deepspeed compression

Posted on November 7, 2022 by

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. 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! In this process, we have identified several best practices for extreme compression: Based on these findings, we greatly simplify the procedure of extreme compression and propose a new extreme compression technique, XTC, that compresses a model to its limit with lightweight layer reduction and robust binarization. To tease apart their effects, we perform a systematic study on the impacts of various techniques currently used for extreme compression. Minjia Zhang, Yuxiong He. ). With DeepSpeed Compression, we can quantize the model in a few minutes with improved accuracy and reduced latency compared to QAT. It delivers significant latency and cost reduction, widely applicable on both various NLP and CV tasks. By Lack of tailored system optimizations for compressed models. Check out DeepSpeed-MII, a new open-source python Liked by Jinwon Lee After several hours of AWS and PyTorch kung-fu, I've managed to train my own Transformer on all 32 Neuron cores of a trn1 . To accommodate users who do not have a fine-tuned model or task-specific model for compression, with the arg --model_name_or_path yoshitomo-matsubara/bert-base-uncased-${TASK_NAME} our python script run_glue_no_trainer.py automatically downloads the models from Hugging Face. (2)modules: the module attention.output.dense is made specific for Hugging Face BERT model. Channel pruning is a feature designed for two back-to-back CONV2d layers (e.g., residual connection in ResNet). . DeepSpeed Compression proposes a seamless pipeline to address the compression composability challenges, as shown in Figure 4. Row pruning can be beneficial to hardware speedup, much better than sparse pruning (but may result in larger accuracy loss compared to sparse pruning). 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. DeepSpeed Team With DeepSpeed you can: DeepSpeed offers a confluence of system innovations, that has made large scale DL training effective, and efficient, greatly improved ease of use, and redefined the DL training landscape in terms of scale that is possible. 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. (2022) Extreme Compression for Pre-trained Transformers Made Simple and Efficient. This is especially useful when the data is not available due to privacy related reasons, for example. When using dynamic, the activation quantization groups will be automatically set to be token-wise (for Transformer-based models) and image-wise (for CNN-based models). I'm using a pretrained ResNet as a simple example to test how DeepSpeed works, in a simple case following this.. For tasks that require less precision, its better to use a smaller, less complex model.. contributing guide for more details on formatting, testing, The list will expand as we continually integrate more state-of-the-art compression methods. huggingface quantizationletterkenny live merch Archives, Collections, Dialog, Commentary, Gallery, Museum 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. (2021) ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning. The results are given below (we also include the fp16 training results). Tutorial for XTC: simple yet effective compression pipeline for extreme compression, 3.1 One-bit or Two-bit BERT-base (12-layer) with 8-bit activation quantization, 3.2 Compressing the 12-layer BERT-base to 1-bit or 2-bit 6/5-layer BERT. Conglong Li, Minjia Zhang, Yuxiong He. 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. Extreme Speed and Scale for DL Training and Inference, Model Implementations for Inference (MII), DeepSpeed-MII: instant speedup on 24,000+ open-source DL models with up to 40x cheaper inference, ZeRO-Inference: Democratizing massive model inference, Azure and DeepSpeed empower easy-to-use and high-performance model training, DeepSpeed Compression: A composable library for extreme compression, Supporting efficient large model training on AMD Instinct GPUs with DeepSpeed, In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC 20), In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 20, Tutorial), ZeRO & Fastest BERT: Increasing the scale and speed of deep learning training in DeepSpeed, DeepSpeed: All the tricks to scale to gigantic models (Mark Saroufim), Turing-NLG, DeepSpeed and the ZeRO optimizer (Yannic Kilcher), Ultimate Guide To Scaling ML Models (The AI Epiphany), Train/Inference dense or sparse models with billions or trillions of parameters, Achieve excellent system throughput and efficiently scale to thousands of GPUs, Train/Inference on resource constrained GPU systems, Achieve unprecedented low latency and high thoughput for inference, Achieve extreme compression for an unparalleled inference latency and model size reduction with low costs. In our paper, we used FP32 ("fp16": {"enabled": false}) to perform training, while directly applying 8-bit quantization ("bits": 8) to the activations and 1-bit quantization ("start_bits": 1, "target_bits": 1) to the attention (query, key, val) and feedforward weight matrices ("modules": ["attention.self", "intermediate", "output.dense"]) at the beginning of the training ("schedule_offset": 0). With pruning, you can lower the overall parameter count in the network (see more in this Coursera lecture). You can often leave it set to the default value 1, but sometimes tuning this hyperparameter leads to better distillation results. (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. (2020) DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters. Learn more: DeepSpeed-Training, DeepSpeed brings together innovations in parallelism technology such as tensor, pipeline, expert and ZeRO-parallelism, and combines them with high performance custom inference kernels, communication optimizations and heterogeneous memory technologies to enable inference at an unprecedented scale, while achieving unparalleled latency, thoughput and cost reduction. We have recently focused on deep learning systems, optimizing deep learnings speed to train, speed to convergence, and speed to develop. 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. Santa Clara, California, United States. It allows for easy composition of multitude of features within a single training, inference or compression pipeline. However, no systematic study on best practices for extreme compression exists, such as using aggressive quantization methods and layer reduction. DeepSpeed Compression by Microsoft. Weight quantization can be enabled and configured using the DeepSpeed config JSON file (configuration details). Existing methods have limited composability from two aspects. Monitor Monitor your model's training metrics live and log for future analysis 1-Cycle Schedule This tutorial shows how to implement 1Cycle schedules for learning rate and 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. But despite their remarkable capabilities, the models large size creates latency and cost constraints that hinder the deployment of applications on top of them. 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. 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! It reduces the Microsoft Turing Image Super Resolution model (T-ISR) model size by. 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. 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. We demonstrated the scalability of ZeroQuant on a GPT-3-style model with 1.3B parameters (GPT-3-1.3B) and one of the largest open-source language models, GPT-NeoX (20B). (2020) Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping. There are two changes to the client code (model_compression/bert/run_glue_no_trainer.py in DeepSpeedExamples): (1) After initialization of the model, apply init_compression function to the model with DeepSpeed JSON configurations. (3)wq1/wq2, users can expand more groups such as wq3, wq4, etc. 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. 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. 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deepspeed compression