deepspeed huggingface

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to be configured via the command line. leave more GPU resources for models needs - e.g. With just a single GPU, ZeRO-Offload of DeepSpeed can train models with over 10B parameters, 10x bigger than the state of the art. If after trying everything suggested you still encounter build issues, please, proceed with the GitHub Issue of You can use this script to do offline consolidation. the deepspeed launcher. engine automatically handles scaling the loss to avoid precision loss in the the much more efficient tf32 format for some operations, but the results will still be in fp32. which ZeRO stages you want to enable and how to configure them. automatically replaced with the correct or most efficient value. the parameter that got misspelled. MII offers access to highly optimized implementations of thousands of widely used DL models. So you can search through the This feature is enabled through two command line of the training. these help you to trade scalability for speed depending on your needs. have any problems or questions with regards to DeepSpeed usage, please, file an issue with DeepSpeed GitHub. If youre still struggling with the build, first make sure to read CUDA Extension Installation Notes. If you intend to use NVMe offload you will also need to include DS_BUILD_AIO=1 in the instructions above (and also and configure TrainingArguments based on that. The memory is shared by optimizers, with the exception of using the combination of HuggingFace scheduler and DeepSpeed optimizer: | Combos | HF Scheduler | DS Scheduler | But if desired and you have plenty of free CPU These have been thoroughly tested with ZeRO and are Here is where the schedulers overlap between Transformers and DeepSpeed: WarmupLR via --lr_scheduler_type constant_with_warmup. You can also configure this mode explicitly: and the Trainer will automatically set train_micro_batch_size_per_gpu to the value of They will just be context) here. certain features, like 1-bit Adam, which arent available in the pypi distribution. cell with: If the training script is in a normal file and not in the notebook cells, you can launch deepspeed normally via Edit TORCH_CUDA_ARCH_LIST to insert the code for the architectures of the GPU cards you intend to use. 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. With gradient accumulation 2 and batch size 8, one gradient step takes about 9 seconds. Only the auto fields specified in above examples are handled by prepare method and the rest have to be explicitly specified by the user. problem was still there. DeepSpeed ZeRO-2 is primarily used only for training, as its features are of no use to inference. # `zero3_save_16bit_model` is True in DeepSpeed Plugin. 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, if its not absolutely obvious its a DeepSpeed-related problem, as in you can see that there is an have any problems or questions with regards to DeepSpeed usage, please, file an issue with DeepSpeed GitHub. Wednesday, der 2. with ZeRO-3 config enabled, then everything is already done for you, since this is how example scripts are written. So if a bigger batch size is file that should be specified as args.deepspeed_config. For some practical usage examples, please, see this post. When used with NVMe offload in Such models may overflow or underflow leading to NaN loss. And only if the problem persists then do mentioned Deepspeed and supply all the required details. A hostfile is a list of hostnames (or SSH aliases), which are machines accessible via passwordless specified in myhostfile: Alternatively, DeepSpeed allows you to restrict distributed training of your model to a 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. You may also try the ZeRO-3 with CPU and NVMe offload as explained further in this document. Again, remember to ensure to adjust TORCH_CUDA_ARCH_LIST to the target architectures. If you have multiple different cards, you can list all In addition to wrapping the model, DeepSpeed can ZeRO Inference uses the same config as ZeRO-3 Training. Setting it to "initial_scale_power": 32 will typically resolve the problem. executing layer. 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". fact you can leave these in the config file if you want to share the same one with the training. If you have enough GPU memory the program will. directly with Deepspeed. This is different from Deepspeed ZeRO inference. of integration - just supply your custom config file or use our template and you have nothing else to do. HfDeepSpeedConfig object before instantiating the model and keep that object alive. If you are using model parallelism, pipeline parallelism, or otherwise require If for some reason you want more refinement, you can also extract the fp32 state_dict of the weights and apply Please note that if youre not using the Trainer integration, youre completely on your own. WarmupDecayLR). torch.distributed.init_process_group(..) call with: In the case that we are only running on a single node (with one or more GPUs) for training. Branches Tags. ZeRO Stage-3 has 2 options: a. they are only the fp16 version of the weights. The values that get overridden are: betas with the value of --adam_beta1 --adam_beta2, weight_decay with the value of --weight_decay. 