This section consists of two parts: (a) we first perform a light-weight layer reduction, and (b) based on the model in (a), we perform 1-bit or 2-bit quantization. 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. agree to a Contributor License Agreement (CLA) declaring that you have the right to, and Limited composability. Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Jeff Rasley, Shaden Smith, Olatunji Ruwase, Yuxiong He. However, to see the real benefit of hardware computation efficiency, the density ratio (percentage of weights to keep after pruning) must be considerably low. On the latter, the emerging compression algorithms show great potential in reducing model size and inference computation. DeepSpeed Team To use DeepSpeed Compression library, you need to install DeepSpeed >= 0.7.0 following the installation guide. DeepSpeed Compression proposes a seamless pipeline to address the compression composability challenges, as shown in Figure 4. Reducing the size of large models is critical when deploying them on both servers and client devices. 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. 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. 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. Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He. The kernels also fuse quantization and dequantization operations before and after GeMM, further reducing the kernel invocation overhead and improving the memory bandwidth utilization. It seamlessly works with the existing DeepSpeed library. 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. 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. However, composing them together is non-trivial. We hope you will try DeepSpeed Compression. Conglong Li, Minjia Zhang, Yuxiong He. Recently we have received many complaints from users about site-wide blocking of their own and blocking of their own activities please go to the settings off state, . Furthermore, our library has multiple built-in state-of-the-art compression methods and supports synergistic composition of these methods together with the system optimizations, offering the best of both worlds while allowing a seamless and easy-to-use pipeline for efficient DL model inference. The results are given below (we also include the fp16 training results). Step 1: Obtain the latest version of the Megatron-DeepSpeed. 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. Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. Please find the code, tutorial, and documents at the DeepSpeed GitHub, and website. (2) After training, apply redundancy_clean function to save the quantized weight. 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. Furthermore, our inference engine supports many-GPU transformer layers for serving transformer models across GPUs using inference-adapted parallelism strategies. To improve the accuracy of binarized/ternarized models, existing methods often adopt complicated and computationally expensive compression pipelines, such as multi-stage distillation. 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.. Otherwise, one needs to consider applying KD with the teacher_layer JSON configuration when calculating the difference between teachers and students output. With our compression composer, applying extreme compression is as easy as adding two new API calls to enable compression and clean the compressed model. As such, we suggest using channel pruning for the first CONV2d layer. 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. At this first release, we open-source the core DeepSpeed Compression components, including the compression composer, which supports various compression methods consisting of INT8/INT4/Ternary/Binary quantization, lightweight layer reduction, pretraining and task specific knowledge distillation, head pruning, row pruning, and channel pruning, for compressing both NLP and computer vision models. It seamlessly works with the existing DeepSpeed library. Why use DeepSpeed Compression: DeepSpeed Compression offers novel state-of-the-art compression techniques to achieve faster model compression with better model quality and lower compression cost. DeepSpeed delivers extreme-scale model training for everyone. For example, popular compression methods such as quantize-aware training (QAT) and multi-stage distillation methods lead to long training time and large hardware resource requirement as the model size grows into multi-billion parameters or at even larger scale, making compressing these models costly and difficult. Please refer to our blog for more details. Very importantly, we quantize these models without requiring any training data, expensive compression time or GPU resources, bringing huge training cost savings compared with QAT! This systematic composition of system technologies for inference falls under the DeepSpeed-Inference. To use DeepSpeed Compression library, you need to install DeepSpeed >= 0.7.0 following the installation guide. For the configurations, see model_compression/bert/config/XTC/ds_config_W1A8_Qgroup1_fp32.json in DeepSpeedExamples. Given that transformers are becoming the standard architecture choice for AI, we believe the investigation and the proposed solution could be highly impactful to power large-scale models on resource-constrained devices. We have recently focused on deep learning systems, optimizing deep learnings speed to train, speed to convergence, and speed to develop. Furthermore, our library has multiple built-in state-of-the-art compression methods. We also empirically found that a staged KD often led to a better pre-trained distilled model on downstream tasks. Furthermore, with the lightweight layer-by-layer knowledge distillation, ZeroQuant can quantize GPT-3-1.3B with mixed INT4/INT8 precision in three hours on a single GPU, which leads to 5000x compression cost reduction compared to quantization-aware training. Performing QAT even with 10% of training samples would still require large amounts of computational resources, which many practitioners cannot afford. 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. It also takes an end-to-end approach to improve the computation efficiency of compressed models via a highly optimized inference engine. 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). train_batch_size: [integer] Value Example The effective training batch size. However, I'm not able to achieve any performance improvement at all, neither with weight quantization, activation quantization or sparse and row pruning. One way to perform pruning is based on the absolute value of the weight parameters, see for instance this paper. 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. (4)dense_ratio, for unstructured sparse pruning, the dense ratio could be less than 0.1 for BRET-base model while still yielding a good accuracy. 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. 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. A longer training iteration with learning rate decay is highly preferred for closing the accuracy gap of extreme quantization; Single-stage knowledge distillation with more training budgets is sufficient to match or even exceed accuracy from multi-stage ones; Training without data augmentation hurts performance on downstream tasks for various compression tasks, especially on smaller tasks; Lightweight layer reduction matches or even exceeds expensive pre-training distillation for task-specific compression. Second, by loading only one layer for low-precision (e.g., INT4) quantization at a time, the maximum memory footprint required to quantize the model depends solely on the size of individual layer instead of the entire model, allowing one to quantize gigantic models with as little as one GPU. Although we started DeepSpeed Compression quite recently, we have successfully leveraged it to optimize several large-scale open-source models and Microsoft production workloads. Layer reduction is about resetting the depth of network architecture and reinitialization of weight parameters, which happens before the training process. To leverage Meta Tensors, all we need to do is wrap our LightningModule in init_meta_context and our model is automatically instantiated with the meta device, which skips CPU instantiation (which would blow up our memory! If a row is pruned, all elements in that row are set to zero. DeepSpeed Compression by Microsoft. With the command above, one can now obtain the results of 1-bit 6-layer model. Thus, we recommend using larger number of groups (e.g., 64) under FP16. 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. (2020) Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping. Second, there is a lack of composability between compression techniques and system optimizations. With DeepSpeed Compression, we can quantize the model in a few minutes with improved accuracy and reduced latency compared to QAT. 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. It offers an easy-to-use API that automatically takes care of the complexities of assembling different compression techniques to deliver the compound benefits of multiple compression methods. (3)wq1/wq2, users can expand more groups such as wq3, wq4, etc. DeepSpeed implements everything described in the ZeRO paper. Compress a model: replace linear/conv2d layer with deepspeed compression-aware modules: Args: model (`torch.nn.Module`) The model to compress. Given that transformers are becoming the standard architecture choice for AI, we believe the investigation and the proposed solution could be highly impactful to power large-scale models on resource-constrained devices. 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. With our compression composer, applying extreme compression is as easy as adding two new API calls to enable compression and clean the compressed model. 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. As we just mentioned, compressed models require specialized system optimizations to maximize latency and cost reduction. Particularly, thanks to the fine-grained quantization scheme, ZeroQuant can convert GPT-3-1.3B (trained with 128 NVIDIA A100 with five days) and GPT-NeoX (trained with 96 A100 with three months) to INT8 without any cost or training data while delivering comparable accuracy. With just a single GPU, ZeRO-Offload of DeepSpeed can train models with over 10B parameters, 10x bigger than the state of the art. This includes the compression composer, which supports some compression techniques for NLP and computer vision models, such as lightweight layer reduction, pretraining and task-specific knowledge distillation, head pruning, row pruning . We build our work on top of DeepSpeed inference, which provides high-performance model serving with inference optimized kernels, parallelism, and memory optimizations, covering a wide variety of models for both latency sensitive and throughput-oriented applications. High compression cost. Although well-performing compression solutions have been proposed independently, combining multiple methods together for the best outcome is still a laborious process, requiring building a complex compression pipeline. We suggest users to further finetune the model after applying redundancy_clean for such cases. You just supply your custom config file . It reduces the Microsoft Turing Image Super Resolution model (T-ISR) model size by. Performing QAT even with 10% of training samples would still require large amounts of computational resources, which many practitioners cannot afford. On the latter, the emerging compression algorithms show great potential in reducing model size and inference computation. We build our work on top of DeepSpeed inference, which provides high-performance model serving with inference optimized kernels, parallelism, and memory optimizations, covering a wide variety of models for both latency sensitive and throughput-oriented applications. As we just mentioned, compressed models require specialized system optimizations to maximize latency and cost reduction. 