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Please feel free to choose to ignore this Paper: ZeRO-Offload: Democratizing Billion-Scale Model Training. The fast scalable training was designed with modern NVMe transfer speeds in mind (as of this While all the efforts were made for things to just work without needing any special changes to your models, in certain things like the Trainer object is not available (e.g. Command line rules. leave more GPU resources for models needs - e.g. Otherwise you just need to pass the usual TrainingArguments arguments. Paper: ZeRO: Memory Optimizations Toward Training Trillion Parameter Models. Pinned memory is set aside to the specific process that requested it DeepSpeed attacks this problem by managing GPU memory by itself and ensuring that long term memory allocations don't mix with short-term ones and thus there is much less fragmentation. Also when loading fp16-pretrained models, you will want to tell from_pretrained to use therefore best to be performed offline after the training is complete. For details, please, see Model Instantiation dtype. Managed to train t5-11b on 1x 40GB gpu w/ Deepspeed (A100-SXM4-40GB) Thank you, @PeterAJansen for letting me use your hardware! for the inference as only ZeRO-3 performs sharding of parameters, whereas ZeRO-1 shards gradients and optimizer states. ZeRO-Offload: Democratizing Billion-Scale Model Training, ZeRO-Infinity: Breaking the GPU You can leave sub_group_size to its default value of 1e9 when not using NVMe offload. 1371. It, however, can import other optimizers from torch. The values that get set are: warmup_max_lr with the value of --learning_rate. context manager (which is also a function decorator), like so: As you can see this gives you a randomly initialized model. arguments: --num_nodes and --num_gpus. ZeRO-3 is quite different from ZeRO-2 because of its param sharding feature. therefore this document focuses on stages 2 and 3. either Issue tracker will do, we will figure it out once you posted it and redirect you to another Issue tracker if Of course, you dont have to use class:~transformers.Trainer and you can adjust the examples above to your own examples. following: Therefore, if your original command line looked as follows: Unlike, torch.distributed.launch where you have to specify how many GPUs to use with --nproc_per_node, with the The new --sharded_ddp and --deepspeed command line Trainer arguments provide FairScale and DeepSpeed integration respectively. Let's look at the results of these six test runs: It's easy to see that both FairScale and DeepSpeed provide great improvements over the baseline, in the total train and evaluation time, but also in the batch size. process. DeepSpeed ZeRO Inference supports ZeRO stage 3 with ZeRO-Infinity. "overlap_comm": true trades off increased GPU RAM usage to lower all-reduce latency. HfDeepSpeedConfig object before instantiating the model and keep that object alive. mpirun as a launcher backend) you simply need to install the mpi4py python package. This allows you to create the configuration on the fly and doesnt require you to write it to DeepSpeed ZeRO-2 is primarily used only for training, as its features are of no use to inference. Replace your initial 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. The full documentation is here. if these mismatch the training may fail in very While the paper doesn't go into details, the source code is available, so it's possible to see how DeepSpeed accomplishes that. 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 deepspeed launcher-specific arguments. including optimizer states cpu offload, uses AdamW optimizer and WarmupLR scheduler and will enable mixed documentation is here. default value in the following cases: Running into OOM during optimizer step: Reduce sub_group_size to reduce memory utilization of temporary buffers. specified in myhostfile: Alternatively, DeepSpeed allows you to restrict distributed training of your model to a This command runs the the standard run_clm.py file from Huggingface's examples with deepspeed, just with 2 lines added to enable gradient checkpointing to use less memory. DeepSpeed will still use the torch distributed NCCL backend and not the MPI models). The problem with running notebook cells as a script is that there is no normal deepspeed launcher to rely on, so to use the launcher for that purpose and this cannot be accomplished by emulating the distributed environment presented # all official t5 models are bf16-pretrained, # - set offload_param.device to "none" or completely remove the `offload_param` section if you don't, # - if using `offload_param` you can manually finetune stage3_param_persistence_threshold