before vectorize the for loop

src/compressed_tensors/quantization/lifecycle/forward.py

@@ -58,19 +58,20 @@ def fake_quantize(
     bit_range = 2**args.num_bits
     max_q = torch.tensor(bit_range / 2 - 1, device=x.device)
     min_q = torch.tensor(-bit_range / 2, device=x.device)
-    # Q = torch.zeros_like(x)
+
     DQ = torch.zeros_like(x)
     num_groups = len(scale)
     group_size = int(x.shape[1] / num_groups)
+
+    # TODO: vectorize the for loop
     for i in range(num_groups):
         sc = scale[i]
         zp = zero_point[i]
 
         idx = i * group_size
         Q = quantize(x[:, idx : (idx + group_size)], sc, zp, min_q, max_q)
         DQ[:, idx : (idx + group_size)] = dequantize(Q, sc, zp)
-    breakpoint()
-    # Q = quantize(x, scale, zero_point, min_q, max_q)
+
     return DQ