shrinath-suresh/blogs-docs-splitted huggingface dataset:
Experiments
We conduct an empirical study about the impact of the different optimizations from the previous section on system performance. For all our experiments, we use a single node with 8 A100 40GB GPUs and run the pretraining for 1000 iterations. All runs also used PyTorch’s automatic mixed precision with the bfloat16 data type. TensorFloat32 format is also enabled to improve matmul performance on the A100. We define throughput as the average number of items (text or image) processed per second (we ignore the first 100 iterations while measuring throughput to account for warmup). We leave training to convergence and its impact on downstream task metrics as an area for future study.
Figure 1 plots the throughput for each model configuration and optimization, both with a local batch size of 8 and then with the maximum batch size possible on 1 node. Absence of a data point for a model variant for an optimization indicates that the model could not be trained on a single node.
Figure 2 plots the maximum possible batch size per worker for each optimization. We observe a few things:

Scaling model size: DDP is only able to fit the 350M and 900M model on a node. With FSDP, due to memory savings, we are able to train ~3x bigger models compared to DDP  (i.e. the 1.8B and 2.7B variants). Combining activation checkpointing (AC) with FSDP enables training even bigger models, on the order of ~10x compared to DDP (i.e. 4.8B and 10B variants)
Throughput:
Throughput:
For smaller model sizes, at a constant batch size of 8, the throughput for DDP is slightly higher than or equal to FSDP, explainable by the additional communication required by FSDP. It is lowest for FSDP and AC combined together. This is because AC re-runs checkpointed forward passes during the backwards pass, trading off additional computation for memory savings. However, in the case of the 2.7B model, FSDP + AC actually has higher throughput compared to FSDP alone. This is because the 2.7B model with FSDP is operating close to the memory limit even at batch size 8 triggering CUDA malloc retries which tend to slow down training. AC helps with reducing the memory pressure and leads to no retries.
For DDP and FSDP + AC, the throughput increases with an increase in batch size for each model. For FSDP alone, this is true for smaller variants. However, with the 1.8B and 2.7B parameter models, we observe throughput degradation when increasing batch size. A potential reason for this, as noted above also, is that at the memory limit, PyTorch’s CUDA memory management may have to retry cudaMalloc calls and/or run expensive defragmentation steps to find free memory blocks to handle the workload’s memory requirements which can result in training slowdown.
For larger models that can only be trained with FSDP (1.8B, 2.7B, 4.8B) the setting with highest throughput achieved is with FSDP + AC scaling to the maximum batch size. For 10B, we observe nearly equal throughput for smaller and maximum batch size. This might be counterintuitive as AC results in increased computation and maxing out batch size potentially leads to expensive defragmentation operations due to operating at CUDA memory limit. However, for these large models, the increase in batch size is large enough to mask this overhead.





  Figure 1: Training throughput for different configurations
Batch size: FSDP alone enables slightly higher batch sizes compared to DDP. Using FSDP + AC enables ~3x batch size compared to DDP for the 350M param model and ~5.5x for 900M param model. Even for 10B, a max batch size of ~20 which is fairly decent. This essentially enables larger global batch size using fewer GPUs which is especially useful for contrastive learning tasks.





  Figure 2: Max local batchsize possible for different configurations

Conclusion
Conclusion
As the world moves towards multimodal foundation models, scaling model parameters and efficient training is becoming an area of focus. The PyTorch ecosystem aims to accelerate innovation in this field by providing different tools to the research community, both for training and scaling multimodal models. With FLAVA, we laid out an example of scaling a model for multimodal understanding. In the future, we plan to add support for other kinds of models like the ones for multimodal generation and demonstrate their scaling factors. We also hope to automate many of these scaling and memory saving techniques (such as sharding and activation checkpointing) to reduce the amount of user experimentation needed to achieve the desired scale and maximum training throughput.
References

Introducing TorchMultimodal - a library for accelerating exploration in Multimodal AI
layout: blog_detail
title: "A BetterTransformer for Fast Transformer Inference"
author: Michael Gschwind, Eric Han, Scott Wolchok, Rui Zhu, Christian Puhrsch
featured-img: "/assets/images/2022-7-12-a-better-transformer-for-fast-transformer-encoder-inference-3.png"

tl;dr Transformers achieve state-of-the-art performance for NLP, and are becoming popular for a myriad of other tasks. They are computationally expensive which has been a blocker to their widespread productionisation. Launching with PyTorch 1.12, BetterTransformer implements a backwards-compatible fast path of torch.nn.TransformerEncoder for Transformer Encoder Inference and does not require model authors to modify their models. BetterTransformer improvements can exceed 2x in speedup and throughput for many common execution scenarios. To use BetterTransformer, install PyTorch 1.12 and start using high-quality, high-performance Transformer models with the PyTorch API today.
Performance Improvements
BetterTransformer launches with accelerated native implementations of MultiHeadAttention and TransformerEncoderLayer for CPUs and GPUs. These fast paths are integrated in the standard PyTorch Transformer APIs, and will accelerate TransformerEncoder, TransformerEncoderLayer and MultiHeadAttention nn.modules. These new modules implement two types of optimizations: (1) fused kernels combine multiple individual operators normally used to implement Transformers to provide a more efficient implementation, and (2) take advantage of sparsity in the inputs to avoid performing unnecessary operations on padding tokens. Padding tokens frequently account for a large fraction of input batches in many Transformer models used for Natural Language Processing.
Backwards compatibility
Advantageously, no model changes are necessary to benefit from the performance boost offered by BetterTransformer. To benefit from fast path execution, inputs and operating conditions must satisfy some access conditions (see below). While the internal implementation of Transformer APIs has changed, PyTorch 1.12 maintains strict compatibility with Transformer modules shipped in previous versions, enabling PyTorch users to use models created and trained with previous PyTorch releases while benefiting from BetterTransformer improvements.
In addition to enabling the PyTorch nn.Modules, BetterTransformer provides improvements for PyTorch libraries. Performance benefits will become available through two different enablement paths:
Torchtext library acceleration: As part of this project, we have optimized Torchtext to build on the PyTorch core API to benefit from BetterTransformer enhancements while maintaining strict and transparent compatibility with previous library versions and models trained with previous Torchtext versions. Using PyTorch Transformers in Torchtext also ensures that Torchtext will benefit from expected future enhancements to the PyTorch Transformer implementation.

Taking advantage of the Fastpath
BetterTransformer is a fastpath for the PyTorch Transformer API. The fastpath is a native, specialized implementation of key Transformer functions for CPU and GPU that applies to common Transformer use cases.
Currently, the BetterTransformer speedup only applies to transformer encoder models used in inference. To benefit from fastpath execution, models must be composed of any of the following components: TransformerEncoder, TransformerEncoderLayer or MultiheadAttention (MHA). Fastpath execution is also subject to some criteria. Most importantly, the model must be executed in inference mode and operate on input tensors that do not collect gradient tape information (e.g., running with torch.no_grad). The full list of conditions can be found at these links for nn.MultiHeadAttention and nn.TransformerEncoder, respectively. If the criteria are not met, control flows to the legacy PyTorch 1.11 Transformer implementation which has the same API, but lacks the fastpath performance boost.
Other transformer models (such as decoder models) which use the PyTorch MultiheadAttention module will benefit from the BetterTransformer fastpath. Planned future work is to expand the end-to-end BetterTransformer fastpath to models based on TransformerDecoder to support popular seq2seq and decoder-only (e.g., OPT) model architectures, and to training.
Speedups
The following graphs show the performance achieved for the BERT-base model with small and large-scale inputs:




Figure 1: PyTorch 1.12 Improvements with BetterTransformer fastpath execution
BetterTransformer includes two types of optimization: (1) fused kernels implementing multiple operations more efficiently in a single kernel, and (2) exploiting sparsity by avoiding unnecessary processing on padding tokens. Enhanced performance for small input sizes benefits primarily from the fused kernel implementations, and shows a constant performance improvement regardless of padding amount. While large inputs still benefit from fused kernels, the computation heavy processing limits the benefits that may be obtained by the fused kernels as baseline performance is already closer to the theoretical peak. However, as we increase the amount of padding, performance increases dramatically as increasingly large amounts of computation can be avoided by exploiting the sparsity introduced by padding in NLP workloads.
Future Work
Future Work
As part of our ongoing work on PyTorch BetterTransformer, we are working on extending BetterTransformer improvements to Transformer Decoders. We aim to expand beyond inference to training as well.
We are partnering to enable BetterTransformer on additional libraries such as FairSeq, MetaSeq, and HuggingFace to benefit all Transformer-based PyTorch models. We’ll provide future updates on the progress of BetterTransformer accelerations for the larger PyTorch ecosystem as part of this blog series.
Acknowledgements: The authors would like to thank Lin Qiao, Ajit Mathews, Andrew Tulloch, Dmytro Dzhulgakov, Natalia Gimelshein, Emad El-Haraty, Mark Saroufim, Adnan Aziz, Geeta Chauhan, and Hamid Shojanazeri for their support, contributions and many helpful suggestions throughout the course of this project, and in the preparation of this blog.
layout: blog_detail
title: "Experience the power of PyTorch 2.0 on AMD Solutions"
author: AMD

PyTorch 2.0 represents a significant step forward for the PyTorch machine learning framework.  The stable release of PyTorch 2.0 brings new features that unlock even higher performance, while remaining backward compatible with prior releases and retaining the Pythonic focus which has helped to make PyTorch so enthusiastically adopted by the AI/ML community. AMD has long been a strong proponent of PyTorch, and we are delighted that the PyTorch 2.0 stable release includes support for AMD Instinct™ and Radeon™ GPUs that are supported by the ROCm™ software platform.
With the stable PyTorch 2.0 release, PyTorch 2.0 introduces torch.compile as a beta feature underpinned by TorchInductor with support for AMD Instinct and Radeon GPUs through OpenAI Triton deep learning compiler. Through TorchInductor, developers can now generate low level kernels using Triton that are portable and performant to hand-written kernels on native hardware centric kernel programming models.
OpenAI Triton is a language and compiler for blocked algorithms, which aims to provide an abstraction layer between CUDA/HIP and Torch at which developers can write efficient kernels more productively.  We have written a new backend which interfaces Triton's custom MLIR dialects with our ROCm compiler stack.
Triton can automatically optimize kernels generated by machine learning compilers such as TorchInductor for multiple AI accelerators including AMD Instinct GPU accelerator by leveraging hardware-specific features of the AMD CDNA™ GPU architecture. This makes it easy for developers and users to switch seamlessly from any HW to AMD Instinct GPU accelerators and get great out of the box performance. 
In addition, compilers like Triton can also enable developers to use high-level programming languages, such as Python, to write machine learning code that can be efficiently compiled and executed on specialized hardware. This can help greatly improve the productivity of machine learning developers, as they can focus on the algorithmic aspects of their models and rely on the compiler to generate efficient code.
By design, PyTorch 2.0 is backward compatible to earlier PyTorch releases. This holds true for the ROCm build of PyTorch 2.0 as well. Developers using PyTorch with AMD GPUs can migrate to PyTorch 2.0 with the confidence that their existing code will continue to work without any required changes, so there is no penalty to access the improvements that come with this release. On the other hand, using PyTorch 2.0 and TorchInductor can result in significant performance improvement over the default eager-mode as shown below.
The initial results using AMD Instinct MI250 GPUs already shows strong performance improvement with minimal optimization on TorchInductor compared to the default eager-mode. We see an average performance increase of up to 1.54X on 44 out of the 45 models on HuggingFace benchmarks suite with CamemBert, DistillGPT2 and T5Small being a few of the standout models with up to 1.5X or more performance improvement over eager-mode. We are looking forward to continued engagement with members of the PyTorch team at Meta to enable further optimization on ROCm software stack and the additional performance improvement for future PyTorch releases.  
 
