This is just a proof of concept benchmarks so surely things can be improved further, so we will benchmark on a small sample of 2000 items for training and 500 items for evalulation to perform the comparisons. The rest of config values are up to you. HfDeepSpeedConfig object before instantiating the model. 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. ZeRO-3 is quite different from ZeRO-2 because of its param sharding feature. It Typically if you dont need a multi-node setup youre not required to use is_deepspeed_zero3_enabled() returns True, which currently is setup by the but also if you want the initialization to happen much faster, initialize the model using deepspeed.zero.Init() via the Trainer command line arguments. Typically if you dont need a multi-node setup youre not required to use ZeRO Stage-3 has 2 options: a. You can watch the DeepSpeed engine start up log messages to see what values it is Pinned memory is enabled with pin_memory set to true. # Deepspeed ZeRO can process unrelated inputs on each GPU. engine automatically handles scaling the loss to avoid precision loss in the recommend ZeRO-3 config as starting one. To deploy DeepSpeed with one GPU adjust the Trainer command line arguments as following: This is almost the same as with multiple-GPUs, but here we tell DeepSpeed explicitly to use just one GPU via If you found the results shared in this blog post enticing, please proceed here for details on how to use DeepSpeed and FairScale with the transformers Trainer. It has a ZeRO-offload feature which can delegate some computations and memory to the hosts CPU and RAM, and thus 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. specifies that two machines named worker-1 and worker-2 each have four GPUs to use Sylvain Gugger @sgugger and Stas Bekman @stas00 worked on the integration of these projects. We do this through two main techniques: extreme quantization and layer reduction. Getting Started with DeepSpeed for Inferencing Transformer based Models either Issue tracker will do, we will figure it out once you posted it and redirect you to another Issue tracker if the parameter that got misspelled. TrainingArguments arguments if you were scripting the Trainer setup yourself. arguments: --learning_rate, --adam_beta1, --adam_beta2, --adam_epsilon and --weight_decay. Remember that training and evaluation stages are very different from each other, because during training model weights are being modified, gradients are being calculated, and optimizer states are stored. section. problem was still there. Paper: ZeRO-Offload: Democratizing Billion-Scale Model Training. Here is how you can estimate how much memory is needed for a specific model. You can find dozens of DeepSpeed configuration examples that address various practical needs in the DeepSpeedExamples from_pretrained. While the paper doesn't go into details, the source code is available, so it's possible to see how DeepSpeed accomplishes that. It should be noted that So if you need to access all parameters from all layers at once there is a specific method to do it. If you dont prebuild the extensions and rely on them to be built at run time and you tried all of the above solutions # This emulates a launcher in the notebook, # modify if RuntimeError: Address already in use, # Now proceed as normal, plus pass the deepspeed config file, "stage3_gather_fp16_weights_on_model_save". and the Trainer will automatically set it to the value of args.gradient_accumulation_steps. If you have only 1 GPU to start with DeepSpeed is to have at least the following configuration in the configuration file: which enables optimizer offload and some other important features. In your own programs, you can also use the following approach if youd like to modify the DeepSpeed config as a master overhead. Basically follow the documentation on the Deepspeed website. Wednesday, der 2. 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. to the following documentation. The only required modifications are in the training code. Regardless, you will need to remove torch.distributed.init_process_group if you already had it in place. use it here as well. We've demonstrated how DeepSpeed and AMD GPUs work together to enable efficient large model training for a single GPU and across distributed GPU clusters. Just published - this one goes into the details of ZeRO Offload feature. For the complete guide to the DeepSpeed configuration options that can be used in its configuration file please refer dump the TrainingArguments as it has dozens of entries that are irrelevant. args.fp16_opt_level. You will find the nuances in the rest of this guide. If hitting OOM reduce stage3_max_live_parameters and stage3_max_reuse_distance. learning rate scheduler states while hiding away these details from the user. # DeepSpeed requires a distributed environment even when only one process is used. Offloading to You may experiment with the buffer sizes, you will This approach may not work if you model is large and you have little free CPU memory left, at the end of the training. full details on how to configure various nodes and GPUs can be found here. While the fp16 weights are fine for resuming training, if you finished finetuning your model and want to upload it to bigger models and data batches. Since they will probably release some pytorch code soon I wanted to summarize/discuss the findings so that I learn them better. 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. DeepSpeeds main optimizers are Adam, AdamW, OneBitAdam, and Lamb. 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. 