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02bc7b8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | import os
import torch
import torch.nn as nn
import numpy as np
from transformers import AutoTokenizer, AutoModelForCausalLM
import tqdm
import json
import math
import torch.nn.functional as F
from datasets import load_dataset
@torch.no_grad()
def quantize_weight_per_channel_absmax(w, n_bits=8):
# w: (out_features, in_features)
scales = w.abs().max(dim=-1, keepdim=True)[0]
q_max = 2 ** (n_bits - 1) - 1
scales.clamp_(min=1e-5).div_(q_max)
w.div_(scales).round_().mul_(scales)
return w
@torch.no_grad()
def quantize_weight_per_tensor_absmax(w, n_bits=8):
# w: (out_features, in_features)
scales = w.abs().max()
q_max = 2 ** (n_bits - 1) - 1
scales.clamp_(min=1e-5).div_(q_max)
w.div_(scales).round_().mul_(scales)
return w
class W8A16Linear(nn.Module):
def __init__(
self,
# bit_width,
in_features,
out_features,
bias=True,
quantize_output=False,
):
super().__init__()
# self.bit_width = bit_width
self.in_features = in_features
self.out_features = out_features
self.register_buffer(
"weight",
torch.randn(
self.out_features,
self.in_features,
dtype=torch.float16,
requires_grad=False,
),
)
if bias:
self.register_buffer(
"bias",
torch.zeros(
(1, self.out_features), dtype=torch.float16, requires_grad=False
),
)
else:
self.register_buffer("bias", None)
def to(self, *args, **kwargs):
super(W8A16Linear, self).to(*args, **kwargs)
self.weight = self.weight.to(*args, **kwargs)
if self.bias is not None:
self.bias = self.bias.to(*args, **kwargs)
return self
@torch.no_grad()
def forward(self, x):
y = torch.functional.F.linear(x, self.weight, self.bias)
return y
@staticmethod
def from_float(
bit, module, weight_quant="per_channel", quantize_output=False
):
assert isinstance(module, torch.nn.Linear)
new_module = W8A16Linear(
# bit,
module.in_features,
module.out_features,
module.bias is not None,
quantize_output=quantize_output,
)
if weight_quant == "per_channel":
new_module.weight = quantize_weight_per_channel_absmax(module.weight, bit)
elif weight_quant == "per_tensor":
new_module.weight = quantize_weight_per_tensor_absmax(module.weight, bit)
else:
raise ValueError(f"Invalid weight_quant: {weight_quant}")
new_module.weight_quant_name = weight_quant
if module.bias is not None:
new_module.bias = module.bias
return new_module
def __repr__(self):
return f"W8A16Linear({self.in_features}, {self.out_features}, bias={self.bias is not None}, weight_quant={self.weight_quant_name})"
def quantize_llama_like(
model, mlp_quant, self_attn_quant, low_bit, weight_quant="per_channel", quantize_bmm_input=False
):
from transformers.models.llama.modeling_llama import (
LlamaAttention,
LlamaMLP,
)
for name, m in model.model.named_modules():
if isinstance(m, LlamaMLP):
if low_bit == 0:
continue
else:
if name in mlp_quant:
bit = low_bit
print(f'{name} {bit} bit quant ')
else:
if low_bit == 4:
bit = 8
print(f'{name} {bit} bit quant ')
elif low_bit == 8:
continue
m.gate_proj = W8A16Linear.from_float(
bit, m.gate_proj, weight_quant=weight_quant
)
m.up_proj = W8A16Linear.from_float(
bit, m.up_proj, weight_quant=weight_quant
)
m.down_proj = W8A16Linear.from_float(
bit, m.down_proj, weight_quant=weight_quant
)
elif isinstance(m, LlamaAttention):
if low_bit == 0:
continue
else:
if name in self_attn_quant:
bit = low_bit
else:
if low_bit == 4:
bit = 8
elif low_bit == 8:
continue
m.q_proj = W8A16Linear.from_float(
bit,
m.q_proj,
weight_quant=weight_quant,
quantize_output=quantize_bmm_input,
)
m.k_proj = W8A16Linear.from_float(
bit,
m.k_proj,
weight_quant=weight_quant,
quantize_output=quantize_bmm_input,
)
m.v_proj = W8A16Linear.from_float(
bit,
m.v_proj,
weight_quant=weight_quant,
quantize_output=quantize_bmm_input,
)
m.o_proj = W8A16Linear.from_float(
bit, m.o_proj, weight_quant=weight_quant
)
return model |