Home>Article>Technology peripherals> H100 reasoning soared 8 times! NVIDIA officially announced open source TensorRT-LLM, supporting 10+ models
The "GPU poor" are about to bid farewell to their predicament!
Just now, NVIDIA released an open source software called TensorRT-LLM, which can accelerate the inference process of large language models running on H100
So, how many times can it be improved?
After adding TensorRT-LLM and its series of optimization functions (including In-Flight batch processing), the total throughput of the model increased by 8 times.
Comparison of GPT-J-6B A100 and H100 with and without TensorRT-LLM
In addition, taking Llama 2 as an example, TensorRT-LLM can improve inference performance by 4.6 times compared to using A100 independently
##Compare Llama 2 70B , Comparison of A100 and H100 with and without TensorRT-LLM
Netizens said that the super powerful H100, combined with TensorRT-LLM, will undoubtedly completely change large-scale language models Reasoning about the current situation!
Currently, due to the huge parameter scale of large models, "deployment and reasoning" difficulty and cost have remained high.
TensorRT-LLM developed by NVIDIA aims to significantly improve the throughput of LLM and reduce costs through GPU
Specifically, TensorRT-LLM encapsulates TensorRT’s deep learning compiler, FasterTransformer’s optimized kernel, pre- and post-processing, and multi-GPU/multi-node communication into a simple open source Python API
NVIDIA has further enhanced FasterTransformer to make it a productized solution.
It can be seen that TensorRT-LLM provides an easy-to-use, open source and modular Python application programming interface.
Coders who don’t need in-depth knowledge of C or CUDA can deploy, run, and debug a variety of large language models and achieve excellent performance and rapid customization. Function
According to NVIDIA’s official blog, TensorRT-LLM uses four methods to improve LLM inference performance on Nvidia GPUs
First of all, TensorRT-LLM is introduced for the current top 10 models, allowing developers to run them immediately.
Secondly, TensorRT-LLM, as an open source software library, allows LLM to perform inference on multiple GPUs and multiple GPU servers simultaneously.
These servers are connected through NVIDIA's NVLink and InfiniBand interconnects respectively.
The third point is about "in-machine batch processing", which is a new scheduling technology that allows tasks of different models to enter and exit the GPU independently of other tasks
Finally, TensorRT-LLM has been optimized to use the H100 Transformer Engine to reduce memory usage and latency during model inference.
Let’s take a detailed look at how TensorRT-LLM improves model performance
Supports rich LLM ecosystemTensorRT -LLM provides excellent support for the open source model ecosystem
What needs to be rewritten is: The largest and most advanced language model, such as Llama 2-70B launched by Meta, needs Multiple GPUs work together to provide responses in real time
Previously, to achieve the best performance of LLM inference, developers had to manually rewrite the AI model and break it into multiple pieces. Then coordinate execution between GPUs
TensorRT-LLM uses tensor parallel technology to distribute the weight matrix to each device, thereby simplifying the process and enabling large-scale efficient inference
Each model can run in parallel on multiple GPUs and multiple servers connected via NVLink, without developer intervention or model changes.
With the introduction of new models and model architectures, developers can optimize using the latest NVIDIA AI kernel (Kernal) open sourced in TensorRT-LLM Model
The content that needs to be rewritten is: the supported kernel fusion (Kernal Fusion) includes the latest FlashAttention implementation, and masked multi-head attention for the context and generation stages of GPT model execution Li et al
In addition, TensorRT-LLM also includes fully optimized, ready-to-run versions of many large language models that are currently popular.
These models include Meta Llama 2, OpenAI GPT-2 and GPT-3, Falcon, Mosaic MPT, BLOOM and more than ten more. All of these models can be called using the easy-to-use TensorRT-LLM Python API
These features help developers build customized large language models faster and more accurately to Meet the different needs of all walks of life.
Nowadays, large language models are used in a wide range of applications.
A single model can be used simultaneously for multiple seemingly disparate tasks - from simple Q&A responses in a chatbot, to document summarization or the generation of long code blocks, the workload is Highly dynamic, the output size needs to meet the needs of tasks of varying orders of magnitude.
The diversity of tasks can make it difficult to batch requests efficiently and perform efficient parallel execution, possibly causing some requests to complete earlier than others.
To manage these dynamic loads, TensorRT-LLM includes an optimized scheduling technology called "In-flight batching".
The core principle of the large language model is that the entire text generation process can be realized through multiple iterations of the model
Through in-flight batch processing , the TensorRT-LLM runtime immediately releases completed sequences from the batch, rather than waiting for the entire batch to complete before moving on to the next set of requests.
When executing a new request, other requests from the previous batch that have not been completed are still being processed.
By doing on-board batching and doing additional kernel-level optimizations, GPU utilization can be improved, resulting in at least doubling the throughput of the actual request benchmark for LLM on the H100
TensorRT-LLM also provides a function called H100 Transformer Engine, which can effectively reduce memory consumption and latency during large model inference. .
Because LLM contains billions of model weights and activation functions, they are usually trained and represented with FP16 or BF16 values, each occupying 16 bits of memory.
However, at inference time, most models can be effectively represented with lower precision using quantization techniques, such as 8-bit or even 4-bit integers (INT8 or INT4).
Quantization is the process of reducing model weights and activation accuracy without sacrificing accuracy. Using lower precision means each parameter is smaller and the model takes up less space in GPU memory.
This can use the same hardware to infer larger models while reducing time consumption on memory operations during execution
Through H100 Transformer Engine technology, the H100 GPU with TensorRT-LLM allows users to easily convert model weights to the new FP8 format, and automatically compile the model to take advantage of the optimized FP8 Kernel.
And this process does not require any code! The FP8 data format introduced by H100 enables developers to quantify their models and dramatically reduce memory consumption without reducing model accuracy.
Compared with other data formats such as INT8 or INT4, FP8 quantization retains higher precision while achieving the fastest performance and is the most convenient to implement. Compared with other data formats such as INT8 or INT4, FP8 quantization retains higher precision while achieving the fastest performance and is the most convenient to implement
Although TensorRT-LLM has not yet been officially released, users can now experience it in advance
The application link is as follows:
https://developer.nvidia.com/tensorrt-llm-early-access/join
NVIDIA also said that TensorRT-LLM will be integrated into the NVIDIA NeMo framework soon.
This framework is part of the AI Enterprise recently launched by NVIDIA, providing enterprise customers with a secure, stable, and highly manageable enterprise-level AI software platform
Developers and researchers can access TensorRT-LLM through the NeMo framework on NVIDIA NGC or the project on GitHub
However, it is important to note that, Users must register for the NVIDIA Developer Program to apply for the early access version.
Users on Reddit had a heated discussion on the release of TensorRT-LLM
Unimaginable After optimizing the hardware specifically for LLM, the effect will be much improved.
But some netizens believe that the purpose of this thing is to help Lao Huang sell more H100s.
Some netizens have different opinions on this. They believe that Tensor RT is also helpful for users who deploy deep learning locally. As long as you have an RTX GPU, you may benefit from similar products in the future
From a more macro perspective, maybe for LLM, it will There will be a series of optimization measures specifically targeted at the hardware level, and there may even be hardware specifically designed for LLM to improve its performance. This situation has occurred in many popular applications, and LLM is no exception
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