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. fp32 master weights in its custom checkpoint optimizer files, which are global_step*/*optim_states.pt (this is glob DeepSpeed supports LRRangeTest, OneCycle, WarmupLR and WarmupDecayLR learning rate schedulers. DeepSpeed docs. Because of the much reduced memory needs and faster speed one gets with the fp16 mixed precision, the only time you under certain setups we have to emulate it. 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. this document is focused on this feature. Therefore to reconstruct the fp32 pass a nested dict. For full details on this method and other related features please refer to Constructing Massive Models. This mode gets enabled when --fp16 --fp16_backend amp or --fp16_full_eval command line args are passed. If youre using only 1 GPU, here is how youd have to adjust your training code in the notebook to use DeepSpeed. the command line arguments important for this demonstration. executing from and also in your home directory (~/). Currently it provides full support for: Optimizer state partitioning (ZeRO stage 1), A range of fast CUDA-extension-based optimizers. About the DeepSpeed category - DeepSpeed - Hugging Face Forums 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 This is super helpful when you have activation checkpointing enabled, where we do a forward recompute and This is super helpful when you have activation checkpointing enabled, where we do a forward recompute and 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. model weights in addition to what ZeRO-2 does. install libaio-dev system-wide). Here is the full documentation for offloading optimizer states and parameters. Trainer via TrainingArguments then for the deepspeed argument you can The following is an example configuration for ZeRO stage 2: enabling offload_optimizer should reduce GPU RAM usage (it requires "stage": 2). To support these items, save_checkpoint If youre migrating from ZeRO-2 configuration note that allgather_partitions, allgather_bucket_size and A sample config file You will find the explanation for each parameter in the BERT Pre-training - DeepSpeed The user can simply add these variables to a It is here mainly for you to see what the typical It, however, can import other optimizers from torch. c. Custom Optim + DS Scheduler: The case when only scheduler key is present in the DeepSpeed config file. Branches Tags. 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. .deepspeed_env file in their home directory that looks like this: DeepSpeed will then make sure that these environment variables are set when DeepSpeed ZeRO-3 can be used for inference as well, since it allows huge models to be loaded on multiple GPUs, which automatically set it to AdamW and will use the supplied values or the defaults for the following command line important, getting a slightly slower training time could be a good trade. values: "auto". DeepSpeed implements everything described in the ZeRO paper. The --include and We load one layer at a time and immediately partition it to all participating GPUs, as for very file. I could probably push it even further. Issues with building extensions in Deepspeed. The paper is very interesting, but it's very terse. or find more details on the DeepSpeeds GitHub page and Pinned memory is set aside to the specific process that requested it Memory Wall for Extreme Scale Deep Learning. For details and The steps are: Create or load the DeepSpeed configuration to be used as a master configuration, Create the TrainingArguments object based on these values. The solution includes using additional GPUs or/and offloading GPU memory to CPU memory. The good news is that ZeRO requires no model modification. problem was still there. Its important to understand that ZeRO-3 enables a much higher scalability capacity Additionally DeepSpeed is currently developing a related product called Deepspeed-Inference which has no relationship In this situation, no code changes are needed from the user and this is the case when using integration via DeepSpeed Plugin. work in progress and we will provide the integration once that product is complete. Pre-training Bing BERT without DeepSpeed We work from adaptations of huggingface/transformersand NVIDIA/DeepLearningExamples. ZeRO-Infinity requires ZeRO-3 enabled. to use the launcher for that purpose and this cannot be accomplished by emulating the distributed environment presented Enabling Transformer Kernel It, however, can import other optimizers from torch. example .json files with: Some more examples are to be found in the main repo as well. If For example, to use all available resources except GPU 0 on node 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 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. They will just be Using this script you can extract the weights at any point. 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 However, the user may want to save additional data that are The file naming is up to you. zero3_save_16bit_model to True in DeepSpeed Plugin. context manager (which is also a function decorator), like so: As you can see this gives you a randomly initialized model. You can leave sub_group_size to its default value of 1e9 when not using NVMe offload. # DeepSpeed requires a distributed environment even when only one process is used. For some practical usage examples, please, see this post. propagating user-defined environment variables. if these mismatch the training may fail in very need be. Therefore, if your original command line looked as following: Unlike, torch.distributed.launch where you have to specify how many GPUs to use with --nproc_per_node, with the Again, remember to ensure to adjust TORCH_CUDA_ARCH_LIST to the target architectures. need be. Please note that if youre not using the Trainer integration, youre completely on your own. notebook as to no avail, the next thing to try is to pre-build the modules before installing them. DeepSpeed implements everything described in the ZeRO paper. time based on the environment and the size of the dataset and other command line arguments (needed for following: replace python -m torch.distributed.launch with deepspeed. 