args.fp16_backend. file. section. The following will run all DeepSpeed tests: Finally, please, remember that, HuggingFace Trainer only integrates DeepSpeed, therefore if you Under DP gpu 0 performs a lot more work than the rest of the gpus, thus resulting in under-utilization of gpus. Assuming all All you need to do is to add this to your code: When this is done CUDA will automatically switch to using tf32 instead of fp32 where its possible. If you own Ampere or newer hardware you can start using bf16 for your training and evaluation. Please see the DeepSpeed JSON config for the full set. Same as with fp16, you can do inference in either the mixed precision bf16 or using the full bf16 mode. 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. Therefore And you can reach the first node with ssh hostname1 and second node with ssh hostname2, and both must be able to reach each other via ssh locally without a password. We can also see how gradient accumulation works: we normalize the loss so we get the average at the end of accumulation and once we have enough steps we run the optimization. When using Trainer everything is automatically taken care of. not using the command line interface to configure the training, and instead instantiate the # DeepSpeed requires a distributed environment even when only one process is used. So for 2 gpus you process 2 inputs at once. Make sure that your nvme_path is actually an NVMe, since it will work with the normal hard drive or SSD, but itll model weights in addition to what ZeRO-2 does. If you have tried to finetune models pre-trained under bf16 mixed precision (e.g. Since for inference there is no need for additional large memory used by the optimizer states and the gradients you The values that get set are: You can, of course, take over any or all of the configuration values and set those yourself: For example, for WarmupDecayLR, you can use the following entry: and total_num_steps, warmup_max_lr, warmup_num_steps and total_num_steps will be set at loading time. And thus we end up with 6 bytes per model parameter for mixed precision inference, plus activation memory. Therefore, by default for half precision training fp16 is used as the default for reduction operations. for the inference as only ZeRO-3 performs sharding of parameters, whereas ZeRO-1 shards gradients and optimizer states. If you use your own trainer, this is just: If you need to switch a tensor to bf16, its just: t.to(dtype=torch.bfloat16). There are multiple other values that are specific to DeepSpeed-only and those you will have to set manually to suit Trainer arguments and DeepSpeed configurations agree. T5) its very likely that you have encountered overflow issues. You can see that NVLink completes the training ~23% faster. Similarly to AdamW, you can configure other officially supported optimizers. Each new generation provides a faster bandwidth, e.g. Lets say your checkpoint folder looks like this: In this example there is just one DeepSpeed checkpoint sub-folder global_step1. and get access to the augmented documentation experience. The smaller the buffer size is, 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. The memory is shared by enable bf16 if you own an Ampere or a newer GPU to make things faster. will want to not use it is when the model youre using doesnt behave well under this training mode. links provide 56.25 GB/sec bandwidth in each direction, and 112.5 GB/sec total bandwidth So that your training cost will be the lowest and you will finish training faster. The script is standalone and you no longer need to and the Trainer will automatically set it to the value of args.gradient_accumulation_steps. In this section we have a look at a few tricks to reduce the memory footprint and speed up training for large models and how they are integrated in the Trainer and Accelerate. Deepspeed is often not the cause of the problem. Of course, this would change as you increase the number of accumulation steps. When you cant fit a model into the available GPU memory, you need to start using a solution that allows you to scale a large model to use multiple GPUs in parallel. Why would you want to use DeepSpeed with just one GPU? (The cache of FP16-casted copies MUST be rebuilt each iteration. Most likely you wont need it, but if you do please refer to Gathering Parameters. You can find exhaustive details and comparison tables in the papers listed at the end of this section. You will find the nuances in the rest of this guide. Train 175+ billion parameter NLP models with model parallel additions Loading Training Checkpoints deepspeed.DeepSpeedEngine.load_checkpoint(self, load_dir, tag=None, load_module_strict=True, load_optimizer_states=True, load_lr_scheduler_states=True, load_module_only=False, custom_load_fn=None) To address these users needs PyTorch and NVIDIA release a new version of NGC docker container which already comes with everything prebuilt and you just need to install your programs on it and it will run out of the box. If you have enough GPU memory the program will. It also o oads activations to CPU when appropriate. For example, are you using the same It is here mainly for you to see what the typical But then youre on your own synchronizing the Trainer command line arguments and the DeepSpeed memory offloading the optimizer states and parameters to CPU memory with "device": "cpu" may solve this limitation. If for some reason you want more refinement, you can also extract the fp32 state_dict of the weights and apply Some other cards may use a PCI-E 12-Pin connectors, and these can deliver up to 500-600W of power. values look like, but we highly recommend using the one with multiple auto settings in it. If we were to save this state_dict it wont be possible to load it back. Gradient checkpointing + FSDP - Accelerate - Hugging Face Forums Next follow the instructions to download and deploy the docker image. normally wont fit. For example, as mentioned earlier, we only employ gradient accumulation when we want to use a batch size beyond the size of the GPU memory. repo: Continuing the code from above, lets say youre looking to configure the Lamb optimizer. It The components on GPU memory are the following: A typical model trained in mixed precision with AdamW requires 18 bytes per model parameter plus activation memory. That subclass has logic to sync the configuration when reducing these buffers youre trading communication speed to avail more GPU RAM. DeepSpeed context of the same application. use the stage3_max_reuse_distance to decide whether to throw away the parameter or to keep it. directly with Deepspeed. model = GPT2LMHeadModel.from_pretrained(model_checkpoint_directory, gradient_checkpointing=True). The important nuance to understand here is that the way ZeRO is designed you can process different inputs on different GPUs in parallel. Make sure you have as many independent 12V PCI-E 8-Pin cables plugged into the card as there are sockets. 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. At times it may take an additional effort to pre-build some components, e.g., if youre using libraries like apex that dont come pre-compiled. Trainer via TrainingArguments then for the deepspeed argument you can If for some reason you get lost, here is the index of all PyTorch NGC images. Of course, this is only me sharing an observation and in no way Im trying to rush you. Rank 0 Skipping step.. Note that in order to use the 8-bit optimizer with an existing pretrained model a change to the embedding layer is needed. Its very confusing but this is how it is. How can I convert a custom PyTorch model (model.pt) to a Huggingface e.g. Now lets look at a simple text-classification fine-tuning on 2 GPUs (Im giving the command for reference): Since the only savings we get are in the model activations saved for the backward passed, its logical that the bigger those activations are, the bigger the saving will be. Gradient_checkpointing = True results in error - Transformers therefore "stage3_gather_16bit_weights_on_model_save": true is required to get the Trainer to save the fp16 Its important to remember that using gradient accumulation you may end up with a much larger effective batch size, so you may need to adjust the learning rate, its warm up and for very short datasets itll impact the loss as the training will end up doing less steps than normal. In your own programs, you can also use the following approach if youd like to modify the DeepSpeed config as a master I use finetune_trainer.py, it saves the best model during checkpoints given an evaluation metric, so sometimes it calls the _save_checkpoint, but if the metric is not higher than the best saved one it would not save the cehckpoint in huggingface code, what I . During training I'm getting often the message OVERFLOW! So the higher X you get in the report of NVX in the output of nvidia-smi topo -m the better. they are only the fp16 version of the weights. Its hard to tell the exact best temperature to strive for when a GPU is heavily loaded, but probably anything under +80C is good, but lower is better - perhaps 70-75C is an excellent range to be in. If thats not the case on your machine make sure to stop all processes that are using GPU memory. When we initialize the Accelerator we can specifiy if we want to use mixed precision training and it will take care of it for us in the prepare call. "HF Transformers models don't know anything about DeepSpeed's activation checkpointing, so if you try to enable that feature in the DeepSpeed config file, . I followed the example given on their github page, I am able to run the sample code with given sample data using tensorflow_datasets.load ('glue/mrpc') . A general rule of thumb is that gradient checkpointing slows down training by about 20%. The document includes This often happens when one takes a model pre-trained in bf16 mixed precision mode and tries to use it under fp16 (with or without mixed precision). The second objective is model performance. The FP32 parameters get updated by the optimizer, so the FP16 copies must be recreated, otherwise the FP16 values will be stale.). If you dont have that hardware you may enable fp16 as long as you dont use any model that was pre-trained in bf16 mixed precision (such as most t5 models). values look like, but we highly recommend using the one with multiple auto settings in it. FSDP supports activation checkpointing once the model has been sharded, and makes it easy to implement. of them like so TORCH_CUDA_ARCH_LIST="6.1;8.6". Tensor Core Requirements define the multiplier based on the dtype and the hardware. context manager (which is also a function decorator), like so: As you can see this gives you a randomly initialized model. Before we start make sure you have installed the following libraries: The nvidia-ml-py3 library allows us to monitor the memory usage of the models from within Python. However, a larger batch size can often result in faster model convergence or better end performance. In this case you usually need to raise the value of initial_scale_power. Question about activation checkpoint with FSDP - PyTorch Forums documented here. default value in the following cases: Note that were listing Stage 0 and 1 last since they are rarely used. Deepspeed supports the full fp32 and the fp16 mixed precision. If you want to create the config file on the fly in the notebook in the current directory, you could have a dedicated If that is not enough you can look into Memory-centric tiling which should shave some more memory, and tuning up buffer sizes in the deepspeed config may help a bit more. Since it has been discovered that more parameters lead to better performance, this technique allows to increase the number of parameters by an order of magnitude without increasing training costs. The paper will also give you the exact details on the savings, but its in the ballpark of O(sqrt(n)), where n is the number of feed-forward layers. If this is not done if one GPU finished generating before other GPUs the whole system will hang as the rest of the GPUs will not be able to received the shard of weights from the GPU that stopped generating. Each PCI-E 8-Pin power cable needs to be plugged into a 12V rail on the PSU side and can supply up to 150W of power. automatically replaced with the correct or most efficient value. In this case, there is no point checkpointing the final layer, as the final layer will instantly need to be re-computed. reduce_scatter configuration parameters are not used in ZeRO-3. When we train models there are a two aspects we want to optimize at the same time: We have seen that each method changes the memory usage and throughput. larger multi-dimensional shape, this means that the parameter is partitioned and what you see is a ZeRO-3 placeholder. For details see fp16 Inference. Checkpointing - Hugging Face Introduction to Model Parallelism - Amazon SageMaker You can check the archs pytorch was built with using: Here is how to find out the arch for one of the installed GPUs. The only thing that it does is handling Deepspeed ZeRO-3 param gathering and automatically splitting the model onto multiple gpus during from_pretrained call. If you keep these in the config file they will just They will just be the slower the communication gets, and the more GPU RAM will be available to other tasks. For the complete guide to the DeepSpeed configuration options that can be used in its configuration file please refer These notes were written primarily for the training mode, but they should mostly apply for inference as well. Gradient accumulation trick and Activation Checkpointing - GitHub TrainingArguments arguments if you were scripting the Trainer setup yourself. If you use gradient accumulation with bf16-enabled, you need to be aware that itll accumulate gradients in bf16, which may not be what you want due to this formats low precision, as it may lead to a lossy accumulation. We can see that the model weights alone take up 1.3 GB of the GPU memory. GPT2Config And then you can adapt the script to handle more gpus if you want to, # The provided deepspeed config also activates CPU memory offloading, so chances are that if you, # have a lot of available CPU memory and you don't mind a slowdown you should be able to load a, # model that doesn't normally fit into a single GPU. memory it can be done in the same training script. the file system before passing it to TrainingArguments. These are the remaining operators: biases, dropout, activations, and residual connections. model = AutoModel.from_pretrained("bigscience/T0_3B"); \ DeepSpeed stores If the attention mask input in the original model's forward function is not a keyword/named argument (e.g., attention_mask=None), user would need to change it to a keyword/named argument and provide that keyword as . example .json files with: Some more examples are to be found in the main repo as well. In total we get 512 sequences each with length 512 and store them in a Dataset with PyTorch format. So This summary is derived from Data Movement Is All You Need: A Case Study on Optimizing Transformers 2020. Lets see how it looks when we add it to the other methods we introduced earlier: We went from 15 GB memory usage to 5 GB - a 3x improvement while maintaining the throughput! ignored. Softmax and layer normalization are less compute-intensive than tensor contractions, and involve one or more reduction operations, the result of which is then applied via a map. explained here. If you want to use more than 1 GPU, you must use a multi-process environment for DeepSpeed to work. You can use this the visible scope of available GPUs. Watch out for future updates that will remove this limitation and make things more In turn it allows you to easily scale across different infrastructures such as CPUs, GPUs, TPUs, or distributed multi-GPU setups without changing any code. and A100. With large For example, for GPU 0: then you know that this cards arch is 8.6. Here is the full description from this comment: Autocast maintains a cache of the FP16 casts of model parameters (leaves). But you have full control over this functionality and if you choose you can add a small overhead and ensure that reductions will be using fp32 as the accumulation dtype and only when the result is ready itll get downcast to the half precision dtype youre training in. RTX-3090 and So once started on the bf16-mode path its best to remain on it and not switch to fp16. GitHub - NVIDIA/Megatron-LM: Ongoing research training transformer But you can still override the value of synced_gpus if need to. Like all cases with reduced precision this may or may not be satisfactory for your needs, so you have to experiment and see. going to use. To enable partition activation, we use the deepspeed.checkpointing API to replace Megatron's activation checkpointing and random state tracker APIs. For example, in LSTM, if user passes (activation, hidden), function should correctly use the first input as activation and the second input as hidden Trainer during train, but you can of course do the math yourself. The zero_optimization section of the configuration file is the most important part (docs), since that is where you define Create or load the DeepSpeed configuration to be used as a master configuration. So a lot less memory is used: 2 bytes per parameter vs 6 bytes with mixed precision! different values in different places. pass a nested dict. Stage 1 is Stage 2 minus gradient sharding. To use the deepspeed launcher instead, you have to first create a hostfile file: Unlike the torch.distributed.run launcher, deepspeed will automatically launch this command on both nodes! larger batch size, or enabling a fitting of a very big model which That is once Deepspeed was removed from the setup, the Increasing the size of deep learning models (layers and parameters) yields better accuracy for complex tasks such as computer vision and natural language processing. If possible try to use one of the existing examples to reproduce the problem with. Add a section on Activation Checkpointing. There are, If you want to use a HF Transformers models you can do, If you write your own model and you want to use DeepSpeeds activation checkpointing you can use the, 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). circumstances you may find the following information to be needed. It is possible to use a non-DeepSpeed optimizer when offload_optimizer is enabled, as long as it has both CPU and Explore Gradient-Checkpointing in PyTorch This is a practical analysis of how Gradient-Checkpointing is implemented in Pytorch, and how to use it in Transformer models like BERT and GPT2. This allows you to create the configuration on the fly and doesnt require you to write it to Usually, biases and layer norm parameters are not weight decayed. You can use automatic mixed precision with either a pytorch-like AMP way or the apex-like way: To configure pytorch AMP-like mode with fp16 (float16) set: and the Trainer will automatically enable or disable it based on the value of When used with NVMe offload in 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. Before beginning to train BLOOM-176B I spent 2 days on this process and was able to increase throughput from 90 to 150 TFLOPs! A100. As a first experiment we will use the Trainer and train the model without any further modifications and a batch size of 4: We see that already a relatively small batch size almost fills up our GPUs entire memory. Since gradient accumulation essentially is identical to having a larger batch size, just as with the larger batch size here you are likely to see a 20-30% speedup due to the optimizer running less often. The script will auto-discover the deepspeed sub-folder using the contents of the file latest, which in the current Gradient checkpointing allows one to trade speed for GPU memory, which either allows one to overcome a GPU OOM, or increase their batch size, which often leads to a better performance. The idea is very similar to gradient accumulation with the distinction that instead of running the forward and backward passes during the accumulation in sequence on a single machine they are performed in parallel on multiple machines. weights. Hello I'm fine-tuning EleutherAI/gpt-j-6B on a conversational dataset (TriviaQA with 80'000 rows) using DeepSpeed on a 24 GB GPU and 150 GB of RAM. Activation checkpointing just means on the backwards, we'll need to re-compute the activations (unless you do CPU checkpointing with Deepspeed or something, where activations are just transferred to the CPU memory). But, of course, feel free to set these explicitly as well. However, not all free GPU memory can be used by the user. For details and to the console, so you can see exactly what was the final configuration passed to it. Or how to escape the dreaded RuntimeError: CUDA error: out of memory error. a starting point. 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 Some configuration values are required by both the Trainer and DeepSpeed to function correctly, As long as you continue training and resuming using DeepSpeed you dont need to worry about anything. Lets have a look at another method with which we can regain some speed: mixed precision training. wont be possible on a single GPU. This approach may not work if you model is large and you have little free CPU memory left, at the end of the training. therefore, to prevent conflicting definitions, which could lead to hard to detect errors, we chose to configure those If you submit a PR that involves DeepSpeed integration please note our CircleCI PR CI setup has no GPUs, so we only run tests requiring gpus on a different CI nightly. That is if you have 2 sockets on the GPU, you want 2 PCI-E 8-Pin cables going from your PSU to the card and not one that has 2 PCI-E 8-Pin connectors at the end! As discussed in this document normally the DeepSpeed configuration is passed as a path to a json file, but if youre Of course, these changes will impact the size of the model you can train. So if you need to access all parameters from all layers at once there is a specific method to do it. For more information please see Resource Configuration (multi-node). as pip install deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl locally or on any other machine. Do not use the 2 splits at one end of the same cable (also known as pigtail cable). Definitely use mixed half-precision over fp32 - so bf16 on Ampere and higher GPUs and fp16 on older gpu architectures. We use the same 8-bit optimizer from the earlier experiments. # 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_16bit_weights_on_model_save", How to Choose Which ZeRO Stage and Offloads To Use For Best Performance, Activation Checkpointing or Gradient Checkpointing, 'from transformers import AutoModel; \ grouped into buckets of sub_group_size and each buckets is updated one at a time. arguments: --learning_rate, --adam_beta1, --adam_beta2, --adam_epsilon and --weight_decay. PDF @microsoft.com arXiv:1910.02054v3 [cs.LG] 13 May 2020 stage3_max_live_parameters is the upper limit on how many full parameters you want to keep on the GPU at any given How do I convert this PyTorch-model to a Huggingface-model? Even when we set the batch size to 1 and use gradient accumulation we can still run out of memory when working with large models. the full fp32 mode, by explicitly disabling the otherwise default fp16 mixed precision mode with: If youre using the Ampere-architecture based GPU, pytorch version 1.7 and higher will automatically switch to using Zero Redundancy Optimizer (ZeRO) is the workhorse of DeepSpeed. You have been warned. Fine-tuning T5 with long sequence length, using activation If you still cant fit a batch size of 1 first check various default values and lower them if you can. Follow the installation guide in the Github repo to install the bitsandbytes library that implements the 8-bit Adam optimizer. automatically set it to AdamW and will use the supplied values or the defaults for the following command line #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node! That's why I would like to use DeepSpeed and fine-tune on 2 or 4 GPUs at the same time. You can find dozens of DeepSpeed configuration examples that address various practical needs in the DeepSpeedExamples The example has copious notes and is self-documenting. Additionally you want the high-end PSU that has stable voltage. 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. on performance unless you are doing activation checkpointing. DeepSpeed docs. And it will shutdown if it gets too hot. and its typically accessed much faster than normal CPU memory. Pinned memory is enabled with pin_memory set to true. If you want to go really deep into understanding these 2 modes, this article is highly recommended, as it has great diagrams, includes multiple benchmarks and profiler outputs on various hardware, explains all the nuances that you may need to know. This effort saved us more than one month of training time. I've gone through the huggingface code of respective classes and found that the feature is present only for the Bert model and not the DistilBert. both configured to offload to cpu. Read [this issue](https://github.com/huggingface/transformers/issues/14819) for more information. its best to specify the desired archs explicitly. On a machine with dual-GPU and which are connected with NVLink, you will most likely see something like: on a different machine w/o NVLink we may see: So the first report NV2 tells us the GPUs are interconnected with 2 NVLinks, and the second report PHB we have a typical consumer-level PCIe+Bridge setup. If you dont prebuild the extensions and rely on them to be built at run time and you tried all of the above solutions which is about the same as fp32 - because both have 8-bits used for the numerical range. Efficient Large-Scale Training with Pytorch FSDP and AWS stage3_gather_16bit_weights_on_model_save enables model fp16 weights consolidation when model gets saved. Everything else you have to do by yourself. fp32 master weights in its custom checkpoint optimizer files, which are global_step*/*optim_states.pt (this is glob Lets add it to the mix of the previous methods: We can see that with these tweaks we use about half the GPU memory as at the beginning while also being slightly faster. The exact number depends on the specific GPU you are using. In total it uses only 19 bits. This document is far from being complete and a lot more needs to be added, so if you have additions or corrections to make please dont hesitate to open a PR or if you arent sure start an Issue and we can discuss the details there.

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