Posts in category: PyTorch

Introduction

We are excited to announce the public release of Holistic Trace Analysis (HTA), an open source performance analysis and visualization Python library for PyTorch users. HTA takes as input Kineto traces collected by the PyTorch profiler, which are complex and challenging to interpret, and up-levels the performance information contained in these traces. It was initially developed internally at Meta to understand and debug performance problems for large-scale distributed training jobs on GPUs. The multidisciplinary team has made a number of enhancements to HTA’s features and scaled them to support state-of-the-art ML workloads.

ML researchers and systems engineers often struggle to computationally scale up their models because they are not aware of the performance bottlenecks in their workloads. The resources requested for a job (e.g. GPUs, memory) are often misaligned with the resources actually required due to lack of visibility “under the hood”. To achieve the best performance from the hardware stack, it is imperative to understand the resource utilization and bottlenecks for distributed training workloads.

The initial HTA implementation was specifically targeted at Deep Learning Based Recommendation Models (DLRM). To make the features in HTA generic and applicable to use cases such as analyzing Vision and NLP models, we decided to refactor the HTA codebase and make the library available to the larger community. This new codebase has implemented several important ideas which lead to significant efficiency and performance improvements.

In this blog, we present several features implemented in the open source version of HTA, which can be used as a Python script as well as interactively in a Jupyter notebook. HTA provides the following features:

1. Breakdown by Dimensions
   1. Temporal: Breakdown of GPU time in terms of time spent in computation, communication, memory events, and idle time on a single node and across all ranks.
   2. Idle Time: Breakdown of GPU idle time into waiting for the host, waiting for another kernel or attributed to an unknown cause.
   3. Kernel: Find kernels with the longest duration on each rank.
   4. Communication Computation Overlap: Calculate the percentage of time when communication overlaps computation.
2. Statistical Analysis
   1. Kernel Duration Distribution: Distribution of average time taken by longest kernels across different ranks.
   2. CUDA Kernel Launch: Distributions of GPU kernels with very small duration, large duration, and excessive launch time.
   3. Augmented Counters (Memory bandwidth, Queue length): Augmented trace files which provide insights into memory copy bandwidth and number of outstanding operations on each CUDA stream.
3. Patterns
   1. Frequent CUDA Kernels: Find the CUDA kernels most frequently launched by any given PyTorch or user defined operator.
4. Trace Comparison
   1. Trace Diff: A trace comparison tool to identify and visualize the differences between traces.

HTA source code is available to users via Github. Users can request new features or build their own analysis using the core libraries and data structures provided in the codebase in addition to the features mentioned above.

GPU Training Performance Debugging 101

To understand the GPU performance in distributed training jobs, we consider how the model operators interact with the GPU devices and how such interactions are reflected in certain measurable metrics.

At a high level, we can break down the GPU operations in a model execution into three broad categories, henceforth referred to as kernel types:

1. Computation (COMP) – Compute kernels execute compiled routines for matrix multiplication and similar numeric calculations. They are responsible for all of the number-crunching necessary for model execution.
2. Communication (COMM) – Communication kernels are routines which are responsible for exchanging and synchronizing data between different GPU devices in a distributed training job. The NVIDIA Collective Communication Library (NCCL) is a widely used communication library and all its kernels have the prefix “nccl”. Example NCCL kernels include NCCL_AllGather, NCCL_ReduceScatter, NCCL_AllReduce, etc.
3. Memory (MEM) – Memory kernels manage the memory allocations/deallocations on the GPU devices and data movement between the memory space on the host and the GPUs. The memory kernels include Memcpy_H2D, Memcpy_D2H, Memcpy_D2D, Memset, etc. Here, H represents the Host and D represents the GPU Device. Thus, H2D, D2H, D2D stands for Host to Device, Device to Host and Device to Device respectively.

Because a modern GPU device like the NVIDIA A100 GPU is a massively parallel device which is capable of running multiple kernels simultaneously, it is possible to overlap the computation, communication, and memory kernels to reduce the model execution time. One common technique to achieve the overlap is to utilize multiple CUDA streams. A CUDA stream is a sequence of operations that execute on a GPU device in the order in which they are issued by the host code. Different CUDA streams can be interleaved and even run concurrently, thus achieving the effect of kernel overlap.

To help understand the above concepts, Figure 1 provides a timeline of the GPU kernels in a sample distributed training job on 8 GPUs for one iteration. In the figure below, each rank represents one GPU and the kernels on each GPU run on 6 CUDA streams. In the right column of the figure, you can see names of the GPU kernels used. In the middle of the figure, you see the overlap between compute and communicate kernels. This figure is created using the plot_timeline example notebook available in HTA.

Figure 1. An example of the execution timeline of GPU Kernels across multiple ranks

The performance of multiple GPU training jobs is affected by multiple factors. Among these factors, how does a model execution create and orchestrate the GPU kernels plays a critical role. HTA provides insights on how the model execution interacts with the GPU devices and highlights the opportunities for performance improvement.

With the features we built in HTA, we aim to provide users insights into “what is happening under the hood in a distributed GPU training?” We briefly describe these features in the next few paragraphs.

Features in Holistic Trace Analysis

For most users, understanding the performance of GPU training jobs is nontrivial. Thus, we built this library to simplify the task of trace analysis and provide the user useful insights by examining the model execution traces. As the first step, we developed features which are important and generic enough so that most users can benefit from this library.

Temporal Breakdown: We begin by asking whether the GPU is spending time on computation, communication, memory events, or is it idle? To answer this question, the temporal breakdown feature presents a breakdown in terms of these categories. To achieve high training efficiency the code should maximize time used by computation kernels and minimize idle time and non-compute time (time used by communication or memory kernels). This is accomplished by implementing concurrent execution of computation kernels with communication or memory kernels. Note that, during concurrent execution of computation kernels with communication/memory kernels the time spent by communication/memory kernels is accounted for under compute time.

Figure 2: Temporal Breakdown across 8 GPUs

Kernel Breakdown: It is natural to ask which kernels are taking the most amount of time. The next feature breaks down the time spent within each kernel type (COMM, COMP, MEM) and sorts them by duration. We present this information for each kernel type and for each rank as a pie chart. See figure 3 below.

Figure 3: Pie chart of top computation and communication kernels

Kernel Duration Distribution: Subsequently, one can also ask – for any given kernel, what is the distribution of the time spent across the ranks? To answer this, HTA generates bar graphs for the average duration of a given kernel across all ranks. Additionally, the error bars in the bar graphs show the minimum and maximum amount of time taken by a given kernel on a given rank. Figure 4 below shows a discrepancy between average duration on rank 0 as compared to other ranks. This anomalous behavior on rank 0 guides the user on where to look for possible bugs.

Figure 4: Average duration of NCCL AllReduce Kernel across 8 ranks

Communication Computation Overlap: In distributed training, a significant amount of time is spent in communication and synchronization events among multiple GPU devices. To achieve high GPU efficiency (i.e. TFLOPS/GPU) it is vital to keep the GPU doing actual computation work. In other words, a GPU should not be blocked because of waiting for data from other GPUs. One way to measure the extent to which computation is blocked by data dependencies is to calculate the computation-communication overlap. Higher GPU efficiency is observed if communication events overlap computation events. Lack of communication and computation overlap will lead to the GPU being idle, thus the efficiency would be low. Thus, the communication computation overlap feature calculates the percentage of time communication and computation overlap in a job for each rank and generates a bar graph representation. See figure below. More precisely, we measure the following ratio

(time spent in computation while communicating) / (time spent in communication)

Figure 5: Communication computation overlap

Augmented Counters (Queue length, Memory bandwidth): To aid in debugging, HTA calculates the memory bandwidth statistics for D2H, H2D and D2D memory copy (memcpy) and memory set (memset) events. Additionally, HTA also computes the number of outstanding CUDA operations on each CUDA stream. We refer to this as queue length. When the queue length on a stream is 1024 or larger new events cannot be scheduled on that stream and the CPU will stall until the GPU events have processed. Additionally, HTA generates a new trace file containing tracks with the memory bandwidth and queue length time series. See Figure 6 below.

Figure 6: Memory Bandwidth and Queue Length

These primary features give us a peek into the system performance and help answer “what is happening in the system?”. As HTA evolves, we hope to address “why is X happening?” and also suggest possible solutions to overcome the bottlenecks.

Installation and Usage

Installation

For installing the HTA please refer to the README. In brief, the user is required to clone the repo and install the necessary Python packages via pip.

Usage

This version of Holistic Trace Analysis is currently in beta and we recommend using HTA in a Jupyter notebook. A demo notebook is provided for your convenience. To get started, import the hta package in a Jupyter notebook, create a TraceAnalysis object and off we go in exactly two lines of code.

from hta.trace_analysis import TraceAnalysis
analyzer = TraceAnalysis(trace_dir = “/trace/folder/path”)

Requirements

• All trace files for a training or inference job must be stored in a unique folder.
• Trace files are in json or gzipped json format.

FAQ

Q. How can I install HTA?

Please see the README in the root directory of the repository.

Q. Is there any documentation on the features and API in HTA?

The documentation and detailed API is available here.

Q. Can you implement feature X?

Depending on how widely the feature is needed and the level of effort required to implement it we would consider developing the feature. Please open a Github Issue and tag it with the feature-request label.

Q. Can I modify the code?

Please do and send a PR along the way, if you think it would be useful for others.

Q. How can I collect traces in PyTorch?

Please refer to this tutorial here.

Q. Can HTA be used at production scale?

Yes, please see a use case study here.

Read More

Serving models in production

In this post we discuss performance tuning of Torchserve for serving your models in production. One of the biggest challenges in the life cycle of a ML project is deploying models in production. This requires a reliable serving solution along with solutions that address the MLOps needs. A robust serving solution needs to provide support for multi model serving, model versioning, metric logging, monitoring and scaling to serve the peak traffic. In this post, we will have an overview of Torchserve and how to tune its performance for production use-cases. We discuss the Animated Drawings app from Meta that can turn your human figure sketches to animations and how it could serve the peak traffic with Torchserve. The Animated Drawing’s workflow is below.

https://ai.facebook.com/blog/using-ai-to-bring-childrens-drawings-to-life/

Many AI systems and tools are designed to handle realistic images of humans, children’s drawings add a level of complexity and unpredictability as they are often constructed in abstract, fanciful ways. These types of morphological and stylistic variations can confuse even state-of-the-art AI systems that excel at spotting objects in photorealistic images and drawings.
Meta AI researchers are working to overcome this challenge so that AI systems will be better able to recognize drawings of human figures in the wildly varied ways that children create them. This great blog post provides more details about the Animated Drawings and the approach taken.

Torchserve

Fig1. Overall flow of Torchserve performance tuning

Once you have trained your model, it needs to be integrated into a larger system to have a full-fledged application, we use the term “model serving” to refer to this integration. Basically model serving is making your trained model available to run inferences and subsequent use of the model.

Torchserve is the Pytorch preferred solution for serving models in production. It is a performant and scalable tool that wraps your model in a HTTP or HTTPS API. It has a frontend implemented in Java that handles multiple tasks from assigning workers for serving models to handling the connection between client and server. Torchserve has a Python backend that is responsible for handling the inference service.

Torchserve supports multi model serving and versioning for AB test, dynamic batching, logging and metrics. It exposes four APIs for inference, explanations, management and metrics.

Inference API is listening on port 8080 and accessible through localhost by default, this can be configured in Torchserve configuration and enable getting predictions from the model.

Explanation API uses Captum under the hood to provide explanations of the model that is being served and listens to the port 8080 as well.

Management API allows to register or unregister and describe a model. It also enables users to scale up or down the number of workers that serve the model.

Metric API by default listens to port 8082 and enables us to monitor the model that is being served.

Torchserve let you scale your model serving and handle the peak traffic by supporting batch inference and multiple workers that serve your model. Scaling can be done through management API and settings through a configuration file. Also, metric API helps you to monitor your model serving through default and customizable metrics.

Other advanced settings such as the length of the queue for the received requests, maximum wait time for a batch of inputs and many other properties are configurable through a config file that can be passed to Torchserve when it is started.

Steps to serve your model with Torchserve

1. Install Torchserve, model archiver and its requirements.
2. Choose a default handler that fits your task (e.g image classification, etc) or author a custom handler.
3. Package your model artifacts (trained model checkpoint and all other necessary files for loading and running your model) and the handler into a “.mar” file using Torcharchive and place it in the model store.
4. Start serving your model.
5. Run inference.
   We will discuss model handlers and metrics in more detail here.

Model handlers

Torchserve uses a handler in the backend to load the models, preprocess the received data, run inference and post-process the response. Handler in torchserve is a python script that all the model initialization, preprocessing, inference and post processing logic goes into.

Torchserve provides an out of the box handler for a number of applications like image classification, segmentation, object detection and text classification. It also supports custom handlers, in case your use case is not supported in default handlers.

It provides a great flexibility in custom handlers, this potentially make Torchserve as multi-framework serving tool. Custom handlers let you define your custom logic to initialize a model that can be used also to load models from other frameworks such as ONNX.

Torchserve handler is made of four main functions, initialize, preprocess, inference and postprocess that each return a list. The code snippet below shows an example of a custom handler.Custom handlers inherit from BaseHandler in Torchserve and can overwrite any of the main functions. Here is an example of the handler used for loading the Detectron2 model for figure detection, this model has been exported to Torchscript and uses model.half() to run the inference with FP16, details are explained in another section in this post.

class MyModelHandler(BaseHandler):
    def initialize(self, context):
        self.manifest = ctx.manifest
        properties = ctx.system_properties
        model_dir = properties.get("model_dir")
        serialized_file = self.manifest["model"]["serializedFile"]
        model_pt_path = os.path.join(model_dir, serialized_file)

        self.device = torch.device(
        "cuda:" + str(properties.get("gpu_id"))
        if torch.cuda.is_available() and properties.get("gpu_id") is not None
        else "cpu"
        )
        self.model = torch.jit.load(model_pt_path, map_location=self.device)

        self.model = self.model.half()

    def preprocess(self, data):

        inputs = []
        for request in batch:

            request_body = request.get("body")

            input_ = io.BytesIO(request_body)
            image = cv2.imdecode(np.fromstring(input_.read(), np.uint8), 1)
            input = torch.Tensor(image).permute(2, 0, 1)
            input = input.to(self.device)
            input = input.half()
            inputs.append({"image": input})

        return inputs

    def inference(self,inputs):
        predictions = self.model(**inputs)
        return predictions

    def postprocess(self, output):
        responses = []
        for inference_output in inference_outputs:
            responses_json = {
            'classes': inference_output['pred_classes'].tolist(),
            'scores': inference_output['scores'].tolist(),
            "boxes": inference_output['pred_boxes'].tolist()
            }
            responses.append(json.dumps(responses_json))

        return responses

Metrics

An essential component in serving models in production is the ability to monitor them. Torchserve collects system level metrics regularly and allows adding custom metrics as well.

System level metrics consist of CPU utilization, available and used disk space and memory on the host machine along with number of requests with different response codes (e.g 200-300, 400-500 and above 500). Custom metrics can be added to the metrics as explained here. TorchServe logs these two sets of metrics to different log files. Metrics are collected by default at:

• System metrics – log_directory/ts_metrics.log
• Custom metrics – log directory/model_metrics.log

As mentioned before, Torchserve also exposes metric API, that by default listens to port 8082 and enables users to query and monitor the collected metrics. The default metrics endpoint returns Prometheus formatted metrics. You can query metrics using curl requests or point a Prometheus Server to the endpoint and use Grafana for dashboards.

While serving a model you can query metrics using curl request as follows:

curl http://127.0.0.1:8082/metrics

In case you are looking into exporting the logged metrics, please refer to this example that uses mtail to export metrics to Prometheus. Tracking these metrics in a dashboard allows you to monitor performance regressions that may have been sporadic or hard to spot during an offline benchmark run.

What to consider for tuning performance of a model in production

The workflow suggested in Fig 1, is the general idea on how to approach model deployment in production with Torchserve.

In many cases serving models in production is optimized based on throughput or latency service level agreement (SLA)s. Usually real-time applications are more concerned about latency whereas off-line applications may care more about higher throughput.

There are a number of main factors contributing to the performance of a serving model in production. In particular, we are focusing on serving Pytorch models with Torchserve here, however most of these factors generalize to all models from other frameworks as well.

• Model optimizations: this is a pre-step for deploying models into production. This is a very broad discussion that we will get into in a series of future blogs. This includes techniques like quantization, pruning to decrease the size of the model, using Intermediate representations (IR graphs) such as Torchscript in Pytorch, fusing kernels and many others. Currently torchprep provides many of these techniques as a CLI tool.
• Batch inference: it refers to feeding multiple inputs into a model, while it is essential during training, it can be very helpful to manage the cost at inference time as well. Hardware accelerators are optimized for parallelism and batching helps to saturate the compute capacity and often leads to higher throughput. The main difference in inference is you can’t wait too long to get a batch filled from clients, something we call dynamic batching

• Number of Workers : Torchserve uses workers to serve models. Torchserve workers are Python processes that hold a copy of the model weights for running inference. Too few workers means you’re not benefitting from enough parallelism but too many can cause worker contention and degrade end to end performance.

• Hardware : choosing the appropriate hardware based on the model, application and latency, throughput budget. This could be one of the supported hardwares in Torchserve, CPU, GPU, AWS Inferentia. Some hardware configurations are intended for best in class performance and others are better suited for cost effective inference. From our experiments we’ve found that GPUs shine best at larger batch sizes whereas the right CPUs and AWS Inferentia can be far more cost effective for lower batch sizes and low latency.

Best Practices for Performance tuning on Torchserve

To get the best performance out of your model while serving it with Torchserve, we are sharing some of the best practices here. Torchserve provides a benchmark suite that provides helpful insight to make informed decisions on different choices as detailed below.

• Optimize your model as the first step, Pytorch model optimization tutorials. Model optimization choices are also closely tied to the hardware of choice. We will discuss it in more detail in another blog post.
• Deciding the hardware for model deployment can be closely related to the latency and throughput budget and cost per inference. Depending on the size of model and application it can vary, for some models like computer vision models it has been historically not affordable to run in production on CPU. However, by having optimizations such IPEX as recently added to Torchserve this has been much more affordable and cost beneficial and you can learn more in this investigative case study

• Workers in Torchserve are Python processes that provide parallelism, setting the number of workers should be done carefully. By default Torchserve launch number of workers equal to VCPUs or available GPUs on the host, this can add a considerable amount of time to the Torchserve start.

  Torchserve exposes a config property to set the number of workers. To provide an efficient parallelism through multiple workers and avoiding them to compete over resources, as a baseline we recommend following setting on CPU and GPU:

  CPU : In the handler, torch.set_num_threads(1) then set the number of workers to num physical cores / 2. But the the best threading configurations can be achieved by leveraging the Intel CPU launcher script.

  GPU: number of available GPUs can be set through number_gpus in config.properties. Torchserve uses round robin to assign workers to GPUs. We recommend setting the number of workers as follows. Number of worker = (Number of available GPUs) / (Number of Unique Models). Note that GPUs that are pre-Ampere do not provide any resource isolation with Multi Instance GPUs.

• Batch size can directly affect the latency and the throughput. To better utilize the compute resources batch size needs to be increased. However, there is a tradeoff between latency and throughput. Larger batch sizes can increase the throughput but results in a higher latency as well. Batch size can be set in Torchserve in two ways, either through model config in config.properties or while registering the model using Management API.

In the next section, we are going to use Torchserve benchmark suite to decide the best combination of model optimization, hardware, workers, and batch size.

Animated Drawings Performance Tuning

To use the Torchserve benchmark suite, first we need to have an archived file, “.mar” file as discussed above, that contains the model, handler and all other artifacts to load and run inference. Animated Drawings uses Detectron2’s implementation of Mask-RCNN for an object detection model.

How to run benchmark suite

The Automated benchmark suite in Torchserve let you benchmark multiple models with different setting including batch size and number of worker and finally generate a report for you. To get started:

git clone https://github.com/pytorch/serve.git

cd serve/benchmarks

pip install -r requirements-ab.txt

apt-get install apache2-utils

Model level settings can be configured in a yaml file similar to

Model_name:
    eager_mode:
        benchmark_engine: "ab"
        url: "Path to .mar file"
        workers:
            - 1
            - 4
        batch_delay: 100
        batch_size:
            - 1
            - 2
            - 4
            - 8
        requests: 10000
        concurrency: 10
        input: "Path to model input"
        backend_profiling: False
        exec_env: "local"
        processors:
            - "cpu"
            - "gpus": "all"

This yaml file will be referenced in the benchmark_config_template.yaml file that includes other settings for generating reports, this can optionally work with AWS cloud watch for logs as well.

python benchmarks/auto_benchmark.py --input benchmark_config_template.yaml

Running the benchmarks, results will be written in “csv” file that can be found in “_ /tmp/benchmark/ab_report.csv_” and full report “/tmp/ts_benchmark/report.md”. It will include items such as Torchserve average latency, model P99 latency, throughput, number of concurrency, number of requests, handler time, and some other metrics. Here we focus on some of the important ones that we track to tune the performance which are, concurrency, model P99 latency, throughput. We look at these numbers specifically in combination with batch size, the used device, number of workers and if any model optimization has been done.

The latency SLA for this model has been set to 100 ms, this is real-time application and as we discussed earlier, latency is more of a concern and throughput ideally should be as high as possible while it does not violate the latency SLA.

Through searching the space, over different batch sizes (1-32), number of workers (1-16) and devices (CPU,GPU), we have run a set of experiments that summarized the best ones in the table below.

The latency of this model on CPU with all of the tried settings in terms of batch size, concurrency and number of workers did not meet the SLA, in fact ~13x higher.

Moving the model serving to GPU, immediately could improve the latency ~**13x **from 305 ms down to 23.6 ms.

One of the simplest optimizations that we could do for the model was lowering its precision to fp16, it is one liner (model.half()) and could reduce the model P99 latency **by **32% and increase the throughput by almost the same amount.

There could be other optimization done by Torchscripting the model and using optimize_for_inference or other tricks including onnx or tensorrt runtime optimizations which leverage aggressive fusions are out of the scope of this post. We will discuss model optimizations in a separate post.

We found both on CPU and GPU , setting **number of workers=1 **worked the best in this case.

• Moving the model to GPU, using number of workers = 1, and batch size = 1 increased the Throughput ~12x compared to CPU and latency ~13x.
• Moving the model to GPU, using model.half(), number of workers = 1, and batch size = 8 yielded best results in terms of Throughput and tolerable latency. Throughput increased ~25x compared to CPU with latency still meeting the SLA (94.4ms).

Note: if you are running the benchmark suite, make sure you are setting a proper batch_delay and set the concurrency of the request to a number proportional to your batch size. Concurrency here means the number of concurrent requests being sent to the server.

Conclusion

In this post, we have discussed the considerations and knobs that Torchserve expose to tune the performance in production. We have discussed the Torchserve benchmark suite as a means to tune the performance and get insights on possible choices for model optimizations, hardware choice and cost in general. We used Animated Drawings app which uses Detectron2’s Mask-RCNN model as a case-study to showcase the performance tuning with benchmark suite.

For more details on Performance tuning in Torchserve please refer to our documentation here.
Also feel free to open a ticket on Torchserve repo for any further questions and feedback.

Acknowledgement

We would like to thank Somya Jain (Meta), Christopher Gustave (Meta) for their great support and guidance throughout many steps of this blog and providing insights to Sketch Animator workflow. Also, special thanks to Li Ning from AWS for the great efforts to make performance tuning much easier on Torchserve with automated benchmark suite.

Read More

TL;DR: We demonstrate the use of PyTorch with FairScale’s FullyShardedDataParallel (FSDP) API in writing large vision transformer models. We discuss our techniques for scaling and optimizing these models on a GPU cluster. The goal of this platform scaling effort is to enable research at scale. This blog does not discuss model accuracy, new model architectures, or new training recipes.

1. Introduction

Latest vision research [1, 2] demonstrates model scaling as a promising research direction. In this project, we aim to enable our platforms to train massive vision transformer (ViT) [3] models. We present our work on scaling the largest trainable ViT from 1B to 120B parameters in FAIR vision platforms. We wrote ViT in PyTorch and leveraged its support for large-scale, distributed training on a GPU cluster.

In the rest of this blog, we will first discuss the main challenges, namely scalability, optimization, and numerical stability. Then we will discuss how we tackle them with techniques including data and model parallelism, automatic mixed precision, kernel fusion, and bfloat16. Finally, we present our results and conclude.

2. Main Challenges

2.1 Scalability

The key scalability challenge is to efficiently shard a model’s operations and state across multiple GPUs. A 100B parameter model requires ~200GB of RAM just for parameters, assuming fp16 representation. So, it is impossible to fit the model on a single GPU (A100 has at most 80GB RAM). Therefore, we need some way to efficiently shard a model’s data (input, parameters, activations, and optimizer state) across multiple GPUs.

Another aspect of this problem is to scale without significantly changing the training recipe. E.g. Certain representation learning recipes use a global batch size of up to 4096 beyond which we start to see accuracy degradation. We cannot scale to more than 4096 GPUs without using some form of tensor or pipeline parallelism.

2.2 Optimization

The key optimization challenge is to maintain high GPU utilization even as we scale the number of model parameters and flops. When we scale models to teraflops and beyond, we start to hit major bottlenecks in our software stack that super-linearly increase training time and reduce accelerator utilization. We require hundreds or thousands of GPUs to run just a single experiment. Improvements in accelerator utilization can lead to significant reductions in cost and improve fleet utilization. It enables us to fund more projects and run more experiments in parallel.

2.3 Numerical Stability

The key stability challenge is to avoid numerical instability and divergence at large scale. We empirically observed in our experiments that the training instability gets severe and hard to deal with when we scale up model sizes, data, batch sizes, learning rate, etc. Vision Transformers particularly face training instability even at a lower parameter threshold. E.g., we find it challenging to train even ViT-H (with just 630M parameters) in mixed-precision mode without using strong data augmentation. We need to study the model properties and training recipes to make sure that the models train stably and converge.

3. Our Solutions

Figure 1 depicts our solutions to each of the challenges.

3.1 Addressing scaling challenges with data parallelism and model parallelism

We apply various forms of data and model parallelism to enable fitting very large models in GPU memory.

We use FairScale’s FullyShardedDataParallel (FSDP) API [4], based on PyTorch, to shard parameters, gradients, and optimizer state across multiple GPUs, thereby reducing the memory footprint per GPU. This process consists of the following three steps:

• Step 1: We wrapped the entire model in a single FSDP instance. This shards the model parameters at the end of a forward pass and gathers parameters at the beginning of a forward pass. This enabled us to scale ~3x from 1.5B to 4.5B parameters.

• Step 2: We experimented with wrapping individual model layers in separate FSDP instances. This nested wrapping further reduced the memory footprint by sharding and gathering parameters of individual model layers instead of an entire model. The peak memory is then determined by an individually wrapped transformer block in GPU memory in this mode instead of the entire model.

• Step 3: We used activation-checkpoint to reduce the memory consumption by activations. It saves the input tensors and discards the intermediate activation tensors during the forward pass. These are recomputed during the backward pass.

In addition, we experimented with model-parallelism techniques such as pipeline parallelism [5], which allow us to scale to more GPUs without increasing the batch size.

3.2 Addressing optimization challenges with advanced AMP and kernel fusion

Advanced AMP

Automatic Mixed Precision (AMP) [6] training refers to training models using a lower precision of bits than FP32 or the default but still maintaining accuracy. We experimented with three levels of AMP as described below:

• AMP O1: This refers to training in mixed precision where weights are in FP32 and some operations are in FP16. With AMP O1, the ops that might impact accuracy remain in FP32 and are not autocasted to FP16.

• AMP O2: This refers to training in mixed precision but with more weights and ops in FP16 than in O1. Weights do not implicitly remain in FP32 and are cast to FP16. A copy of the master weights is maintained in the FP32 precision that is used by the optimizer. If we want the normalization layer weights in FP32 then we need to explicitly use layer wrapping to ensure that.

• Full FP16: This refers to training in full FP16 where weights and operations are in FP16. FP16 is challenging to enable for training due to convergence issues.

We found that AMP O2 with LayerNorm wrapping in FP32 leads to the best performance without sacrificing accuracy.

Kernel Fusion

• To reduce GPU kernel launch overhead and increase GPU work granularity, we experimented with kernel fusions, including fused dropout and fused layer-norm, using the xformers library [7].

3.3 Addressing stability challenges by studying ops numerical stability and training recipes

BFloat16 in general but with LayerNorm in FP32

The bfloat16 (BF16) [8] floating-point format provides the same dynamic range as FP32 with a memory footprint identical to FP16. We found that we could train models in the BF16 format using the same set of hyperparameters as in FP32, without special parameter tuning. Nevertheless, we found that we need to keep LayerNorm in FP32 mode in order for the training to converge.

