手动安装AIACC-Inference（AIACC推理加速）Torch版 - GPU 云服务器

背景信息

AIACC-Inference（AIACC推理加速）Torch版通过对模型的计算图进行切割，执行层间融合，以及高性能OP实现，大幅度提升PyTorch的推理性能。您无需指定精度和输入尺寸，即可通过JIT编译的方式对PyTorch框架下的深度学习模型进行推理优化。

AIACC-Inference（AIACC推理加速）Torch版通过调用aiacctorch.compile(model)接口即可实现推理性能加速。您只需先使用torch.jit.script或者torch.jit.trace接口，将PyTorch模型转换为TorchScript模型，更多信息，请参见PyTorch官方文档。本文将为您提供分别使用torch.jit.script和torch.jit.trace接口实现推理性能加速的示例。

准备并安装AIACC-Inference（AIACC推理加速）Torch版软件包

AIACC-Inference（AIACC推理加速）Torch版为您提供了Conda一键安装包以及whl包两种软件包，您可以根据自身业务场景选择一种进行安装。

Conda安装包
Conda一键安装包中已经预装了大部分依赖包，您只需手动安装CUDA驱动，再安装Conda包即可。具体操作如下：

注意请勿随意更改Conda安装包中的预装依赖包信息，否则可能会因为版本不匹配导致Demo运行报错。
1. 远程登录实例。
2. 自行安装CUDA 470.57.02或以上版本的驱动。
3. 下载Conda安装包。
```
wget https://aiacc-inference-public.oss-cn-beijing.aliyuncs.com/aiacc-inference-torch/aiacc-inference-torch-miniconda-latest.tar.bz2
```
4. 解压Conda安装包。
```
mkdir ./aiacc-inference-miniconda && tar -xvf ./aiacc-inference-torch-miniconda-latest.tar.bz2 -C ./aiacc-inference-miniconda
```
5. 加载Conda安装包。
```
source ./aiacc-inference-miniconda/bin/activate
```
whl安装包
您需要手动安装相关依赖包后再安装whl软件包。具体操作如下：
1. 远程登录实例。
2. 选择以下任一方式安装相关依赖包。由于whl软件包依赖大量不同的软件组合，请您谨慎设置。
  - 方式一
    1. 自行安装如下版本的依赖包：
      
      CUDA 11.1
      
      cuDNN 8.3.1.22
      
      TensorRT 8.2.3.0
    2. 将TensorRT及CUDA的相关依赖库放置在系统LD_LIBRARY_PATH环境变量中。
      以下命令以CUDA的相关依赖库位于/usr/local/cuda/目录下，TensorRT的相关依赖库位于/usr/local/TensorRT/目录下为例，您需要根据实际情况替换。
      export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=/usr/local/TensorRT/lib:$LD_LIBRARY_PATH
    3. 执行环境变量。
      source ~/.bashrc
  - 方式二
    使用NVIDIA的pip包安装相关依赖包。
```
pip install nvidia-pyindex && \ 
pip install nvidia-tensorrt==8.2.3.0
```
3. 安装PyTorch 1.9.0+cu111。
```
pip3 install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
```
4. 下载并安装aiacctorch。
```
pip install aiacctorch -f https://aiacc-inference-public.oss-cn-beijing.aliyuncs.com/aiacc-inference-torch/aiacctorch_stable.html -f https://download.pytorch.org/whl/torch_stable.html
```

基于ResNet50模型执行推理

以下示例将以安装了Conda软件包为例，基于ResNet50模型，并调用torch.jit.script接口执行推理任务，执行1000次后取平均时间，将推理耗时从3.68 ms降低至0.396 ms以内。

原始版本

原始代码如下所示：

import time
import torch
import torchvision.models as models
mod = models.resnet50(pretrained=True).eval()
mod_jit = torch.jit.script(mod)
mod_jit = mod_jit.cuda()

in_t = torch.randn([1,3,224,224]).float().cuda()

