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Scaling up BERT-like model Inference on modern CPU - Part 2 | mfuntowicz | November 4, 2021 | bert-cpu-scaling-part-2 | partnerships, intel, guide, nlp | https://huggingface.co/blog/bert-cpu-scaling-part-2 | OpenMP In the previous section we talked about the memory management within machine learning software involving mostly CPU-bound workloads. Such software often relies on intermediary frameworks such as PyTorch or TensorFlow for Deep Learning which commonly abstract away all the underlying, highly parallelized, operator implementations. Writing such highly parallel and optimized algorithms is a real engineering challenge, and it requires a very low-level understanding of all the actual elements coming into play operated by the CPU (synchronization, memory cache, cache validity, etc.). In this context, it is very important to be able to leverage primitives to implement such powerful algorithms, reducing the delivery time and computation time by a large margin compared to implementing everything from scratch. There are many libraries available which provide such higher-level features to accelerate the development of algorithms. Among the most common, one can look at OpenMP, Thread Building Blocks and directly from the C++ when targeting a recent version of the standard. In the following part of this blog post, we will restrict ourselves to OpenMP and especially comparing the GNU, open source and community-based implementation, to the Intel OpenMP one. The latter especially targets Intel CPUs and is optimized to provide best of class performances when used as a drop-in replacement against the GNU OpenMP one. OpenMP exposes many environment variables to automatically configure the underlying resources which will be involved in the computations, such as the number of threads to use to dispatch computation to (intra-op threads), the way the system scheduler should bind each of these threads with respect to the CPU resources (threads, cores, sockets) and some other variables which bring further control to the user. Intel OpenMP exposes more of these environment variables to provide the user even more flexibility to adjust the performance of its software. Figure 21. OpenMP vs Intel OpenMP latencies running PyTorch Figure 22. OpenMP vs Intel OpenMP latencies running PyTorch As stated above, tuning OpenMP is something you can start to tweak when you tried all the other, system related, tuning knobs. It can bring a final speed up to you model with just a single environment variable to set. Also, it is important to note that tuning OpenMP library will only work within software that uses the OpenMP API internally. More specially, now only PyTorch and TorchScript really make usage of OpenMP and thus benefit from OpenMP backend tuning. This also explains why we reported latencies only for these two frameworks. | [
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Scaling up BERT-like model Inference on modern CPU - Part 2 | mfuntowicz | November 4, 2021 | bert-cpu-scaling-part-2 | partnerships, intel, guide, nlp | https://huggingface.co/blog/bert-cpu-scaling-part-2 | Introduction: Using Intel Software to Optimize AI Efficiency on CPU As we detailed in our previous blog post, Intel Xeon CPUs provide a set of features especially designed for AI workloads such as AVX512 or VNNI (Vector Neural Network Instructions) for efficient inference using integer quantized neural network for inference along with additional system tools to ensure the work is being done in the most efficient way. In this blog post, we will focus on software optimizations and give you a sense of the performances of the new Ice Lake generation of Xeon CPUs from Intel. Our goal is to give you a full picture of what’s available on the software side to make the most out of your Intel hardware. As in the previous blog post, we show the performance with benchmark results and charts, along with new tools to make all these knobs and features easy to use. Back in April, Intel launched its latest generation of Intel Xeon processors, codename Ice Lake, targeting more efficient and performant AI workloads. More precisely, Ice Lake Xeon CPUs can achieve up to 75% faster inference on a variety of NLP tasks when comparing against the previous generation of Cascade Lake Xeon processors. This is achieved by a combination of both hardware and software improvements, such as new instructions and PCIe 4.0 featured on the new Sunny Cove architecture to supports Machine Learning and Deep Learning workloads. Last but not least, Intel worked on dedicated optimizations for various frameworks which now come with Intel’s flavors like Intel’s Extension for Scikit Learn, Intel TensorFlow and Intel PyTorch Extension. All these features are very low-level in the stack of what Data Scientists and Machine Learning Engineers use in their day-to-day toolset. In a vast majority of situations, it is more common to rely on higher level frameworks and libraries to handle multi-dimensional arrays manipulation such as PyTorch and TensorFlow and make use of highly tuned mathematical operators such as BLAS (Basic Linear Algebra Subroutines) for the computational part. In this area, Intel plays an essential role by providing software components under the oneAPI umbrella which makes it very easy to use highly efficient linear algebra routines through Intel oneMKL (Math Kernel Library), higher-level parallelization framework with Intel OpenMP or the Threading Building Blocks (oneTBB). Also, oneAPI provides some domain-specific libraries such as Intel oneDNN for deep neural network primitives (ReLU, fully-connected, etc.) or oneCCL for collective communication especially useful when using distributed setups to access efficient all-reduce operations over multiple hosts. Some of these libraries, especially MKL or oneDNN, are natively included in frameworks such as PyTorch and TensorFlow (since 2.5.0) to bring all the performance improvements to the end user out of the box. When one would like to target very specific hardware features, Intel provides custom versions of the most common software, especially optimized for the Intel platform. This is for instance the case with TensorFlow, for which Intel provides custom, highly tuned and optimized versions of the framework, or with the Intel PyTorch Extension (IPEX) framework which can be considered as a feature laboratory before upstreaming to PyTorch. | [
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Scaling up BERT-like model Inference on modern CPU - Part 2 | mfuntowicz | November 4, 2021 | bert-cpu-scaling-part-2 | partnerships, intel, guide, nlp | https://huggingface.co/blog/bert-cpu-scaling-part-2 | More Efficient AI Processing on latest Intel Ice Lake CPUs In order to report the performances of the Ice Lake product lineup we will closely follow the methodology we used for the first blog post of this series. As a reminder, we will adopt the exact same schema to benchmark the various setups we will highlight through this second blog post. More precisely, the results presented in the following sections are based on: - PyTorch: 1.9.0 - TensorFlow: 2.5.0 - Batch Sizes: 1, 4, 8, 16, 32, 128 - Sequence Lengths: 8, 16, 32, 64, 128, 384, 512 We will present the results through metrics accepted by the field to establish the performances of the proposed optimizations: - Latency: Time it takes to execute a single inference request (i.e., “forward call”) through the model, expressed in millisecond. - Throughput: Number of inference requests (i.e., “forward calls”) the system can sustain within a defined period, expressed in call/sec. We will also provide an initial baseline showing out-of-the-box results and a second baseline applying all the different optimizations we highlighted in the first blogpost. Everything was run on an Intel provided cloud instance featuring the Ice Lake Xeon Platinum 8380 CPU operating on Ubuntu 20.04.2 LTS. You can find the same processors on the various cloud providers: - AWS m6i / c6i instances - Azure Ev5 / Dv5 series Figure 3. Intel Ice Lake Xeon 8380 Specifications | [
