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reformat code

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PanQiWei 2023-05-04 22:16:08 +08:00
parent 1c6bb69fae
commit 6cba6e7123
2 changed files with 153 additions and 92 deletions
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20
auto_gptq/nn_modules/qlinear.py
View file

@ -62,25 +62,27 @@ class QuantLinear(nn.Module):
if self.bits in [2, 4, 8]: if self.bits in [2, 4, 8]:
self.wf = torch.tensor(list(range(0, 32, self.bits)), dtype=torch.int32).unsqueeze(0) self.wf = torch.tensor(list(range(0, 32, self.bits)), dtype=torch.int32).unsqueeze(0)
elif self.bits == 3: elif self.bits == 3:
self.wf = torch.tensor([ 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, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 0],
[0, 2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 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) ],
dtype=torch.int32
).reshape(1, 3, 12)
self.kernel_switch_threshold = kernel_switch_threshold self.kernel_switch_threshold = kernel_switch_threshold
self.quant_cuda_available = _quant_cuda_available self.quant_cuda_available = _quant_cuda_available
if infeatures % 256 != 0 or outfeatures % 256 != 0: if infeatures % 256 != 0 or outfeatures % 256 != 0:
self.quant_cuda_available = False self.quant_cuda_available = False
def pack(self, linear, scales, zeros, g_idx=None): def pack(self, linear, scales, zeros, g_idx=None):
W = linear.weight.data.clone() W = linear.weight.data.clone()
if isinstance(linear, nn.Conv2d): if isinstance(linear, nn.Conv2d):
W = W.flatten(1) W = W.flatten(1)
if isinstance(linear, transformers.pytorch_utils.Conv1D): if isinstance(linear, transformers.pytorch_utils.Conv1D):
W = W.t() W = W.t()
self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx
scales = scales.t().contiguous() scales = scales.t().contiguous()
@ -195,7 +197,7 @@ class QuantLinear(nn.Module):
else: else:
if self.wf.device != self.qzeros.device: 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]: if self.bits in [2, 4, 8]:
zeros = torch.bitwise_right_shift( zeros = torch.bitwise_right_shift(
torch.unsqueeze(self.qzeros, 2).expand(-1, -1, 32 // self.bits), torch.unsqueeze(self.qzeros, 2).expand(-1, -1, 32 // self.bits),

225
auto_gptq/nn_modules/qlinear_old.py
View file

@ -17,10 +17,19 @@ except ImportError:
_quant_cuda_available = False _quant_cuda_available = False
class QuantLinear(nn.Module): 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__() 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.") raise NotImplementedError("Only 2,3,4,8 bits are supported.")
self.infeatures = infeatures self.infeatures = infeatures
self.outfeatures = outfeatures self.outfeatures = outfeatures
@ -28,30 +37,47 @@ class QuantLinear(nn.Module):
self.groupsize = groupsize if groupsize != -1 else infeatures self.groupsize = groupsize if groupsize != -1 else infeatures
self.maxq = 2 ** self.bits - 1 self.maxq = 2 ** self.bits - 1
self.register_buffer('qweight', torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32)) self.register_buffer(
self.register_buffer('qzeros', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures // 32 * self.bits), dtype=torch.int32)) 'qweight',
self.register_buffer('scales', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures), dtype=torch.float16)) torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32)
self.register_buffer('g_idx',torch.tensor([i // self.groupsize for i in range(infeatures)], 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: if bias:
self.register_buffer('bias', torch.zeros((outfeatures),dtype=torch.float16)) self.register_buffer('bias', torch.zeros((outfeatures), dtype=torch.float16))
else: else:
self.bias = None self.bias = None
self.half_indim = self.infeatures // 2 self.half_indim = self.infeatures // 2
self.faster = faster if bits != 8 else False self.faster = faster if bits != 8 else False
# is performed by unpacking the weights and using torch.matmul # is performed by unpacking the weights and using torch.matmul
if self.bits in [2,4,8]: if self.bits in [2, 4, 8]:
self.wf = torch.tensor(list(range(0,32,self.bits)), dtype=torch.int32).unsqueeze(0) self.wf = torch.tensor(list(range(0, 32, self.bits)), dtype=torch.int32).unsqueeze(0)
elif self.bits == 3: elif self.bits == 3:
self.wf = torch.tensor([[0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 0], self.wf = torch.tensor(
