mirror of https://github.com/hpcaitech/ColossalAI
284 lines
11 KiB
Python
284 lines
11 KiB
Python
# copied from https://github.com/qwopqwop200/GPTQ-for-LLaMa/blob/past/quant.py
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import math
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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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def quantize(x, scale, zero, maxq):
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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(self, bits, perchannel=False, sym=True, mse=False, norm=2.4, grid=100, maxshrink=.8):
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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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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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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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except:
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print('CUDA extension not installed.')
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# Assumes layer is perfectly divisible into 256 * 256 blocks
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class QuantLinear(nn.Module):
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def __init__(self, bits, groupsize, infeatures, outfeatures):
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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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if groupsize != -1 and groupsize < 32 and groupsize != int(math.pow(2, int(math.log2(groupsize)))):
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raise NotImplementedError("groupsize supports powers of 2 greater than 32. (e.g. : 32,64,128,etc)")
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groupsize = groupsize if groupsize != -1 else infeatures
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self.groupsize = groupsize
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self.register_buffer(
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'qzeros', torch.zeros((math.ceil(infeatures / groupsize), outfeatures // 256 * (bits * 8)),
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dtype=torch.int))
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self.register_buffer('scales', torch.zeros((math.ceil(infeatures / groupsize), outfeatures)))
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self.register_buffer('bias', torch.zeros(outfeatures))
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self.register_buffer('qweight', torch.zeros((infeatures // 256 * (bits * 8), outfeatures), dtype=torch.int))
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self._initialized_quant_state = False
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def pack(self, linear, scales, zeros):
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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()
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if linear.bias is not None:
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self.bias = linear.bias.clone()
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intweight = []
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for idx in range(self.infeatures):
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g_idx = idx // self.groupsize
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intweight.append(
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torch.round((linear.weight.data[:, idx] + scale_zeros[g_idx]) / self.scales[g_idx]).to(torch.int)[:,
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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((intweight.shape[0] // 256 * (self.bits * 8), intweight.shape[1]), dtype=np.uint32)
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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] // 256 * (self.bits * 8)), 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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intermediate_dtype = torch.float32
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if not self._initialized_quant_state:
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# Do we even have a bias? Check for at least one non-zero element.
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if self.bias is not None and bool(torch.any(self.bias != 0)):
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# Then make sure it's the right type.
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self.bias.data = self.bias.data.to(intermediate_dtype)
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else:
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self.bias = None
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outshape = list(x.shape)
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outshape[-1] = self.outfeatures
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x = x.reshape(-1, x.shape[-1])
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if self.bias is None:
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y = torch.zeros(x.shape[0], outshape[-1], dtype=intermediate_dtype, device=x.device)
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else:
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y = self.bias.clone().repeat(x.shape[0], 1)
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output_dtype = x.dtype
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x = x.to(intermediate_dtype)
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if self.bits == 2:
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quant_cuda.vecquant2matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize)
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elif self.bits == 3:
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quant_cuda.vecquant3matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize)
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elif self.bits == 4:
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quant_cuda.vecquant4matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize)
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elif self.bits == 8:
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quant_cuda.vecquant8matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize)
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else:
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raise NotImplementedError("Only 2,3,4,8 bits are supported.")
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y = y.to(output_dtype)
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return y.reshape(outshape)
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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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setattr(module, attr, QuantLinear(bits, groupsize, tmp.in_features, tmp.out_features))
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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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