349 lines
14 KiB
Python
349 lines
14 KiB
Python
import math
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from logging import getLogger
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import numpy as np
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import torch
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import torch.nn as nn
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logger = getLogger(__name__)
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def quantize(x, scale, zero, maxq):
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if maxq < 0:
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return (x > scale / 2).float() * scale + (x < zero / 2).float() * zero
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q = torch.clamp(torch.round(x / scale) + zero, 0, maxq)
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return scale * (q - zero)
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class Quantizer(nn.Module):
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def __init__(self, shape=1):
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super(Quantizer, self).__init__()
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self.register_buffer('maxq', torch.tensor(0))
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self.register_buffer('scale', torch.zeros(shape))
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self.register_buffer('zero', torch.zeros(shape))
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def configure(
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self,
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bits, perchannel=False, sym=True,
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mse=False, norm=2.4, grid=100, maxshrink=.8,
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trits=False
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):
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self.maxq = torch.tensor(2 ** bits - 1)
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self.perchannel = perchannel
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self.sym = sym
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self.mse = mse
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self.norm = norm
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self.grid = grid
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self.maxshrink = maxshrink
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if trits:
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self.maxq = torch.tensor(-1)
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def find_params(self, x, weight=False):
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dev = x.device
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self.maxq = self.maxq.to(dev)
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shape = x.shape
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if self.perchannel:
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if weight:
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x = x.flatten(1)
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else:
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if len(shape) == 4:
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x = x.permute([1, 0, 2, 3])
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x = x.flatten(1)
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if len(shape) == 3:
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x = x.reshape((-1, shape[-1])).t()
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if len(shape) == 2:
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x = x.t()
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else:
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x = x.flatten().unsqueeze(0)
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tmp = torch.zeros(x.shape[0], device=dev)
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xmin = torch.minimum(x.min(1)[0], tmp)
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xmax = torch.maximum(x.max(1)[0], tmp)
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if self.sym:
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xmax = torch.maximum(torch.abs(xmin), xmax)
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tmp = xmin < 0
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if torch.any(tmp):
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xmin[tmp] = -xmax[tmp]
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tmp = (xmin == 0) & (xmax == 0)
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xmin[tmp] = -1
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xmax[tmp] = +1
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if self.maxq < 0:
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self.scale = xmax
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self.zero = xmin
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else:
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self.scale = (xmax - xmin) / self.maxq
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if self.sym:
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self.zero = torch.full_like(self.scale, (self.maxq + 1) / 2)
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else:
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self.zero = torch.round(-xmin / self.scale)
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if self.mse:
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best = torch.full([x.shape[0]], float('inf'), device=dev)
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for i in range(int(self.maxshrink * self.grid)):
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p = 1 - i / self.grid
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xmin1 = p * xmin
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xmax1 = p * xmax
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scale1 = (xmax1 - xmin1) / self.maxq
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zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero
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q = quantize(x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq)
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q -= x
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q.abs_()
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q.pow_(self.norm)
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err = torch.sum(q, 1)
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tmp = err < best
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if torch.any(tmp):
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best[tmp] = err[tmp]
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self.scale[tmp] = scale1[tmp]
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self.zero[tmp] = zero1[tmp]
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if not self.perchannel:
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if weight:
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tmp = shape[0]
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else:
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tmp = shape[1] if len(shape) != 3 else shape[2]
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self.scale = self.scale.repeat(tmp)
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self.zero = self.zero.repeat(tmp)
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if weight:
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shape = [-1] + [1] * (len(shape) - 1)
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self.scale = self.scale.reshape(shape)
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self.zero = self.zero.reshape(shape)
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return
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if len(shape) == 4:
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self.scale = self.scale.reshape((1, -1, 1, 1))
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self.zero = self.zero.reshape((1, -1, 1, 1))
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if len(shape) == 3:
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self.scale = self.scale.reshape((1, 1, -1))
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self.zero = self.zero.reshape((1, 1, -1))
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if len(shape) == 2:
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self.scale = self.scale.unsqueeze(0)
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self.zero = self.zero.unsqueeze(0)
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def quantize(self, x):
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if self.ready():
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return quantize(x, self.scale, self.zero, self.maxq)
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return x
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def enabled(self):
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return self.maxq > 0
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def ready(self):
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return torch.all(self.scale != 0)
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try:
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import quant_cuda
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is_cuda = True
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except:
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logger.warning('CUDA extension not installed.')
