shrinath-suresh/llama-finetune
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layout: blog_detail
title: "PyTorch Trace Analysis for the Masses"
author: Anupam Bhatnagar, Xizhou Feng, Brian Coutinho, Yifan Liu, Sung-Han Lin, Louis Feng, and Yuzhen Huang
We are excited to announce the public release of Holistic Trace Analysis (HTA), an open source performance analysis and visualization Python ... | https://pytorch.org/blog/trace-analysis-for-masses/ | pytorch blogs |
ML researchers and systems engineers often struggle to computationally scale up their models because they are not aware of the performance bottlenecks in their workloads. The resources requested for a job (e.g. GPUs, memory) are often misaligned with the resources actually required due to lack of visibility “under the ... | https://pytorch.org/blog/trace-analysis-for-masses/ | pytorch blogs |
In this blog, we present several features implemented in the open source version of HTA, which can be used as a Python script as well as interactively in a Jupyter notebook. HTA provides the following features:
Breakdown by Dimensions
Temporal: Breakdown of GPU time in terms of time spent in computation, communication... | https://pytorch.org/blog/trace-analysis-for-masses/ | pytorch blogs |
Augmented Counters (Memory bandwidth, Queue length): Augmented trace files which provide insights into memory copy bandwidth and number of outstanding operations on each CUDA stream.
Patterns
Frequent CUDA Kernels: Find the CUDA kernels most frequently launched by any given PyTorch or user defined operator.
Trace Co... | https://pytorch.org/blog/trace-analysis-for-masses/ | pytorch blogs |
At a high level, we can break down the GPU operations in a model execution into three broad categories, henceforth referred to as kernel types:
1. Computation (COMP) - Compute kernels execute compiled routines for matrix multiplication and similar numeric calculations. They are responsible for all of the number-crunch... | https://pytorch.org/blog/trace-analysis-for-masses/ | pytorch blogs |
Memory (MEM) - Memory kernels manage the memory allocations/deallocations on the GPU devices and data movement between the memory space on the host and the GPUs. The memory kernels include Memcpy_H2D, Memcpy_D2H, Memcpy_D2D, Memset, etc. Here, H represents the Host and D represents the GPU Device. Thus, H2D, D2H, D2D ... | https://pytorch.org/blog/trace-analysis-for-masses/ | pytorch blogs |
To help understand the above concepts, Figure 1 provides a timeline of the GPU kernels in a sample distributed training job on 8 GPUs for one iteration. In the figure below, each rank represents one GPU and the kernels on each GPU run on 6 CUDA streams. In the right column of the figure, you can see names of the GPU ke... | https://pytorch.org/blog/trace-analysis-for-masses/ | pytorch blogs |
The performance of multiple GPU training jobs is affected by multiple factors. Among these factors, how does a model execution create and orchestrate the GPU kernels plays a critical role. HTA provides insights on how the model execution interacts with the GPU devices and highlights the opportunities for performance i... | https://pytorch.org/blog/trace-analysis-for-masses/ | pytorch blogs |
Temporal Breakdown: We begin by asking whether the GPU is spending time on computation, communication, memory events, or is it idle? To answer this question, the temporal breakdown feature presents a breakdown in terms of these categories. To achieve high training efficiency the code should maximize time used by comput... | https://pytorch.org/blog/trace-analysis-for-masses/ | pytorch blogs |
Kernel Breakdown: It is natural to ask which kernels are taking the most amount of time. The next feature breaks down the time spent within each kernel type (COMM, COMP, MEM) and sorts them by duration. We present this information for each kernel type and for each rank as a pie chart. See figure 3 below.