[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) [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.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: if infeatures % 256 != 0 or outfeatures % 256 != 0:
self.quant_cuda_available = False self.quant_cuda_available = False
def pack(self, linear, scales, zeros, g_idx): def pack(self, linear, scales, zeros, g_idx):
scales = scales.t().contiguous() scales = scales.t().contiguous()
zeros = zeros.t().contiguous() zeros = zeros.t().contiguous()
@ -59,24 +85,29 @@ class QuantLinear(nn.Module):
self.scales = scales.clone().half() self.scales = scales.clone().half()
if linear.bias is not None: if linear.bias is not None:
self.bias = linear.bias.clone().half() self.bias = linear.bias.clone().half()
intweight = [] intweight = []
for idx in range(self.infeatures): for idx in range(self.infeatures):
g_idx = idx // self.groupsize 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.append(
intweight = torch.cat(intweight,dim=1) 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.t().contiguous()
intweight = intweight.numpy().astype(np.uint32) intweight = intweight.numpy().astype(np.uint32)
i = 0
row = 0
qweight = np.zeros( qweight = np.zeros(
(intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32 (intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32
) )
i = 0
row = 0
while row < qweight.shape[0]: while row < qweight.shape[0]:
if self.bits in [2,4,8]: if self.bits in [2, 4, 8]:
for j in range(i, i + (32//self.bits)): for j in range(i, i + (32 // self.bits)):
qweight[row] |= intweight[j] << (self.bits * (j - i)) qweight[row] |= intweight[j] << (self.bits * (j - i))
i += 32//self.bits i += 32 // self.bits
row += 1 row += 1
elif self.bits == 3: elif self.bits == 3:
for j in range(i, i + 10): for j in range(i, i + 10):
@ -99,20 +130,20 @@ class QuantLinear(nn.Module):
row += 1 row += 1
else: else:
raise NotImplementedError("Only 2,3,4,8 bits are supported.") raise NotImplementedError("Only 2,3,4,8 bits are supported.")
qweight = qweight.astype(np.int32) qweight = qweight.astype(np.int32)
self.qweight = torch.from_numpy(qweight) self.qweight = torch.from_numpy(qweight)
zeros -= 1; zeros -= 1
zeros = zeros.numpy().astype(np.uint32) zeros = zeros.numpy().astype(np.uint32)
qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32) qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32)
i = 0 i = 0
col = 0 col = 0
while col < qzeros.shape[1]: while col < qzeros.shape[1]:
if self.bits in [2,4,8]: if self.bits in [2, 4, 8]:
for j in range(i, i + (32//self.bits)): for j in range(i, i + (32 // self.bits)):
qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i)) qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
i += 32//self.bits i += 32 // self.bits
col += 1 col += 1
elif self.bits == 3: elif self.bits == 3:
for j in range(i, i + 10): for j in range(i, i + 10):
@ -135,84 +166,112 @@ class QuantLinear(nn.Module):
col += 1 col += 1
else: else:
raise NotImplementedError("Only 2,3,4,8 bits are supported.") raise NotImplementedError("Only 2,3,4,8 bits are supported.")
qzeros = qzeros.astype(np.int32) qzeros = qzeros.astype(np.int32)
self.qzeros = torch.from_numpy(qzeros) self.qzeros = torch.from_numpy(qzeros)
def forward(self, x): def forward(self, x):
out_shape = x.shape[:-1] + (self.outfeatures, ) out_shape = x.shape[:-1] + (self.outfeatures,)
x = x.reshape(-1,x.shape[-1]) 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): 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) out = torch.zeros(x.shape[0], out_shape[-1], dtype=torch.float, device=x.device)
if self.faster: if self.faster:
x = x.half() x = x.half()
if self.bits == 2: 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: 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: 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: else:
raise NotImplementedError("Only 2,3,4 bits are supported.") raise NotImplementedError("Only 2,3,4 bits are supported.")
else: else:
x = x.float() x = x.float()
if self.bits == 2: 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: 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: 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: 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: else:
raise NotImplementedError("Only 2,3,4,8 bits are supported.") raise NotImplementedError("Only 2,3,4,8 bits are supported.")
