Sharing details for those who need. trainer. Branch not found: {{ refName . It provides a smart GPU memory management system, that minimizes memory fragmentation, which again allows you to fit install libaio-dev system-wide). While there is an ongoing effort to distill some of those huge models to be of a more manageable size -- that effort isn't producing models small enough soon enough. cell with: If the training script is in a normal file and not in the notebook cells, you can launch deepspeed normally via worker-2 and GPUs 0 and 1 on worker-3: Similarly, you can use only GPUs 0 and 1 on worker-2: When training across multiple nodes we have found it useful to support With 120x16, there is an OOM error. if these mismatch the training may fail in very exception and you can see that DeepSpeed modules are involved, first re-test your setup without DeepSpeed in it. at a cost of speed. Again, remember to ensure to adjust TORCH_CUDA_ARCH_LIST to the target architectures. circumstances you may find the following information to be needed. on performance unless you are doing activation checkpointing. Important: all processes must call this method and not just the process with rank 0. A short time later DeepSpeed has been released and it gave to the world the open source implementation of most of the ideas in that paper (a few ideas are still in works) and in parallel a team from Facebook released FairScale which also implemented some of the core ideas from the ZeRO paper. Just remember that may have different This allows you to create the configuration on the fly and doesnt require you to write it to the file system before passing it to TrainingArguments. Therefore, in the rest of this guide you will find a special configuration value: auto, which when set will be That is once Deepspeed was removed from the setup, the As discussed in this document normally the DeepSpeed configuration is passed as a path to a json file, but if youre One other problem that a lot of people complain about on pytorch forums is GPU memory fragmentation. This blog post will describe how you can . memory offloading the optimizer states and parameters to CPU memory with "device": "cpu" may solve this limitation. models). at the beginning of this section. If you arent sure, please do not worry, smart partitioning and tiling algorithms each GPU needs to send and receive very small amounts of data during Therefore its important that this object remains alive while the program is still running. By default the PyTorch default is used. default options when doing. That is once Deepspeed was removed from the setup, the MII supported models achieve significantly lower latency and cost . DeepSpeed-MII is a new open-source python library from DeepSpeed, aimed towards making low-latency, low-cost inference of powerful models not only feasible but also easily accessible. full details on how to configure various nodes and GPUs can be found here. certain features, like 1-bit Adam, which arent available in the pypi distribution. doc. This feature offloads some of the processing and memory needs to the host's CPU, thus allowing more to be fit onto the GPU. If you dont want to offload to CPU memory, use none instead of cpu for the device entry. difficult to detect ways. shell from a cell. documented here. example .json files with: Some more examples are to be found in the main repo as well. If you have enough GPU memory make sure to disable the CPU/NVMe offload as itll make everything faster. DeepSpeeds main optimizers are Adam, AdamW, OneBitAdam, and Lamb. making less memory available to other processes. your cards are the same you can get the arch via: So if you get 8, 6, then use TORCH_CUDA_ARCH_LIST="8.6". have the configuration file or a Trainer to do the extraction. example .json files with: Some more examples are to be found in the main repo as well. fp32 master weights in its custom checkpoint optimizer files, which are global_step*/*optim_states.pt (this is glob Currently it provides full support for: Optimizer state partitioning (ZeRO stage 1) Gradient partitioning (ZeRO stage 2) Parameter partitioning (ZeRO stage 3) Custom mixed precision training handling A range of fast CUDA-extension-based optimizers ZeRO-Offload to CPU and NVMe For full information please see memory estimators. DeepSpeed features can be enabled, disabled, or configured using a config JSON To deploy DeepSpeed with one GPU adjust the Trainer command line arguments as follows: This is almost the same as with multiple-GPUs, but here we tell DeepSpeed explicitly to use just one GPU via you plan to resume the training. You can, of course, modify your own trainer to integrate DeepSpeed and FairScale, based on each project's instructions or you can "cheat" and see how we did it in the transformers Trainer. each process needs to save its master weights and scheduler+optimizer states. When not using DeepSpeeds learning rate scheduler: Saving and loading the training state is handled via the save_checkpoint and The following configuration example enables NVMe to offload both optimizer states and the params: You can choose to offload both optimizer states and params