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import torch
import torch.nn as nn
import quant
from gptq import GPTQ
from utils import find_layers, DEV, set_seed, get_wikitext2, get_ptb, get_c4, get_ptb_new, get_c4_new, get_loaders
import transformers
from transformers import AutoTokenizer
class ModelInference:
def __init__(self, model_name, load=None, wbits=16, groupsize=-1):
self.model_name = model_name
self.load = load
self.wbits = wbits
self.groupsize = groupsize
if self.load:
self.model = self.load_quant(self.model_name, self.load, self.wbits, self.groupsize)
else:
self.model = self.get_llama(self.model_name)
self.model.eval()
self.model.to(DEV)
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name, use_fast=False)
def get_llama(model):
def skip(*args, **kwargs):
pass
torch.nn.init.kaiming_uniform_ = skip
torch.nn.init.uniform_ = skip
torch.nn.init.normal_ = skip
from transformers import LlamaForCausalLM
model = LlamaForCausalLM.from_pretrained(model, torch_dtype='auto')
model.seqlen = 2048
return model
def load_quant(model, checkpoint, wbits, groupsize=-1, fused_mlp=True, eval=True, warmup_autotune=True):
from transformers import LlamaConfig, LlamaForCausalLM
config = LlamaConfig.from_pretrained(model)
def noop(*args, **kwargs):
pass
torch.nn.init.kaiming_uniform_ = noop
torch.nn.init.uniform_ = noop
torch.nn.init.normal_ = noop
torch.set_default_dtype(torch.half)
transformers.modeling_utils._init_weights = False
torch.set_default_dtype(torch.half)
model = LlamaForCausalLM(config)
torch.set_default_dtype(torch.float)
if eval:
model = model.eval()
layers = find_layers(model)
for name in ['lm_head']:
if name in layers:
del layers[name]
quant.make_quant_linear(model, layers, wbits, groupsize)
del layers
print('Loading model ...')
if checkpoint.endswith('.safetensors'):
from safetensors.torch import load_file as safe_load
model.load_state_dict(safe_load(checkpoint), strict=False)
else:
model.load_state_dict(torch.load(checkpoint), strict=False)
if eval:
quant.make_quant_attn(model)
quant.make_quant_norm(model)
if fused_mlp:
quant.make_fused_mlp(model)
if warmup_autotune:
quant.autotune_warmup_linear(model, transpose=not (eval))
if eval and fused_mlp:
quant.autotune_warmup_fused(model)
model.seqlen = 2048
print('Done.')
return model
def generate_text(self, text, min_length=10, max_length=50, top_p=0.95, temperature=0.8):
input_ids = self.tokenizer.encode(text, return_tensors="pt").to(DEV)
with torch.no_grad():
generated_ids = self.model.generate(
input_ids,
do_sample=True,
min_length=min_length,
max_length=max_length,
top_p=top_p,
temperature=temperature,
)
return self.tokenizer.decode([el.item() for el in generated_ids[0]])
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