Convert Tokenizers By Default

optimum/commands/export/openvino.py

@@ -126,10 +126,15 @@ def parse_args_openvino(parser: "ArgumentParser"):
             "OpenVINO native inference code that expects kv-cache inputs and outputs in the model."
         ),
     )
+    optional_group.add_argument(
+        "--disable-convert-tokenizer",
+        action="store_true",
+        help="Do not add converted tokenizer and detokenizer OpenVINO models.",
+    )
     optional_group.add_argument(
         "--convert-tokenizer",
         action="store_true",
-        help="Add converted tokenizer and detokenizer with OpenVINO Tokenizers",
+        help="[Deprecated] Add converted tokenizer and detokenizer with OpenVINO Tokenizers.",
     )
 
     optional_group.add_argument(
@@ -247,6 +252,9 @@ def run(self):
             model.save_pretrained(self.args.output)
 
         else:
+            if self.args.convert_tokenizer:
+                logger.warning("`--convert-tokenizer` option is deprecated. Tokenizer will be converted by default.")
+
             # TODO : add input shapes
             main_export(
                 model_name_or_path=self.args.model,
@@ -258,7 +266,7 @@ def run(self):
                 pad_token_id=self.args.pad_token_id,
                 ov_config=ov_config,
                 stateful=not self.args.disable_stateful,
-                convert_tokenizer=self.args.convert_tokenizer,
+                convert_tokenizer=not self.args.disable_convert_tokenizer,
                 library_name=library_name,
                 # **input_shapes,
             )

optimum/exporters/openvino/__main__.py

@@ -22,11 +22,10 @@
 from optimum.exporters import TasksManager
 from optimum.exporters.onnx.base import OnnxConfig
 from optimum.exporters.onnx.constants import SDPA_ARCHS_ONNX_EXPORT_NOT_SUPPORTED
+from optimum.exporters.openvino.convert import export_from_model, export_tokenizer
+from optimum.intel.utils.import_utils import is_openvino_tokenizers_available, is_transformers_version
 from optimum.utils.save_utils import maybe_load_preprocessors
 
-from ...intel.utils.import_utils import is_openvino_tokenizers_available, is_transformers_version
-from .convert import export_from_model, export_tokenizer
-
 
 if TYPE_CHECKING:
     from optimum.intel.openvino.configuration import OVConfig
@@ -187,12 +186,6 @@ def main_export(
                 f"The task could not be automatically inferred as this is available only for models hosted on the Hugging Face Hub. Please provide the argument --task with the relevant task from {', '.join(TasksManager.get_all_tasks())}. Detailed error: {e}"
             )
 
-    if convert_tokenizer and not is_openvino_tokenizers_available():
-        logger.warning(
-            "`convert_tokenizer` requires openvino-tokenizers, please install it with `pip install optimum-intel[openvino-tokenizers]`"
-        )
-        convert_tokenizer = False
-
     do_gptq_patching = False
     custom_architecture = False
     loading_kwargs = {}
@@ -348,7 +341,7 @@ class StoreAttr(object):
         **kwargs_shapes,
     )
 
-    if convert_tokenizer:
+    if convert_tokenizer and is_openvino_tokenizers_available():
         if library_name != "diffusers":
             tokenizer = next(
                 (preprocessor for preprocessor in preprocessors if isinstance(preprocessor, PreTrainedTokenizerBase)),
@@ -371,6 +364,8 @@ class StoreAttr(object):
             tokenizer_2 = getattr(model, "tokenizer_2", None)
             if tokenizer_2 is not None:
                 export_tokenizer(tokenizer_2, output, suffix="_2")
+    elif convert_tokenizer and not is_openvino_tokenizers_available():
+        logger.warning("Tokenizer won't be converted.")
 
     # Unpatch modules after GPTQ export
     if do_gptq_patching:

optimum/exporters/openvino/convert.py

@@ -20,7 +20,6 @@
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
 
-from transformers import T5Tokenizer, T5TokenizerFast
 from transformers.utils import is_tf_available, is_torch_available
 
 from openvino.runtime import PartialShape, save_model
@@ -49,9 +48,6 @@
 )
 
 
-UNSUPPORTED_TOKENIZER_CLASSES = (T5Tokenizer, T5TokenizerFast)
-
-
 logger = logging.getLogger(__name__)
 
 if is_torch_available():
@@ -662,10 +658,6 @@ def export_tokenizer(
 ):
     from optimum.intel.openvino import OV_DETOKENIZER_NAME, OV_TOKENIZER_NAME  # avoid circular imports
 