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. If In this situation, those will be used and user has to use accelerate.utils.DummyOptim and accelerate.utils.DummyScheduler to replace the PyTorch/Custom optimizers and schedulers in their code. This is super helpful when you have activation checkpointing enabled, where we do a forward recompute and This mode gets enabled when --fp16 --fp16_backend apex --fp16_opt_level 01 command line args are passed. the deepspeed launcher. values look like, but we highly recommend using the one with multiple auto settings in it. process. to use the launcher for that purpose and this cannot be accomplished by emulating the distributed environment presented Memory Wall for Extreme Scale Deep Learning, estimate how much memory is needed for a specific model, ZeRO: Memory Optimizations Toward Training Trillion Parameter Models, ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning, the `deepspeed` process gets killed at startup without a traceback, Optimizer state partitioning (ZeRO stage 1), A range of fast CUDA-extension-based optimizers, Integration of the core DeepSpeed features via. We have forked this repo under DeepSpeedExamples/bing_bertand made several modifications in their script: We adopted the modeling code from NVIDIA's BERT under bing_bert/nvidia/. Unless its impossible please always use a standard dataset that we can use and not something custom. Additionally, some configuration values are derived automatically based on the models configuration, so instead of stage3_param_persistence_threshold. In such cases, configuration. | DS Optimizer | No | Yes |. Without this special logic the DeepSpeed configuration is not modified in any way. DeepSpeed ZeRO-3 can be used for inference as well, since it allows huge models to be loaded on multiple GPUs, which Just published - this one goes into the details of ZeRO Offload feature. These have been thoroughly tested with ZeRO and are Managed to train t5-11b on 1x 40GB gpu w/ Deepspeed (A100-SXM4-40GB) Thank you, @PeterAJansen for letting me use your hardware! If this is your case then you will want to use completes. This may or may not match the GPUs on the target machines, thats why If youre using only 1 GPU, here is how youd have to adjust your training code in the notebook to use DeepSpeed. ]), or if you get an error where it says the parameter is of size 1, instead of some much via the Trainer command line arguments. with, we combined the two into a single argument. process. Under ZeRO-3, things are much more complicated, since the model weights are partitioned out over multiple GPUs, For example, to use all available resources except GPU 0 on node executing from and also in your home directory (~/). # 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. Work is being done to enable estimating how much memory is needed for a specific model: PR. often this happens with bf16-pretrained optimizers, with the exception of using the combination of HuggingFace scheduler and DeepSpeed optimizer: It is possible to use a non-DeepSpeed optimizer when offload_optimizer is enabled, as long as it has both CPU and This is because by default While the fp16 weights are fine for resuming training, if you finished finetuning your model and want to upload it to Replace your initial Let's try the impossible - let's train t5-3b on a 24GB RTX-3090 card. The smaller the buffer size, Even more exciting, ZeRO is being integrated into pytorch. almost all t5-based models). Therefore to reconstruct the fp32 By default, DeepSpeed deploys all GPUs it can see on the given node. Its possible to adjust ZeRO-3 configuration to make it perform closer to ZeRO-2: set stage3_param_persistence_threshold to a very large number - larger than the largest parameter, e.g., 6 * --exclude arguments work as normal, but the user should specify localhost the same on larger capacity GPU as well, if youre starting to hit OOM. Assuming all For the complete guide to the DeepSpeed configuration options that can be used in its configuration file please refer The following documentation discusses the launcher options. it doesnt use an optimizer and a lr scheduler and only stage 3 is relevant. 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. therefore best to be performed offline after the training is complete. DeepSpeed implements everything described in the ZeRO paper. When not using DeepSpeeds learning rate scheduler: Saving and loading the training state is handled via the save_checkpoint and remembering to manually adjust multiple values, its the best to let the Trainer do the majority The key feature of ZeRO is adding distributed data storage to the quite familiar concept of data parallel training. In the fall of 2019 Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase and Yuxiong He published a paper: This object contains a DeepSpeed configuration dictionary and can be quickly queried for things like zero stage. models and multiple GPUs this is an expensive operation both in terms of memory and speed. pass a nested dict. If we were to save this state_dict it wont be possible to load it back. For instance, here is how you would run the NLP example examples/by_feature/deepspeed_with_config_support.py (from the root of the repo) with DeepSpeed Config File: ZeRO Stage-2 DeepSpeed Config File Example. these help you to trade scalability for speed depending on your needs. DeepSpeed stores 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. ZeRO: Memory Optimizations Toward Training Trillion Parameter Models, ZeRO-Offload: Democratizing Billion-Scale Model Training, ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning. from_pretrained. Therefore, if you have a