fix quantization for onnxruntime v1.16.0

optimum/onnxruntime/quantization.py

@@ -279,6 +279,10 @@ def compute_ranges(self) -> Dict[str, Tuple[float, float]]:
             )
 
         LOGGER.info("Computing calibration ranges")
+
+        if parse(ort_version) >= Version("1.16.0"):
+            return self._calibrator.compute_data()
+
         return self._calibrator.compute_range()
 
     def quantize(
@@ -351,8 +355,13 @@ def quantize(
                 has_subgraphs = True
                 break
 
-        if quantization_config.is_static and has_subgraphs:
-            raise NotImplementedError("Static quantization is currently not supported for models with" " subgraphs.")
+        if has_subgraphs:
+            if quantization_config.is_static:
+                raise NotImplementedError("Static quantization is currently not supported for models with subgraphs.")
+            if parse(ort_version) == Version("1.16.0"):
+                raise ValueError(
+                    "ONNX Runtime version v1.16.0 is not compatible with quantization for models with subgraphs, please downgrade to 1.15.1 or upgrade to a higher version. Reference: https://github.com/microsoft/onnxruntime/pull/17651"
+                )
 
         quantizer_factory = QDQQuantizer if use_qdq else ONNXQuantizer
 

tests/cli/test_cli.py

@@ -21,6 +21,9 @@
 import unittest
 from pathlib import Path
 
+from onnxruntime import __version__ as ort_version
+from packaging.version import Version, parse
+
 import optimum.commands
 
 
@@ -84,14 +87,22 @@ def test_quantize_commands(self):
             export_commands = [
                 f"optimum-cli export onnx --model hf-internal-testing/tiny-random-BertModel {tempdir}/encoder",
                 f"optimum-cli export onnx --model hf-internal-testing/tiny-random-gpt2 {tempdir}/decoder",
-                f"optimum-cli export onnx --model hf-internal-testing/tiny-random-t5 {tempdir}/encoder-decoder",
+                # f"optimum-cli export onnx --model hf-internal-testing/tiny-random-t5 {tempdir}/encoder-decoder",
             ]
             quantize_commands = [
                 f"optimum-cli onnxruntime quantize --onnx_model {tempdir}/encoder --avx2 -o {tempdir}/quantized_encoder",
                 f"optimum-cli onnxruntime quantize --onnx_model {tempdir}/decoder --avx2 -o {tempdir}/quantized_decoder",
-                f"optimum-cli onnxruntime quantize --onnx_model {tempdir}/encoder-decoder --avx2 -o {tempdir}/quantized_encoder_decoder",
+                # f"optimum-cli onnxruntime quantize --onnx_model {tempdir}/encoder-decoder --avx2 -o {tempdir}/quantized_encoder_decoder",
             ]
 
+            if parse(ort_version) != Version("1.16.0"):
+                export_commands.append(
+                    f"optimum-cli export onnx --model hf-internal-testing/tiny-random-t5 {tempdir}/encoder-decoder"
+                )
+                quantize_commands.append(
+                    f"optimum-cli onnxruntime quantize --onnx_model {tempdir}/encoder-decoder --avx2 -o {tempdir}/quantized_encoder_decoder"
+                )
+
             for export, quantize in zip(export_commands, quantize_commands):
                 subprocess.run(export, shell=True, check=True)
                 subprocess.run(quantize, shell=True, check=True)

tests/onnxruntime/test_quantization.py

@@ -19,7 +19,9 @@
 from pathlib import Path
 
 from onnx import load as onnx_load
+from onnxruntime import __version__ as ort_version
 from onnxruntime.quantization import QuantFormat, QuantizationMode, QuantType
+from packaging.version import Version, parse
 from parameterized import parameterized
 from transformers import AutoTokenizer
 
@@ -112,9 +114,9 @@ def test_dynamic_quantization(self, model_cls, model_name, expected_quantized_ma
             self.assertEqual(expected_quantized_matmuls, num_quantized_matmul)
             gc.collect()
 
+    @unittest.skipIf(parse(ort_version) == Version("1.16.0"), "not supported with this onnxruntime version")
     def test_dynamic_quantization_subgraphs(self):
         qconfig = AutoQuantizationConfig.avx512(is_static=False, per_channel=True)
-        # with tempfile.TemporaryDirectory() as tmp_dir:
         tmp_dir = tempfile.mkdtemp()
         output_dir = Path(tmp_dir)
         model = ORTModelForCausalLM.from_pretrained(