Change transformers.onnx to use optimum.exporters.onnx (#20529)

* Change transformers.onnx to use optimum.exporters.onnx

* Update doc

* Remove print

* Fix transformers.onnx cli

* Update documentation

* Update documentation

* Small fixes

* Fix log message

* Apply suggestions

* Update src/transformers/onnx/__main__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Apply suggestions

* Add missing line break

* Ran make fix-copies

* Update src/transformers/onnx/__main__.py
Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>

* Update src/transformers/onnx/__main__.py
Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>
Co-authored-by: Michael Benayoun <michael@huggingface.co>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>

docs/source/en/serialization.mdx

@@ -17,15 +17,6 @@ exporting them to a serialized format that can be loaded and executed on special
 runtimes and hardware. In this guide, we'll show you how to export 🤗 Transformers
 models to [ONNX (Open Neural Network eXchange)](http://onnx.ai).
 
-<Tip>
-
-Once exported, a model can be optimized for inference via techniques such as
-quantization and pruning. If you are interested in optimizing your models to run with
-maximum efficiency, check out the [🤗 Optimum
-library](https://github.com/huggingface/optimum).
-
-</Tip>
-
 ONNX is an open standard that defines a common set of operators and a common file format
 to represent deep learning models in a wide variety of frameworks, including PyTorch and
 TensorFlow. When a model is exported to the ONNX format, these operators are used to
@@ -41,6 +32,23 @@ you to convert model checkpoints to an ONNX graph by leveraging configuration ob
 These configuration objects come ready made for a number of model architectures, and are
 designed to be easily extendable to other architectures.
 
+<Tip>
+
+You can also export 🤗 Transformers models with the [`optimum.exporters.onnx` package](https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model)
+from 🤗 Optimum.
+
+Once exported, a model can be:
+
+- Optimized for inference via techniques such as quantization and graph optimization.
+- Run with ONNX Runtime via [`ORTModelForXXX` classes](https://huggingface.co/docs/optimum/onnxruntime/package_reference/modeling_ort),
+which follow the same `AutoModel` API as the one you are used to in 🤗 Transformers.
+- Run with [optimized inference pipelines](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/pipelines),
+which has the same API as the [`pipeline`] function in 🤗 Transformers.
+
+To explore all these features,  check out the [🤗 Optimum library](https://github.com/huggingface/optimum).
+
+</Tip>
+
 Ready-made configurations include the following architectures:
 
 <!--This table is automatically generated by `make fix-copies`, do not fill manually!-->
@@ -117,6 +125,14 @@ In the next two sections, we'll show you how to:
 
 ## Exporting a model to ONNX
 
+<Tip>
+
+The recommended way of exporting a model is now to use
+[`optimum.exporters.onnx`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli),
+do not worry it is very similar to `transformers.onnx`!
+
+</Tip>
+
 To export a 🤗 Transformers model to ONNX, you'll first need to install some extra
 dependencies:
 
@@ -245,6 +261,14 @@ python -m transformers.onnx --model=local-tf-checkpoint onnx/
 
 ## Selecting features for different model tasks
 
+<Tip>
+
+The recommended way of exporting a model is now to use `optimum.exporters.onnx`.
+You can check the [🤗 Optimum documentation](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#selecting-a-task)
+to learn how to select a task.
+
+</Tip>
+
 Each ready-made configuration comes with a set of _features_ that enable you to export
 models for different types of tasks. As shown in the table below, each feature is
 associated with a different `AutoClass`:
@@ -312,6 +336,15 @@ exported separately as two ONNX files named `encoder_model.onnx` and `decoder_mo
 
 ## Exporting a model for an unsupported architecture
 
+<Tip>
+
+If you wish to contribute by adding support for a model that cannot be currently exported, you should first check if it is
+supported in [`optimum.exporters.onnx`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/package_reference/configuration#supported-architectures),
+and if it is not, [contribute to 🤗 Optimum](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/contribute)
+directly.
+
+</Tip>
+
 If you wish to export a model whose architecture is not natively supported by the
 library, there are three main steps to follow:
 
@@ -499,4 +532,4 @@ file
 
 Check out how the configuration for [IBERT was
 contributed](https://github.com/huggingface/transformers/pull/14868/files) to get an
-idea of what's involved.
\ No newline at end of file
+idea of what's involved.

src/transformers/onnx/__main__.py

@@ -11,60 +11,63 @@
 # 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.
-
+import subprocess
+import sys
+import warnings
 from argparse import ArgumentParser
 from pathlib import Path
 
-from ..models.auto import AutoFeatureExtractor, AutoProcessor, AutoTokenizer
-from ..onnx.utils import get_preprocessor
+from packaging import version
+
+from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer
 from ..utils import logging
+from ..utils.import_utils import is_optimum_available
 from .convert import export, validate_model_outputs
 from .features import FeaturesManager
+from .utils import get_preprocessor
+
 
+MIN_OPTIMUM_VERSION = "1.5.0"
 
 ENCODER_DECODER_MODELS = ["vision-encoder-decoder"]
 
