go/protobuf/for_core_protos_go_proto";
+
+// RPC options for distributed runtime.
+message RPCOptions {
+ // If true, always use RPC to contact the session target.
+ //
+ // If false (the default option), TensorFlow may use an optimized
+ // transport for client-master communication that avoids the RPC
+ // stack. This option is primarily for used testing the RPC stack.
+ bool use_rpc_for_inprocess_master = 1;
+
+ // The compression algorithm to be used. One of "deflate", "gzip".
+ string compression_algorithm = 2;
+
+ // If compression_algorithm is set, the compression level to be used.
+ // From 0 (no compression), up to 3.
+ int32 compression_level = 3;
+
+ // Setting cache_rpc_response to true will enable sender side caching of
+ // response for RecvTensorAsync and RecvBufAsync to allow receiver to retry
+ // requests . This is only necessary when the network fabric is experiencing a
+ // significant error rate. Without it we'll fail a step on an network error,
+ // while with it we'll be able to complete long steps (like complex
+ // initializations) in the face of some network errors during RecvTensor.
+ bool cache_rpc_response = 4;
+
+ // Disables TCP connection sharing when opening a new RPC channel.
+ bool disable_session_connection_sharing = 5;
+
+ // Setting num_channels_per_target > 0 allows uses of multiple channels to
+ // communicate to the same target. This can be used to improve the aggregate
+ // throughput on high speed links (e.g 100G) where single connection is not
+ // sufficient to maximize link utilization. Note that a single RPC only goes
+ // on a single channel, this only helps in situations where there are multiple
+ // transfers to the same target overlapping in time.
+ int32 num_channels_per_target = 6;
+}
diff --git a/src/test/java/com/github/tadayosi/tensorflow/serving/client/ConfigurationTest.java b/src/test/java/com/github/tadayosi/tensorflow/serving/client/ConfigurationTest.java
new file mode 100644
index 0000000..b1246fd
--- /dev/null
+++ b/src/test/java/com/github/tadayosi/tensorflow/serving/client/ConfigurationTest.java
@@ -0,0 +1,34 @@
+package com.github.tadayosi.tensorflow.serving.client;
+
+import org.junit.jupiter.api.AfterEach;
+import org.junit.jupiter.api.Test;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertNotNull;
+
+class ConfigurationTest {
+
+ @AfterEach
+ void cleanSystemProperties() {
+ System.clearProperty("tfsc4j.target");
+ System.clearProperty("tfsc4j.credentials");
+ }
+
+ @Test
+ void testLoad() {
+ var config = Configuration.load();
+ assertNotNull(config);
+ }
+
+ @Test
+ @SuppressWarnings("OptionalGetWithoutIsPresent")
+ void testSystemProperties() {
+ System.setProperty("tfsc4j.target", "dns:///test.com:8501");
+ System.setProperty("tfsc4j.credentials", "aaaaa");
+
+ var config = Configuration.load();
+
+ assertEquals("dns:///test.com:8501", config.getTarget().get());
+ assertEquals("aaaaa", config.getCredentials().get());
+ }
+}
diff --git a/src/test/java/com/github/tadayosi/tensorflow/serving/client/TensorFlowServingClientTest.java b/src/test/java/com/github/tadayosi/tensorflow/serving/client/TensorFlowServingClientTest.java
new file mode 100644
index 0000000..f43498b
--- /dev/null
+++ b/src/test/java/com/github/tadayosi/tensorflow/serving/client/TensorFlowServingClientTest.java
@@ -0,0 +1,30 @@
+package com.github.tadayosi.tensorflow.serving.client;
+
+import com.google.protobuf.Int64Value;
+import org.junit.jupiter.api.Test;
+import org.testcontainers.junit.jupiter.Testcontainers;
+import tensorflow.serving.GetModelStatus;
+import tensorflow.serving.Model;
+
+import static org.junit.jupiter.api.Assertions.assertNotNull;
+
+@Testcontainers
+class TensorFlowServingClientTest extends TensorFlowServingTestSupport {
+
+ private static final String DEFAULT_MODEL = "half_plus_two";
+ private static final String DEFAULT_MODEL_VERSION = "123";
+ private static final String TEST_DATA = "src/test/resources/data/kitten.jpg";
+
+ @Test
+ void testGetModelStatus() {
+ var request = GetModelStatus.GetModelStatusRequest.newBuilder()
+ .setModelSpec(Model.ModelSpec.newBuilder()
+ .setName(DEFAULT_MODEL)
+ .setVersion(Int64Value.of(Long.parseLong(DEFAULT_MODEL_VERSION))))
+ .build();
+ var response = client.getModelStatus(request);
+ assertNotNull(response);
+ System.out.println(response);
+ }
+
+}
diff --git a/src/test/java/com/github/tadayosi/tensorflow/serving/client/TensorFlowServingTestSupport.java b/src/test/java/com/github/tadayosi/tensorflow/serving/client/TensorFlowServingTestSupport.java
new file mode 100644
index 0000000..667a657
--- /dev/null
+++ b/src/test/java/com/github/tadayosi/tensorflow/serving/client/TensorFlowServingTestSupport.java
@@ -0,0 +1,31 @@
+package com.github.tadayosi.tensorflow.serving.client;
+
+import org.junit.jupiter.api.BeforeEach;
+import org.testcontainers.containers.GenericContainer;
+import org.testcontainers.containers.wait.strategy.Wait;
+import org.testcontainers.images.builder.Transferable;
+import org.testcontainers.junit.jupiter.Container;
+import org.testcontainers.utility.DockerImageName;
+
+class TensorFlowServingTestSupport {
+
+ private static final String IMAGE_NAME = "bitnami/tensorflow-serving";
+
+ @Container
+ @SuppressWarnings("resource")
+ static GenericContainer<?> tensorflowServing = new GenericContainer<>(DockerImageName.parse(IMAGE_NAME))
+ .withExposedPorts(8501)
+ .withCopyToContainer(Transferable.of("testdata/saved_model_half_plus_two_cpu"), "/bitnami/model-data")
+ .withEnv("TENSORFLOW_SERVING_MODEL_NAME", "half_plus_two")
+ .withEnv("TENSORFLOW_SERVING_ENABLE_MONITORING", "yes")
+ .waitingFor(Wait.forListeningPorts(8501));
+
+ protected TensorFlowServingClient client;
+
+ @BeforeEach
+ void setUp() {
+ client = TensorFlowServingClient.builder()
+ .target("localhost:" + tensorflowServing.getMappedPort(8501))
+ .build();
+ }
+}
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diff --git a/src/test/resources/log4j2.properties b/src/test/resources/log4j2.properties
new file mode 100644
index 0000000..604d9cb
--- /dev/null
+++ b/src/test/resources/log4j2.properties
@@ -0,0 +1,17 @@
+appender.out.type = Console
+appender.out.name = out
+appender.out.layout.type = PatternLayout
+appender.out.layout.pattern = %d{HH:mm:ss} %level {%thread} %-30.30c{1} : %msg%n
+appender.out.filter.threshold.type = ThresholdFilter
+appender.out.filter.threshold.level = info
+
+appender.file.type = File
+appender.file.name = file
+appender.file.append = true
+appender.file.fileName = target/test.log
+appender.file.layout.type = PatternLayout
+appender.file.layout.pattern = %d{HH:mm:ss.SSS} %-5level {%thread} %-30.30c{1} : %msg%n
+
+rootLogger.level = info
+rootLogger.appenderRef.out.ref = out
+rootLogger.appenderRef.file.ref = file
diff --git a/src/test/resources/testdata/saved_model_half_plus_two_cpu/00000123/assets/foo.txt b/src/test/resources/testdata/saved_model_half_plus_two_cpu/00000123/assets/foo.txt
new file mode 100644
index 0000000..f9ff036
--- /dev/null
+++ b/src/test/resources/testdata/saved_model_half_plus_two_cpu/00000123/assets/foo.txt
@@ -0,0 +1 @@
+asset-file-contents
\ No newline at end of file
diff --git a/src/test/resources/testdata/saved_model_half_plus_two_cpu/00000123/saved_model.pb b/src/test/resources/testdata/saved_model_half_plus_two_cpu/00000123/saved_model.pb
new file mode 100644
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diff --git a/src/test/resources/testdata/saved_model_half_plus_two_cpu/00000123/variables/variables.data-00000-of-00001 b/src/test/resources/testdata/saved_model_half_plus_two_cpu/00000123/variables/variables.data-00000-of-00001
new file mode 100644
index 0000000000000000000000000000000000000000..a378d172b66af3595a20b49a506ef16b03d8d017
GIT binary patch
literal 20
TcmZQzV6bO^U<U>U2QUTz4K@M4
literal 0
HcmV?d00001
diff --git a/src/test/resources/testdata/saved_model_half_plus_two_cpu/00000123/variables/variables.index b/src/test/resources/testdata/saved_model_half_plus_two_cpu/00000123/variables/variables.index
new file mode 100644
index 0000000000000000000000000000000000000000..5ae2cc0f2d45a17b3b86dbac99d1b2a64e2be29a
GIT binary patch
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HcmV?d00001
diff --git a/src/test/resources/tfsc4j.properties b/src/test/resources/tfsc4j.properties
new file mode 100644
index 0000000..5523cc0
--- /dev/null
+++ b/src/test/resources/tfsc4j.properties
@@ -0,0 +1,2 @@
+#target = <target>
+#credentials = <credentials>