Posts in category: PyTorch

The PyTorch 2.0 release includes a new high-performance implementation of the PyTorch Transformer API with the goal of making training and deployment of state-of-the-art Transformer models affordable. Following the successful release of “fastpath” inference execution (“Better Transformer”), this release introduces high-performance support for training and inference using a custom kernel architecture for scaled dot product attention (SPDA).

You can take advantage of the new fused SDPA kernels either by calling the new SDPA operator directly (as described in the SDPA tutorial), or transparently via integration into the pre-existing PyTorch Transformer API. All features of the PyTorch Transformer API will continue to work compatibly, with many features mapped to high-performance SDPA kernels, while other features are impossible to support with higher performance (e.g., need_weights, as per below) while expanded high-performance support for other features may still be under active development.

Similar to the “fastpath” architecture, custom kernels are fully integrated into the PyTorch Transformer API – thus, using the native Transformer and MultiHeadAttention API will enable users to transparently see significant speed improvements. Unlike the “fastpath” architecture, the newly introduced “custom kernels” support many more use cases including models using Cross-Attention, Transformer Decoders, and for training models, in addition to the existing fastpath inference for fixed and variable sequence length Transformer Encoder and Self Attention use cases.

To take full advantage of different hardware models and Transformer use cases, multiple SDPA custom kernels are supported, with custom kernel selection logic that will pick the highest-performance kernel for a given model and hardware type. In particular, the first custom kernels included with the PyTorch 2.0 release are the Flash Attention kernel (sdpa_flash, for 16-bit floating point training and inference on Nvidia GPUs with SM80+ architecture level) and the xFormers memory-efficient attention kernel (sdpa_mem_eff, for 16-bit and 32-bit floating point training and inference on a broad range of Nvidia GPUs). A general-purpose kernel sdpa_math provides an implementation when the custom kernels are not applicable.

As mentioned, custom kernels provide a wider range of support for execution scenarios To ensure efficient execution (e,g., to use GPU tensor cores), model configurations need to meet a small number of requirements. This list of requirements will evolve over time, prospectively relaxing constraints limiting the usage of currently supported custom kernels, or providing additional kernels in the future.

For the most up to date list of custom kernels and dispatch constraints, you can refer to sdp_utils.h. As of PyTorch 2.0, the existing fused SDPA kernels have the following constraints:

• Flash Attention only supports 16 bit floating point data types (float16 and bfloat16).
• The head dimension must be a multiple of 8 for 16-bit floating point numbers and a multiple of 4 for 32-bit floating point numbers. At present, the maximum head_dim support for the Flash Attention custom kernel is 128.
• The CUDA architecture level must be sm5x or better for the mem_efficient kernel, and sm80 for Flash Attention.
• Flash Attention supports arbitrary dropout, in PyTorch 2.0 the mem_efficient kernel does not support dropout (i.e., dropout must be set to zero for this kernel to be selected in PyTorch 2.0).
• To support variable-sequence length batches, all SDPA kernels support Nested Tensor inputs that combine input data and padding information using variable sequence length tensors for forward. (You can find more information about Nested Tensors in the Nested Tensor tutorial.)
• You can specify both a key_padding_mask and an attn_mask by combining them before passing them to the SDPA operator. In particular, you can use the per-batch-element key padding mask of the nn.Transformer API to implement training for variable-sequence length inputs in a batch.
• At present, the only attention mask supported by fused kernel implementation is the causal mask commonly used for training. To specify the causal mask in custom kernels, it must be specified with the is_causal boolean and attn_mask must be None.
• Support for Nested Tensors is still under development. Specifically, in PyTorch 2.0, only the sdpa_math kernel supports training with Nested Tensors. Also, PyTorch 2.0 does not support Nested Tensors as part of code being compiled with torch.compile().
• The SDPA operator does not support returning averaged attention weights because computing them defeats the optimizations that enabled fused kernels to execute more efficiently. The argument need_weights for torch.nn.MultiheadAttention’s forward function defaults to True. In order to use the fused kernels, need_weights needs to be set to need_weights=False.

We find that an attention mask is rarely used in real-world applications, except for the causal mask during training. Consequently, we reduce kernel complexity and compute cost by building in the option to use a causal mask as attention mask, and select this new capability with the is_causal parameter introduced in conjunction with the new SDPA operator.

Providing the is_causal Boolean flag for the frequently used causal mask also obviates the expensive and memory-intensive allocation of a causal mask, increasing training memory efficiency by allowing more memory to be used for large batch sizes, and reduce memory bandwidth and cache contention – which are both at a premium in GPU accelerators – by not needing to load an attention mask tensor.

If the constraints of none of the available custom kernels are met, then training falls back to using the default sdpa_math kernel, implementing the mathematical equations for scaled dot product attention using a sequence of PyTorch operator to implement SDPA. This is the most general “catch-all” fallback kernel to ensure successful training for all models.

