simcop2387/AutoGPTQ
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages 1 Wiki Activity

reformat code

Browse source
This commit is contained in:
PanQiWei 2023-05-04 22:16:08 +08:00
parent 1c6bb69fae
commit 6cba6e7123
2 changed files with 153 additions and 92 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

14
auto_gptq/nn_modules/qlinear.py
View file

@ -62,12 +62,14 @@ class QuantLinear(nn.Module):
if self.bits in [2, 4, 8]:
self.wf = torch.tensor(list(range(0, 32, self.bits)), dtype=torch.int32).unsqueeze(0)
elif self.bits == 3:
self.wf = torch.tensor([
[0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 0],
[0, 1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31],
[0, 2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 0],
],
dtype=torch.int32).reshape(1, 3, 12)
self.wf = torch.tensor(
[
[0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 0],
[0, 1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31],
[0, 2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 0],
],
dtype=torch.int32
).reshape(1, 3, 12)
self.kernel_switch_threshold = kernel_switch_threshold
self.quant_cuda_available = _quant_cuda_available

185
auto_gptq/nn_modules/qlinear_old.py
View file

@ -18,9 +18,18 @@ except ImportError:
class QuantLinear(nn.Module):
def __init__(self, bits, groupsize, infeatures, outfeatures, bias, faster=True, kernel_switch_threshold=128, is_cuda=_quant_cuda_available):
def __init__(
self,
bits,
groupsize,
infeatures,
outfeatures,
bias,
faster=True,
kernel_switch_threshold=128
):
super().__init__()
if bits not in [2,3,4,8]:
if bits not in [2, 3, 4, 8]:
raise NotImplementedError("Only 2,3,4,8 bits are supported.")
self.infeatures = infeatures
self.outfeatures = outfeatures
@ -28,27 +37,44 @@ class QuantLinear(nn.Module):
self.groupsize = groupsize if groupsize != -1 else infeatures
self.maxq = 2 ** self.bits - 1
self.register_buffer('qweight', torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32))
self.register_buffer('qzeros', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures // 32 * self.bits), dtype=torch.int32))
self.register_buffer('scales', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures), dtype=torch.float16))
self.register_buffer('g_idx',torch.tensor([i // self.groupsize for i in range(infeatures)], dtype=torch.int32))
self.register_buffer(
'qweight',
torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32)
)
self.register_buffer(
'qzeros',
torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures // 32 * self.bits), dtype=torch.int32)
)
self.register_buffer(
'scales',
torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures), dtype=torch.float16)
)
self.register_buffer(
'g_idx',
torch.tensor([i // self.groupsize for i in range(infeatures)], dtype=torch.int32)
)
if bias:
self.register_buffer('bias', torch.zeros((outfeatures),dtype=torch.float16))
self.register_buffer('bias', torch.zeros((outfeatures), dtype=torch.float16))
else:
self.bias = None
self.half_indim = self.infeatures // 2
self.faster = faster if bits != 8 else False
# is performed by unpacking the weights and using torch.matmul
if self.bits in [2,4,8]:
self.wf = torch.tensor(list(range(0,32,self.bits)), dtype=torch.int32).unsqueeze(0)
if self.bits in [2, 4, 8]:
self.wf = torch.tensor(list(range(0, 32, self.bits)), dtype=torch.int32).unsqueeze(0)
elif self.bits == 3:
self.wf = torch.tensor([[0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 0],
[0, 1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31],
[0, 2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 0],], dtype=torch.int32).reshape(1,3,12)
self.wf = torch.tensor(
[
[0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 0],
[0, 1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31],
[0, 2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 0],
],
dtype=torch.int32
).reshape(1, 3, 12)
self.kernel_switch_threshold = kernel_switch_threshold
self.is_cuda = is_cuda
self.quant_cuda_available = _quant_cuda_available
