File size: 9,112 Bytes
e75cdce |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 |
import bitsandbytes as bnb
from accelerate import init_empty_weights
from bitsandbytes.nn.modules import Params4bit, Int8Params
import torch
def Params4bitCuda(self, device):
self.data = self.data.cuda(device)
self.quant_state[0] = self.quant_state[0].cuda(device)
self.quant_state[4][0] = self.quant_state[4][0].cuda(device)
self.quant_state[4][1][0] = self.quant_state[4][1][0].cuda(device)
self.quant_state[4][1][1] = self.quant_state[4][1][1].cuda(device)
self.quant_state[6] = self.quant_state[6].cuda(device)
return self
class Linear4bitOnline(torch.nn.Module):
def __init__(self, weight, bias, quant_type):
super().__init__()
self.weight = Params4bit(
weight.data, requires_grad=False, compress_statistics=True, quant_type=quant_type
)
self.compute_dtype = None
#self.weight.cuda(weight.device)
self.bias = bias
def forward(self, x: torch.Tensor):
# weights are cast automatically as Int8Params, but the bias has to be cast manually
if self.bias is not None and self.bias.dtype != x.dtype:
self.bias.data = self.bias.data.to(x.dtype)
if getattr(self.weight, "quant_state", None) is None:
print(
"FP4 quantization state not initialized. Please call .cuda() or .to(device) on the LinearFP4 layer first."
)
inp_dtype = x.dtype
if self.compute_dtype is not None:
x = x.to(self.compute_dtype)
bias = None if self.bias is None else self.bias.to(self.compute_dtype)
out = bnb.matmul_4bit(
x, self.weight.t(), bias=bias, quant_state=self.weight.quant_state
)
out = out.to(inp_dtype)
return out
class Linear8bitLtOnline(torch.nn.Module):
def __init__(
self,
weight,
bias,
has_fp16_weights=True,
memory_efficient_backward=False,
threshold=0.0,
index=None,
):
super().__init__()
assert (
not memory_efficient_backward
), "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0"
self.state = bnb.MatmulLtState()
self.index = index
# Necessary for stacked layers
self.state.threshold = threshold
self.state.has_fp16_weights = has_fp16_weights
self.state.memory_efficient_backward = memory_efficient_backward
if threshold > 0.0 and not has_fp16_weights:
self.state.use_pool = True
self.weight = Int8Params(
weight.data,
has_fp16_weights=has_fp16_weights,
requires_grad=has_fp16_weights,
)
self.bias = bias
def init_8bit_state(self):
self.state.CB = self.weight.CB
self.state.SCB = self.weight.SCB
self.weight.CB = None
self.weight.SCB = None
def forward(self, x: torch.Tensor):
self.state.is_training = self.training
if self.weight.CB is not None:
self.init_8bit_state()
# weights are cast automatically as Int8Params, but the bias has to be cast manually
if self.bias is not None and self.bias.dtype != x.dtype:
self.bias.data = self.bias.data.to(x.dtype)
out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)
if not self.state.has_fp16_weights:
if self.state.CB is not None and self.state.CxB is not None:
# we converted 8-bit row major to turing/ampere format in the first inference pass
# we no longer need the row-major weight
del self.state.CB
self.weight.data = self.state.CxB
return out
def quantize_offline(model, bits: int):
assert (bits == 4), f'bits: {bits} is not supported'
for i, layer in enumerate(model.model.layers):
layer.self_attn.W_pack = bnb.nn.Linear4bit(
layer.self_attn.W_pack.weight.shape[1],
layer.self_attn.W_pack.weight.shape[0],
False,
torch.float16,
compress_statistics=True,
quant_type="nf4",
)
layer.self_attn.o_proj = bnb.nn.Linear4bit(
layer.self_attn.o_proj.weight.shape[1],
layer.self_attn.o_proj.weight.shape[0],
False,
torch.float16,
compress_statistics=True,
quant_type="nf4",
)
layer.mlp.gate_proj = bnb.nn.Linear4bit(
layer.mlp.gate_proj.weight.shape[1],
layer.mlp.gate_proj.weight.shape[0],
False,
torch.float16,
compress_statistics=True,
quant_type="nf4",
)
layer.mlp.down_proj = bnb.nn.Linear4bit(
layer.mlp.down_proj.weight.shape[1],
layer.mlp.down_proj.weight.shape[0],
False,
torch.float16,
compress_statistics=True,
quant_type="nf4",
)
layer.mlp.up_proj = bnb.nn.Linear4bit(
layer.mlp.up_proj.weight.shape[1],
layer.mlp.up_proj.weight.shape[0],
False,
torch.float16,
compress_statistics=True,
quant_type="nf4",
)
return model
def quantize_online(model, bits: int):
def quant(weight, bias=None):
if bits == 8:
linear = Linear8bitLtOnline(
weight,
bias,
has_fp16_weights=False,
threshold=6.0,
)
if bias is not None:
linear.bias = torch.nn.Parameter(bias)
elif bits == 4:
linear = Linear4bitOnline(
weight,
bias,
quant_type="nf4", #fp4/nf4
)
else:
raise ValueError("quantize only support 4/8 bit")
return linear
for i, layer in enumerate(model.model.layers):
layer.self_attn.W_pack = quant(layer.self_attn.W_pack.weight)
layer.self_attn.o_proj = quant(layer.self_attn.o_proj.weight)
layer.mlp.gate_proj = quant(layer.mlp.gate_proj.weight)
layer.mlp.down_proj = quant(layer.mlp.down_proj.weight)
layer.mlp.up_proj = quant(layer.mlp.up_proj.weight)
return model
def init_model_weight_int4(config, model, state_dict):
#replace Params4bit.cuda with Params4bitCuda
Params4bit.cuda = Params4bitCuda
for i in range(config.num_hidden_layers):
weight_data = state_dict[f'model.layers.{i}.self_attn.W_pack.weight.data']
weight_quant_state = state_dict[f'model.layers.{i}.self_attn.W_pack.weight.quant_state']
model.model.layers[i].self_attn.W_pack.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
weight_data = state_dict[f'model.layers.{i}.self_attn.o_proj.weight.data']
weight_quant_state = state_dict[f'model.layers.{i}.self_attn.o_proj.weight.quant_state']
model.model.layers[i].self_attn.o_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
weight_data = state_dict[f'model.layers.{i}.mlp.gate_proj.weight.data']
weight_quant_state = state_dict[f'model.layers.{i}.mlp.gate_proj.weight.quant_state']
model.model.layers[i].mlp.gate_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
weight_data = state_dict[f'model.layers.{i}.mlp.up_proj.weight.data']
weight_quant_state = state_dict[f'model.layers.{i}.mlp.up_proj.weight.quant_state']
model.model.layers[i].mlp.up_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
weight_data = state_dict[f'model.layers.{i}.mlp.down_proj.weight.data']
weight_quant_state = state_dict[f'model.layers.{i}.mlp.down_proj.weight.quant_state']
model.model.layers[i].mlp.down_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
model.model.layers[i].input_layernorm.weight = state_dict[f'model.layers.{i}.input_layernorm.weight']
model.model.layers[i].post_attention_layernorm.weight = state_dict[f'model.layers.{i}.post_attention_layernorm.weight']
model.model.embed_tokens.weight = state_dict['model.embed_tokens.weight']
model.model.norm.weight = state_dict['model.norm.weight']
model.lm_head.weight = state_dict['lm_head.weight']
return model |