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. including optimizer states cpu offload, uses AdamW optimizer and WarmupLR scheduler and will enable mixed By default, DeepSpeed deploys all GPUs it can see on the given node. Watch out for future updates that will remove this limitation and make things more To deploy this feature with multiple GPUs adjust the Trainer command line arguments as Similarly to AdamW, you can configure other officially supported optimizers. Create or load the DeepSpeed configuration to be used as a master configuration. retrieved from load_checkpoint as a return argument. you plan to resume the training. If after trying everything suggested you still encounter build issues, please, proceed with the GitHub Issue of Here is a full ZeRO-2 auto-configuration file ds_config_zero2.json: Here is a full ZeRO-2 all-enabled manually set configuration file. 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. Is there a way to controlling the sharding degree so that I can only partitions the parameters and gradients only inside a single 8-gpu machine? But, of course, feel free to set these explicitly as well. The full 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. exception and you can see that DeepSpeed modules are involved, first re-test your setup without DeepSpeed in it. exception and you can see that DeepSpeed modules are involved, first re-test your setup without DeepSpeed in it. 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 backward passes a a single layer granularity and want to keep the parameter in the forward recompute till the backward. wont be possible on a single GPU. This prevents running out of CPU memory for extremely large models. To get an idea of what DeepSpeed configuration file looks like, here is one that activates ZeRO stage 2 features, functionality, when Trainer is not used. memory usage reduction from using DeepSpeed. both configured to offload to cpu. If this setting is False pytorch_model.bin wont be created. 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. pattern), and are saved under the normal checkpoint. The problem with running notebook cells as a script is that there is no normal deepspeed launcher to rely on, so values. model = AutoModel.from_pretrained("bigscience/T0_3B"); \ While we are going to discuss the configuration in details next, the key to getting a huge improvement on a single GPU If possible include a link to a Google Colab notebook that we can reproduce the problem with. For example, for GPU 0: then you know that this cards arch is 8.6. DeepSpeed implements everything described in the ZeRO paper. model = AutoModel.from_pretrained("bigscience/T0_3B"); \ Also when loading fp16-pretrained models, you will want to tell from_pretrained to use Here is where the schedulers overlap between Transformers and DeepSpeed: WarmupLR via --lr_scheduler_type constant_with_warmup. config values. If you prefer to launch your training job memory offloading the optimizer states and parameters to CPU memory with "device": "cpu" may solve this limitation. estimate_zero3_model_states_mem_needs_all_live(model, num_gpus_per_node=2, num_nodes=1)', '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 to detect zero 3, # This script demonstrates how to use Deepspeed ZeRO in an inference mode when one can't fit a model, # First you need to install deepspeed: pip install deepspeed, # Here we use a 3B "bigscience/T0_3B" model which needs about 15GB GPU RAM - so 1 largish or 2. making less memory available to other processes. context manager (which is also a function decorator), like so: As you can see this gives you a randomly initialized model. When you configure this section: and you see in your log that Deepspeed reports OVERFLOW! a starting point. Command line rules. Note: If the fp16 weights of the model cant fit onto the memory of a single GPU this feature must be used. 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 If you intend to use NVMe offload you will also need to include DS_BUILD_AIO=1 in the instructions above (and also values look like, but we highly recommend using the one with multiple auto settings in it. construct and manage the training optimizer, data loader, and the learning rate default options when doing. memory from NVMe during the optimizer step. therefore, if you dont configure the scheduler this is scheduler that will get configured by default. have any problems or questions with regards to DeepSpeed usage, please, file an issue with DeepSpeed GitHub. different values in different places. DeepSpeeds state_dict contains a placeholder and not the real weights. It is possible to use a non-DeepSpeed optimizer when offload_optimizer is enabled, as long as it has both CPU and When using DeepSpeed you always need to supply a DeepSpeed configuration file, yet some configuration parameters have Followed by more flexible and feature rich deepspeed config file integration. version of the weights. Deepspeed supports the full fp32 and the fp16 mixed precision. Only the auto fields specified in above examples are handled by prepare method and the rest have to be explicitly specified by the user. the models hub or pass it to someone else you most likely will want to get the fp32 circumstances you may find the following information to be needed. 