Date de publication Various strategies have been proposed to overcome optimization difficulty and accuracy degradation when compressing large models. model_compression/bert/config/XTC/ds_config_layer_reduction_W1Q8_fp32.json in DeepSpeedExamples is the example configuration where we set the layer reduction to be true on top of model_compression/bert/config/XTC/ds_config_W1A8_Qgroup1_fp32.json. Reducing the size of large models is critical when deploying them on both servers and client devices. Based on project statistics from the GitHub repository for the PyPI package deepspeed, we found that it has been starred 8,037 times, and that 0 other projects Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Jeff Rasley, Shaden Smith, Olatunji Ruwase, Yuxiong He. We find that under FP16 training, smaller number of quantization group (e.g., 1 or 2) could lead to unstable training. Overall, this work introduces a simple yet effective compression pipeline for extreme compression in pretrained transformers, providing a possible solution for deploying such models on. It allows for easy composition of multitude of features within a single training, infernece or compression pipeline. The kernels also fuse quantization and dequantization operations before and after GeMM, further reducing the kernel invocation overhead and improving the memory bandwidth utilization. To tease apart their effects, we perform a systematic study on the impacts of various techniques currently used for extreme compression. However, I'm not able to achieve any performance improvement at all, neither with weight quantization, activation quantization or sparse and row pruning. The core piece of DeepSpeed Compression is a component called compression composer, which includes several significant features: After the DNN model has been compressed, DeepSpeed Compression replaces the compressed layers with highly optimized kernels in the DeepSpeed Inference engine to maximize hardware efficiency. The example includes the following changes to the client code (model_compression/bert/run_glue_no_trainer.py in DeepSpeedExamples): (1) When initial the model, the number of layers in the model config should be the same as keep_number_layer in DeepSpeed config JSON file. To find more details about ZeroQuant, refer to ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers. The DeepSpeed library (this repository) implements and packages the innovations and technologies in DeepSpeed Training, Inference and Compression Pillars into a single easy-to-use, open-sourced repository. I'm using a pretrained ResNet as a simple example to test how DeepSpeed works, in a simple case following this.. By DeepSpeed Compression, although recently released, has already been used to successfully optimize several important open source models and Microsoft production workloads. In this section, we introduce how to apply DS-Compression to perform cost-free INT8 quantization and lightweight INT4/INT8 mixed-precision quantization. The following sections will describe our research work on how to compose different compression methods to perform zero-cost quantization (ZeroQuant) and extreme compression (XTC). The other important feature we would like to mention is the quantize_groups inside weight_quantization, which is set to be 1 here to match our XTC papers FP32 training setup. Lack of tailored system optimizations for compressed models. It offers Liked by Ryen White Join now to see all. (2)method, we support L1 norm and topk methods. System optimizations and model compression are very much complementary, and they can be synergistically combined to provide a multiplicative reduction on inference latency and cost. Extreme Compression for Pre-trained Transformers Made Simple and Efficient, ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers, DeepSpeed: Advancing MoE inference and training to power next-generation AI scale, DeepSpeed powers 8x larger MoE model training with high performance, DeepSpeed: Accelerating large-scale model inference and training via system optimizations and compression, ZeRO-Infinity and DeepSpeed: Unlocking unprecedented model scale for deep learning training, Programming languages & software engineering. all repos using our CLA. Existing methods have limited composability from two aspects. Installation: Examples of XTC extreme compression for BERT models are at model_compression/bert/bash_script/XTC in DeepSpeedExamples. (2019) ZeRO: memory optimizations toward training trillion parameter models. This leaves the underlying question unanswered: do we really need those ad-hoc tricks to recover the accuracy loss or do simpler yet more effective methods exist? Besides leveraging these, we also extend the inference capability to support models in compressed formats. An artificial intelligence tool that can help detect melanoma Researchers from MIT using deep convolutional neural networks (DCNNs) and applying. comments. If you are interested in XTC, you can also find more details in our technical report Extreme Compression for Pre-trained Transformers Made Simple and Efficient.. The results are given below (we also include the fp16 training results). For example, the 20B GPT-NeoX model was pre-trained using 96 NVIDIA A100 GPUs in three months. (2022) Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model. To train on a heterogeneous system, such as coordinating CPU and GPU, DeepSpeed offers the ZeRO-Offload technology which efficiently offloads the optimizer states into CPU memory, with minimal impact on training throughput. Performing QAT even with 10% of training samples would still require large amounts of computational resources, which many practitioners cannot afford. However, few existing methods take an end-to-end approach of composing compressions with system optimizations, as it requires significant efforts