-    if isinstance(tokenizer, UNSUPPORTED_TOKENIZER_CLASSES):
-        logger.info(f"OpenVINO Tokenizer export for {type(tokenizer).__name__} is not supported.")
-        return
-
     try:
         from openvino_tokenizers import convert_tokenizer
     except ModuleNotFoundError:
@@ -681,13 +673,13 @@ def export_tokenizer(
     try:
         converted = convert_tokenizer(tokenizer, with_detokenizer=True)
     except NotImplementedError:
-        logger.warning("Detokenizer is not supported, convert tokenizer only.")
+        logger.info("Detokenizer is not supported, convert tokenizer only.")
         converted = convert_tokenizer(tokenizer, with_detokenizer=False)
     except OVTypeError:
-        logger.warning(f"OpenVINO Tokenizer export for {type(tokenizer).__name__} is not supported.")
+        logger.debug(f"OpenVINO Tokenizer export for {type(tokenizer).__name__} is not supported.")
         return
     except Exception as exception:
-        logger.warning(
+        logger.debug(
             f"OpenVINO Tokenizer export for {type(tokenizer).__name__} is not supported. Exception: {exception}"
         )
         return

optimum/intel/utils/import_utils.py

@@ -11,7 +11,7 @@
 #  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 functools
 import importlib.util
 import logging
 import operator as op
@@ -95,32 +95,6 @@
     except ImportError:
         _openvino_available = False
 
-_openvino_tokenizers_available = importlib.util.find_spec("openvino_tokenizers") is not None and _openvino_available
-_openvino_tokenizers_version = "N/A"
-if _openvino_tokenizers_available:
-    try:
-        _openvino_tokenizers_version = importlib_metadata.version("openvino_tokenizers")
-    except importlib_metadata.PackageNotFoundError:
-        _openvino_tokenizers_available = False
-
-if _openvino_tokenizers_available and _openvino_tokenizers_version != "N/A":
-    _compatible_openvino_version = next(
-        (
-            requirement.split("==")[-1]
-            for requirement in importlib_metadata.requires("openvino-tokenizers")
-            if requirement.startswith("openvino==")
-        ),
-        "",
-    )
-    _openvino_tokenizers_available = _compatible_openvino_version == ov_major_version
-    if not _openvino_tokenizers_available:
-        logger.warning(
-            "OpenVINO Tokenizer version is not compatible with OpenVINO version. "
-            f"Installed OpenVINO version: {ov_major_version},"
-            f"OpenVINO Tokenizers requires {_compatible_openvino_version}. "
-            f"OpenVINO Tokenizers models will not be added during export."
-        )
-
 _nncf_available = importlib.util.find_spec("nncf") is not None
 _nncf_version = "N/A"
 if _nncf_available:
@@ -196,8 +170,81 @@ def is_openvino_available():
     return _openvino_available
 
 
+@functools.lru_cache(1)
 def is_openvino_tokenizers_available():
-    return _openvino_tokenizers_available
+    if not is_openvino_available():
+        return False
+
+    if importlib.util.find_spec("openvino_tokenizers") is None:
+        logger.info(
+            "OpenVINO Tokenizers is not available. To deploy models in production "
+            "with C++ code, please follow installation instructions: "
+            "https://github.com/openvinotoolkit/openvino_tokenizers?tab=readme-ov-file#installation\n"
+        )
+        return False
+
+    try:
+        pip_metadata_version = importlib_metadata.version("openvino")
+    except importlib_metadata.PackageNotFoundError:
+        pip_metadata_version = False
+    try:
+        pip_metadata_version = importlib_metadata.version("openvino-nightly")
+        is_nightly = True
+    except importlib_metadata.PackageNotFoundError:
+        is_nightly = False
+
+    try:
+        import openvino_tokenizers
+
+        openvino_tokenizers._get_factory()
+    except RuntimeError:
+        tokenizers_version = openvino_tokenizers.__version__
+
+        if tokenizers_version == "0.0.0.0":
+            try:
+                tokenizers_version = importlib_metadata.version("openvino_tokenizers") or tokenizers_version
+            except importlib_metadata.PackageNotFoundError:
+                pass
+        message = (
+            "OpenVINO and OpenVINO Tokenizers versions are not binary compatible.\n"
+            f"OpenVINO version:            {_openvino_version}\n"
+            f"OpenVINO Tokenizers version: {tokenizers_version}\n"
+            "First 3 numbers should be the same. Update OpenVINO Tokenizers to compatible version. "
+        )
+        if not pip_metadata_version:
+            message += (
+                "For archive installation of OpenVINO try to build OpenVINO Tokenizers from source: "
+                "https://github.com/openvinotoolkit/openvino_tokenizers/tree/master?tab=readme-ov-file"
+                "#build-and-install-from-source"
+            )
+            if sys.platform == "linux":
+                message += (
+                    "\nThe PyPI version of OpenVINO Tokenizers is built on CentOS and may not be compatible with other "
+                    "Linux distributions; rebuild OpenVINO Tokenizers from source."
+                )
+        else:
+            message += (
+                "It is recommended to use the same day builds for pre-release version. "
+                "To install both OpenVINO and OpenVINO Tokenizers release version perform:\n"
+            )
+            if is_nightly:
+                message += "pip uninstall -y openvino-nightly && "
+            message += "pip install --force-reinstall openvino openvino-tokenizers\n"
+            if is_nightly:
+                message += (
+                    "openvino-nightly package will be deprecated in the future - use pre-release drops instead. "
+                )
+            message += "To update both OpenVINO and OpenVINO Tokenizers to the latest pre-release version perform:\n"
+            if is_nightly:
+                message += "pip uninstall -y openvino-nightly && "
+            message += (
+                "pip install --pre -U openvino openvino-tokenizers "
+                "--extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly"
+            )
+        logger.warning(message)
+        return False
+
+    return True
 
