MixtralForCausalLM DeepSpeed Inference节约HOST内存【最新的方案】

2024-06-17 16:44
文章标签 内存 最新 方案 host 节约 deepspeed inference mixtralforcausallm

本文主要是介绍MixtralForCausalLM DeepSpeed Inference节约HOST内存【最新的方案】，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

MixtralForCausalLM DeepSpeed Inference节约HOST内存【最新的方案】

  • 一.效果
  • 二.特别说明
  • 三.测试步骤
    • 1.创建Mixtral-8x7B配置文件(简化了)
    • 2.生成随机模型,运行cpu float32推理,输出结果
    • 3.加载模型,cuda 单卡推理
    • 4.DS 4 TP cuda 推理
    • 5.分别保存DS 4TP每个rank上engine.module的权值
    • 6.DS 4TP推理,init_empty_weights初始化模型,每个rank加载自己engine.module的权值

本文演示了MixtralForCausalLM DeepSpeed Inference如果节约HOST内存
方法:每个rank分别保存,并且使用accelerate的init_empty_weights
增加的功能:

  • safetensors分块的存储与加载
  • 解决register_buffer persistent=False,参数初始化的问题

一.效果

运行方式HOST内存占用备注
单卡推理13198 MB
DS 4TP13246 MB/GPU
DS 4TP 优化内存占用后369 MB/GPU直接加载到设备,更节约HOST内存

二.特别说明

  • 1.MixtralRotaryEmbedding中self.register_buffer(“sin_cached”, emb.sin().to(dtype), persistent=False)
    因为persistent为False。所以不会保存到state_dict中,module.to_empty(device)也不会保留它的值
    只能在模型初始化之后保存出来,之后engine.moudle加载完权值之后再把这个buffer替换进去

三.测试步骤

1.创建Mixtral-8x7B配置文件(简化了)

mkdir skip_init_demo
cd skip_init_demo
tee ./config.json <<-'EOF'
{"architectures": ["MixtralForCausalLM"],"attention_dropout": 0.0,"bos_token_id": 1,"eos_token_id": 2,"hidden_act": "silu","hidden_size": 1024,"initializer_range": 0.02,"intermediate_size": 4096,"max_position_embeddings": 1024,"model_type": "mixtral","num_attention_heads": 32,"num_experts_per_tok": 2,"num_hidden_layers": 32,"num_key_value_heads": 8,"num_local_experts": 8,"output_router_logits": false,"rms_norm_eps": 1e-05,"rope_theta": 1000000.0,"router_aux_loss_coef": 0.02,"sliding_window": 128,"tie_word_embeddings": false,"torch_dtype": "bfloat16","transformers_version": "4.36.0.dev0","use_cache": true,"vocab_size": 32000
}
EOF

2.生成随机模型,运行cpu float32推理,输出结果

rm -rf Mixtral-8x7B
tee gen_model.py <<-'EOF'
import torch
import os
import time
def main():torch.manual_seed(1)from transformers import MixtralForCausalLM, MixtralConfigconfig=MixtralConfig.from_pretrained("./config.json")model = MixtralForCausalLM(config).half()    model.eval()model.save_pretrained("./Mixtral-8x7B",safe_serialization=True)torch.manual_seed(2)input_tokens=torch.randint(0,32000,(1,128))model=model.float()output=model(input_tokens)output=output.logits.detach().reshape(-1).cpu().numpy()[:8]print(output)if __name__ == "__main__":main()
EOF
python gen_model.py
du Mixtral-8x7B -lh

输出

6.3G    Mixtral-8x7B[-0.9623295  -0.36580455  0.767425    1.7021806  -0.17950581  0.36059803-0.49157432 -0.58618194]

