Model parallelism pytorch. In the forward pass, the module is .

Model parallelism pytorch 0. This hybrid approach balances the trade-offs of each method, optimizing memory usage and minimizing communication overhead, enabling the training of extremely large models on large GPU clusters. Oct 31, 2025 · Training large models inevitably requires a solid understanding of parallelism techniques. In the modern machine learning the various approaches to parallelism are used to: fit very large models onto limited hardware - e. 知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业、友善的社区氛围、独特的产品机制以及结构化和易获得的优质内容，聚集了中文互联网科技、商业、影视 The high-level idea of model parallel is to place different sub-networks of a model onto different devices, and implement the forward method accordingly to move intermediate outputs across devices. The model is replicated on all the devices Dec 5, 2017 · Hi, I’m trying to implement the following paper: Population based training for a simple CIFAR classifier. I first describe the difference between Data Parallelism and Model Paralleli Implement distributed data parallelism based on torch. While Pipeline Parallelism split the hidden layers into multiple GPUs. May 30, 2025 · This guide shows you how to implement model parallelism for large language models using PyTorch, covering both tensor parallelism and pipeline parallelism approaches with practical code examples. I’m confused by so many of the multiprocessing methods out there (e. Previous posts have explained how to use DataParallel to train a neural network on multiple GPUs; this feature replicates the same model to all GPUs, where each GPU consumes a different partition of the input data. In High-Performance Computing (HCP), this is called . This is a built-in feature of Pytorch. However, for smaller models, the communication overhead may outweigh its benefits. Comparing with DDP, FSDP reduces GPU memory footprint by sharding model parameters, gradients, and optimizer states. Single-Machine Model Parallel Best Practices Author: Shen Li Model parallel is widely-used in distributed training techniques. Question: So how to combine data parallelism with model parallelism for multiple nodes? mrshenli (Shen Li) December 27, 2019, 6:44pm 2 The high-level idea of model parallel is to place different sub-networks of a model onto different devices, and implement the forward method accordingly to move intermediate outputs across devices. 12 release. DataParallel and nn. This article was inspired by the excellent “How to Scale Your Model” blog series. . Naive Model Parallel (Vertical) and Pipeline Parallel Naive Model Parallel (MP) is where one spreads groups of model layers across multiple GPUs. Aug 25, 2024 · An advantage of data parallelism over model parallelism is that the GPUs can run in parallel. Defaults to "auto", which sets this size to the number of nodes in the cluster. This tutorial is an extension of the Sequence-to-Sequence Modeling with nn. Dec 19, 2024 · In summary, we hope our work on torch. And the paper published by PyTorch offers insights into their optimization techniques and experiments. nn. This tutorial uses a gpt-style transformer model to demonstrate implementing distributed pipeline parallelism with torch. The TorchTitan project demonstrates a “3D parallel” application on the Llama model. These PyTorch APIs are currently still Apr 23, 2024 · Parallelism in PyTorch encompasses techniques like Data Parallelism, where data is distributed across cores with the same model, and Distributed Data Parallel, which extends this concept to multiple machines. This container parallelizes the application of the given module by splitting the input across the specified devices by chunking in the batch dimension (other objects will be copied once per device). The mechanism is relatively simple - switch the desired layers . Unlike data parallelism, which is limited to sharding computations across the batch dimension, TP further distributes the computation along the feature dimension, allowing multiple GPUs to process the same sample simultaneously. pipelining makes pipeline parallelism more accessible and efficient for large-model training and inference. A complete tutorial on how to train a model on multiple GPUs or multiple servers. The model is divided into different parts (e. Its aim is to make cutting-edge NLP easier to use for everyone May 12, 2025 · Above is the overall runtime design for single-GPU inference without model parallelism. g. Oct 11, 2019 · But when I try to combine data parallelism with model parallelism, it failed. I choose “torch. DistributedDataParallel (model, device_ids= [args. nn. Module): def init (self, split_gpus): self. If your model does not fit on a single GPU, you can use FSDP and request more GPUs to reduce the memory footprint for each GPU. Unlike DataParallel, DDP takes a more sophisticated approach by distributing both data and the model PyTorch Fully Sharded Data Parallel (FSDP) is used to speed-up model training time by parallelizing training data as well as sharding model parameters, optimizer states, and gradients across multiple pytorch instances. Amazon SageMaker model parallelism is a software library on top of PyTorch. In PyTorch, the DistributedSampler ensures each device gets a non-overlapping input batch. Defaults to "auto", which sets this size to the number of GPUs in a single node. We parallelize module or sub_modules based on a parallelize_plan. By leveraging features such as model partitioning, a declarative schedule format, and automated backwards partitioning, we are able to provide a wide range of complex schedules as defaults to the user. module1 = (s&hellip; Jun 14, 2023 · Model Training with PyTorch’s Fully Sharded Data Parallel on Spark The model training is performed through PyTorch’s distributed training on Spark, using PySpark’s TorchDistributor on Azure Databricks. Sep 12, 2024 · Tensor Parallelism Tensor parallelism (TP) is a widely used model parallelism technique. Is there any tutorial regarding implementation of DDP + Model Parallelism together? Oct 25, 2023 · Tired of being limited by the capacity of a single GPU while working with large PyTorch models? Fear not! With tensor parallelism, you can run large models smoothly across multiple GPUs with just one line of code. distributed at module level. The SageMaker model parallel library v2 (SMP v2) is compatible with the native PyTorch APIs and capabilities. What is Model Parallelism? ¶ There are different types of model parallelism, each with its own trade-offs. transformers is the pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, …), inference engines (vLLM, SGLang, TGI Oct 10, 2024 · Accelerating Deep Learning with Data and Model Parallelization in PyTorch Speeding up the training process of deep learning models is crucial, especially as models grow larger and datasets become … Training Transformer models using Pipeline Parallelism Author: Pritam Damania This tutorial demonstrates how to train a large Transformer model across multiple GPUs using pipeline parallelism. parallel. Questions: Is this possible in pyTorch? If not, is this possible in Torch? Would inter-GPU communication (say, for While Data Parallelism replicates the model across devices and processes different data batches, it doesn't help when the model itself is too large to fit into the memory of a single accelerator (GPU). These models would then periodically and DataParallel # class torch. Jan 26, 2021 · This approach helps achieve Model Parallelism just with PyTorch and without using any PyTorch wrappers such as Pytorch-Lightning. Aug 31, 2023 · The above figure illustrates the unsharded 2B parameters model peak memory footprint stands at 26GB (red dashed line). 2 Model Parallelism. I want to split it over several GPUs such that the memory cost is shared between GPUs. parallelize_module(module, device_mesh=None, parallelize_plan=None, *, src_data_rank=0) [source] # Apply Tensor Parallelism in PyTorch by parallelizing modules or sub-modules based on a user-specified plan. It is a general and flexible framework, supporting pipeline and tensor parallelism with memory-saving features. By splitting the training process across multiple machines, DDP helps reduce training time and facilitates scaling to larger models and datasets. As shown below in the In the modern machine learning the various approaches to parallelism are used to: fit very large models onto limited hardware - e. Currently, PiPPy focuses on pipeline parallelism, a technique in which the code of the model is partitioned and multiple micro-batches execute different parts of the model code Jul 7, 2025 · By leveraging FSDP, we have shown that it is possible to fully fine-tune the Llama-3 8B model, a feat that is not achievable with Differentially Private Distributed Data Parallel (DP-DDP) due to memory constraints. But when I increase the size of my model, the forward pass doesn’t fit on one GPU. DistributedDataParallel (DDP) class for data parallel training: multiple workers train the same global model on different data shards, compute local gradients, and synchronize them using AllReduce. Each method has its own pros and cons, but in this blog, we will focus on Tensor Parallelism using PyTorch. Jan 21, 2025 · Distributed Data Parallelism (DDP) in PyTorch is a module that enables users to train models across multiple GPUs and machines efficiently. ” Training on a Subset of Available Devices What if you want to use model parallelism or data parallelism but you don’t want to take up all available devices for a single model? In that case, you can restrict which devices Pytorch can see for each model . spawn, launch utility). Mar 11, 2025 · Figure 1: Layout illustration of 2D Sparse Parallelism With 2D sparse parallelism we address these challenges, instead of sharding tables across all ranks, we first evenly divide all ranks into several parallel groups: Within each group, we use model parallel for the embedding tables, such as column-wise/row-wise sharding. Is it right @wayi ? Tensor parallelism is a technique for training large models by distributing layers across multiple devices, improving memory management and efficiency by reducing inter-device communication. harding model weights (model parallelism) reduces the peak memory footprint, and thus, enables larger model training with a given TPU pod slice. The parallelize Mar 4, 2020 · For more information, see “Getting Started with Distributed Data Parallel. In the forward pass, the module is Tensor parallelism is a technique for training large models by distributing layers across multiple devices, improving memory management and efficiency by reducing inter-device communication. In many cases these strategies are some flavour of model parallelism however we only introduce concepts at a Single-Machine Model Parallel Best Practices Author: Shen Li Model parallel is widely-used in distributed training techniques. This article is your user-friendly guide to getting started with tensor parallel, troubleshooting any issues you might face, and much more! Why Choose Tensor Parallel? The magic of [docs] classModelParallelStrategy(ParallelStrategy):"""Enables user-defined parallelism applied to a model. However, I have several hundred thousand crops I need to run on the model so it is only practical if I run Jul 3, 2023 · By leveraging deep learning frameworks, such as TensorFlow and PyTorch, and incorporating the recommended techniques, practitioners can effectively implement model parallelism and harness the full Jan 16, 2019 · Another option would be to use some helper libraries for PyTorch: PyTorch Ignite library Distributed GPU training In there there is a concept of context manager for distributed configuration on: nccl - torch native distributed configuration on multiple GPUs xla-tpu - TPUs distributed configuration PyTorch Lightning Multi-GPU training This is of possible the best option IMHO to train on CPU/GPU May 6, 2025 · This article explores four major multi-GPU training strategies — Data Parallelism, Model Parallelism, Tensor Parallelism, and Pipeline Parallelism — explaining each with examples in PyTorch Model parallelism is a distributed training method in which the deep learning (DL) model is partitioned across multiple GPUs and instances. tensor. Data parallelism is a way to process multiple data batches across multiple devices simultaneously to achieve better performance. In the forward pass, they compute their output signal Model parallelism is a distributed training method in which the deep learning model is partitioned across multiple devices, within or across instances. , layers or modules), with each part assigned to a separate GPU. This makes it convenient for you to adapt your PyTorch Fully Sharded Data Parallel (FSDP) training script to the SageMaker Training platform and This tutorial introduces more advanced features of Fully Sharded Data Parallel (FSDP) as part of the PyTorch 1. One can wrap a Module in DataParallel and it will be parallelized over multiple GPUs in the batch dimension This repository contains recipes for running inference and training on Large Language Models (LLMs) using PyTorch's multi-GPU support. For such scenarios, we need Model Parallelism, which involves partitioning a single model across multiple devices. t5-11b is 45GB in just model params significantly speed up training - finish training that would take a year in hours We will first discuss in depth various 1D parallelism techniques and their pros and cons and then look at how they can be combined into 2D and 3D You should be familiar with: PyTorch basics Writing distributed applications Distributed model training This tutorial uses the torch. Aug 19, 2022 · I have an existing DDP pipeline that can fit on my single-node multiple GPU system. Data Parallelism is implemented using torch. Mar 1, 2025 · Recommendation: In actual projects, start with simple synchronous parallelism (BSP), and use PyTorch DDP or similar tools for multi-GPU training. Jan 21, 2025 · TorchAO, a PyTorch-native library, provides a streamlined experience for quantization, sparsity, and tensor parallelism (with DTensor) SGLang, a fast, efficient and hackable serving framework for Large Language Model (LLM) and Vision Language Models (VLM) with extensive model support Jan 15, 2020 · Example # Let us start with a simple torch. This tutorial is a gentle introduction to PyTorch DistributedDataParallel (DDP) which enables data parallel training in PyTorch. Each of these models would then update a global dict with its validation accuracy as well as its parameters. State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2. DataParallel is easier to use (just wrap the model and run your training script). Jun 13, 2025 · The entrypoint to parallelize your nn. Tensor Parallelism itself can be divided into two different Nov 12, 2024 · PyTorch’s DistributedDataParallel module incorporate these data parallelism modules gracefully. Jun 12, 2024 · Therefore I assume that model parallelism alone is not enough, since a single layer (the first) does not fit on a single GPU. Composability with other PyTorch parallel techniques such as data parallel (DDP, FSDP) or tensor parallel. In the forward pass, the module is The high-level idea of model parallel is to place different sub-networks of a model onto different devices, and implement the forward method accordingly to move intermediate outputs across devices. If the network environment is heterogeneous, there are many nodes, or the task requires extremely high throughput, you can try asynchronous parallelism (ASP) or parameter server solutions, and cooperate with Gradient Accumulation to balance bandwidth Dec 19, 2018 · As I understand, in model parallelism, you divide a model and train each part separately in different GPUs. What Is Model Parallelism for Large Language Models? Model parallelism distributes different parts of a neural network across multiple GPUs or devices. Mar 5, 2023 · Efficient model sharding with tensor parallelism How can we parallelize a model more efficiently? Rather than parallelizing the model layer by layer, we can split the model into shards that are distributed across multiple devices and execute in parallel. pipelining APIs. Jul 8, 2019 · Pytorch has two ways to split models and data across multiple GPUs: nn. If your model fits on a single GPU and you have a large training set that is taking a long time to train, you can use DDP and request more GPUs to increase training speed. gpu]), this creates one DDP instance on one process, there could be other DDP instances from other processes in the same group working together with this DDP instance. Note that, to optimize performance, the first and third linear layers of SwiGLU activation are merged together as GroupedGEMM13 / GEMM13. Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation, etc in 100+ languages. . In model parallelism, the DL model is split, and each worker loads a different part of the DL model for training (see Figure 5). As only part of a model operates on any individual device, a set of devices can collectively serve a larger model. The devices to synchronize across are specified by the input process_group, which is the entire world by default. Dec 6, 2023 · The most popular way of parallelizing computation across multiple GPUs is data parallelism (DP), where the model is copied across devices and the batch is split so that each part runs on a different device. Nov 2, 2024 · Enter Distributed Data Parallel (DDP) — PyTorch’s answer to efficient multi-GPU training. Model Parallelism in PyTorch The above description shows that distributed model parallel training has two main parts. It can perform distributed data parallel Apr 22, 2020 · So, for model = nn. 1 is now available with some exciting new features. Jul 14, 2021 · Hello, I have 4 GPUs available to me, and I’m trying to run inference utilizing all of them. It demonstrates how to set up parallelism using torch. It makes it feasible to train models that cannot fit on a single GPU. Feb 11, 2021 · In conclusion, single machine model parallelism can be done as shown in the article I listed in my question, multi node training without model parallelism (with DDP) is shown in the example listed by @conrad & multi node training with model parallelism can only be implemented using PyTorch RPC. Feb 28, 2017 · Hi! I have a model that is too large to fit inside a single TITAN X (even with 1 batch size). An example code I’ve found is this: class Network (nn. Optional: Data Parallelism # Created On: Nov 14, 2017 | Last Updated: Nov 19, 2018 | Last Verified: Nov 05, 2024 Authors: Sung Kim and Jenny Kang In this tutorial, we will learn how to use multiple GPUs using DataParallel. The worker (s) that hold the input layer of the DL model are fed with the training data. Its pipeline parallelism engine enables load-balancing auto-partitioning and pipelining runtime for arbitrary model architectures based on module-server design. Data Parallelism is when we split the mini-batch of samples into multiple smaller mini-batches and run the computation for each of the smaller mini-batches in parallel. 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. save How FSDP2 works # In DistributedDataParallel (DDP) training, each rank owns a model replica and processes a batch of data, finally it uses all-reduce to sync gradients across ranks. DDP achieves parallelism by partitioning input data into smaller chunks, processing them on multiple GPUs, and PyTorch Distributed Data Parallel (DDP) is used to speed-up model training time by parallelizing training data across multiple identical model instances. It’s very easy to use GPUs with PyTorch. Sep 18, 2022 · Also, Mesh-TensorFlow and Megatron-LM create a tensor parallelism framework for optimally training billion-parameter models based on TensorFlow and PyTorch, respectively. Data parallelism in PyTorch involves distributing the data across multiple GPUs and performing operations in parallel. 2D Parallelism combines Tensor Parallelism (TP) and Fully Sharded Data Parallelism (FSDP) to leverage the memory efficiency of FSDP and the computational scalability of TP. tensor_parallel_size ¶ (Union [Literal ['auto'], int]) – The number of devices within a tensor-parallel group. t5-11b is 45GB in just model params significantly speed up training - finish training that would take a year in hours We will first discuss in depth various 1D parallelism techniques and their pros and cons and then look at how they can be combined into 2D and 3D Jul 1, 2025 · The PyTorch documentation is very complete on this subject: You only need to make some small modifications for the parallelism model to work with batch segmentation (Pipeline Parallelism) on Jean Zay. You can put the model on a GPU: Mar 6, 2025 · Learn how to speed up PyTorch model training using data parallelism. Data Parallel ¶ Most users with just 2 GPUs already enjoy the increased training speed up thanks to DataParallel (DP) and DistributedDataParallel (DDP) that are almost trivial to use. 1 - Model Parallelism Training and More Logging Options Lightning 1. Model Parallel GPU Training When training large models, fitting larger batch sizes, or trying to increase throughput using multi-GPU compute, Lightning provides advanced optimized distributed training strategies to support these cases and offer substantial improvements in memory usage. That is, place different parts of the same model on different GPUs and train it end-to-end. t5-11b is 45GB in just model params significantly speed up training - finish training that would take a year in hours We will first discuss in depth various 1D parallelism techniques and their pros and cons and then look at how they can be combined into 2D and 3D In many cases these strategies are some flavour of model parallelism however we only introduce concepts at a high level to get you started. DistributedDataParallel example. pool, torch. State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2. The example uses Wikihow and for simplicity, we will showcase the training on a Aug 16, 2021 · Entire workflow for pytorch DistributedDataParallel, including Dataloader, Sampler, training, and evaluating. This container provides data parallelism by synchronizing gradients across each model replica. Fully Sharded Data Parallelism (FSDP) shards both model parameters and optimizer states across multiple GPUs, significantly reducing memory usage per GPU. Having prior knowledge of Autograd and model training is assumed. distributed and optimizes inference pipelines for large models across multiple GPUs. DistributedDataParallel. Sep 13, 2022 · Each GPU or node has resource limits. Model parallelism enables the training to be parallelized while the distributed training can eventually scale out. There are 2 types:Model Parallelismand Data Parallelism. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature. The integration with TorchTitan Dec 27, 2022 · “3. In this post, I’ll give a practical, in-depth overview of the most common approaches — DDP, FSDP, and TP — and how they’re actually used in real PyTorch training setups. Tensor Parallelism itself can be divided into two different Data Parallel ¶ Most users with just 2 GPUs already enjoy the increased training speed up thanks to DataParallel (DP) and DistributedDataParallel (DDP) that are almost trivial to use. Its aim is to make cutting-edge NLP easier to use for everyone Jun 16, 2025 · First-class support for cross-host pipeline parallelism, as this is where PP is typically used (over slower interconnects). This method is most effective for models with very large layers, significantly enhancing performance and memory efficiency. In this tutorial, we fine-tune a HuggingFace (HF) T5 model with FSDP for text summarization as a working example. Tensor Model Parallelism (TMP) is a specific type of model parallelism where data_parallel_size ¶ (Union [Literal ['auto'], int]) – The number of devices within a data-parallel group. This articles aims to a one-stop reference guide for understanding the different types of distributed training in PyTorch. DistributedDataParallel” to achieve data parallelism, but node2 can’t receive the gradients from node1. Currently supports up to 2D parallelism. This approach is called tensor parallelism or model sharding. Linear as the local model, wraps it with DDP, and then runs one forward pass, one backward pass, and an optimizer step on the DDP model. Jan 29, 2025 · Model parallelization in PyTorch allows the training of deep learning models that require more memory than a single GPU can provide. By distributing different parts of the model across multiple GPUs, we can overcome memory limitations and potentially speed up the training process. Mar 14, 2022 · How FSDP Works FSDP is a type of data-parallel training, but unlike traditional data-parallel, which maintains a per-GPU copy of a model’s parameters, gradients and optimizer states, it shards all of these states across data-parallel workers and can optionally offload the sharded model parameters to CPUs. I was thinking about tensor parallelism, with references like: Aug 20, 2024 · Tensor Parallelism shard hidden layers into multiple GPUs but all the GPUs get all the hidden layers. t5-11b is 45GB in just model params significantly speed up training - finish training that would take a year in hours We will first discuss in depth various 1D parallelism techniques and their pros and cons and then look at how they can be combined into 2D and 3D Frameworks are Essential: Libraries like DeepSpeed (which offers flexible pipeline and tensor parallelism implementations, along with memory optimization like ZeRO), Megatron-LM (pioneering efficient tensor parallelism for Transformers), Colossal-AI, and PyTorch's Fully Sharded Data Parallelism (FSDP) (which can be configured for model sharding Distributed Training leverages parallel executionto accelerate training of Deep Learning models such as LLMs and LMMs. Refer to the FairScale documentation for more information about model parallelism. Multiprocessing. DataParallel. Apr 5, 2024 · Implementation in Pytorch Lightning We need several ingredients for data parallelism: A dataloader that can handle distributed training An all-reduce function that harmonizes the model replicas A framework for the different parallel parts to communicate with each other In PyTorch Lightning, the Lightning Trainer handles the entire training process. It is essential to design model parallelism in multiple GPUs to realize this. This introduction page provides a high-level overview about model parallelism, a description of how it can help overcome issues that arise when training DL models that are typically very large in size, and examples of what the SageMaker model Dec 10, 2020 · PyTorch Lightning 1. Transformer and TorchText tutorial and scales up the same model to demonstrate how pipeline parallelism can be used to train Jun 23, 2024 · PyTorch Distributed Checkpoint ensures the model’s state can be saved and restored accurately across all nodes in the training cluster in parallel, regardless of any changes in the cluster’s composition due to node failures or additions. To get familiar with FSDP, please refer to the FSDP getting started tutorial. Specifically, it supports the combination of Fully Sharded Data-Parallel 2 (FSDP2) with Tensor Parallelism (DTensor). While that series is clear and insightful 6 days ago · Conclusion Model parallelism in PyTorch is a powerful technique for training large neural network models that cannot fit on a single GPU. This example uses a torch. Discover the benefits, challenges, and implementation strategies to In the modern machine learning the various approaches to parallelism are used to: fit very large models onto limited hardware - e. The PiPPy project consists of a compiler and runtime stack for automated parallelism and scaling of PyTorch models. Although it can significantly accelerate the training It centralizes the model definition so that this definition is agreed upon across the ecosystem. multiprocessing, multiprocessing. The main functions to do so is DistributedDataParallel. Module using Tensor Parallelism is: torch. Sep 18, 2022 · Amazon SageMaker model parallelism is a software library on top of PyTorch. DataParallel(module, device_ids=None, output_device=None, dim=0) [source] # Implements data parallelism at the module level. I have a model that I trained. As a part of this I need to train multiple models, with different hyperparameters, in parallel (they will be fed the same data). 2. Jul 15, 2024 · Unlock the power of PyTorch scaling with our comprehensive guide to model and data parallelism. Insights&Codes. Learn advanced techniques, avoid common pitfalls, and supercharg May 20, 2025 · PyTorch has an excellent support for production ready distributed training. distributed. Dec 5, 2017 · Hi, I’m trying to implement the following paper: Population based training for a simple CIFAR classifier. gdqb nrcmvl jllyjp ensc huep ezkgw npwpj kda jrco blqzxhh jhievd mfxdz jojzgr hnpq znc