to control, # - which params should remain on gpus - the larger the value the smaller the offload size, # For indepth info on Deepspeed config see, # https://huggingface.co/docs/transformers/main/main_classes/deepspeed, # keeping the same format as json for consistency, except it uses lower case for true/false, # next line instructs transformers to partition the model directly over multiple gpus using. c. Custom Optim + DS Scheduler: The case when only scheduler key is present in the DeepSpeed config file. Zero Redundancy Optimizer (ZeRO) is the workhorse of DeepSpeed. DeepSpeed ZeRO-3 can be used for inference as well, since it allows huge models to be loaded on multiple GPUs, which values. DeepSpeed works with the PyTorch Trainer but not TF TFTrainer. footprint (5e8 x 2Bytes x 2 x 4.5). Edit TORCH_CUDA_ARCH_LIST to insert the code for the architectures of the GPU cards you intend to use. and the Trainer will automatically configure it based on the values of args.fp16_backend and Parameters are If either Issue tracker will do, we will figure it out once you posted it and redirect you to another Issue tracker if ZeRO-Infinity further extends ZeRO-3 to support NVMe memory and multiple other speed and scalability improvements. And only if the problem persists then do mentioned Deepspeed and supply all the required details. trainer. the parameter that got misspelled. engine automatically handles scaling the loss to avoid precision loss in the d. DS Optim + Custom Scheduler: The case when only optimizer key is present in the DeepSpeed config file. stage3_max_live_parameters is the upper limit on how many full parameters you want to keep on the GPU at any given Here is an example of running run_translation.py under DeepSpeed deploying all available GPUs: Note that in the DeepSpeed documentation you are likely to see --deepspeed --deepspeed_config ds_config.json - i.e. large models it wont be possible to load it on one GPU and then spread it out to multiple GPUs, due to memory unique to a given model training. torch.distributed.init_process_group(..) call with: In the case that we are only running on a single node (with one or more GPUs) pytorch_model.bin will now contain the full fp32 model weights consolidated from multiple GPUs. Here is an example of running run_translation.py under DeepSpeed deploying all available GPUs: Note that in the DeepSpeed documentation you are likely to see --deepspeed --deepspeed_config ds_config.json - i.e. Default param values for sacrebleu. The document includes to be configured via the command line. sync the configuration with values of TrainingArguments by replacing special placeholder Instead, you have to use the following syntax: In this example, we tell DeepSpeed to use GPU 1 (second gpu). The HfDeepSpeedConfig is used to integrate Deepspeed into the Transformers core the step value is stored as part of the client_sd. DeepSpeed can be activated in HuggingFace examples using the deepspeed command-line argument, `--deepspeed=deepspeed_config.json`. You have been warned. For example, if you If this is your case then you will want to use on performance unless you are doing activation checkpointing. I think they are working on ZeRO stage 3 as well. reduce_scatter configuration parameters are not used in ZeRO-3. Transformers integrates DeepSpeed via 2 options: Integration of the core DeepSpeed features via Trainer. # python -m torch.distributed.run --nproc_per_node=1 t0.py, # python -m torch.distributed.run --nproc_per_node=2 t0.py, # To avoid warnings about parallelism in tokenizers, # batch size has to be divisible by world_size, but can be bigger than world_size, # - enable bf16 if you use Ampere or higher GPU - this will run in mixed precision and will be. If we were to save this state_dict it wont be possible to load it back. This section has to be configured exclusively via DeepSpeed configuration - the Trainer provides torch.distributed calls before calling deepspeed.initialize(..) we provide If you dont use Trainer and want to use your own Trainer where you integrated DeepSpeed For example, for GPU 0: then you know that this cards arch is 8.6. fact you can leave these in the config file if you want to share the same one with the training. If youre migrating from ZeRO-2 configuration note that allgather_partitions, allgather_bucket_size and DeepSpeed implements everything described in the ZeRO paper. DeepSpeed has direct integrations with HuggingFace Transformersand PyTorch Lightning. stage3_param_persistence_threshold. therefore best to be performed offline after the training is complete. the same MPI support with an additional DeepSpeed API call. no equivalent command line arguments. You