torch.median -> torch.quantile + minor fix

nncf/torch/quantization/init_range.py

@@ -14,6 +14,7 @@
 from typing import Callable, Dict, List, Tuple
 
 import numpy as np
+import torch
 
 from nncf.common.graph.layer_attributes import WeightedLayerAttributes
 from nncf.common.quantization.initialization.range import RangeInitCollectorParams
@@ -277,6 +278,16 @@ def __init__(
         self.hook_handles = []
         self.batch_size = batch_size
 
+    def _get_fwd_hook(
+        self, collector: TensorStatisticCollectorBase
+    ) -> Callable[[torch.Module, torch.Tensor, torch.Tensor], torch.Tensor]:
+        hook = register_inputs_hook_factory(collector=collector)
+
+        def fwd_hook(module, input_, output):
+            hook(input_[0])
+
+        return fwd_hook
+
     def _prepare_initialization(self):
         for name, data in self.modules_to_init.items():
             quantizer_module, init_config, is_weights, input_shape = data
@@ -311,9 +322,7 @@ def _prepare_initialization(self):
 
             self.collectors_and_modules_to_init[name] = collector, quantizer_module
 
-            self.hook_handles.append(
-                quantizer_module.register_forward_hook(register_inputs_hook_factory(collector=collector))
-            )
+            self.hook_handles.append(quantizer_module.register_forward_hook(self._get_fwd_hook(collector)))
 
     def _apply_initializers(self):
         for handle in self.hook_handles:

nncf/torch/tensor_statistics/collectors.py

@@ -81,7 +81,7 @@ def median(x: NNCFTensor, axis: Union[int, tuple, list], keepdims=False) -> NNCF
         # See https://github.com/pytorch/pytorch/issues/61582
         if not isinstance(axis, int):
             return PTNNCFTensor(torch.tensor(np.median(x.tensor.detach().cpu().numpy(), axis=axis, keepdims=keepdims)))
-        return PTNNCFTensor(x.tensor.median(dim=axis, keepdim=keepdims).values)
+        return PTNNCFTensor(torch.quantile(x.tensor, q=0.5, dim=axis, keepdim=keepdims).values)
 
     @classmethod
     def masked_mean(cls, x: NNCFTensor, axis: Union[int, tuple], mask: NNCFTensor, keepdims=False) -> NNCFTensor: