Huggingface load model on multiple gpus example.
Huggingface load model on multiple gpus example requires_grad = False for param in model. It supports ONNX Runtime (ORT), a model accelerator, for a wide range of hardware and frameworks including CPUs. AutoTokenizer. The key is to find the right balance between GPU memory utilization (data throughput/training time) and training The only settings to configure in this guide are where to save the checkpoint, how to evaluate model performance during training, and pushing the model to the Hub. , replicates your model across all the gpus To load a model in 4-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. The model takes up about 32GB when loaded, so each graphic is taken up to about 8GB (8*4). For example, what would be the Jun 17, 2024 · In the era of large-scale deep learning models, the need for efficient training and finetuning on large datasets across multiple GPUs has become critical. To use multiple GPUs, you must use a multi-process environment, which means you have to use the DeepSpeed launcher which can’t be emulated as shown here. from_pretrained(load_path, device_map = 'auto') model = BetterTransformer. Defaults to -1 for CPU inference. I Oct 22, 2024 · I am trying to fine-tune llama on multiple GPU using trl library, and trying to achieve data-parallel and model-parallel both. from_pretrained( llama_model_id May 26, 2023 · Found the following statement: You don’t need to prepare a model if it is used only for inference without any kind of mixed precision in accelerate. I am running the model on notebook. Mar 4, 2024 · I tried DataParallel and DistributedDataParallel, but didn’t Work. Single GPU - 32466MiB Two GPUs - 26286MiB + 14288MiB = 40574MiB PEFT, a library of parameter-efficient fine-tuning methods, enables training and storing large models on consumer GPUs. Hi folks, I tried running the 7b-chat-hf variant from meta (fp16) with 2*RTX3060 (2*12GB). My current machine has 8 gpu cards and I only want to use some of them. I cannot use CUDA_VISIBLE_DEVICES since I need all of them to be visible in the script. 31. from sentence_transformers import SentenceTransformer, losses from torch. Aug 13, 2023 · hi All, @philschmid , I hope you are doing well. The BitsAndBytesConfig is passed to the quantization_config parameter in from_pretrained() . requires_grad = True GPU. save_state. My guess is that it provides data parallelism (i. I’m following the training framework in the official example to train the model. It just puts everything on gpu:0, so I cannot use Aug 13, 2023 · Hi, Is there any way to load a Hugging Face model in multi GPUs and use those GPUs for inferences as well? Like, there is this model which can be loaded on a single GPU (default cuda:0) and run for inference as below: … This should happen as early as possible in your training script as it will initialize everything necessary for distributed training. 00 MiB (GPU 0; 39. Knowledge distillation is a good example of using multiple models, but only training one of them. 59 GiB already allocated; 42. distributed. distributed module Mar 9, 2023 · To get a decent model, you need at least to play with 10B+ scale models which would require up to 40GB GPU memory in full precision, just to fit the model on a single GPU device without doing any training at all! Next, the weights are loaded into the model for inference. Running FP4 models - multi GPU setup The way to load your mixed 4-bit model in multiple GPUs is as follows (same command as single GPU setup): I have 4 gpu's. from_pretraine… Aug 28, 2023 · Feature request. Set the environment variables MODEL_NAME and DATASET_NAME to the model and dataset respectively. Would be great to add a code example of how to do multi-GPU processing with 🤗 Datasets in the documentation. generate on a DataParallel layer isn't possible, and model. @philschmid @nielsr your help would be appreciated import os import torch import pandas as pd from datasets import load_dataset os. Therefore, only rank 0 is loading the pre-trained model leading to efficient usage of Mar 8, 2022 · This just hangs when loading the model. Aug 4, 2023 · According to the main page of the Trainer API, “The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex and Native AMP for PyTorch. This is important for the use-case of an end-user running a model locally for chat. @diegomontoya The reason for setting max_memory[0]=10GiB is because of moving lm_head to GPU 0 in an ad-hoc way (and loading the input tensor to GPU 0 before running forward pass). We load the model and the adapters across multiple GPUs and the activations and gradients will be naively communicated across the GPUs. Load a model as a backbone. would you please help me to understand how I can change the code or add any extra lines to run it in multiple gpus? for me trainer in Hugging face always needs GPU :0 be free , even if I use GPU 1,2,. Load a pretrained processor. launch with deepspeed. self_attn. perf_counter() tokenizer Aug 29, 2020 · Hi! How would I run generation on multiple GPUs at the same time? Running model. Jun 19, 2023 · For example “gpt2:lmheadmodel”? How do I find the model is supported? Is there any tutorial, how to add unsupported model? Has anyone tried to run the model on multiple GPUs, if it does not fit one? Let me give you a concrete example. data import DataLoader # Replace 'model_name' and 'max_seq_length' with your actual model name and max sequence length model_name = 'your_model_name' max_seq_length = Model sharding is a technique that distributes models across GPUs when the models don’t fit on a single GPU. This is only supported for one GPU. distributed, torchX, torchrun, Ray Train, PTL etc) or can the HF Trainer alone use multiple GPUs without being launched by a third-party distributed launcher? Tensor Parallelism. It allows If the student model fits on a single GPU, we can use ZeRO-2 for training and ZeRO-3 to shard the teacher for inference. You can control which GPU’s to use using CUDA_VISIBLE_DEVICES environment variable i. I want to load the model directly into GPU when executing from_pretrained. environ["MASTER_ADDR Jun 13, 2024 · How can i use SFTTrainer to leverage all GPUs automatically? If I add device_map=“auto” I get a Cuda out of memory exception. While training using model-parallel, I noticed that gpu:0 is actively computing, while other GPUs set idle despite their VRAM are consumed. There are several ways to split a model, but the typical method distributes the model layers across GPUs. What you’re looking for is a distributed training. This is the case for the Pipelines in 🤗 transformers, fastai and many others. ⇨ Single GPU. If you have access to more than one GPU, there a few options for efficiently loading and distributing a large model across your hardware. Huggingface’s Transformers library Aug 4, 2024 · Can I please ask if it’s possible to do multi gpu training if the whole model itself doesn’t fit on one gpu when loaded? For example, I’m training using the Trainer from huggingface Llama3. This performs fine-tuning training on the well-known BERT transformer model in its base configuration, using the GLUE MRPC dataset concerning whether or not a sentence is a paraphrase of another. We’ll walk through the necessary steps to configure your Nov 22, 2024 · So I need to load multiple large models in a single script and control which GPUs they are kept on. Note that here we can run the inference on multiple GPUs using the model-parallel tensor-slicing across GPUs even though the original model was trained without any model parallelism and the checkpoint is also a single GPU checkpoint. The base classes PreTrainedModel, TFPreTrainedModel, and FlaxPreTrainedModel implement the common methods for loading/saving a model either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace’s AWS S3 repository). Sep 4, 2023 · I followed the accelerate doc. Jun 28, 2022 · CPU/Disk Offloading to enable training humongous models that won’t fit the GPU memory On a single 24GB NVIDIA Titan RTX GPU, one cannot train GPT-XL Model (1. Thanks. PyTorch model weights are normally instantiated as torch. Here is an example: ⇨ Single GPU. utils. to('cuda') now the model is loaded into GPU. from_pretrained() and both GPUs memory is almost full (11GB~, 11GB~) which is good. Dec 21, 2022 · Dear Huggingface community, I’m using Owl-Vit in order to analyze a lot of input images, passing a set of labels. Deployment with multiple GPUs To deploy this feature with multiple GPUs adjust the Trainer command line arguments as following: replace python -m torch. I know I’ll eventually want to learn about DeepSpeed as well but for now I am focusing on the base features of Accelerate. You must revise several lines of code to match your intended configuration (e. from transformers import pipeline from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig import time import torch from accelerate import init_empty_weights, load_checkpoint_and_dispatch t1= time. You don’t need to indicate the kind of environment you are in (just one machine with a GPU, one match with several GPUs, several machines with multiple GPUs or a TPU), the library will detect this automatically. I write the code following popular repositories in GitHub. Sep 27, 2022 · We'll explain what each of those arguments do in a moment, but first just consider the traditional model loading pipeline in PyTorch: it usually consists of: Create the model; Load in memory its weights (in an object usually called state_dict) Load those weights in the created model; Move the model on the device for inference Model sharding is a technique that distributes models across GPUs when the models don’t fit on a single GPU. First I wonder what does accelerate do when using the --multi_gpu flag. Many frameworks automatically use the GPU if one is available. It allows Aug 17, 2022 · I've extensively look over the internet, hugging face's (hf's) discuss forum & repo but found no end to end example of how to properly do ddp/distributed data parallel with HF (links at the end). Jan 24, 2024 · Next, train a small GPT-3 model with 8 GPUs in one node. from_pretrained("google/ul2", device_map = 'auto') Aug 10, 2024 · In this article, we’ll learn how to effectively distribute HuggingFace models across multiple GPUs to enhance performance. If you run the script as is it shouldn’t detect multiple GPUs and use them. For example, load the optimum/roberta-base-squad2 checkpoint for question answering inference. Start by computing the text embeddings with the text encoders. Since some computations are performed in full and some in half precision this approach is also called mixed precision training. We will look at how we can use DeepSpeed ZeRO Stage-3 with CPU offloading of optimizer states, gradients and parameters to train GPT-XL Model. Accelerate. 25 MiB Jul 16, 2023 · Hi, I want to fine-tune llama with Lora on multiple GPUs on my private dataset. GPU. Optimum provides the ORTModel class for loading ONNX models. Model sharding is a technique that distributes models across GPUs when the models don’t fit on a single GPU. This will use the Accelerate library to automatically determine how to load the model weights across multiple devices. to("cuda") [inputs will be on cuda:0] I want lo load them on all GPU's. float32 and then again to load them in your desired data type, like torch. I’m using the huggingface trainer to try this and confirmed the training_args resolved n_gpu to 2 or 4 depending on how many I configure modal to have. The script creates and saves the following files to your repository: saved model checkpoints Aug 4, 2023 · Hey @muellerzr I have a question related to this. Can I use Accelerate + DeepSpeed to train a model with this configuration ? Can’t seem to be able to find any writeups or example how to perform the “accelerate config”. _ba Jan 23, 2021 · For example, when you call load_dataset() you should pass streaming=True or verify that when you use your data you don’t use random access (since it’s an iterable dataset). Sep 10, 2023 · I am training the model using multiple gpu what is the right way to save the checkpoing currently i am confused with how this works? should i check is it the main process or not using accelerate. For example, to distribute 600MB of memory to the first GPU and 1GB of memory to the second GPU: Aug 21, 2023 · hi All, would you please give me some idea how I can run the attached code with multiple GPUs, with define number of 1,2? As I understand the trainer in HF always goes with gpu:0, but I need to specify the number of GPUs like 1,2. PartialState to create a distributed environment; your setup is automatically detected so you don’t need to explicitly define the rank or world_size. The file naming is up to you. Is this possible? Nov 27, 2023 · meta-llama/Llama-2–7b, 100 prompts, 100 tokens generated per prompt, 1–5x NVIDIA GeForce RTX 3090 (power cap 290 W) Multi GPU inference (batched) ⇨ Single GPU. To use DeepSpeed in a Jupyter Notebook, you need to emulate a distributed environment because the launcher doesn’t support deployment from a notebook. Sep 28, 2023 · Loading a HF Model in Multiple GPUs and Run Inferences in those GPUs. Expand the list below to see which models support tensor parallelism. co, or a path to a directory containing model weights saved using save_pretrained, e. float32 and it can be an issue if you try to load a model as a different data type. Do I need to launch HF with a torch launcher (torch. Any idea what could be wrong? I have a very vanilla ROCm 6. Essentially this is what I have: from tr Sep 23, 2024 · cd examples python . This guide will show you how to run inference on two execution providers that ONNX Runtime supports for NVIDIA GPUs: Oct 22, 2024 · I am trying to fine-tune llama on multiple GPU using trl library, and trying to achieve data-parallel and model-parallel both. Example: cuda: 0,1,2,3 Models. 1 8b in full precision on 4 gpus of 16 GB VRAM each. parameters(): param. I have 8 Tesla-V100 GPU cards, each of which has 32GB grap… Set up a BitsAndBytesConfig and set load_in_8bit=True to load a model in 8-bit precision. I successfully ran my code on 1 GPU. I just want to do the most naive data parallelism with Multi-GPU LLM inference (llama). Let suppose that I use model from HF library, but I am using my own trainers,dataloader,collators etc. I feel like this is an unexpected act, expecting all GPUs would be busy during training. Would above steps result in successful Oct 21, 2022 · This tutorial assumes you have a basic understanding of PyTorch and how to train a simple model. Sorry for fine tuning llama2, I create csv file with the Alpaca structure which has text column including ### instruction ### input ### response, for fine tuning the model I am confused which method with PEFT and QLora should I use, I am confused with many codes, would you please refer me to any code that is right for fine tuning with alpaca Note that device_map is optional but setting device_map = 'auto' is prefered for inference as it will dispatch efficiently the model on the available ressources. May 7, 2025 · HF always loads your model to GPU if it's available. 45 GiB total capacity; 38. This way we can only load onto one gpu inputs = inputs. My code is based on some very basic llama generation code: model = AutoModelForCausalLM. When I run the training, the number of steps equals 2 days ago · The above script modifies the model in HuggingFace text-generation pipeline to use DeepSpeed inference. Aug 18, 2023 · Hi All, @phucdoitoan , I am using this code but my issue is that I need multiple gpus, for example using GPU 1,2,3 (not gpu 0) . onnx file. To ensure optimal performance and flexibility, we have partnered with open-source communities and hardware vendors to provide multiple ways to run the model locally. Load a pretrained model. This paradigm, termed as “Naive Pipeline Parallelism” (NPP) is a simple way to parallelize the model across multiple GPUs. Keep the text encoders on two GPUs by setting device_map="balanced". The example below assumes two 16GB GPUs are available for inference. Currently the docs has a small section on this saying "your big GPU call goes here", however it didn't work for me out-of-the-box. This is significantly faster than using ZeRO-3 for both models. Let’s say that I would load this model EleutherAI/gpt-neo-125m · Hugging Face. generate run on a single GPU. from_pretrained("bert-base-uncased") would be loaded to CPU until executing. 3 GB of the GPU memory. GPUs are commonly used to train deep learning models due to their high memory bandwidth and parallel processing capabilities. If I pass “auto” to the device_map, it will always use all GPUs. When save_total_limit=1 and load_best_model_at_end, it is possible that two checkpoints are saved: the last one and the best one (if they are different). The exact number depends on the specific GPU you are using. For example, when multiplying the input tensors with the first weight tensor, the matrix multiplication is equivalent to splitting the weight tensor column-wise, multiplying each column with the input separately, and then concatenating the separate outputs. 10: 9395: October 16, 2024 I see only "GPU 0" is used to load the model, not Sep 28, 2020 · I would like to train some models to multiple GPUs. sh. We observe that inference is faster on a multi-GPU instance than on a single-GPU instance ; is the pipe. distributed module Next, the weights are loaded into the model for inference. There are a lot of resources on how to optimize LLM inference for latency with a batch size of 1. You should also specify where to save the model in OUTPUT_DIR, and the name of the model to save to on the Hub with HUB_MODEL_ID. load_model_func_kwargs (dict, optional) — Additional keyword arguments for loading model which can be passed to the underlying load function, such as optional arguments for DeepSpeed’s load_checkpoint function or a map_location to load the model and optimizer on. Mar 14, 2024 · Some highlights of Zero-2 are that it only divides optimizer states and gradients across GPUs but the model params are copied on each GPU while in Zero-3, model weights are also distributed across May 2, 2024 · Hey Guys, I have a multiple AMD GPU setup and have run into a bit of trouble with transformers + accelerate. Depending on your GPU and model size, it is possible to even train models with billions of parameters. I know that we can run the model on multiple gpu's using device="auto", but how to convert the input token's to load on multiple gpu's. Jun 25, 2024 · Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cuda:3! (when checking argument for argument index in method wrapper_CUDA__index_select) Mar 4, 2023 · @SamuelAzran Yeah, that script assumes you have four 16 GB RAM GPUs and you need to offload it to CPU when you have only one. This is minimal example I cooked up to demonstrate the issue. This checkpoint contains a model. Knowledge distillation. To train this model you need to add additional modules inside the model such as adapters using `peft` library and freeze the model weights. However, the Accelerator fails to work properly. Each machine has 4 GPUs. While I know how to train using multiple GPUs, it is not clear how to use multiple GPUs when coming to this stage. Trainer requires a function to compute and report your metric. My problem is: I have 8 gpu machine (each has 40GB gpu memory), but the below code does use only one of them to process batches. Oct 15, 2018 · Training neural networks with larger batches in PyTorch: gradient accumulation, gradient checkpointing, multi-GPUs and distributed setups… Dec 9, 2023 · In ctransformers library, I can only load around a dozen supported models. Load a tokenizer with AutoTokenizer. In most cases, this allows costly operations to be placed on GPU and significantly accelerate inference. from_model_id in the LangChain framework, you can use the device_map="auto" parameter. The key is to find the right balance between GPU memory utilization (data throughput/training time) and training Load a pretrained image processor; Load a pretrained feature extractor. any help would be appreciated. I’m training environment is the one-machine-multiple-gpu setup. save_state when i do . If you have multiple-GPUs and/or the model is too large for a single GPU, you can specify device_map="auto", which requires and uses the Accelerate library to automatically determine how to load the model weights. For example, lets say I want to load one LLM on the first 4 GPUs and the another LLM on the last 4 GPUs. The main training script is ds_pretrain_gpt_125M_flashattn. The model predicts binary masks that states the presence or not of the object of interest given an image. Model fits onto a single GPU: Normal use; Model doesn’t fit onto a single GPU: ZeRO + Offload CPU and optionally NVMe; as above plus Memory Centric Tiling (see below for details) if the largest layer can’t fit into a single GPU; Largest Layer not fitting into a single GPU: ZeRO - Enable Memory Centric Tiling (MCT). It enables fitting larger model sizes into memory and is faster because each GPU can process a tensor slice. The load_checkpoint_and_dispatch() method loads a checkpoint inside your empty model and dispatches the weights for each layer across all available devices, starting with the fastest devices (GPU, MPS, XPU, NPU, MLU, SDAA, MUSA) first before moving to the slower ones (CPU and hard drive). 0 install (see this gist for docker-compose Note that device_map is optional but setting device_map = 'auto' is prefered for inference as it will dispatch efficiently the model on the available ressources. model. e if CUDA_VISIBLE_DEVICES=1,2 then it’ll use the 1 and 2 cuda devices. To clarify, I have 3200 examples and I set per_device_train_batch_size=4 Text-to-image models like Stable Diffusion are conditioned to generate images given a text prompt. Training multiple disjoint models at once. I am trying to finetune a model that is loaded on 8bit using Peft/Lora library in huggingface. v_proj. Jan 23, 2024 · Hence, every GPU mainly have to load the model parameters, no matter the GPU count. /nlp_example. It comes from the accelerate module; see here. Tried to allocate 160. Llama 3 8B Instruct loads fine and produces sensible output when I use just one card, but when I change to device_map=‘auto’ it appears to work, but only produces garbage output. I can load the tokenizer as Set up a BitsAndBytesConfig and set load_in_8bit=True to load a model in 8-bit precision. Model parallelism distributes a model across multiple GPUs. Nov 23, 2022 · You can read Distributed inference with multiple GPUs with using accelerate which is library designed to make it easy to train or run inference across distributed setups. These methods only fine-tune a small number of extra model parameters, also known as adapters, on top of the pretrained model. 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. With accelerate it does it too. For a classification task, you’ll use evaluate. I can successfully specify 1 GPU using device_map='cuda:3' for smaller model, how to do this on multiple GPU like CUDA:[4,5,6] for larger model? May 15, 2023 · Hey, we have this sample using Instruct-pix2pix diffuser . import os import torch import Apr 10, 2024 · Hi, is there a way to create an instance of LLM and load that model into two different GPUs? Note that the instance will be created in two different celery tasks (asynchronous task/job) Oct 21, 2022 · This tutorial assumes you have a basic understanding of PyTorch and how to train a simple model. Handling big models for inference Below is a fully working example for me to load code llama into multiple GPUs. On the forward pass, the first GPU processes a batch of data and passes it to the next group of layers on the next GPU. Can someone help me understand if separate scripts like torchrun (or maybe Load those weights inside the model; While this works very well for regularly sized models, this workflow has some clear limitations when we deal with a huge model: in step 1, we load a full version of the model in RAM, and spend some time randomly initializing the weights (which will be discarded in step 3). from_pretrained(load_path) model = AutoModelForSequenceClassification. We can observe that during loading the pre-trained model rank 0 & rank 1 have CPU total peak memory of 32744 MB and 1506 MB, respectively. float16. Jan 26, 2021 · Note: Though we have 2 * 32GB of GPU available, and we should be able to fine-tune the RoBERTa-Large model on a single 32GBB GPU with lower batch sizes, we wanted to share this idea/method to help A string, being the model id of a pretrained model hosted inside a model repo on huggingface. I was trying to use a pretained m2m 12B model for language processing task (44G model file). cc @stevhliu. For example, you will see the Total optimization steps will decrease proportionally to the number of GPUs. Feb 23, 2022 · I'm using an tweaked version of the uer/roberta-base-chinese-extractive-qa model. The model predicts much better results if input 2D points and/or input bounding boxes are provided; You can prompt multiple points for the same image, and predict a single mask. May 2, 2022 · Due to this, any optimizer created before model wrapping gets broken and occupies more memory. json is the DeepSpeed configuration file as documented here. This is a simple way to parallelize the model across multiple GPUs. Since the model is present on the GPU in both 16-bit and 32-bit precision this can use more GPU memory (1. Accelerate will automatically wrap the model and create an optimizer for you in case of single model with a warning message. To begin, create a Python file and initialize an accelerate. transform(model) May 30, 2022 · This might be a simple question, but bugged me the whole afternoon. Llama 3, Oct 4, 2020 · There is an argument called device_map for the pipelines in the transformers lib; see here. is_main_process and save the state using accelerate. Nov 22, 2024 · Hi, So I need to load multiple large models in a single script and control which GPUs they are kept on. , how often to save the model checkpoints, and how to set up the 3D parallelism configuration). `is_model_parallel` attribute will be force-set to `True` to avoid any unexpected behavior such as device placement mismatching. Fine-tuning the model is not supported yet Jun 23, 2022 · Hi, I want to train Trainer scripts on single-node, multi-GPU setting. cuda. Feb 15, 2023 · When you load the model using from_pretrained(), you need to specify which device you want to load the model to. Multiple GPUs. Where I should focus to implement multiple GPU training? I need to make changes only in the Trainer class? If yes, can you give me a brief description? Thank you in avance. Aug 11, 2023 · You have loaded a model on multiple GPUs. A tokenizer converts your input into a format that can be processed by the model. Oct 31, 2023 · I encountered the following issues while using the device_map provided by Hugging Face for model parallel inference: I am running the code from the example code provided by Hugging Face, which can be Since the model is present on the GPU in both 16-bit and 32-bit precision this can use more GPU memory (1. Jun 17, 2024 · PyTorch’s Fully Sharded Data Parallel (FSDP) is a powerful tool designed to address these challenges by enabling efficient distributed training and finetuning across multiple GPUs. But when I tried to ran it on multiple GPUs, I met the following problem (I used TORCH_DISTRIBUTED_DEBUG=DETAIL to debug): Parameter at index 127 with name base_model. Aug 15, 2024 · Question Validation I have searched both the documentation and discord for an answer. Oct 21, 2021 · I’m training my own prompt-tuning model using