Align scale dtype with model precision in GPTQ (#2403)

Summary:
For general usage, align the data type of scale with model precision Instead of the default use of bfloat16.
Pull Request resolved: #2403
Approved by: https://github.com/liangan1, https://github.com/jerryzh168

Author: xiaowangintel

torchao/quantization/GPTQ/GPTQ.py

@@ -295,7 +295,7 @@ def __torch_function__(
                     SQNR(DQ, DQ_from_qtensor),
                 )
 
-                qparams2 = cls.get_qparams_func(W)
+                qparams2 = cls.get_qparams_func(W, W.dtype)
                 Q2 = cls.quantize_func(W, qparams2)
                 DQ2 = cls.dequantize_func(Q2, qparams2).to(W.dtype)
                 old_q_out = (
@@ -444,7 +444,9 @@ def faster_quant(cls, H, W, device):
                 group_end = min(group_start + group_size, columns)
                 if group_start % group_size == 0:
                     # needed for when group_size == columns so only calculate qparams once
-                    cur_qparams = cls.get_qparams_func(W[:, group_start:group_end])
+                    cur_qparams = cls.get_qparams_func(
+                        W[:, group_start:group_end], orig_dtype
+                    )
                     all_qparams.append(cur_qparams)
 
                 for index in range(group_start, group_end):  # within each group
@@ -679,10 +681,11 @@ def __init__(
         else:
             self.zero_point_domain = ZeroPointDomain.FLOAT
 
-        self.get_qparams_func = lambda w: get_groupwise_affine_qparams(
+        self.get_qparams_func = lambda w, precision: get_groupwise_affine_qparams(
             w,
             n_bit,
             group_size,
+            dtype=precision,
             zero_point_domain=self.zero_point_domain,
         )
         self.quantize_func = (