3.加载模型,cuda 单卡推理

tee open_model.py <<-'EOF'
import torch
import os
import psutil
import time
from transformers.modeling_utils import load_sharded_checkpoint,load_state_dict
import json
from safetensors import safe_opendef get_mem_info():pid = os.getpid()current_process = psutil.Process(pid)memory_info = current_process.memory_info()print(f"RSS: {memory_info.rss / (1024 * 1024):.2f}MB VMS:{memory_info.vms / (1024 * 1024):.2f}MB")def main():from transformers import MixtralForCausalLM, MixtralConfigget_mem_info()config=MixtralConfig.from_pretrained("./config.json")model = MixtralForCausalLM(config).half()get_mem_info()with open("Mixtral-8x7B/model.safetensors.index.json", "r") as file:index_data = json.load(file)weight_files = index_data.get('weight_map', [])state_dict = {}for k,v in weight_files.items():weights_path = os.path.join("Mixtral-8x7B", v)with safe_open(weights_path, framework="pt") as f:for k in f.keys():state_dict[k] = f.get_tensor(k)       model.load_state_dict(state_dict, strict=True)get_mem_info()model=model.to("cuda:0")torch.manual_seed(2)input_tokens=torch.randint(0,32000,(1,128)).to("cuda:0")output=model(input_tokens)output=output.logits.detach().reshape(-1).cpu().numpy()[:8]print(output)if __name__ == "__main__":main()
EOF
python open_model.py

输出:

RSS: 251.70MB VMS:3292.21MB
RSS: 6697.91MB VMS:13695.17MB
RSS: 13198.57MB VMS:26385.02MB[-0.9633789  -0.36450195  0.76708984  1.703125   -0.1772461   0.3581543-0.48901367 -0.5888672 ]

4.DS 4 TP cuda 推理

tee open_model.py <<-'EOF'
import torch
import os
import psutil
import time
from transformers.modeling_utils import load_sharded_checkpoint,load_state_dict
import deepspeed
from deepspeed.accelerator import get_accelerator
import json
from safetensors import safe_opendeepspeed.init_distributed(dist_backend='nccl')
world_size = torch.distributed.get_world_size()
local_rank=int(os.environ['LOCAL_RANK'])
rank=torch.distributed.get_rank()def get_mem_info(prefix):pid = os.getpid()current_process = psutil.Process(pid)memory_info = current_process.memory_info()print(f"{prefix} RANK:{os.environ['LOCAL_RANK']} RSS: {memory_info.rss / (1024 * 1024):.2f}MB VMS:{memory_info.vms / (1024 * 1024):.2f}MB")def main():torch.set_num_threads(1)from transformers import MixtralForCausalLM, MixtralConfigget_mem_info("Init")config=MixtralConfig.from_pretrained("./config.json")model = MixtralForCausalLM(config).half()get_mem_info("ModelCreate")print("-----------------------")with open("Mixtral-8x7B/model.safetensors.index.json", "r") as file:index_data = json.load(file)weight_files = index_data.get('weight_map', [])state_dict = {}for k,v in weight_files.items():weights_path = os.path.join("Mixtral-8x7B", v)with safe_open(weights_path, framework="pt") as f:for k in f.keys():state_dict[k] = f.get_tensor(k)model.load_state_dict(state_dict, strict=True)get_mem_info("LoadState")print("-----------------------")engine = deepspeed.init_inference(model,tensor_parallel={"tp_size": world_size},dtype=torch.float16,replace_with_kernel_inject=False)device=get_accelerator().current_device_name()print("device:",device)torch.manual_seed(2)input_tokens=torch.randint(0,32000,(1,128)).to(device)output=engine(input_tokens)output=output.logits.detach().reshape(-1).cpu().numpy()[:8]if rank==0:print(output)if __name__ == "__main__":main()
EOF
deepspeed --num_gpus=4 open_model.py

输出:


Init RANK:1 RSS: 270.02MB VMS:3414.44MB
Init RANK:3 RSS: 270.43MB VMS:3414.45MB
Init RANK:2 RSS: 270.22MB VMS:3414.45MB
Init RANK:0 RSS: 270.38MB VMS:3486.45MBModelCreate RANK:0 RSS: 6757.33MB VMS:9965.12MB
ModelCreate RANK:3 RSS: 6727.30MB VMS:9862.06MB
ModelCreate RANK:2 RSS: 6757.18MB VMS:9893.12MB
ModelCreate RANK:1 RSS: 6756.99MB VMS:9893.12MBLoadState RANK:2 RSS: 13248.96MB VMS:22772.97MB
LoadState RANK:0 RSS: 13245.91MB VMS:22616.97MB
LoadState RANK:3 RSS: 13233.00MB VMS:22490.91MB
LoadState RANK:1 RSS: 13246.22MB VMS:23240.97MB[-0.96240234 -0.36547852  0.7680664   1.703125   -0.17382812  0.359375-0.49169922 -0.5883789 ]

5.分别保存DS 4TP每个rank上engine.module的权值

tee open_model.py <<-'EOF'
import torch
import os
import psutil
import time
from transformers.modeling_utils import load_sharded_checkpoint,load_state_dict
import deepspeed
from deepspeed.accelerator import get_accelerator
import json
from safetensors import safe_open
from safetensors.torch import save_file, load_filedeepspeed.init_distributed(dist_backend='nccl')
world_size = torch.distributed.get_world_size()
local_rank=int(os.environ['LOCAL_RANK'])
rank=torch.distributed.get_rank()def get_mem_info(prefix):pid = os.getpid()current_process = psutil.Process(pid)memory_info = current_process.memory_info()print(f"{prefix} RANK:{os.environ['LOCAL_RANK']} RSS: {memory_info.rss / (1024 * 1024):.2f}MB VMS:{memory_info.vms / (1024 * 1024):.2f}MB")def save_state_dict(state_dict,save_dir):max_bytes_per_file = 1 * 1024 * 1024 * 1024  # 1GB# 计算每个 tensor 的大小并拆分 state_dictsplit_state_dicts = []current_state_dict = {}current_size = 0for param_name, param_tensor in state_dict.items():tensor_size = param_tensor.element_size() * param_tensor.nelement()# 如果当前 tensor 超过了文件大小，先保存已有 tensorsif current_size + tensor_size > max_bytes_per_file:split_state_dicts.append(current_state_dict)current_state_dict = {}current_size = 0current_state_dict[param_name] = param_tensorcurrent_size += tensor_size# 添加最后一个 state_dictif current_state_dict:split_state_dicts.append(current_state_dict)# 保存拆分后的 state_dicts 并生成索引文件os.makedirs(save_dir, exist_ok=True)index = {"metadata": {"total_parts": len(split_state_dicts)},"weight_map": []}for i, sd in enumerate(split_state_dicts):part_file = os.path.join(save_dir, f"model_part_{i}.safetensors")save_file(sd, part_file)index["weight_map"].append(f"model_part_{i}.safetensors")# 保存索引文件index_file = os.path.join(save_dir, "index.json")with open(index_file, 'w') as f:json.dump(index, f, indent=4)def main():from transformers import MixtralForCausalLM, MixtralConfigget_mem_info("Init")config=MixtralConfig.from_pretrained("./config.json")model = MixtralForCausalLM(config).half()get_mem_info("ModelCreate")print("-----------------------")with open("Mixtral-8x7B/model.safetensors.index.json", "r") as file:index_data = json.load(file)weight_files = index_data.get('weight_map', [])state_dict = {}for k,v in weight_files.items():weights_path = os.path.join("Mixtral-8x7B", v)with safe_open(weights_path, framework="pt") as f:for k in f.keys():state_dict[k] = f.get_tensor(k)model.load_state_dict(state_dict, strict=True)get_mem_info("LoadState")print("-----------------------")engine = deepspeed.init_inference(model,tensor_parallel={"tp_size": world_size},dtype=torch.float16,replace_with_kernel_inject=False)save_state_dict(engine.module.state_dict(), f"./Mixtral-8x7B-{local_rank}")
if __name__ == "__main__":main()
EOF
deepspeed --num_gpus=4 open_model.py
du Mixtral-8x7B-* -lh

