Fixed test case for E2M1.

Author: andreyanufr

nncf/quantization/quantize_model.py

@@ -585,11 +585,12 @@ def compress_weights(
     if backend == BackendType.OPENVINO:
         from nncf.openvino.quantization.quantize_model import compress_weights_impl as ov_compress_weights_impl
 
-        if any((scale_estimation, gptq, lora_correction)) and (dataset is None or mode == CompressWeightsMode.E2M1):
-            msg = (
-                "Scale estimation, GPTQ or Lora Correction algorithm is defined, "
-                "but dataset is None or mode is E2M1."
-            )
+        if any((scale_estimation, gptq, lora_correction)) and dataset is None:
+            msg = "Scale estimation, GPTQ or Lora Correction algorithm is defined, but dataset is None."
+            raise nncf.ParameterNotSupportedError(msg)
+
+        if any((awq, scale_estimation, gptq, lora_correction)) and mode == CompressWeightsMode.E2M1:
+            msg = "AWQ, Scale estimation, GPTQ or Lora Correction algorithm is defined, but mode is E2M1."
             raise nncf.ParameterNotSupportedError(msg)
 
         if gptq and lora_correction:

tests/openvino/native/quantization/test_weights_compression.py

@@ -691,7 +691,6 @@ def test_raise_error_with_unsupported_params_for_e2m1(algo):
     "algo",
     (
         "lora_correction",
-        "awq",
         "scale_estimation",
         "gptq",
     ),