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Scaling up BERT-like model Inference on modern CPU - Part 2 | mfuntowicz | November 4, 2021 | bert-cpu-scaling-part-2 | partnerships, intel, guide, nlp | https://huggingface.co/blog/bert-cpu-scaling-part-2 | Baseline: Eager frameworks latencies Frameworks operating in eager mode usually discover the actual graph while executing it. More precisely, the actual computation graph is not known beforehand and you gradually (eagerly) execute one operator which will become the input of the next one, etc. until you reach leaf nodes (outputs). These frameworks usually provide more flexibility in the algorithm you implement at the cost of increased runtime overhead and slightly potential more memory usage to keep track of all the required elements for the backward pass. Last but not least, it is usually harder through these frameworks to enable graph optimizations such as operator fusion. For instance, many deep learning libraries such as oneDNN have optimized kernels for Convolution + ReLU but you actually need to know before executing the graph that this pattern will occur within the sequence of operation, which is, by design, not something possible within eager frameworks. Figure 4. PyTorch latencies with respect to the number of cores involved Figure 5. Google's TensorFlow latencies with respect to the number of cores involved Figure 6. Google's TensorFlow with oneDNN enabled latencies with respect to the number of cores involved Figure 7. Intel TensorFlow latencies with respect to the number of cores involved The global trend highlights the positive impact of the number of cores on the observed latencies. In most of the cases, increasing the number of cores reduces the computation time across the different workload sizes. Still, putting more cores to the task doesn't result in monotonic latency reductions, there is always a trade-off between the workload’s size and the number of resources you allocate to execute the job. As you can see on the charts above, one very common pattern tends to arise from using all the cores available on systems with more than one CPU (more than one socket). The inter-socket communication introduces a significant latency overhead and results in very little improvement to increased latency overall. Also, this inter-socket communication overhead tends to be less and less perceptive as the workload becomes larger, meaning the usage of all computational resources benefits from using all the available cores. In this domain, it seems PyTorch (Figure 1.) and Intel TensorFlow (Figure 4.) seem to have slightly better parallelism support, as showed on the sequence length 384 and 512 for which using all the cores still reduces the observed latency. | [
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Scaling up BERT-like model Inference on modern CPU - Part 2 | mfuntowicz | November 4, 2021 | bert-cpu-scaling-part-2 | partnerships, intel, guide, nlp | https://huggingface.co/blog/bert-cpu-scaling-part-2 | Conclusion - Accelerating Transformers for Production In this post, we showed how the new Intel Ice Lake Xeon CPUs are suitable for running AI workloads at scale along with the software elements you can swap and tune in order to exploit the full potential of the hardware. All these items are to be considered after setting-up the various lower-level knobs detailed in the previous blog to maximize the usage of all the cores and resources. At Hugging Face, we are on a mission to democratize state-of-the-art Machine Learning, and a critical part of our work is to make these state-of-the-art models as efficient as possible, to use less energy and memory at scale, and to be more affordable to run by companies of all sizes. Our collaboration with Intel through the 🤗 Hardware Partner Program enables us to make advanced efficiency and optimization techniques easily available to the community, through our new 🤗 Optimum open source library dedicated to production performance. For companies looking to accelerate their Transformer models inference, our new 🤗 Infinity product offers a plug-and-play containerized solution, achieving down to 1ms latency on GPU and 2ms on Intel Xeon Ice Lake CPUs. If you found this post interesting or useful to your work, please consider giving Optimum a star. And if this post was music to your ears, consider joining our Machine Learning Optimization team! | [
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Scaling up BERT-like model Inference on modern CPU - Part 2 | mfuntowicz | November 4, 2021 | bert-cpu-scaling-part-2 | partnerships, intel, guide, nlp | https://huggingface.co/blog/bert-cpu-scaling-part-2 | Performance tuning knobs As highlighted above, we are going to cover a new set of tunable items to improve the performance of our AI application. From a high-level point of view, every machine learning and deep learning framework is made of the same ingredients: 1. A structural way of representing data in memory (vector, matrices, etc.) 2. Implementation of mathematical operators 3. Efficient parallelization of the computations on the target hardware In addition to the points listed above, deep learning frameworks provide ways to represent data flow and dependencies to compute gradients. This falls out of the scope of this blog post, and it leverages the same components as the ones listed above! Figure 1. Intel libraries overview under the oneAPI umbrella | [
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Scaling up BERT-like model Inference on modern CPU - Part 2 | mfuntowicz | November 4, 2021 | bert-cpu-scaling-part-2 | partnerships, intel, guide, nlp | https://huggingface.co/blog/bert-cpu-scaling-part-2 | 2. Efficient parallelization of computations Now that we have an efficient way to represent our data, we need a way to take the most out of the computational hardware at our disposal. Interestingly, when it comes to inference, CPUs have a potential advantage over GPUs in the sense they are everywhere, and they do not require specific application components and administration staff to operate them. Modern CPUs come with many cores and complex mechanisms to increase the general performances of software. Yet, as we highlighted on the first blog post, they also have features which can be tweaked depending on the kind of workload (CPU or I/O bound) you target, to further improve performances for your application. Still, implementing parallel algorithms might not be as simple as throwing more cores to do the work. Many factors, such as data structures used, concurrent data access, CPU caches invalidation - all of which might prevent your algorithm from being effectively faster. As a reference talk, we recommend the talk from Scott Meyers: CPU Caches and Why You Care if you are interested in diving more into the subject. Thankfully, there are libraries which make the development process of such parallel algorithms easier and less error-prone. Among the most common parallel libraries we can mention OpenMP and TBB (Threading Building Blocks), which work at various levels, from programming API in C/C++ to environment variable tuning and dynamic scheduling. On Intel hardware, it is advised to use the Intel implementation of the OpenMP specification often referred as "IOMP" available as part of the Intel oneAPI toolkit. Figure 2. Code snippet showing parallel computation done through OpenMP [comment]: <> () | [
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Scaling up BERT-like model Inference on modern CPU - Part 2 | mfuntowicz | November 4, 2021 | bert-cpu-scaling-part-2 | partnerships, intel, guide, nlp | https://huggingface.co/blog/bert-cpu-scaling-part-2 | Establishing the baseline As mentioned previously, the baselines will be composed of two different setups: - Out-of-the-box: We are running the workloads as-is, without any tuning - Optimized: We apply the various knobs present in Blog #1 Also, from the comments we had about the previous blog post, we wanted to change the way we present the framework within the resulting benchmarks. As such, through the rest of this second blog post, we will split framework benchmarking results according to the following: - Frameworks using “eager” mode for computations (PyTorch, TensorFlow) - Frameworks using “graph” mode for computations (TorchScript, TensorFlow Graph, Intel Tensorflow) | [
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Scaling up BERT-like model Inference on modern CPU - Part 2 | mfuntowicz | November 4, 2021 | bert-cpu-scaling-part-2 | partnerships, intel, guide, nlp | https://huggingface.co/blog/bert-cpu-scaling-part-2 | Automatic Performances Tuning: Bayesian Optimization with Intel SigOpt As mentioned above, many knobs can be tweaked to improve latency and throughput on Intel CPUs, but because there are many, tuning all of them to get optimal performance can be cumbersome. For instance, in our experiments, the following knobs were tuned: - The number of cores: although using as many cores as you have is often a good idea, it does not always provide the best performance because it also means more communication between the different threads. On top of that, having better performance with fewer cores can be very useful as it allows to run multiple instances at the same time, resulting in both better latency and throughput. - The memory allocator: which memory allocator out of the default malloc, Google's tcmalloc and Facebook's jemalloc provides the best performance? - The parallelism library: which parallelism library out of GNU OpenMP and Intel OpenMP provides the best performance? - Transparent Huge Pages: does enabling Transparent Huge Pages (THP) on the system provide better performance? - KMP block time parameter: sets the time, in milliseconds, that a thread should wait, after completing the execution of a parallel region, before sleeping. Of course, the brute force approach, consisting of trying out all the possibilities will provide the best knob values to use to get optimal performance but, the size of the search space being N x 3 x 2 x 2 x 2 = 24N, it can take a lot of time: on a machine with 80 physical cores, this means trying out at most 24 x 80 = 1920 different setups! 😱 Fortunately, Intel's SigOpt, through Bayesian optimization, allows us to make these tuning experiments both faster and more convenient to analyse, while providing similar performance than the brute force approach. When we analyse the relative difference between the absolute best latency and what SigOpt provides, we observe that although it is often not as good as brute force (except for sequence length = 512 in that specific case), it gives very close performance, with 8.6% being the biggest gap on this figure. Figure 23. Absolute best latency found by SigOpt automatic tuning vs brute force Figure 24. Relative best latency found by SigOpt automatic tuning vs brute force SigOpt is also very useful for analysis: it provides a lot of figures and valuable information. First, it gives the best value it was able to find, the corresponding knobs, and the history of trials and how it improved as trials went, for example, with sequence length = 20: Figure 25. SigOpt best value reporting Figure 26. SigOpt best value reporting In this specific setup, 16 cores along with the other knobs were able to give the best results, that is very important to know, because as mentioned before, that means that multiple instances of the model can be run in parallel while still having the best latency for each. It also shows that it had converged at roughly 20 trials, meaning that maybe 25 trials instead of 40 would have been enough. A wide range of other valuable information is available, such as Parameter Importance: As expected, the number of cores is, by far, the most important parameter, but the others play a part too, and it is very experiment dependent. For instance, for the sequence length = 512 experiment, this was the Parameter Importance: Figure 27. SigOpt best value for Batch Size = 1, Sequence Length = 20 Figure 28. SigOpt best value for Batch Size = 1, Sequence Length = 512 Here not only the impact of using OpenMP vs Intel OpenMP was bigger than the impact of the allocator, the relative importance of each knob is more balanced than in the sequence length = 20 experiment. And many more figures, often interactive, are available on SigOpt such as: - 2D experiment history, allowing to compare knobs vs knobs or knobs vs objectives - 3D experiment history, allowing to do the same thing as the 2D experiment history with one more knob / objective. | [
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Scaling up BERT-like model Inference on modern CPU - Part 2 | mfuntowicz | November 4, 2021 | bert-cpu-scaling-part-2 | partnerships, intel, guide, nlp | https://huggingface.co/blog/bert-cpu-scaling-part-2 | Memory allocator benchmarks Again, we first compare performance against frameworks executing in an eager fashion. This is potentially the use case where the allocator can play the biggest role: As the graph is unknown before its execution, each framework must manage the memory required for each operation when it meets the actual execution of the above node, no planning ahead possible. In this context, the allocator is a major component due to all the system calls to allocate and reclaim memory. Figure 13. PyTorch memory allocator and cores scaling latencies Figure 14. Google's TensorFlow memory allocator and cores scaling latencies Figure 15. Google's TensorFlow with oneDNN enabled memory allocator and cores scaling latencies Figure 16. Intel TensorFlow memory allocator and cores scaling latencies As per the graph above, you can notice that the standard library allocator (glibc) is often behind performance-wise but provides reasonable performance. Jemalloc allocator is sometimes the fastest around but in very specific situations, where the concurrency is not that high, this can be explained by the underlying structure jemalloc uses internally which is out of the scope of this blog, but you can read the Facebook Engineering blog if you want to know more about it. Finally, tcmalloc seems to be the one providing generally best performances across all the workloads benchmarked here. Again, tcmalloc has a different approach than Jemalloc in the way it allocates resources, especially tcmalloc maintains a pool of memory segments locally for each thread, which reduces the necessity to have global, exclusive, critical paths. Again, for more details, I invite you to read the full blog by Google Abseil team. Now, back to the graph mode where we benchmark framework having an omniscient representation of the overall computation graph. Figure 17. TorchScript memory allocator and cores scaling latencies Figure 18. Google's TensorFlow memory allocator and cores scaling latencies Figure 19. Google's TensorFlow with oneDNN enabled memory allocator and cores scaling latencies Figure 20. Intel TensorFlow memory allocator and cores scaling latencies This time, by knowing the underlying structure of the operator flows and matrix shapes involved then the framework can plan and reserve the required resources beforehand. In this context, and as it is shown in the chart above, the difference between framework is very small and there is no clear winner between jemalloc and tcmalloc. Of course, glibc is still slightly behind as a general-purpose memory allocator, but the margin is less significant than in the eager setup. To sum it up, tuning the memory allocator can provide an interesting item to grab the last milliseconds' improvement at the end of the optimization process, especially if you are already using traced computation graphs. | [
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Scaling up BERT-like model Inference on modern CPU - Part 2 | mfuntowicz | November 4, 2021 | bert-cpu-scaling-part-2 | partnerships, intel, guide, nlp | https://huggingface.co/blog/bert-cpu-scaling-part-2 | Deep Dive: Leveraging advanced Intel features to improve AI performances | [