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is_cuda = False
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def make_quant(module, names, bits, groupsize, name=''):
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if isinstance(module, QuantLinear):
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return
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for attr in dir(module):
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tmp = getattr(module, attr)
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name1 = name + '.' + attr if name != '' else attr
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if name1 in names:
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delattr(module, attr)
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setattr(module, attr, QuantLinear(bits, groupsize, tmp.in_features, tmp.out_features, tmp.bias is not None))
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for name1, child in module.named_children():
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make_quant(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1)
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class QuantLinear(nn.Module):
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def __init__(self, bits, groupsize, infeatures, outfeatures, bias, kernel_switch_threshold=128, is_cuda=is_cuda):
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super().__init__()
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if bits not in [2, 3, 4, 8]:
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raise NotImplementedError("Only 2,3,4,8 bits are supported.")
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self.infeatures = infeatures
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self.outfeatures = outfeatures
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self.bits = bits
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self.groupsize = groupsize if groupsize != -1 else infeatures
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self.maxq = 2 ** self.bits - 1
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self.register_buffer('qweight', torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32))
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self.register_buffer('qzeros',
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torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures // 32 * self.bits),
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dtype=torch.int32))
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self.register_buffer('scales',
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torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures), dtype=torch.float16))
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self.register_buffer('g_idx', torch.tensor([i // self.groupsize for i in range(infeatures)], dtype=torch.int32))
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if bias:
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self.register_buffer('bias', torch.zeros((outfeatures), dtype=torch.float16))
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else:
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self.bias = None
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# is performed by unpacking the weights and using torch.matmul
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if self.bits in [2, 4, 8]:
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self.register_buffer('wf', torch.tensor(list(range(0, 32, self.bits)), dtype=torch.int32).unsqueeze(0),
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persistent=False)
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elif self.bits == 3:
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self.register_buffer('wf', torch.tensor([[0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 0],
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[0, 1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31],
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[0, 2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 0], ],
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dtype=torch.int32).reshape(1, 3, 12), persistent=False)
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self.kernel_switch_threshold = kernel_switch_threshold
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self.is_cuda = is_cuda
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def pack(self, linear, scales, zeros, g_idx=None):
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self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx
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scales = scales.t().contiguous()
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zeros = zeros.t().contiguous()
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scale_zeros = zeros * scales
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self.scales = scales.clone().half()
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if linear.bias is not None:
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self.bias = linear.bias.clone().half()
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intweight = []
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for idx in range(self.infeatures):
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intweight.append(torch.round(
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(linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]]) / self.scales[self.g_idx[idx]]).to(
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torch.int)[:, None])
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intweight = torch.cat(intweight, dim=1)
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intweight = intweight.t().contiguous()
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intweight = intweight.numpy().astype(np.uint32)
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qweight = np.zeros(
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(intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32
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)
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i = 0
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row = 0
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while row < qweight.shape[0]:
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if self.bits in [2, 4, 8]:
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for j in range(i, i + (32 // self.bits)):
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qweight[row] |= intweight[j] << (self.bits * (j - i))
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i += 32 // self.bits
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row += 1
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elif self.bits == 3:
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for j in range(i, i + 10):
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qweight[row] |= intweight[j] << (3 * (j - i))
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i += 10
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qweight[row] |= intweight[i] << 30
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row += 1
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qweight[row] |= (intweight[i] >> 2) & 1
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i += 1
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for j in range(i, i + 10):
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qweight[row] |= intweight[j] << (3 * (j - i) + 1)
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i += 10
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qweight[row] |= intweight[i] << 31
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row += 1
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qweight[row] |= (intweight[i] >> 1) & 0x3
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i += 1
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for j in range(i, i + 10):
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qweight[row] |= intweight[j] << (3 * (j - i) + 2)
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i += 10
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row += 1
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else:
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raise NotImplementedError("Only 2,3,4,8 bits are supported.")
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qweight = qweight.astype(np.int32)
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self.qweight = torch.from_numpy(qweight)
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zeros -= 1;
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zeros = zeros.numpy().astype(np.uint32)
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qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32)
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i = 0
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col = 0
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while col < qzeros.shape[1]:
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if self.bits in [2, 4, 8]:
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for j in range(i, i + (32 // self.bits)):
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qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
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i += 32 // self.bits
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col += 1
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elif self.bits == 3:
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for j in range(i, i + 10):
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qzeros[:, col] |= zeros[:, j] << (3 * (j - i))
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i += 10
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qzeros[:, col] |= zeros[:, i] << 30
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col += 1
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qzeros[:, col] |= (zeros[:, i] >> 2) & 1
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i += 1
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for j in range(i, i + 10):
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qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 1)
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i += 10
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qzeros[:, col] |= zeros[:, i] << 31
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col += 1
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qzeros[:, col] |= (zeros[:, i] >> 1) & 0x3
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i += 1
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for j in range(i, i + 10):
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qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 2)
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i += 10
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col += 1
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else:
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raise NotImplementedError("Only 2,3,4,8 bits are supported.")