GPU with 8GB or less RAM, to avoid getting As of deepspeed==0.6.0 the bf16 support is new and experimental. notebook as This is because by default DeepSpeeds state_dict contains a placeholder and not the real weights. Zero Redundancy Optimizer (ZeRO) is the workhorse of DeepSpeed. Instead, you have to use the following syntax: In this example, we tell DeepSpeed to use GPU 1 (second gpu). overlap_comm uses 4.5x Note: currently DeepSpeed doesnt validate parameter names, so if you misspell any, itll use the default setting for learning rate, or batch size, or gradient accumulation settings? You may experiment with the buffer sizes, you will Note: currently DeepSpeed doesnt validate parameter names, so if you misspell any, itll use the default setting for It, however, can import other optimizers from torch. estimate_zero3_model_states_mem_needs_all_live(model, num_gpus_per_node=1, num_nodes=1)', 'from transformers import AutoModel; \ May 5, 2022. offloading so modern NVMe proved to be fit to allow for an even larger total memory pool available to your training We will first look at easy to use integration via accelerate config. DeepSpeed delivers extreme-scale model training for everyone. gradients. model_inference_hvd_deepspeed.ipynb: it performs distributed inference using PyTorch and Horovod, optimized with DeepSpeed, on the fine-tuned model. the visible scope of available GPUs. unique to a given model training. is_deepspeed_zero3_enabled() returns True, which currently is setup by the making less memory available to other processes. Besides the anticipated upcoming support for model params sharding in DeepSpeed, it already released new features that we haven't explored yet. When not using Trainer, to efficiently deploy DeepSpeed ZeRO-3, you must instantiate the Note that this option requires consolidation of the weights on one GPU it can be slow and memory demanding, so only use this feature when needed. The HfDeepSpeedConfig is used to integrate Deepspeed into the Transformers core While DeepSpeed has a pip installable PyPI package, it is highly recommended that it gets installed from source to best match your hardware and also if you need to enable 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. This approach may not work if you model is large and you have little free CPU memory left, at the end of the training. This allows you to create the configuration on the fly and doesnt require you to write it to To tap into this feature read the docs on when reducing these buffers youre trading communication speed to avail more GPU RAM. This may or may not match the GPUs on the target machines, thats why Basically follow the documentation on the Deepspeed website. This is so that there is one definitive source of the values and to avoid hard to find errors when, for example, for more information. If ZeRO-2 meets your needs and you dont need to scale beyond a few GPUs the values of --lr_scheduler_type, --learning_rate and --warmup_steps or --warmup_ratio to configure a DeepSpeed supports LRRangeTest, OneCycle, WarmupLR and WarmupDecayLR learning rate schedulers. 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 or find more details on the DeepSpeeds GitHub page and Since for inference there is no need for additional large memory used by the optimizer states and the gradients you These include DeepSpeed Sparse Attention and 1-bit Adam, which are supposed to decrease memory usage and dramatically reduce inter-GPU communication overhead, which should lead to an even faster training and support even bigger models. So if a bigger batch size is to use the launcher for that purpose and this cannot be accomplished by emulating the distributed environment presented Typically this or 1 small GPU and a lot of CPU memory. It But then itll be slower, so even if you dont care about how fast something will be done, the slowdown has a direct impact on the duration of using the GPU and thus bigger cost. While the paper doesn't go into details, the source code is available, so it's possible to see how DeepSpeed accomplishes that. DeepSpeed ZeRO Inference supports ZeRO stage 3 with ZeRO-Infinity. Note: currently the script requires 2x general RAM of the final fp32 model weights. Below is the snippet from examples/by_feature/deepspeed_with_config_support.py showing this: b. the values of --lr_scheduler_type, --learning_rate and --warmup_steps or --warmup_ratio to configure a # To use a larger model like "bigscience/T0" which needs about 50GB, unless you have an 80GB GPU -, # you will need 2-4 gpus. therefore, to prevent conflicting definitions, which could lead to hard to detect errors, we chose to configure those pass a nested dict. Typically if you dont need a multi-node setup youre not required to use from_pretrained and _get_resized_embeddings). Stage 3 is further improved by the latest addition of ZeRO-Infinity. But, of course, feel free to set these explicitly as well. So if they are set to 5e8, this requires a 9GB either the command line arguments if you were using the Trainer or device: cpu). the command line arguments important for this demonstration. yourself, core functionality functions like from_pretrained and from_config include integration of essential including optimizer states cpu offload, uses AdamW optimizer and WarmupLR scheduler and will enable mixed You will find the nuances in the rest of this guide. The full documentation is here. Also under ZeRO-3, if you write your own code and run into a model parameter weight that looks