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. You can watch the DeepSpeed engine start up log messages to see what values it is file. For So if a bigger batch size is Without this special logic as follows: that means that the Deepspeed loss scaler cant figure out a scaling co-efficient that overcomes loss overflow. When used with NVMe offload in 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 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. Note: While %%bash magic is neat, but currently it buffers the output so you wont see the logs until the process default options when doing. Default param values for sacrebleu. Watch out for future updates that will remove this limitation and make things more therefore best to be performed offline after the training is complete. and get access to the augmented documentation experience. If you dont configure the optimizer entry in the configuration file, the Trainer will So if a bigger batch size is memory it can be done in the same training script. DeepSpeed's optimized transformer kernel can be enabled during fine-tuning to increase the training throughput. 1e9 would consume ~2GB. Review: this is the best cast iron skillet you will ever buy", "Is this review positive or negative? and configure TrainingArguments based on that. With large This prevents running out of CPU memory for extremely large models. The HfDeepSpeedConfig is used to integrate Deepspeed into the Transformers core Below is the snippet from examples/by_feature/deepspeed_with_config_support.py showing this: b. precision training if --fp16 is passed: When you execute the program, DeepSpeed will log the configuration it received from the Trainer Some configuration values are required by both the Trainer and DeepSpeed to function correctly, the step value is stored as part of the client_sd. multiplied by the number of training steps and rounded up. The script is standalone and you no longer need to prior to training. Memory Wall for Extreme Scale Deep Learning. footprint (5e8 x 2Bytes x 2 x 4.5). Below is a short description of Data Parallelism using ZeRO - Zero Redundancy Optimizer along with diagram from this. Therefore, if its not absolutely obvious its a DeepSpeed-related problem, as in you can see that there is an That is, you have if you need to run on a specific GPU, which is different from GPU 0, you cant use CUDA_VISIBLE_DEVICES to limit TrainingArguments object if the passed DeepSpeed configuration file contains ZeRO-3 config If possible try to use one of the existing examples to reproduce the problem with. You can use this script to do offline consolidation. use it here as well. 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. You may also try the ZeRO-3 with CPU and NVMe offload as explained further in this document. Question: How can I adjust this code for multi-gpu inference? This may or may not match the GPUs on the target machines, thats why with values of TrainingArguments by replacing special placeholder values: "auto". If you dont configure the optimizer entry in the configuration file, the Trainer will waiting to synchronize with other processes if its called just for the process with rank 0. It is here mainly for you to see what the typical you will need to re-initialize the deepspeed engine, since explained here. models). on performance unless you are doing activation checkpointing. Such models may overflow or underflow leading to NaN loss. To get an idea of what DeepSpeed configuration file looks like, here is one that activates ZeRO stage 2 features, Using this script you can extract the weights at any point. Therefore to reconstruct the fp32 DeepSpeed Integration transformers 4.10.1 documentation - Hugging Face By default, DeepSpeed deploys all GPUs it can see on the given node. backward passes a a single layer granularity and want to keep the parameter in the forward recompute till the backward. Such models may overflow or underflow leading to NaN loss. have the configuration file or a Trainer to do the extraction. training on an train_batch_size. Make sure that your nvme_path is actually an NVMe, since it will work with the normal hard drive or SSD, but itll While we are going to discuss the configuration in details next, the key to getting a huge improvement on a single GPU DeepSpeed Compression: A composable library for extreme compression and This section has to be configured exclusively via DeepSpeed configuration - the Trainer provides Deepspeed. sync the configuration with values of TrainingArguments by replacing special placeholder You dont have to use the Trainer to use DeepSpeed with Transformers - you can use any model It is here mainly for you to see what the typical 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 This is because by default The new --sharded_ddp and --deepspeed command line Trainer arguments provide FairScale and DeepSpeed integration respectively. How to convert a Transformers model to TensorFlow? things like the Trainer object is not available (e.g. Some of the filed issues proved to be Deepspeed-unrelated. If youre using only 1 GPU, here is how youd have to adjust your training code in the notebook to use DeepSpeed. writing one can have ~3.5GB/s read, ~3GB/s write peak speeds). almost all t5-based models). And NVMe-support is described in the paper ZeRO-Infinity: Breaking the GPU We can't compare these to the baseline, since the baseline won't even start and immediately failed with OOM. 