if infeatures % 256 != 0 or outfeatures % 256 != 0:
self.quant_cuda_available = False
@ -63,20 +89,25 @@ class QuantLinear(nn.Module):
intweight = []
for idx in range(self.infeatures):
g_idx = idx // self.groupsize
intweight.append(torch.round((linear.weight.data[:,idx] + scale_zeros[g_idx]) / self.scales[g_idx]).to(torch.int)[:,None])
intweight = torch.cat(intweight,dim=1)
intweight.append(
torch.round(
(linear.weight.data[:, idx] + scale_zeros[g_idx]) / self.scales[g_idx]
).to(torch.int)[:, None]
)
intweight = torch.cat(intweight, dim=1)
intweight = intweight.t().contiguous()
intweight = intweight.numpy().astype(np.uint32)
i = 0
row = 0
qweight = np.zeros(
(intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32
)
i = 0
row = 0
while row < qweight.shape[0]:
if self.bits in [2,4,8]:
for j in range(i, i + (32//self.bits)):
if self.bits in [2, 4, 8]:
for j in range(i, i + (32 // self.bits)):
qweight[row] |= intweight[j] << (self.bits * (j - i))
i += 32//self.bits
i += 32 // self.bits
row += 1
elif self.bits == 3:
for j in range(i, i + 10):
@ -103,16 +134,16 @@ class QuantLinear(nn.Module):
qweight = qweight.astype(np.int32)
self.qweight = torch.from_numpy(qweight)
zeros -= 1;
zeros -= 1
zeros = zeros.numpy().astype(np.uint32)
qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32)
i = 0
col = 0
while col < qzeros.shape[1]:
if self.bits in [2,4,8]:
for j in range(i, i + (32//self.bits)):
if self.bits in [2, 4, 8]:
for j in range(i, i + (32 // self.bits)):
qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
i += 32//self.bits
i += 32 // self.bits
col += 1
elif self.bits == 3:
for j in range(i, i + 10):
@ -140,71 +171,100 @@ class QuantLinear(nn.Module):
self.qzeros = torch.from_numpy(qzeros)
def forward(self, x):
out_shape = x.shape[:-1] + (self.outfeatures, )
x = x.reshape(-1,x.shape[-1])
if self.is_cuda is True and (self.kernel_switch_threshold is False or x.shape[0] < self.kernel_switch_threshold):
out_shape = x.shape[:-1] + (self.outfeatures,)
x = x.reshape(-1, x.shape[-1])
if self.quant_cuda_available is True and (
self.kernel_switch_threshold is False or x.shape[0] < self.kernel_switch_threshold
):
out = torch.zeros(x.shape[0], out_shape[-1], dtype=torch.float, device=x.device)
if self.faster:
x = x.half()
if self.bits == 2:
quant_cuda.vecquant2matmul_faster_old(x, self.qweight, out, self.scales.float(), self.qzeros, self.groupsize, self.half_indim)
quant_cuda.vecquant2matmul_faster_old(
x, self.qweight, out, self.scales.float(), self.qzeros, self.groupsize, self.half_indim
)
elif self.bits == 3:
quant_cuda.vecquant3matmul_faster_old(x, self.qweight, out, self.scales.float(), self.qzeros, self.groupsize, self.half_indim)
quant_cuda.vecquant3matmul_faster_old(
x, self.qweight, out, self.scales.float(), self.qzeros, self.groupsize, self.half_indim
)
elif self.bits == 4:
quant_cuda.vecquant4matmul_faster_old(x, self.qweight, out, self.scales.float(), self.qzeros, self.groupsize, self.half_indim)
quant_cuda.vecquant4matmul_faster_old(
x, self.qweight, out, self.scales.float(), self.qzeros, self.groupsize, self.half_indim
)
else:
raise NotImplementedError("Only 2,3,4 bits are supported.")
else:
x = x.float()
if self.bits == 2:
quant_cuda.vecquant2matmul_old(x, self.qweight, out, self.scales.float(), self.qzeros, self.groupsize)
quant_cuda.vecquant2matmul_old(
x, self.qweight, out, self.scales.float(), self.qzeros, self.groupsize
)
elif self.bits == 3:
quant_cuda.vecquant3matmul_old(x, self.qweight, out, self.scales.float(), self.qzeros, self.groupsize)
quant_cuda.vecquant3matmul_old(
x, self.qweight, out, self.scales.float(), self.qzeros, self.groupsize
)
elif self.bits == 4:
quant_cuda.vecquant4matmul_old(x, self.qweight, out, self.scales.float(), self.qzeros, self.groupsize)
quant_cuda.vecquant4matmul_old(
x, self.qweight, out, self.scales.float(), self.qzeros, self.groupsize
)
elif self.bits == 8:
quant_cuda.vecquant8matmul_old(x, self.qweight, out, self.scales.float(), self.qzeros, self.groupsize)
quant_cuda.vecquant8matmul_old(
