File size: 2,638 Bytes
5472531 |
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 |
from dataclasses import dataclass, field
from pathlib import Path
import sys
import torch
from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, modeling_utils
@dataclass
class AWQConfig:
ckpt: str = field(
default=None,
metadata={
"help": "Load quantized model. The path to the local AWQ checkpoint."
},
)
wbits: int = field(default=16, metadata={"help": "#bits to use for quantization"})
groupsize: int = field(
default=-1,
metadata={"help": "Groupsize to use for quantization; default uses full row."},
)
def load_awq_quantized(model_name, awq_config: AWQConfig, device):
print("Loading AWQ quantized model...")
try:
from tinychat.utils import load_quant
from tinychat.modules import make_quant_norm, make_quant_attn, make_fused_mlp
except ImportError as e:
print(f"Error: Failed to import tinychat. {e}")
print("Please double check if you have successfully installed AWQ")
print("See https://github.com/lm-sys/FastChat/blob/main/docs/awq.md")
sys.exit(-1)
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
model_name, use_fast=False, trust_remote_code=True
)
def skip(*args, **kwargs):
pass
torch.nn.init.kaiming_uniform_ = skip
torch.nn.init.kaiming_normal_ = skip
torch.nn.init.uniform_ = skip
torch.nn.init.normal_ = skip
modeling_utils._init_weights = False
torch.set_default_dtype(torch.half)
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
if any(name in find_awq_ckpt(awq_config) for name in ["llama", "vicuna"]):
model = load_quant.load_awq_llama_fast(
model,
find_awq_ckpt(awq_config),
awq_config.wbits,
awq_config.groupsize,
device,
)
make_quant_attn(model, device)
make_quant_norm(model)
make_fused_mlp(model)
else:
model = load_quant.load_awq_model(
model,
find_awq_ckpt(awq_config),
awq_config.wbits,
awq_config.groupsize,
device,
)
return model, tokenizer
def find_awq_ckpt(awq_config: AWQConfig):
if Path(awq_config.ckpt).is_file():
return awq_config.ckpt
for ext in ["*.pt", "*.safetensors"]:
matched_result = sorted(Path(awq_config.ckpt).glob(ext))
if len(matched_result) > 0:
return str(matched_result[-1])
print("Error: AWQ checkpoint not found")
sys.exit(1)
|