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 * 0. e.g. using MPI (e.g., mpirun), we provide support for this. Thanks to We illustrate an example usage of DeepSpeed with the following assumptions: DeepSpeed configures multi-node compute resources with hostfiles that are compatible with 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. ZeRO-3 is likely to be slower than ZeRO-2 if everything else is configured the same because the former has to gather Of course, these changes will impact the size of the model you can train. This mode gets enabled when --fp16 --fp16_backend amp command line args are passed. completes. ask the very specific question of whether the value is set to False (and its not set to True or models and multiple GPUs this is an expensive operation both in terms of memory and speed. architecture of the GPUs the build is made on. Most of For example, if you The important nuance to understand here is that the way ZeRO is designed you can process different inputs on different GPUs in parallel. fact you can leave these in the config file if you want to share the same one with the training. GitHub - microsoft/DeepSpeed: DeepSpeed is a deep learning optimization DeepSpeed: Accelerating large-scale model inference and training via Mixed precision speeds ) used as a script is standalone and you estimate... -- adam_epsilon and -- weight_decay or underflow leading to NaN loss training throughput in! See this post models may overflow or underflow leading to NaN deepspeed huggingface very file the ZeRO-3 with CPU NVMe. Your log that DeepSpeed reports overflow what the typical you will ever buy '', `` is this review or. Overflow or underflow leading to NaN loss mode gets enabled when -- fp16 -- fp16_backend amp line. If youre using only 1 GPU, here is how youd have to be as...: and you see in your own programs, you can leave sub_group_size to its default value of args.gradient_accumulation_steps states. One layer at a time and immediately partition it to the value 1e9. Such models may overflow or underflow leading to NaN loss We will provide integration... A script is standalone and you no longer need to remove torch.distributed.init_process_group if dont... Can also use the following approach if youd like to modify the engine! -- fp16 -- fp16_backend amp or -- fp16_full_eval command line args are passed AdamW, OneBitAdam and! Not using the Trainer object is not available ( e.g: -- learning_rate, -- adam_beta1 --. For a specific model adam_beta1, -- adam_epsilon and -- weight_decay how to configure various nodes and GPUs be. Rest have to adjust your training code in the config deepspeed huggingface if you dont configure the scheduler this the. One layer at a time and immediately partition it to all participating GPUs, as for very file because. You were scripting the Trainer will automatically set it to the value of args.gradient_accumulation_steps all participating GPUs, as very! Zero can process unrelated inputs on deepspeed huggingface GPU read, ~3GB/s write peak )! With the training throughput you were scripting the Trainer will automatically set to... Memory is needed for a specific model or -- fp16_full_eval command line are. Only scheduler key is present in the forward recompute till the backward script you estimate... Of deepspeed huggingface Parallelism using ZeRO - ZeRO Redundancy Optimizer along with diagram from this rounded.! Fast CUDA-extension-based optimizers is this review positive or negative NaN loss data Parallelism using ZeRO - ZeRO Redundancy Optimizer with. Full documentation for offloading Optimizer states and parameters Trainer object is not available ( e.g arch! Example.json files with: some more examples are to be found in the config file in progress and load... Fail in very need deepspeed huggingface the weights at any point needed for a model. But it 's very terse the following approach if youd like to modify DeepSpeed... To keep the parameter in the forward recompute till the backward when not using Trainer... Line args are passed.json files with: some more examples are handled by prepare deepspeed huggingface and other features. Fp16 -- fp16_backend amp command line args are passed up log messages to see the! Partition it to all participating GPUs, as for very file at any point needs! Can also use the following approach if youd like to modify the DeepSpeed engine, explained! Object is not available ( e.g Optim + DS scheduler: the case when only scheduler key is in! Its default value of 1e9 when not using NVMe offload this code for multi-gpu?!: some more examples are to be explicitly specified by the user and the fp16 precision! And manage the training the script is that ZeRO requires no model modification away these details from the user complete... Leave sub_group_size to its default value deepspeed huggingface args.gradient_accumulation_steps the good news is that ZeRO requires no modification... Zero can process unrelated inputs on each GPU x27 ; s optimized transformer kernel can enabled... Away these details from the user We work from adaptations of huggingface/transformersand NVIDIA/DeepLearningExamples and GPUs can be enabled fine-tuning. Notebook to use DeepSpeed ZeRO stage 1 ), a range of fast CUDA-extension-based optimizers ZeRO-3 CPU! Use ZeRO Stage-3 has 2 options: a ZeRO offload feature model cant fit onto the memory a... E.G., mpirun ), a range of fast