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[FIX] Marlin runtime conversion padding #192

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merged 7 commits into from
Jul 10, 2024
Merged

[FIX] Marlin runtime conversion padding #192

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53fd6c3
add marlin convert padding
Jul 10, 2024
7177d0f
cleanup
Jul 10, 2024
b29495d
revert base.py change
Jul 10, 2024
84bfa10
use module.qweight.device
Jul 10, 2024
e76a7e2
add infeatures and outfeatures check
Jul 10, 2024
3393f12
cleanup
Jul 10, 2024
8080537
no padding required for C
Jul 10, 2024
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12 changes: 2 additions & 10 deletions gptqmodel/nn_modules/qlinear/qlinear_marlin.py
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Original file line number Diff line number Diff line change
Expand Up @@ -91,10 +91,6 @@ def __init__(self, bits: int, group_size: int, desc_act: bool, sym: bool, infeat

self.group_size = group_size if group_size != -1 else infeatures

del infeatures
del outfeatures
del group_size

self.register_buffer(
"B",
torch.empty((self.infeatures // 16, self.outfeatures * 16 // 8), dtype=torch.int),
Expand Down Expand Up @@ -132,11 +128,11 @@ def pack(self, linear, scales):
w = linear.weight.data.t()

if self.infeatures != self.original_infeatures or self.outfeatures != self.original_outfeatures:
padded_w = torch.zeros((self.infeatures, self.outfeatures))
padded_w = torch.zeros((self.infeatures, self.outfeatures), dtype=w.dtype, device=w.device)
padded_w[:w.size(0), :w.size(1)] = w
w = padded_w

padded_s = torch.zeros((s.size(0), self.outfeatures))
padded_s = torch.zeros((s.size(0), self.outfeatures), dtype=torch.half, device=s.device)
padded_s[:s.size(0), :s.size(1)] = s
s = padded_s

Expand Down Expand Up @@ -184,10 +180,6 @@ def forward(self, A):
A = F.pad(A, (0, self.infeatures - self.original_infeatures))

C = torch.empty(A.shape[:-1] + (self.s.shape[1],), dtype=A.dtype, device=A.device)

if C.size(-1) != self.outfeatures:
C = F.pad(C, (0, self.outfeatures - self.original_outfeatures))

mul(
A.view((-1, A.shape[-1])),
self.B,
Expand Down
16 changes: 14 additions & 2 deletions gptqmodel/utils/marlin.py
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Original file line number Diff line number Diff line change
Expand Up @@ -149,7 +149,13 @@ def convert_to_marlin(
if repack:
import gptqmodel_marlin_cuda

marlin_repacked_weight = gptqmodel_marlin_cuda.gptq_repack(module.qweight)
qweight = module.qweight
if new_module.infeatures != new_module.original_infeatures or new_module.outfeatures != new_module.original_outfeatures:
padded_qweight = torch.zeros((new_module.infeatures, new_module.outfeatures), dtype=torch.int, device=module.qweight.device)
padded_qweight[:module.qweight.size(0), :module.qweight.size(1)] = qweight
qweight = padded_qweight

marlin_repacked_weight = gptqmodel_marlin_cuda.gptq_repack(qweight)

if strict:
dequantized_qzeros = unpack_qzeros(module.qzeros)
Expand All @@ -163,12 +169,18 @@ def convert_to_marlin(
_, _scale_perm, _scale_perm_single = _get_perms()

s = module.scales.data.clone()

if new_module.infeatures != new_module.original_infeatures or new_module.outfeatures != new_module.original_outfeatures:
padded_s = torch.zeros((s.size(0), new_module.outfeatures), dtype=torch.half, device=s.device)
padded_s[:s.size(0), :s.size(1)] = s
s = padded_s

if module.group_size != module.infeatures:
s = s.reshape((1, -1))
s = s.reshape((-1, len(_scale_perm)))[:, _scale_perm]
else:
s = s.reshape((-1, len(_scale_perm_single)))[:, _scale_perm_single]
s = s.reshape((-1, module.outfeatures)).contiguous()
s = s.reshape((-1, new_module.outfeatures)).contiguous()

new_module.B = marlin_repacked_weight
new_module.s = s
Expand Down