3.4 Final training recipe

A summary of the final training recipe.

1. Wrap the outer model in an FSDP instance. Enable parameter sharding after the forward pass.
2. Wrap individual ViT blocks with activation checkpointing, nested FSDP wrapping, and parameter flattening.
3. Enable mixed precision mode (AMP O2) with bfloat16 representation. Maintain the optimizer state in FP32 precision to enhance numerical stability.
4. Wrap normalization layers like LayerNorm in FP32 for better numerical stability.
5. Maximize the Nvidia TensorCore utilization by keeping matrix dimensions to be multiple of 8. For More details check Nvidia Tensor Core Performance Guide.

4. Results

In this section, we show the scaling results of ViT on three types of tasks: (1) image classification, (2) object detection (3) video understanding. Our key result is that we are able to train massive ViT backbones across these vision tasks after applying the discussed scaling and optimization techniques. This enables vision research at a much larger scale. We trained the models to convergence to verify that we maintain the current baselines even with all the optimizations. A common trend in Figures 2, 3, 4 is that we are able to train up to 25B-param models with an epoch time of less than 4 hours on 128 A100 GPUs. The 60B and 120B models are relatively slower to train.

Figure 2 shows the image-classification scaling result. It plots the epoch time for training ViTs on ImageNet using 128 A100-80GB GPUs with different model sizes.

Figure 2: Image-classification scaling result.

Figure 3 shows the object-detection scaling result. It plots the epoch time for training ViTDet [9] with different ViT backbones on COCO using 128 A100-80GB GPUs.

Figure 3: Object-detection scaling result.

Figure 4 shows the video-understanding scaling result. It plots the epoch time for training MViTv2 [10] models on Kinetics 400 [11] using 128 V100 (32 GB) GPUs in FP32.

Figure 4: Video-understanding scaling result.

Figure 5 shows the optimization result with the ViT-H model in Figure 2 on 8 A100-40GB GPUs.
Three versions are used: (1) the baseline uses PyTorch’s DDP [12] with AMP O1, (2) FSDP + AMP-O2 + other optimizations, and (3) FSDP + FP16 + other optimizations. These optimizations altogether speed up the training by up to 2.2x.

Figure 5: Training speedups from various optimizations.

5. Concluding Remarks

We have demonstrated the use of PyTorch with FairScale’s FullyShardedDataParallel (FSDP) API in writing large vision transformer models. We discuss our techniques for scaling and optimizing these models on a GPU cluster. We hope that this article can motivate others to develop large-scale ML models with PyTorch and its ecosystem.

References

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Cutting-edge AI models are becoming extremely large. The cost and overhead of training these models is increasing rapidly, and involves large amounts of engineering and guesswork to find the right training regime. FSDP reduces these costs significantly by enabling you to train much larger models with the same amount of resources. FSDP lowers the memory footprint on your GPUs, and is usable via a lightweight configuration that requires substantially less effort, typically with just a few lines of code.

The main performance gains in FSDP come from maximizing the overlap between network communication and model computation, and eliminating the memory redundancy inherent in traditional data parallel training (DDP). PyTorch FSDP can train models approximately 4x larger on the same server resources as DDP and 20x larger if we combine activation checkpointing and activation offloading.

Since PyTorch 1.12, FSDP is now in beta status, and has added a number of new features that can be tuned to further accelerate your model training.

In this series of blog posts, we will explain multiple performance optimizations you can run with FSDP to boost your distributed training speed and model sizes within the context of your available server resources. We use the HuggingFace T5 3B, 11B and DeepVit, in fine-tuning mode, as the running examples throughout the series.

As a preview of some of the optimizations discussed in this series, we show the before and after performance scaled in Flops below (Note that these results can vary based on your server resources and model architecture).

*T5 3B Performance measured on AWS A100 and A10 servers. Original with no optimizations and Tuned with the applied optimization

*T5 11B Performance measured on A100 servers. Original with no optimizations and Tuned with the applied optimization

In this first post, we will provide a quick overview of FSDP and how it can make training large- scale AI models more efficient. We will highlight briefly the multiple performance options available, and dive deeper into the details on these in upcoming posts. We will then conclude with an overview on how to leverage AWS parallel cluster for large- scale training with FSDP.

Performance optimization gains on T5 models over non-optimized.

In our experiments with the T5 3B model, using the transformer wrapping policy resulted in >2x higher throughput measured in TFLOPS versus the default wrapping policy. Activation checkpointing resulted in 10x improvement by reinvesting the freed memory from the checkpoints into larger batch size. Mixed precision with BFloat16 resulted in ~5x improvement versus FP32 and finally the full sharding strategy versus zero2 (DDP) resulted in 1.5x improvement.

We ran similar experiments for a larger model, T5 11B, but the larger model size resulted in some changes to the experiment space. Specifically, we found that two optimizations, transformer wrapping policy and activation checkpointing, were needed to enable us to run these experiments on 3 nodes (each node had 8 A100 gpus with 80 GB of memory). With these optimizations, we could fit a batch size of 50 and get higher throughput compared to removing each one of them. Thus rather than running on/off solely for a single optimization test as with the 3B model, the larger model experiments were done with 1 of 3 optimizations turned on/off while always running the other two in order to allow a usable batch size for both test states for each item.

Based on TFLOP comparisons, with the 11B model, we saw even more payoff from the optimizations. Mixed precision(~10x improvement) and activation checkpointing (~100x improvement) had a much larger impact with the 11B model compared to the 3B parameter model. With mixed precision we could fit ~2x larger batch sizes and with activation checkpointing >15x batch sizes (from 3 with no activation checkpointing to 50 with activation checkpointing) which translated into large throughput improvements.

We also have observed that for these larger models > 3B, using Zero2 sharding strategy would result in minimal room left in memory for the batch data, and had to go with very small batch sizes (e.g 1-2) that essentially makes full sharding strategy a necessity to enable fitting larger batches sizes.

Note – this tutorial assumes a basic understanding of FSDP. To learn more about basics of FSDP please refer to the getting started and advanced FSDP tutorials.

What is FSDP? How does it make Large-Scale Training More Efficient

FSDP expands upon distributed data parallel, by parallelizing not just data, but the model parameters, the optimizer states and gradients associated with the model. Specifically – each GPU only stores a subset of the entire model and the associated subset of optimizer states and gradients.

To show the evolution of distributed training, we can start from the beginning, where AI models were simply trained on a single GPU.

DDP (Distributed Data Parallel) was the initial step up from training with only a single GPU, and was an effort to address the data and model size growth, where multiple GPUs each housed their own copy of the same model. The gain here is that the data for each batch could be split and processed independently on each GPU, all at the same time,thus parallelizing the processing of the data set and increasing training speed by the increasing number of GPUs. The tradeoff is the need to communicate the gradients between each GPU to synchronize the models after the backward pass.

FSDP expands on scaling models by removing the redundancy of optimizer calculations and state storage, as well as gradient and memory storage of model parameters that are present in DDP (DDP = Distributed Data Parallel). This redundancy reduction, along with increased communication overlap where model parameter communication takes place at the same time as model computation, is what allows FSDP to train much larger models with the same resources as DDP.

A key point is that this efficiency also allows for AI models that are larger than a single GPU to be trained. The model size available for training is now increased to the aggregate memory of all GPUs, rather than the size of a single GPU. (And as a point of note, FSDP can go beyond aggregated GPU memory by leveraging CPU memory as well, though we will not directly cover this aspect here).

As discussed in a previous blog post, with DDP the largest model that we could train on 32, A100 gpus with 40 GB memory (4 nodes) was up to 3B parameters, and batch size of 128, with the help of activation checkpointing. By contrast, using FSDP we were able to train up to 81B model size, combining activation checkpointing, along with activation and parameter offloading. In another experiment, we benchmarked a 1T parameter model with FSDP using 512 gpus.

For intuition on the parameter level workings of FSDP, below we show an animation detailing how the model parameters are sharded and communicated assuming a two GPU scenario and a simple 8 parameter model:

Above – the animations walk through the steps involved with the initial sharding of the model amongst ranks, and we start the all_gathers and forward pass

We continue through the model with the forward pass. After each FSDP unit completes, non-locally owned params are dropped to free memory, and optionally activations can be checkpointed. This continues until we finish the forward pass and compute the loss.

During the backward pass, another all_gather is used to load the parameters and the gradients are computed. These gradients are then reduce_scattered so that the local owners of each param can aggregate and prepare to update the weights.

Finally, each rank passes the summed gradients through the optimizer states and updates the weights to complete the mini-batch.

With the model now distributed across the entire set of available GPUs, the logical question is how data moves through the model given this sharding of model parameters.

This is accomplished by FSDP coordinating with all GPUs to effectively share (communicate) the respective parts of the model. The model is decomposed into FSDP units and parameters within each unit are flattened and then sharded across all GPUs. Within each FSDP unit, GPU’s are assigned interleaving ownership of individual model parameters.

By interleaving, we mean the following – assuming 2 gpus with an id of 1 and 2, the FSDP unit ownership pattern would be [12121212], rather than a contiguous chunk of [111222].

During training, an all_gather is initiated and the locally owned model parameters within a FSDP unit are shared by the owner GPU with the other non-owners, when they need it, on a ‘just in time’ type basis. FSDP prefetches parameters to overlap all_gather communication with computation.

When those requested parameters arrive, the GPU uses the delivered parameters, in combination with the parameters it already owns, to create a fully populated FSDP unit. Thus there is a moment where each GPU hits peak memory usage while holding a fully populated FSDP unit.

It then processes the data through the FSDP unit, and drops the parameters it received from other GPU’s to free up memory for the next unit…the process continues over and over proceeding through the entire model to complete the forward pass.The process is then repeated (in general) for the backward pass.(note – this is a simplified version for understanding..there is additional complexity but this should help construct a basic mental model of the FSDP process).

This eliminates much of the memory redundancy present in DDP, but imposes the cost of higher amounts of network communication to shuttle these requested parameters back and forth amongst all the GPUs.Overlapping the communication timing with the computation taking place is the basis of many of the performance improvements we’ll discuss in this series. The key gains are frequently based on the fact that communication can often take place at the same time as computation.As you can surmise, having high communication speed is vital for FSDP performance.

How do I optimize my training with FSDP?

There are four main performance improvements we will cover – the transformer wrapper, activation checkpointing, mixed precision, and selecting the proper sharding strategy. The flowchart below will help as a checklist for tuning options that we will discuss in this post.

Wrapping policy – for transformers, use Transformer wrapping policy

The first performance optimization is leveraging the FSDP transformer wrapper for transformer models.

One of the pre-defined wrapping policy is size_based_autowrap_policy. With size_based_autowrap_policy, FSDP will traverse the module structure from bottom to top, a new FSDP unit will be created once the current unit has at least the min_num_params specified within the size policy (this defaults to 1e8, or 100M). If the module can not be created as an FSDP unit, FSDP will continue to check its parent module. This size based wrapping policy may not be ideal for some model structures, PyTorch distributed team is actively working on a new default wrapping policy in the next release which is based on size and also module execution order, users can simply tune the size and achieve the optimized performance.

In the current release, you can greatly improve your performance when running Transformer models by using the ‘transformer wrapper’. You will need to provide the appropriate layer class for your model. Here, layer class is the class that houses the Multi-Head Attention and Feed Forward Network.

FSDP will then form the FSDP units around the layer class rather than arbitrary breaks based on parameter size. By sharding the model around layer classes that are uniformly repeated within the transformer, FSDP can create uniform FSDP units that better balance the overlap of computation and communication. By contrast, size based wrapping can produce very uneven or skewed shards for models, which then have uneven matching of compute vs communication overlap. As discussed earlier, the main driver of FSDP high performance is the overlap of communication and computation, and hence why the Transformer wrapper provides improved performance. Note that the Transformer wrapper can also be used for non-transformer models if these models have a list of uniform layers.

Let’s compare the performance difference on a T5, 3B parameter model when running under the default wrapper and the transformer wrapper.

For default wrapping, we don’t need to take any action – we simply pass the model to FSDP as shown:

model = FSDP(
      model,
      device_id=torch.cuda.current_device(),
  )

In this case FSDP will simply wrap the whole model in a single FSDP unit.

Running on an NVIDIA A100-SXM4–40GB with 8 GPUs, we are able to reach 2.3 TFlops and 95% GPU memory utilization with a batch size of 14.

However, since T5 is a transformer model, we are better served to leverage the transformer wrapper for this model.

To use that, we need to isolate the layer class for the transformer, and then pass it in to create our transformer wrapper.

from transformers.models.t5.modeling_t5 import T5Block

And now we can create our Transformer wrapper:

transformer_auto_wrapper_policy = functools.partial(
        transformer_auto_wrap_policy,
        transformer_layer_cls={
            T5Block,  # < ---- Your Transformer layer class
        },
    )

With our model aware wrapper ready, we can initialize FSDP:

# invoke FSDP with your transformer wrapper policy:
model = FSDP(
        model,
        auto_wrap_policy=transformer_auto_wrapper_policy,
        device_id=torch.cuda.current_device(),  # streaming init
    )

Running this wrapped model, we can see some substantial performance gains.We can fit nearly double the batch size, going to 28, and with better memory and communication efficiency, we see a TFlops increase to 5.07 from 2.3.

Thus, we’ve increased our training throughput by over 200% (2.19x) due to providing greater model info to FSDP! The transformer wrapping policy results in more fine-grained and balanced FSDP units each holding a layer class, which leads to a more effective communication-computation overlap.

Above: Graphical comparison of TFlops based on wrapper type

If you are training a Transformer model, it pays to configure your training with FSDP using the transformer wrapper. For more information on how to isolate your layer class, please see our in depth video on Transformer wrapping here, where we walk through a number of transformers showing where the layer class can be found.

Mixed precision – use BF16 if you have an Ampere architecture GPU

FSDP supports a flexible mixed precision policy that gives you granular control over parameters, gradients and buffer data types. This lets you easily leverage BFloat16 or FP16 to increase your training speed by up to 70%.

*Note that BFloat 16 is only available on Ampere type GPUs. On AWS this is available with p4dn and g5 instances.

By way of comparison, we can show a 77% speed improvement when comparing fully tuned BFloat16 vs FP32 on an 8B DeepVit model.

We have obtained even greater acceleration using BFloat16 in fine-tuning a 3B HuggingFace T5 model as shown in the figures below. We observed that because of the lower precision the validation loss of BFloat16 is slightly behind in the first few epochs, but it is able to catch up and results in the same final accuracy as FP32.

To use mixed precision, we create a policy with our desired data types, and pass it in during the FSDP initialization.

To create our policy, we need to import the MixedPrecision class, and then define our custom policy using our customized class:

from torch.distributed.fsdp import MixedPrecision
bfSixteen = MixedPrecision(
   param_dtype=torch.bfloat16,
   # Gradient communication precision.
   reduce_dtype=torch.bfloat16,
   # Buffer precision.
   buffer_dtype=torch.bfloat16,
)
model = FSDP(
       model,
       auto_wrap_policy=transformer_auto_wrapper_policy,
       mixed_precision=bfloatPolicy)

You can mix and match the precision for parameters, gradients and buffers as you prefer:

comboPolicy = MixedPrecision(
        # Param precision
        param_dtype=torch.bfloat16,
        # Gradient communication precision.
        reduce_dtype=torch.float32,
        # Buffer precision.
        buffer_dtype=torch.float32,
    )

For training with FP16, you will need to also use the ShardedGradScaler, which we will cover in subsequent posts. For BFloat16, it is a drop-in replacement.

AnyPrecision Optimizer – going beyond mixed precision with full BF16 training

Mixed precision training, both in FSDP and elsewhere, maintains the working weights in the reduced datatype (BF16 or FP16) while keeping the master weights in full FP32. The reason for the master weights in FP32 is that running in pure BF16 will result in ‘weight stagnation’, where very small weight updates are lost due to the lower precision, and the accuracy flatlines over time while FP32 weights can continue to improve from these small updates.

In order to resolve this dilemma, we can use the new AnyPrecision optimizer available in TorchDistX (Torch Distributed Experimental) that allows you to successfully train and keep the master weights in pure BF16 instead of FP32. In addition, unlike the typical storage of optimizer states in FP32, AnyPrecision is able to maintain states in pure BF16 as well.

AnyPrecision enables pure BF16 training by maintaining an extra buffer that tracks the precision lost during the weight updates and re-applies that during the next update…effectively resolving the weight stagnation issue without requiring FP32.

As a comparison of the throughput gains available with pure BF16 training using AnyPrecision, we ran experiments using FSDP with the T5 11B model with regular FP32 training, Mixed Precision training with BF16, and pure BF16 training using the AnyPrecision optimizer on 3 nodes with A100 gpus as mentioned previously.

As shown above, training with AnyPrecision and pure BF16 resulted in 2x the throughput vs Mixed Precision, and over 20x improvement vs FP32.

The potential tradeoff is the impact on final accuracy – in the cases we tested, the accuracy was equal or better than FP32 due to a regularization effect from the slightly reduced precision, but your results may vary.

AnyPrecision optimizer is available for you to test with here, and is a drop in replacement for AdamW optimizer.

Activation checkpointing – increasing throughput by trading compute for memory

FSDP supports activation checkpointing once the model has been sharded, and makes it easy to implement. The graph above shows ~4x throughput improvement using activation checkpointing.

Activation checkpointing is where the intermediate activations are freed during the forward pass, and a checkpoint is left as a placeholder. This generally increases available GPU memory by over 30%.

The tradeoff is that during the backward pass, these previously removed intermediate activations must be re-calculated again using information in the checkpoint (duplicate compute), but by leveraging the increased GPU memory, one can increase the batch size such that the net throughput can increase substantially.

# verify we have FSDP activation support ready by importing:
from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
   checkpoint_wrapper,
   CheckpointImpl,
   apply_activation_checkpointing_wrapper,
)

The steps required to implement activation checkpointing is to first import the FSDP checkpointing functions. We need declare our checkpointer wrapper type which is non-reentrant and create a check function to identify which layer to wrap as follows

non_reentrant_wrapper = partial(
    checkpoint_wrapper,
    offload_to_cpu=False,
    checkpoint_impl=CheckpointImpl.NO_REENTRANT,
)
check_fn = lambda submodule: isinstance(submodule, T5Block)

apply_activation_checkpointing_wrapper(
       model, checkpoint_wrapper_fn=non_reentrant_wrapper, check_fn=check_fn
   )

Important note – this must be run after the model has been initialized with FSDP.

However, hopefully you’ve seen how some initial tuning with FSDP options can have a large impact on your training performance.

With that, we turn our attention from how to scale within FSDP, to how to scale your server hardware for FSDP using AWS.

Large Scale Training with FSDP on AWS – For multi-node prioritize high speed network

AWS provides several services that can be used to run distributed training with FSDP: Amazon EC2 Accelerated Computing instances, AWS ParallelCluster, and Amazon Sagemaker.

In this series of blog posts, we used Amazon EC2 p4d instances in a single-instance multi-GPU configuration and in a multi-instance configuration using AWS ParallelCluster and SageMaker in order to run our training jobs.

Here, we’ll focus specifically on AWS parallel cluster and provide an overview of how to utilize it for training purposes.

AWS ParallelCluster Setup

AWS ParallelCluster is an open source, cluster management tool that makes it easy for you to deploy and manage High Performance Computing (HPC) clusters on AWS. AWS ParallelCluster uses yaml configuration files to provision all the necessary resources. It also supports multiple instance types, job submission queues, shared file systems like Amazon EFS (NFS) or Amazon FSx for Lustre, and job schedulers like AWS Batch and Slurm.

Workflow on Clusters

The high level idea is to have a cluster that has a head node which controls the compute nodes. The actual training job runs on the compute nodes. Overall steps to run a training job on a cluster are as follows:

1. Set up an AWS ParallelCuster (we discuss below)
2. Connect to the head node, and import the training code/ setup the environment.
3. Pull the data and place it in a shared folder that compute nodes can access (FSx Lustre drive).
4. Run the training job using a job scheduler (in this case Slurm).

Setup AWS ParallelCuster

To setup AWS ParallelCluster,

1. Deploy a network stack. This step is optional since you could use your account default VPC and let AWS ParallelCluster create your subnets and security groups. However, we prefer to compartmentalize our desired network infrastructure and do this deployment via a CloudFormation stack.

   Since we deploy a public and a private subnet, we want to create them into an Availability Zone that contains our target instances, in this case p4d. We consult their availability in the region we use (us-east-1) through the following AWS CLI command:

   aws ec2 describe-instance-type-offerings --location-type availability-zone --filters Name=instance-type,Values=p4d.24xlarge --region us-east-1 --output table

   We see three availability zones containing p4d instances, we pick one of them (us-east-1c, yours may be different) when deploying our network stack. This can be done with the AWS Console or the AWS CLI. In our case we use the latter as follows

   aws cloudformation create-stack --stack-name VPC-Large-Scale --capabilities CAPABILITY_IAM --template-body file://VPC-Large-Scale.yaml --parameters ParameterKey=SubnetsAZ,ParameterValue=us-east-1c

   CloudFormation will deploy our new VPC, subnets, security groups and endpoints on our behalf. Once done, you can retrieve the IDs of the public and private subnets by querying the stack outputs and the values PublicSubnet and PrivateSubnet.

   For example, using the AWS CLI for the private subnet:

   aws cloudformation describe-stacks --stack-name VPC-Large-Scale --query "Stacks[0].Outputs[?OutputKey=='PrivateSubnet'].OutputValue" --output text

2. Create ParallelCluster, The cluster configuration file specifies the resources for our cluster. These resources include instance type for Head node, compute nodes, access to S3 buckets, shared storage where our data will be located. We will use Amazon FSx for Lustre that offers a fully managed shared storage service with Lustre.

   Here is an example of a cluster configuration file. We can use AWs ParallelCluster CLI to create the cluster. Please note that the private and public subnet IDs will need to be replaced by the ones you retrieved earlier. You will be able to control the cluster using the AWS ParallelCluster CLI to start, stop, pause, etc.

   pcluster create-cluster --cluster-name my-hpc-cluster --cluster-configuration cluster.yaml
   

3. SSH to Head node – once the cluster is ready, we can connect to the Head node using the SSH protocol, pull our training code with and place the data in the shared storage specified in the cluster configuration file.

   pcluster ssh --cluster-name cluster -i your-key_pair
   

4. Launch the training job – now that we have the data and training code, we can launch the slurm job for training. Here is an example of a slurm script to launch the job using torchrun.

More details on how to set up the cluster is out of the scope of this post, however we will have a separate post on it.

What’s next?

With this post we provided a high level overview of FSDP and how it efficiently scales distributed AI training. The flowchart included will help provide a checklist for you to review tuning options discussed such as the transformer wrapper and activation checkpointing.

In the next posts, we will continue with the T5 model and go deeper into each of the topics above, specifically with sharding strategy and other optimizations to provide more insight and details. For now, a good reference for the sharding strategy is in our video tutorial here:

If you have questions or find an issue, please find the authors Less, Hamid and Geeta or open an issue on PyTorch github.

Special thanks to:

Pytorch Distributed team, Shen Li, Rohan Varma, Yanli Zhao, Andrew Gu, Anjali Sridhar, Ana Simoes, Pierre-Yves Aquilanti, Sundar Ranganathan, and the broader AWS team for supporting us with providing infrastructure and technical support for running the large scale experiments.

Resources:

FSDP video series

Getting started with FSDP

Advanced tutorial on FSDP

API documentation

Read More

Large model training using a cloud native approach is of growing interest for many enterprises given the emergence and success of foundation models. Some AI practitioners may assume that the only way they can achieve high GPU utilization for distributed training jobs is to run them on HPC systems, such as those inter-connected with Infiniband and may not consider Ethernet connected systems. We demonstrate how the latest distributed training technique, Fully Sharded Data Parallel (FSDP) from PyTorch, successfully scales to models of size 10B+ parameters using commodity Ethernet networking in IBM Cloud.

PyTorch FSDP Scaling

As models get larger, the standard techniques for data parallel training work only if the GPU can hold a full replica of the model, along with its training state (optimizer, activations, etc.). However, GPU memory increases have not kept up with the model size increases and new techniques for training such models have emerged (e.g., Fully Sharded Data Parallel, DeepSpeed), which allow us to efficiently distribute the model and data over multiple GPUs during training. In this blog post, we demonstrate a path to achieve remarkable scaling of model training to 64 nodes (512 GPUs) using PyTorch native FSDP APIs as we increase model sizes to 11B.

What is Fully Sharded Data Parallel?

FSDP extends the distributed data parallel training (DDP) approach by sharding model parameters, gradient and optimizer states into K FSDP units, determined by using a wrapping policy. FSDP achieves large model training efficiency in terms of resources and performance by significantly reducing the memory footprint on each GPU and overlapping computation and communication.

Resource efficiency is achieved with memory footprint reduction by having all GPUs own a portion of each FSDP unit. To process a given FSDP unit, all GPUs share their locally owned portion via all_gather communication calls.

Performance efficiency is accomplished by overlapping all_gather communication calls for upcoming FSDP units with computation of the current FSDP unit. Once the current FSDP unit has been processed, the non-locally owned parameters are dropped, freeing memory for the upcoming FSDP units. This process achieves training efficiency by the overlap of computation and communication, while also reducing the peak memory needed by each GPU.

In what follows, we demonstrate how FSDP allows us to keep hundreds of GPUs highly utilized throughout a distributed training job, while running over standard Ethernet networking (system description towards the end of the blog). We chose the T5 architecture for our experiments and leveraged the code from the FSDP workshop. In each of our experiments, we start with a single node experiment to create a baseline and report the metric seconds/iteration normalized by the batch size as well as compute the teraflops based on the Megatron-LM paper (see Appendix for details of teraflop computation for T5). Our experiments aim to maximize the batch size (while avoiding cudaMalloc retries) to take full advantage of overlap in computation and communications, as discussed below. Scaling is defined as the ratio of the seconds/iteration normalized by batch size for N nodes versus a single node, representing how well we can utilize the additional GPUs as more nodes are added.

Experimental Results

Our first set of experiments using the T5-3B configuration (mixed precision with BF16, activation checkpointing, and transformer wrapping policy) demonstrated scaling efficiency of 95% as we increased the number of GPUs from 8 to 512 (1 to 64 nodes, respectively). We achieved these results without any modifications to the existing FSDP APIs. We observed that, for this scale, over Ethernet based network, there is sufficient bandwidth to enable continuous overlap of communication and computation.

However, when we increased the T5 model size to 11B, the scaling efficiency declined substantially to 20%. The PyTorch profiler shows that overlap of communication and computation was very limited. Further investigation into the network bandwidth usage revealed that the poor overlap is being caused by latency in the communication of individual packets and not the bandwidth required (in fact, our peak bandwidth utilization is 1/4th of that available). This led us to hypothesize that if we can increase the compute time by increasing the batch size, we can better overlap communication and computation. However, given we are already at maximum GPU memory allocation, we must identify opportunities to rebalance the memory allocation to allow for increase in batch size. We identified that the model state was being allocated a lot more memory than was needed. The primary function of these reservations is to have pre-reserved memory ready to aggressively send/receive tensors during the communication periods and too few buffers can result in increased wait times, whereas too many buffers result in smaller batch sizes.

To achieve better efficiency, the PyTorch distributed team introduced a new control knob, the rate_limiter which controls how much memory is allocated for send/receive of tensors, alleviating the memory pressure and providing room for higher batch sizes. In our case, the rate_limiter could increase the batch size from 20 to 50, thus increasing compute time by 2.5x and allowing for much greater overlap of communication and computation. With this fix, we increased the scaling efficiency to >75% (at 32 nodes)!

Continued investigation into the factors limiting scaling efficiency uncovered that the rate limiter was creating a recurring pipeline bubble of GPU idle time. This was due to the rate limiter using a block and flush approach for the allocation and release of each set of memory buffers. By waiting for the entire block to complete before initiating a new all_gather, the GPU was idling at the start of each block, while waiting for the new set of all_gather parameters to arrive. This bubble was alleviated by moving to a sliding window approach. Upon the completion of a single all_gather step and its computation (rather than a block of them), the memory is freed and the next all_gather is immediately issued in a much more uniform manner. This improvement eliminated the pipeline bubble and boosted the scaling efficiencies to >90% (at 32 nodes).

Figure 1: Scaling of T5-XL (3B) and T5-XXL (11B) from 1 node to 64 nodes

Figure 2: TFLOPs/sec usage for T5-XL(3B) and T5-XXL (11B) as we increase number of nodes

IBM Cloud AI System and Middleware

The AI infrastructure used for this work is a large-scale AI system on IBM Cloud consisting of nearly 200 nodes, each node with 8 NVIDIA A100 80GB cards, 96 vCPUs, and 1.2TB CPU RAM. The GPU cards within a node are connected via NVLink with a card-to-card bandwidth of 600GBps. Nodes are connected by 2 x 100Gbps Ethernet links with SRIOV based TCP/IP stack, providing a usable bandwidth of 120Gbps.