输出

1.7G    Mixtral-8x7B-0
1.7G    Mixtral-8x7B-1
1.7G    Mixtral-8x7B-2
1.7G    Mixtral-8x7B-3

6.DS 4TP推理,init_empty_weights初始化模型,每个rank加载自己engine.module的权值

tee open_model.py <<-'EOF'
import torch
import os
import psutil
import time
from accelerate import init_empty_weights
from transformers.modeling_utils import load_sharded_checkpoint,load_state_dict
import deepspeed
from deepspeed.accelerator import get_accelerator
import json
from safetensors import safe_open
from safetensors.torch import save_file, load_filedeepspeed.init_distributed(dist_backend='nccl')
world_size = torch.distributed.get_world_size()
local_rank=int(os.environ['LOCAL_RANK'])
rank=torch.distributed.get_rank()def get_mem_info(prefix):pid = os.getpid()current_process = psutil.Process(pid)memory_info = current_process.memory_info()print(f"{prefix} RANK:{os.environ['LOCAL_RANK']} RSS: {memory_info.rss / (1024 * 1024):.2f}MB VMS:{memory_info.vms / (1024 * 1024):.2f}MB")def my_load_state_dict(model,save_dir):index_file = os.path.join(save_dir, "index.json")with open(index_file, "r") as file:index_data = json.load(file)weight_files = index_data.get('weight_map', [])state_dict = {}for v in weight_files:weights_path = os.path.join(save_dir, v)with safe_open(weights_path, framework="pt") as f:for k in f.keys():state_dict[k] = f.get_tensor(k)model.load_state_dict(state_dict, strict=True)def main():from transformers import MixtralForCausalLM, MixtralConfigget_mem_info("Init")config=MixtralConfig.from_pretrained("./config.json")with init_empty_weights():model = MixtralForCausalLM(config).half()get_mem_info("ModelCreate")print("-----------------------")buffer_dict = {}for name, param in model.named_buffers():buffer_dict[name] = paramengine = deepspeed.init_inference(model,tensor_parallel={"tp_size": world_size},dtype=torch.float16,replace_with_kernel_inject=False)my_load_state_dict(engine.module,f"./Mixtral-8x7B-{local_rank}")for name, param in engine.module.named_buffers():param.copy_(buffer_dict[name])get_mem_info("LoadState")device=get_accelerator().current_device_name()torch.manual_seed(2)input_tokens=torch.randint(0,32000,(1,128)).to(device)output=engine(input_tokens)output=output.logits.detach().reshape(-1).cpu().numpy()[:8]if rank==0:print(output)
if __name__ == "__main__":main()
EOF
deepspeed --num_gpus=4 open_model.py

输出


Init RANK:1 RSS: 269.73MB VMS:3382.40MB
Init RANK:2 RSS: 269.45MB VMS:3382.39MB
Init RANK:3 RSS: 269.86MB VMS:3382.39MB
Init RANK:0 RSS: 269.96MB VMS:3454.39MBModelCreate RANK:1 RSS: 300.44MB VMS:17064.71MB
ModelCreate RANK:0 RSS: 297.03MB VMS:17136.70MB
ModelCreate RANK:2 RSS: 299.22MB VMS:17064.70MB
ModelCreate RANK:3 RSS: 300.66MB VMS:17065.70MBLoadState RANK:0 RSS: 366.28MB VMS:20159.03MB
LoadState RANK:3 RSS: 369.87MB VMS:20152.03MB
LoadState RANK:2 RSS: 368.37MB VMS:20151.02MB
LoadState RANK:1 RSS: 369.16MB VMS:20087.04MB[-0.96240234 -0.36547852  0.7680664   1.703125   -0.17382812  0.359375-0.49169922 -0.5883789 ]

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