Update submodule onnxruntime-extensions. (#8282)

* Update submodule onnxruntime-extensions to latest.

* Add document for onnxruntime-extensions.

* Update cgmanifest.json for onnxruntime-extensions.

* Add example in JavaScript.

Co-authored-by: Zuwei Zhao <zuzhao@microsoft.com>

cgmanifests/submodules/cgmanifest.json

@@ -381,6 +381,16 @@
         "comments": "git submodule at cmake/external/onnx-tensorrt/third_party/onnx/third_party/pybind11/tools/clang"
       }
     },
+    {
+      "component": {
+        "type": "git",
+        "git": {
+          "commitHash": "b708aeaaf44416bba84b87206fb550196fc60d5a",
+          "repositoryUrl": "https://github.com/microsoft/onnxruntime-extensions.git"
+        },
+        "comments": "git submodule at cmake/external/onnxruntime-extensions"
+      }
+    },
     {
       "component": {
         "type": "git",

cmake/external/onnxruntime-extensions

@@ -1 +1 @@
-Subproject commit 800e360ef3603c743392b96a9adca3e210e4d2b2
+Subproject commit b708aeaaf44416bba84b87206fb550196fc60d5a

docs/onnxruntime_extensions.md

@@ -0,0 +1,93 @@
+# ONNXRuntime Extensions
+
+ONNXRuntime Extensions is a comprehensive package to extend the capability of the ONNX conversion and inference. Please visit the documentation [onnxruntime-extensions](https://github.com/microsoft/onnxruntime-extensions) to learn more about ONNXRuntime Extensions.
+
+## Custom Operators Supported
+onnxruntime-extensions supports many useful custom operators to enhance the text processing capability of ONNXRuntime, which include some widely used **string operators** and popular **tokenizers**. For custom operators supported and how to use them, please check the documentation [custom operators](https://github.com/microsoft/onnxruntime-extensions/blob/main/docs/custom_text_ops.md).
+
+## Build ONNXRuntime with Extensions
+We have supported build onnxruntime-extensions as a static library and link it into ONNXRuntime. To enable custom operators in onnxruntime-extensions, you should add argument `--enable_onnxruntime_extensions` when build ONNXRuntime.
+
+## E2E Example using Custom Operators
+A common NLP task would probably contain several steps, including pre-processing, DL model and post-processing. It would be very efficient and productive to convert the pre/post processing code snippets into ONNX model since ONNX graph is actually a computation graph, and it can represent the most programming code, theoretically.
+
+Here is an E2E NLP example to show the usage of onnxruntime-extensions:
+### Create E2E Model
+You could use ONNX helper functions to create an ONNX model with custom operators.
+```python
+import onnx
+from onnx import helper
+
+# ...
+e2e_nodes = []
+
+# tokenizer node
+tokenizer_node = helper.make_node(
+    'GPT2Tokenizer', # custom operator supported in onnxruntime-extensions
+    inputs=['input_str'],
+    outputs=['token_ids', 'attention_mask'],
+    vocab=get_file_content(vocab_file),
+    merges=get_file_content(merges_file),
+    name='gpt2_tokenizer',
+    domain='ai.onnx.contrib' # domain of custom operator
+)
+e2e_nodes.append(tokenizer_node)
+
+# deep learning model
+dl_model = onnx.load("dl_model.onnx")
+dl_nodes = dl_model.graph.node
+e2e_nodes.extend(dl_nodes)
+
+# construct E2E ONNX graph and model
+e2e_graph = helper.make_graph(
+    e2e_nodes,
+    'e2e_graph',
+    [input_tensors],
+    [output_tensors],
+)
+# ...
+```
+For more usage of ONNX helper, please visit the document [Python API Overview](https://github.com/onnx/onnx/blob/master/docs/PythonAPIOverview.md).
+
+### Run E2E Model in Python
+```python
+import onnxruntime as _ort
+from onnxruntime_extensions import get_library_path as _lib_path
+
+so = _ort.SessionOptions()
+# register onnxruntime-extensions library
+so.register_custom_ops_library(_lib_path())
+
+# run onnxruntime session
+sess = _ort.InferenceSession(e2e_model, so)
+sess.run(...)
+```
+
+### Run E2E Model in JavaScript
+To run E2E ONNX model in JavaScript, you need to first [prepare ONNX Runtime WebAssembly artifacts](https://github.com/microsoft/onnxruntime/tree/master/js), include the generated `ort.min.js`, and then load and run the model in JS.
+```js
+// use an async context to call onnxruntime functions
+async function main() {
+    try {
+        // create a new session and load the e2e model
+        const session = await ort.InferenceSession.create('./e2e_model.onnx');
+
+        // prepare inputs
+        const tensorA = new ort.Tensor(...);
+        const tensorB = new ort.Tensor(...);
+
+        // prepare feeds: use model input names as keys
+        const feeds = { a: tensorA, b: tensorB };
+
+        // feed inputs and run
+        const results = await session.run(feeds);
+
+        // read from results
+        const dataC = results.c.data;
+        document.write(`data of result tensor 'c': ${dataC}`);
+
+    } catch (e) {
+        document.write(`failed to inference ONNX model: ${e}.`);
+    }
+}
+```