to NVMe, or just one of them or none. In DeepSpeed Compression, we provide extreme compression techniques to reduce model size by 32x with almost no accuracy loss or to achieve 50x model size reduction while retaining 97% of the accuracy. While the paper doesn't go into details, the source code is available, so it's possible to see how DeepSpeed accomplishes that. Choose a base branch. Create or load the DeepSpeed configuration to be used as a master configuration. therefore, to prevent conflicting definitions, which could lead to hard to detect errors, we chose to configure those explained here. estimate_zero3_model_states_mem_needs_all_live(model, num_gpus_per_node=1, num_nodes=1)', 'from transformers import AutoModel; \ Why would you want to use DeepSpeed with just one GPU? When you will face a real project that will be running for hours and perhaps days, definitely spend more time to make sure you use the most optimal hyper-parameters to get your job done faster and at a minimal cost. the slower the communication gets, and the more GPU RAM will be available to other tasks. Instead, you have to use the following syntax: In this example, we tell DeepSpeed to use GPU 1 (second gpu). Custom Optim + Custom Scheduler: The case when both optimizer and scheduler keys are absent in the DeepSpeed config file. In the above example we can see that the code remains unchanged if the optimizer and scheduler keys are absent in the DeepSpeed config file. So use this method to MII offers access to highly optimized implementations of thousands of widely used DL models. For some practical usage examples, please, see this post. DeepSpeed context of the same application. This is also the default value for --lr_scheduler_type, Under ZeRO-3, things are much more complicated, since the model weights are partitioned out over multiple GPUs, As long as you dont enable offload_optimizer you can mix and match DeepSpeed and HuggingFace schedulers and These are automatically handled by prepare method You saw its dramatic impact in the success at running t5-3b on a 24GB GPU. documentation is here. with, then you dont need this argument. Most models trained on TPU and often the ones released by Google are in this category (e.g. Zero Redundancy Optimizer (ZeRO) is the workhorse of DeepSpeed. config_file_or_dict (Union[str, Dict]) , Returns the set value or default if no value is set. For DeepSpeed you need to write a simple configuration file and change your command line's launcher, with Fairscale you only need to add the --sharded_ddp command line argument, so you may want to try it first as it's the most low-hanging fruit. memory from NVMe during the optimizer step. Optimizer Step is taking a long time: Increase sub_group_size to improve bandwidth utilization as a result of Here is an example of the auto-configured optimizer entry for AdamW: Note that the command line arguments will set the values in the configuration file. A sample config file arguments: --learning_rate, --adam_beta1, --adam_beta2, --adam_epsilon and --weight_decay. Thanks to For a full set of features see API DeepSpeed stores Here is an example of the auto-configured scheduler entry for WarmupLR: Since auto is used the Trainer arguments will set the correct values in the configuration Therefore to reconstruct the fp32 the full Deepspeed config file in the report. backward passes a a single layer granularity and want to keep the parameter in the forward recompute till the backward. # **it has to be run before loading the model AutoModelForSeq2SeqLM.from_pretrained(model_name)**, # otherwise the model will first be loaded normally and only partitioned at forward time which is, # less efficient and when there is little CPU RAM may fail, # initialise Deepspeed ZeRO and store only the engine object. explained here. Collaborate on models, datasets and Spaces, Faster examples with accelerated inference, "import torch; print(torch.cuda.get_device_capability())", "import torch; print(torch.cuda.get_arch_list())", "import torch; \ If you have saved at least one checkpoint, and you want to use the latest one, you can do the following: If youre using the --load_best_model_at_end class:~transformers.TrainingArguments argument (to track the best The document includes Besides the anticipated upcoming support for model params sharding in DeepSpeed, it already released new features that we haven't explored yet. Here is an example of how one could do DeepSpeed ZeRO Inference without using Trainer when one cant fit a model onto a single GPU. Using this script you can extract the weights at any point. is shown below. the visible scope of available GPUs. Also under ZeRO-3, if you write your own code and run into a model parameter weight that looks like: stress on tensor([1. The script is standalone and you no longer need to from_pretrained and _get_resized_embeddings). With just a single GPU, ZeRO-Offload of DeepSpeed can train models with over 10B parameters, 10x bigger than the state of the art. Therefore to reconstruct the fp32 to be configured via the command line. on performance unless you are doing activation checkpointing. ZeRO-3 is likely to be slower than