transformers package. e. In this Sep 28, 2023 · Here’s how I load the model: tokenizer = AutoTokenizer. When running on a machine with GPU, you can specify the device=n parameter to put the model on the specified device. However, using more GPUs will speed up your training, as each GPU can process different data samples in parallel (data parallelism). Allow Accelerate to automatically distribute the model across your available hardware by setting device_map=“auto” . Oct 31, 2023 · I encountered the following issues while using the device_map provided by Hugging Face for model parallel inference: I am running the code from the example code provided by Hugging Face, which can be For example, for save_total_limit=5 and load_best_model_at_end, the four last checkpoints will always be retained alongside the best model. Hence, it is highly recommended and efficient to prepare model before creating optimizer. Question I'm trying to load an embedding model from HuggingFace on multiple available GPUs using this code: embed_model = HuggingFaceEmbedding(self. From my Jul 2, 2024 · When I run my self contained script on one or multiple GPUs then the memory utilization on the same model is as follows. GPU Inference . Oct 5, 2023 · from transformers import AutoModelForCausalLM model = AutoModelForCausalLM. 25 MiB Oct 11, 2022 · I need just inference. You can specify a custom model dispatch, but you can also have it inferred automatically with device_map=" auto". g. Thus, add the following argument, and the transformers library will take care of the rest: model = AutoModelForSeq2SeqLM. 5B parameters) even with a batch size of 1. I share the code I’m using for this below. Accelerate is a library designed to simplify distributed training on any type of setup with PyTorch by uniting the most common frameworks (Fully Sharded Data Parallel (FSDP) and DeepSpeed) for it into a single interface. Aug 20, 2020 · It starts training on multiple GPU’s if available. Accelerator. . model. If the student model fits on a single GPU, we can use ZeRO-2 for training and ZeRO-3 to shard the teacher for inference. I was able to load the model shards into both GPUs using "device_map" in AutoModelForCausalLM. lora_B. The model is quantized. ” It seems like a user does not have to configure anything when using the Trainer class for doing distributed training. It allows Jun 9, 2023 · Hey there, so I have a model that takes 45+ GB to just LOAD, and I’m trying to load the model onto 4 A100 GPU’s, with 40 GB VRAM each and I do the following model = model. Oct 13, 2021 · Hi, I wonder how to setup Accelerate or possibly train a model if I have 2 physical machines sitting in the same network. However, I am not able to find which distribution strategy this Trainer API supports. Nearly every NLP task begins with a tokenizer. from_pretrained(…,device_map=“auto”) and it still just gives me this error: OutOfMemoryError: CUDA out of memory. 🤗Accelerate. is_available() to detect if GPUs are available. py . These features are supported by the Accelerate library, so make sure it is installed first. Tensor parallelism shards a model onto multiple GPUs and parallelizes computations such as matrix multiplication. It will showcase training on multiple GPUs through a process called Distributed Data Parallelism (DDP) through three different levels of increasing abstraction: Native PyTorch DDP through the pytorch. To train the model i use trainer api, since trainer api documentation says that it supports multi-gpu training. The Trainer checks torch. Sep 13, 2023 · Below is the output snippet on a 7B model on 2 GPUs measuring the memory consumed and model parameters at various stages. Training a model can be taxing on your hardware, but if you enable gradient_checkpointing and mixed_precision, it is possible to train a model on a single 24GB GPU. As Llama2 chat was fine-tuned on specific input syntax, we have to make sure that our input string is matching that syntax. Set up a BitsAndBytesConfig and set load_in_8bit=True to load a model in 8-bit precision. 