Switch nncf.quantize to ptq for PT backend

openvinotoolkit · Oct 26, 2023 · 5e44302 · 5e44302
1 parent faf687c
commit 5e44302
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Showing 8 changed files with 61 additions and 394 deletions.
diff --git a/nncf/experimental/torch/quantization/__init__.py b/nncf/experimental/torch/quantization/__init__.py
diff --git a/nncf/experimental/torch/quantization/quantize_model.py b/nncf/experimental/torch/quantization/quantize_model.py
diff --git a/nncf/quantization/quantize_model.py b/nncf/quantization/quantize_model.py
@@ -59,7 +59,7 @@ def quantize(
         - `performance`: Symmetric quantization of weights and activations.
         - `mixed`: Symmetric quantization of weights and asymmetric quantization of activations.
         Default value is None. In this case, `mixed` preset is used for `transformer`
-        model type otherwise `performace`.
+        model type otherwise `performance`.
     :type  preset: nncf.QuantizationPreset
     :param target_device: A target device the specificity of which will be taken
         into account while compressing in order to obtain the best performance
@@ -185,7 +185,7 @@ def quantize_with_accuracy_control(
         - `performance`: Symmetric quantization of weights and activations.
         - `mixed`: Symmetric quantization of weights and asymmetric quantization of activations.
         Default value is None. In this case, `mixed` preset is used for `transformer`
-        model type otherwise `performace`.
+        model type otherwise `performance`.
     :type preset: nncf.QuantizationPreset
     :param target_device: A target device the specificity of which will be taken
         into account while compressing in order to obtain the best performance
@@ -317,7 +317,7 @@ def quantize_with_tune_hyperparams(
         - `performance`: Symmetric quantization of weights and activations.
         - `mixed`: Symmetric quantization of weights and asymmetric quantization of activations.
         Default value is None. In this case, `mixed` preset is used for `transformer`
-        model type otherwise `performace`.
+        model type otherwise `performance`.
     :param target_device: A target device the specificity of which will be taken
         into account while compressing in order to obtain the best performance
         for this type of device.