mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-04-21 14:52:20 +00:00
Removing dead code.
This commit is contained in:
Ubuntu
Nicolas Patry
parent
9a12941bef
commit
92f85c964d
@ -318,127 +318,8 @@ class QuantLinear(nn.Module):
|
||||
self.outfeatures = qweight.shape[1]
|
||||
self.infeatures = qweight.shape[0] * 32 // 4
|
||||
|
||||
# expected = (math.ceil(self.infeatures / self.groupsize), self.outfeatures // 32 * self.bits)
|
||||
# assert tuple(self.qzeros.shape) == expected, f"{self.qzeros.shape} != {expected}"
|
||||
# expected = (math.ceil(self.infeatures / self.groupsize), self.outfeatures)
|
||||
# assert tuple(self.scales.shape) == expected, f"{self.scales.shape} != {expected}"
|
||||
# assert self.g_idx.shape == (math.ceil(self.infeatures / self.group_size), outfeatures)
|
||||
|
||||
# def new(cls, bits, groupsize, infeatures, outfeatures, bias):
|
||||
# if bits not in [2, 4, 8]:
|
||||
# raise NotImplementedError("Only 2,4,8 bits are supported.")
|
||||
|
||||
# self.infeatures = infeatures
|
||||
# self.outfeatures = outfeatures
|
||||
# self.bits = bits
|
||||
# self.maxq = 2**self.bits - 1
|
||||
# self.groupsize = groupsize if groupsize != -1 else infeatures
|
||||
|
||||
# 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))
|
||||
# else:
|
||||
# self.bias = None
|
||||
|
||||
def pack(self, linear, scales, zeros, g_idx=None):
|
||||
self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx
|
||||
|
||||
scales = scales.t().contiguous()
|
||||
zeros = zeros.t().contiguous()
|
||||
scale_zeros = zeros * scales
|
||||
self.scales = scales.clone().half()
|
||||
if linear.bias is not None:
|
||||
self.bias = linear.bias.clone().half()
|
||||
|
||||
intweight = []
|
||||
for idx in range(self.infeatures):
|
||||
intweight.append(torch.round((linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]]) / self.scales[self.g_idx[idx]]).to(torch.int)[:, None])
|
||||
intweight = torch.cat(intweight, dim=1)
|
||||
intweight = intweight.t().contiguous()
|
||||
intweight = intweight.numpy().astype(np.uint32)
|
||||
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)):
|
||||
qweight[row] |= intweight[j] << (self.bits * (j - i))
|
||||
i += 32 // self.bits
|
||||
row += 1
|
||||
else:
|
||||
raise NotImplementedError("Only 2,4,8 bits are supported.")
|
||||
|
||||
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
|
||||
col = 0
|
||||
while col < qzeros.shape[1]:
|
||||
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
|
||||
col += 1
|
||||
else:
|
||||
raise NotImplementedError("Only 2,4,8 bits are supported.")
|
||||
|
||||
qzeros = qzeros.astype(np.int32)
|
||||
self.qzeros = torch.from_numpy(qzeros)
|
||||
|
||||
def forward(self, x):
|
||||
out_shape = x.shape[:-1] + (self.outfeatures, )
|
||||
out = QuantLinearFunction.apply(x.reshape(-1, x.shape[-1]), self.qweight, self.scales, self.qzeros, self.g_idx, self.bits, self.maxq)
|
||||
out = out + self.bias if self.bias is not None else out
|
||||
return out.reshape(out_shape)
|
||||
|
||||
|
||||
def make_quant_linear(module, names, bits, groupsize, name=''):
|
||||
if isinstance(module, QuantLinear):
|
||||
return
|
||||
for attr in dir(module):
|
||||
tmp = getattr(module, attr)
|
||||
name1 = name + '.' + attr if name != '' else attr
|
||||
if name1 in names:
|
||||
delattr(module, attr)
|
||||
setattr(module, attr, QuantLinear(bits, groupsize, tmp.in_features, tmp.out_features, tmp.bias is not None))
|
||||
for name1, child in module.named_children():
|
||||
make_quant_linear(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1)
|
||||
|
||||
|
||||
def autotune_warmup_linear(model, transpose=False):
|
||||
"""
|
||||
Pre-tunes the quantized kernel
|
||||
"""
|
||||
from tqdm import tqdm
|
||||
|
||||
kn_values = {}
|
||||
|
||||
for _, m in model.named_modules():
|
||||
if not isinstance(m, QuantLinear):
|
||||
continue
|
||||
|
||||
k = m.infeatures
|
||||
n = m.outfeatures
|
||||
|
||||
if (k, n) not in kn_values:
|
||||
kn_values[(k, n)] = (m.qweight.cuda(), m.scales.cuda(), m.qzeros.cuda(), m.g_idx.cuda(), m.bits, m.maxq)
|
||||
|
||||
print(f'Found {len(kn_values)} unique KN Linear values.')
|
||||
|
||||
print('Warming up autotune cache ...')
|
||||
with torch.no_grad():
|
||||
for m in tqdm(range(0, 12)):
|
||||
m = 2**m # [1, 2048]
|
||||
for (k, n), (qweight, scales, qzeros, g_idx, bits, maxq) in kn_values.items():
|
||||
a = torch.randn(m, k, dtype=torch.float16, device='cuda')
|
||||
matmul248(a, qweight, scales, qzeros, g_idx, bits, maxq)
|
||||
if transpose:
|
||||
a = torch.randn(m, n, dtype=torch.float16, device='cuda')
|
||||
transpose_matmul248(a, qweight, scales, qzeros, g_idx, bits, maxq)
|
||||
del kn_values
|
||||
|
||||
Loading…
Reference in New Issue
Block a user