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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 |