Image 1: AMD MI250 GPU performance improvement for TorchInductor vs eager-mode using HuggingFace MI200-89.
PyTorch 2.0 follows the same set of install options as before to build and install for supporting AMD GPUs. These include an installable Python package hosted at pytorch.org, AMD’s public PyTorch docker image, and of course the option to build from source using the upstream PyTorch repository. As with PyTorch builds for other platforms, the specific command line to be run for pip-based install is provided by the configurator at https://pytorch.org/get-started/locally/.
The GPUs supported by the ROCm software platform which forms the basis for PyTorch support on AMD GPUs are documented at https://docs.amd.com/bundle/Hardware_and_Software_Reference_Guide/page/Hardware_and_Software_Support.html
Conclusion
Conclusion
PyTorch 2.0 represents a major step in continuing to broaden support for ML developers by increasing performance while maintaining a simple, Pythonic interface. This performance uplift is made possible in large part by the new TorchInductor infrastructure, which in turn harnesses the Triton ML programming language and just-in-time compiler. AMD’s support for these technologies allows users to realize the full promise of the new PyTorch architecture.  Our GPU support in PyTorch 2.0 is just one manifestation of a larger vision around AI and machine learning. AI/ML plays an important role in multiple AMD product lines, including Instinct and Radeon GPUs, Alveo™ data center accelerators, and both Ryzen™ and EPYC processors. These hardware and software initiatives are all part of AMD’s Pervasive AI vision, and we look forward to addressing the many new challenges and opportunities of this dynamic space.
MI200-89 – PyTorch Inductor mode HuggingFace Transformers training speedup, running the standard PyTorch 2.0 test suite, over PyTorch eager-mode comparison based on AMD internal testing on a single GCD as of 3/10/2023 using a 2P AMD EPYC™ 7763 production server with 4x AMD Instinct™ MI250 (128GB HBM2e) 560W GPUs with Infinity Fabric™ technology; host ROCm™ 5.3, guest ROCm™ 5.4.4, PyTorch 2.0.0, Triton 2.0. Server manufacturers may vary configurations, yielding different results. Performance may vary based on factors including use of latest drivers and optimizations. 
© 2023 Advanced Micro Devices, Inc. All rights reserved. AMD, the AMD Arrow logo, AMD CDNA, AMD Instinct, EPYC, Radeon, ROCm, Ryzen, and combinations thereof are trademarks of Advanced Micro Devices, Inc. Other product names used in this publication are for identification purposes only and may be trademarks of their respective owners.
layout: blog_detail
title: 'Announcing PyTorch Developer Day 2021'
author: Team PyTorch
featured-img: 'assets/images/ptdevday21.gif'

We are excited to announce PyTorch Developer Day (#PTD2), taking place virtually from December 1 & 2, 2021. Developer Day is designed for developers and users to discuss core technical developments, ideas, and roadmaps. 



Event Details
Technical Talks Live Stream - December 1, 2021
Join us for technical talks on a variety of topics, including updates to the core framework, new tools and libraries to support development across a variety of domains, responsible AI and industry use cases. All talks will take place on December 1 and will be live streamed on PyTorch channels.
Stay up to date by following us on our social channels: Twitter, Facebook, or LinkedIn.
Poster Exhibition & Networking - December 2, 2021
On the second day, we’ll be hosting an online poster exhibition on Gather.Town. There will be opportunities to meet the authors and learn more about their PyTorch projects as well as network with the community. This poster and networking event is limited to people composed of PyTorch maintainers and contributors, long-time stakeholders and experts in areas relevant to PyTorch’s future. Conversations from the networking event will strongly shape the future of PyTorch. As such, invitations are required to attend the networking event. 
Apply for an invitation to the networking event by clicking here.
Call for Content Now Open
Call for Content Now Open
Submit your poster abstracts today! Please send us the title and brief summary of your project, tools and libraries that could benefit PyTorch researchers in academia and industry, application developers, and ML engineers for consideration. The focus must be on academic papers, machine learning research, or open-source projects related to PyTorch development, Responsible AI or Mobile. Please no sales pitches. Deadline for submission is September 24, 2021. 
You can submit your poster abstract during your application & registration process here.
Visit the event website for more information and we look forward to having you at PyTorch Developer Day. For any questions about the event, contact pytorch@fbreg.com.
layout: blog_detail
title: 'Efficient PyTorch: Tensor Memory Format Matters'
author: 'Dhruv Matani, Suraj Subramanian'
featured-img: ''

Ensuring the right memory format for your inputs can significantly impact the running time of your PyTorch vision models. When in doubt, choose a Channels Last memory format.
When dealing with vision models in PyTorch that accept multimedia (for example image Tensorts) as input, the Tensor’s memory format can significantly impact the inference execution speed of your model on mobile platforms when using the CPU backend along with XNNPACK. This holds true for training and inference on server platforms as well, but latency is particularly critical for mobile devices and users.