else: else:
if self.wf.device != self.qzeros.device: 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]: 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) zeros = torch.bitwise_right_shift(
torch.bitwise_and(zeros, (2 ** self.bits) - 1, out=zeros) torch.unsqueeze(self.qzeros, 2).expand(-1, -1, 32 // self.bits),
self.wf.unsqueeze(0)
zeros = zeros + 1 ).to(torch.int16 if self.bits == 8 else torch.int8)
zeros = zeros.reshape(-1, 1, zeros.shape[1] * zeros.shape[2]) torch.bitwise_and(zeros, (2 ** self.bits) - 1, out=zeros)
scales = self.scales zeros = zeros + 1
scales = scales.reshape(-1, 1, scales.shape[-1]) zeros = zeros.reshape(-1, 1, zeros.shape[1] * zeros.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) scales = self.scales
torch.bitwise_and(weight,(2 ** self.bits) - 1, out=weight) scales = scales.reshape(-1, 1, scales.shape[-1])
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: 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 = self.qzeros.reshape(
zeros = (zeros >> self.wf.unsqueeze(0)) self.qzeros.shape[0], self.qzeros.shape[1] // 3, 3, 1
zeros[:,:,0,10] = (zeros[:,:,0,10]&0x3) | ((zeros[:,:,1,0] << 2)&0x4) ).expand(-1, -1, -1, 12)
zeros[:,:,1,11] = (zeros[:,:,1,11]&0x1) | ((zeros[:,:,2,0] << 1)&0x6) zeros = (zeros >> self.wf.unsqueeze(0))
zeros = zeros & 0x7 zeros[:, :, 0, 10] = (zeros[:, :, 0, 10] & 0x3) | ((zeros[:, :, 1, 0] << 2) & 0x4)
zeros = torch.cat([zeros[:,:,0,:11], zeros[:,:,1,1:12], zeros[:,:,2,1:11]], dim=2) zeros[:, :, 1, 11] = (zeros[:, :, 1, 11] & 0x1) | ((zeros[:, :, 2, 0] << 1) & 0x6)
zeros = zeros & 0x7
zeros = zeros + 1 zeros = torch.cat([zeros[:, :, 0, :11], zeros[:, :, 1, 1:12], zeros[:, :, 2, 1:11]], dim=2)
zeros = zeros.reshape(-1, 1, zeros.shape[1] * zeros.shape[2])
zeros = zeros + 1
scales = self.scales zeros = zeros.reshape(-1, 1, zeros.shape[1] * zeros.shape[2])
scales = scales.reshape(-1, 1, scales.shape[-1])
scales = self.scales
weight = self.qweight.reshape(self.qweight.shape[0]//3, 3, 1, self.qweight.shape[1]).expand(-1, -1, 12, -1) scales = scales.reshape(-1, 1, scales.shape[-1])
weight = (weight >> self.wf.unsqueeze(-1))&0x7
weight[:,0,10] = (weight[:,0,10]&0x3) | ((weight[:,1,0] << 2)&0x4) weight = self.qweight.reshape(
weight[:,1,11] = (weight[:,1,11]&0x1) | ((weight[:,2,0] << 1)&0x6) self.qweight.shape[0] // 3, 3, 1, self.qweight.shape[1]
weight = weight & 0x7 ).expand(-1, -1, 12, -1)
weight = torch.cat([weight[:,0,:11], weight[:,1,1:12], weight[:,2,1:11]], dim=1) weight = (weight >> self.wf.unsqueeze(-1)) & 0x7
weight = weight.reshape(-1, self.groupsize, weight.shape[2]) 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 = (scales * (weight - zeros)) weight = (scales * (weight - zeros))
weight = weight.reshape(weight.shape[0] * weight.shape[1], weight.shape[2]) weight = weight.reshape(weight.shape[0] * weight.shape[1], weight.shape[2])
out = torch.matmul(x.half(), weight) out = torch.matmul(x.half(), weight)
out = out.reshape(out_shape) out = out.reshape(out_shape)
out = out + self.bias if self.bias is not None else out out = out + self.bias if self.bias is not None else out
return out return out
__all__ = ["QuantLinear"] __all__ = ["QuantLinear"]