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Scaling up BERT-like model Inference on modern CPU - Part 2 | mfuntowicz | November 4, 2021 | bert-cpu-scaling-part-2 | partnerships, intel, guide, nlp | https://huggingface.co/blog/bert-cpu-scaling-part-2 | Tuning the Memory Allocator: Can this impact the latencies observed? One crucial component every program dynamically allocating memory relies on is the memory allocator. If you are familiar with C/C++ programming this component provides the low bits to malloc/free or new/delete. Most of the time you don’t have to worry too much about it and the default ones (glibc for instance on most Linux distributions) will provide great performances out of the box. Still, in some situations it might not provide the most efficient performances, as these default allocators are most of the time designed to be “good” most of the time, and not fine-tuned for specific workloads or parallelism. So, what are the alternatives, and when are they more suitable than the default ones? Well, again, it depends on the kind of context around your software. Possible situations are a heavy number of allocations/de-allocations causing fragmentation over time, specific hardware and/or architecture you’re executing your software on and finally the level of parallelism of your application. Do you see where this is going? Deep learning and by extension all the applications doing heavy computations are heavily multi-threaded, that’s also the case for software libraries such as PyTorch, TensorFlow and any other frameworks targeting Machine Learning workloads. The default memory allocator strategies often rely on global memory pools which require the usage of synchronization primitives to operate, increasing the overall pressure on the system, reducing the performance of your application. Some recent works by companies such as Google, Facebook and Microsoft provided alternative memory allocation strategies implemented in custom memory allocator libraries one can easily integrate directly within its software components or use dynamic shared library preload to swap the library being used to achieve the allocation/de-allocation. Among these libraries, we can cite a few of them such as tcmalloc, jemalloc and mimalloc. Figure 12. Various memory allocators benchmarked on different tasks Through this blog post we will only focus on benchmarking tcmalloc and jemalloc as potential memory allocators drop-in candidates. To be fully transparent, for the scope of the results below we used tcmalloc as part of the gperftools package available on Ubuntu distributions version 2.9 and jemalloc 5.1.0-1. | [
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Scaling up BERT-like model Inference on modern CPU - Part 2 | mfuntowicz | November 4, 2021 | bert-cpu-scaling-part-2 | partnerships, intel, guide, nlp | https://huggingface.co/blog/bert-cpu-scaling-part-2 | 1. Memory allocation and management libraries This blog post will deliberately skip the first point about the data representation as it is something rather framework specific. For reference, PyTorch uses its very own implementation, called ATen, while TensorFlow relies on the open source library Eigen for this purpose. While it’s very complex to apply generic optimizations to different object structures and layouts, there is one area where we can have an impact: Memory Allocation. As a short reminder, memory allocation here refers to the process of programmatically asking the operating system a dynamic (unknown beforehand) area on the system where we will be able to store items into, such as the malloc and derived in C or the new operator in C++. Memory efficiency, both in terms of speed but also in terms of fragmentation, is a vast scientific and engineering subject with multiple solutions depending on the task and underlying hardware. Over the past years we saw more and more work in this area, with notably: - jemalloc (Facebook - 2005) - mimalloc (Microsoft - 2019) - tcmalloc (Google - 2020) Each pushes forward different approaches to improve aspects of the memory allocation and management on various software. | [
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Scaling up BERT-like model Inference on modern CPU - Part 2 | mfuntowicz | November 4, 2021 | bert-cpu-scaling-part-2 | partnerships, intel, guide, nlp | https://huggingface.co/blog/bert-cpu-scaling-part-2 | Baseline: Graph frameworks latencies This time we compare performance when using frameworks in “Graph” mode, where the graph is fully known beforehand, and all the allocations and optimizations such as graph pruning and operators fusing can be made. Figure 8. TorchScript latencies with respect to the number of cores involved Figure 9. Google's TensorFlow latencies with respect to the number of cores involved Figure 10. Google's TensorFlow with oneDNN enabled latencies with respect to the number of cores involved Figure 11. Intel TensorFlow latencies with respect to the number of cores involved This is often referred to as “tracing” the graph and, as you can see here, the results are not that different from TorchScript (Graph execution mode from PyTorch) vs TensorFlow(s). All TensorFlow implementations seem to perform better than TorchScript when the parallelization is limited (low number of cores involved in the intra operation computations) but this seems not to scale efficiently as we increase the computation resources, whereas TorchScript seems to be able to better leverage the power of modern CPUs. Still, the margin between all these frameworks in most cases very limited. | [
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Scaling up BERT-like model Inference on modern CPU - Part 2 | mfuntowicz | November 4, 2021 | bert-cpu-scaling-part-2 | partnerships, intel, guide, nlp | https://huggingface.co/blog/bert-cpu-scaling-part-2 | 3. Optimized mathematical operators Now that we covered the necessary building blocks for designing efficient data structures and parallel algorithms, the last remaining piece is the one running the computation, the one implementing the variety of mathematical operators and neural network layers to do what we love most, designing neural networks! 😊 In every programmer toolkit, there are multiple levels which can bring mathematical operations support, which can then be optimized differently depending on various factors such as the data storage layout being used (Contiguous memory, Chunked, Packed, etc.), the data format representing each scalar element (Float32, Integer, Long, Bfloat16, etc.) and of course the various instructions being supported by your processor. Nowadays, almost all processors support basic mathematical operations on scalar items (one single item at time) or in vectorized mode (meaning they operate on multiple items within the same CPU instructions, referred as SIMD “Single Instruction Multiple Data”). Famous sets of SIMD instructions are SSE2, AVX, AVX2 and the AVX-512 present on the latest generations of Intel CPUs being able to operate over 16 bytes of content within a single CPU clock. Most of the time, one doesn't have to worry too much about the actual assembly being generated to execute a simple element-wise addition between two vectors, but if you do, again there are some libraries which allow you to go one level higher than writing code calling CPU specific intrinsic to implement efficient mathematical kernels. This is for instance what Intel’s MKL “Math Kernel Library” provides, along with the famous BLAS “Basic Linear Algebra Subroutines” interface to implement all the basic operations for linear algebra. Finally, on top of this, one can find some domain specific libraries such as Intel's oneDNN which brings all the most common and essential building blocks required to implement neural network layers. Intel MKL and oneDNN are natively integrated within the PyTorch framework, where it can enable some performance speedup for certain operations such as Linear + ReLU or Convolution. On the TensorFlow side, oneDNN can be enabled by setting the environment variable TF_ENABLE_ONEDNN_OPTS=1 (TensorFlow >= 2.5.0) to achieve similar machinery under the hood. | [
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Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers | patrickvonplaten | November 15, 2021 | fine-tune-xlsr-wav2vec2 | guide, audio | https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 | Training Training will take multiple hours depending on the GPU allocated to this notebook. While the trained model yields somewhat satisfying results on Common Voice\'s test data of Turkish, it is by no means an optimally fine-tuned model. The purpose of this notebook is just to demonstrate how to fine-tune XLS-R XLSR-Wav2Vec2\'s on an ASR dataset. Depending on what GPU was allocated to your google colab it might be possible that you are seeing an "out-of-memory" error here. In this case, it\'s probably best to reduce per_device_train_batch_size to 8 or even less and increase gradient_accumulation. python trainer.train() Print Output: | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.8842 | 3.67 | 400 | 0.6794 | 0.7000 | | 0.4115 | 7.34 | 800 | 0.4304 | 0.4548 | | 0.1946 | 11.01 | 1200 | 0.4466 | 0.4216 | | 0.1308 | 14.68 | 1600 | 0.4526 | 0.3961 | | 0.0997 | 18.35 | 2000 | 0.4567 | 0.3696 | | 0.0784 | 22.02 | 2400 | 0.4193 | 0.3442 | | 0.0633 | 25.69 | 2800 | 0.4153 | 0.3347 | | 0.0498 | 29.36 | 3200 | 0.4077 | 0.3195 | The training loss and validation WER go down nicely. You can now upload the result of the training to the Hub, just execute this instruction: python trainer.push_to_hub() You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier \"your-username/the-name-you-picked\" so for instance: python from transformers import AutoModelForCTC, Wav2Vec2Processor model = AutoModelForCTC.from_pretrained("patrickvonplaten/wav2vec2-large-xls-r-300m-tr-colab") processor = Wav2Vec2Processor.from_pretrained("patrickvonplaten/wav2vec2-large-xls-r-300m-tr-colab") For more examples of how XLS-R can be fine-tuned, please take a look at the official 🤗 Transformers examples. | [
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Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers | patrickvonplaten | November 15, 2021 | fine-tune-xlsr-wav2vec2 | guide, audio | https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 | Evaluation As a final check, let\'s load the model and verify that it indeed has learned to transcribe Turkish speech. Let\'s first load the pretrained checkpoint. python model = Wav2Vec2ForCTC.from_pretrained(repo_name).to("cuda") processor = Wav2Vec2Processor.from_pretrained(repo_name) Now, we will just take the first example of the test set, run it through the model and take the argmax(...) of the logits to retrieve the predicted token ids. python input_dict = processor(common_voice_test[0]["input_values"], return_tensors="pt", padding=True) logits = model(input_dict.input_values.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1)[0] It is strongly recommended to pass the sampling_rate argument to this function.Failing to do so can result in silent errors that might be hard to debug. We adapted common_voice_test quite a bit so that the dataset instance does not contain the original sentence label anymore. Thus, we re-use the original dataset to get the label of the first example. python common_voice_test_transcription = load_dataset("common_voice", "tr", data_dir="./cv-corpus-6.1-2020-12-11", split="test") Finally, we can decode the example. python print("Prediction:") print(processor.decode(pred_ids)) print("\nReference:") print(common_voice_test_transcription[0]["sentence"].lower()) Print Output: | pred_str | target_text | |----------|:-------------:| | hatta küçük şeyleri için bir büyt bir şeyleri kolluyor veyınıki çuk şeyler için bir bir mizi inciltiyoruz | hayatta küçük şeyleri kovalıyor ve yine küçük şeyler için birbirimizi incitiyoruz. | Alright! The transcription can definitely be recognized from our prediction, but it is not perfect yet. Training the model a bit longer, spending more time on the data preprocessing, and especially using a language model for decoding would certainly improve the model\'s overall performance. For a demonstration model on a low-resource language, the results are quite acceptable however 🤗. | [
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Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers | patrickvonplaten | November 15, 2021 | fine-tune-xlsr-wav2vec2 | guide, audio | https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 | Set-up Trainer Let\'s start by defining the data collator. The code for the data collator was copied from this example. Without going into too many details, in contrast to the common data collators, this data collator treats the input_values and labels differently and thus applies to separate padding functions on them (again making use of XLS-R processor\'s context manager). This is necessary because in speech input and output are of different modalities meaning that they should not be treated by the same padding function. Analogous to the common data collators, the padding tokens in the labels with -100 so that those tokens are not taken into account when computing the loss. python import torch from dataclasses import dataclass, field from typing import Any, Dict, List, Optional, Union @dataclass class DataCollatorCTCWithPadding: """ Data collator that will dynamically pad the inputs received. Args: processor (:class:`~transformers.Wav2Vec2Processor`) The processor used for proccessing the data. padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the maximum acceptable input length for the model if that argument is not provided. * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). """ processor: Wav2Vec2Processor padding: Union[bool, str] = True def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: # split inputs and labels since they have to be of different lengths and need # different padding methods input_features = [{"input_values": feature["input_values"]} for feature in features] label_features = [{"input_ids": feature["labels"]} for feature in features] batch = self.processor.pad( input_features, padding=self.padding, return_tensors="pt", ) with self.processor.as_target_processor(): labels_batch = self.processor.pad( label_features, padding=self.padding, return_tensors="pt", ) # replace padding with -100 to ignore loss correctly labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100) batch["labels"] = labels return batch python data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) Next, the evaluation metric is defined. As mentioned earlier, the predominant metric in ASR is the word error rate (WER), hence we will use it in this notebook as well. python wer_metric = load_metric("wer") The model will return a sequence of logit vectors: \( \mathbf{y}1, \ldots, \mathbf{y}_m \) with \( \mathbf{y}_1 = f{\theta}(x_1, \ldots, x_n)[0] \) and \( n >> m \). A logit vector \( \mathbf{y}_1 \) contains the log-odds for each word in the vocabulary we defined earlier, thus \( \text{len}(\mathbf{y}_i) = \) config.vocab_size. We are interested in the most likely prediction of the model and thus take the argmax(...) of the logits. Also, we transform the encoded labels back to the original string by replacing -100 with the pad_token_id and decoding the ids while making sure that consecutive tokens are not grouped to the same token in CTC style \( {}^1 \). python def compute_metrics(pred): pred_logits = pred.predictions pred_ids = np.argmax(pred_logits, axis=-1) pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id pred_str = processor.batch_decode(pred_ids) # we do not want to group tokens when computing the metrics label_str = processor.batch_decode(pred.label_ids, group_tokens=False) wer = wer_metric.compute(predictions=pred_str, references=label_str) return {"wer": wer} Now, we can load the pretrained checkpoint of Wav2Vec2-XLS-R-300M. The tokenizer\'s pad_token_id must be to define the model\'s pad_token_id or in the case of Wav2Vec2ForCTC also CTC\'s blank token \( {}^2 \). To save GPU memory, we enable PyTorch\'s gradient checkpointing and also set the loss reduction to \"mean\". Because the dataset is quite small (\~6h of training data) and because Common Voice is quite noisy, fine-tuning Facebook\'s wav2vec2-xls-r-300m checkpoint seems to require some hyper-parameter tuning. Therefore, I had to play around a bit with different values for dropout, SpecAugment\'s masking dropout rate, layer dropout, and the learning rate until training seemed to be stable enough. Note: When using this notebook to train XLS-R on another language of Common Voice those hyper-parameter settings might not work very well. Feel free to adapt those depending on your use case. python from transformers import Wav2Vec2ForCTC model = Wav2Vec2ForCTC.from_pretrained( "facebook/wav2vec2-xls-r-300m", attention_dropout=0.0, hidden_dropout=0.0, feat_proj_dropout=0.0, mask_time_prob=0.05, layerdrop=0.0, ctc_loss_reduction="mean", pad_token_id=processor.tokenizer.pad_token_id, vocab_size=len(processor.tokenizer), ) The first component of XLS-R consists of a stack of CNN layers that are used to extract acoustically meaningful - but contextually independent - features from the raw speech signal. This part of the model has already been sufficiently trained during pretraining and as stated in the paper does not need to be fine-tuned anymore. Thus, we can set the requires_grad to False for all parameters of the feature extraction part. python model.freeze_feature_extractor() In a final step, we define all parameters related to training. To give more explanation on some of the parameters: - group_by_length makes training more efficient by grouping training samples of similar input length into one batch. This can significantly speed up training time by heavily reducing the overall number of useless padding tokens that are passed through the model - learning_rate and weight_decay were heuristically tuned until fine-tuning has become stable. Note that those parameters strongly depend on the Common Voice dataset and might be suboptimal for other speech datasets. For more explanations on other parameters, one can take a look at the docs. During training, a checkpoint will be uploaded asynchronously to the Hub every 400 training steps. It allows you to also play around with the demo widget even while your model is still training. Note: If one does not want to upload the model checkpoints to the Hub, simply set push_to_hub=False. python from transformers import TrainingArguments training_args = TrainingArguments( output_dir=repo_name, group_by_length=True, per_device_train_batch_size=16, gradient_accumulation_steps=2, evaluation_strategy="steps", num_train_epochs=30, gradient_checkpointing=True, fp16=True, save_steps=400, eval_steps=400, logging_steps=400, learning_rate=3e-4, warmup_steps=500, save_total_limit=2, push_to_hub=True, ) Now, all instances can be passed to Trainer and we are ready to start training! python from transformers import Trainer trainer = Trainer( model=model, data_collator=data_collator, args=training_args, compute_metrics=compute_metrics, train_dataset=common_voice_train, eval_dataset=common_voice_test, tokenizer=processor.feature_extractor, ) ------------------------------------------------------------------------ \( {}^1 \) To allow models to become independent of the speaker rate, in CTC, consecutive tokens that are identical are simply grouped as a single token. However, the encoded labels should not be grouped when decoding since they don\'t correspond to the predicted tokens of the model, which is why the group_tokens=False parameter has to be passed. If we wouldn\'t pass this parameter a word like "hello" would incorrectly be encoded, and decoded as "helo". \( {}^2 \) The blank token allows the model to predict a word, such as "hello" by forcing it to insert the blank token between the two l\'s. A CTC-conform prediction of "hello" of our model would be [PAD] [PAD] "h" "e" "e" "l" "l" [PAD] "l" "o" "o" [PAD]. | [
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Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers | patrickvonplaten | November 15, 2021 | fine-tune-xlsr-wav2vec2 | guide, audio | https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 | Create Wav2Vec2CTCTokenizer A pre-trained XLS-R model maps the speech signal to a sequence of context representations as illustrated in the figure above. However, for speech recognition the model has to to map this sequence of context representations to its corresponding transcription which means that a linear layer has to be added on top of the transformer block (shown in yellow in the diagram above). This linear layer is used to classify each context representation to a token class analogous to how a linear layer is added on top of BERT\'s embeddings for further classification after pre-training (cf. with \'BERT\' section of the following blog post). after pretraining a linear layer is added on top of BERT\'s embeddings for further classification - cf. with \'BERT\' section of this blog post. The output size of this layer corresponds to the number of tokens in the vocabulary, which does not depend on XLS-R\'s pretraining task, but only on the labeled dataset used for fine-tuning. So in the first step, we will take a look at the chosen dataset of Common Voice and define a vocabulary based on the transcriptions. First, let\'s go to Common Voice official website and pick a language to fine-tune XLS-R on. For this notebook, we will use Turkish. For each language-specific dataset, you can find a language code corresponding to your chosen language. On Common Voice, look for the field \"Version\". The language code then corresponds to the prefix before the underscore. For Turkish, e.g. the language code is "tr". Great, now we can use 🤗 Datasets\' simple API to download the data. The dataset name is "common_voice", the configuration name corresponds to the language code, which is "tr" in our case. Common Voice has many different splits including invalidated, which refers to data that was not rated as \"clean enough\" to be considered useful. In this notebook, we will only make use of the splits "train", "validation" and "test". Because the Turkish dataset is so small, we will merge both the validation and training data into a training dataset and only use the test data for validation. python from datasets import load_dataset, load_metric, Audio common_voice_train = load_dataset("common_voice", "tr", split="train+validation") common_voice_test = load_dataset("common_voice", "tr", split="test") Many ASR datasets only provide the target text, 'sentence' for each audio array 'audio' and file 'path'. Common Voice actually provides much more information about each audio file, such as the 'accent', etc. Keeping the notebook as general as possible, we only consider the transcribed text for fine-tuning. python common_voice_train = common_voice_train.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "segment", "up_votes"]) common_voice_test = common_voice_test.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "segment", "up_votes"]) Let\'s write a short function to display some random samples of the dataset and run it a couple of times to get a feeling for the transcriptions. python from datasets import ClassLabel import random import pandas as pd from IPython.display import display, HTML def show_random_elements(dataset, num_examples=10): assert num_examples <= len(dataset), "Can't pick more elements than there are in the dataset." picks = [] for _ in range(num_examples): pick = random.randint(0, len(dataset)-1) while pick in picks: pick = random.randint(0, len(dataset)-1) picks.append(pick) df = pd.DataFrame(dataset[picks]) display(HTML(df.to_html())) Print Output: | Idx | Sentence | |----------|:-------------:| | 1 | Jonuz, kısa süreli görevi kabul eden tek adaydı. | | 2 | Biz umudumuzu bu mücadeleden almaktayız. | | 3 | Sergide beş Hırvat yeniliği sergilendi. | | 4 | Herşey adıyla bilinmeli. | | 5 | Kuruluş özelleştirmeye hazır. | | 6 | Yerleşim yerlerinin manzarası harika. | | 7 | Olayların failleri bulunamadı. | | 8 | Fakat bu çabalar boşa çıktı. | | 9 | Projenin değeri iki virgül yetmiş yedi milyon avro. | | 10 | Büyük yeniden yapım projesi dört aşamaya bölündü. | Alright! The transcriptions look fairly clean. Having translated the transcribed sentences, it seems that the language corresponds more to written-out text than noisy dialogue. This makes sense considering that Common Voice is a crowd-sourced read speech corpus. We can see that the transcriptions contain some special characters, such as ,.?!;:. Without a language model, it is much harder to classify speech chunks to such special characters because they don\'t really correspond to a characteristic sound unit. E.g., the letter "s" has a more or less clear sound, whereas the special character "." does not. Also in order to understand the meaning of a speech signal, it is usually not necessary to include special characters in the transcription. Let\'s simply remove all characters that don\'t contribute to the meaning of a word and cannot really be represented by an acoustic sound and normalize the text. python import re chars_to_remove_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\']' def remove_special_characters(batch): batch["sentence"] = re.sub(chars_to_remove_regex, '', batch["sentence"]).lower() return batch python common_voice_train = common_voice_train.map(remove_special_characters) common_voice_test = common_voice_test.map(remove_special_characters) Let\'s look at the processed text labels again. python show_random_elements(common_voice_train.remove_columns(["path","audio"])) Print Output: | Idx | Transcription | |----------|:-------------:| | 1 | birisi beyazlar için dediler | | 2 | maktouf'un cezası haziran ayında sona erdi | | 3 | orijinalin aksine kıyafetler çıkarılmadı | | 4 | bunların toplam değeri yüz milyon avroyu buluyor | | 5 | masada en az iki seçenek bulunuyor | | 6 | bu hiç de haksız bir heveslilik değil | | 7 | bu durum bin dokuz yüz doksanlarda ülkenin bölünmesiyle değişti | | 8 | söz konusu süre altı ay | | 9 | ancak bedel çok daha yüksek olabilir | | 10 | başkent fira bir tepenin