Outline of this article
Outline of this article

Deep Dive into matrix storage/memory representation in C++. Introduction to Row and Column major order.
Impact of looping over a matrix in the same or different order as the storage representation, along with an example.
Introduction to Cachegrind; a tool to inspect the cache friendliness of your code.
Memory formats supported by PyTorch Operators.
Best practices example to ensure efficient model execution with XNNPACK optimizations

Matrix Storage Representation in C++
Images are fed into PyTorch ML models as multi-dimensional Tensors. These Tensors have specific memory formats. To understand this concept better, let’s take a look at how a 2-d matrix may be stored in memory.
Broadly speaking, there are 2 main ways of efficiently storing multi-dimensional data in memory.
Row Major Order: In this format, the matrix is stored in row order, with each row stored before the next row in memory. I.e. row N comes before row N+1.
Column Major Order: In this format, the matrix is stored in column-order, with each column stored before the next column in memory. I.e. column N comes before column N+1.

You can see the differences graphically below.



C++ stores multi-dimensional data in row-major format.

Efficiently accessing elements of a 2d matrix
Similar to the storage format, there are 2 ways to access data in a 2d matrix.

Loop Over Rows first: All elements of a row are processed before any element of the next row.
Loop Over Columns first: All elements of a column are processed before any element of the next column.
For maximum efficiency, one should always access data in the same format in which it is stored. I.e. if the data is stored in row-major order, then one should try to access it in that order.
The code below (main.cpp) shows 2 ways of accessing all the elements of a 2d 4000x4000 matrix.
```python
include 
include 
// loop1 accesses data in matrix 'a' in row major order,
// since i is the outer loop variable, and j is the
// inner loop variable.
int loop1(int a[4000][4000]) {
 int s = 0;
 for (int i = 0; i < 4000; ++i) {
   for (int j = 0; j < 4000; ++j) {
     s += a[i][j];
   }
 }
 return s;
}
// loop2 accesses data in matrix 'a' in column major order
// since j is the outer loop variable, and i is the
// inner loop variable.
int loop2(int a[4000][4000]) {
 int s = 0;
 for (int j = 0; j < 4000; ++j) {
   for (int i = 0; i < 4000; ++i) {
     s += a[i][j];
   }
s += a[i][j];
   }
 }
 return s;
}
int main() {
 static int a[4000][4000] = {0};
 for (int i = 0; i < 100; ++i) {
   int x = rand() % 4000;
   int y = rand() % 4000;
   a[x][y] = rand() % 1000;
 }
auto start = std::chrono::high_resolution_clock::now();
 auto end = start;
 int s = 0;
if defined RUN_LOOP1
start = std::chrono::high_resolution_clock::now();
s = 0;
 for (int i = 0; i < 10; ++i) {
   s += loop1(a);
   s = s % 100;
 }
 end = std::chrono::high_resolution_clock::now();
std::cout << "s = " << s << std::endl;
 std::cout << "Time for loop1: "
   << std::chrono::duration(end - start).count()
   << "ms" << std::endl;
endif
if defined RUN_LOOP2
start = std::chrono::high_resolution_clock::now();
 s = 0;
 for (int i = 0; i < 10; ++i) {
   s += loop2(a);
   s = s % 100;
 }
 end = std::chrono::high_resolution_clock::now();
std::cout << "s = " << s << std::endl;
 std::cout << "Time for loop2: "
   << std::chrono::duration(end - start).count()
<< "ms" << std::endl;
endif
}
Let’s build and run this program and see what it prints.
g++ -O2 main.cpp -DRUN_LOOP1 -DRUN_LOOP2
./a.out
Prints the following:
s = 70
Time for loop1: 77.0687ms
s = 70
Time for loop2: 1219.49ms

loop1() is **15x faster** than loop2(). Why is that? Let’s find out below!

## Measure cache misses using Cachegrind

[Cachegrind](https://courses.cs.washington.edu/courses/cse326/05wi/valgrind-doc/cg_main.html) is a cache profiling tool used to see how many I1 (first level instruction), D1 (first level data), and LL (last level) cache misses your program caused.

Let’s build our program with just loop1() and just loop2() to see how cache friendly each of these functions is.

### Build and run/profile just loop1()

```python
g++ -O2 main.cpp -DRUN_LOOP1
valgrind --tool=cachegrind ./a.out

Prints:
```python
==3299700==
==3299700== I   refs:      643,156,721
==3299700== I1  misses:          2,077
==3299700== LLi misses:          2,021