Figure 3: P... | https://pytorch.org/blog/trace-analysis-for-masses/ | pytorch blogs |
Kernel Duration Distribution: Subsequently, one can also ask - for any given kernel, what is the distribution of the time spent across the ranks? To answer this, HTA generates bar graphs for the average duration of a given kernel across all ranks. Additionally, the error bars in the bar graphs show the minimum and max... | https://pytorch.org/blog/trace-analysis-for-masses/ | pytorch blogs |
Communication Computation Overlap: In distributed training, a significant amount of time is spent in communication and synchronization events among multiple GPU devices. To achieve high GPU efficiency (i.e. TFLOPS/GPU) it is vital to keep the GPU doing actual computation work. In other words, a GPU should not be block... | https://pytorch.org/blog/trace-analysis-for-masses/ | pytorch blogs |
(time spent in computation while communicating) / (time spent in communication)
Figure 5: Communication computation overlap
Augmented Counters (Queue length, Memory bandwidth): To aid in debugging, HTA calculates the memory bandwidth statistics for D2H, H2D and D2D memory copy (memcpy) and memory set (memset) events. ... | https://pytorch.org/blog/trace-analysis-for-masses/ | pytorch blogs |
Figure 6: Memory Bandwidth and Queue Length
These primary features give us a peek into the system performance and help answer “what is happening in the system?”. As HTA evolves, we hope to address “why is X happening?” and also suggest possible solutions to overcome the bottlenecks.
Installation and Usage
Installation
... | https://pytorch.org/blog/trace-analysis-for-masses/ | pytorch blogs |
Usage
This version of Holistic Trace Analysis is currently in beta and we recommend using HTA in a Jupyter notebook. A demo notebook is provided for your convenience. To get started, import the hta package in a Jupyter notebook, create a TraceAnalysis object and off we go in exactly two lines of code.
from hta.trace_an... | https://pytorch.org/blog/trace-analysis-for-masses/ | pytorch blogs |
The documentation and detailed API is available here.
Q. Can you implement feature X?
Depending on how widely the feature is needed and the level of effort required to implement it we would consider developing the feature. Please open a Github Issue and tag it with the feature-request label.
Q. Can I modify the code?
P... | https://pytorch.org/blog/trace-analysis-for-masses/ | pytorch blogs |
layout: blog_detail
title: 'PyTorch adds new dev tools as it hits production scale'
author: The PyTorch Team
This is a partial re-post of the original blog post on the Facebook AI Blog. The full post can be viewed here
Since its release just a few months ago, PyTorch 1.0 has been rapidly adopted as a powerful, flexib... | https://pytorch.org/blog/pytorch-adds-new-dev-tools/ | pytorch blogs |
Building on the initial launch of PyTorch in 2017, we partnered with the AI community to ship the stable release of PyTorch 1.0 last December. Along with enhanced production-oriented capabilities and deep integration with leading cloud platforms, PyTorch 1.0 expands on the open source library’s core features, with the ... | https://pytorch.org/blog/pytorch-adds-new-dev-tools/ | pytorch blogs |
Leading businesses across industries are beginning to use PyTorch to both facilitate their research and then also deploy at large scale for applications such as translation, computer vision, conversational interfaces, pharmaceutical research, factory optimization, and automated driving research. Community adoption of P... | https://pytorch.org/blog/pytorch-adds-new-dev-tools/ | pytorch blogs |
ATOM is building a platform to generate and optimize new drug candidates significantly faster and with greater success than conventional processes. Using machine learning frameworks such as PyTorch, ATOM was able to design a variational autoencoder for representing diverse chemical structures and designing new drug ca... | https://pytorch.org/blog/pytorch-adds-new-dev-tools/ | pytorch blogs |
Toyota Research Institute (TRI) is developing a two-pronged approach toward automated driving with Toyota Guardian and Toyota Chauffeur technologies. The Machine Learning Team at TRI is creating new deep learning algorithms to leverage Toyota's 10 million sales per year data advantage. The flexibility of PyTorch has v... | https://pytorch.org/blog/pytorch-adds-new-dev-tools/ | pytorch blogs |
Key features of PyTorch v1.1 include:
TensorBoard: First-class and native support for visualization and model debugging with TensorBoard, a web application suite for inspecting and understanding training runs and graphs. PyTorch now natively supports TensorBoard with a simple “from torch.utils.tensorboard import Summa... | https://pytorch.org/blog/pytorch-adds-new-dev-tools/ | pytorch blogs |
Distributed Training: Improved performance for common models such as CNNs, added support for multi device modules including the ability to split models across GPUs while still using Distributed Data Parallel (DDP) and support for modules where not all parameters are used in every iteration (e.g. control flow, like ada... | https://pytorch.org/blog/pytorch-adds-new-dev-tools/ | pytorch blogs |
This ecosystem includes open source projects and tools that have been deployed at production scale, as well as products and services from our partnership with industry leaders who share our vision of an open and collaborative AI community. Here are a few of the latest tools:
BoTorch: BoTorch is a research framework bu... | https://pytorch.org/blog/pytorch-adds-new-dev-tools/ | pytorch blogs |
PyTorch-BigGraph: PBG is a distributed system for creating embeddings of very large graphs with billions of entities and trillions of edges. It includes support for sharding and negative sampling and it offers sample use cases based on Wikidata embeddings.
Google AI Platform Notebooks: AI Platform Notebooks is a new, ... | https://pytorch.org/blog/pytorch-adds-new-dev-tools/ | pytorch blogs |
BigGAN-PyTorch:This is a full PyTorch reimplementation that uses gradient accumulation to provide the benefits of big batches on as few as four GPUs.
GeomLoss: A Python API that defines PyTorch layers for geometric loss functions between sampled measures, images, and volumes. It includes MMD, Wasserstein, Sinkhorn, an... | https://pytorch.org/blog/pytorch-adds-new-dev-tools/ | pytorch blogs |
Curve-GCN: A real-time, interactive image annotation approach that uses an end-to-end-trained graph convolutional network (GCN). It supports object annotation by either polygons or splines, facilitating labeling efficiency for both line-based and curved objects. Curve-GCN runs 10x faster than traditional methods, such... | https://pytorch.org/blog/pytorch-adds-new-dev-tools/ | pytorch blogs |
University-level classes — including Stanford NLP, UC Berkeley Computer Vision, and Caltech Robotics courses — are now being taught on PyTorch. In addition, massive open online courses (MOOCs) are training thousands of new PyTorch developers.
Today, we’re announcing a new Udacity course, building upon the Intro to Deep... | https://pytorch.org/blog/pytorch-adds-new-dev-tools/ | pytorch blogs |
The fast.ai community is also continuing to invest energy and resources in PyTorch. In June, fast.ai will launch a new course called Deep Learning from the Foundations, which will show developers how to go all the way from writing matrix multiplication from scratch to how to train and implement a state-of-the-art Image... | https://pytorch.org/blog/pytorch-adds-new-dev-tools/ | pytorch blogs |
As part of the course, fast.ai will also release new software modules, including fastai.audio, which brings the power of fast.ai’s deep abstractions and curated algorithms to the new PyTorch.audio module, and show how fastai.vision can be used to create stunning high-resolution videos from material such as old classic ... | https://pytorch.org/blog/pytorch-adds-new-dev-tools/ | pytorch blogs |
Getting started with PyTorch
Everyone in the AI community — including those new to ML development as well as researchers and engineers looking for ways to accelerate their end-to-end workflows — can experiment with PyTorch instantly by visiting pytorch.org and launching a tutorial in Colab. There are also many easy way... | https://pytorch.org/blog/pytorch-adds-new-dev-tools/ | pytorch blogs |
layout: blog_detail
title: "Introducing Accelerated PyTorch Training on Mac"
author: PyTorch
featured-img: "/assets/images/METAPT-002-BarGraph-02-static.png"
In collaboration with the Metal engineering team at Apple, we are excited to announce support for GPU-accelerated PyTorch training on Mac. Until now, PyTorch tr... | https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/ | pytorch blogs |
Metal Acceleration
Accelerated GPU training is enabled using Apple’s Metal Performance Shaders (MPS) as a backend for PyTorch. The MPS backend extends the PyTorch framework, providing scripts and capabilities to set up and run operations on Mac. MPS optimizes compute performance with kernels that are fine-tuned for th... | https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/ | pytorch blogs |
In the graphs below, you can see the performance speedup from accelerated GPU training and evaluation compared to the CPU baseline:
Accelerated GPU training and evaluation speedups over CPU-only (times faster)
Getting Started
To get started, just install the latest Preview (Nightly) build on your Apple silicon Mac... | https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/ | pytorch blogs |
* Testing conducted by Apple in April 2022 using production Mac Studio systems with Apple M1 Ultra, 20-core CPU, 64-core GPU 128GB of RAM, and 2TB SSD. Tested with macOS Monterey 12.3, prerelease PyTorch 1.12, ResNet50 (batch size=128), HuggingFace BERT (batch size=64), and VGG16 (batch size=64). Performance tests are ... | https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/ | pytorch blogs |
layout: blog_detail
title: "Accelerating Hugging Face and TIMM models with PyTorch 2.0"
author: Mark Saroufim
featured-img: "assets/images/pytorch-2.0-feature-img.png"
torch.compile() makes it easy to experiment with different compiler backends to make PyTorch code faster with a single line decorator torch.compile().... | https://pytorch.org/blog/Accelerating-Hugging-Face-and-TIMM-models/ | pytorch blogs |
What makes this announcement different for us is we’ve already benchmarked some of the most popular open source PyTorch models and gotten substantial speedups ranging from 30% to 2x https://github.com/pytorch/torchdynamo/issues/681.
There are no tricks here, we’ve pip installed popular libraries like https://github.com... | https://pytorch.org/blog/Accelerating-Hugging-Face-and-TIMM-models/ | pytorch blogs |
Sylvain Gugger the primary maintainer of transformers and accelerate: "With just one line of code to add, PyTorch 2.0 gives a speedup between 1.5x and 2.x in training Transformers models. This is the most exciting thing since mixed precision training was introduced!"
This tutorial will show you exactly how to replicate... | https://pytorch.org/blog/Accelerating-Hugging-Face-and-TIMM-models/ | pytorch blogs |
binaries which you can download with
docker pull ghcr.io/pytorch/pytorch-nightly
And for ad hoc experiments just make sure that your container has access
to all your GPUs
docker run --gpus all -it ghcr.io/pytorch/pytorch-nightly:latest /bin/bash
Getting started
a toy exmaple
Let’s start with a simple example and ma... | https://pytorch.org/blog/Accelerating-Hugging-Face-and-TIMM-models/ | pytorch blogs |
example that features torch.cos() and torch.sin() which are examples of pointwise ops as in they operate element by element on a vector. A more famous pointwise op you might actually want to use would be something like torch.relu().
Pointwise ops in eager mode are suboptimal because each one would need to read a tensor... | https://pytorch.org/blog/Accelerating-Hugging-Face-and-TIMM-models/ | pytorch blogs |
torch.compile() supports many different backends but one that we’re particularly excited about is Inductor which generates Triton kernels https://github.com/openai/triton which are written in Python yet outperform the vast majority of handwritten CUDA kernels. Suppose our example above was called trig.py we can actuall... | https://pytorch.org/blog/Accelerating-Hugging-Face-and-TIMM-models/ | pytorch blogs |
tmp1 = tl.sin(tmp0)
tmp2 = tl.sin(tmp1)
tl.store(out_ptr0 + (x0 + tl.zeros([XBLOCK], tl.int32)), tmp2, xmask)
And you can verify that fusing the two `sins` did actually occur because the two `sin` operations occur within a single Triton kernel and the temporary variables are held in registers with very fast ac... | https://pytorch.org/blog/Accelerating-Hugging-Face-and-TIMM-models/ | pytorch blogs |
You may have noticed how we also passed in the name of a compiler explicitly here with “inductor” but it’s not the only available backend, you can run in a REPL torch._dynamo.list_backends() to see the full list of available backends. For fun you should try out aot_cudagraphs or nvfuser.