üzerinde yer alıyor | Good! This looks better. We have removed most special characters from transcriptions and normalized them to lower-case only. Before finalizing the pre-processing, it is always advantageous to consult a native speaker of the target language to see whether the text can be further simplified. For this blog post, Merve was kind enough to take a quick look and noted that \"hatted\" characters - like â - aren\'t really used anymore in Turkish and can be replaced by their \"un-hatted\" equivalent, e.g. a. This means that we should replace a sentence like "yargı sistemi hâlâ sağlıksız" to "yargı sistemi hala sağlıksız". Let\'s write another short mapping function to further simplify the text labels. Remember, the simpler the text labels, the easier it is for the model to learn to predict those labels. python def replace_hatted_characters(batch): batch["sentence"] = re.sub('[â]', 'a', batch["sentence"]) batch["sentence"] = re.sub('[î]', 'i', batch["sentence"]) batch["sentence"] = re.sub('[ô]', 'o', batch["sentence"]) batch["sentence"] = re.sub('[û]', 'u', batch["sentence"]) return batch python common_voice_train = common_voice_train.map(replace_hatted_characters) common_voice_test = common_voice_test.map(replace_hatted_characters) In CTC, it is common to classify speech chunks into letters, so we will do the same here. Let\'s extract all distinct letters of the training and test data and build our vocabulary from this set of letters. We write a mapping function that concatenates all transcriptions into one long transcription and then transforms the string into a set of chars. It is important to pass the argument batched=True to the map(...) function so that the mapping function has access to all transcriptions at once. python def extract_all_chars(batch): all_text = " ".join(batch["sentence"]) vocab = list(set(all_text)) return {"vocab": [vocab], "all_text": [all_text]} python vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names) vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names) Now, we create the union of all distinct letters in the training dataset and test dataset and convert the resulting list into an enumerated dictionary. python vocab_list = list(set(vocab_train["vocab"][0]) | set(vocab_test["vocab"][0])) python vocab_dict = {v: k for k, v in enumerate(sorted(vocab_list))} vocab_dict Print Output: bash { ' ': 0, 'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26, 'ç': 27, 'ë': 28, 'ö': 29, 'ü': 30, 'ğ': 31, 'ı': 32, 'ş': 33, '̇': 34 } Cool, we see that all letters of the alphabet occur in the dataset (which is not really surprising) and we also extracted the special characters "" and '. Note that we did not exclude those special characters because: The model has to learn to predict when a word is finished or else the model prediction would always be a sequence of chars which would make it impossible to separate words from each other. One should always keep in mind that pre-processing is a very important step before training your model. E.g., we don\'t want our model to differentiate between a and A just because we forgot to normalize the data. The difference between a and A does not depend on the \"sound\" of the letter at all, but more on grammatical rules - e.g. use a capitalized letter at the beginning of the sentence. So it is sensible to remove the difference between capitalized and non-capitalized letters so that the model has an easier time learning to transcribe speech. To make it clearer that " " has its own token class, we give it a more visible character |. In addition, we also add an \"unknown\" token so that the model can later deal with characters not encountered in Common Voice\'s training set. python vocab_dict["|"] = vocab_dict[" "] del vocab_dict[" "] Finally, we also add a padding token that corresponds to CTC\'s \"blank token\". The \"blank token\" is a core component of the CTC algorithm. For more information, please take a look at the \"Alignment\" section here. python vocab_dict["[UNK]"] = len(vocab_dict) vocab_dict["[PAD]"] = len(vocab_dict) len(vocab_dict) Cool, now our vocabulary is complete and consists of 39 tokens, which means that the linear layer that we will add on top of the pretrained XLS-R checkpoint will have an output dimension of 39. Let\'s now save the vocabulary as a json file. python import json with open('vocab.json', 'w') as vocab_file: json.dump(vocab_dict, vocab_file) In a final step, we use the json file to load the vocabulary into an instance of the Wav2Vec2CTCTokenizer class. python from transformers import Wav2Vec2CTCTokenizer tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("./", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|") If one wants to re-use the just created tokenizer with the fine-tuned model of this notebook, it is strongly advised to upload the tokenizer to the Hugging Face Hub. Let\'s call the repo to which we will upload the files "wav2vec2-large-xlsr-turkish-demo-colab": python repo_name = "wav2vec2-large-xls-r-300m-tr-colab" and upload the tokenizer to the 🤗 Hub. python tokenizer.push_to_hub(repo_name) Great, you can see the just created repository under https://huggingface.co/<your-username>/wav2vec2-large-xls-r-300m-tr-colab | [
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Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers | patrickvonplaten | November 15, 2021 | fine-tune-xlsr-wav2vec2 | guide, audio | https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 | Preprocess Data So far, we have not looked at the actual values of the speech signal but just the transcription. In addition to sentence, our datasets include two more column names path and audio. path states the absolute path of the audio file. Let\'s take a look. python common_voice_train[0]["path"] XLS-R expects the input in the format of a 1-dimensional array of 16 kHz. This means that the audio file has to be loaded and resampled. Thankfully, datasets does this automatically by calling the other column audio. Let try it out. python common_voice_train[0]["audio"] bash {'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., -8.8930130e-05, -3.8027763e-05, -2.9146671e-05], dtype=float32), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/05be0c29807a73c9b099873d2f5975dae6d05e9f7d577458a2466ecb9a2b0c6b/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_21921195.mp3', 'sampling_rate': 48000} Great, we can see that the audio file has automatically been loaded. This is thanks to the new "Audio" feature introduced in datasets == 1.18.3, which loads and resamples audio files on-the-fly upon calling. In the example above we can see that the audio data is loaded with a sampling rate of 48kHz whereas 16kHz are expected by the model. We can set the audio feature to the correct sampling rate by making use of cast_column: python common_voice_train = common_voice_train.cast_column("audio", Audio(sampling_rate=16_000)) common_voice_test = common_voice_test.cast_column("audio", Audio(sampling_rate=16_000)) Let\'s take a look at "audio" again. python common_voice_train[0]["audio"] bash {'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., -7.4556941e-05, -1.4621433e-05, -5.7861507e-05], dtype=float32), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/05be0c29807a73c9b099873d2f5975dae6d05e9f7d577458a2466ecb9a2b0c6b/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_21921195.mp3', 'sampling_rate': 16000} This seemed to have worked! Let\'s listen to a couple of audio files to better understand the dataset and verify that the audio was correctly loaded. python import IPython.display as ipd import numpy as np import random rand_int = random.randint(0, len(common_voice_train)-1) print(common_voice_train[rand_int]["sentence"]) ipd.Audio(data=common_voice_train[rand_int]["audio"]["array"], autoplay=True, rate=16000) Print Output: bash sunulan bütün teklifler i̇ngilizce idi It seems like the data is now correctly loaded and resampled. It can be heard, that the speakers change along with their speaking rate, accent, and background environment, etc. Overall, the recordings sound acceptably clear though, which is to be expected from a crowd-sourced read speech corpus. Let\'s do a final check that the data is correctly prepared, by printing the shape of the speech input, its transcription, and the corresponding sampling rate. python rand_int = random.randint(0, len(common_voice_train)-1) print("Target