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qzeros = qzeros.astype(np.int32)
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self.qzeros = torch.from_numpy(qzeros)
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def forward(self, x):
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out_shape = x.shape[:-1] + (self.outfeatures,)
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x = x.reshape(-1, x.shape[-1])
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if self.is_cuda is True and (
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self.kernel_switch_threshold is False or x.shape[0] < self.kernel_switch_threshold):
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out = torch.zeros((x.shape[0], self.outfeatures), device='cuda', dtype=torch.float32)
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if self.bits == 2:
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quant_cuda.vecquant2matmul(x.float(), self.qweight, out, self.scales.float(), self.qzeros, self.g_idx)
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elif self.bits == 3:
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quant_cuda.vecquant3matmul(x.float(), self.qweight, out, self.scales.float(), self.qzeros, self.g_idx)
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elif self.bits == 4:
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quant_cuda.vecquant4matmul(x.float(), self.qweight, out, self.scales.float(), self.qzeros, self.g_idx)
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elif self.bits == 8:
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quant_cuda.vecquant8matmul(x.float(), self.qweight, out, self.scales.float(), self.qzeros, self.g_idx)
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out = out.half()
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else:
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if self.bits in [2, 4, 8]:
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zeros = torch.bitwise_right_shift(torch.unsqueeze(self.qzeros, 2).expand(-1, -1, 32 // self.bits),
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self.wf.unsqueeze(0)).to(
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torch.int16 if self.bits == 8 else torch.int8)
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torch.bitwise_and(zeros, (2 ** self.bits) - 1, out=zeros)
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zeros = zeros + 1
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zeros = zeros.reshape(self.scales.shape)
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weight = torch.bitwise_right_shift(torch.unsqueeze(self.qweight, 1).expand(-1, 32 // self.bits, -1),
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self.wf.unsqueeze(-1)).to(
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torch.int16 if self.bits == 8 else torch.int8)
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torch.bitwise_and(weight, (2 ** self.bits) - 1, out=weight)
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elif self.bits == 3:
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zeros = self.qzeros.reshape(self.qzeros.shape[0], self.qzeros.shape[1] // 3, 3, 1).expand(-1, -1, -1,
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12)
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zeros = (zeros >> self.wf.unsqueeze(0))
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zeros[:, :, 0, 10] = (zeros[:, :, 0, 10] & 0x3) | ((zeros[:, :, 1, 0] << 2) & 0x4)
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zeros[:, :, 1, 11] = (zeros[:, :, 1, 11] & 0x1) | ((zeros[:, :, 2, 0] << 1) & 0x6)
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zeros = zeros & 0x7
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zeros = torch.cat([zeros[:, :, 0, :11], zeros[:, :, 1, 1:12], zeros[:, :, 2, 1:11]], dim=2)
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zeros = zeros + 1
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zeros = zeros.reshape(self.scales.shape)
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weight = self.qweight.reshape(self.qweight.shape[0] // 3, 3, 1, self.qweight.shape[1]).expand(-1, -1,
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12, -1)
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weight = (weight >> self.wf.unsqueeze(-1)) & 0x7
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weight[:, 0, 10] = (weight[:, 0, 10] & 0x3) | ((weight[:, 1, 0] << 2) & 0x4)
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weight[:, 1, 11] = (weight[:, 1, 11] & 0x1) | ((weight[:, 2, 0] << 1) & 0x6)
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weight = weight & 0x7
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weight = torch.cat([weight[:, 0, :11], weight[:, 1, 1:12], weight[:, 2, 1:11]], dim=1)
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weight = weight.reshape(weight.shape[0] * weight.shape[1], weight.shape[2])
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weights = (self.scales[self.g_idx.long()] * (weight - zeros[self.g_idx.long()]))
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out = torch.matmul(x.half(), weights)
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out = out.reshape(out_shape)
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out = out + self.bias if self.bias is not None else out
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return out
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__all__ = [
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"quantize",
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"make_quant",
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"Quantizer",
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"QuantLinear"
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]
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