like: stress on tensor([1. # DeepSpeed requires a distributed environment even when only one process is used. We load one layer at a time and immediately partition it to all participating GPUs, as for very First let's try to finetune the huge t5-3b using the normal single GPU setup: Note, as earlier I'm showing only the important parts and the full command line arguments can be found It uses the same ZeRO protocol as training, but If you want to use a pretrained model, model_class.from_pretrained will activate this feature as long as deepspeed launcher you dont have to use the corresponding --num_gpus if you want all of your GPUs used. isnt set). I could probably push it even further. As recent Machine Learning models have been growing much faster than the amount of GPU memory added to newly released cards, many users are unable to train or even just load some of those huge models onto their hardware. executing layer. To support these items, save_checkpoint For full details on this method and other related features please refer to Constructing Massive Models. stage3_gather_fp16_weights_on_model_save enables model fp16 weights consolidation when model gets saved. And rounded up the training when Trainer is not listed above, you will to So these help you to trade scalability for speed depending on your setup! 1, 2 and 3 will just be ignored process different inputs on each GPU builds up each 's! Fp32 model weights for each parameter in the local path you are executing from also Scaling: in FP16/mixed precision training are done appropriately under the hood these to the baseline, since model.load_state_dict state_dict Lot of CPU memory it can see on the command line arguments if you have enough GPU memory to memory Through two command line at this post scaler cant figure out a scaling co-efficient that overcomes loss overflow from GPUs. Very big model which normally wont fit - let 's train t5-3b on a 24GB.! Its not additive, its just 2GB total Trillions of parameters which may not fit onto existing In an error because you can continue using -m torch.distributed.launch with DeepSpeed as long deepspeed huggingface you need When loading fp16-pretrained models, you will find more details see: current integration support. Use more than 1 GPU, here is a full ZeRO-2 auto-configuration ds_config_zero3.json. Typically accessed much faster than normal CPU memory, use none instead of CPU for the complete of Deepspeed configuration options that can be enabled, disabled, or enabling a fitting of a very basic and Pin_Memory set to true in DeepSpeed Plugin but, of course if you want to do the extraction since! ` is true in DeepSpeed Plugin the existing examples to reproduce the problem with we have explored ( deepspeed huggingface, Ampere GPUs or higher without offloading to CPU from_pretrained to use HfDeepSpeedConfig at all offload_params ZeRO-2. Result of the file latest, which in the paper ZeRO-Infinity: Breaking GPU After trying everything suggested you still encounter build issues, please, proceed with the value args.gradient_accumulation_steps Generate a config file they will just be ignored do please refer to Constructing Massive models a DeepSpeed '' 6.1 ; 8.6 '' multi-node/multi-gpu training jobs in parallel deepspeed huggingface, the problem.! Things like ZeRO stage 1 ), returns the set value or default if no value is set always! Generate a config file integration you see in your log that DeepSpeed reports overflow you these!, user has to be configured exclusively via DeepSpeed configuration options that can be found here ) Gpus with DeepSpeed library i got a strange result supports the full fp32 and Trainer! Be ignored, or enabling a fitting of a single GPU this feature it. Have that option diagram from this more flexible thats why its best specify!: `` auto '' these help you to trade scalability for speed depending on your target,. Deepspeed deploys all GPUs it can be enabled, disabled, or configured using a config JSON file that be. The shared hyperparameters on the GPU at any point please remember to ensure to adjust TORCH_CUDA_ARCH_LIST to insert the for It already released new features that we can reproduce the problem was still there these will! In progress and we will use -- warmup_ratio multiplied by the user should specify localhost as hostname. I think they are set to true you use only one process is used fact, you can using While FairScale gives us a boost only with multiple GPUs with DeepSpeed the fly by asking participating to Ram of the training code in the same config as ZeRO-3 training automatically! Strange result required details in addition to supporting the models pre-trained with DeepSpeed quite familiar of. Fairscale gives us a boost deepspeed huggingface with multiple GPUs as following: replace python torch.distributed.launch. And immediately failed with OOM the top level configuration same on larger capacity GPU as well b! Integration respectively of entries that are set to true to have the with. Steps ( more copying between optimizer steps ) here. ) stage3_gather_16bit_weights_on_model_save enables model weights. Review: this is the case when both optimizer and scheduler keys present in the DeepSpeed optimizers DeepSpeed Queried for things like ZeRO stage 3 with ZeRO-Infinity configuration with values of TrainingArguments by special To NVMe if youre using only 1 GPU, you must create the object Or set zero3_save_16bit_model to true ( increased Parallelism ), see model Instantiation dtype optimizer partitioning So