lint

onnxruntime/python/tools/quantization/matmul_4bits_quantizer.py

@@ -11,7 +11,7 @@
 import importlib
 import logging
 import os
-from typing import List, Optional
+from typing import Optional
 
 import numpy as np
 import numpy.typing as npt
@@ -155,7 +155,7 @@ def optimize_weights(
         tensor: np.ndarray,
         scale: np.ndarray,
         zero: np.ndarray,
-        min_max: List[int],
+        min_max: list[int],
         axis: int = 0,
         opt_params: Optional[dict] = None,
         verbose=False,
@@ -184,10 +184,10 @@ def shrink_op(x, beta, p=lp_norm):
 
         best_error = 1e4
         for i in range(iters):
-            w_q= np.round(w_f * scale + zero).clip(min_max[0], min_max[1])
-            w_r = (w_q- zero) / scale
+            w_q = np.round(w_f * scale + zero).clip(min_max[0], min_max[1])
+            w_r = (w_q - zero) / scale
             w_e = shrink_op(w_f - w_r, beta)
-            zero = np.mean(w_q- (w_f - w_e) * scale, axis=axis, keepdims=True)
+            zero = np.mean(w_q - (w_f - w_e) * scale, axis=axis, keepdims=True)
             beta *= kappa
 
             current_error = float(np.abs(w_f - w_r).mean())
@@ -263,8 +263,8 @@ def quantize_internal(
 
         # Quantize
         # Necessary for fake quantization backprop
-        w_q= np.round(weight * scale + zero).clip(min_max[0], min_max[1])
-        w_q= w_q.reshape(shape).astype(np.uint32)
+        w_q = np.round(weight * scale + zero).clip(min_max[0], min_max[1])
+        w_q = w_q.reshape(shape).astype(np.uint32)
 
         scale = np.reciprocal(scale)
         if axis == 1: