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layout: blog_detail
title: "Understanding LazyTensor System Performance with PyTorch/XLA on Cloud TPU"
author: Vaibhav Singh
featured-img: ""
Introduction
Ease of use, expressivity, and debuggability are among the core principles of PyTorch. One of the key drivers for the ease of use is that PyTorch execution is by d... | https://pytorch.org/blog/understanding-lazytensor-system-performance-with-pytorch-xla-on-cloud-tpu/ | pytorch blogs |
In this post we will explore some of the basic concepts of the LazyTensor System with the goal of applying these concepts to understand and debug performance of LazyTensor based implementations in PyTorch. Although we will use PyTorch/XLA on Cloud TPU as the vehicle for exploring these concepts, we hope that these idea... | https://pytorch.org/blog/understanding-lazytensor-system-performance-with-pytorch-xla-on-cloud-tpu/ | pytorch blogs |
The starting point of a LazyTensor system is a custom tensor type. In PyTorch/XLA, this type is called XLA tensor. In contrast to PyTorch’s native tensor type, operations performed on XLA tensors are recorded into an IR graph. Let’s examine an example that sums the product of two tensors:
import torch
import torch_xla
... | https://pytorch.org/blog/understanding-lazytensor-system-performance-with-pytorch-xla-on-cloud-tpu/ | pytorch blogs |
The operations will continue until PyTorch/XLA encounters a barrier. This barrier can either be a [mark step()](https://github.com/pytorch/xla/blob/ff079bb48744e5aa6696201ccf34057f15fc7cac/torch_xla/core/xla_model.py#L751) api call or any other event which forces the execution of the graph recorded so far.
```python
xm... | https://pytorch.org/blog/understanding-lazytensor-system-performance-with-pytorch-xla-on-cloud-tpu/ | pytorch blogs |
Compile Once, Execute Often
XLA compilation passes offer optimizations (e.g. op-fusion, which reduces HBM pressure by using scratch-pad memory for multiple ops, ref ) and leverages lower level XLA infrastructure to optimally use the underlying hardware. However, there is one caveat, compilation passes are expensive, i.... | https://pytorch.org/blog/understanding-lazytensor-system-performance-with-pytorch-xla-on-cloud-tpu/ | pytorch blogs |
10000000 loops, best of 5: 34.2 ns per loop
```
You notice that the slowest step is quite longer than the fastest. This is because of the graph compilation overhead which is incurred only once for a given shape of graph, input shape, and output shape. Subsequent steps are faster because no graph compilation is necessar... | https://pytorch.org/blog/understanding-lazytensor-system-performance-with-pytorch-xla-on-cloud-tpu/ | pytorch blogs |
We saw that the computation graph is compiled and executed when a LazyTensor barrier is encountered. There are three scenarios when the LazyTensor barrier is automatically or manually introduced. The first is the explicit call of mark_step() api as shown in the preceding example. mark_step() is also called implicitly a... | https://pytorch.org/blog/understanding-lazytensor-system-performance-with-pytorch-xla-on-cloud-tpu/ | pytorch blogs |
The second scenario where a barrier is introduced is when PyTorch/XLA finds an op with no mapping (lowering) to equivalent XLA HLO ops. PyTorch has 2000+ operations. Although most of these operations are composite (i.e. can be expressed in terms of other fundamental operations), some of these operations do not have cor... | https://pytorch.org/blog/understanding-lazytensor-system-performance-with-pytorch-xla-on-cloud-tpu/ | pytorch blogs |
The third and final scenario which results in a LazyTensor barrier is when there is a control structure/statement or another method which requires the value of a tensor. This statement would at the minimum cause the execution of the computation graph leading to the tensor (if the graph has already been seen) or cause c... | https://pytorch.org/blog/understanding-lazytensor-system-performance-with-pytorch-xla-on-cloud-tpu/ | pytorch blogs |
z = torch.einsum('bs,st->bt', y, x)
step_loss = z.sum().view(1,)
if acc:
loss = torch.cat((loss, step_loss))
else:
loss = step_loss
xm.mark_step()
return loss
import time
def measure_time(acc=False):
exec_times = []
iter_count = 100
x = torch.rand((512, 8)).to(dev)
y = torch.rand((512, 512)).t... | https://pytorch.org/blog/understanding-lazytensor-system-performance-with-pytorch-xla-on-cloud-tpu/ | pytorch blogs |
Note that static and dynamic cases have the same computation but dynamic graph compiles every time, leading to the higher overall run-time. In practice, the training step with recompilation can sometimes be an order of magnitude or slower. In the next section we discuss some of the PyTorch/XLA tools to debug training ... | https://pytorch.org/blog/understanding-lazytensor-system-performance-with-pytorch-xla-on-cloud-tpu/ | pytorch blogs |
The second component offered by PyTorch/XLA profiler is the inline trace annotation. For example:
import torch_xla.debug.profiler as xp
def train_imagenet():
print('==> Preparing data..')
img_dim = get_model_property('img_dim')
....
server = xp.start_server(3294)
def train_loop_fn(loader, epoch):
....
... | https://pytorch.org/blog/understanding-lazytensor-system-performance-with-pytorch-xla-on-cloud-tpu/ | pytorch blogs |
Op trace along with the client-side debugging function is a powerful set of tools to debug and optimize your training performance with PyTorch/XLA. For more detailed instructions on the profiler usage, the reader is encouraged to explore blogs part-1, part-2, and part-3 of the blog series on PyTorch/XLA performance de... | https://pytorch.org/blog/understanding-lazytensor-system-performance-with-pytorch-xla-on-cloud-tpu/ | pytorch blogs |
Summary
In this article we have reviewed the fundamentals of the LazyTensor system. We built on those fundamentals with PyTorch/XLA to understand the potential causes of training performance degradation. We discussed why “compile once and execute often” helps to get the best performance on LazyTensor systems, and why t... | https://pytorch.org/blog/understanding-lazytensor-system-performance-with-pytorch-xla-on-cloud-tpu/ | pytorch blogs |
Refrences
[[1]] LazyTensor: combining eager execution with domain-specific compilers | https://pytorch.org/blog/understanding-lazytensor-system-performance-with-pytorch-xla-on-cloud-tpu/ | pytorch blogs |
layout: blog_detail
title: "PyTorch Conference 2023: Join us in San Francisco October 16-17"
We’re thrilled to announce the upcoming PyTorch Conference 2023! On October 16-17, the conference will showcase PyTorch 2.0, the next-generation release of the popular machine learning framework. As part of the Linux Founda... | https://pytorch.org/blog/pytorch-conference-2023/ | pytorch blogs |
The conference agenda features an engaging lineup of events, including an opening reception, engaging community and partner discussions, informative panels, poster sessions, enlightening use cases and community stories, as well as discussions on the latest trends in machine learning and deep learning development and de... | https://pytorch.org/blog/pytorch-conference-2023/ | pytorch blogs |
How will your presentation help better the open source ecosystem?
To help you shape your proposal, here are some suggested topics for the conference:
Deployments on AWS, Azure
Use cases and real-world applications
Foundational models
AI practices
Production considerations
PyTorch 2.X features and updates
Training tech... | https://pytorch.org/blog/pytorch-conference-2023/ | pytorch blogs |
Register Today
Registration is now open! Get your ticket today and secure your spot: https://events.linuxfoundation.org/pytorch-conference/register/
Thank you for your interest, and we look forward to a successful PyTorch Conference 2023! | https://pytorch.org/blog/pytorch-conference-2023/ | pytorch blogs |
layout: blog_detail
title: "Accelerating Large Language Models with Accelerated Transformers"
author: Lucas Pasqualin, Driss Guessous, Christian Puhrsch, Bertrand Maher, Michael Gschwind
| https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
TL;DR. We show how to use Accelerated PyTorch 2.0 Transformers and the newly introduced torch.compile() method to accelerate Large Language Models on the example of nanoGPT, a compact open-source implementation of the GPT model from Andrej Karpathy. Using the new scaled dot product attention operator introduced with A... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
Recent times have seen exponential adoption of large language models (LLMs) and Generative AI in everyday life. Tightly coupled with these ever-growing models is the ever-growing training cost - in terms of both time and hardware utilization. The PyTorch team has tackled these challenges head on with Accelerated PyTorc... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
In this blog post, we explore training optimizations gained by utilizing custom kernel implementations of SDPA - also known as scaled dot product attention - a critical layer in transformer models. The custom kernel for SDPA replaces several discrete sequential operations with one globally optimized kernel which avoids... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
Figure 1: The Transformer model architecture based on “Attention is All You Need”. With the new PyTorch SDPA operator, Multi-Head Attention is efficiently implemented by a linear layer for the in-projection, the SDPA operator, and a linear layer for the out-projection.
With the new scaled_dot_product_attention operat... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
head_dim: Head Dimension
q, k, v = _in_projection(query, key, value, q_proj_weight, k_proj_weight, v_proj_weight, b_q, b_k, b_v)
q = q.view(bsz, num_heads, tgt_len, head_dim)
k = k.view(bsz, num_heads, src_len, head_dim)
v = v.view(bsz, num_heads, src_len, head_dim)
# Scaled Dot Product Attention
attn_output = scaled_d... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
``
PyTorch 2. supports multiple different kernels optimized for specific use cases, with specific requirements. A kernel picker picks the best kernel for a particular combination of input parameters. If no optimized "custom kernel" for a particular combination of input parameters can be identified, the kernel picker se... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
An optimized kernel based on the paper “Self-Attention Does Not Need O(n^2) Memory" and implemented in xFormer, which supports both 32 and 16 bit floating data types on a wider range of architectures (SM40 and later). This blog post refers to this kernel as the mem_efficient kernel.
Note that both optimized kernels (t... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
Enabling Accelerated Transformers with nanoGPT
The SDPA operator being a critical component of the GPT model, we identified the open source nanoGPT model as an excellent candidate for both demonstrating the ease of implementation and benefits of PyTorch 2.0’s Accelerated Transformers. The following demonstrates the ex... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
Step 1: Identify the existing SDPA implementation
In the case of nanoGPT, SDPA is implemented in the model’s CausalSelfAttention class. The original implementation at time of writing is adapted below for this post.
Step 2: Replace with Torch’s scaled_dot_product_attention
At this point we can note the following:
* Li... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
Alternatively, the original mask can be passed into the attn_mask field however due to the mentioned kernel constraints that would limit the implementation to only support the generic sdpa_math kernel.
Step 3 (Bonus): Faster matmuls with padding
On top of the performance improvements from SDPA, our analysis yielded a... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
The vocab size determines the dimensions of matmuls in the output layer of GPT, and these are so large that they were taking a majority of the time for the entire training loop! We discovered that they were achieving performance significantly below the peak throughput achievable on the A100 GPU, and guessed from NVIDI... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
baseline (nanoGPT implementation): ~143ms
sdpa_math (generic): ~134ms (6.71% faster)
mem_efficient kernel: ~119ms (20.16% faster)
flash_attention kernel: ~113ms (26.54% faster)
flash_attention + padded vocab: ~87ms (64.37% faster)
All code was run on an 8 x NVIDIA Corporation A100 server with 80 GB HBM [A100 SXM4 80... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
Enhancing Numerical Model Stability
In addition to being faster, PyTorch's implementation offers increased numerical stability by avoiding loss of precision in many execution scenarios. There is a great explanation here, but essentially the PyTorch implementation scales the Query and Key matrices before multiplication,... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
Improved Memory Consumption
Yet another large advantage of using the torch SDPA kernels is the reduced memory footprint, which allows for the utilization of larger batch sizes. The following chart compares the best validation loss after one hour of training for both flash attention and the baseline implementations of c... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
Conclusion
Accelerated PyTorch 2 Transformers were designed to make the training and production deployment of state-of-the-art transformer models affordable and integrated with PyTorch 2.0 model JIT compilation. The newly introduced PyTorch SDPA operator provides improved performance for training Transformer models an... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
In this section we provide a more in depth explanation of the previously mentioned enhanced numerical stability which is gained by prescaling SDPA’s input vectors. The following is a simplified version of nanoGPT’s mathematical implementation of SDPA. The important thing to note here is that the query undergoes matrix ... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
scaling_factor = math.sqrt(math.sqrt(embed_size))
q = q / scaling_factor # notice q is scaled here !
same as above, but with scaling factor
att = q @ (k.transpose(-2, -1) / scaling_factor)
att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
att = F.softmax(att0, dim=-1)
Dropout is set to 0, so we can safel... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
need_attn_weights=False,
is_causal=False,
)
torch.allclose(y_sdpa, y_nanogpt) # False, indicating fp issues
torch.allclose(y_sdpa, y_scale_before) # True, as expected
## Appendix B: Reproducing Experiment Results
Researchers seeking to reproduce these results should start with the following commit from Andrej’s nan... | https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
with torch.backends.cuda.sdp_kernel (
enable_math = False,
enable_flash = False,
enable_mem_efficient = True
):
train(model)
| https://pytorch.org/blog/accelerating-large-language-models/ | pytorch blogs |
layout: blog_detail
title: "Introducing Hidet: A Deep Learning Compiler for Efficient Model Serving"
author: Team Hidet
Hidet is a powerful deep learning compiler that simplifies the process of implementing high-performing deep learning operators on modern accelerators (e.g., NVIDIA GPUs). With the new feature of tor... | https://pytorch.org/blog/introducing-hidet/ | pytorch blogs |
pip install hidet
Hidet is integrated with PyTorch as a torch.compile(...) backend following the Custom Backends tutorial. You can specify hidet as the backend when you compile a model. (Note: requires PyTorch version 2.0+):
torch.compile(..., backend='hidet')
| https://pytorch.org/blog/introducing-hidet/ | pytorch blogs |
torch.compile(..., backend='hidet')
Hidet converts the given PyTorch model in the torch.fx.Graph format into its internal graph representation, and conducts a series of optimizations. Hidet provides a few options to configure the optimizations. For example, we can use hidet.torch.dynamo_config.use_tensor_core(True) to... | https://pytorch.org/blog/introducing-hidet/ | pytorch blogs |
model = torch.hub.load(
'pytorch/vision:v0.6.0', 'resnet50', pretrained=True
).cuda().half().eval()
Configure hidet to use tensor core and enable tuning
hidet.torch.dynamo_config.use_tensor_core(True)
hidet.torch.dynamo_config.search_space(2)
Compile the model using Hidet
model_opt = torch.compile(model, backend='... | https://pytorch.org/blog/introducing-hidet/ | pytorch blogs |
We encourage you to try out the above script on your own NVIDIA GPU(s)! If you run this script on an `aws.g5.2xlarge` instance, you would get the result shown in the following figure. Hidet achieves the speedup because it could automatically fuse multiple operators, tune operator schedules, and use CUDA Graph to reduce... | https://pytorch.org/blog/introducing-hidet/ | pytorch blogs |
from hidet.lang import f32, attr
from hidet.lang.cuda import threadIdx, blockIdx, blockDim
with hidet.script_module() as script_module:
@hidet.script
def matmul(
a: f32[m_size, k_size],
b: f32[k_size, n_size],
c: f32[m_size, n_size]
):
attr... | https://pytorch.org/blog/introducing-hidet/ | pytorch blogs |
func = matmul(m, n, k)
func(a, b, c)
return c
a = torch.randn([3, 4], device='cuda')
b = torch.randn([4, 5], device='cuda')
c = NaiveMatmul.apply(a, b)
cc = torch.matmul(a, b)
torch.testing.assert_close(c, cc)
```
More optimizations can be applied, see the example in our documentation to learn more. | https://pytorch.org/blog/introducing-hidet/ | pytorch blogs |
Hidet Script vs. Triton: Triton greatly simplifies the CUDA programming by introducing the tile-based programming model where the parallel execution unit is thread blocks instead of threads. However, this simplification also prevents the tensor program developers from manipulating the fine-grained computation and memor... | https://pytorch.org/blog/introducing-hidet/ | pytorch blogs |
More about Hidet
Hidet originates from a research project led by the EcoSystem lab at the University of Toronto (UofT) and AWS. The authors propose a new way, named the task-mapping programming paradigm, to construct tensor programs. It aims to simplify the tensor programming without sacrificing any optimization opport... | https://pytorch.org/blog/introducing-hidet/ | pytorch blogs |
Acknowledgement
We would like to thank Jerry Park, Mark Saroufim, Jason Liang and Helen Suk for their valuable help on preparing the blog post and feedback on the text. We also would like to thank Nikita Shulga, Jason Ansel, and Dmytro Dzhulgakov for reviewing and improving our PR https://github.com/pytorch/pytorch/pul... | https://pytorch.org/blog/introducing-hidet/ | pytorch blogs |
layout: blog_detail
title: "PyTorch, a year in...."
author: "The PyTorch Team"
date: 2018-01-19 12:00:00 -0500
redirect_from: /2018/01/19/a-year-in.html
Today marks 1 year since PyTorch was released publicly. It's been a wild ride — our quest to build a flexible deep learning research platform. Over the last year, we... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
Research papers, packages and Github
Within days of release, users from the community started to implement their favorite research papers in PyTorch and release the code on Github. Open-source code is a primary and essential tool for researchers today.
Folks came together to create torchtext, torchvision and torchaudio... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
Jun-Yan Zhu, Taesung Park, Phillip Isola, Alyosha Efros and team from U.C.Berkeley released the hugely popular Cycle-GAN and pix2pix which does image to image transforms.
The researchers at HarvardNLP and Systran started developing and improving OpenNMT in PyTorch, seeded by initial reimplementation of the [Lua]Torc... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
Salesforce Research released several packages, including their highlight release of PyTorch-QRNN, a type of RNN that is 2x to 17x faster than standard LSTMs optimized by CuDNN. James Bradbury and team form one of the most active and engaging forces in the PyTorch community.
We're releasing @PyTorch-QRNN, 2-17x faster t... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
Researchers from Uber, Northeastern and Stanford came together to form an active probabilistic programming community around their packages Pyro and ProbTorch. They are actively developing the torch.distributions core package. This community is so active and fast-moving, we had our first pytorch-probabilistic-programmin... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
NVIDIA Researchers released three high-quality repositories that implemented pix2pix-HD, Sentiment Neuron and FlowNet2 papers. Their analysis of scalability of different Data Parallel models in PyTorch was helpful to the community.
The Allen Institute for AI released AllenNLP which includes several state-of-the-art... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
We also had our first Kaggle winning team grt123 in July. They won the DataScience Bowl 2017 on Lung Cancer detection and subsequently released their PyTorch implementations.
On the visualization front, Tzu-Wei Huang implemented a TensorBoard-PyTorch plugin and Facebook AI Research released PyTorch compatibility for t... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
There are countless good projects that we haven't highlighted for the lack of space, you can find a curated list here.
We would also like to give a huge shout-out to folks who actively help others out on the Forums, especially ptrblck, jpeg729, QuantScientist, albanD, Thomas Viehmann and chenyuntc. You are providing an... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
More than half a million downloads of PyTorch binaries. 651,916 to be precise.
5,400 users wrote 21,500 posts discussing 5,200 topics on our forums discuss.pytorch.org (http://discuss.pytorch.org/)
131 mentions of PyTorch on Reddit's /r/machinelearning since the day of release. In the same period, TensorFlow was menti... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
Courses, Tutorials and Books
When we released PyTorch, we had good API documentation, but our tutorials were limited to a few ipython notebooks — helpful, but not good enough.
Sasank Chilamkurthy took it upon himself to revamp the tutorials into the beautiful website that it is today.
Sean Robertson and Justin Johns... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
Goku Mohandas and Delip Rao switched the code content of their book-in-progress to use PyTorch.
We've seen quite a few university machine learning courses being taught with PyTorch as the primary tool, such as Harvard's CS287. Taking it one step further and democratizing learning, we had three online courses pop up tha... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
Sung Kim from HKUST released an online course on Youtube that was aimed towards a general audience, titled: “PyTorch Zero to All”.
Engineering
Over the last year we implemented multiple features, improved performance across the board and fixed lots of bugs. A full list of the work we've done is found in our release n... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
Distributed PyTorch
In August, we released a small distributed package that followed the highly popular MPI-collective approach. The package has multiple backends such as TCP, MPI, Gloo and NCCL2 to support various types of CPU/GPU collective operations and use-cases, and integrates distributed technologies such as Inf... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
Sparse Tensors
In March, we released a small package supporting sparse Tensors and in May we released CUDA support for the sparse package. The package is small and limited in functionality, and is used for implementing Sparse Embeddings and commonly used sparse paradigms in deep learning. This package is still small in... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
Rewrote the code for several neural network operators (too many to list), but notably nn.Embedding and group convolutions.
Reducing framework overhead by 10x across board
Since PyTorch is a dynamic graph framework, we create a new graph on the fly at every iteration of a training loop. Hence, the framework overhead ha... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
ATen
As we embarked upon a redesign of the PyTorch internals, we built the ATen C++11 library that now powers all of the PyTorch backend. ATen has an API that mirrors PyTorch's Python API, which makes it a convenient C++ library for Tensor computation. ATen can be built and used independently of PyTorch.
Exporting mode... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
The subsequent trace can be either used to run the current PyTorch model more efficiently (by running optimization passes on it), or be converted to the ONNX format to be shipped to other frameworks such as Caffe2, MXNet, TensorFlow and others or directly to the hardware accelerated libraries like CoreML or TensorRT. O... | https://pytorch.org/blog/a-year-in/ | pytorch blogs |
Talk to your doctor to find out if PyTorch is right for you.— Sean Robertson (@sprobertson) May 26, 2017
PyTorch gave me so much life that my skin got cleared, my grades are up, my bills are paid and my crops are watered.— Adam Will ð️ð (@adam_will_do_it) May 26, 2017
| https://pytorch.org/blog/a-year-in/ | pytorch blogs |
So have I! But my hair is also shiner and I've lost weight. @PyTorch for the win. https://t.co/qgU4oIOB4K— Mariya (@thinkmariya) May 26, 2017
| https://pytorch.org/blog/a-year-in/ | pytorch blogs |
layout: blog_detail
title: "Out of the box acceleration and memory savings of 🤗 decoder models with PyTorch 2.0"
author: Felix Marty, Younes Belkada, Hamid Shojanazeri
As part of PyTorch 2.0 release, an accelerated implementation of the attention mechanism as part of the “Better Transformer” project (and known in Py... | https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
After seeing 20-30% speedups at inference for diffusion models, we went ahead and implemented an integration with 🤗 Transformers models through the 🤗 Optimum library. Similar to the previous integration for encoder models, the integration replaces modules from Transformers with efficient implementations that use torc... | https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
model = BetterTransformer.reverse(model)
model.save_pretrained(“fine_tuned_model”)
model.push_to_hub(“fine_tuned_model”)
Summarizing our findings below about `torch.nn.functional.scaled_dot_product_attention`:
* It is most useful to fit larger models, sequence length, or batch size to train on a given hardware.
* Memo... | https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
model = model.to_bettertransformer()
To convert your model using the BetterTransformer API. You can already try this feature out by installing transformers from source.
Benchmark and usage with 🤗 Transformers
torch.nn.functional.scaled_dot_product_attention is usable with any architecture that uses standard attention... | https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
return attn_weight @ V
```
In the 🤗 Optimum integration with Transformers models, the following architectures are supported for now: gpt2, gpt-neo, gpt-neox, gptj, t5, bart, codegen, pegasus, opt, LLaMA, blenderbot, m2m100. You can expect this list to be extended in the near future!
To validate the benefits from the n... | https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
Training benchmark on a single A100-SXM4-80GB, Nvidia DGX
Out of this benchmark, the most interesting finding is that native SDPA allows for the usage of longer sequence lengths and batch sizes without running into out of memory issues. Moreover, up to 20% speedups can be seen during inference, and even larger durin... | https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
Training benchmark on two A100-SXM4-80GB, Nvidia DGX, using 🤗 Accelerate library for distributed training
Note that some kernels support only the sm_80 compute capability (which is the one from A100 GPUs), which limits usability on a wide range of hardware, notably if the head dimension is not a power of two. For ex... | https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
The native scaled_dot_product_attention relies on three possible backend implementations: flash attention, memory-efficient attention, and the so-called math implementation which provides a hardware-neutral fallback for all PyTorch platforms.
When fused kernels are available for a given problem size, flash-attention or... | https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
The “math” implementation is simply an implementation using the PyTorch’s C++ API. Interesting to note in this implementation is that the query and key tensors are scaled individually for numerical stability, thus launching two aten::div operations instead of possibly only one in an eager implementation that does not c... | https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
Benchmarking torch.nn.functional.scaled_dot_product_attention, we notice a decrease in the speedup / memory gains as the head dimension increases. This is an issue for some architectures like EleutherAI/gpt-neo-2.7B, that has a relatively large head dimension of 128, or EleutherAI/gpt-j-6B (and derived models as Pygmal... | https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
Using memory-efficient attention SDP kernel (forward-only), A100
Using math (without dropout), A100
Using flash attention SDP kernel (without dropout), A100
Using memory-efficient attention SDP kernel (without dropout), A100
| https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
We see that for the same problem size, be it for inference-only or training, the speedup decreases with higher head dimension, e.g. from 3.4x for headdim=8 to 1.01x for headdim=128 using flash attention kernel.
The reduced memory saving is expected with larger head dimensions. Recall the standard attention computation... | https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
In flash attention, the tradeoff is between the head dimension d and the shared memory size M of a GPU streaming multiprocessor, with a total number of memory accesses of O(N² * d²/M). Thus, the memory accesses scale quadratically in the head dimension, contrary to the standard attention that scales linearly. The reaso... | https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
However, some architectures as OPT or T5 do not use a scaling in the attention, which as of Pytorch 2.0.0 forces it to artificially rescale before the scaled_dot_product_attention call. This introduces an unnecessary overhead, as an additional multiplication is necessary, on top of unneeded divisions in the attention.
... | https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
However, as we have seen, some architectures require a custom attention mask, as T5 that uses positional bias. Moreover, in the case of a batch size larger than one where some inputs may be padded, a custom attention mask also needs to be passed. For this latter case, an alternative would be to use NestedTensor, which ... | https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
Note that xformers, from which PyTorch’s SDPA partially takes inspiration, currently supports arbitrary attention masks: https://github.com/facebookresearch/xformers/blob/658ebab39545f180a6075385b3897921623d6c3b/xformers/ops/fmha/cutlass.py#L147-L156 . HazyResearch implementation of flash attention also supports an equ... | https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
In the future, we would like to adapt the API to enable users to use SDPA in encoder-based models as well.
We thank Benjamin Lefaudeux, Daniel Haziza and Francisco Massa for their advice on the head dimension influence, as well as Michael Gschwind, Christian Puhrsch and Driss Guessous for their feedback on the blog pos... | https://pytorch.org/blog/out-of-the-box-acceleration/ | pytorch blogs |
layout: blog_detail
title: 'Announcing the Winners of the 2020 Global PyTorch Summer Hackathon'
author: Team PyTorch
More than 2,500 participants in this year’s Global PyTorch Summer Hackathon pushed the envelope to create unique new tools and applications for PyTorch developers and researchers.
Notice: None of th... | https://pytorch.org/blog/announcing-the-winners-of-the-2020-global-pytorch-summer-hackathon/ | pytorch blogs |
PyTorch Responsible AI Development Tools: a tool, library, or web/mobile app to support researchers and developers in creating responsible AI that factors in fairness, security, privacy, and more throughout its entire development process.
The virtual hackathon ran from June 22 to August 25, with more than 2,500 regist... | https://pytorch.org/blog/announcing-the-winners-of-the-2020-global-pytorch-summer-hackathon/ | pytorch blogs |
DeMask is an end-to-end model for enhancing speech while wearing face masks — offering a clear benefit during times when face masks are mandatory in many spaces and for workers who wear face masks on the job. Built with Asteroid, a PyTorch-based audio source separation toolkit, DeMask is trained to recognize distortion... | https://pytorch.org/blog/announcing-the-winners-of-the-2020-global-pytorch-summer-hackathon/ | pytorch blogs |
model = ConvTasNet(n_src=2)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
loss = PITLossWrapper(
lambda x, y: (x - y).pow(2).mean(-1), # MSE
pit_from="pw_pt", # Point in the pairwise matrix.
