Export to ONNX doc refocused on using optimum, added tflite (#23434)

* doc refocused on using optimum, tflite

* minor updates to fix checks

* Apply suggestions from code review
Co-authored-by: regisss <15324346+regisss@users.noreply.github.com>

* TFLite to separate page, added links

* Removed the onnx list builder

* make style

* Update docs/source/en/serialization.mdx
Co-authored-by: regisss <15324346+regisss@users.noreply.github.com>

---------
Co-authored-by: regisss <15324346+regisss@users.noreply.github.com>

docs/source/en/_toctree.yml

@@ -93,6 +93,8 @@
       title: Run training on Amazon SageMaker
     - local: serialization
       title: Export to ONNX
+    - local: tflite
+      title: Export to TFLite
     - local: torchscript
       title: Export to TorchScript
     - local: benchmarks

docs/source/en/tflite.mdx

+<!--Copyright 2023 The HuggingFace Team. 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.
+-->
+
+# Export to TFLite
+
+[TensorFlow Lite](https://www.tensorflow.org/lite/guide) is a lightweight framework for deploying machine learning models 
+on resource-constrained devices, such as mobile phones, embedded systems, and Internet of Things (IoT) devices. 
+TFLite is designed to optimize and run models efficiently on these devices with limited computational power, memory, and 
+power consumption.
+A TensorFlow Lite model is represented in a special efficient portable format identified by the `.tflite` file extension. 
+
+🤗 Optimum offers functionality to export 🤗 Transformers models to TFLite through the `exporters.tflite` module. 
+For the list of supported model architectures, please refer to [🤗 Optimum documentation](https://huggingface.co/docs/optimum/exporters/tflite/overview).
+
+To export a model to TFLite, install the required dependencies:
+ 
+```bash
+pip install optimum[exporters-tf]
+```
+
+To check out all available arguments, refer to the [🤗 Optimum docs](https://huggingface.co/docs/optimum/main/en/exporters/tflite/usage_guides/export_a_model), 
+or view help in command line:
+
+```bash
+optimum-cli export tflite --help
+```
+
+To export a model's checkpoint from the 🤗 Hub, for example, `bert-base-uncased`, run the following command:
+
+```bash
+optimum-cli export tflite --model bert-base-uncased --sequence_length 128 bert_tflite/
+```
+
+You should see the logs indicating progress and showing where the resulting `model.tflite` is saved, like this:
+
+```bash
+Validating TFLite model...
+	-[✓] TFLite model output names match reference model (logits)
+	- Validating TFLite Model output "logits":
+		-[✓] (1, 128, 30522) matches (1, 128, 30522)
+		-[x] values not close enough, max diff: 5.817413330078125e-05 (atol: 1e-05)
+The TensorFlow Lite export succeeded with the warning: The maximum absolute difference between the output of the reference model and the TFLite exported model is not within the set tolerance 1e-05:
+- logits: max diff = 5.817413330078125e-05.
+ The exported model was saved at: bert_tflite
+ ```
+
+The example above illustrates exporting a checkpoint from 🤗 Hub. When exporting a local model, first make sure that you 
+saved both the model's weights and tokenizer files in the same directory (`local_path`). When using CLI, pass the 
+`local_path` to the `model` argument instead of the checkpoint name on 🤗 Hub. 
\ No newline at end of file

utils/check_table.py

@@ -173,56 +173,9 @@ def check_model_table(overwrite=False):
             )
 
 
-def has_onnx(model_type):
-    """
-    Returns whether `model_type` is supported by ONNX (by checking if there is an ONNX config) or not.
-    """
-    config_mapping = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING
-    if model_type not in config_mapping:
-        return False
-    config = config_mapping[model_type]
-    config_module = config.__module__
-    module = transformers_module
-    for part in config_module.split(".")[1:]:
-        module = getattr(module, part)
-    config_name = config.__name__
-    onnx_config_name = config_name.replace("Config", "OnnxConfig")
-    return hasattr(module, onnx_config_name)
-
-
-def get_onnx_model_list():
-    """
-    Return the list of models supporting ONNX.
-    """
-    config_mapping = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING
-    model_names = config_mapping = transformers_module.models.auto.configuration_auto.MODEL_NAMES_MAPPING
-    onnx_model_types = [model_type for model_type in config_mapping.keys() if has_onnx(model_type)]
-    onnx_model_names = [model_names[model_type] for model_type in onnx_model_types]
-    onnx_model_names.sort(key=lambda x: x.upper())
-    return "\n".join([f"- {name}" for name in onnx_model_names]) + "\n"
-
-
-def check_onnx_model_list(overwrite=False):
-    """Check the model list in the serialization.mdx is consistent with the state of the lib and maybe `overwrite`."""
-    current_list, start_index, end_index, lines = _find_text_in_file(
-        filename=os.path.join(PATH_TO_DOCS, "serialization.mdx"),
-        start_prompt="<!--This table is automatically generated by `make fix-copies`, do not fill manually!-->",
-        end_prompt="In the next two sections, we'll show you how to:",
-    )
-    new_list = get_onnx_model_list()
-
-    if current_list != new_list:
-        if overwrite:
-            with open(os.path.join(PATH_TO_DOCS, "serialization.mdx"), "w", encoding="utf-8", newline="\n") as f:
-                f.writelines(lines[:start_index] + [new_list] + lines[end_index:])
-        else:
-            raise ValueError("The list of ONNX-supported models needs an update. Run `make fix-copies` to fix this.")
-
-
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
     args = parser.parse_args()
 
     check_model_table(args.fix_and_overwrite)
-    check_onnx_model_list(args.fix_and_overwrite)