# Warming up
for _ in range(10):
    mod_jit(in_t)

    inference_count = 1000
    # inference test
    start = time.time()
    for _ in range(inference_count):
        mod_jit(in_t)
        end = time.time()
        print(f"use {(end-start)/inference_count*1000} ms each inference")
print(f"{inference_count/(end-start)} step/s")

执行结果如下，显示推理耗时大约为3.68 ms。

加速版本

您仅需要在原始版本的示例代码中增加如下两行内容，即可实现性能加速。

import aiacctorch
aiacctorch.compile(mod_jit)

更新后的代码如下：

import time
import aiacctorch #import aiacc包
import torch
import torchvision.models as models
mod = models.resnet50(pretrained=True).eval()
mod_jit = torch.jit.script(mod)
mod_jit = mod_jit.cuda()
mod_jit = aiacctorch.compile(mod_jit) #进行编译

in_t = torch.randn([1,3,224,224]).float().cuda()

# Warming up
for _ in range(10):
    mod_jit(in_t)

inference_count = 1000
# inference test
start = time.time()
for _ in range(inference_count):
    mod_jit(in_t)
end = time.time()
print(f"use {(end-start)/inference_count*1000} ms each inference")
print(f"{inference_count/(end-start)} step/s")

执行结果如下，显示推理耗时为0.396 ms。相较于之前的3.68 ms，推理性能有了显著提升。

基于Bert-Base模型执行推理

以下示例将基于Bert-Base模型，并调用torch.jit.trace接口执行推理任务，将推理耗时从4.95 ms降低至0.419 ms以内。

安装transformers包。
```
pip install transformers
```

分别运行原始版本和加速版本的Demo，并查看运行结果。

原始版本

原始代码如下所示：

from transformers import BertModel, BertTokenizer, BertConfig
import torch
import time

enc = BertTokenizer.from_pretrained("bert-base-uncased")

# Tokenizing input text
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
tokenized_text = enc.tokenize(text)

# Masking one of the input tokens
masked_index = 8
tokenized_text[masked_index] = '[MASK]'
indexed_tokens = enc.convert_tokens_to_ids(tokenized_text)
segments_ids = [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, ]

# Creating a dummy input
tokens_tensor = torch.tensor([indexed_tokens]).cuda()
segments_tensors = torch.tensor([segments_ids]).cuda()
dummy_input = [tokens_tensor, segments_tensors]

# Initializing the model with the torchscript flag
# Flag set to True even though it is not necessary as this model does not have an LM Head.
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
                    num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, torchscript=True)

# Instantiating the model
model = BertModel(config)

# The model needs to be in evaluation mode
model.eval()

# If you are instantiating the model with `from_pretrained` you can also easily set the TorchScript flag
model = BertModel.from_pretrained("bert-base-uncased", torchscript=True)

model = model.eval().cuda()

# Creating the trace
traced_model = torch.jit.trace(model, dummy_input)

# Warming up
for _ in range(10):
    all_encoder_layers, pooled_output = traced_model(*dummy_input)

    inference_count = 1000
    # inference test
    start = time.time()
    for _ in range(inference_count):
        traced_model(*dummy_input)
        end = time.time()
        print(f"use {(end-start)/inference_count*1000} ms each inference")
print(f"{inference_count/(end-start)} step/s")

执行结果如下，显示推理耗时大约为4.95 ms。

加速版本

您仅需要在原始版本的示例代码中增加如下两行内容，即可实现性能加速。

import aiacctorch
aiacctorch.compile(traced_model)

更新后的代码如下：

from transformers import BertModel, BertTokenizer, BertConfig
import torch
import aiacctorch #import aiacc包
import time

enc = BertTokenizer.from_pretrained("bert-base-uncased")

# Tokenizing input text
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
tokenized_text = enc.tokenize(text)

# Masking one of the input tokens
masked_index = 8
tokenized_text[masked_index] = '[MASK]'
indexed_tokens = enc.convert_tokens_to_ids(tokenized_text)
segments_ids = [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, ]