5x the original model is on the GPU), especially for small batch sizes. Only causal Sep 10, 2023 · Intro. load to load the accuracy function from the Evaluate library. get_classifier(). Loading a HF Model in Multiple GPUs and Run Inferences in Oct 9, 2023 · Hi, I’ve been looking this problem up all day, however, I cannot find a good practice for running multi-GPU LLM inference, information about DP/deepspeed documentation is so outdated. Loading an entire model onto each GPU and sending chunks of a batch through each GPU’s model copy at a time; Loading parts of a model onto each GPU and processing a single input at one time; Loading parts of a model onto each GPU and using what is called scheduled Pipeline Parallelism to combine the two prior techniques. It allows To load a model in 4-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. when i do this only one random state is being stored or irrespective of the process should i just call accelerate. I Mar 20, 2024 · I’m trying to fine tune mosaicml/mpt-7b-instruct using cloud resources (modal. to("cuda:" + gpu_id) running the pipeline on multiple GPUs? what explains the speedup on a multi-GPU machine vs single-GPU machine? Load those weights inside the model; While this works very well for regularly sized models, this workflow has some clear limitations when we deal with a huge model: in step 1, we load a full version of the model in RAM, and spend some time randomly initializing the weights (which will be discarded in step 3). json, where ds_config. At the moment, my code works well but run just on 1 GPU: model = OwlViTForObjectDetection. Running FP4 models - multi GPU setup The way to load your mixed 8-bit model in multiple GPUs is as follows (same command as single GPU setup): May 17, 2022 · So if I understand correctly, for launching very large models such as BLOOM 176B for inference, I can do this via Accelerate as long as one node has enough GPUs and GPU memory. module. A PreTrainedModel object. com) with mutliple GPUs but still running out of memory even on the large GPUs. prepare() documentation: Accelerator In data-parallel multi-gpu inference, we want a model copy to reside on each GPU. The model is loaded using from_pretrained with the keywork arguments in args. model_init_kwargs. Aug 13, 2023 · Is there any way to load a Hugging Face model in multi GPUs and use those GPUs for inferences as well? Like, there is this model which can be loaded on a single GPU (default cuda:0) and run for inf Next, the weights are loaded into the model for inference. Tensor parallelism is a technique used to fit a large model in multiple GPUs. default As you can see in this example, by adding 5-lines to any standard PyTorch training script you can now run on any kind of single or distributed node setting (single CPU, single GPU, multi-GPUs and TPUs) as well as with or without mixed precision (fp8, fp16, bf16). /my_model_directory/'. As you are performing transfer learning in this example, the encoder of the model starts out frozen so the head of the model can be trained only initially: Copied for param in model. For example, to distribute 600MB of memory to the first GPU and 1GB of memory to the second GPU: Oct 11, 2022 · I need just inference. However, it is possible to place supported operations on an NVIDIA GPU, while leaving any unsupported ones on CPU. Dec 16, 2022 · I am trying to learn how to train large(r) language models and Accelerate seems to be the tool for me. Aug 13, 2023 · Hi, Is there any way to load a Hugging Face model in multi GPUs and use those GPUs for inferences as well? Like, there is this model which can be loaded on a single GPU (default cuda:0) and run for inference as below: … ⇨ Single GPU. , '. For example, you’d need twice as much memory to load the weights in torch. add a new argument --deepspeed ds_config. from May 13, 2024 · I have a local server with multiple GPUs and I am trying to load a local model and specify which GPU to use since we want to split GPU between team members. This example shows to perform inference on multiple chats simultaneously, where each chat is of course constituted of multiple messages. How can I run local inference on CPU (not just on GPU) from any open-source LLM quantized in the GGUF format (e. I although I have 4x Nvidia T4 GPUs Cuda is installed and my environment can see the available GPUs. In other cases, or if you use PyTorch directly, you may need to move your models and data to the GPU to ensure computation is done on the accelerator and not on the CPU. layers. We can see that the model weights alone take up 1. NOTE: The total size of DeepSeek-V3 models on HuggingFace is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. Nov 9, 2023 · To load a model using multiple devices with HuggingFacePipeline. Jul 10, 2023 · Can i train my models using naive model parallelism by following the below steps: For loading model to multiple gpu’s (2 in my case), i use device_map=“auto” in from_pretrained method. Jun 9, 2023 · Hey there, so I have a model that takes 45+ GB to just LOAD, and I’m trying to load the model onto 4 A100 GPU’s, with 40 GB VRAM each and I do the following model = model. irevrq bwse tewb dhgbqus rmuke sftlg etqg wtdt vihbn ftunu