Revert "Fix overflow in collectors" (openvinotoolkit#2384)

Reverts openvinotoolkit#2337

nncf/common/graph/layer_attributes.py

@@ -17,7 +17,6 @@
 
 
 class Dtype(Enum):
-    BOOL = "bool"
     FLOAT = "float"
     INTEGER = "int"
 

nncf/common/tensor_statistics/collectors.py

@@ -174,7 +174,7 @@ def max(x1: NNCFTensor, x2: NNCFTensor) -> NNCFTensor:
 
     @staticmethod
     @abstractmethod
-    def mean(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False) -> NNCFTensor:
+    def mean(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims=False) -> NNCFTensor:
         """
         Computes the mean of elements across given dimensions of NNCFTensor.
 

nncf/onnx/statistics/collectors.py

@@ -52,17 +52,12 @@ def max(x1: NNCFTensor, x2: NNCFTensor) -> NNCFTensor:
         return ONNXNNCFTensor(np.maximum(x1.tensor, x2.tensor))
 
     @staticmethod
-    def mean(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False) -> NNCFTensor:
-        comp_dtype, out_dtype = _get_computing_dtype(x.tensor.dtype)
-        return ONNXNNCFTensor(
-            np.mean(x.tensor, axis=axis, keepdims=keepdims, dtype=comp_dtype).astype(dtype=out_dtype, copy=False)
-        )
+    def mean(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims=False) -> NNCFTensor:
+        return ONNXNNCFTensor(np.mean(x.tensor, axis=axis, keepdims=keepdims))
 
     @staticmethod
-    def median(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False) -> NNCFTensor:
-        comp_dtype, out_dtype = _get_computing_dtype(x.tensor.dtype)
-        t = x.tensor.astype(dtype=comp_dtype, copy=False)
-        return ONNXNNCFTensor(np.median(t, axis=axis, keepdims=keepdims).astype(dtype=out_dtype, copy=False))
+    def median(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims=False) -> NNCFTensor:
+        return ONNXNNCFTensor(np.median(x.tensor, axis=axis, keepdims=keepdims))
 
     @classmethod
     def masked_mean(
@@ -74,12 +69,8 @@ def masked_mean(
     ) -> NNCFTensor:
         if mask is None:
             return cls.mean(x, axis=axis, keepdims=keepdims)
-        comp_dtype, out_dtype = _get_computing_dtype(x.tensor.dtype)
         masked_x = np.ma.array(x.tensor, mask=mask.tensor)
-        result = np.ma.mean(masked_x, axis=axis, keepdims=keepdims, dtype=comp_dtype)
-        if isinstance(result, np.ma.MaskedArray):
-            result = result.data
-        return ONNXNNCFTensor(result.astype(dtype=out_dtype, copy=False))
+        return ONNXNNCFTensor(np.ma.mean(masked_x, axis=axis, keepdims=False).data)
 
     @classmethod
     def masked_median(
@@ -91,21 +82,8 @@ def masked_median(
     ) -> NNCFTensor:
         if mask is None:
             return cls.median(x, axis=axis, keepdims=keepdims)
-        comp_dtype, out_dtype = _get_computing_dtype(x.tensor.dtype)
-        t = x.tensor.astype(dtype=comp_dtype, copy=False)
-        masked_x = np.ma.array(t, mask=mask.tensor)
-        result = np.ma.median(masked_x, axis=axis, keepdims=keepdims)
-        if isinstance(result, np.ma.MaskedArray):
-            result = result.data
-        return ONNXNNCFTensor(result.astype(dtype=out_dtype, copy=False))
-
-    @staticmethod
-    def mean_per_channel(x: NNCFTensor, axis: int) -> NNCFTensor:
-        if len(x.shape) < 3:
-            return ONNXNNCFTensor.mean(x, axis=0)
-        x = np.moveaxis(x.tensor, axis, 1)
-        t = x.reshape(x.shape[0], x.shape[1], -1)
-        return ONNXNNCFCollectorTensorProcessor.mean(ONNXNNCFTensor(t), axis=(0, 2))
+        masked_x = np.ma.array(x.tensor, mask=mask.tensor)
+        return ONNXNNCFTensor(np.ma.median(masked_x, axis=axis, keepdims=keepdims).data)
 
     @staticmethod
     def logical_or(input_: NNCFTensor, other: NNCFTensor) -> NNCFTensor:
@@ -152,6 +130,14 @@ def percentile(
     ) -> List[TensorElementsType]:
         raise NotImplementedError()
 
