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llama_transformers_example.py
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llama_transformers_example.py
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import os
import time
import psutil
import random
import torch
import numpy as np
from torch.nn.parameter import Parameter
from transformers import AutoTokenizer, LlamaModel, LlamaForCausalLM, LlamaTokenizer, AutoConfig, AutoModelForCausalLM
# model_dir = "decapoda-research/llama-7b-hf"
model_dir = "/root/project/huggingface/llama-7b-hf/"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
def set_random_seed(seed):
random.seed(seed)
torch.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
def test_from_fp16():
torch.set_printoptions(precision=6, sci_mode=False)
torch.set_grad_enabled(False)
set_random_seed(1)
# model_name = '/root/data/models/2023/llama-13B-v1/'
model_name = '/home/llama_13B_v1/'
MAX_NEW_TOKENS = 32
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
config = AutoConfig.from_pretrained(model_name)
# config.num_hidden_layers = 1
free_in_GB = int(torch.cuda.mem_get_info()[0]/1024**3)
max_memory = f'{int(torch.cuda.mem_get_info()[0]/1024**3)-1}GB'
n_gpus = torch.cuda.device_count()
max_memory = {i: max_memory for i in range(n_gpus)}
model = AutoModelForCausalLM.from_pretrained(model_name, config=config, torch_dtype=torch.float16)
model.eval()
from eetq.utils import eet_accelerator
eet_accelerator(model, quantize=True, fused_attn=True, dev="cuda:0")
model.to("cuda:0")
# for k, v in model.state_dict().items():
# print(k, v.shape, v.dtype, v.device)
# torch.save(model, "eetq_llama13B_model_fused_attn_v2.pt")
text = '中国的首都在'
kwargs = {
"input_text": str(text),
"max_new_tokens": int(MAX_NEW_TOKENS),
"do_sample": bool(False),
# "num_beams": 1,
"temperature": float(0.75),
"top_k": int(1),
"top_p": float(0.7),
"use_cache": bool(True),
}
inputs = tokenizer(kwargs["input_text"], return_tensors='pt')
kwargs["inputs"] = inputs.input_ids.to('cuda:0')
del kwargs["input_text"]
# warm up
generate_ids = model.generate(**kwargs)
for i in range(1):
print('i:', i)
torch.cuda.synchronize()
t1 = time.perf_counter()
with torch.no_grad():
generate_ids = model.generate(**kwargs)
torch.cuda.synchronize()
t2 = time.perf_counter()
print("time:", t2 - t1)
outputs_str = tokenizer.batch_decode(
generate_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False)
print("ori_str", outputs_str)
print("***********************************")
max_memory_allocated = torch.cuda.max_memory_allocated() / 1e9
print('当前进程号: {}, 内存使用:{:.4f} GB'.format(os.getpid(), psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024))
print("max GPU memory allocated: {:.4f} GB".format(max_memory_allocated))
def test_from_ckpt():
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
torch.set_printoptions(precision=6, sci_mode=False)
torch.set_grad_enabled(False)
set_random_seed(1)
model_name = '/root/data/models/2023/llama-13B-v1/'
MAX_NEW_TOKENS = 32
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
config = AutoConfig.from_pretrained(model_name)
free_in_GB = int(torch.cuda.mem_get_info()[0]/1024**3)
max_memory = f'{int(torch.cuda.mem_get_info()[0]/1024**3)-1}GB'
n_gpus = torch.cuda.device_count()
max_memory = {i: max_memory for i in range(n_gpus)}
with init_empty_weights():
model = AutoModelForCausalLM.from_pretrained(model_name, config=config, torch_dtype=torch.float16)
from eetq.utils import eet_accelerator
eet_accelerator(model, quantize=True, fused_attn=False, dev="cuda:0")
print("***********************************")
model = load_checkpoint_and_dispatch(model, "llama13B_eetq_dict.pt", device_map="auto", no_split_module_classes=["LlamaDecoderLayer"])
# model.load_state_dict(torch.load("checkpoint_int8.pt", map_location="cpu"), strict=False)
model.to("cuda:0")
text = '中国的首都在'
kwargs = {
"input_text": str(text),
"max_new_tokens": int(MAX_NEW_TOKENS),
"do_sample": bool(False),
"num_beams": 2,
"temperature": float(0.75),
"top_k": int(1),
"top_p": float(0.7),
"use_cache": bool(True),
}
inputs = tokenizer(kwargs["input_text"], return_tensors='pt')
kwargs["inputs"] = inputs.input_ids.to('cuda:0')
del kwargs["input_text"]
# warm up
generate_ids = model.generate(**kwargs)
for i in range(1):
# print('i:', i)
torch.cuda.synchronize()
t1 = time.perf_counter()
with torch.no_grad():
generate_ids = model.generate(**kwargs)
torch.cuda.synchronize()
t2 = time.perf_counter()
print("time:", t2 - t1)