 
-def main():
-    parser = ArgumentParser("Hugging Face Transformers ONNX exporter")
-    parser.add_argument(
-        "-m", "--model", type=str, required=True, help="Model ID on huggingface.co or path on disk to load model from."
-    )
-    parser.add_argument(
-        "--feature",
-        choices=list(FeaturesManager.AVAILABLE_FEATURES),
-        default="default",
-        help="The type of features to export the model with.",
-    )
-    parser.add_argument("--opset", type=int, default=None, help="ONNX opset version to export the model with.")
-    parser.add_argument(
-        "--atol", type=float, default=None, help="Absolute difference tolerance when validating the model."
-    )
-    parser.add_argument(
-        "--framework",
-        type=str,
-        choices=["pt", "tf"],
-        default=None,
-        help=(
-            "The framework to use for the ONNX export."
-            " If not provided, will attempt to use the local checkpoint's original framework"
-            " or what is available in the environment."
-        ),
-    )
-    parser.add_argument("output", type=Path, help="Path indicating where to store generated ONNX model.")
-    parser.add_argument("--cache_dir", type=str, default=None, help="Path indicating where to store cache.")
-    parser.add_argument(
-        "--preprocessor",
-        type=str,
-        choices=["auto", "tokenizer", "feature_extractor", "processor"],
-        default="auto",
-        help="Which type of preprocessor to use. 'auto' tries to automatically detect it.",
+def export_with_optimum(args):
+    if is_optimum_available():
+        from optimum.version import __version__ as optimum_version
+
+        parsed_optimum_version = version.parse(optimum_version)
+        if parsed_optimum_version < version.parse(MIN_OPTIMUM_VERSION):
+            raise RuntimeError(
+                f"transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You "
+                "can upgrade optimum by running: pip install -U optimum[exporters]"
+            )
+    else:
+        raise RuntimeError(
+            "transformers.onnx requires optimum to run, you can install the library by running: pip install "
+            "optimum[exporters]"
+        )
+    cmd_line = [
+        sys.executable,
+        "-m",
+        "optimum.exporters.onnx",
+        f"--model {args.model}",
+        f"--task {args.feature}",
+        f"--framework {args.framework}" if args.framework is not None else "",
+        f"{args.output}",
+    ]
+    proc = subprocess.Popen(" ".join(cmd_line), stdout=subprocess.PIPE, shell=True)
+    proc.wait()
+
+    logger.info(
+        "The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as "
+        "transformers.onnx is deprecated, and will be removed in v5. You can find more information here: "
+        "https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."
     )
 
-    # Retrieve CLI arguments
-    args = parser.parse_args()
-    args.output = args.output if args.output.is_file() else args.output.joinpath("model.onnx")
 
+def export_with_transformers(args):
+    args.output = args.output if args.output.is_file() else args.output.joinpath("model.onnx")
     if not args.output.parent.exists():
         args.output.parent.mkdir(parents=True)
 
@@ -172,6 +175,63 @@ def main():
 
         validate_model_outputs(onnx_config, preprocessor, model, args.output, onnx_outputs, args.atol)
         logger.info(f"All good, model saved at: {args.output.as_posix()}")
+        warnings.warn(
+            "The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"
+            " using optimum.exporters.onnx in future. You can find more information here:"
+            " https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model.",
+            FutureWarning,
+        )
+
+
+def main():
+    parser = ArgumentParser("Hugging Face Transformers ONNX exporter")
+    parser.add_argument(
+        "-m", "--model", type=str, required=True, help="Model ID on huggingface.co or path on disk to load model from."
+    )
+    parser.add_argument(
+        "--feature",
+        default="default",
+        help="The type of features to export the model with.",
+    )
+    parser.add_argument("--opset", type=int, default=None, help="ONNX opset version to export the model with.")
+    parser.add_argument(
+        "--atol", type=float, default=None, help="Absolute difference tolerance when validating the model."
+    )
+    parser.add_argument(
+        "--framework",
+        type=str,
+        choices=["pt", "tf"],
+        default=None,
+        help=(
+            "The framework to use for the ONNX export."
+            " If not provided, will attempt to use the local checkpoint's original framework"
+            " or what is available in the environment."
+        ),
+    )
+    parser.add_argument("output", type=Path, help="Path indicating where to store generated ONNX model.")
+    parser.add_argument("--cache_dir", type=str, default=None, help="Path indicating where to store cache.")
+    parser.add_argument(
+        "--preprocessor",
+        type=str,
+        choices=["auto", "tokenizer", "feature_extractor", "processor"],
+        default="auto",
+        help="Which type of preprocessor to use. 'auto' tries to automatically detect it.",
+    )
+    parser.add_argument(
+        "--export_with_transformers",
+        action="store_true",
+        help=(
+            "Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be "
+            "useful when exporting a model supported in transformers but not in optimum, otherwise it is not "
+            "recommended."
+        ),
+    )
+
+    args = parser.parse_args()
+    if args.export_with_transformers or not is_optimum_available():
+        export_with_transformers(args)
+    else:
+        export_with_optimum(args)
 
 
 if __name__ == "__main__":

src/transformers/utils/import_utils.py

@@ -571,6 +571,10 @@ def is_accelerate_available():
     return importlib.util.find_spec("accelerate") is not None
 
 
+def is_optimum_available():
+    return importlib.util.find_spec("optimum") is not None
+
+
 def is_safetensors_available():
     return importlib.util.find_spec("safetensors") is not None