ZeRO-2 if everything else is configured the same because the former has to gather section. We do however use it internally in several places, one such example is when loading pretrained model weights in This is so that there is one The solution includes using additional GPUs or/and offloading GPU memory to CPU memory. That subclass has logic to If no hostfile is pass a nested dict. backward passes a a single layer granularity and want to keep the parameter in the forward recompute till the backward. The example has copious notes and is self-documenting. For the complete guide to the DeepSpeed configuration options that can be used in its configuration file please refer This feature can improve the throughput at the cost of Additionally, some configuration values are derived automatically based on the models configuration, so instead of # For Zero Stages 1 and 2, models are saved as usual in the output directory. when reducing these buffers youre trading communication speed to avail more GPU RAM. default value in the following cases: Running into OOM during optimizer step: Reduce sub_group_size to reduce memory utilization of temporary buffers. at a cost of speed. The DeepSpeed implements everything described in the ZeRO paper. This prevents running out of CPU memory for extremely large models. be ignored. For full details on this method and other related features please refer to Constructing Massive Models. If you dont prebuild the extensions and rely on them to be built at run time and you tried all of the above solutions leave more GPU resources for models needs - e.g. the values of --lr_scheduler_type, --learning_rate and --warmup_steps or --warmup_ratio to configure a By default DeepSpeed will "overlap_comm": true trades off increased GPU RAM usage to lower all-reduce latency. bigger models and data batches. The --include and For details and pass a nested dict. Wednesday, der 2. if the schedule is supposed to execute at any other interval (e.g., training epochs), then the user should NOT pass the scheduler to DeepSpeed during initialization and must manage it explicitly. We will look at the changes needed in the code when using these. Trainer uses the HfTrainerDeepSpeedConfig subclass instead. definitive source of the values and to avoid hard to find errors when for example, the learning rate is set to Loss Scaling: in FP16/mixed precision training, the DeepSpeed This object contains a DeepSpeed configuration dictionary and can be quickly queried for things like zero stage. One often gets an OOM error that may look like this: The program wants to allocate ~1.5GB and the GPU still has some 6-7GBs of unused memory, but it reports to have only ~100MB of contiguous free memory and it fails with the OOM error. from_pretrained and _get_resized_embeddings). If you keep these in the config file they will just but also if you want the initialization to happen much faster, initialize the model using deepspeed.zero.Init() scheduler based on the parameters passed to deepspeed.initialize and the So # To use a larger model like "bigscience/T0" which needs about 50GB, unless you have an 80GB GPU -, # you will need 2-4 gpus. Some of the filed issues proved to be Deepspeed-unrelated. "stage3_gather_fp16_weights_on_model_save": 'import torch; print(f"torch: {torch.__version__}")', 'import transformers; print(f"transformers: {transformers.__version__}")', 'import deepspeed; print(f"deepspeed: {deepspeed.__version__}")', # deepspeed config object or path to the file, # must run before instantiating the model, Performance and Scalability: How To Fit a Bigger Model and Train It Faster, ZeRO-Infinity: Breaking the GPU Please do not And only if the problem persists then do mentioned Deepspeed and supply all the required details. its important that this object remains alive while the program is still running. stas00 wants to merge 10 commits into huggingface: main. advanced install. Note: If the fp16 weights of the model cant fit onto the memory of a single GPU this feature must be used. need be. The script will auto-discover the deepspeed sub-folder using the contents of the file latest, which in the current Assuming all For more details see: Current integration doesnt support Pipeline Parallelism of DeepSpeed. While Fairscale gives us a boost only with multiple GPUs, DeepSpeed has a gift even for those of us with a single GPU. Only the auto fields specified in above examples are handled by prepare method and the rest have to be explicitly specified by the user. Currently it provides full support for: ZeRO-Offload has its own dedicated paper: ZeRO-Offload: Democratizing Billion-Scale Model Training. Once a Transformer-based model is trained (for example, through DeepSpeed or HuggingFace), the model checkpoint can be loaded with DeepSpeed in inference mode where the user can specify the parallelism degree. with your own trainer, and you will have to adapt the latter according to the DeepSpeed integration instructions. trainer. Therefore please remember to tune the shared hyperparameters on the command line. of configuration for you. Similarly to AdamW, you can configure other officially supported optimizers.