Hugging Face models
Let’s do so... | https://pytorch.org/blog/Accelerating-Hugging-Face-and-TIMM-models/ | pytorch blogs |
from transformers import BertTokenizer, BertModel
Copy pasted from here https://huggingface.co/bert-base-uncased
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained("bert-base-uncased").to(device="cuda:0")
model = torch.compile(model) # This is the only line of code that we ... | https://pytorch.org/blog/Accelerating-Hugging-Face-and-TIMM-models/ | pytorch blogs |
Similarly let’s try out a TIMM example
import timm
import torch
model = timm.create_model('resnext101_32x8d', pretrained=True, num_classes=2)
opt_model = torch.compile(model, backend="inductor")
opt_model(torch.randn(64,3,7,7))
Our goal with PyTorch was to build a breadth-first compiler that would speed up the vast ma... | https://pytorch.org/blog/Accelerating-Hugging-Face-and-TIMM-models/ | pytorch blogs |
layout: blog_detail
title: 'PyTorch 1.6 now includes Stochastic Weight Averaging'
author: Pavel Izmailov, Andrew Gordon Wilson and Vincent Queneneville-Belair
Do you use stochastic gradient descent (SGD) or Adam? Regardless of the procedure you use to train your neural network, you can likely achieve significantly be... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
SWA has a wide range of applications and features:
* SWA significantly improves performance compared to standard training techniques in computer vision (e.g., VGG, ResNets, Wide ResNets and DenseNets on ImageNet and CIFAR benchmarks [1, 2]).
* SWA provides state-of-the-art performance on key benchmarks in semi-supervis... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
SWA for low precision training, SWALP, can match the performance of full-precision SGD training, even with all numbers quantized down to 8 bits, including gradient accumulators [6].
SWA in parallel, SWAP, was shown to greatly speed up the training of neural networks by using large batch sizes and, in particular, set a... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
In short, SWA performs an equal average of the weights traversed by SGD (or any stochastic optimizer) with a modified learning rate schedule (see the left panel of Figure 1.). SWA solutions end up in the center of a wide flat region of loss, while SGD tends to converge to the boundary of the low-loss region, making it ... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
Is this just Averaged SGD?
At a high level, averaging SGD iterates dates back several decades in convex optimization [7, 8], where it is sometimes referred to as Polyak-Ruppert averaging, or averaged SGD. But the details matter. Averaged SGD is often used in conjunction with a decaying learning rate, and an exponential... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
How does Stochastic Weight Averaging Work?
There are two important ingredients that make SWA work. First, SWA uses a modified learning rate schedule so that SGD (or other optimizers such as Adam) continues to bounce around the optimum and explore diverse models instead of simply converging to a single solution. For exa... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
Figure 2. Illustration of the learning rate schedule adopted by SWA. Standard decaying schedule is used for the first 75% of the training and then a high constant value is used for the remaining 25%. The SWA averages are formed during the last 25% of training.
One important detail is the batch normalization. Batch nor... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
How to use SWA in PyTorch?
In torch.optim.swa_utils we implement all the SWA ingredients to make it convenient to use SWA with any model. In particular, we implement AveragedModel class for SWA models, SWALR learning rate scheduler, and update_bn utility function to update SWA batch normalization statistics at the end ... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
optimizer.zero_grad()
loss_fn(model(input), target).backward()
optimizer.step()
if epoch > swa_start:
swa_model.update_parameters(model)
swa_scheduler.step()
else:
scheduler.step()
Update bn statistics for the swa_model at the end
torch.optim.swa_utils.updat... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
ema_avg = lambda averaged_model_parameter, model_parameter, num_averaged:\
0.1 * averaged_model_parameter + 0.9 * model_parameter
ema_model = torch.optim.swa_utils.AveragedModel(model, avg_fn=ema_avg)
In practice, we find an equal average with the modified learning rate schedule in Figure 2 provides the best performan... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100)
swa_start = 75
for epoch in range(100):
# <train epoch>
if i > swa_start:
swa_model.update_parameters(model)
swa_scheduler.step()
else:
scheduler.step()
Finally, update_bn is a utility function ... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
Why does it work?
There are large flat regions of the loss surface [9]. In Figure 3 below, we show a visualization of the loss surface in a subspace of the parameter space containing a path connecting two independently trained SGD solutions, such that the loss is similarly low at every point along the path. SGD converg... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
Figure 3: visualization of mode connectivity for ResNet-20 with no skip connections on CIFAR-10 dataset. The visualization is created in collaboration with Javier Ideami (https://losslandscape.com/). For more details, see this blogpost.
We expect solutions that are centered in the flat region of the loss to generalize... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
Figure 4. Train loss and test error along the line connecting the SWA solution (circle) and SGD solution (square). The SWA solution is centered in a wide region of low train loss, while the SGD solution lies near the boundary. Because of the shift between train loss and test error surfaces, the SWA solution leads to... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
| SWA | 74.4 ± 0.3 | 79.8 ± 0.4 | 82.5 ± 0.2 |
Semi-Supervised Learning
In a follow-up paper SWA was applied to semi-supervised learning, where it improved the best reported results in multiple settings [2]. For example, with SWA you can get 95% accuracy on CIFAR-10 if you only have the training labels for 4k training... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
Reinforcement Learning
In another follow-up paper SWA was shown to improve the performance of policy gradient methods A2C and DDPG on several Atari games and MuJoCo environments [3]. This application is also an instance of where SWA is used with Adam. Recall that SWA is not specific to SGD and can benefit essentially a... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
Low Precision Training
We can filter through quantization noise by combining weights that have been rounded down with weights that have been rounded up. Moreover, by averaging weights to find a flat region of the loss surface, large perturbations of the weights will not affect the quality of the solution (Figures 9 and... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
Figure 9. Quantizing a solution leads to a perturbation of the weights which has a greater effect on the quality of the sharp solution (left) compared to wide solution (right).
Figure 10. The difference between standard low precision training and SWALP.
Another work, SQWA, presents an approach for quantization ... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
SWA can be viewed as taking the first moment of SGD iterates with a modified learning rate schedule. We can directly generalize SWA by also taking the second moment of iterates to form a Gaussian approximate posterior over the weights, further characterizing the loss geometry with SGD iterates. This approach,SWA-Gauss... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
Figure 6. SWAG posterior approximation and the loss surface for a ResNet-20 without skip-connections trained on CIFAR-10 in the subspace formed by the two largest eigenvalues of the SWAG covariance matrix. The shape of SWAG distribution is aligned with the posterior: the peaks of the two distributions coincide, and bo... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
Figure 7. MultiSWAG generalizes SWAG and deep ensembles, to perform Bayesian model averaging over multiple basins of attraction, leading to significantly improved performance. By contrast, as shown here, deep ensembles select different modes, while standard variational inference (VI) marginalizes (model averages) with... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
Indeed, we see in Figure 8 that MultiSWAG entirely mitigates double descent -- more flexible models have monotonically improving performance -- and provides significantly improved generalization over SGD. For example, when the ResNet-18 has layers of width 20, Multi-SWAG achieves under 30% error whereas SGD achieves ov... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
Another method, Subspace Inference, constructs a low-dimensional subspace around the SWA solution and marginalizes the weights in this subspace to approximate the Bayesian model average [5]. Subspace Inference uses the statistics from the SGD iterates to construct both the SWA solution and the subspace. The method achi... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
Try it Out!
One of the greatest open questions in deep learning is why SGD manages to find good solutions, given that the training objectives are highly multimodal, and there are many settings of parameters that achieve no training loss but poor generalization. By understanding geometric features such as flatness, whic... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
We encourage you to try out SWA! SWA is now as easy as any standard training in PyTorch. And even if you have already trained your model, you can use SWA to significantly improve performance by running it for a small number of epochs from a pre-trained model.
[1] Averaging Weights Leads to Wider Optima and Better Gene... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
[4] A Simple Baseline for Bayesian Uncertainty in Deep Learning
Wesley Maddox, Timur Garipov, Pavel Izmailov, Andrew Gordon Wilson; Neural Information Processing Systems (NeurIPS), 2019.