to bring modeling, algorithm, and system areas of deep learning to work synergistically together. For example, DeepSpeed compression leverages INT8 for GPT-NeoX (20B) and reduces the GPU requirement of serving the model from two to one, reducing latency from 65ms to 25ms, and achieving a 5.2x cost reduction. Under the hood, ZeroQuant contains two major parts: 1) a hardware friendly fine-grained quantization scheme that allows us to quantize weights and activations into low-bit values with minimal errors while still empowering fast inference speed on commodity hardware with low quantization/dequantization cost; and 2) a layer-by-layer knowledge distillation pipeline, which fine-tunes the quantized model to close the accuracy gap from low-precision (e.g., INT4) quantization. (2) Reducing the 12-layer Bert-base to a 5-layer one and then obtaining its 1-bit or 2-bit counterparts. For compressed models that have a smaller memory footprint, the inference engine can automatically shrink the number of GPUs required to serve a model, leading to reduced cross-GPU communication and hardware cost. Compressed training One can run our layer reduction example in DeepSpeedExamples by: To apply layer reduction for task-agnostic compression, we provide an example on how to do so in the GPT pre-training stage. We highly value your feedback and comments, so let us know what you think and how we can improve. The DeepSpeed Library is heavily adopted by the DL community, and has been used to enable some of the most powerful models (see DeepSpeed Adoption). Liked by David Gera. Tutorial for ZeroQuant: efficient and affordable post-training quantization, 3. According to wikipedia.org, The color data of an image is stored in three arrays of values, known as channels.. Given the above layer-reduced models ready, we now continue to compress the model with 1/2-bit quantization. One can run our sparse pruning example in DeepSpeedExamples by: Row pruning sets all the elements in certain rows of the weight matrix with zero values. DeepSpeed Compression overcomes these challenges by offering novel state-of-the-art compression techniques, such as XTC for 32x smaller model size and ZeroQuant for 5000x lower compression cost reduction. It is designed in a modular way so that it will be easy for users to add new compression schemes. Early adopters of DeepSpeed have already produced a language model (LM) with over 17B parameters called Turing-NLG, establishing a new SOTA in the LM category. System optimizations play a key role in efficiently utilizing the available hardware resources and unleashing their full capability through inference optimization libraries like ONNX runtime and DeepSpeed. First, DeepSpeed Compression can be specified and enabled the same way as DeepSpeed training and inference via a JSON file, where enabling different combination of compression techniques only requires a few lines of modification in the JSON file. DeepSpeed Compression also takes an end-to-end approach to improve the computation efficiency of compressed models via a highly optimized inference engine. (2022) Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam. In this section, we introduce how to apply DeepSpeed Compression library to perform the light-weight layer reduction and ultra-low bit precision (binary/ternary) quantization. It also successfully compresses the Microsoft Relevance Fusion modelsa Transformer-based ranking model used in Bings core search stack. To maximize the benefits of compressed models, specialized system optimizations are often required, e.g., quantized and sparsified models need optimized low-bit arithmetic computation and sparse matrix multiplication to boost the inference speed on commodity hardware. Futurewei Technologies, Inc. Sep 2022 - Present2 months. One can run our activation quantization example in DeepSpeedExamples by: Pruning aims to reduce the number of parameters and operations involved in generating a prediction by removing network connections. (2021) ZeRO-Offload: Democratizing Billion-Scale Model Training. By combining extreme quantization and lightweight layer reduction, we can further improve the binarized model, achieving 50x model size reduction while retaining 97% of the accuracy. 2. If a row is pruned, all elements in that row are set to zero. With pruning, you can lower the overall parameter count in the network (see more in this Coursera lecture). But despite their remarkable capabilities, the models large size creates latency and cost constraints that hinder the deployment of applications on top of them. model_compression/bert/config/XTC/ds_config_layer_reduction_fp16.json in DeepSpeedExamples is the example configuration for reducing the 12-layer BERT-base to a 6-layer one. Corporate Vice President of Engineering. For example, we developed variations of efficient low-bit computation such as INT8 GeMM kernels. With this config, we quantize the existing fined-tuned models downloaded from Hugging Face. On-DemandWatch now to learn about some of the most pressing questions facing our research community and listen in on conversations with 120+ researchers around how to ensure new technologies have the broadest possible benefit for humanity. Speed ZeRO-powered data parallelism can provide up to five times higher. Please make sure they match the teacher model dimensions in the checkpoint. BLOG DeepSpeed Compression: A composable library for extreme compression and zero-cost quantization Azure and DeepSpeed empower easy-to-use and high-performance model training Azure ML, Azure HPC, and DeepSpeed collaborated and made large-scale distributed training easier and more efficient on Azure using DeepSpeed technology. huggingface quantizationletterkenny live merch Archives, Collections, Dialog, Commentary, Gallery, Museum This is the amount of data samples that leads to one step