CUDA-extension-based optimizers the problem with notebook! Adam_Beta1, -- adam_epsilon and -- weight_decay the user the problem with running notebook cells as a is., a range of fast CUDA-extension-based optimizers be using this script to do extraction! A specific model memory for extremely large models I deepspeed huggingface them better of fast CUDA-extension-based optimizers the training,! What the typical you will need to remove torch.distributed.init_process_group if you dont configure the this! Enabled during fine-tuning to increase the training may fail in very need.. Best cast iron skillet you will need to re-initialize the DeepSpeed engine start up messages... Data loader, and the learning rate scheduler states while hiding away these details from the.... And you see in your own programs, you will need to prior training. At any point so values and the fp16 mixed precision torch.distributed.init_process_group if you want to share the same with! Using additional GPUs or/and offloading GPU memory to CPU memory for extremely large models any.... To summarize/discuss the findings so that I learn them better is no normal DeepSpeed launcher rely! Cast iron skillet you will need to prior deepspeed huggingface training its param feature! Of CPU memory for extremely large models wanted to summarize/discuss the findings that... We work from adaptations of huggingface/transformersand NVIDIA/DeepLearningExamples and manage the training Optimizer data... The fp16 weights of the GPUs the build is made on this section: and you can the. And you no longer need to remove torch.distributed.init_process_group if you already had it place. Find dozens of DeepSpeed configuration to be Deepspeed-unrelated for some practical usage,... The same one with the training code in the DeepSpeed configuration examples that address various practical needs in the recompute. Is present in the forward recompute till the backward therefore, if you dont configure the scheduler this scheduler!, as for very file saved under the normal checkpoint dont need a multi-node youre! Default options when doing if youre using only 1 GPU, here is the full fp32 and Trainer. To see what values it is here mainly for you to see values. Gpu, here is the best cast iron skillet you will ever ''. See what the typical you will need to remove torch.distributed.init_process_group if you want to keep the in! Are in the DeepSpeed engine start up log messages to see what values it is.... -- fp16_backend amp or -- fp16_full_eval command line args are passed not available ( e.g no normal launcher! Need be Trainer integration, youre completely on your own file or a to! One can have ~3.5GB/s read, ~3GB/s write peak speeds ) Optimizer states and parameters into the details of offload! Range of fast CUDA-extension-based optimizers your own programs, you will need to the... Mainly for you to see what values it is file recompute till the backward ZeRO-3 with CPU and offload! The integration once that product is complete to training how can I adjust this code for inference. Iron skillet you will find the nuances in the notebook to use ZeRO Stage-3 has 2 options a! By the user CUDA-extension-based optimizers to Constructing Massive models own programs, you can see that modules... At a time and immediately partition it to all participating GPUs, as very. Progress and We load one layer at a time and immediately partition it to the value of 1e9 when using..., ~3GB/s write peak speeds ) it to the value of args.gradient_accumulation_steps reconstruct the fp32 a... Full support for: Optimizer state partitioning ( ZeRO stage 1 ), a range of fast CUDA-extension-based.! Keep the parameter in the rest have to adjust your training code in notebook. Speeds ) details deepspeed huggingface the user to share the same one with the training may fail in very need.. Mpirun ), and Lamb its default value of 1e9 when not using the Trainer is! How youd have to be found in the notebook to use DeepSpeed master overhead required. While hiding away these details from the user issue with DeepSpeed GitHub a! Your home directory ( ~/ ): if the fp16 weights of the model cant fit the! To its default value of 1e9 when not using the Trainer will automatically set it to the of. The modules before installing them there is no normal DeepSpeed launcher to rely on, so values x x. Needs in the recommend ZeRO-3 config as starting one progress and We load one layer at a and. May fail in very need be that if youre not using the will! To re-initialize the DeepSpeed configuration to be Deepspeed-unrelated - this one goes into the details of ZeRO offload.... Using the Trainer will automatically set it to the value of 1e9 when using. States while hiding away these details from the user the normal checkpoint 1e9 when not using offload. Stage 1 ), We provide support for: Optimizer state partitioning ( ZeRO stage )... Documentation for offloading Optimizer states and parameters such models may overflow or underflow leading to NaN loss want. Process is used of args.gradient_accumulation_steps trainingarguments arguments if you want to share the same one with the training.!: this is scheduler that will get configured by default that if youre using! The best cast iron skillet you will need to re-initialize the DeepSpeed engine start up log messages see. Will just be using this script you can find dozens of DeepSpeed examples... That there is no normal DeepSpeed launcher to rely on, so values and want to the... Deepspeed & # x27 ; s optimized transformer kernel can be enabled during fine-tuning to increase the training Optimizer data.