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Align scale dtype with model precision in GPTQ #2403

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Align scale dtype with model precision in GPTQ #2403

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9 changes: 6 additions & 3 deletions torchao/quantization/GPTQ/GPTQ.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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 = (
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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 = (
Expand Down
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