# Creating a dummy input
tokens_tensor = torch.tensor([indexed_tokens]).cuda()
segments_tensors = torch.tensor([segments_ids]).cuda()
dummy_input = [tokens_tensor, segments_tensors]

# Initializing the model with the torchscript flag
# Flag set to True even though it is not necessary as this model does not have an LM Head.
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
    num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, torchscript=True)

# Instantiating the model
model = BertModel(config)

# The model needs to be in evaluation mode
model.eval()

# If you are instantiating the model with `from_pretrained` you can also easily set the TorchScript flag
model = BertModel.from_pretrained("bert-base-uncased", torchscript=True)

model = model.eval().cuda()

# Creating the trace
traced_model = torch.jit.trace(model, dummy_input)
traced_model = aiacctorch.compile(traced_model) #进行编译

# Warming up
for _ in range(10):
    all_encoder_layers, pooled_output = traced_model(*dummy_input)

inference_count = 1000
# inference test
start = time.time()
for _ in range(inference_count):
    traced_model(*dummy_input)
end = time.time()
print(f"use {(end-start)/inference_count*1000} ms each inference")
print(f"{inference_count/(end-start)} step/s")

执行结果如下，显示推理耗时为0.419 ms。相较于之前的4.95 ms，推理性能有了显著提升。

基于ResNet50模型执行动态尺寸推理

在AIACC-Inference-Torch中，我们无需关心动态尺寸的问题，AIACC-Inference-Torch能够支持不同的输入尺寸。以下示例基于ResNet50模型，输入3个不同的长宽尺寸，带您体验使用AIACC-Inference-Torch进行加速的过程。

import time
import aiacctorch #import aiacc包
import torch
import torchvision.models as models
mod = models.resnet50(pretrained=True).eval()
mod_jit = torch.jit.script(mod)
mod_jit = mod_jit.cuda()
mod_jit = aiacctorch.compile(mod_jit) #进行编译

in_t = torch.randn([1,3,224,224]).float().cuda()
in_2t = torch.randn([1,3,448,448]).float().cuda()
in_3t = torch.randn([16,3,640,640]).float().cuda()

# Warming up
for _ in range(10):
    mod_jit(in_t)
    mod_jit(in_3t)

inference_count = 1000
# inference test
start = time.time()
for _ in range(inference_count):
    mod_jit(in_t)
    mod_jit(in_2t)
    mod_jit(in_3t)
end = time.time()
print(f"use {(end-start)/(inference_count*3)*1000} ms each inference")
print(f"{inference_count/(end-start)} step/s")

执行结果如下，显示推理耗时大约为9.84 ms。

说明为了缩短模型编译的时间，应在warming up阶段推理最大及最小的tensor尺寸，避免在执行时重复编译。例如，已知推理尺寸在1×3×224×224至16×3×640×640之间时，应在warming up时推理这两个尺寸。

性能数据对比参考

以下数据为AIACC-Inference-HRT与PyTorch的性能对比结果，采用的环境配置如下：

实例规格：配置NVIDIA A10的GPU实例。
CUDA版本：11.5。
CUDA Driver版本：470.57.02。


Model	Input-Size	AIACC-Inference-Torch (ms)	Pytorch Half (ms)	Pytorch Float (ms)	加速比
resnet50	1x3x224x224	0.46974873542785645	3.4382946491241455	2.9194235801696777	6.22
mobilenet-v2-100	1x3x224x224	0.23872756958007812	2.8045766353607178	2.0068271160125732	8.69
SRGAN-X4	1x3x272x480	23.070229649543762	35.863523721694946	132.00348043441772	5.74
YOLO-V3	1x3x640x640	3.869319200515747	8.807475328445435	15.704705834388735	4.06
bert-base-uncased	1x128，1x128	0.9421144723892212	3.1525989770889282	3.761411190032959	4.00
bert-large-uncased	1x128，1x128	1.3300731182098389	6.11789083480835	7.110695481300354	5.34

GPU 云服务器：手动安装AIACC-Inference（AIACC推理加速）Torch版

前提条件