outputs_str = tokenizer.batch_decode(
generate_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False)
print("ori_str", outputs_str)
print("***********************************")
max_memory_allocated = torch.cuda.max_memory_allocated() / 1e9
print('当前进程号: {}, 内存使用:{:.4f} GB'.format(os.getpid(), psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024))
print("max GPU memory allocated: {:.4f} GB".format(max_memory_allocated))
def test_lora():
torch.set_printoptions(precision=6, sci_mode=False)
torch.set_grad_enabled(False)
set_random_seed(1)
# model_name = '/root/data/models/2023/llama-13B-v1/'
model_name = "/home/llama_13B_v1"
MAX_NEW_TOKENS = 32
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
config = AutoConfig.from_pretrained(model_name)
# config.num_hidden_layers = 1
model = AutoModelForCausalLM.from_pretrained(model_name, config=config, torch_dtype=torch.float16)
model.eval()
from eetq.utils import eet_accelerator
from peft import PeftConfig, PeftModel, get_peft_model, LoraConfig, TaskType
# lora
lora_rank = 8
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM, inference_mode=True,
peft_type="LORA",
r=lora_rank,
target_modules=["q_proj", "k_proj", "v_proj"],
lora_alpha=32, lora_dropout=0.1
)
model = get_peft_model(model, peft_config)
# print(model)
model = model.merge_and_unload()
import peft.tuners.lora as lora
eet_accelerator(model, quantize=True, fused_attn=False, dev="cuda:0")
model.to("cuda:0")
text = '中国的首都在'
kwargs = {
"input_text": str(text),
"max_new_tokens": int(MAX_NEW_TOKENS),
"do_sample": bool(False),
# "num_beams": 1,
"temperature": float(0.75),
"top_k": int(1),
"top_p": float(0.7),
"use_cache": bool(True),
}
inputs = tokenizer(kwargs["input_text"], return_tensors='pt')
kwargs["inputs"] = inputs.input_ids.to('cuda:0')
del kwargs["input_text"]
# warm up
generate_ids = model.generate(**kwargs)
for i in range(1):
print('i:', i)
torch.cuda.synchronize()
t1 = time.perf_counter()
with torch.no_grad():
generate_ids = model.generate(**kwargs)
torch.cuda.synchronize()
t2 = time.perf_counter()
print("time:", t2 - t1)
outputs_str = tokenizer.batch_decode(
generate_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False)
print("ori_str", outputs_str)
# save
# torch.save(model, "eetq_llama13B_model.pt")
print("***********************************")
max_memory_allocated = torch.cuda.max_memory_allocated() / 1e9
print('当前进程号: {}, 内存使用:{:.4f} GB'.format(os.getpid(), psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024))
print("max GPU memory allocated: {:.4f} GB".format(max_memory_allocated))
def test_load():
torch.set_printoptions(precision=6, sci_mode=False)
torch.set_grad_enabled(False)
set_random_seed(1)
model_name = '/root/data/models/2023/llama-13B-v1/'
#model_name = "/home/llama_13B_v1"
MAX_NEW_TOKENS = 32
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
config = AutoConfig.from_pretrained(model_name)
from eetq.utils import eet_accelerator
# from peft import PeftConfig, PeftModel, get_peft_model, LoraConfig, TaskType
model = torch.load("eetq_llama13B_model_fused_attn_v2.pt", map_location="cuda:0")
# model = torch.load("eetq_llama13B_model.pt",map_location="cuda:0")
# model = torch.load("/project/tgi_0907/text-generation-inference/model_llama7b.pt", map_location="auto")
model.to("cuda:0")
text = '中国的首都在'
kwargs = {
"input_text": str(text),
"max_new_tokens": int(MAX_NEW_TOKENS),
"do_sample": bool(False),
# "num_beams": 2,
"temperature": float(0.75),
"top_k": int(1),
"top_p": float(0.7),
"use_cache": bool(True),
}
inputs = tokenizer(kwargs["input_text"], return_tensors='pt')
kwargs["inputs"] = inputs.input_ids.to('cuda:0')
del kwargs["input_text"]
# warm up
generate_ids = model.generate(**kwargs)
for i in range(1):
print('i:', i)
torch.cuda.synchronize()
t1 = time.perf_counter()
with torch.no_grad():
generate_ids = model.generate(**kwargs)
torch.cuda.synchronize()
t2 = time.perf_counter()
print("time:", t2 - t1)
outputs_str = tokenizer.batch_decode(
generate_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False)
print("ori_str", outputs_str)
print("***********************************")
max_memory_allocated = torch.cuda.max_memory_allocated() / 1e9
print('当前进程号: {}, 内存使用:{:.4f} GB'.format(os.getpid(), psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024))
print("max GPU memory allocated: {:.4f} GB".format(max_memory_allocated))
if __name__ == "__main__":
# test_from_fp16()
# test_from_ckpt()
# test_lora()
test_load()