[5] Subspace Inference for Bayesian Deep Learning
Pavel Izmailov, Wesley Maddox, Polina Kirichenko, Timur Garipov, Dmitry Vetrov, An... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
[9] Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry Vetrov,
Andrew Gordon Wilson. Neural Information Processing Systems (NeurIPS), 2018.
[10] Bayesian Deep Learning and a Probabilistic Perspective of Generalization
Andrew Gordon Wilson, Pavel Izm... | https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ | pytorch blogs |
layout: blog_detail
title: "Introducing TorchRec, and other domain library updates in PyTorch 1.11"
author: Team PyTorch
featured-img: "assets/images/pytorch-logo.jpg"
We are introducing the beta release of TorchRec and a number of improvements to the current PyTorch domain libraries, alongside the PyTorch 1.11 relea... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
TorchText - Added beta support for RoBERTa and XLM-R models, byte-level BPE tokenizer, and text datasets backed by TorchData. See the release notes here.
TorchVision - Added 4 new model families and 14 new classification datasets such as CLEVR, GTSRB, FER2013. See the release notes here.
TorchRec 0.1
We announced Tor... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
In particular, the library includes:
Modeling primitives, such as embedding bags and jagged tensors, that enable easy authoring of large, performant multi-device/multi-node models using hybrid data-parallelism and model-parallelism.
Optimized RecSys kernels powered by FBGEMM, including support for sparse and quantized... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
Please check the TorchRec announcement post here, video tutorial, install instructions here, test drive the feature through this tutorial here, and refer to the reference document here.
TorchAudio 0.11
TorchAudio: Building Blocks for Audio and Speech Processing
We published a paper, TorchAudio: Building Blocks for Audi... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
Emformer is an efficient memory-transformer-based streaming acoustic model that has demonstrated state-of-the-art streaming automatic speech recognition (ASR) performance in low-latency, resource-constrained scenarios, such as on-device applications (citation: https://arxiv.org/abs/2010.10759).
The TorchAudio v0.11 re... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
LibriSpeech Emformer RNN-T training recipe (GitHub) and corresponding pre-trained streaming ASR inference pipeline (docs)
Also there are prototype features that are available from nightly builds or the main branch.
Training recipes trained on MuST-C and TED-LIUM3 datasets. (GitHub)
Pre-trained pipelines correspondin... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
Collectively, these features cover the full development lifecycle of a streaming ASR model, from definition through training and inference, and enable users to easily develop their own Emformer- and RNN-T-based models.
Special thanks to Yangyang Shi, Jay Mahadeokar, and Gil Keren for their code contributions and guidan... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
(Beta) HuBERT Pretrain Model
The masked prediction training of HuBERT model requires the masked logits, unmasked logits, and feature norm as the outputs. The logits are for cross-entropy losses and the feature norm is for penalty loss. The release adds HuBERTPretrainModel and corresponding factory functions (hubert_pre... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
(Prototype) CTC Beam Search Decoder
In recent releases, TorchAudio has added support for ASR models fine-tuned on CTC loss. The addition of an inference time CTC beam search decoder enables running end-to-end ASR evaluation using TorchAudio utils.
The CTC decoder in TorchAudio supports customizable beam search decoding... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
Streaming API makes it easy to develop and test the model in online inference. It utilizes ffmpeg under the hood, and enables reading media from online services and hardware devices, decoding media in an incremental manner, and applying filters and preprocessing.
Please checkout the API tutorial and the documentation. ... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
(Beta) RoBERTa and XLM-R Models
TorchText has added support for pre-trained RoBERTa and XLM-R models. It would allow users to train end-2-end Transformer Encoder based models on standard NLP tasks using TorchText.
More specifically:
The models are torchscriptable and hence can be employed for production use-cases.
The... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
(Beta) byte-level BPE tokenizer
TorchText has added support for a Byte-Level BPE tokenizer, as used in GPT-2. This tokenizer is also used for tokenizing inputs to the pre-trained RoBERTa models described previously. In addition to the RoBERTa vocab, users can also load their own custom BPE vocab to use the tokenizer. F... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
These DataPipes work out-of-the-box with PyTorch DataLoader and would enable new functionalities like auto-sharding. Users can now easily do data manipulation and pre-processing using user-defined functions and transformations in a functional style programming. Datasets backed by DataPipes also enable standard flow-con... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
#1 Object Detection
FCOS is a popular, fully convolutional, anchor-free model for object detection. In this release we include a community-contributed model implementation as well as pre-trained weights. The model was trained on COCO train2017 and can be used as follows:
import torch
from torchvision import models
x =... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
We would like to thank Hu Ye and Zhiqiang Wang for contributing to the model implementation and initial training. This was the first community-contributed model in a long while, and given its success, we decided to use the learnings from this process and create a new model contribution guidelines.
#2 Optical Flow suppo... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
We implemented a torchscript-compatible RAFT model with pre-trained weights (both normal and “small” versions), and added support for training and evaluating optical flow models. Our training scripts support distributed training across processes and nodes, leading to much faster training time than the original implemen... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
#3. Image Classification
Vision Transformer (ViT) and ConvNeXt are two popular architectures which can be used as image classifiers or as backbones for downstream vision tasks. In this release we include 8 pre-trained weights for their classification variants. The models were trained on ImageNet and can be used as fol... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
| vit_l_32 | 76.972 | 93.07 |
| convnext_tiny | 82.52 | 96.146 |
| convnext_small | 83.616 | 96.65 |
| convnext_base | 84.062 | 96.87 |
| convnext_large | 84.414 | 96.976 |
The above models have been trained using an adjusted version of our new training recipe and this allows u... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
reader.seek(seek_time)
New Datasets
We have implemented 14 new classification datasets: CLEVR, GTSRB, FER2013, SUN397, Country211, Flowers102, fvgc_aircraft, OxfordIIITPet, DTD, Food 101, Rendered SST2, Stanford cars, PCAM, and EuroSAT.
As part of our work on Optical Flow support (see above for more details), we also ... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
New model contribution guidelines have been published following the success of the FCOS model which was contributed by the community. These guidelines aim to be an overview of the model contribution process for anyone who would like to suggest, implement and train a new model.
Upcoming Prototype API - We are currently... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
Changes in our deprecation policy - Up until now, torchvision would almost never remove deprecated APIs. In order to be more aligned and consistent with pytorch core, we are updating our deprecation policy. We are now following a 2-release deprecation cycle: deprecated APIs will raise a warning for 2 versions, and wil... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
Captum 0.5
Captum is a PyTorch library for model interpretability. For this release, we expanded Captum with influential instances and added support for both similarity based influences and novel algorithms, TracIn and its variants. TracIn variants offer faster approximation of influence scores based on random projecti... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
TracInCP approximates the influential score of each training example on a given test example based on the dot-product similarity between loss gradients w.r.t. model parameters for test and training examples. Note that if we use training examples as test examples then we compute self influence. This method and its vari... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
TracInCPFastRandProj uses a nearest neighbor approximation library such as annoy to compute the dot product between the training and test quantities. In order to reduce the dimensionality of layer activations and corresponding gradients this method, in addition, allows to project those vectors into a lower dimensional... | https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
Thanks for reading, If you’re interested in these updates and want to join the PyTorch community, we encourage you to join the discussion forums and open GitHub issues. To get the latest news from PyTorch, follow us on Twitter, Medium, YouTube, and LinkedIn.
Cheers!
Team PyTorch
TorchRec 0.1
TorchAudio 0.11
| https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
TorchText 0.12
<li>
<a class="reference internal title-link has-children" href="#torchvision-012">TorchVision 0.12</a>
</li>
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| https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
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| https://pytorch.org/blog/pytorch-1.11-new-library-releases/ | pytorch blogs |
layout: blog_detail
title: "PyTorch 2.0 & XLA—The Latest Cutting Edge Features"
author: Jack Cao, Milad Mohammadi, Alex Wertheim, Yeounoh Chung, Joe Spisak, Will Cromar, Shauheen Zahirazami
| https://pytorch.org/blog/pytorch-2.0-xla/ | pytorch blogs |
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