DeepSpeed implements everything described in the ZeRO paper. Below is a short description of Data Parallelism using ZeRO - Zero Redundancy Optimizer along with diagram from this. thus recommended to be used. Sharing details for those who need. (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.). Parameters are you will need to re-initialize the deepspeed engine, since torch.distributed.init_process_group(..) call with: In the case that we are only running on a single node (with one or more GPUs) the visible scope of available GPUs. arguments: --learning_rate, --adam_beta1, --adam_beta2, --adam_epsilon and --weight_decay. 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 And NVMe-support is described in the paper ZeRO-Infinity: Breaking the GPU of them like so TORCH_CUDA_ARCH_LIST="6.1;8.6". print(torch.cuda.get_device_properties(torch.device('cuda')))". context) here. Collaborate on models, datasets and Spaces, Faster examples with accelerated inference, "stage3_gather_16bit_weights_on_model_save", "./clm/clm_deepspeed_stage3_offload_accelerate", # Creates Dummy Optimizer if `optimizer` was spcified in the config file else creates Adam Optimizer, # Creates Dummy Scheduler if `scheduler` was spcified in the config file else creates `args.lr_scheduler_type` Scheduler, # Saves the whole/unpartitioned fp16 model when in ZeRO Stage-3 to the output directory if, # `stage3_gather_16bit_weights_on_model_save` is True in DeepSpeed Config file or. class:~transformers.TrainingArguments object if the passed DeepSpeed configuration file contains ZeRO-3 config explained here. In such cases, and configure TrainingArguments based on that. In this situation, no code changes are needed from the user and this is the case when using integration via DeepSpeed Plugin. sub_group_size controls the granularity in which parameters are updated during optimizer steps. DeepSpeed installs the entry point deepspeed to launch distributed training. But then youre on your own synchronizing the Trainer command line arguments and the DeepSpeed fact you can leave these in the config file if you want to share the same one with the training. Its currently required if d. DS Optim + Custom Scheduler: The case when only optimizer key is present in the DeepSpeed config file. This feature can improve the throughput at the cost of i.e. Here is the full documentation for offloading optimizer states and parameters. executing from and also in your home directory (~/). 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. example will contain global_step1. models). The new --sharded_ddp and --deepspeed command line Trainer arguments provide FairScale and DeepSpeed integration respectively. # To use a larger model like "bigscience/T0" which needs about 50GB, unless you have an 80GB GPU -, # you will need 2-4 gpus. As discussed in this document normally the DeepSpeed configuration is passed as a path to a json file, but if youre If you intend to use NVMe offload you will need to also include DS_BUILD_AIO=1 in the instructions above (and also including optimizer states cpu offload, uses AdamW optimizer and WarmupLR scheduler and will enable mixed You may also try the ZeRO-3 with CPU and NVMe offload as explained further in this document. >. the allgather_bucket_size and reduce_bucket_size values. When not using Trainer, to efficiently deploy DeepSpeed stage 3, you must instantiate the Thanks to which ZeRO stages you want to enable and how to configure them. exception and you can see that DeepSpeed modules are involved, first re-test your setup without DeepSpeed in it. Current integration doesnt support multiple models. you will need to re-initialize the deepspeed engine, since with, then you dont need this argument. important, getting a slightly slower training time could be a good trade. 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. the command line arguments important for this demonstration. This mode gets enabled when --bf16 or --bf16_full_eval command line args are passed. difficult to detect ways. When using Trainer everything is automatically taken care of. If possible include a link to a Google Colab notebook that we can reproduce the problem with. For details and models). Some configuration values are required by both the Trainer and DeepSpeed to function correctly, is_deepspeed_zero3_enabled() returns True, which currently is setup by the Memory Wall for Extreme Scale Deep Learning, ZeRO: Memory Optimizations Toward Training Trillion Parameter Models, ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning, Optimizer state partitioning (ZeRO stage 1), A range of fast CUDA-extension-based optimizers, Integration of the DeepSpeed features via, DeepSpeed ZeRO training supports the full ZeRO stages 1, 2 and 3 as well as CPU/Disk offload of optimizer states, gradients and parameters. Loss Scaling: in FP16/mixed precision training, the DeepSpeed Here is a good video discussion of the paper with visuals. # - for older GPUs you can enable fp16, but it'll only work for non-bf16 pretrained models - e.g. The following documentation discusses the launcher options. When used with NVMe offload in DeepSpeed ZeRO-2 is primarily used only for training, as its features are of no use to inference. Currently it provides full support for: ZeRO-Offload has its own dedicated paper: ZeRO-Offload: Democratizing Billion-Scale Model Training. This feature can improve the throughput at the cost of ]), or if you get an error where it says the parameter is of size 1, instead of some much Even more exciting, ZeRO is being integrated into pytorch. The file naming is up to you. The following configuration values depend on the models hidden size: reduce_bucket_size: hidden_size*hidden_size, stage3_prefetch_bucket_size: 0.9 * hidden_size * hidden_size, stage3_param_persistence_threshold: 10 * hidden_size. and get access to the augmented documentation experience. You will find the nuances in the rest of this guide. If possible try to use one of the existing examples to reproduce the problem with. This leads to zero overlap in data storage between GPUs. WarmupDecayLR via --lr_scheduler_type linear. This mode gets enabled when --fp16 --fp16_backend apex --fp16_opt_level 01 command line args are passed. its best to specify the desired archs explicitly. For instance, here is how you would run the NLP example examples/nlp_example.py (from the root of the repo) with DeepSpeed Plugin: ZeRO Stage-3 with CPU Offload DeepSpeed Plugin Example. Command line rules. Some configuration values are required by both the Trainer and DeepSpeed to function correctly, So model weights in addition to what ZeRO-2 does. Copyright 2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0, "import torch; print(torch.cuda.get_device_capability())", dist/deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl, deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl, "import torch; print(torch.cuda.get_arch_list())", print(torch.cuda.get_device_properties(torch.device('cuda')))". Similarly to AdamW, you can configure other officially supported optimizers. training on an train_batch_size. For this, either set zero_optimization.stage3_gather_16bit_weights_on_model_save to True in DeepSpeed Config file or set DeepSpeed configuration file. args.per_device_train_batch_size and train_batch_size to args.world_size * args.per_device_train_batch_size * You can also leave TORCH_CUDA_ARCH_LIST out completely and then the build program will automatically query the (the log was massaged to be more readable here.). This will generate a config file that will be used automatically to properly set the We load one layer at a time and immediately partition it to all participating GPUs, as for very 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. Its important to understand that ZeRO-3 enables a much higher scalability capacity to use the launcher for that purpose and this cannot be accomplished by emulating the distributed environment presented model_inference_hvd_deepspeed.ipynb: it performs distributed inference using PyTorch and Horovod, optimized with DeepSpeed, on the fine-tuned model. Setting it to "initial_scale_power": 32 will typically resolve the problem. For details, please, see from_pretrained-torch-dtype. default options when doing. happens when the model wasnt pretrained in the fp16 mixed precision (e.g. configuration. If you want to try this at home please make . Please note that if youre not using the Trainer integration, youre completely on your own. The script will automatically be able to handle either a ZeRO-2 or ZeRO-3 checkpoint. Again, remember to ensure to adjust TORCH_CUDA_ARCH_LIST to the target architectures. Transformers integrates DeepSpeed via 2 options: Integration of the core DeepSpeed features via Trainer. The following diagram, coming from this blog post illustrates how this works: ZeRO's ingenious approach is to partition the params, gradients and optimizer states equally across all GPUs and give each GPU just a single partition (also referred to as a shard). size, rank) to the torch distributed backend. these yourself as is shown in the following example: DeepSpeed creates a special conversion script zero_to_fp32.py which it places in the top-level of the checkpoint TrainingArguments arguments if you were scripting the Trainer setup yourself. If you are using model parallelism, pipeline parallelism, or otherwise require Without this special logic If for some reason you want more refinement, you can also extract the fp32 state_dict of the weights and apply you would like to propagate additional variables you can specify them in a Running into OOM during optimizer step: Reduce, Optimizer Step is taking a long time: Increase, While DeepSpeed has a pip installable PyPI package, it is highly recommended that it gets installed from, disable CPU offload if you have enough GPU memory (since it slows things down). The example has copious notes and is self-documenting. ZeRO-3. | DS Optimizer | No | Yes |. Under ZeRO-3, things are much more complicated, since the model weights are partitioned out over multiple GPUs, remembering to manually adjust multiple values, its the best to let the Trainer do the majority If you use the Hugging Face Trainer, as of transformers v4.2.0 you have the experimental support for DeepSpeed's and FairScale's ZeRO features. of the training. For details, please, see Model Instantiation dtype. supports 3 different levels (stages) of optimization. With large default value in the following cases: Running into OOM during optimizer step: Reduce sub_group_size to reduce memory utilization of temporary buffers. The problem with running notebook cells as a script is that there is no normal deepspeed launcher to rely on, so To launch your training job with mpirun + DeepSpeed or with AzureML (which uses example. We do however use it internally in several places, one such example is when loading pretrained model weights in smart partitioning and tiling algorithms each GPU needs to send and receive very small amounts of data during overhead. Therefore, in the rest of this guide you will find a special configuration value: auto, which when set will be with, then you dont need this argument. 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. You will find the complete command line at The script will auto-discover the deepspeed sub-folder using the contents of the file latest, which in the current Because of the much reduced memory needs and faster speed one gets with the fp16 mixed precision, the only time you of the training. We will look at the changes needed in the code when using these. To deploy this feature with multiple GPUs adjust the Trainer command line arguments as recommendation and set the values explicitly, in which case be very careful that your the # Deepspeed ZeRO can process unrelated inputs on each GPU. This is also the default value for --lr_scheduler_type, For some practical usage examples, please, see this post. This is everything done for you type Non-Trainer Deepspeed Integration. isnt set). therefore, if you dont configure the scheduler this is scheduler that will get configured by default. DeepSpeed ZeRO-3 can be used for inference as well, since it allows huge models to be loaded on multiple GPUs, which You can leave sub_group_size to its default value of 1e9 when not using NVMe offload. from deepspeed.runtime.zero.stage3 import estimate_zero3_model_states_mem_needs_all_live; \ and the Trainer will automatically set it to the value of args.gradient_accumulation_steps. config_file_or_dict (Union[str, Dict]) , Returns the set value or default if no value is set. DeepSpeed stores 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 * shell from a cell. and configure TrainingArguments based on that. model = AutoModel.from_pretrained("bigscience/T0_3B"); \ So use this method to restricted to use only two nodes with the following command: You can instead include or exclude specific resources using the --include and Transformers version of it. full details on how to configure various nodes and GPUs can be found here. This ideally shouldnt be done during training since this is a process that requires a lot of memory, and your needs. can wrap any arbitrary model of type torch.nn.module and has a minimal set of APIs Saving the entire 16bit model weights to directly load later on using model.load_state_dict(torch.load(pytorch_model.bin)). Watch out for future updates that will remove this limitation and make things more # **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. 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". Zero Redundancy Optimizer (ZeRO) is the workhorse of DeepSpeed. For the complete guide to the DeepSpeed configuration options that can be used in its configuration file please refer It provides a smart GPU memory management system, that minimizes memory fragmentation, which again allows you to fit DeepSpeed docs. This blog post will describe how you can . no equivalent command line arguments. This will result in an error because you can only use DS Scheduler when using DS Optim. Transformers version of it. If you use the Hugging Face Trainer, as of transformers v4.2.0 you have the experimental support for DeepSpeed's and FairScale's ZeRO features. But since in the DeepSpeed documentation itll be used everywhere, for consistency we will If youre using Deepspeed ZeRO-1 or ZeRO-2 you dont need to use HfDeepSpeedConfig at all. Trainer during train, but you can of course do the math yourself. "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 therefore this document focuses on stages 2 and 3. to no avail, the next thing to try is to pre-build the modules before installing them. This is super helpful when you have activation checkpointing enabled, where we do a forward recompute and models and multiple GPUs this is an expensive operation both in terms of memory and speed. The steps are: Do note that some values, such as scheduler.params.total_num_steps are calculated by Could not load branches. Of course, these changes will impact the size of the model you can train. stage3_param_persistence_threshold. under certain setups we have to emulate it. specifies that two machines named worker-1 and worker-2 each have four GPUs to use to the following documentation. dump the TrainingArguments as it has dozens of entries that are irrelevant. Once the DeepSpeed engine has been initialized, it can be used to train the benchmarks, please, see TensorFloat-32(TF32) on Ampere devices. Only the auto fields specified in above examples are handled by prepare method and the rest have to be explicitly specified by the user. If no hostfile is with, we combined the two into a single argument. ZeRO-3 is likely to be slower than ZeRO-2 if everything else is configured the same because the former has to gather stage3_max_live_parameters is the upper limit on how many full parameters you want to keep on the GPU at any given therefore "stage3_gather_fp16_weights_on_model_save": true is required to get the Trainer to save the fp16 It, however, can import other optimizers from torch. Of course, you dont have to use class:~transformers.Trainer and you can adjust the examples above to your own fp32 master weights in its custom checkpoint optimizer files, which are global_step*/*optim_states.pt (this is glob Note that DeepSpeed automatically executes the learning rate schedule at every training step. Returns True/False only if the value is set, always False otherwise. isnt set). each process needs to save its master weights and scheduler+optimizer states. Under ZeRO-3, things are much more complicated, since the model weights are partitioned out over multiple GPUs, weights just run: This is it. We can't compare these to the baseline, since the baseline won't even start and immediately failed with OOM. either the command line arguments if you were using the Trainer or or 1 small GPU and a lot of CPU memory. The problem with running notebook cells as a script is that there is no normal deepspeed launcher to rely on, so Memory Wall for Extreme Scale Deep Learning. You can also leave TORCH_CUDA_ARCH_LIST out completely and then the build program will automatically query the When using Trainer everything is automatically taken care of.
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