)
system = System(model, optimizer, loss, train_loader, val_loader)
trainer = Trainer(fast_dev_run=True)
trainer.... | https://pytorch.org/blog/announcing-the-winners-of-the-2020-global-pytorch-summer-hackathon/ | pytorch blogs |
TorchExpo is a collection of models and extensions that simplifies taking PyTorch from research to production in mobile devices. This library is more than a web and mobile application, and also comes with a Python library. The Python library is available via pip install and it helps researchers convert a state-of-the-a... | https://pytorch.org/blog/announcing-the-winners-of-the-2020-global-pytorch-summer-hackathon/ | pytorch blogs |
Q&Aid is a conceptual health-care chatbot aimed at making health-care diagnoses and facilitating communication between patients and doctors. It relies on a series of machine learning models to filter, label, and answer medical questions, based on a medical image and/or questions in text provided by a patient. The trans... | https://pytorch.org/blog/announcing-the-winners-of-the-2020-global-pytorch-summer-hackathon/ | pytorch blogs |
Rasoee is an application that can take images as input and output the name of the dish. It also lists the ingredients and recipe, along with the link to the original recipe online. Additionally, users can choose a cuisine from the list of cuisines in the drop menu, and describe the taste and/or method of preparation in... | https://pytorch.org/blog/announcing-the-winners-of-the-2020-global-pytorch-summer-hackathon/ | pytorch blogs |
Rexana is an AI voice assistant meant to lay the foundation for a physical robot that can complete basic tasks around the house. The system is capable of autonomous navigation (knowing its position around the house relative to landmarks), recognizing voice commands, and object detection and recognition — meaning it can... | https://pytorch.org/blog/announcing-the-winners-of-the-2020-global-pytorch-summer-hackathon/ | pytorch blogs |
FairTorch is a fairness library for PyTorch. It lets developers add constraints to their models to equalize metrics across subgroups by simply adding a few lines of code. Model builders can choose a metric definition of fairness for their context, and enforce it at time of training. The library offers a suite of metric... | https://pytorch.org/blog/announcing-the-winners-of-the-2020-global-pytorch-summer-hackathon/ | pytorch blogs |
Fluence is a PyTorch-based deep learning library for language research. It specifically addresses the large compute demands of natural language processing (NLP) research. Fluence aims to provide low-resource and computationally efficient algorithms for NLP, giving researchers algorithms that can enhance current NLP met... | https://pytorch.org/blog/announcing-the-winners-of-the-2020-global-pytorch-summer-hackathon/ | pytorch blogs |
Causing (CAUSal INterpretation using Graphs) is a multivariate graphic analysis tool for bringing transparency to neural networks. It explains causality and helps researchers and developers interpret the causal effects of a given equation system to ensure fairness. Developers can input data and a model describing the d... | https://pytorch.org/blog/announcing-the-winners-of-the-2020-global-pytorch-summer-hackathon/ | pytorch blogs |
layout: blog_detail
title: "Get Started with PyTorch 2.0 Summary and Overview"
author: Team PyTorch
featured-img: "assets/images/Pytorch_2_0_Animation_AdobeExpress.gif"
Introducing PyTorch 2.0, our first steps toward the next generation 2-series release of PyTorch. Over the last few years we have innovated and iterat... | https://pytorch.org/blog/getting-started-with-pytorch-2.0/ | pytorch blogs |
Finally we are launching a new “Ask the Engineers: 2.0 Live Q&A” series that allows you to go deeper on a range of topics with PyTorch subject matter experts. We hope this content is helpful for the entire community and level of users/contributors.
https://pytorch.org/get-started/pytorch-2.0 | https://pytorch.org/blog/getting-started-with-pytorch-2.0/ | pytorch blogs |
layout: blog_detail
title: 'An Overview of the PyTorch Mobile Demo Apps'
author: Jeff Tang and Mark Saroufim
featured-img: 'assets/images/android-demo-app.png'
date: 2021-06-18 12:00:00 -0500
PyTorch Mobile provides a runtime environment to execute state-of-the-art machine learning models on mobile devices. Latency i... | https://pytorch.org/blog/mobile-demo-apps-overview/ | pytorch blogs |
Computer Vision
Image Classification
This app demonstrates how to use PyTorch C++ libraries on iOS and Android to classify a static image with the MobileNetv2/3 model.
iOS #1 iOS #2 Android #1 Android #2
iOS Android
Live Image Classification
This app demonstrates how to run a quantized MobileNetV2 and Resnet18 mo... | https://pytorch.org/blog/mobile-demo-apps-overview/ | pytorch blogs |
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