simcop2387/AutoGPTQ
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fix bug

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qwopqwop200 2023-09-06 16:41:41 +09:00 • committed by GitHub
parent f752336cda
commit ad5b0d72ee
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6 changed files with 1 additions and 11 deletions
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2
auto_gptq/modeling/_base.py
View file

@ -39,7 +39,7 @@ class BaseQuantizeConfig(PushToHubMixin):
damp_percent: float = field(default=0.01)
desc_act: bool = field(default=True)
static_groups: bool = field(default=False)
sym: bool = field(default=True)
sym: bool = field(default=False)
true_sequential: bool = field(default=True)
model_name_or_path: Optional[str] = field(default=None)
model_file_base_name: Optional[str] = field(default=None)

3
auto_gptq/nn_modules/qlinear/qlinear_cuda.py
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@ -157,7 +157,6 @@ class QuantLinear(nn.Module):
qweight = qweight.astype(np.int32)
self.qweight = torch.from_numpy(qweight)
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
@ -221,7 +220,6 @@ class QuantLinear(nn.Module):
).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(self.scales.shape)
weight = torch.bitwise_right_shift(
@ -239,7 +237,6 @@ class QuantLinear(nn.Module):
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(self.scales.shape)
weight = self.qweight.reshape(

3
auto_gptq/nn_modules/qlinear/qlinear_cuda_old.py
View file

@ -157,7 +157,6 @@ class QuantLinear(nn.Module):
qweight = qweight.astype(np.int32)
self.qweight = torch.from_numpy(qweight)
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
@ -231,7 +230,6 @@ class QuantLinear(nn.Module):
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])
scales = self.scales
@ -248,7 +246,6 @@ class QuantLinear(nn.Module):
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])
scales = self.scales

1
auto_gptq/nn_modules/qlinear/qlinear_exllama.py
View file

@ -146,7 +146,6 @@ class QuantLinear(nn.Module):
qweight = qweight.astype(np.int32)
self.qweight = torch.from_numpy(qweight)
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

1
auto_gptq/nn_modules/qlinear/qlinear_triton.py
View file

@ -114,7 +114,6 @@ class QuantLinear(nn.Module, TritonModuleMixin):
qweight = qweight.astype(np.int32)
self.qweight = torch.from_numpy(qweight)
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

2
auto_gptq/nn_modules/triton_utils/kernels.py
View file

@ -144,7 +144,6 @@ def quant_matmul_248_kernel(
zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = (zeros >> zeros_shifter[None, :]) & maxq
zeros = (zeros + 1)
a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
@ -290,7 +289,6 @@ def transpose_quant_matmul_248_kernel(
zeros = tl.load(zeros_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = (zeros >> zeros_shifter[None, :]) & maxq
zeros = (zeros + 1)
a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated