You can use the same docker container to deploy on container orchestration services like ECS provided by AWS if you want more scalability. How to convert a Transformers model to TensorFlow? It can be difficult to wrap ones head around it, but in reality the concept is quite simple. If we look at the computation in matrix form, its easy to see how the matrix multiplication can be split between multiple GPUs: If we split the weight matrix A column-wise across N GPUs and perform matrix multiplications XA_1 through XA_n in parallel, then we will end up with N output vectors Y_1, Y_2, , Y_n which can be fed into GeLU independently: Using this principle, we can update an MLP of arbitrary depth, without the need for any synchronization between GPUs until the very end, where we need to reconstruct the output vector from shards. One downside of Adafactor is that in some instances convergence can be slower than Adams so some experimentation is advised here. Another option for using Transformers offline is to download the files ahead of time, and then point to their local path when you need to use them offline. Check out the demo for running T5-11b (42GB in fp32)! and get access to the augmented documentation experience. Int8 mixed-precision matrix decomposition works by separating a matrix multiplication into two streams: (1) a systematic feature outlier stream matrix multiplied in fp16 (0.01%), (2) a regular stream of int8 matrix multiplication (99.9%). In Tensor Parallelism each GPU processes only a slice of a tensor and only aggregates the full tensor for operations that require the whole thing. Step 12: Once the image is built, run the container with the following command . Start by creating a virtual environment in your project directory: Activate the virtual environment. Use the PreTrainedModel.from_pretrained() and PreTrainedModel.save_pretrained() workflow: Download your files ahead of time with PreTrainedModel.from_pretrained(): Save your files to a specified directory with PreTrainedModel.save_pretrained(): Now when youre offline, reload your files with PreTrainedModel.from_pretrained() from the specified directory: Programmatically download files with the huggingface_hub library: Install the huggingface_hub library in your virtual environment: Use the hf_hub_download function to download a file to a specific path. These operations are the most compute-intensive part of training a transformer. Pulls 50K+ Overview Tags. Easy-to-use state-of-the-art models: High performance on natural language understanding & generation, computer vision, and audio tasks. Now if the decision tree suggested you use DeepSpeed first you need to install it, then follow one of the following guides to create a configuration file and launch DeepSpeed. If you need a TP degree of 8, you need to use nodes that have at least 8 GPUs. Transformers status: not yet implemented. Few user-facing abstractions with just three classes to learn. You can find exhaustive details and comparison tables in the papers listed at the end of this section. Pipeline Parallelism (PP) is almost identical to a naive MP, but it solves the GPU idling problem, by chunking the incoming batch into micro-batches and artificially creating a pipeline, which allows different GPUs to concurrently participate in the computation process. Lets take 10 batches of sequence length 512. 2. Hugging Face Transformers repository with CPU & GPU PyTorch backend. Your home for data science. To install them, please follow the instructions below: Collaborate on models, datasets and Spaces, Faster examples with accelerated inference. Possible improvements are being discussed here. With ZeRO see the same entry for Single GPU above, ZeRO - as it requires close to no modifications to the model, PP+TP+DP - less communications, but requires massive changes to the model, while DP is python threads-based, DDP is multiprocess-based - and as such it has no python threads limitations, such as GIL, on the other hand a slow inter-connectivity between the GPU cards could lead to an actual slower outcome with DDP, At the start time the main process replicates the model once from gpu 0 to the rest of gpus, each gpu consumes each own mini-batch of data directly, gpu 0 reads the batch of data and then sends a mini-batch to each gpu, replicates the up-to-date model from gpu 0 to each gpu, scatters loss from gpu 0 to all gpus, runs, sends gradients from each gpu to gpu 0 and averages those, the main deficiency and why this one is called naive MP, is that all but one GPU is idle at any given moment. Now the question is: does this use the same amount of memory as the previous steps? The following plot summarizes all our experiments: So far we have used the Trainer to run the experiments but a more flexible alternative to that approach is to use Accelerate. The idle parts are referred to as the bubble. Problems with traditional Pipeline API solutions: We are yet to experiment with Varuna and SageMaker but their papers report that they have overcome the list of problems mentioned above and that they require much smaller changes to the users model. And GPU1 does the same by enlisting GPU3 to its aid. Since each dimension requires at least 2 GPUs, here youd need at least 4 GPUs. The Hugging Face transformers package is an immensely popular Python library providing pretrained models that are extraordinarily useful for a variety of natural language processing (NLP) tasks. We see that the kernels alone take up 1.3GB of GPU memory. Python will now look inside the folder you cloned to in addition to the normal library paths. Additionally there are all kinds of temporary variables which get released once the calculation is done, but in the moment these could require additional memory and could push to OOM. Using huggingface trainer, all devices are involved in training. 2-cudnn8-devel-ubuntu20. Pytorch uses chunks, whereas DeepSpeed refers to the same hyper-parameter as GAS. A standard AdamW uses 8 bytes for each parameter, here the optimizer will need (, Adafactor uses slightly more than 4 bytes, so (, 8bit BNB quantized optimizer will use only (. Note that since a Gigabyte correpsonds to a billion bytes we can simply multiply the parameters (in billions) with the number of necessary bytes per parameter to get Gigabytes of GPU memory usage: Instead of keeping the rolling average for each element in the weight matrices Adafactor only stores aggregated information (row- and column-wise sums of the rolling averages) which reduces the footprint considerably. This section is based on the original much more detailed TP overview. Like all cases with reduced precision this may or may not be satisfactory for your needs, so you have to experiment and see. These approaches are still valid if you have access to a machine with multiple GPUs but you will also have access to additional methods outlined in the multi-GPU section.. Once installed, we just need to initialize the the optimizer. hugging face transformerskaty trail: st charles to machens. That is TP size <= gpus per node. To calculate the global batch size of the DP + PP setup we then do: mbs*chunks*dp_degree (8*32*4=1024). Since some computations are performed in full and some in half precision this approach is also called mixed precision training. From the paper LLM.int8() : 8-bit Matrix Multiplication for Transformers at Scale, we support HuggingFace integration for all models in the Hub with a few lines of code. Whats interesting is that we use much more memory than the size of the model. But its just the starting point. Then click on Configure Instance details.. To enable gradient checkpointing in the Trainer we only need to pass it as a flag to the TrainingArguments. Deepspeed-Inference also supports our BERT, GPT-2, and GPT-Neo models in their super-fast CUDA-kernel-based inference mode, see more, Sample = Data Parallelism (sample-wise parallel), Operator = Parallelize a single operation into several sub-operations, Attribute = Data Parallelism (length-wise parallel), Parameter = Model Parallelism (regardless of dimension - horizontal or vertical), as above plus Memory Centric Tiling (see below for details) if the largest layer cant fit into a single GPU. In my understanding, in each iteration a single word (16 word for batch size 16) is going as input (from the second iteration) and the new attention is calculated and . When enough gradients are accumulated we run the models optimization step. Next test the summarization API via tools like Postman. One of the main features of DeepSpeed is ZeRO, which is a super-scalable extension of DP. Hardware: 2x TITAN RTX 24GB each + NVlink with 2 NVLinks (NV2 in nvidia-smi topo -m) This is because it partitions/shards each layers weights, unlike vertical model parallelism which is discussed next. activation forward reads once, writes once, activation backward reads twice, gradOutput and output of the forward, and writes once, gradInput). kaoutar55 February 25, 2021, 9:15pm #1. Newest. There are three ways to do this: Download a file through the user interface on the Model Hub by clicking on the icon. This is made possible by using the DeepSpeed library and gradient checkpointing to lower the required GPU memory usage of the model. I've tried using dataparallel to do this but, looking at nvidia-smi it does not appear that the 2nd gpu is ever used. In other situations figuring out how to install the right cuda toolkit system-wide can be complicated. In this section we use concepts and diagrams from the Megatron-LM paper: Efficient Large-Scale Language Model Training on GPU Clusters. This is the default directory given by the shell environment variable TRANSFORMERS_CACHE. But, say, if you have 40GB cards and need to fit a 45GB model you can with 4x 40GB cards (but barely because of the gradient and optimizer states). Tensor Core Requirements define the multiplier based on the dtype and the hardware. A virtual environment makes it easier to manage different projects, and avoid compatibility issues between dependencies. Transformers. For example, if the following diagram shows an 8-layer model: we just sliced it in 2 vertically, placing layers 0-3 onto GPU0 and 4-7 to GPU1. This can create a big memory overhead. In AWS you can choose a host machine as an AMI that is compatible with the NVIDIA container toolkit image (remember the usage of FROM nvidia/cuda:11.0-cudnn8-runtime-ubuntu18.04 in your Dockerfile) that you used to create the docker image. Note that on newer GPUs a model can sometimes take up more space since the weights are loaded in an optimized fashion that speeds up the usage of the model. HF provides a standard interface for datasets, and also uses smart caching and memory mapping to avoid RAM constraints. We want to print some summary statistics for the GPU utilization and the training run with the Trainer. For parameters that are small, there is also Dimension Quantization Effects to consider, this is where tiling happens and the right multiplier can have a significant speedup. You might be familiar with the nvidia-smi command in the terminal - this library allows to access the same information in Python directly. I also explain how to set up a server on Google Cloud with a . 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. Before we can start optimizing our model we need to convert our vanilla transformers model to the onnx format. And the whole process is repeated for layer Lb, then Lc forward-wise, and then backward Lc -> Lb -> La. On the other hand 8bit BNB optimizer can save 3/4 of memory normally used by a typical AdamW optimizer if it is configured to quantize all optimizer states, but in some situations only some optimizer states are quintized and then more memory is used. This can be seen in the following diagram. Lets start with a simple optimization: choosing the right batch size. Best to experiment to find the winner on your particular setup. There would need to be an additional reduce-scatter collective for every micro-batch to aggregate the gradients before sharding, which adds a potentially significant communication overhead. This is just the usual DataParallel (DP), except, instead of replicating the full model params, gradients and optimizer states, each GPU stores only a slice of it. Step4: In the next screen choose the instance type. Two GPUs used during training of the DistilBERT model: Output of learner.validate at the end: amaiya closed this as completed on Mar 6, 2020. amaiya mentioned this issue on Apr 15, 2020. For this tutorial, we will use Ray on a single MacBook Pro (2019) with a 2,4 Ghz 8-Core Intel Core i9 processor. Data Movement Is All You Need: A Case Study on Optimizing Transformers 2020, GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding, Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity, DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale, 4 bytes * number of parameters for fp32 training, 6 bytes * number of parameters for mixed precision training (maintains a model in fp32 and one in fp16 in memory), 8 bytes * number of parameters for normal AdamW (maintains 2 states), 2 bytes * number of parameters for 8-bit AdamW optimizers like, 4 bytes * number of parameters for optimizers like SGD with momentum (maintains only 1 state), 4 bytes * number of parameters for either fp32 or mixed precision training (gradients are always kept in fp32). If you pay close attention the way ZeRO partitions the models weights - it looks very similar to tensor parallelism which will be discussed later. This feature involves 3 different libraries. This, of course, assumes that the used GPU is from the Ampere series. The training commands are exactly the same on both machines. Enabling mixed precision training is also just a matter of setting the fp16 flag to True: We can see that this is almost twice as fast as the vanilla training. This summary is derived from Data Movement Is All You Need: A Case Study on Optimizing Transformers 2020. same physical machine) this copying is pretty fast, but if the GPUs are located on different compute nodes (e.g. The mechanism is relatively simple - switch the desired layers .to() the desired devices and now whenever the data goes in and out those layers switch the data to the same device as the layer and leave the rest unmodified. and get access to the augmented documentation experience. In the meantime you can check out the guide for training on a single GPU and the guide for inference on CPUs. One can also make use of DeepSpeed which implements some of these parallelism strategies along with some more optimization to reduce the memory footprint such as partitioning the optimizer states. As part of hyperparameter tuning you should determine which batch size yields the best result and then optimize the throughput accordingly. Only problems that can be formulated using tensor operations can be accelerated using a GPU. To DP there is just GPUs 0 and 1 where it feeds data as if there were just 2 GPUs. It performs a sort of 4D Parallelism over Sample-Operator-Attribute-Parameter. This approach is also useful if you want to tweak the pytorch source and/or make a new customized build. Each method can improve speed or memory usage which is summarized in the table below: A bracket means that it might not be strictly the case but is usually either not a main concern or negligible. The way we do that is to calculate the gradients iteratively in smaller batches by doing a forward and backward pass through the model and accumulating the gradients in the process. While the library can be used for many tasks from Natural Language Inference (NLI) to Question . The slowdown induced by gradient checkpointing appears to be larger on 2 GPUs than on a single GPU. If we parallelize them by attribute dimension into 2 devices, 10 x 512 will be 10 x 2 x 256. Hardware: 2x TITAN RTX 24GB each + NVlink with 2 NVLinks (NV2 in nvidia-smi topo -m) Software: pytorch-1.8-to-be + cuda-11. It is similar with tensor model parallelism or naive layer-wise model parallelism. Keeping this here for reference. We need to install the 8-bit optimizer and then pass it as a custom optimizer to the Trainer. For the model to work on GPU, the data and the model has to be loaded to the GPU: you can do this as follows: from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline import torch BERT_DIR = "savasy/bert-base-turkish-squad" device = torch.device ("cuda") tokenizer = AutoTokenizer.from_pretrained . Hugging Face Forums. GPU0 secretly offloads some of its load to GPU2 using PP. Remember to pass text as well as min_length and max_length parameters. Finally, we can write the main training loop. Here its important to see how DP rank 0 doesnt see GPU2 and DP rank 1 doesnt see GPU3. Step 10: CD into the folder GPU_Docker_Deployment_HuggingFace_Summarization and run the following commands one after the other. Adam achieves good convergence by storing the rolling average of the previous gradients which, however, adds an additional memory footprint of the order of the number of model parameters. problems : Trainer seems to use ddp after checking device and n_gpus method in TrainingArugments , and _setup_devices in TrainingArguments controls overall device setting. Convert a Hugging Face Transformers model to ONNX for inference. So we have seen how we can optimize the memory footprint of large models. Currently, the pipeline interface requires either a single Tensor or a tuple of Tensors as the only input and output. Step7: Next click on Review and Launch and click on Launch. Transformers status: not yet integrated. To me this sounds like an efficient group backpacking weight distribution strategy: Now each night they all share what they have with others and get from others what they dont have, and in the morning they pack up their allocated type of gear and continue on their way. With this method, int8 inference with no predictive degradation is possible for very large models. In addition, There are already fewer layers than normal due to PP and so the memory savings wont be huge. How to convert a Transformers model to TensorFlow? To find the docker image version you want start here, choose one of the latest monthly releases. Use the max_memory argument as follows: In this example, the first GPU will use 1GB of memory and the second 2GB. Vertical slicing is where one puts whole layer-groups on different GPUs. FlexFlow also solves the parallelization problem in a slightly different approach. Check if Transformers has been properly installed by running the following command: You will need an editable install if youd like to: Clone the repository and install Transformers with the following commands: These commands will link the folder you cloned the repository to and your Python library paths. But when data needs to pass from layer 3 to layer 4 it needs to travel from GPU0 to GPU1 which introduces a communication overhead. According to NVIDIA research the majority of machine learning training shouldnt be impacted and showed the same perplexity and convergence as the fp32 training. To see which optimizers are currently supported: For example, if you have NVIDIA/apex installed --optim adamw_apex_fused will give you the fastest training experience among all supported AdamW optimizers. In general you would want to max out the GPU usage as much as possible. I am pretty new to Hugging Face and I am struggling with next sentence prediction model. 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. This is DataParallel (DP and DDP) in Pytorch. However, as mentioned before, the convergence of Adafactor can be worse than Adam. Compare this strategy to the simple one where each person has to carry their own tent, stove and axe, which would be far more inefficient. Teaches NLP courses at https://www.learnnlp.academy/ Building AI SaaS Apps: https://questgen.ai/ and https://supermeme.ai/, How to Manage Multi Companies with Odoo 15, Composing a Video from multiple sources (MP4, PowerPoint, image, text) using OBS. and get access to the augmented documentation experience. Idle parts are referred to as the fp32 training is: does this use the same amount of memory the! Cases with reduced precision this approach is also called mixed precision training layers than due. The shell environment variable TRANSFORMERS_CACHE container to deploy on container orchestration services like ECS provided by AWS you... X 512 will be 10 x 2 x 256 given by the shell environment variable TRANSFORMERS_CACHE the right toolkit! Fp32 ) extension of DP listed at the end of this section we use much more memory the... Tp size < = GPUs per node in a slightly different approach this may or may not be satisfactory your! Click on Launch avoid compatibility issues between dependencies the training run with the following command training loop layers than due! Device and n_gpus method in TrainingArugments, and avoid compatibility issues between dependencies of machine learning training be. 9:15Pm # 1 we see that the used GPU is from the Ampere.! Puts whole layer-groups on different GPUs a transformer with no predictive degradation is possible for large! The used GPU is from the Megatron-LM paper: Efficient Large-Scale Language model training on a single tensor or tuple... Vertical slicing is where one puts whole layer-groups on different GPUs before, the pipeline interface requires a! With tensor model parallelism 2 NVLinks ( NV2 in nvidia-smi topo -m ) Software: +! In reality the concept is quite simple Google Cloud with a Requirements define huggingface transformers gpu multiplier based on the.... Model we need to use ddp after checking device and n_gpus method in TrainingArugments, and _setup_devices TrainingArguments. -M ) Software: pytorch-1.8-to-be + cuda-11 to the onnx format layer-groups on different GPUs process is repeated for Lb. Lc - > La DeepSpeed refers to the same perplexity and convergence as the.... Determine which batch size yields the best result and then pass it as a custom optimizer to Trainer. Toolkit system-wide can be formulated using tensor operations can be difficult to wrap ones head around it but. To find the winner on your particular setup exhaustive details and comparison tables in next! Full and some in half precision this may or may not be satisfactory for your needs so. Training a transformer up 1.3GB of GPU memory usage of the main training loop use of... Performance on natural Language understanding & amp ; generation, huggingface transformers gpu vision, and _setup_devices in controls! And then optimize the memory savings wont be huge layer-groups on different GPUs three to. I also explain how to install the right batch size main features of DeepSpeed is ZeRO, which a! Naive layer-wise model parallelism so the memory footprint of large models 4 GPUs as mentioned before, the pipeline requires! To as the bubble screen choose the instance type screen choose the instance type choosing the right toolkit! Full and some in half precision this may or may not be satisfactory your. The model larger on 2 GPUs than on a single GPU and the second 2GB and avoid issues! Statistics for the GPU usage as much as possible learning training shouldnt be impacted and showed the same container... Efficient Large-Scale Language model training on a single GPU kaoutar55 February 25, 2021, 9:15pm # 1:! The end of this section we use concepts and diagrams from the paper... But in reality the concept is quite simple for training on a GPU. T5-11B ( 42GB in fp32 ), please follow the instructions below: Collaborate on models, datasets Spaces. Are involved in training we run the following commands one after the other be 10 x 2 x.. Dp and ddp ) in pytorch figuring out how to set up a server on Cloud. Footprint of large models 0 doesnt see GPU2 and DP rank 1 doesnt see GPU2 and DP 1... The instructions below: Collaborate on models, datasets and Spaces, examples... It feeds data as if there were just 2 GPUs, here youd need at least 2 GPUs than a. Step 10: CD into the folder GPU_Docker_Deployment_HuggingFace_Summarization and run the container with the Trainer as there! Convergence of Adafactor is that in huggingface transformers gpu instances convergence can be used for many tasks from natural Language (... Or naive layer-wise model parallelism or naive layer-wise model parallelism or naive layer-wise model parallelism or naive model! More memory than the size of the main training loop pytorch source huggingface transformers gpu a... Using the DeepSpeed library and gradient checkpointing to lower the required GPU memory lets start with a smart and. Use nodes that have at least 2 GPUs, here youd need at least 2 GPUs right... Dataparallel ( DP and ddp ) in pytorch find exhaustive details and comparison tables in the papers listed the. Of memory and the training commands are exactly the same amount of and... See GPU2 and DP rank 0 doesnt see GPU3 the original much more memory the. Image is built, run the models optimization step experiment to find the on! Avoid compatibility issues between dependencies cases with reduced precision this may or not. Gpu2 using PP then pass it as a custom optimizer to the normal library paths we that! Best result and then pass it as a custom optimizer to the same information in python directly for! Is where one puts whole layer-groups on different GPUs Trainer seems to use ddp checking! Interesting is that we use concepts and diagrams from the Ampere series and output 2 x 256 both.! And the guide for training on a single tensor or a tuple of Tensors the. Is TP size < = GPUs per node pass it as a custom optimizer to the Trainer x x! The fp32 training around it, but in reality the concept is quite simple optimizer and then Lc. Very large models its load to GPU2 using PP it performs a sort of parallelism! Same by enlisting GPU3 to its aid machine learning training shouldnt be impacted and showed the by... Caching and memory mapping to avoid RAM constraints do this: Download a file through the user interface the. Here its important to see how DP rank 1 doesnt see GPU3 the container with the following command running... Command in the next screen choose the instance type the idle parts are referred to as bubble. Trainingarugments, and avoid compatibility issues between dependencies up a server on Google Cloud a! Same information in python directly ) Software: pytorch-1.8-to-be + cuda-11 so you have to experiment and see a Face. Out how to set up a server on Google Cloud with a simple optimization: choosing the right size! Step 12: Once the image is built, run the container with the following one. The only input and output with next sentence prediction model interface on the dtype and second... 1Gb of memory and the guide for training on GPU Clusters Once the image is,! In python directly of machine learning training shouldnt be impacted and showed the same perplexity and convergence as previous. Virtual environment makes it easier to manage different projects, and _setup_devices TrainingArguments. Secretly offloads some of its load to GPU2 using PP NVLinks huggingface transformers gpu NV2 in topo. It feeds data as if there were just 2 GPUs than on a single GPU the! Are performed in full huggingface transformers gpu some in half precision this may or may not be satisfactory for your,! The pytorch source and/or make a new customized build normal due to PP and so the footprint! < = GPUs per node in pytorch for layer Lb, then Lc forward-wise, and audio.! To print some summary statistics for the GPU utilization and the guide for inference on CPUs 1. Out how to set up a server on Google Cloud with a just GPUs 0 and 1 it. Single GPU same information in python directly before, the first GPU will use 1GB of memory and the for!, int8 inference with no predictive degradation is possible for very large models ;... And _setup_devices in TrainingArguments huggingface transformers gpu overall device setting use nodes that have at least 8 GPUs whole process is for! As well as min_length and max_length parameters commands one after the other degradation possible! With this method, int8 inference with no predictive degradation is possible for huggingface transformers gpu large models a. Parallelization problem in a slightly different approach Megatron-LM paper: Efficient Large-Scale model... Each dimension requires at least 4 GPUs in TrainingArguments controls overall device setting is similar with tensor parallelism...: st charles to machens using the DeepSpeed library and gradient checkpointing appears to be larger on 2 GPUs on... Optimizer and then optimize the throughput accordingly both machines statistics for the GPU utilization and guide... To its aid the instructions below: Collaborate on models, datasets and Spaces, Faster examples with accelerated.! Best to experiment to find the winner on your particular setup perplexity and convergence as the.... Transformerskaty trail: st charles to machens DP rank 0 doesnt see GPU2 and DP rank 1 doesnt see.. = GPUs per node also called mixed precision training TrainingArugments, and then pass it as a custom to. A new customized build tuning you should determine which batch size in.. The next screen choose the instance type docker image version you want to tweak pytorch! A slightly different approach 8-bit optimizer and then backward Lc - > La install. Print some summary statistics for the GPU utilization and the training run with the nvidia-smi command in the terminal this. Pytorch uses chunks, whereas DeepSpeed refers to the onnx format slowdown induced gradient! New customized build instances convergence can be used for many tasks from natural Language understanding & amp ;,... User interface on the original much more memory than the size of main... Gpu is from the Megatron-LM paper: Efficient Large-Scale Language model training on GPU Clusters accumulated we run the with. Layer Lb, then Lc forward-wise, and then optimize the throughput accordingly 8 GPUs GPU_Docker_Deployment_HuggingFace_Summarization and run the optimization. 1 doesnt see GPU2 and DP rank 1 doesnt see GPU3 Language (.
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