Raise an error when OVQuantizer is invoked on an already compressed model

Author: l-bat

optimum/intel/openvino/quantization.py

@@ -315,6 +315,31 @@ def quantize(
         else:
             raise TypeError(f"Unsupported model type: {type(self.model)}")
 
+    def _check_model_state(self, sub_model_names: List[str] = None):
+        message_template = (
+            "Couldn't apply optimization to the model because it was already compressed with config: {}. "
+            "To avoid this issue, set load_in_8bit=False in the from_pretrained method when using the optimum-intel API, "
+            "or explicitly specify the desired weight format using --weight_format fp16/fp32 for CLI."
+        )
+
+        def check_rt_info(ov_model):
+            rt_info = ov_model.get_rt_info()
+            if "nncf" in rt_info:
+                model_weight_compression_config = rt_info["nncf"].get("weight_compression", None)
+                model_quantization_config = rt_info["nncf"].get("quantization", None)
+                if model_weight_compression_config is not None:
+                    raise RuntimeError(message_template.format(model_weight_compression_config))
+                elif model_quantization_config is not None:
+                    raise RuntimeError(message_template.format(model_quantization_config))
+
+        if sub_model_names is None:
+            check_rt_info(self.model.model)
+        else:
+            for name in sub_model_names:
+                if hasattr(self.model, name):
+                    ov_model = getattr(self.model, name).model
+                    check_rt_info(ov_model)
+
     def _quantize_ovbasemodel(
         self,
         ov_config: OVConfig,
@@ -325,7 +350,7 @@ def _quantize_ovbasemodel(
         remove_unused_columns: bool = True,
         **kwargs,
     ):
-        from optimum.intel.openvino.modeling_seq2seq import _OVModelForWhisper
+        from optimum.intel.openvino.modeling_seq2seq import _OVModelForWhisper, OVModelForSeq2SeqLM
         from optimum.intel.openvino.modeling_visual_language import OVModelForVisualCausalLM
 
         if is_diffusers_available():
@@ -404,6 +429,7 @@ def _quantize_ovbasemodel(
                         "text_encoder_2",
                         "text_encoder_3",
                     ]
+                    self._check_model_state(sub_model_names)
                     sub_models = filter(lambda x: x, (getattr(self.model, name) for name in sub_model_names))
                     for sub_model in sub_models:
                         _weight_only_quantization(sub_model.model, quantization_config_copy, **kwargs)
@@ -421,6 +447,7 @@ def _quantize_ovbasemodel(
                     self.model.clear_requests()
                 else:
                     # The model may be for example OVModelForImageClassification, OVModelForAudioClassification, etc.
+                    self._check_model_state()
                     self.model.model = _hybrid_quantization(
                         self.model.model, quantization_config, calibration_dataset, **kwargs
                     )
@@ -436,19 +463,31 @@ def _quantize_ovbasemodel(
                         "transformer",
                         "text_encoder_3",
                     ]
+                    self._check_model_state(sub_model_names)
                     sub_models = filter(lambda x: x, (getattr(self.model, name) for name in sub_model_names))
                     for sub_model in sub_models:
                         _weight_only_quantization(sub_model.model, quantization_config, **kwargs)
                     self.model.clear_requests()
                 elif isinstance(self.model, OVModelForVisualCausalLM):
                     language_model = self.model.language_model
-                    _weight_only_quantization(language_model.model, quantization_config, calibration_dataset, **kwargs)
                     sub_model_names = ["vision_embeddings", "text_embeddings"] + self.model.additional_parts
+                    self._check_model_state(sub_model_names + ["language_model"])
+                    _weight_only_quantization(language_model.model, quantization_config, calibration_dataset, **kwargs)
                     sub_models = [getattr(self.model, f"{name}_model") for name in sub_model_names]
                     for sub_model in sub_models:
                         _weight_only_quantization(sub_model, OVWeightQuantizationConfig(bits=8, sym=True), **kwargs)
                     self.model.clear_requests()
+                elif isinstance(self.model, OVModelForSeq2SeqLM):
+                    sub_model_names = ["encoder", "decoder"]
+                    if self.model.decoder_with_past is not None:
+                        sub_model_names.append("decoder_with_past")
+                    self._check_model_state(sub_model_names)
+                    sub_models = [getattr(self.model, name) for name in sub_model_names]
+                    for sub_model in sub_models:
+                        _weight_only_quantization(sub_model, quantization_config, **kwargs)
+                    self.model.clear_requests()
                 else:
+                    self._check_model_state()
                     _weight_only_quantization(self.model.model, quantization_config, calibration_dataset, **kwargs)
                     self.model.request = None
         else:
@@ -460,6 +499,7 @@ def _quantize_ovbasemodel(
 
             # Quantize model(s)
             if isinstance(self.model, _OVModelForWhisper):
+                self._check_model_state(["encoder_model", "decoder_model", "decoder_with_past_model"])
                 self._quantize_whisper_model(quantization_config, calibration_dataset, **kwargs)
             else:
                 quantized_model = _full_quantization(

tests/openvino/test_quantization.py

@@ -723,6 +723,23 @@ def test_ovmodel_load_with_compressed_weights(self, model_cls, model_type, trust
             _, num_weight_nodes = get_num_quantized_nodes(model)
             self.assertEqual(expected_ov_int8[i], num_weight_nodes["int8"])
 
+    @parameterized.expand(SUPPORTED_ARCHITECTURES_WITH_AUTO_COMPRESSION)
+    def test_raise_error_WC_over_WC(self, model_cls, model_type, trust_remote_code):
+        model = model_cls.from_pretrained(
+            MODEL_NAMES[model_type],
+            export=True,
+            load_in_8bit=True,
+            trust_remote_code=trust_remote_code,
+        )
+        quantization_config = OVWeightQuantizationConfig(bits=4, sym=True)
+        quantizer = OVQuantizer(model)
+        if isinstance(model, OVModelOpenCLIPForZeroShotImageClassification):
+            with pytest.raises(TypeError):
+                quantizer.quantize(ov_config=OVConfig(quantization_config=quantization_config))
+        else:
+            with pytest.raises(RuntimeError):
+                quantizer.quantize(ov_config=OVConfig(quantization_config=quantization_config))
+
     @parameterized.expand(SUPPORTED_ARCHITECTURES_WITH_HYBRID_QUANTIZATION)
     def test_ovmodel_hybrid_quantization(self, model_cls, model_type, expected_fake_nodes, expected_int8_nodes):
         model_id = MODEL_NAMES[model_type]