can also configure this mode explicitly: and the Trainer will automatically set train_micro_batch_size_per_gpu to the value of with, we combined the two into a single argument. the models hub or pass it to someone else you most likely will want to get the fp32 python zero_to_fp32.py -h will give you usage details. In fact, you can continue using -m torch.distributed.launch with DeepSpeed as long as you dont need to use default value in the following cases: ZeRO-Infinity allows for training incredibly large models by extending GPU and CPU memory with NVMe memory. With gradient accumulation 2 and batch size 8, one gradient step takes about 9 seconds. So if you need to access all parameters from all layers at once there is a specific method to do it. Pre-training Bing BERT without DeepSpeed We work from adaptations of huggingface/transformersand NVIDIA/DeepLearningExamples. DeepSpeed ZeRO-2 is primarily used only for training, as its features are of no use to inference. To configure gradient gradient clipping set: and the Trainer will automatically set it to the value of args.max_grad_norm. The program failed with OOM at BS=30. If its clear to you that the issue is in the DeepSpeed core and not the integration part, please file the Issue or find more details on the DeepSpeeds GitHub page and That's why it's not fast, especially when a model is large. size, rank) to the torch distributed backend. DeepSpeed will use directly with Deepspeed. If youre still struggling with the build, first make sure to read CUDA Extension Installation Notes. both configured to offload to cpu. The following is an example of configuration for ZeRO stage 2: Additionally, deepspeed==0.4.4 added a new option round_robin_gradients which you can enable with: This is a stage 2 optimization for CPU offloading that parallelizes gradient copying to CPU memory among ranks by fine-grained gradient partitioning. For full details on this method and other related features please refer to Constructing Massive Models. As long as you continue training and resuming using DeepSpeed you dont need to worry about anything. If after trying everything suggested you still encounter build issues, please, proceed with the GitHub Issue of values look like, but we highly recommend using the one with multiple auto settings in it. subset of the available nodes and GPUs. MII supported models achieve significantly lower latency and cost . Most likely you wont need it, but if you do please refer to Gathering Parameters. # Deepspeed ZeRO can process unrelated inputs on each GPU. It is here mainly for you to see what the typical You may experiment with the buffer sizes, you will therefore "stage3_gather_fp16_weights_on_model_save": true is required to get the Trainer to save the fp16 stage3_gather_fp16_weights_on_model_save enables model fp16 weights consolidation when model gets saved. Otherwise will use --warmup_ratio example .json files with: Some more examples are to be found in the main repo as well. example will contain global_step1. You have already integrated DeepSpeed into your model. both configured to offload to cpu. stage3_param_persistence_threshold. Here the solution is to either use fp32 or bf16 if your hardware supports it (TPU, Ampere GPUs or newer). And NVMe-support is described in the paper ZeRO-Infinity: Breaking the GPU While we are going to discuss the configuration in details next, the key to getting a huge improvement on a single GPU In such cases, Then its a tradeoff of cost vs speed. ZeRO Inference uses the same config as ZeRO-3 Training. Again, remember to ensure to adjust TORCH_CUDA_ARCH_LIST to the target architectures. If for some reason you want more refinement, you can also extract the fp32 state_dict of the weights and apply flexible. So experiment and compare which works the best. Zero Redundancy Optimizer (ZeRO) is the workhorse of DeepSpeed. A weakref of this object is stored in the modules globals to be able to access the config from areas where larger multi-dimensional shape, this means that the parameter is partitioned and what you see is a ZeRO-3 placeholder. support 3 different levels (stages) of optimization. If you want to create the config file on the fly in the notebook in the current directory, you could have a dedicated Here is how to file an issue so that we could quickly get to the bottom of the issue and help you to unblock your work. appropriately under the hood. ask the very specific question of whether the value is set to True (and its not set to False or wont be possible on a single GPU. The DeepSpeed launcher will look in the local path you are and its typically accessed much faster than normal CPU memory. To get an idea of what DeepSpeed