The IBM Cloud AI System has been production-ready since May of 2022 and is configured with the OpenShift container platform to run AI workloads. We also built a software stack for production AI workloads that provide end-to-end tools for training workloads. The middleware leverages Ray for pre and post processing workloads and PyTorch for training of models. We also integrate a Kubernetes native scheduler, MCAD, that manages multiple jobs with job queuing, gang scheduling, prioritization, and quota management. A multi-NIC CNI discovers all available network interfaces and handles them as a single NIC pool enabling optimized use of the network interfaces in Kubernetes. Finally, CodeFlare CLI supports a single pane for observability of the full stack using a desktop CLI (e.g., GPU utilization, application metrics like loss, gradient norm).

Figure 3: Foundation Model Middleware Stack

Conclusion and Future Work

In conclusion, we demonstrated how we can achieve remarkable scaling of FSDP APIs over non-InfiniBand networks. We identified the bottleneck that had limited scaling to less than 20% efficiency for 11B parameter model training. After identifying the issue, we were able to correct this with a new rate limiter control to ensure a more optimal balance of reserved memory and communication overlap relative to compute time. With this improvement, we were able to achieve 90% scaling efficiency (a 4.5x improvement), at 256 GPUs and 80% at 512 GPUs for training of the 11B parameter model. In addition, the 3B parameter model scales extremely well with 95% efficiency even as we increase the number of GPUs to 512.

This is a first in the industry to achieve such scaling efficiencies for up to 11B parameter models using Kubernetes with vanilla Ethernet and PyTorch native FSDP API’s. This improvement enables users to train huge models on a Hybrid Cloud platform in a cost efficient and sustainable manner.

We plan on continuing to investigate scaling with decoder only models and increasing the size of these models to 100B+ parameters. From a system design perspective, we are exploring capabilities such as RoCE and GDR that can improve latencies of communications over Ethernet networks.

Acknowledgements

This blog was possible because of contributions from both PyTorch Distributed and IBM Research teams.

From the PyTorch Distributed team, we would like to thank Less Wright, Hamid Shojanazeri, Geeta Chauhan, Shen Li, Rohan Varma, Yanli Zhao, Andrew Gu, Anjali Sridhar, Chien-Chin Huang, and Bernard Nguyen.

From the IBM Research team, we would like to thank Linsong Chu, Sophia Wen, Lixiang (Eric) Luo, Marquita Ellis, Davis Wertheimer, Supriyo Chakraborty, Raghu Ganti, Mudhakar Srivatsa, Seetharami Seelam, Carlos Costa, Abhishek Malvankar, Diana Arroyo, Alaa Youssef, Nick Mitchell.

Appendix

Teraflop computation

The T5-XXL (11B) architecture has two types of T5 blocks, one is an encoder and the second is a decoder. Following the approach of Megatron-LM, where each matrix multiplication requires 2m×k×n FLOPs, where the first matrix is of size m×k and the second is k×n. The encoder block consists of self-attention and feed forward layers, whereas the decoder block consists of self-attention, cross-attention, and feed forward layers.

The attention (both self and cross) block consists of a QKV projection, which requires 6Bsh² operations, an attention matrix computation requiring 2Bs²h operations, an attention over values which needs 2Bs²h computations, and the post-attention linear projection requires 2Bsh² operations. Finally, the feed forward layer requires 15Bsh² operations.

The total for an encoder block is 23Bsh²+4Bs²h, whereas for a decoder block, it comes to 31Bsh²+8Bs²h. With a total of 24 encoder and 24 decoder blocks and 2 forward passes (as we discard the activations) and one backward pass (equivalent to two forward passes), the final FLOPs computation comes to be 96×(54Bsh²+ 12Bs²h) + 6BshV. Here, B is the batch size per GPU, s is sequence length, h is hidden state size, and V is vocabulary size.
We repeat a similar computation for T5-XL (3B) architecture, which is slightly different.

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tl;dr

Multi-Objective Optimization in Ax enables efficient exploration of tradeoffs (e.g. between model performance and model size or latency) in Neural Architecture Search. This method has been successfully applied at Meta for a variety of products such as On-Device AI. In this post, we provide an end-to-end tutorial that allows you to try it out yourself.

Introduction

Neural networks continue to grow in both size and complexity. Developing state-of-the-art architectures is often a cumbersome and time-consuming process that requires both domain expertise and large engineering efforts. In an attempt to overcome these challenges, several Neural Architecture Search (NAS) approaches have been proposed to automatically design well-performing architectures without requiring a human in-the-loop.

Despite being very sample-inefficient, naïve approaches like random search and grid search are still popular for both hyperparameter optimization and NAS (a study conducted at NeurIPS 2019 and ICLR 2020 found that 80% of NeurIPS papers and 88% of ICLR papers tuned their ML model hyperparameters using manual tuning, random search, or grid search). But as models are often time-consuming to train and may require large amounts of computational resources, minimizing the number of configurations that are evaluated is important.

Ax is a general tool for black-box optimization that allows users to explore large search spaces in a sample-efficient manner using state-of-the art algorithms such as Bayesian Optimization. At Meta, Ax is used in a variety of domains, including hyperparameter tuning, NAS, identifying optimal product settings through large-scale A/B testing, infrastructure optimization, and designing cutting-edge AR/VR hardware.

In many NAS applications, there is a natural tradeoff between multiple metrics of interest. For instance, when deploying models on-device we may want to maximize model performance (e.g., accuracy), while simultaneously minimizing competing metrics such as power consumption, inference latency, or model size, in order to satisfy deployment constraints. In many cases, we have been able to reduce computational requirements or latency of predictions substantially by accepting a small degradation in model performance (in some cases we were able to both increase accuracy and reduce latency!). Principled methods for exploring such tradeoffs efficiently are key enablers of Sustainable AI.

At Meta, we have successfully used multi-objective Bayesian NAS in Ax to explore such tradeoffs. Our methodology is being used routinely for optimizing AR/VR on-device ML models. Beyond NAS applications, we have also developed MORBO which is a method for high-dimensional multi-objective optimization that can be used to optimize optical systems for augmented reality (AR).

Fully automated Multi-Objective NAS with Ax

Ax’s Scheduler allows running experiments asynchronously in a closed-loop fashion by continuously deploying trials to an external system, polling for results, leveraging the fetched data to generate more trials, and repeating the process until a stopping condition is met. No human intervention or oversight is required. Features of the Scheduler include:

• Customizability of parallelism, failure tolerance, and many other settings;

• A large selection of state-of-the-art optimization algorithms;

• Saving in-progress experiments (to a SQL DB or json) and resuming an experiment from storage;

• Easy extensibility to new backends for running trial evaluations remotely.

The following illustration from the Ax scheduler tutorial summarizes how the scheduler interacts with any external system used to run trial evaluations:

To run automated NAS with the Scheduler, the main things we need to do are:

• Define a Runner, which is responsible for sending off a model with a particular architecture to be trained on a platform of our choice (like Kubernetes, or maybe just a Docker image on our local machine). In the tutorial below, we use TorchX for handling deployment of training jobs.

• Define a Metric, which is responsible for fetching the objective metrics (such as accuracy, model size, latency) from the training job. In our tutorial, we use Tensorboard to log data, and so can use the Tensorboard metrics that come bundled with Ax.

Tutorial

In our tutorial we show how to use Ax to run multi-objective NAS for a simple neural network model on the popular MNIST dataset. While the underlying methodology can be used for more complicated models and larger datasets, we opt for a tutorial that is easily runnable end-to-end on a laptop in less than an hour. In our example, we will tune the widths of two hidden layers, the learning rate, the dropout probability, the batch size, and the number of training epochs. The goal is to trade off performance (accuracy on the validation set) and model size (the number of model parameters) using multi-objective Bayesian optimization.

The tutorial makes use of the following PyTorch libraries:

• PyTorch Lightning (specifying the model and training loop)

• TorchX (for running training jobs remotely / asynchronously)

• BoTorch (the Bayesian optimization library that powers Ax’s algorithms)

The complete runnable example is available as a PyTorch Tutorial.

Results

The final results from the NAS optimization performed in the tutorial can be seen in the tradeoff plot below. Here, each point corresponds to the result of a trial, with the color representing its iteration number, and the star indicating the reference point defined by the thresholds we imposed on the objectives. We see that our method was able to successfully explore the trade-offs between validation accuracy and number of parameters and found both large models with high validation accuracy as well as small models with lower validation accuracy. Depending on the performance requirements and model size constraints, the decision maker can now choose which model to use or analyze further.

Visualizations

Ax provides a number of visualizations that make it possible to analyze and understand the results of an experiment. Here, we will focus on the performance of the Gaussian process models that model the unknown objectives, which are used to help us discover promising configurations faster. Ax makes it easy to better understand how accurate these models are and how they perform on unseen data via leave-one-out cross-validation. In the figures below, we see that the model fits look quite good – predictions are close to the actual outcomes, and predictive 95% confidence intervals cover the actual outcomes well. Additionally, we observe that the model size (num_params) metric is much easier to model than the validation accuracy (val_acc) metric.

Takeaways

• We showed how to run a fully automated multi-objective Neural Architecture Search using Ax.

• Using the Ax Scheduler, we were able to run the optimization automatically in a fully asynchronous fashion – this can be done locally (as done in the tutorial) or by deploying trials remotely to a cluster (simply by changing the TorchX scheduler configuration).

• The state-of-the-art multi-objective Bayesian optimization algorithms available in Ax allowed us to efficiently explore the tradeoffs between validation accuracy and model size.

