126 lines
4.5 KiB
Python
126 lines
4.5 KiB
Python
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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import intel_extension_for_pytorch as ipex
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class QuantLinear(nn.Module):
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def __init__(self, qweight, qzeros, scales, g_idx, bias, bits, groupsize):
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super().__init__()
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self.register_buffer("qweight", qweight)
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self.register_buffer("qzeros", qzeros)
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self.register_buffer("scales", scales)
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self.register_buffer("g_idx", g_idx)
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if bias is not None:
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self.register_buffer("bias", bias)
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else:
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self.bias = None
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if bits not in [4]:
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raise NotImplementedError("Only 4 bits are supported.")
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self.bits = bits
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self.maxq = 2**self.bits - 1
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self.groupsize = groupsize
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self.outfeatures = qweight.shape[1]
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self.infeatures = qweight.shape[0] * 32 // bits
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self.woq_linear = (
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ipex.llm.quantization.IPEXWeightOnlyQuantizedLinear.from_weight(
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self.qweight,
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self.scales,
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self.qzeros,
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self.infeatures,
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self.outfeatures,
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bias=self.bias,
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group_size=self.groupsize,
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g_idx=g_idx,
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quant_method=ipex.llm.quantization.QuantMethod.GPTQ_GEMM,
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dtype=ipex.llm.quantization.QuantDtype.INT4,
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)
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)
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@classmethod
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def new(cls, bits, groupsize, infeatures, outfeatures, bias):
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if bits not in [4]:
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raise NotImplementedError("Only 4 bits are supported.")
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qweight = torch.zeros((infeatures // 32 * bits, outfeatures), dtype=torch.int32)
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qzeros = torch.zeros(
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(math.ceil(infeatures / groupsize), outfeatures // 32 * bits),
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dtype=torch.int32,
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)
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scales = torch.zeros(
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(math.ceil(infeatures / groupsize), outfeatures), dtype=torch.float16
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)
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g_idx = torch.tensor(
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[i // groupsize for i in range(infeatures)], dtype=torch.int32
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)
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if bias:
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bias = torch.zeros((outfeatures), dtype=torch.float16)
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else:
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bias = None
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return cls(qweight, qzeros, scales, g_idx, bias, bits, groupsize)
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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(
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torch.round(
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(linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]])
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/ self.scales[self.g_idx[idx]]
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).to(torch.int)[:, None]
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)
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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 [4]:
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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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else:
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raise NotImplementedError("Only 4 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(
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(zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32
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)
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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 [4]:
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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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else:
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raise NotImplementedError("Only 4 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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out = self.woq_linear(x.reshape(-1, x.shape[-1]))
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return out.reshape(out_shape)
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