x, self.qweight, out, self.scales.float(), self.qzeros, self.groupsize
)
else:
raise NotImplementedError("Only 2,3,4,8 bits are supported.")
else:
if self.wf.device != self.qzeros.device:
self.wf = self.wf.to(self.qzeros.device)
self.wf = self.wf.to(self.qzeros.device)
if self.bits in [2,4,8]:
zeros = torch.bitwise_right_shift(torch.unsqueeze(self.qzeros, 2).expand(-1, -1, 32 // self.bits), self.wf.unsqueeze(0)).to(torch.int16 if self.bits == 8 else torch.int8)
torch.bitwise_and(zeros, (2 ** self.bits) - 1, out=zeros)
if self.bits in [2, 4, 8]:
zeros = torch.bitwise_right_shift(
torch.unsqueeze(self.qzeros, 2).expand(-1, -1, 32 // self.bits),
self.wf.unsqueeze(0)
).to(torch.int16 if self.bits == 8 else torch.int8)
torch.bitwise_and(zeros, (2 ** self.bits) - 1, out=zeros)
zeros = zeros + 1
zeros = zeros.reshape(-1, 1, zeros.shape[1] * zeros.shape[2])
zeros = zeros + 1
zeros = zeros.reshape(-1, 1, zeros.shape[1] * zeros.shape[2])
scales = self.scales
scales = scales.reshape(-1, 1, scales.shape[-1])
scales = self.scales
scales = scales.reshape(-1, 1, scales.shape[-1])
weight = torch.bitwise_right_shift(torch.unsqueeze(self.qweight, 1).expand(-1, 32 // self.bits, -1), self.wf.unsqueeze(-1)).to(torch.int16 if self.bits == 8 else torch.int8)
torch.bitwise_and(weight,(2 ** self.bits) - 1, out=weight)
weight = weight.reshape(-1, self.groupsize, weight.shape[2])
weight = torch.bitwise_right_shift(
torch.unsqueeze(self.qweight, 1).expand(-1, 32 // self.bits, -1),
self.wf.unsqueeze(-1)
).to(torch.int16 if self.bits == 8 else torch.int8)
torch.bitwise_and(weight, (2 ** self.bits) - 1, out=weight)
weight = weight.reshape(-1, self.groupsize, weight.shape[2])
elif self.bits == 3:
zeros = self.qzeros.reshape(self.qzeros.shape[0], self.qzeros.shape[1]//3, 3, 1).expand(-1, -1, -1, 12)
zeros = (zeros >> self.wf.unsqueeze(0))
zeros[:,:,0,10] = (zeros[:,:,0,10]&0x3) | ((zeros[:,:,1,0] << 2)&0x4)
zeros[:,:,1,11] = (zeros[:,:,1,11]&0x1) | ((zeros[:,:,2,0] << 1)&0x6)
zeros = zeros & 0x7
zeros = torch.cat([zeros[:,:,0,:11], zeros[:,:,1,1:12], zeros[:,:,2,1:11]], dim=2)
zeros = self.qzeros.reshape(
self.qzeros.shape[0], self.qzeros.shape[1] // 3, 3, 1
).expand(-1, -1, -1, 12)
zeros = (zeros >> self.wf.unsqueeze(0))
zeros[:, :, 0, 10] = (zeros[:, :, 0, 10] & 0x3) | ((zeros[:, :, 1, 0] << 2) & 0x4)
zeros[:, :, 1, 11] = (zeros[:, :, 1, 11] & 0x1) | ((zeros[:, :, 2, 0] << 1) & 0x6)
zeros = zeros & 0x7
zeros = torch.cat([zeros[:, :, 0, :11], zeros[:, :, 1, 1:12], zeros[:, :, 2, 1:11]], dim=2)
zeros = zeros + 1
zeros = zeros.reshape(-1, 1, zeros.shape[1] * zeros.shape[2])
zeros = zeros + 1
zeros = zeros.reshape(-1, 1, zeros.shape[1] * zeros.shape[2])
scales = self.scales
scales = scales.reshape(-1, 1, scales.shape[-1])
scales = self.scales
scales = scales.reshape(-1, 1, scales.shape[-1])
weight = self.qweight.reshape(
self.qweight.shape[0] // 3, 3, 1, self.qweight.shape[1]
).expand(-1, -1, 12, -1)
weight = (weight >> self.wf.unsqueeze(-1)) & 0x7
weight[:, 0, 10] = (weight[:, 0, 10] & 0x3) | ((weight[:, 1, 0] << 2) & 0x4)
weight[:, 1, 11] = (weight[:, 1, 11] & 0x1) | ((weight[:, 2, 0] << 1) & 0x6)
weight = weight & 0x7
weight = torch.cat([weight[:, 0, :11], weight[:, 1, 1:12], weight[:, 2, 1:11]], dim=1)
weight = weight.reshape(-1, self.groupsize, weight.shape[2])
else:
raise NotImplementedError("Only 2,3,4,8 bits are supported.")
weight = self.qweight.reshape(self.qweight.shape[0]//3, 3, 1, self.qweight.shape[1]).expand(-1, -1, 12, -1)
weight = (weight >> self.wf.unsqueeze(-1))&0x7
weight[:,0,10] = (weight[:,0,10]&0x3) | ((weight[:,1,0] << 2)&0x4)
weight[:,1,11] = (weight[:,1,11]&0x1) | ((weight[:,2,0] << 1)&0x6)
weight = weight & 0x7
weight = torch.cat([weight[:,0,:11], weight[:,1,1:12], weight[:,2,1:11]], dim=1)
weight = weight.reshape(-1, self.groupsize, weight.shape[2])
weight = (scales * (weight - zeros))
weight = weight.reshape(weight.shape[0] * weight.shape[1], weight.shape[2])
@ -214,5 +274,4 @@ class QuantLinear(nn.Module):
return out
__all__ = ["QuantLinear"]