Advanced Functionality

Ax has a number of other advanced capabilities that we did not discuss in our tutorial. Among these are the following:

Early Stopping

When evaluating a new candidate configuration, partial learning curves are typically available while the NN training job is running. We can use the information contained in the partial curves to identify under-performing trials to stop early in order to free up computational resources for more promising candidates. While not demonstrated in the above tutorial, Ax supports early stopping out-of-the-box – see our early stopping tutorial for more details.

High-dimensional search spaces

In our tutorial, we used Bayesian optimization with a standard Gaussian process in order to keep the runtime low. However, these models typically scale to only about 10-20 tunable parameters. Our new SAASBO method (paper, Ax tutorial, BoTorch tutorial) is very sample-efficient and enables tuning hundreds of parameters. SAASBO can easily be enabled by passing use_saasbo=True to choose_generation_strategy.

Acknowledgements

We thank the TorchX team (in particular Kiuk Chung and Tristan Rice) for their help with integrating TorchX with Ax, and the Adaptive Experimentation team @ Meta for their contributions to Ax and BoTorch.

References

D. Eriksson, P. Chuang, S. Daulton, M. Balandat. Optimizing model accuracy and latency using Bayesian multi-objective neural architecture search. Meta Research blog, July 2021.

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Introduction

In recent years, scaling model sizes has become a promising area of research. In the field of NLP, language models have gone from hundreds of millions of parameters (BERT) to hundreds of billions of parameters (GPT-3) demonstrating significant improvements on downstream tasks. The scaling laws for large scale language models have also been studied extensively in the industry. A similar trend can be observed in the vision field, with the community moving to transformer based models (like Vision Transformer, Masked Auto Encoders) as well. It is clear that individual modalities – text, image, video – have benefited massively from recent advancements in scale, and frameworks have quickly adapted to accommodate larger models.

At the same time, multimodality is becoming increasingly important in research with tasks like image-text retrieval, visual question-answering, visual dialog and text to image generation gaining traction in real world applications. Training large scale multimodal models is the natural next step and we already see several efforts in this area like CLIP from OpenAI, Parti from Google and CM3 from Meta.

In this blog, we present a case study demonstrating the scaling of FLAVA to 10B params using techniques from PyTorch Distributed. FLAVA is a vision and language foundation model, available in TorchMultimodal, which has shown competitive performance on both unimodal and multimodal benchmarks. We also give the relevant code pointers in this blog. The instructions for running an example script to scale FLAVA can be found here.

Scaling FLAVA Overview

FLAVA is a foundation multimodal model which consists of transformer based image and text encoders followed by a transformer-based multimodal fusion module. It is pretrained on both unimodal and multimodal data with a diverse set of losses. This includes masked language, image and multimodal modeling losses that require the model to reconstruct the original input from its context (self-supervised learning). It also uses image text matching loss over positive and negative examples of aligned image-text pairs as well as CLIP style contrastive loss. In addition to multimodal tasks (like image-text retrieval), FLAVA demonstrated competitive performance on unimodal benchmarks as well (GLUE tasks for NLP and image classification for vision).

The original FLAVA model has ~350M parameters and uses ViT-B16 configurations (from the Vision Transformer paper) for image and text encoders. The multimodal fusion transformer follows the unimodal encoders but with half the number of layers. We explore increasing the size of each encoder to larger ViT variants.

Another aspect of scaling is adding the ability to increase the batch size. FLAVA makes use of contrastive loss over in-batch negatives, which typically benefits from large batch size (as studied here). The largest training efficiency or throughput is also generally achieved when operating near maximum possible batch sizes as determined by the amount of GPU memory available (also see the experiments section).

The following table displays the different model configurations we experimented with. We also determine the maximum batch size that was able to fit in memory for each configuration in the experiments section.

Optimization overview

PyTorch offers several native techniques to efficiently scale models. In the following sections, we go over some of these techniques and show how they can be applied to scale up a FLAVA model to 10 billion parameters.

Distributed Data Parallel

A common starting point for distributed training is data parallelism. Data parallelism replicates the model across each worker (GPU), and partitions the dataset across the workers. Different workers process different data partitions in parallel and synchronize their gradients (via all reduce) before model weights are updated. The figure below showcases the flow (forward, backward, and weight update steps) for processing a single example for data parallelism:

Source: https://engineering.fb.com/2021/07/15/open-source/fsdp/

PyTorch provides a native API, DistributedDataParallel (DDP) to enable data parallelism which can be used as a module wrapper as showcased below. Please see PyTorch Distributed documentation for more details.

from torchmultimodal.models.flava.model import flava_model_for_pretraining
import torch
import torch.distributed as dist

model = flava_model_for_pretraining().cuda()
# Initialize PyTorch Distributed process groups
# Please see https://pytorch.org/tutorials/intermediate/dist_tuto.html for details
dist.init_process_group(backend=”nccl”)
# Wrap model in DDP
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[torch.cuda.current_device()])

Fully Sharded Data Parallel

GPU memory usage of a training application can roughly be broken down into model inputs, intermediate activations (needed for gradient computation), model parameters, gradients, and optimizer states. Scaling a model will typically increase each of these elements. Scaling a model with DDP can eventually result in out-of-memory issues when a single GPU’s memory becomes insufficient since it replicates the parameters, gradients, and optimizer states on all workers.

To reduce this replication and save GPU memory, we can shard the model parameters, gradients, and optimizer states across all workers with each worker only managing a single shard. This technique was popularized by the ZeRO-3 approach developed by Microsoft. A PyTorch-native implementation of this approach is available as FullyShardedDataParallel (FSDP) API, released as a beta feature in PyTorch 1.12. During a module’s forward and backward passes, FSDP unshards the model parameters as needed for computation (using all-gather) and reshards them after computation. It synchronizes gradients using the reduce-scatter collective to ensure sharded gradients are globally averaged. The forward and backward pass flow of a model wrapped in FSDP are detailed below:

Source: https://engineering.fb.com/2021/07/15/open-source/fsdp/

To use FSDP, the submodules of a model need to be wrapped with the API to control when specific submodules are sharded or unsharded. FSDP provides an auto-wrapping API (see the auto_wrap_policy argument) that can be used out of the box as well as several wrapping policies and the ability to write your own policy.

The following example demonstrates wrapping the FLAVA model with FSDP. We specify the auto-wrapping policy as transformer_auto_wrap_policy. This will wrap individual transformer layers (TransformerEncoderLayer), the image transformer (ImageTransformer), text encoder (BERTTextEncoder) and multimodal encoder (FLAVATransformerWithoutEmbeddings) as individual FSDP units. This uses a recursive wrapping approach for efficient memory management. For example, after an individual transformer layer’s forward or backward pass is finished, its parameters are discarded, freeing up memory thereby reducing peak memory usage.

FSDP also provides a number of configurable options to tune the performance of applications. For example, in our use case, we illustrate the use of the new limit_all_gathers flag, which prevents all-gathering model parameters too early thereby alleviating memory pressure on the application. We encourage users to experiment with this flag which can potentially improve the performance of applications with high active memory usage.

import torch
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy
from torchmultimodal.models.flava.model import flava_model_for_pretraining
from torchmultimodal.models.flava.text_encoder import BertTextEncoder
from torchmultimodal.models.flava.image_encoder import ImageTransformer
from torchmultimodal.models.flava.transformer import FLAVATransformerWithoutEmbeddings
from torchmultimodal.modules.layers.transformer import TransformerEncoderLayer

model = flava_model_for_pretraining().cuda()
dist.init_process_group(backend=”nccl”)

model = FSDP(
               model,
               device_id=torch.cuda.current_device(),
               auto_wrap_policy=partial(
                   transformer_auto_wrap_policy,
                   transformer_layer_cls={
                       TransformerEncoderLayer,
                       ImageTransformer,
                       BERTTextEncoder,
                       FLAVATransformerWithoutEmbeddings
                   },
               ),
               limit_all_gathers=True,
           )

Activation Checkpointing

As discussed above, intermediate activations, model parameters, gradients, and optimizer states contribute to the overall GPU memory usage. FSDP can reduce memory consumption due to the latter three but does not reduce memory consumed by activations. Memory used by activations increases with increase in batch size or number of hidden layers. Activation checkpointing is a technique to decrease this memory usage by recomputing the activations during the backward pass instead of holding them in memory for a specific checkpointed module. For example, we observed ~4x reduction in the peak active memory after forward pass by applying activation checkpointing to the 2.7B parameter model.

PyTorch offers a wrapper based activation checkpointing API. In particular, checkpoint_wrapper allows users to wrap an individual module with checkpointing, and apply_activation_checkpointing allows users to specify a policy with which to wrap modules within an overall module with checkpointing. Both these APIs can be applied to most models as they do not require any modifications to the model definition code. However, if more granular control over checkpointed segments, such as checkpointing specific functions within a module, is required, the functional torch.utils.checkpoint API can be leveraged, although this requires modification to the model code. The application of the activation checkpointing wrapper to individual FLAVA transformer layers (denoted by TransformerEncoderLayer) is shown below. For a thorough description of activation checkpointing, please see the description in the PyTorch documentation.

from torchmultimodal.models.flava.model import flava_model_for_pretraining
from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import apply_activation_checkpointing, checkpoint_wrapper, CheckpointImpl
from torchmultimodal.modules.layers.transformer import TransformerEncoderLayer

model = flava_model_for_pretraining()
checkpoint_tformer_layers_policy = lambda submodule: isinstance(submodule, TransformerEncoderLayer)

apply_activation_checkpointing(
               model,
               checkpoint_wrapper_fn=checkpoint_wrapper,
               check_fn=checkpoint_tformer_layers_policy,
           )

Used together, wrapping FLAVA transformer layers with activation checkpointing and wrapping the overall model with FSDP as demonstrated above, we are able to scale FLAVA to 10B parameters.

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:

1. 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)
2. 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

3. 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

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
• FLAVA paper
• Introducing Pytorch FSDP

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