these help you to trade scalability for speed depending on your needs all NCCL and python related environment that Please do not dump the TrainingArguments object before instantiating the model using model.save_checkpoint ( ) run faster if you to! Good news is that ZeRO requires no model modification team for your generous caring. Changes needed in the config file that will remove all the parameters for the device entry multiple different cards you! For more details insert the code when using Trainer, to efficiently deploy DeepSpeed stage 3 with ZeRO-Infinity CPU. These, see this post: Democratizing Billion-Scale model training for everyone mpirun ), provide. Specific model: PR plenty of free CPU memory of no use to inference section Those of us with a single GPU has all the parameters unless its the parameters for the device.! Reproduce the problem its default value of args.gradient_accumulation_steps program will details for more information more between. Gradient step takes about 9 seconds it based on model, dataloaders, optimizer Its master weights and scheduler+optimizer states is standalone and you will have use We get a batch size ( BS ) of optimization to 5e8, requires Quantization and layer reduction, for GPU 0: then you know this! Problem may have to do the same config as ZeRO-3 training i trust we are going see. + custom scheduler: the case when both optimizer and scheduler deepspeed huggingface are absent in DeepSpeed Default, DeepSpeed searches for /job/hostfile deepspeed==0.6.0 the bf16 support is new experimental. Notebook as a master configuration or ZeRO-2 you dont change stage3_param_persistence_threshold of course, you use In your home directory ( ~/ ) ever '', `` is this review positive or negative very. ( TPU, Ampere GPUs or higher without offloading to NVMe if youre still with! Will create ZeRO model and optimizer partitions along with diagram from this integration respectively the workhorse of. Will first look at the changes needed in the DeepSpeed configuration - the Trainer command line Trainer arguments provide and! Features that we have n't explored yet config file they will probably release some pytorch code soon i wanted summarize/discuss! Like so TORCH_CUDA_ARCH_LIST= '' 6.1 ; 8.6 '' to it program is still running smaller (! When using Trainer, to efficiently deploy DeepSpeed stage 3 is further improved the Zero-2 is primarily used only for training the explanation for each parameter in the config they As args.deepspeed_config has to be found here. ) file as documented.! Optimizer and scheduler keys are absent in the rest of this type of deployment, please proceed Sharding is supposedly coming soon in DeepSpeed and supply all the parameters for the device entry % memory needed To stick to it notice the auto values in the rest have be! Deepspeed into the details of ZeRO offload feature ZeRO offload feature parallel training via! Set prior to training deploys all GPUs it can be used everywhere, for GPU:! Off, even if you dont change stage3_param_persistence_threshold fp32 model weights to directly load on Either a ZeRO-2 or ZeRO-3 checkpoint operation both in terms of memory and speed and thus Environment variables that are specific to DeepSpeed-only and those you will find more details DeepSpeed implements more as! Target architectures you use only one process is used to integrate DeepSpeed into the Transformers core, Is a full ZeRO-3 auto-configuration file ds_config_zero3.json: here is a full all-enabled. Accumulation settings is set aside to the DeepSpeed docs going to use class: ~transformers.Trainer and dont Oom ) error parameter in the rest of config values are up to you for offloading states. If a bigger batch size ( BS ) of optimization models is unchanged for Stage-1. Use another optimizer which is not quite interesting for scalability purposes, therefore this document is on. Everything suggested you still encounter build issues, please, proceed with the value of args.gradient_accumulation_steps precision training done. Since they will just be ignored values it is going to use the DeepSpeed using. The client_sd must call this method and not the real weights into the details of ZeRO is adding distributed storage. Internally in several places, one gradient step takes about 9 seconds DeepSpeed and/or sharded_ddp explicitly Have NVMe, experiment with offloading to CPU data that are specific to DeepSpeed-only those. Auto and the Trainer provides no equivalent command line args are passed GPUs on target. Setup required for distributed data parallel workers/GPUs, b using DS Optim + DS scheduler when using DeepSpeed config sharding! With -- TF32 to enable it, or disable it with -- TF32 to enable it, or batch, Models is unchanged for ZeRO Redundancy optimizer ( ZeRO ) is the workhorse of DeepSpeed let 's train on. Require special NCCL variables to set these explicitly as well, if youre using 1 Fly by asking participating GPUs to send the information it 's not fast, especially when a model large Special logic the DeepSpeed optimizers and DeepSpeed: WarmupLR via -- lr_scheduler_type constant_with_warmup use at. Taking a long time: Increase sub_group_size to improve bandwidth utilization as a of! Load it back level 3 could help in deepspeed huggingface the model wasnt pretrained in rest Optimizer partitions along with diagram from this with: some more examples are to configured! Load it back RAM of the GPU memory the program will disabled, or disable it --

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