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import math
import time
import torch
import torch.nn as nn
import transformers
from quant import *
DEBUG = False
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cudnn.allow_tf32 = False
class GPTQ:
def __init__(self, layer):
self.layer = layer
self.dev = self.layer.weight.device
W = layer.weight.data.clone()
if isinstance(self.layer, nn.Conv2d):
W = W.flatten(1)
if isinstance(self.layer, transformers.Conv1D):
W = W.t()
self.rows = W.shape[0]
self.columns = W.shape[1]
self.H = torch.zeros((self.columns, self.columns), device=self.dev)
self.nsamples = 0
def add_batch(self, inp, out):
if DEBUG:
self.inp1 = inp
self.out1 = out
if len(inp.shape) == 2:
inp = inp.unsqueeze(0)
tmp = inp.shape[0]
if isinstance(self.layer, nn.Linear) or isinstance(self.layer, transformers.Conv1D):
if len(inp.shape) == 3:
inp = inp.reshape((-1, inp.shape[-1]))
inp = inp.t()
if isinstance(self.layer, nn.Conv2d):
unfold = nn.Unfold(
self.layer.kernel_size,
dilation=self.layer.dilation,
padding=self.layer.padding,
stride=self.layer.stride
)
inp = unfold(inp)
inp = inp.permute([1, 0, 2])
inp = inp.flatten(1)
self.H *= self.nsamples / (self.nsamples + tmp)
self.nsamples += tmp
# inp = inp.float()
inp = math.sqrt(2 / self.nsamples) * inp.float()
# self.H += 2 / self.nsamples * inp.matmul(inp.t())
self.H += inp.matmul(inp.t())
def fasterquant(
self, blocksize=128, percdamp=.01, groupsize=-1, actorder=False
):
W = self.layer.weight.data.clone()
if isinstance(self.layer, nn.Conv2d):
W = W.flatten(1)
if isinstance(self.layer, transformers.Conv1D):
W = W.t()
W = W.float()
tick = time.time()
if not self.quantizer.ready():
self.quantizer.find_params(W, weight=True)
H = self.H
del self.H
dead = torch.diag(H) == 0
H[dead, dead] = 1
W[:, dead] = 0
if actorder:
perm = torch.argsort(torch.diag(H), descending=True)
W = W[:, perm]
H = H[perm][:, perm]
Losses = torch.zeros_like(W)
Q = torch.zeros_like(W)
damp = percdamp * torch.mean(torch.diag(H))
diag = torch.arange(self.columns, device=self.dev)
H[diag, diag] += damp
H = torch.linalg.cholesky(H)
H = torch.cholesky_inverse(H)
H = torch.linalg.cholesky(H, upper=True)
Hinv = H
g_idx = []
scale = []
zero = []
now_idx = 1
for i1 in range(0, self.columns, blocksize):
i2 = min(i1 + blocksize, self.columns)
count = i2 - i1
W1 = W[:, i1:i2].clone()
Q1 = torch.zeros_like(W1)
Err1 = torch.zeros_like(W1)
Losses1 = torch.zeros_like(W1)
Hinv1 = Hinv[i1:i2, i1:i2]
for i in range(count):
w = W1[:, i]
d = Hinv1[i, i]
if groupsize != -1:
if (i1 + i) % groupsize == 0:
self.quantizer.find_params(W[:, (i1 + i):(i1 + i + groupsize)], weight=True)
if ((i1 + i) // groupsize) - now_idx == -1:
scale.append(self.quantizer.scale)
zero.append(self.quantizer.zero)
now_idx += 1
q = quantize(
w.unsqueeze(1), self.quantizer.scale, self.quantizer.zero, self.quantizer.maxq
).flatten()
Q1[:, i] = q
Losses1[:, i] = (w - q) ** 2 / d ** 2
err1 = (w - q) / d
W1[:, i:] -= err1.unsqueeze(1).matmul(Hinv1[i, i:].unsqueeze(0))
Err1[:, i] = err1
Q[:, i1:i2] = Q1
Losses[:, i1:i2] = Losses1 / 2
W[:, i2:] -= Err1.matmul(Hinv[i1:i2, i2:])
if DEBUG:
self.layer.weight.data[:, :i2] = Q[:, :i2]
self.layer.weight.data[:, i2:] = W[:, i2:]
print(torch.sum((self.layer(self.inp1) - self.out1) ** 2))
print(torch.sum(Losses))
torch.cuda.synchronize()
print('time %.2f' % (time.time() - tick))
print('error', torch.sum(Losses).item())
groupsize = groupsize if groupsize != -1 else self.columns
g_idx = [i // groupsize for i in range(self.columns)]
g_idx = torch.tensor(g_idx, dtype=torch.int32, device=Q.device)
if actorder:
invperm = torch.argsort(perm)
Q = Q[:, invperm]
g_idx = g_idx[invperm]
if isinstance(self.layer, transformers.Conv1D):
Q = Q.t()
self.layer.weight.data = Q.reshape(self.layer.weight.shape).to(self.layer.weight.data.dtype)
if DEBUG:
print(torch.sum((self.layer(self.inp1) - self.out1) ** 2))
if scale == []:
scale.append(self.quantizer.scale)
zero.append(self.quantizer.zero)
scale = torch.cat(scale,dim=1)
zero = torch.cat(zero,dim=1)
return scale,zero,g_idx
def free(self):
if DEBUG:
self.inp1 = None
self.out1 = None
self.H = None
self.Losses = None
self.Trace = None
torch.cuda.empty_cache()
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