configuration file looks like, here is one that activates ZeRO stage 2 features, Stage 2 : Shards optimizer states + gradients across data parallel workers/GPUs, c. Stage 3: Shards optimizer states + gradients + model parameters across data parallel workers/GPUs, d. Optimizer Offload: Offloads the gradients + optimizer states to CPU/Disk building on top of ZERO Stage 2, e. Param Offload: Offloads the model parameters to CPU/Disk building on top of ZERO Stage 3. This is super helpful when you have activation checkpointing enabled, where we do a forward recompute and While you don't really need to understand how any of these projects work and you can just deploy them via the transformers Trainer, should you want to figure out the whys and hows please refer to the following resources. In the example above, In addition to the paper, I highly recommend to read the following detailed blog posts with diagrams: We were quite astonished at the amazing level of support we received from the FairScale and DeepSpeed developer teams while working on integrating those projects into transformers. To configure gradient gradient clipping set: and the Trainer will automatically set it to the value of args.max_grad_norm. It Below is the snippet from examples/by_feature/deepspeed_with_config_support.py showing this: b. isnt set). Typically if you dont need a multi-node setup youre not required to use Besides the anticipated upcoming support for model params sharding in DeepSpeed, it already released new features that we haven't explored yet. turn off offload_params since ZeRO-2 doesnt have that option. It provides a smart GPU memory management system, that minimizes memory fragmentation, which again allows you to fit which ZeRO stages you want to enable and how to configure them. Followed by more flexible and feature rich deepspeed config file integration. For a practical usage example of this type of deployment, please, see this post. If you have NVMe, experiment with offloading to NVMe if youre running under SSH, and slot counts, which specify the number of GPUs available on the system. use the stage3_max_reuse_distance to decide whether to throw away the parameter or to keep it. Trainer arguments and DeepSpeed configurations agree. to the following documentation. 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. DeepSpeed ZeRO-3 can be used for inference as well, since it allows huge models to be loaded on multiple GPUs, which If you want to use another optimizer which is not listed above, you will have to add to the top level configuration. the same on larger capacity GPU as well, if youre starting to hit OOM. via the Trainer command line arguments. Pinned memory is enabled with pin_memory set to true. It is possible to use a non-DeepSpeed optimizer when offload_optimizer is enabled, as long as it has both CPU and config_file_or_dict (Union[str, Dict]) , Returns the set value or default if no value is set. If you dont prebuild the extensions and rely on them to be built at run time and you tried all of the above solutions You will find the nuances in the rest of this guide. As ZeRO stands for Zero Redundancy Optimizer, it's easy to see that it lives up to its name. ZeRO-Infinity, sub_group_size therefore controls the granularity in which model states are moved in and out of CPU ZeRO-3 is quite different from ZeRO-2 because of its param sharding feature. So use this method to NVMe is discussed further down. Trainer uses the HfTrainerDeepSpeedConfig subclass instead. This allows you to create the configuration on the fly and doesnt require you to write it to Create or load the DeepSpeed configuration to be used as a master configuration. backend. memory it can be done in the same training script. Unless its impossible please always use a standard dataset that we can use and not something custom. going to use. Such models may overflow or underflow leading to NaN loss. models and multiple GPUs this is an expensive operation both in terms of memory and speed. deepspeed launcher you dont have to use the corresponding --num_gpus if you want all of your GPUs used. of them like so TORCH_CUDA_ARCH_LIST="6.1;8.6". | DS Optimizer | No | Yes |. HuggingFace Transformers users can now easily accelerate their models with DeepSpeed through a simple --deepspeedflag + config file See more details. 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 Please note that if youre not using the Trainer integration, youre completely on your own. 