In addition to the existing Transformer API, model developers may also use the scaled dot product attention kernels directly by calling the new scaled_dot_product_attention() operator. This operator may be used to efficiently implement multi-head attention by combining it with in-projection and outprojection, as described in the SDPA tutorial.

In addition to adding custom kernels, Accelerated PyTorch 2 Transformers are integrated with PyTorch 2.0 compilation. To use your model while benefiting from the additional acceleration of PT2-compilation (for inference or training), pre-process the model with

model = torch.compile(model)

We have achieved major speedups for training transformer models and in particular large language models with Accelerated PyTorch 2 Transformers using a combination of custom kernels and torch.compile().

Figure: Using scaled dot product attention with custom kernels and torch.compile delivers significant speedups for training large language models, such as for nanoGPT shown here.

Finally, because the custom kernels are much more memory efficient, try to increase the size of training batches to achieve faster training with increased batch size.

In addition to automatic kernel selection, a context manager enables developers to override the kernel selection algorithm – this is not required for day to day operation, but enables developers to debug their code as well as enable performance engineers to override kernel selection. The SDPA tutorial provides additional information on using the SDPA context manager.

In addition to availability as part of the nn.Transformer API, Accelerated PyTorch 2 Transformer custom kernels are also available in conjunction with the torchtext, torchvision, and fairseq domain libraries with the launch of PyTorch 2.0.

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PyTorch 2.0 has just been released. Its flagship new feature is torch.compile(), a one-line code change that promises to automatically improve performance across codebases. We have previously checked on that promise in Hugging Face Transformers and TIMM models, and delved deep into its motivation, architecture and the road ahead.

As important as torch.compile() is, there’s much more to PyTorch 2.0. Notably, PyTorch 2.0 incorporates several strategies to accelerate transformer blocks, and these improvements are very relevant for diffusion models too. Techniques such as FlashAttention, for example, have become very popular in the diffusion community thanks to their ability to significantly speed up Stable Diffusion and achieve larger batch sizes, and they are now part of PyTorch 2.0.

In this post we discuss how attention layers are optimized in PyTorch 2.0 and how these optimization are applied to the popular 🧨 Diffusers library. We finish with a benchmark that shows how the use of PyTorch 2.0 and Diffusers immediately translates to significant performance improvements across different hardware.

Accelerating transformer blocks

PyTorch 2.0 includes a scaled dot-product attention function as part of torch.nn.functional. This function encompasses several implementations that can be applied depending on the inputs and the hardware in use. Before PyTorch 2.0, you had to search for third-party implementations and install separate packages in order to take advantage of memory optimized algorithms, such as FlashAttention. The available implementations are:

• FlashAttention, from the official FlashAttention project.
• Memory-Efficient Attention, from the xFormers project.
• A native C++ implementation suitable for non-CUDA devices or when high-precision is required.

All these methods are available by default, and PyTorch will try to select the optimal one automatically through the use of the new scaled dot-product attention (SDPA) API. You can also individually toggle them for finer-grained control, see the documentation for details.

Using scaled dot-product attention in diffusers

The incorporation of Accelerated PyTorch 2.0 Transformer attention to the Diffusers library was achieved through the use of the set_attn_processor method, which allows for pluggable attention modules to be configured. In this case, a new attention processor was created, which is enabled by default when PyTorch 2.0 is available. For clarity, this is how you could enable it manually (but it’s usually not necessary since diffusers will automatically take care of it):

from diffusers import StableDiffusionPipeline
from diffusers.models.cross_attention import AttnProcessor2_0

pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipe.to("cuda")
pipe.unet.set_attn_processor(AttnProcessor2_0())

prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]

Stable Diffusion Benchmark

We ran a number of tests using accelerated dot-product attention from PyTorch 2.0 in Diffusers. We installed diffusers from pip and used nightly versions of PyTorch 2.0, since our tests were performed before the official release. We also used torch.set_float32_matmul_precision('high') to enable additional fast matrix multiplication algorithms.

We compared results with the traditional attention implementation in diffusers (referred to as vanilla below) as well as with the best-performing solution in pre-2.0 PyTorch: PyTorch 1.13.1 with the xFormers package (v0.0.16) installed.

Results were measured without compilation (i.e., no code changes at all), and also with a single call to torch.compile() to wrap the UNet module. We did not compile the image decoder because most of the time is spent in the 50 denoising iterations that run UNet evaluations.

Results in float32

The following figures explore performance improvement vs batch size for various representative GPUs belonging to different generations. We collected data for each combination until we reached maximum memory utilization. Vanilla attention runs out of memory earlier than xFormers or PyTorch 2.0, which explains the missing bars for larger batch sizes. Similarly, A100 (we used the 40 GB version) is capable of running batch sizes of 64, but the other GPUs could only reach 32 in our tests.

We found very significant performance improvements over vanilla attention across the board, without even using torch.compile(). An out of the box installation of PyTorch 2.0 and diffusers yields about 50% speedup on A100 and between 35% and 50% on 4090 GPUs, depending on batch size. Performance improvements are more pronounced for modern CUDA architectures such as Ada (4090) or Ampere (A100), but they are still very significant for older architectures still heavily in use in cloud services.

In addition to faster speeds, the accelerated transformers implementation in PyTorch 2.0 allows much larger batch sizes to be used. A single 40GB A100 GPU runs out of memory with a batch size of 10, and 24 GB high-end consumer cards such as 3090 and 4090 cannot generate 8 images at once. Using PyTorch 2.0 and diffusers we could achieve batch sizes of 48 for 3090 and 4090, and 64 for A100. This is of great significance for cloud services and applications, as they can efficiently process more images at a time.

When compared with PyTorch 1.13.1 + xFormers, the new accelerated transformers implementation is still faster and requires no additional packages or dependencies. In this case we found moderate speedups of up to 2% on datacenter cards such as A100 or T4, but performance was great on the two last generations of consumer cards: up to 20% speed improvement on 3090 and between 10% and 45% on 4090, depending on batch size.

When torch.compile() is used, we get an additional performance boost of (typically) 2% and 3% over the previous improvements. As compilation takes some time, this is better geared towards user-facing inference services or training.

Results in float16

When we consider float16 inference, the performance improvements of the accelerated transformers implementation in PyTorch 2.0 are between 20% and 28% over standard attention, across all the GPUs we tested, except for the 4090, which belongs to the more modern Ada architecture. This GPU benefits from a dramatic performance improvement when using PyTorch 2.0 nightlies. With respect to optimized SDPA vs xFormers, results are usually on par for most GPUs, except again for the 4090. Adding torch.compile() to the mix boosts performance a few more percentage points across the board.

Conclusions

PyTorch 2.0 comes with multiple features to optimize the crucial components of the foundational transformer block, and they can be further improved with the use of torch.compile. These optimizations lead to significant memory and time improvements for diffusion models, and remove the need for third-party library installations.

To take advantage of these speed and memory improvements all you have to do is upgrade to PyTorch 2.0 and use diffusers >= 0.13.0.

For more examples and in-detail benchmark numbers, please also have a look at the Diffusers with PyTorch 2.0 docs.

Acknowledgement

The authors are grateful to the PyTorch team for their insights, assistance and suggestions during the elaboration of this post, and for creating such excellent software. We are particularly indebted to Hamid Shojanazeri, Grigory Sizov, Christian Puhrsch, Driss Guessous, Michael Gschwind and Geeta Chauhan.

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We are excited to announce the release of PyTorch® 2.0 which we highlighted during the PyTorch Conference on 12/2/22! PyTorch 2.0 offers the same eager-mode development and user experience, while fundamentally changing and supercharging how PyTorch operates at compiler level under the hood with faster performance and support for Dynamic Shapes and Distributed.

This next-generation release includes a Stable version of Accelerated Transformers (formerly called Better Transformers); Beta includes torch.compile as the main API for PyTorch 2.0, the scaled_dot_product_attention function as part of torch.nn.functional, the MPS backend, functorch APIs in the torch.func module; and other Beta/Prototype improvements across various inferences, performance and training optimization features on GPUs and CPUs. For a comprehensive introduction and technical overview of torch.compile, please visit the 2.0 Get Started page.

Along with 2.0, we are also releasing a series of beta updates to the PyTorch domain libraries, including those that are in-tree, and separate libraries including TorchAudio, TorchVision, and TorchText. An update for TorchX is also being released as it moves to community supported mode. More details can be found in this library blog.

This release is composed of over 4,541 commits and 428 contributors since 1.13.1. We want to sincerely thank our dedicated community for your contributions. As always, we encourage you to try these out and report any issues as we improve 2.0 and the overall 2-series this year.

Summary:

• torch.compile is the main API for PyTorch 2.0, which wraps your model and returns a compiled model. It is a fully additive (and optional) feature and hence 2.0 is 100% backward compatible by definition.
• As an underpinning technology of torch.compile, TorchInductor with Nvidia and AMD GPUs will rely on OpenAI Triton deep learning compiler to generate performant code and hide low level hardware details. OpenAI Triton-generated kernels achieve performance that’s on par with hand-written kernels and specialized cuda libraries such as cublas.
• Accelerated Transformers introduce high-performance support for training and inference using a custom kernel architecture for scaled dot product attention (SPDA). The API is integrated with torch.compile() and model developers may also use the scaled dot product attention kernels directly by calling the new scaled_dot_product_attention() operator.
• Metal Performance Shaders (MPS) backend provides GPU accelerated PyTorch training on Mac platforms with added support for Top 60 most used ops, bringing coverage to over 300 operators.
• Amazon AWS optimizes the PyTorch CPU inference on AWS Graviton3 based C7g instances. PyTorch 2.0 improves inference performance on Graviton compared to the previous releases, including improvements for Resnet50 and Bert.
• New prototype features and technologies across TensorParallel, DTensor, 2D parallel, TorchDynamo, AOTAutograd, PrimTorch and TorchInductor.

*To see a full list of public 2.0, 1.13 and 1.12 feature submissions click here.

Stable Features

[Stable] Accelerated PyTorch 2 Transformers (previously known as “Better Transformer”)

The PyTorch 2.0 release includes a new high-performance implementation of the PyTorch Transformer API, formerly known as “Better Transformer API, “ now renamed Accelerated PyTorch 2 Transformers. In releasing accelerated PT2 Transformers, our goal is to make training and deployment of state-of-the-art Transformer models affordable across the industry. This release introduces high-performance support for training and inference using a custom kernel architecture for scaled dot product attention (SPDA).

Similar to the “fastpath” architecture, custom kernels are fully integrated into the PyTorch Transformer API – thus, using the native Transformer and MultiHeadAttention API will enable users to:

• transparently see significant speed improvements;
• support many more use cases including models using Cross-Attention, Transformer Decoders, and for training models; and
• continue to use fastpath inference for fixed and variable sequence length Transformer Encoder and Self Attention use cases.

To take full advantage of different hardware models and Transformer use cases, multiple SDPA custom kernels are supported (see below), with custom kernel selection logic that will pick the highest-performance kernel for a given model and hardware type. In addition to the existing Transformer API, model developers may also use the

scaled dot product attention kernels directly by calling the new scaled_dot_product_attention() operator. Accelerated PyTorch 2 Transformers are integrated with torch.compile() . To use your model while benefiting from the additional acceleration of PT2-compilation (for inference or training), pre-process the model with model = torch.compile(model).

We have achieved major speedups for training transformer models and in particular large language models with Accelerated PyTorch 2 Transformers using a combination of custom kernels and torch.compile().

Figure: Using scaled dot product attention with custom kernels and torch.compile delivers significant speedups for training large language models, such as for nanoGPT shown here.

Beta Features

[Beta] torch.compile

torch.compile is the main API for PyTorch 2.0, which wraps your model and returns a compiled model. It is a fully additive (and optional) feature and hence 2.0 is 100% backward compatible by definition.

Underpinning torch.compile are new technologies – TorchDynamo, AOTAutograd, PrimTorch and TorchInductor:

• TorchDynamo captures PyTorch programs safely using Python Frame Evaluation Hooks and is a significant innovation that was a result of 5 years of our R&D into safe graph capture.
• AOTAutograd overloads PyTorch’s autograd engine as a tracing autodiff for generating ahead-of-time backward traces.
• PrimTorch canonicalizes ~2000+ PyTorch operators down to a closed set of ~250 primitive operators that developers can target to build a complete PyTorch backend. This substantially lowers the barrier of writing a PyTorch feature or backend.
• TorchInductor is a deep learning compiler that generates fast code for multiple accelerators and backends. For NVIDIA and AMD GPUs, it uses OpenAI Triton as a key building block. For intel CPUs, we generate C++ code using multithreading, vectorized instructions and offloading appropriate operations to mkldnn when possible.

With all the new technologies, torch.compile is able to work 93% of time across 165 open-source models and runs 20% faster on average at float32 precision and 36% faster on average at AMP precision.

For more information, please refer to https://pytorch.org/get-started/pytorch-2.0/ and for TorchInductor CPU with Intel here.

[Beta] PyTorch MPS Backend

MPS backend provides GPU-accelerated PyTorch training on Mac platforms. This release brings improved correctness, stability, and operator coverage.

MPS backend now includes support for the Top 60 most used ops, along with the most frequently requested operations by the community, bringing coverage to over 300 operators. The major focus of the release was to enable full OpInfo-based forward and gradient mode testing to address silent correctness issues. These changes have resulted in wider adoption of MPS backend by 3rd party networks such as Stable Diffusion, YoloV5, WhisperAI, along with increased coverage for Torchbench networks and Basic tutorials. We encourage developers to update to the latest macOS release to see the best performance and stability on the MPS backend.

Links

1. MPS Backend
2. Developer information
3. Accelerated PyTorch training on Mac
4. Metal, Metal Performance Shaders & Metal Performance Shaders Graph

[Beta] Scaled dot product attention 2.0

We are thrilled to announce the release of PyTorch 2.0, which introduces a powerful scaled dot product attention function as part of torch.nn.functional. This function includes multiple implementations that can be seamlessly applied depending on the input and hardware in use.

In previous versions of PyTorch, you had to rely on third-party implementations and install separate packages to take advantage of memory-optimized algorithms like FlashAttention. With PyTorch 2.0, all these implementations are readily available by default.

These implementations include FlashAttention from HazyResearch, Memory-Efficient Attention from the xFormers project, and a native C++ implementation that is ideal for non-CUDA devices or when high-precision is required.

PyTorch 2.0 will automatically select the optimal implementation for your use case, but you can also toggle them individually for finer-grained control. Additionally, the scaled dot product attention function can be used to build common transformer architecture components.

Learn more with the documentation and this tutorial.

[Beta] functorch -> torch.func

Inspired by Google JAX, functorch is a library that offers composable vmap (vectorization) and autodiff transforms. It enables advanced autodiff use cases that would otherwise be tricky to express in PyTorch. Examples include:

• model ensembling
• efficiently computing jacobians and hessians
• computing per-sample-gradients (or other per-sample quantities)

We’re excited to announce that, as the final step of upstreaming and integrating functorch into PyTorch, the functorch APIs are now available in the torch.func module. Our function transform APIs are identical to before, but we have changed how the interaction with NN modules work. Please see the docs and the migration guide for more details.

Furthermore, we have added support for torch.autograd.Function: one is now able to apply function transformations (e.g. vmap, grad, jvp) over torch.autograd.Function.

[Beta] Dispatchable Collectives

Dispatchable collectives is an improvement to the existing init_process_group() API which changes backend to an optional argument. For users, the main advantage of this feature is that it will allow them to write code that can run on both GPU and CPU machines without having to change the backend specification. The dispatchability feature will also make it easier for users to support both GPU and CPU collectives, as they will no longer need to specify the backend manually (e.g. “NCCL” or “GLOO”). Existing backend specifications by users will be honored and will not require change.

Usage example:

import torch.distributed.dist
…
# old
dist.init_process_group(backend=”nccl”, ...)
dist.all_reduce(...) # with CUDA tensors works
dist.all_reduce(...) # with CPU tensors does not work

# new
dist.init_process_group(...) # backend is optional
dist.all_reduce(...) # with CUDA tensors works
dist.all_reduce(...) # with CPU tensors works

Learn more here.

[Beta] torch.set_default_device and torch.device as context manager

torch.set_default_device allows users to change the default device that factory functions in PyTorch allocate on. For example, if you torch.set_default_device(‘cuda’), a call to torch.empty(2) will allocate on CUDA (rather than on CPU). You can also use torch.device as a context manager to change the default device on a local basis. This resolves a long standing feature request from PyTorch’s initial release for a way to do this.

Learn more here.

[Beta] “X86” as the new default quantization backend for x86 CPU

The new X86 quantization backend, which utilizes FBGEMM and oneDNN kernel libraries, replaces FBGEMM as the default quantization backend for x86 CPU platforms and offers improved int8 inference performance compared to the original FBGEMM backend, leveraging the strengths of both libraries, with 1.3X – 2X inference performance speedup measured on 40+ deep learning models. The new backend is functionally compatible with the original FBGEMM backend.

Table: Geomean Speedup of X86 Quantization Backend vs. FBGEMM Backend

By default, users on x86 platforms will utilize the x86 quantization backend and their PyTorch programs will remain unchanged when using the default backend. Alternatively, users have the option to specify “X86” as the quantization backend explicitly. Example code is show below:

import torch
from torch.ao.quantization import get_default_qconfig_mappingfrom torch.quantization.quantize_fx
import prepare_fx, convert_fx
 
# get default configuration
qconfig_mapping = get_default_qconfig_mapping()
 
# or explicitly specify the backend
# qengine = 'x86'
# torch.backends.quantized.engine = qengine
# qconfig_mapping = get_default_qconfig_mapping(qengine)
 
# construct fp32 model
model_fp32 = ...
 
# prepare
prepared_model = prepare_fx(model_fp32, qconfig_mapping, example_inputs=x)
 
# calibrate
...
 
# convert
quantized_model = convert_fx(prepared_model)

Find more information: https://github.com/pytorch/pytorch/issues/83888 and https://www.intel.com/content/www/us/en/developer/articles/technical/accelerate-pytorch-int8-inf-with-new-x86-backend.html.

[Beta] GNN inference and training optimization on CPU

PyTorch 2.0 includes several critical optimizations to improve GNN inference and training performance on CPU. Before 2.0, GNN models of PyG suffers from low efficiency on CPU due to lack of performance tuning for several critical kernels (scatter/gather, etc) and the lack of GNN-related sparse matrix multiplication ops. To be specific, optimizations include:

• scatter_reduce: performance hotspot in Message Passing when the edge index is stored in Coordinate format (COO).
• gather: backward of scatter_reduce, specially tuned for the GNN compute when the index is an expanded tensor.
• torch.sparse.mm with reduce flag: performance hotspot in Message Passing when the edge index is stored in Compressed Sparse Row (CSR). Supported reduce flag of: sum, mean, amax, amin.

On PyG benchmarks/examples, OGB benchmarks, a 1.12x – 4.07x performance speedup is measured (1.13.1 compared with 2.0) for single node inference and training.

Learn more: PyG CPU Performance Optimization.

[Beta] Accelerating inference on CPU with PyTorch by leveraging oneDNN Graph

oneDNN Graph API extends oneDNN with a flexible graph API to maximize the optimization opportunity for generating efficient code on AI hardware.

• It automatically identifies the graph partitions to be accelerated via fusion.
• The fusion patterns focus on fusing compute-intensive operations such as convolution, matmul and their neighbor operations for both inference and training use cases.
• Although work is ongoing to integrate oneDNN Graph with TorchDynamo as well, its integration with the PyTorch JIT Fuser attained beta status in PyTorch 2.0 for Float32 & BFloat16 inference (on machines that support AVX512_BF16 ISA).

From a developer’s/researcher’s perspective, the usage is quite simple & intuitive, with the only change in code being an API invocation:

• Leverage oneDNN Graph, with JIT-tracing, a model is profiled with an example input.
• The context manager with torch.jit.fuser(“fuser3”): can also be used instead of invoking torch.jit.enable_onednn_fusion(True).
• For accelerating BFloat16 inference, we rely on eager-mode AMP (Automatic Mixed Precision) support in PyTorch & disable JIT mode’s AMP, as both of them are currently divergent:

# Assuming we have a model of the name 'model'
 
example_input = torch.rand(1, 3, 224, 224)
 
# enable oneDNN Graph
torch.jit.enable_onednn_fusion(True)
# Disable AMP for JIT
torch._C._jit_set_autocast_mode(False)
with torch.no_grad(), torch.cpu.amp.autocast():
	model = torch.jit.trace(model, (example_input))
	model = torch.jit.freeze(model)
 	# 2 warm-ups (2 for tracing/scripting with an example, 3 without an example)
	model(example_input)
	model(example_input)
 
	# speedup would be observed in subsequent runs.
	model(example_input)

Learn more here.

Prototype Features

Distributed API

[Prototype] DTensor

PyTorch DistributedTensor (DTensor) is a prototyping effort with distributed tensor primitives to allow easier distributed computation authoring in the SPMD (Single Program Multiple Devices) paradigm. The primitives are simple but powerful when used to express tensor distributions with both sharded and replicated parallelism strategies. PyTorch DTensor empowered PyTorch Tensor Parallelism along with other advanced parallelism explorations. In addition, it also offers a uniform way to save/load state_dict for distributed checkpointing purposes, even when there’re complex tensor distribution strategies such as combining tensor parallelism with parameter sharding in FSDP. More details can be found in this RFC and the DTensor examples notebook.

[Prototype] TensorParallel

We now support DTensor based Tensor Parallel which users can distribute their model parameters across different GPU devices. We also support Pairwise Parallel which shards two concatenated linear layers in a col-wise and row-wise style separately so that only one collective(all-reduce/reduce-scatter) is needed in the end. More details can be found in this example.

[Prototype] 2D Parallel

We implemented the integration of the aforementioned TP with FullyShardedDataParallel(FSDP) as 2D parallel to further scale large model training. More details can be found in this slide and code example.

[Prototype] torch.compile(dynamic=True)

Experimental support for PT2 compilation with dynamic shapes is available in this release. Inference compilation with inductor for simple models is supported, but there are a lot of limitations:

• Training available in a future release (This is partially fixed in nightlies!)
• Minifier available in a future release.
• It is easy to end up in a situation where the dimension you wanted to be dynamic gets specialized anyway. Some of these issues are fixed in nightlies, others are not.
• We do not appropriately propagate Inductor guards to the top-level, this is tracked at #96296.
• Data-dependent operations like nonzero still require a graph break.
• Dynamic does not work with non-standard modes like reduce-overhead or max-autotune.
• There are many bugs in Inductor compilation. To track known bugs, check the dynamic shapes label on the PyTorch issue tracker.

For the latest and greatest news about dynamic shapes support on master, check out our status reports.

Highlights/Performance Improvements

Deprecation of Cuda 11.6 and Python 1.7 support for PyTorch 2.0

If you are still using or depending on CUDA 11.6 or Python 3.7 builds, we strongly recommend moving to at least CUDA 11.7 and Python 3.8, as it would be the minimum versions required for PyTorch 2.0. For more detail, please refer to the Release Compatibility Matrix for PyTorch releases.

