Fix collecting duplicate tensors in quantization calibration dataset (#…

…577)

* Added deepcopying of inputs collected by InferRequestWrapper. Added a test covering the fixed issue.

* Phrasing tweaks

* Add soundfile to test requirements

* Added librosa to test requirements

* Added copying to other data cache appends

* Remove the need for real test data

* Process __call__ call properly

* Addressed suggested changes

optimum/intel/openvino/quantization.py

@@ -12,6 +12,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
+import copy
 import inspect
 import logging
 import os
@@ -87,11 +88,14 @@ def __init__(self, request, data_cache=None):
         self.data_cache = data_cache
 
     def __call__(self, *args, **kwargs):
-        self.data_cache.append(*args)
+        # If __call__ is invoked then self.request must be an instance of CompiledModel
+        signature = inspect.signature(self.request)
+        bound_args = signature.bind(*args, **kwargs).arguments
+        self.data_cache.append(copy.deepcopy(bound_args["inputs"]))
         return self.request(*args, **kwargs)
 
     def infer(self, inputs: Any = None, share_inputs: bool = False):
-        self.data_cache.append(inputs)
+        self.data_cache.append(copy.deepcopy(inputs))
         return self.request.infer(inputs, share_inputs)
 
     def start_async(
@@ -102,7 +106,7 @@ def start_async(
         *,
         shared_memory: Any = None,
     ):
-        self.data_cache.append(inputs)
+        self.data_cache.append(copy.deepcopy(inputs))
         self.request.infer(inputs, share_inputs, share_outputs=True)
 
     def wait(self):

tests/openvino/test_quantization.py

@@ -16,10 +16,12 @@
 
 import tempfile
 import unittest
+from collections import defaultdict
 from functools import partial
 
 import evaluate
 import numpy as np
+import torch
 from datasets import load_dataset
 from parameterized import parameterized
 import openvino.runtime as ov
@@ -30,6 +32,7 @@
     AutoModelForCausalLM,
     AutoModelForTokenClassification,
     AutoTokenizer,
+    AutoProcessor,
     TrainingArguments,
     default_data_collator,
 )
@@ -45,6 +48,7 @@
     OVModelForSeq2SeqLM,
     OVModelForSequenceClassification,
     OVModelForTokenClassification,
+    OVModelForSpeechSeq2Seq,
     OVStableDiffusionPipeline,
     OVStableDiffusionXLPipeline,
     OVQuantizer,
@@ -54,6 +58,7 @@
 
 
 from optimum.intel.openvino.configuration import INT8_WEIGHT_COMPRESSION_CONFIG
+from optimum.intel.openvino.quantization import InferRequestWrapper
 from optimum.intel.utils.import_utils import is_openvino_version
 from utils_tests import MODEL_NAMES, get_num_quantized_nodes, _ARCHITECTURES_TO_EXPECTED_INT8
 
@@ -589,3 +594,38 @@ def compute_metrics(p):
             tokens = tokenizer("This is a sample input", return_tensors="pt")
             outputs = model(**tokens)
             self.assertTrue("logits" in outputs)
+
+
+class InferRequestWrapperTest(unittest.TestCase):
+    MODEL_ID = ("openai/whisper-tiny.en",)
+
+    @staticmethod
+    def _generate_random_audio_data(processor):
+        t = np.linspace(0, 1.0, int(1000), endpoint=False)
+        audio_data = 0.5 * np.sin((2 + np.random.random()) * np.pi * t)
+        input_features = processor(
+            audio_data,
+            sampling_rate=16000,
+            return_tensors="pt",
+        ).input_features
+        return input_features
+
+    @parameterized.expand(MODEL_ID)
+    def test_calibration_data_uniqueness(self, model_id):
+        ov_model = OVModelForSpeechSeq2Seq.from_pretrained(model_id, export=True, compile=True)
+        processor = AutoProcessor.from_pretrained(model_id)
+
+        calibration_data = []
+        ov_model.decoder_with_past.request = InferRequestWrapper(ov_model.decoder_with_past.request, calibration_data)
+        for _ in range(2):
+            input_features = self._generate_random_audio_data(processor)
+            ov_model.generate(input_features)
+
+        data_hashes_per_key = defaultdict(list)
+        for inputs_dict in calibration_data:
+            for k, v in inputs_dict.items():
+                x = (v.numpy() if isinstance(v, torch.Tensor) else v).copy()
+                data_hashes_per_key[k].append(hash(x.tobytes()))
+        for k, data_hashes in data_hashes_per_key.items():
+            # All hashes can not be equal because calibration dataset contains at least 2 different samples
+            self.assertTrue(any(data_hashes[0] != it for it in data_hashes))