Removing percentile argument from compute encodings (#2768)

Signed-off-by: Priyanka Dangi <quic_pdangi@quicinc.com>

TrainingExtensions/torch/src/python/aimet_torch/experimental/v2/quantization/encoding_analyzer.py

@@ -341,37 +341,31 @@ class PercentileEncodingAnalyzer(EncodingAnalyzer[_Histogram]):
     """
     Encoding Analyzer for Percentile calibration technique
     """
-    def __init__(self, shape: tuple, num_bins: int = 2048):
+    def __init__(self, shape: tuple, num_bins: int = 2048, percentile: float = 100):
         if num_bins <= 0:
             raise ValueError('Number of bins cannot be less than or equal to 0.')
+
+        if percentile < 50 or percentile > 100:
+            raise ValueError('Percentile value must be within 50-100 range')
+
         observer = _HistogramObserver(shape=shape, num_bins=num_bins)
         super().__init__(observer)
+        self.percentile = percentile
 
     @torch.no_grad()
     def update_stats(self, input_tensor: torch.Tensor) -> _Statistics:
         new_stats = self.observer.collect_stats(input_tensor)
         self.observer.merge_stats(new_stats, input_tensor)
         return new_stats
 
-    def compute_dynamic_encodings(self, input_tensor: torch.Tensor, bitwidth: int,\
-                                  is_symmetric: bool, percentile: float)-> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]:
-        return self.compute_encodings_from_stats(
-            self.observer.collect_stats(input_tensor), bitwidth, is_symmetric, percentile)
-
-    def compute_encodings(self, bitwidth: int, is_symmetric: bool, percentile: float) -> torch.Tensor:
-        return self.compute_encodings_from_stats(self.observer.get_stats(), bitwidth, is_symmetric, percentile)
-
     # pylint: disable=too-many-locals
     @torch.no_grad()
-    def compute_encodings_from_stats(self, stats: List[_Histogram], bitwidth: int, is_symmetric: bool, percentile: float)\
+    def compute_encodings_from_stats(self, stats: List[_Histogram], bitwidth: int, is_symmetric: bool)\
             -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]:
 
         if bitwidth <= 0:
             raise ValueError('Bitwidth cannot be less than or equal to 0.')
 
-        if percentile < 50 or percentile > 100:
-            raise ValueError('Percentile value must be within 50-100 range')
-
         if stats[0].histogram is None:
             raise StatisticsNotFoundError('No statistics present to compute encodings.')
 
@@ -381,10 +375,10 @@ def compute_encodings_from_stats(self, stats: List[_Histogram], bitwidth: int, i
         for list_elem in stats:
             cum_sum = torch.cumsum(list_elem.histogram, dim=0)
             # trim percentile value from min and max
-            max_index = torch.searchsorted(cum_sum, torch.quantile(cum_sum, percentile/100))
-            min_index = torch.searchsorted(cum_sum, torch.quantile(cum_sum, 1 - percentile/100))
+            max_index = torch.searchsorted(cum_sum, torch.quantile(cum_sum, self.percentile/100))
+            min_index = torch.searchsorted(cum_sum, torch.quantile(cum_sum, 1 - self.percentile/100))
 
-            if percentile == 100:
+            if self.percentile == 100:
                 min_index = 0
                 max_index = -1
             curr_min = list_elem.bin_edges[min_index]