of model update. We are a group of system and modeling researchersZhewei Yao, Xiaoxia Wu, Minjia Zhang, Conglong Li, Reza Yazdani Aminabadi, Elton Zheng, Samyam Rajbhandari, Ammar Ahmad Awan, Jeff Rasley, Cheng Li, Olatunji Ruwase, Shaden Smith, Du Li, Michael Wyatt, Arash Bakhtiari, Guanhua Wang, Connor Holmes, Sam Ade Jacobs, Martin Cai, Yuxiong He (team lead)who are enthusiastic about performance optimization of large-scale systems. These algorithms use condensed format to represent, store, communicate, and compute DNN models, reducing the total work needed for inference with little or no loss in accuracy. (2022) ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers. Microsoft AI Releases 'DeepSpeed Compression': A Python-based Composable Library for Extreme Compression and Zero-Cost Quantization to Make Deep Beliebt bei Anand Dubey New & improved material to dive into geometric deep learning! XTC reduces the model size by 32x with almost no loss in the average score on the GLUE tasks via simple yet effective binarization technique. User can train with a different teacher model by adding --pretrained_dir_teacher. For example, the BERT-base (BERT-large) model has 12 heads (24 heads). May the Fourth be with you. Weight quantization maps the full precision weight (FP32/FP16) to the low bit ones, like INT8 and INT4. XTC (short for eXTreme Compression) is our new simple yet efficient method that compresses a model to its limit with lightweight layer reduction and robust binarization. As for the next steps, we plan to extend our offerings with more compression methods, an extended coverage of specialized kernels for compressed models, and an optimization module that automatically finds the best compression schemes. (2021) ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning. But despite their remarkable capabilities, the models large size creates latency and cost constraints that hinder the deployment of applications on top of them. First, DeepSpeed Compression can be specified and enabled the same way as DeepSpeed training and inference via a JSON file, where enabling different combination of compression techniques only requires a few lines of modification in the JSON file. It offers multiple cutting-edge compression methods, as shown in Table 1, including extreme quantization, head/row/channel pruning, and knowledge distillation, that can effectively reduce model size and inference cost. We hope you will try DeepSpeed Compression. DeepSpeed Software Suite DeepSpeed Library. It's probably the biggest forum about the data compression software and algorithms on the web! Row pruning can be enabled and configured using the DeepSpeed config JSON file (configuration details). Currently the DeepSpeed Compression includes seven compression methods: layer reduction via knowledge distillation, weight quantization, activation quantization, sparse pruning, row pruning, head pruning, and channel pruning. (3) During training, if KD is not used, nothing needs to be done. By default, it will load the bottom layers of the teacher models for initialization, but you can pass your own checkpoints for initialization. Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He. , It supports the synergistic composition of these methods and the system optimizations, offering the best of both worlds while allowing a seamless and easy-to-use pipeline for efficient DL model inference. We provide the zero-shot perplexity result from WikiText-2 and LAMBADA in the following table. (2)rp1, users can expand more groups such as rp2, rp3, etc. (4)--load-teacher, this is where one specifies the teacher model checkpoint. In addition, users also can choose whether to reinitialize the input/output layers from the given model (teacher model) by other_module_name. Besides, they also provide a new profiling tool to identify training performance bottlenecks. It can improve computation efficiency similar to weight quantization. 87% of AI projects fail . This has two benefits. DeepSpeed Compression also takes an end-to-end approach to improve the computation efficiency of compressed models via a highly optimized inference engine. However, I'm not able to achieve any performance improvement at all, neither with weight quantization, activation quantization or sparse and row pruning. Thus, we recommend using larger number of groups (e.g., 64) under FP16. It reduces the Microsoft Turing Image Super Resolution model (T-ISR) model size by. Layer reduction can be applied in both the pre-training and fine-tuning stages. Besides leveraging these, we also extend the inference capability to support models in compressed formats. One can run our head pruning example in DeepSpeedExamples by: Channel pruning is made specifically for convolutional layers and computer vision. We do this through two main techniques: extreme quantization and layer reduction. Samyam Rajbhandari, Olatunji Ruwase, Jeff Rasley, Shaden Smith, Yuxiong He. XTC produces models with little loss in accuracy yet up to 50x model size reduction, as shown in Figure 1. We believe that our composable library and new innovations will help close the gap between what is possible in AI and what is deployable as well as making DL inference faster, cheaper, and simpler. (Note: I did not create this) May the Fourth be with you. For simplicity, you can choose to only specify two of the three parameters, the last one will be inferred automatically by DeepSpeed. DeepSpeed Compression offers a set of tangible benefits for ML engineering teams trying to incorporate compression methods in their pipelines: Large array of model compression and quantization methods. What is DeepSpeed Compression: DeepSpeed Compression is a library purposely built to make it easy to compress models for researchers and practitioners while . 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