+    @staticmethod
+    def mean_per_channel(x: NNCFTensor, axis: int) -> NNCFTensor:
+        if len(x.shape) < 3:
+            return ONNXNNCFTensor(np.mean(x.tensor, axis=0))
+        x = np.moveaxis(x.tensor, axis, 1)
+        t = x.reshape(x.shape[0], x.shape[1], -1)
+        return ONNXNNCFTensor(np.mean(t, axis=(0, 2)))
+
     @staticmethod
     def transpose(x: NNCFTensor, axes: Tuple[int, ...]) -> NNCFTensor:
         return ONNXNNCFTensor(np.transpose(x.tensor, axes))
@@ -233,20 +219,3 @@ def _register_input(self, x: ONNXNNCFTensor):
 
     def _get_statistics(self) -> ONNXRawTensorStatistic:
         return ONNXRawTensorStatistic(self._all_values)
-
-
-def _get_computing_dtype(dtype: np.dtype) -> Tuple[Optional[np.dtype], Optional[np.dtype]]:
-    """
-    Determines the appropriate dtypes for intermediate computations and the final output,
-    aiming to prevent overflow while maintaining precision.
-
-    :param dtype: The dtype of the processed tensor.
-    :return:
-        - comp_dtype: The recommended dtype for intermediate computations to avoid overflow.
-            If None, no dtype change is necessary for intermediate computations.
-        - out_dtype: The recommended dtype for the final output, balancing precision and memory usage.
-            If None, the input dtype is preserved for the output.
-    """
-    if dtype in [np.float32, np.float16]:
-        return (np.float64, dtype)
-    return (None, None)

nncf/openvino/statistics/collectors.py

@@ -70,16 +70,11 @@ def max(x1: NNCFTensor, x2: NNCFTensor) -> NNCFTensor:
 
     @staticmethod
     def mean(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False) -> NNCFTensor:
-        comp_dtype, out_dtype = _get_computing_dtype(x.tensor.dtype)
-        return OVNNCFTensor(
-            np.mean(x.tensor, axis=axis, keepdims=keepdims, dtype=comp_dtype).astype(dtype=out_dtype, copy=False)
-        )
+        return OVNNCFTensor(np.mean(x.tensor, axis=axis, keepdims=keepdims))
 
     @staticmethod
     def median(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False) -> NNCFTensor:
-        comp_dtype, out_dtype = _get_computing_dtype(x.tensor.dtype)
-        t = x.tensor.astype(dtype=comp_dtype, copy=False)
-        return OVNNCFTensor(np.median(t, axis=axis, keepdims=keepdims).astype(dtype=out_dtype, copy=False))
+        return OVNNCFTensor(np.median(x.tensor, axis=axis, keepdims=keepdims))
 
     @classmethod
     def masked_mean(
@@ -91,12 +86,11 @@ def masked_mean(
     ) -> NNCFTensor:
         if mask is None:
             return cls.mean(x, axis=axis, keepdims=keepdims)
-        comp_dtype, out_dtype = _get_computing_dtype(x.tensor.dtype)
         masked_x = np.ma.array(x.tensor, mask=mask.tensor)
-        result = np.ma.mean(masked_x, axis=axis, keepdims=keepdims, dtype=comp_dtype)
+        result = np.ma.mean(masked_x, axis=axis, keepdims=keepdims)
         if isinstance(result, np.ma.MaskedArray):
-            result = result.data
-        return OVNNCFTensor(result.astype(dtype=out_dtype, copy=False))
+            return OVNNCFTensor(result.data)
+        return OVNNCFTensor(result)
 
     @classmethod
     def masked_median(
@@ -108,21 +102,19 @@ def masked_median(
     ) -> NNCFTensor:
         if mask is None:
             return cls.median(x, axis=axis, keepdims=keepdims)
-        comp_dtype, out_dtype = _get_computing_dtype(x.tensor.dtype)
-        t = x.tensor.astype(dtype=comp_dtype, copy=False)
-        masked_x = np.ma.array(t, mask=mask.tensor)
+        masked_x = np.ma.array(x.tensor, mask=mask.tensor)
         result = np.ma.median(masked_x, axis=axis, keepdims=keepdims)
         if isinstance(result, np.ma.MaskedArray):
-            result = result.data
-        return OVNNCFTensor(result.astype(dtype=out_dtype, copy=False))
+            return OVNNCFTensor(result.data)
+        return OVNNCFTensor(result)
 