 
 def is_nncf_available():

setup.py

@@ -58,13 +58,8 @@
 QUALITY_REQUIRE = ["black~=23.1", "ruff>=0.0.241"]
 
 EXTRAS_REQUIRE = {
-    "neural-compressor": [
-        "neural-compressor>=2.2.0",
-        "onnxruntime<1.15.0",
-        "accelerate",
-    ],
-    "openvino": ["openvino>=2023.3", "nncf>=2.8.1"],
-    "openvino-tokenizers": ["openvino-tokenizers[transformers]"],
+    "neural-compressor": ["neural-compressor>=2.2.0", "onnxruntime<1.15.0", "accelerate"],
+    "openvino": ["openvino>=2023.3", "nncf>=2.8.1", "openvino-tokenizers[transformers]"],
     "nncf": ["nncf>=2.8.1"],
     "ipex": ["intel-extension-for-pytorch", "transformers>=4.36.0,<4.39.0"],
     "diffusers": ["diffusers"],

tests/openvino/test_exporters_cli.py

@@ -66,7 +66,7 @@ class OVCLIExportTestCase(unittest.TestCase):
     )
     EXPECTED_NUMBER_OF_TOKENIZER_MODELS = {
         "gpt2": 2,
-        "t5": 0,  # failed internal sentencepiece check - no <s> token in the vocab
+        "t5": 0,  # no .model file in the repository
         "albert": 0,  # not supported yet
         "distilbert": 1,  # no detokenizer
         "roberta": 2,
@@ -125,26 +125,26 @@ def test_exporters_cli(self, task: str, model_type: str):
         for arch in SUPPORTED_ARCHITECTURES
         if not arch[0].endswith("-with-past") and not arch[1].endswith("-refiner")
     )
-    @unittest.skipIf(not is_openvino_tokenizers_available(), reason="OpenVINO Tokenizers not available")
     def test_exporters_cli_tokenizers(self, task: str, model_type: str):
         with TemporaryDirectory() as tmpdir:
             output = subprocess.check_output(
-                f"optimum-cli export openvino --model {MODEL_NAMES[model_type]} --convert-tokenizer --task {task} {tmpdir}",
+                f"optimum-cli export openvino --model {MODEL_NAMES[model_type]} --task {task} {tmpdir}",
                 shell=True,
                 stderr=subprocess.STDOUT,
             ).decode()
-            save_dir = Path(tmpdir)
-            number_of_tokenizers = sum("tokenizer" in file for file in map(str, save_dir.rglob("*.xml")))
-            self.assertEqual(
-                self.EXPECTED_NUMBER_OF_TOKENIZER_MODELS[model_type],
-                number_of_tokenizers,
-                f"OVT: {is_openvino_tokenizers_available() }",
-            )
+            if not is_openvino_tokenizers_available():
+                self.assertTrue(
+                    "OpenVINO Tokenizers is not available." in output
+                    or "OpenVINO and OpenVINO Tokenizers versions are not binary compatible." in output,
+                    msg=output,
+                )
+                return
+
+            number_of_tokenizers = sum("tokenizer" in file for file in map(str, Path(tmpdir).rglob("*.xml")))
+            self.assertEqual(self.EXPECTED_NUMBER_OF_TOKENIZER_MODELS[model_type], number_of_tokenizers, output)
 
             if number_of_tokenizers == 1:
                 self.assertTrue("Detokenizer is not supported, convert tokenizer only." in output, output)
-            elif number_of_tokenizers == 0 and task not in ("image-classification", "audio-classification"):
-                self.assertTrue(("OpenVINO Tokenizer export for" in output and "is not supported." in output), output)
 
     @parameterized.expand(SUPPORTED_ARCHITECTURES)
     def test_exporters_cli_fp16(self, task: str, model_type: str):