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). DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. For example, we developed variations of efficient low-bit computation such as INT8 GeMM kernels. It delivers significant latency and cost reduction, widely applicable on both various NLP and CV tasks. It can improve computation efficiency similar to weight quantization. Using fp32 clearly results in more stable performance than fp16, although fp16 can speed up the training time. DeepSpeed releases a new pillar, DeepSpeed compression, to tackle latency & cost challenges on deploying large-scale deep learning models. In particularly, we will guide you on implementing the XTC methods, namely: (1) Obtaining a 1-bit or 2-bit BERT-base (12-layer) with 8-bit activation quantization. Users can also use their own models with better accuracy as the teacher and the student model initialization. Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, Yuxiong He. For example, XTC requires composition of lightweight layer reduction, binarization, and knowledge distillation. For now, it can only be applied to output matrix of the Transformer (i.e., attention.output.dense in BERT). Corporate Vice President of Engineering. To tease apart their effects, we perform a systematic study on the impacts of various techniques currently used for extreme compression. These kernels load INT8 parameters and activations from GPU device memory to the registers and use the customized INT8 GeMM implemented on top of CUTLASS tuned for different batch sizes to deliver faster GeMM computation. The DeepSpeed team introduces two compressed-training strategies to support fast and low-cost training while simultaneously delivering high accuracy. We highly recommend you also to read our blog to learn more about (at a high level) why we build DeepSpeed Compression and what benefits it provides to users. Therefore, we suggest an easy approach to early-stop KD by not setting --kd in the script provided (e.g., disabling KD in the remaining 40% of training). With pruning, you can lower the overall parameter count in the network (see more in this Coursera lecture). Neural network model compression and acceleration. Existing methods for compressing large models incur high training costs. To resolve those issues, we propose a method called ZeroQuant, which quantizes large-scale models with little or no fine-tuning cost on limited resources. 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. Please find the code, tutorial, and documents at the DeepSpeed GitHub, and website. NOTE: Head pruning is a feature designed for the attention layers (e.g., Multi Head Attention in Transformers). model_compression/bert/config/ds_config_W1A8_Qgroup64_fp16.json in DeepSpeedExamples is the FP16 example configurations, where "fp16": {"enabled": true} and "weight_quantization": {"shared_parameters": {"quantize_weight_in_forward": false}} are different from FP32 case. 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. deepspeed_config (`DeepSpeedConfig`) The path of ds_config: mpu: The mpu module for Row/Column parallelism """ compress_methods = get_compression_config (check_deepspeed_config (deepspeed_config)) if . However, no systematic study on best practices for extreme compression exists, such as using aggressive quantization methods and layer reduction. huggingface quantizationletterkenny live merch Archives, Collections, Dialog, Commentary, Gallery, Museum In this section, we introduce how to apply DS-Compression to perform cost-free INT8 quantization and lightweight INT4/INT8 mixed-precision quantization. Layer reduction can be applied in both the pre-training and fine-tuning stages. 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? There is no structure pattern in the zero values. With this config, we quantize the existing fined-tuned models downloaded from Hugging Face. Very importantly, we quantize these models without requiring any training data, expensive compression time or GPU resources, bringing huge training cost savings compared with QAT! 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. Out-of-box, MII offers support for thousands of widely used DL models, optimized using DeepSpeed-Inference, that can be deployed with a few lines of code, while achieving significant latency reduction compared to their vanilla open-sourced versions. Most contributions require you to It reduces the Microsoft Turing Image Super Resolution model (T-ISR) model size by. Head pruning is beneficial to hardware speedup. These optimizations target improving the inference system efficiency while preserving the model sizes, the amount of computation, and model accuracy: the total work remains the same, but the processing capability and speed are higher. In our XTC work (paper, tutorial), we also discuss when to apply layer reduction. This has two benefits. One can run our BERT example in DeepSpeedExamples by: NOTE: right now, we only support zero cost quantization. We find that under FP16 training, smaller number of quantization group (e.g., 1 or 2) could lead to unstable training. , Samyam Rajbhandari, Olatunji Ruwase, Jeff Rasley, Shaden Smith, Yuxiong He. (2) Then we need to re-initialize the model based on the DeepSpeed JSON configurations using the function init_compression imported from deepspeed.compression.compress. 