update wc_reference_data.yaml

nncf/torch/quantization/layers.py

@@ -46,7 +46,8 @@
 from nncf.torch.quantization.quantize_functions import ExportQuantizeToONNXQuantDequant
 from nncf.torch.quantization.quantize_functions import TuneRange
 from nncf.torch.quantization.quantize_functions import asymmetric_quantize
-from nncf.torch.quantization.quantize_functions import decompress
+from nncf.torch.quantization.quantize_functions import decompress_asymmetric
+from nncf.torch.quantization.quantize_functions import decompress_symmetric
 from nncf.torch.quantization.quantize_functions import get_scale_zp_from_input_low_input_high
 from nncf.torch.quantization.quantize_functions import symmetric_quantize
 from nncf.torch.return_types import maybe_get_values_from_torch_return_type
@@ -1061,7 +1062,7 @@ def __init__(self, scale: torch.Tensor, zero_point: torch.Tensor, result_dtype:
         self.result_dtype = result_dtype
 
     def forward(self, x):
-        result = decompress(x, self._scale, self._zero_point)
+        result = decompress_asymmetric(x, self._scale, self._zero_point)
         result = result.type(dtype=self.result_dtype) if self.result_dtype is not None else result
         return result
 
@@ -1081,6 +1082,6 @@ def __init__(self, scale: torch.Tensor, result_dtype: torch.dtype = None):
         self.result_dtype = result_dtype
 
     def forward(self, x):
-        result = decompress(x, self._scale)
+        result = decompress_symmetric(x, self._scale)
         result = result.type(dtype=self.result_dtype) if self.result_dtype is not None else result
         return result

nncf/torch/quantization/quantize_functions.py

@@ -8,7 +8,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Optional
+from typing import Any
 
 import torch
 
@@ -249,17 +249,29 @@ def backward(ctx: Any, *grad_outputs: Any) -> Any:
 
 
 @register_operator()
-def decompress(input: torch.Tensor, scale: torch.Tensor, zero_point: Optional[torch.Tensor] = None) -> torch.Tensor:
+def decompress_asymmetric(input: torch.Tensor, scale: torch.Tensor, zero_point: torch.Tensor) -> torch.Tensor:
     """
-    Decompress the input tensor.
+    Decompress the asymmetrically quantized input tensor.
 
     :param input: An input tensor
     :param scale: A scale tensor
     :param zero_point: A zero point tensor
     :return: The decompressed tensor
     """
     input = input.type(dtype=scale.dtype)
-    if zero_point is not None:
-        input -= zero_point
+    decompressed_input = (input - zero_point) * scale
+    return decompressed_input
+
+
+@register_operator()
+def decompress_symmetric(input: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
+    """
+    Decompress the symmetrically quantized input tensor.
+
+    :param input: An input tensor
+    :param scale: A scale tensor
+    :return: The decompressed tensor
+    """
+    input = input.type(dtype=scale.dtype)
     decompressed_input = input * scale
     return decompressed_input

tests/post_training/data/wc_reference_data.yaml

@@ -24,5 +24,5 @@ tinyllama_int8_data_free_backend_TORCH:
   num_int8: 312
 tinyllama_data_aware_gptq_backend_OV:
   metric_value: 0.83387
-  num_int4: 188
+  num_int4: 94
   num_int8: 124