     @staticmethod
     def mean_per_channel(x: NNCFTensor, axis: int) -> NNCFTensor:
         if len(x.shape) < 3:
-            return OVNNCFCollectorTensorProcessor.mean(x, axis=0)
+            return OVNNCFTensor(np.mean(x.tensor, axis=0))
         x = np.moveaxis(x.tensor, axis, 1)
         t = x.reshape(x.shape[0], x.shape[1], -1)
-        return OVNNCFCollectorTensorProcessor.mean(OVNNCFTensor(t), axis=(0, 2))
+        return OVNNCFTensor(np.mean(t, axis=(0, 2)))
 
     @staticmethod
     def transpose(x: NNCFTensor, axes: Tuple[int, ...]) -> NNCFTensor:
@@ -344,20 +336,3 @@ def get_raw_stat_collector(num_samples, inplace=False):
     StatisticsType.QUANTILE: OVQuantileReducer,
     StatisticsType.ABS_QUANTILE: OVAbsQuantileReducer,
 }
-
-
-def _get_computing_dtype(dtype: np.dtype) -> Tuple[Optional[np.dtype], Optional[np.dtype]]:
-    """
-    Determines the appropriate dtypes for intermediate computations and the final output,
-    aiming to prevent overflow while maintaining precision.
-
-    :param dtype: The dtype of the processed tensor.
-    :return:
-        - comp_dtype: The recommended dtype for intermediate computations to avoid overflow.
-            If None, no dtype change is necessary for intermediate computations.
-        - out_dtype: The recommended dtype for the final output, balancing precision and memory usage.
-            If None, the input dtype is preserved for the output.
-    """
-    if dtype in [np.float32, np.float16]:
-        return (np.float64, dtype)
-    return (None, None)

nncf/tensorflow/tensor_statistics/collectors.py

@@ -57,22 +57,22 @@ def max(x1: tf.Tensor, x2: tf.Tensor) -> NNCFTensor:
         return TFNNCFTensor(tf.math.maximum(x1.tensor, x2.tensor))
 
     @staticmethod
-    def mean(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False) -> NNCFTensor:
+    def mean(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims=False) -> NNCFTensor:
         return TFNNCFTensor(tf.math.reduce_mean(x.tensor, axis=axis, keepdims=keepdims))
 
     @staticmethod
-    def median(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False) -> NNCFTensor:
+    def median(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims=False) -> NNCFTensor:
         raise NotImplementedError()
 
     @classmethod
     def masked_mean(
-        cls, x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], mask: NNCFTensor, keepdims: bool = False
+        cls, x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], mask: NNCFTensor, keepdims=False
     ) -> NNCFTensor:
         raise NotImplementedError()
 
     @classmethod
     def masked_median(
-        cls, x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], mask: NNCFTensor, keepdims: bool = False
+        cls, x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], mask: NNCFTensor, keepdims=False
     ) -> NNCFTensor:
         raise NotImplementedError()
 

nncf/torch/tensor_statistics/collectors.py

@@ -71,66 +71,56 @@ def max(cls, *args) -> NNCFTensor:
         return cls.reduce_max(stacked, axis=0, keepdims=False)
 
     @staticmethod
-    def mean(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False) -> NNCFTensor:
-        comp_dtype, out_dtype = _get_computing_dtype(x.tensor.dtype)
-        return PTNNCFTensor(
-            torch.mean(x.tensor, axis=axis, keepdims=keepdims, dtype=comp_dtype).to(dtype=out_dtype, copy=False)
-        )
+    def mean(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims=False) -> NNCFTensor:
+        return PTNNCFTensor(x.tensor.mean(dim=axis, keepdim=keepdims))
 
     @staticmethod
-    def median(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims: bool = False) -> NNCFTensor:
+    def median(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims=False) -> NNCFTensor:
         # See https://github.com/pytorch/pytorch/issues/61582
         if not isinstance(axis, int):
             device = x.tensor.device
-            comp_dtype, out_dtype = _get_computing_dtype(x.tensor.dtype)
-            np_tensor = x.tensor.detach().cpu().to(dtype=comp_dtype, copy=False).numpy()
-            result = torch.tensor(np.median(np_tensor, axis=axis, keepdims=keepdims))
-            return PTNNCFTensor(result.type(out_dtype).to(device))
+            result = torch.tensor(np.median(x.tensor.detach().cpu().numpy(), axis=axis, keepdims=keepdims))
+            return PTNNCFTensor(result.type(x.tensor.dtype).to(device))
         return PTNNCFCollectorTensorProcessor.quantile(x, quantile=[0.5], axis=axis, keepdims=keepdims)[0]
 