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. Data augmentation introduces in TinyBERT will help significantly for smaller tasks (such as mrpc, rte, sst-b and cola). For example, popular compression methods such as quantize-aware training (QAT) and multi-stage distillation methods lead to long training time and large hardware resource requirement as the model size grows into multi-billion parameters or at even larger scale, making compressing these models costly and difficult. (2)quantize_weight_in_forward must be set to true for FP32 optimizer training and false for FP16. Head pruning can be enabled and configured using the DeepSpeed config JSON file (configuration details). The recommended method to try DeepSpeed on Azure is through AzureML recipes. If the model is very deep, you may consider using this method. We applied INT8 quantization of DeepSpeed Compression to optimize two large-scale open-source models in GPT-3 style: GPT-J (6B) and GPT-NeoX (20B) on the Azure AI platform. First, there is limited composability among multiple compression methods. For tasks that require less precision, its better to use a smaller, less complex model.. (2)--kd-beta-ce, this specifies the knowledge distillation coefficient. For example, an image with three channels passing through ResNet-18 produces 64 channels after the first layer. It offers Liked by Ryen White Join now to see all. Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He. 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. For Hugging Face BERT example, set config.num_hidden_layers = ds_config["compression_training"]["layer_reduction"]["keep_number_layer"]. Motivated by combining the best of both worlds, we are proud to announce DeepSpeed Compressiona composable library that combines novel compression technologies and highly efficient system optimizations to make DL model size smaller and inference speed faster, all with much lowered compression cost. DeepSpeed welcomes your contributions! What is DeepSpeed Compression: DeepSpeed Compression is a library purposely built to make it easy to compress models for researchers and practitioners while delivering faster speed, smaller model size, and significantly reduced compression cost. Limited composability. (3) During training, if KD is not used, nothing needs to be done. DeepSpeed releases a new pillar, DeepSpeed compression, to tackle latency & cost challenges on deploying large-scale deep learning models. (2020) DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters. The client code change is the same as weight quantization. July 20, 2022, Par 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. XTC produces models with little loss in accuracy yet up to 50x model size reduction, as shown in Figure 1. It also takes an end-to-end approach to improve the computation efficiency of compressed models via a highly optimized inference engine. By (2)rp1, users can expand more groups such as rp2, rp3, etc. 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. (2021) 1-bit LAMB: Communication Efficient Large-Scale Large-Batch Training with LAMBs Convergence Speed. DeepSpeed-Compression To further increase the inference efficiency, DeepSpeed offers easy-to-use and flexible-to-compose compression techniques for researchers and practitioners to compress their models while delivering faster speed, smaller model size, and significantly reduced compression cost. Deep learning based Image/Video . Each of these features is explained further below. See more in this blog. Various strategies have been proposed to overcome optimization difficulty and accuracy degradation when compressing large models. The list will expand as we continually integrate more state-of-the-art compression methods. It is a simple and hyper-parameter tuning friendly method. One can run our row pruning example in DeepSpeedExamples by: Head pruning is designed specifically for networks with multi-head attention, such as transformer-based models (see more in this blog). Some of the components are same as weight quantization, such as schedule_offset and quantization_type. 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 et DeepSpeed. Each of these features is explained further below. 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. In addition, users also can choose whether to reinitialize the input/output layers from the given model (teacher model) by other_module_name. By combining extreme quantization and lightweight layer reduction, we can further improve the binarized model, achieving 50x model size reduction while keeping 97% of the accuracy. I'm using a pretrained ResNet as a simple example to test how DeepSpeed works, in a simple case following this.. Beyond open-source models, DeepSpeed Compression has also demonstrated its effectiveness to optimize production workloads in Microsoft: DeepSpeed Compression is still at its early stage and under active development, but wed like to share the results and tools to DeepSpeed users as soon as possible. (3)related_modules, as mentioned in when to use row pruning, if we do row pruning, the follow-up matrix will be affected. Besides, they also provide a new profiling tool to identify training performance bottlenecks. In particular, increased inference time and memory consumption inhibit deployment of models on latency-sensitive and resource-constrained applications on both server and client devices. It allows for easy composition of multitude of features within a single training, inference or compression pipeline. An artificial intelligence tool that can help detect melanoma Researchers from MIT using deep convolutional neural networks (DCNNs) and applying. Together, the compression composer and inference engine achieve the best of both worlds of compression and system optimization, delivering a compound effect of inference cost reduction. Besides leveraging these, we also extend the inference capability to support models in compressed formats. A longer training iteration with learning rate decay is highly preferred for closing the accuracy gap of extreme quantization; Single-stage knowledge distillation with more training budgets is sufficient to match or even exceed accuracy from multi-stage ones; Training without data augmentation hurts performance on downstream tasks for various compression tasks, especially on smaller tasks; Lightweight layer reduction matches or even exceeds expensive pre-training distillation for task-specific compression.
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