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; \ Now update your transformers to v4.2.0 or higher, then install DeepSpeed: and let's try again, this time adding DeepSpeed to the command line: et voila! Then answer the following questions to generate a basic DeepSpeed config. Why would you want to use DeepSpeed with just one GPU? If you want to use a pretrained model, model_class.from_pretrained will activate this feature as long as I could probably push it even further. HfDeepSpeedConfig object before instantiating the model. print(torch.cuda.get_device_properties(torch.device('cuda')))". when reducing these buffers youre trading communication speed to avail more GPU RAM. Here is an example of a possible sequence: If youre using the official example scripts and your command line arguments include --deepspeed ds_config.json Important: all processes must call this method and not just the process with rank 0. With large Note: currently DeepSpeed doesnt validate parameter names, so if you misspell any, itll use the default setting for with your own trainer, and you will have to adapt the latter according to the DeepSpeed integration instructions. version of the weights. If you want to use more than 1 GPU, you must use a multi-process environment for DeepSpeed to work. The performance will likely improve significantly with just offload_params turned off, even if you dont change Command line rules. Deepspeed supports the full fp32 and the fp16 mixed precision. | HF Optimizer | Yes | Yes | the command line arguments important for this demonstration. Thanks to While all the efforts were made for things to just work without needing any special changes to your models, in certain Everything else you have to do by yourself. I trust we are going to see new gifts from the FairScale team as well. If you encounter any issues with the integration part of either of these projects please open an Issue in transformers. In this situation, user has to use accelerate.utils.DummyScheduler to replace the PyTorch/Custom scheduler in their code. A hostfile is a list of hostnames (or SSH aliases), which are machines accessible via passwordless The paper is very interesting, but it's very terse. It uses the same ZeRO protocol as training, but the learning rate is set to different values in different places. I thought FaireScale_zero_dp_2 or level 3 could help in this, but not. from deepspeed.runtime.zero.stage3 import estimate_zero3_model_states_mem_needs_all_live; \ notebook as Note: If the fp16 weights of the model cant fit onto the memory of a single GPU this feature must be used. DeepSpeed ZeRO-2 is primarily used only for training, as its features are of no use to inference. So if a bigger batch size is The rest of config values are up to you. The For example, to use run_translation.py you would launch it with: or with %%bash magic, where you can write a multi-line code for the shell program to run: In such case you dont need any of the code presented at the beginning of this section. If hitting OOM reduce stage3_max_live_parameters and stage3_max_reuse_distance. That is, you have (Remember this is just the memory for params, optimizer states and gradients - you will need a bit more memory for cuda kernels, activations and temps.). Trainer uses the HfTrainerDeepSpeedConfig subclass instead. Here is the full documentation. sub_group_size controls the granularity in which parameters are updated during optimizer steps. Finally, please, remember that, HuggingFace Trainer only integrates DeepSpeed, therefore if you recommend ZeRO-3 config as starting one. In fact, you can continue using -m torch.distributed.launch with DeepSpeed as long as you dont need to use DeepSpeed/ZeRO-3 can handle models with Trillions of parameters which may not fit onto the existing RAM. normally wont fit. going to be used again in near future (less than stage3_max_reuse_distance) then we keep it to reduce communication The fast scalable training was designed with modern NVMe transfer speeds in mind (as of this dump the TrainingArguments as it has dozens of entries that are irrelevant. The smaller the buffer size is, By default, DeepSpeed deploys all GPUs it can see on the given node. and get access to the augmented documentation experience. But here are a few quick insights that may help understand how ZeRO manages these amazing feats. You may want to change its DeepSpeed implements everything described in the ZeRO paper. Again, remember to ensure to adjust TORCH_CUDA_ARCH_LIST to the target architectures. But if you dont need the distributed environment setup until after deepspeed.initialize() you dont have to use this function, as DeepSpeed will automatically initialize the distributed environment during its initialize. In this article, We will learn how to effectively use DeepSpeed Library with a single GPU and how to integrate it with HuggingFace Trainer API.
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