Python 3.11 support on Anaconda Platform

Due to lack of Python 3.11 support for packages that PyTorch depends on, including NumPy, SciPy, SymPy, Pillow and others on the Anaconda platform. We will not be releasing Conda binaries compiled with Python 3.11 for PyTorch Release 2.0. The Pip packages with Python 3.11 support will be released, hence if you intend to use PyTorch 2.0 with Python 3.11 please use our Pip packages. Please note: Conda packages with Python 3.11 support will be made available on our nightly channel. Also we are planning on releasing Conda Python 3.11 binaries as part of future release once Anaconda provides these key dependencies. More information and instructions on how to download the Pip packages can be found here.

Optimized PyTorch Inference with AWS Graviton processors

The optimizations focused on three key areas: GEMM kernels, bfloat16 support, primitive caching and the memory allocator. For aarch64 platforms, PyTorch supports Arm Compute Library (ACL) GEMM kernels via Mkldnn(OneDNN) backend. The ACL library provides Neon/SVE GEMM kernels for fp32 and bfloat16 formats. The bfloat16 support on c7g allows efficient deployment of bfloat16 trained, AMP (Automatic Mixed Precision) trained, or even the standard fp32 trained models. The standard fp32 models leverage bfloat16 kernels via OneDNN fast math mode, without any model quantization. Next we implemented primitive caching for conv, matmul and inner product operators. More information on the updated PyTorch user guide with the upcoming 2.0 release improvements and TorchBench benchmark details can be found here.

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Last year, Meta announced that PyTorch joined the Linux Foundation as a neutral home for growing the machine learning project and community with AMD representation as a part of the founding membership and governing board.

PyTorch Foundation’s mission is to drive AI adoption by democratizing its software ecosystem through open source principles aligning with the AMD core principle of an Open software ecosystem. AMD strives to foster innovation through the support for latest generations of hardware, tools, libraries, and other components to simplify and accelerate adoption of AI across a broad range of scientific discoveries.

The progressive improvements on both the AMD CDNA™ architecture as well as ROCm and PyTorch shows single GPU model throughput increase from AMD Instinct MI100 to the latest generation AMD Instinct MI200 family GPUs going from ROCm 4.2 to ROCm 5.3 and from PyTorch 1.7 to PyTorch 1.12.

Graph 1: ML model performance over generation using Microsoft Superbench Suite ^{1, 2, 3}

Below are a few of the key updates for ROCm support since the PyTorch 1.12 release

Full Continuous Integration (CI) for ROCm on PyTorch

With the ROCm support for PyTorch move from “Beta” to “Stable,” all the functions and features commits are now verified through a full Continuous Integration (CI) process. The CI process helps ensure the proper build and test process ahead of an expected Docker and PIP wheel release with stable commits forthcoming.

Support for Kineto Profiler

The addition of Kineto profiler support to ROCm now helps developers and users understand performance bottlenecks through effective diagnosis and profiling tools. The tool also provides recommendations to improve known issues and visualization through TensorBoard UI.

Key PyTorch Libraries support added

PyTorch ecosystem libraries like TorchText (Text classification), TorchRec (libraries for recommender systems – RecSys), TorchVision (Computer Vision), TorchAudio (audio and signal processing) are fully supported since ROCm 5.1 and upstreamed with PyTorch 1.12.

Key libraries provided with the ROCm software stack including MIOpen (Convolution models), RCCL (ROCm Collective Communications) and rocBLAS (BLAS for transformers) were further optimized to offer new potential efficiencies and higher performance.

MIOpen innovates on several fronts, such as implementing fusion to optimize for memory bandwidth and GPU launch overheads, providing an auto-tuning infrastructure to overcome the large design space of problem configurations, and implementing different algorithms to optimize convolutions for different filter and input sizes. MIOpen is one of the first libraries to publicly support the bfloat16 data-type for convolutions, allowing efficient training at lower precision maintaining expected accuracy.

RCCL (pronounced “Rickle”) is a stand-alone library of standard collective communication routines for GPUs, implementing all-reduce, all-gather, reduce, broadcast, reduce-scatter, gather, scatter, and all-to-all. There is support for direct GPU-to-GPU send and receive operations. It has been optimized to achieve high bandwidth on platforms using PCIe®, Infinity Fabric™ (GPU to GPU) as well as networking using InfiniBand Verbs or TCP/IP sockets. RCCL supports an arbitrary number of GPUs installed in single or multiple nodes and can be used in either single- or multi-process (e.g., MPI) applications.

Along with the above key highlights, over 50 features and functionality improvements were completed jointly between AMD and PyTorch to add stable support for ROCm. These include improvements to tools, compilers, runtime, graph optimizations through TorchScript, INT8 quant path usage, and ONNX runtime integration including support for Navi 21 based Radeon™ PRO datacenter graphics card to name a few.

AITemplate Inference Engine

MetaAI recently published a blog announcing the release of its open source AITemplate (link) for a unified inference system supporting AMD Instinct GPU accelerators using the AMD ROCm stack. This Python based framework can help significantly improve performance through increased utilization of AMD matrix cores for transformer blocks. This is achieved through the AMD Composable Kernel (CK) library which provides performance critical Kernels for ML AI workloads across multiple architectures including GPUs and CPUs through HIP & C++.

Moreover, the AITemplate also provides out-of-the-box support for widely used AI models like BERT, ResNET, Vision Transformer, Stable Diffusion etc. simplifying deployment process through these pretrained models.

What’s coming with future ROCm releases?

Unified memory models for CPU + GPU

As system architecture evolves to address the complexity of large problem sizes and data sets, memory management becomes a key performance bottle neck that needs a cohesive strategy to be addressed through innovations at both hardware and software levels. AMD is uniquely positioned to address this problem with its effective data center solutions integrating AMD EPYC™ CPU cores with its AMD Instinct GPU compute units in a truly unified datacenter APU (Accelerated Processing Unit) form factor set to be launched in 2H 2023.

The software work to leverage the unified CPU + GPU memory has already started in collaboration with the PyTorch team, to enable the usage of a fast, low latency, synchronized memory model that enables not only AMD but also other AI accelerators to address the complex memory management problem of today. We are looking forward to this joint effort and announcement soon.

Acknowledgement

The content in this blog highlights the joint work between AMD and key PyTorch contributors including Meta, working on many of the core features, as well as Microsoft enabling ONNX Runtime support. We are looking forward to working with the other founding members at the PyTorch Foundation on the next steps and improvements to democratize and grow adoption of PyTorch across the industry.

CAUTIONARY STATEMENT

This blog contains forward-looking statements concerning Advanced Micro Devices, Inc. (AMD) such as the availability, timing and expected benefits of an AMD datacenter APU form factor, which are made pursuant to the Safe Harbor provisions of the Private Securities Litigation Reform Act of 1995. Forward-looking statements are commonly identified by words such as “would,” “may,” “expects,” “believes,” “plans,” “intends,” “projects” and other terms with similar meaning. Investors are cautioned that the forward-looking statements in this blog are based on current beliefs, assumptions and expectations, speak only as of the date of this blog and involve risks and uncertainties that could cause actual results to differ materially from current expectations. Such statements are subject to certain known and unknown risks and uncertainties, many of which are difficult to predict and generally beyond AMD’s control, that could cause actual results and other future events to differ materially from those expressed in, or implied or projected by, the forward-looking information and statements. Investors are urged to review in detail the risks and uncertainties in AMD’s Securities and Exchange Commission filings, including but not limited to AMD’s most recent reports on Forms 10-K and 10-Q. AMD does not assume, and hereby disclaims, any obligation to update forward-looking statements made in this blog, except as may be required by law.

Endnotes

1. MI100D-01 SuperBench v0.5 model training results based on AMD internal testing as of 11/09/2022 measuring the total training throughput, at half precision, using a 2P AMD EPYC™ 7763 CPU server tested with 1x AMD Instinct™ MI100 (32GB HBM2e) 300W GPU, SBIOS 2.2, Ubuntu® 20.04.5 LTS, host ROCm™ 5.2.0, guest ROCm 4.2, PyTorch 1.7.0. Server manufacturers may vary configurations, yielding different results. Performance may vary based factors including use of latest drivers and optimizations.
2. MI200D-01 SuperBench v0.6 model training results based on AMD internal testing as of 11/09/2022 measuring the total training throughput, at half precision, using a 2P AMD EPYC™ 7763 CPU server tested with 1x AMD Instinct™ MI210 (64GB HBM2e) 300W GPU, SBIOS 2.2, Ubuntu 20.04.5 LTS, host ROCm 5.3.0, guest ROCm 5.3, PyTorch 1.12. Server manufacturers may vary configurations, yielding different results. Performance may vary based factors including use of latest drivers and optimizations.
3. MI200D-02: SuperBench v0.6 model training results based on AMD internal testing as of 11/09/2022 measuring the total training throughput, at half precision, using a 2P AMD EPYC™️ 7763 CPU server tested with 1x AMD Instinct™️ MI250 (128GB HBM2e) 560W GPU, SBIOS M12, Ubuntu 20.04 LTS, host ROCm 5.3.0, guest ROCm 5.3, PyTorch 1.12. Server manufacturers may vary configurations, yielding different results. Performance may vary based factors including use of latest drivers and optimizations.

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