update from keras2onnx to tf2onnx (#15162)

setup.py

@@ -114,7 +114,6 @@ _deps = [
     "jax>=0.2.8",
     "jaxlib>=0.1.65",
     "jieba",
-    "keras2onnx",
     "nltk",
     "numpy>=1.17",
     "onnxconverter-common",
@@ -147,6 +146,7 @@ _deps = [
     "starlette",
     "tensorflow-cpu>=2.3",
     "tensorflow>=2.3",
+    "tf2onnx",
     "timeout-decorator",
     "timm",
     "tokenizers>=0.10.1",
@@ -229,8 +229,8 @@ extras = {}
 extras["ja"] = deps_list("fugashi", "ipadic", "unidic_lite", "unidic")
 extras["sklearn"] = deps_list("scikit-learn")
 
-extras["tf"] = deps_list("tensorflow", "onnxconverter-common", "keras2onnx")
-extras["tf-cpu"] = deps_list("tensorflow-cpu", "onnxconverter-common", "keras2onnx")
+extras["tf"] = deps_list("tensorflow", "onnxconverter-common", "tf2onnx")
+extras["tf-cpu"] = deps_list("tensorflow-cpu", "onnxconverter-common", "tf2onnx")
 
 extras["torch"] = deps_list("torch")
 
@@ -243,7 +243,7 @@ else:
 
 extras["tokenizers"] = deps_list("tokenizers")
 extras["onnxruntime"] = deps_list("onnxruntime", "onnxruntime-tools")
-extras["onnx"] = deps_list("onnxconverter-common", "keras2onnx") + extras["onnxruntime"]
+extras["onnx"] = deps_list("onnxconverter-common", "tf2onnx") + extras["onnxruntime"]
 extras["modelcreation"] = deps_list("cookiecutter")
 
 extras["sagemaker"] = deps_list("sagemaker")

src/transformers/convert_graph_to_onnx.py

@@ -294,7 +294,7 @@ def convert_pytorch(nlp: Pipeline, opset: int, output: Path, use_external_format
 
 def convert_tensorflow(nlp: Pipeline, opset: int, output: Path):
     """
-    Export a TensorFlow backed pipeline to ONNX Intermediate Representation (IR
+    Export a TensorFlow backed pipeline to ONNX Intermediate Representation (IR)
 
     Args:
         nlp: The pipeline to be exported
@@ -312,10 +312,10 @@ def convert_tensorflow(nlp: Pipeline, opset: int, output: Path):
     try:
         import tensorflow as tf
 
-        from keras2onnx import __version__ as k2ov
-        from keras2onnx import convert_keras, save_model
+        from tf2onnx import __version__ as t2ov
+        from tf2onnx import convert_keras, save_model
 
-        print(f"Using framework TensorFlow: {tf.version.VERSION}, keras2onnx: {k2ov}")
+        print(f"Using framework TensorFlow: {tf.version.VERSION}, tf2onnx: {t2ov}")
 
         # Build
         input_names, output_names, dynamic_axes, tokens = infer_shapes(nlp, "tf")

src/transformers/dependency_versions_table.py

@@ -24,7 +24,6 @@ deps = {
     "jax": "jax>=0.2.8",
     "jaxlib": "jaxlib>=0.1.65",
     "jieba": "jieba",
-    "keras2onnx": "keras2onnx",
     "nltk": "nltk",
     "numpy": "numpy>=1.17",
     "onnxconverter-common": "onnxconverter-common",
@@ -57,6 +56,7 @@ deps = {
     "starlette": "starlette",
     "tensorflow-cpu": "tensorflow-cpu>=2.3",
     "tensorflow": "tensorflow>=2.3",
+    "tf2onnx": "tf2onnx",
     "timeout-decorator": "timeout-decorator",
     "timm": "timm",
     "tokenizers": "tokenizers>=0.10.1",

src/transformers/file_utils.py

@@ -175,12 +175,12 @@ except importlib_metadata.PackageNotFoundError:
     _sympy_available = False
 
 
-_keras2onnx_available = importlib.util.find_spec("keras2onnx") is not None
+_tf2onnx_available = importlib.util.find_spec("tf2onnx") is not None
 try:
-    _keras2onnx_version = importlib_metadata.version("keras2onnx")
-    logger.debug(f"Successfully imported keras2onnx version {_keras2onnx_version}")
+    _tf2onnx_version = importlib_metadata.version("tf2onnx")
+    logger.debug(f"Successfully imported tf2onnx version {_tf2onnx_version}")
 except importlib_metadata.PackageNotFoundError:
-    _keras2onnx_available = False
+    _tf2onnx_available = False
 
 _onnx_available = importlib.util.find_spec("onnxruntime") is not None
 try:
@@ -429,8 +429,8 @@ def is_coloredlogs_available():
     return _coloredlogs_available
 
 
-def is_keras2onnx_available():
-    return _keras2onnx_available
+def is_tf2onnx_available():
+    return _tf2onnx_available
 
 
 def is_onnx_available():

src/transformers/testing_utils.py

@@ -35,7 +35,6 @@ from .file_utils import (
     is_faiss_available,
     is_flax_available,
     is_ftfy_available,
-    is_keras2onnx_available,
     is_librosa_available,
     is_onnx_available,
     is_pandas_available,
@@ -49,6 +48,7 @@ from .file_utils import (
     is_soundfile_availble,
     is_spacy_available,
     is_tensorflow_probability_available,
+    is_tf2onnx_available,
     is_tf_available,
     is_timm_available,
     is_tokenizers_available,
@@ -246,9 +246,9 @@ def require_rjieba(test_case):
         return test_case
 
 
-def require_keras2onnx(test_case):
-    if not is_keras2onnx_available():
-        return unittest.skip("test requires keras2onnx")(test_case)
+def require_tf2onnx(test_case):
+    if not is_tf2onnx_available():
+        return unittest.skip("test requires tf2onnx")(test_case)
     else:
         return test_case
 

tests/test_modeling_tf_common.py

@@ -36,8 +36,8 @@ from transformers.testing_utils import (
     _tf_gpu_memory_limit,
     is_pt_tf_cross_test,
     is_staging_test,
-    require_keras2onnx,
     require_tf,
+    require_tf2onnx,
     slow,
 )
 from transformers.utils import logging
@@ -254,14 +254,14 @@ class TFModelTesterMixin:
 
             self.assertEqual(len(incompatible_ops), 0, incompatible_ops)
 
-    @require_keras2onnx
+    @require_tf2onnx
     @slow
     def test_onnx_runtime_optimize(self):
         if not self.test_onnx:
             return
 
-        import keras2onnx
         import onnxruntime
+        import tf2onnx
 
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
@@ -269,9 +269,9 @@ class TFModelTesterMixin:
             model = model_class(config)
             model(model.dummy_inputs)
 
-            onnx_model = keras2onnx.convert_keras(model, model.name, target_opset=self.onnx_min_opset)
+            onnx_model_proto, _ = tf2onnx.convert.from_keras(model, opset=self.onnx_min_opset)
 
-            onnxruntime.InferenceSession(onnx_model.SerializeToString())
+            onnxruntime.InferenceSession(onnx_model_proto.SerializeToString())
 
     def test_keras_save_load(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()