     @classmethod
     def masked_mean(
-        cls, x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], mask: NNCFTensor, keepdims: bool = False
+        cls, x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], mask: NNCFTensor, keepdims=False
     ) -> NNCFTensor:
         if mask is None:
             return cls.mean(x, axis=axis, keepdims=keepdims)
         device = x.tensor.device
-        comp_dtype, out_dtype = _get_computing_dtype(x.tensor.dtype)
-        np_tensor = x.tensor.detach().cpu().to(dtype=comp_dtype, copy=False).numpy()
-        masked_x = np.ma.array(np_tensor, mask=mask.tensor.detach().cpu().numpy())
-        result = np.ma.mean(masked_x, axis=axis, keepdims=keepdims)
+        masked_x = np.ma.array(x.tensor.detach().cpu().numpy(), mask=mask.tensor.detach().cpu().numpy())
+        result = np.ma.mean(masked_x, axis=axis, keepdims=keepdims).astype(masked_x.dtype)
         if isinstance(result, np.ma.MaskedArray):
             result = result.data
-        return PTNNCFTensor(torch.tensor(result).to(device=device, dtype=out_dtype))
+        return PTNNCFTensor(torch.tensor(result).to(device=device))
 
     @classmethod
     def masked_median(
-        cls, x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], mask: NNCFTensor, keepdims: bool = False
+        cls, x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], mask: NNCFTensor, keepdims=False
     ) -> NNCFTensor:
-        # Implemented in numpy as torch.masked.median is not implemented yet
+        # Implemented in numy as torch.masked.median is not implemented yet
         if mask is None:
             return cls.median(x, axis=axis, keepdims=keepdims)
-
         device = x.tensor.device
-        comp_dtype, out_dtype = _get_computing_dtype(x.tensor.dtype)
-        np_tensor = x.tensor.detach().cpu().to(dtype=comp_dtype, copy=False).numpy()
-        masked_x = np.ma.array(np_tensor, mask=mask.tensor.detach().cpu().numpy())
+        masked_x = np.ma.array(x.tensor.detach().cpu().numpy(), mask=mask.tensor.detach().cpu().numpy())
         result = np.ma.median(masked_x, axis=axis, keepdims=keepdims).astype(masked_x.dtype)
         if isinstance(result, np.ma.MaskedArray):
             result = result.data
-        return PTNNCFTensor(torch.tensor(result).to(device=device, dtype=out_dtype))
+        return PTNNCFTensor(torch.tensor(result).to(device=device))
 
     @staticmethod
     def mean_per_channel(x: NNCFTensor, axis: int) -> NNCFTensor:
         if len(x.shape) < 3:
-            return PTNNCFCollectorTensorProcessor.mean(x, axis=0)
+            return PTNNCFTensor(torch.mean(x.tensor, axis=0))
         x = torch.moveaxis(x.tensor, axis, 1)
         t = x.reshape(x.shape[0], x.shape[1], -1)
-        return PTNNCFCollectorTensorProcessor.mean(PTNNCFTensor(t), axis=(0, 2))
+        return PTNNCFTensor(torch.mean(t, axis=(0, 2)))
 
     @staticmethod
     def batch_mean(x: NNCFTensor) -> NNCFTensor:
-        return PTNNCFCollectorTensorProcessor.mean(x, axis=0, keepdims=True)
+        return PTNNCFTensor(torch.mean(x.tensor, axis=0, keepdims=True))
 
     @staticmethod
     def transpose(x: NNCFTensor, axes: Tuple[int, ...]) -> NNCFTensor:
@@ -564,20 +554,3 @@ def get_mean_statistic_collector(
     StatisticsType.QUANTILE: PTQuantileReducer,
     StatisticsType.ABS_QUANTILE: PTAbsQuantileReducer,
 }
-
-
-def _get_computing_dtype(dtype: torch.dtype) -> Tuple[Optional[torch.dtype], Optional[torch.dtype]]:
-    """
-    Determines the appropriate dtypes for intermediate computations and the final output,
-    aiming to prevent overflow while maintaining precision.
-
-    :param dtype: The dtype of the processed tensor.
-    :return:
-        - comp_dtype: The recommended dtype for intermediate computations to avoid overflow.
-            If None, no dtype change is necessary for intermediate computations.
-        - out_dtype: The recommended dtype for the final output, balancing precision and memory usage.
-            If None, the input dtype is preserved for the output.
-    """
-    if dtype in [torch.float32, torch.float16]:
-        return (torch.float64, dtype)
-    return (None, None)