text:", common_voice_train[rand_int]["sentence"]) print("Input array shape:", common_voice_train[rand_int]["audio"]["array"].shape) print("Sampling rate:", common_voice_train[rand_int]["audio"]["sampling_rate"]) Print Output: bash Target text: makedonya bu yıl otuz adet tyetmiş iki tankı aldı Input array shape: (71040,) Sampling rate: 16000 Good! Everything looks fine - the data is a 1-dimensional array, the sampling rate always corresponds to 16kHz, and the target text is normalized. Finally, we can leverage Wav2Vec2Processor to process the data to the format expected by Wav2Vec2ForCTC for training. To do so let\'s make use of Dataset\'s map(...) function. First, we load and resample the audio data, simply by calling batch["audio"]. Second, we extract the input_values from the loaded audio file. In our case, the Wav2Vec2Processor only normalizes the data. For other speech models, however, this step can include more complex feature extraction, such as Log-Mel feature extraction. Third, we encode the transcriptions to label ids. Note: This mapping function is a good example of how the Wav2Vec2Processor class should be used. In \"normal\" context, calling processor(...) is redirected to Wav2Vec2FeatureExtractor\'s call method. When wrapping the processor into the as_target_processor context, however, the same method is redirected to Wav2Vec2CTCTokenizer\'s call method. For more information please check the docs. python def prepare_dataset(batch): audio = batch["audio"] # batched output is "un-batched" batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0] batch["input_length"] = len(batch["input_values"]) with processor.as_target_processor(): batch["labels"] = processor(batch["sentence"]).input_ids return batch Let\'s apply the data preparation function to all examples. python common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names) common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names) Note: Currently datasets make use of torchaudio and librosa for audio loading and resampling. If you wish to implement your own costumized data loading/sampling, feel free to just make use of the "path" column instead and disregard the "audio" column. Long input sequences require a lot of memory. XLS-R is based on self-attention. The memory requirement scales quadratically with the input length for long input sequences (cf. with this reddit post). In case this demo crashes with an \"Out-of-memory\" error for you, you might want to uncomment the following lines to filter all sequences that are longer than 5 seconds for training. python #max_input_length_in_sec = 5.0 #common_voice_train = common_voice_train.filter(lambda x: x < max_input_length_in_sec * processor.feature_extractor.sampling_rate, input_columns=["input_length"]) Awesome, now we are ready to start training! Training -------- The data is processed so that we are ready to start setting up the training pipeline. We will make use of 🤗\'s Trainer for which we essentially need to do the following: - Define a data collator. In contrast to most NLP models, XLS-R has a much larger input length than output length. E.g., a sample of input length 50000 has an output length of no more than 100. Given the large input sizes, it is much more efficient to pad the training batches dynamically meaning that all training samples should only be padded to the longest sample in their batch and not the overall longest sample. Therefore, fine-tuning XLS-R requires a special padding data collator, which we will define below - Evaluation metric. During training, the model should be evaluated on the word error rate. We should define a compute_metrics function accordingly - Load a pretrained checkpoint. We need to load a pretrained checkpoint and configure it correctly for training. - Define the training configuration. After having fine-tuned the model, we will correctly evaluate it on the test data and verify that it has indeed learned to correctly transcribe speech. | [
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Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers | patrickvonplaten | November 15, 2021 | fine-tune-xlsr-wav2vec2 | guide, audio | https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 | Create Wav2Vec2FeatureExtractor Speech is a continuous signal, and, to be treated by computers, it first has to be discretized, which is usually called sampling. The sampling rate hereby plays an important role since it defines how many data points of the speech signal are measured per second. Therefore, sampling with a higher sampling rate results in a better approximation of the real speech signal but also necessitates more values per second. A pretrained checkpoint expects its input data to have been sampled more or less from the same distribution as the data it was trained on. The same speech signals sampled at two different rates have a very different distribution. For example, doubling the sampling rate results in data points being twice as long. Thus, before fine-tuning a pretrained checkpoint of an ASR model, it is crucial to verify that the sampling rate of the data that was used to pretrain the model matches the sampling rate of the dataset used to fine-tune the model. XLS-R was pretrained on audio data of Babel, Multilingual LibriSpeech (MLS), Common Voice, VoxPopuli, and VoxLingua107 at a sampling rate of 16kHz. Common Voice, in its original form, has a sampling rate of 48kHz, thus we will have to downsample the fine-tuning data to 16kHz in the following. A Wav2Vec2FeatureExtractor object requires the following parameters to be instantiated: - feature_size: Speech models take a sequence of feature vectors as an input. While the length of this sequence obviously varies, the feature size should not. In the case of Wav2Vec2, the feature size is 1 because the model was trained on the raw speech signal \( {}^2 \). - sampling_rate: The sampling rate at which the model is trained on. - padding_value: For batched inference, shorter inputs need to be padded with a specific value - do_normalize: Whether the input should be zero-mean-unit-variance normalized or not. Usually, speech models perform better when normalizing the input - return_attention_mask: Whether the model should make use of an attention_mask for batched inference. In general, XLS-R models checkpoints should always use the attention_mask. python from transformers import Wav2Vec2FeatureExtractor feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True) Great, XLS-R\'s feature extraction pipeline is thereby fully defined! For improved user-friendliness, the feature extractor and tokenizer are wrapped into a single Wav2Vec2Processor class so that one only needs a model and processor object. python from transformers import Wav2Vec2Processor processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) Next, we can prepare the dataset. | [
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Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers | patrickvonplaten | November 15, 2021 | fine-tune-xlsr-wav2vec2 | guide, audio | https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 | # Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers New (11/2021): This blog post has been updated to feature XLSR\'s successor, called XLS-R. Wav2Vec2 is a pretrained model for Automatic Speech Recognition (ASR) and was released in September 2020 by Alexei Baevski, Michael Auli, and Alex Conneau. Soon after the superior performance of Wav2Vec2 was demonstrated on one of the most popular English datasets for ASR, called LibriSpeech, Facebook AI presented a multi-lingual version of Wav2Vec2, called XLSR. XLSR stands for cross-lingual speech representations and refers to model\'s ability to learn speech representations that are useful across multiple languages. XLSR\'s successor, simply called XLS-R (refering to the \'\'XLM-R for Speech\'\'), was released in November 2021 by Arun Babu, Changhan Wang, Andros Tjandra, et al. XLS-R used almost half a million hours of audio data in 128 languages for self-supervised pre-training and comes in sizes ranging from 300 milion up to two billion parameters. You can find the pretrained checkpoints on the 🤗 Hub: - Wav2Vec2-XLS-R-300M - Wav2Vec2-XLS-R-1B - Wav2Vec2-XLS-R-2B Similar to BERT\'s masked language modeling objective, XLS-R learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network during self-supervised pre-training (i.e. diagram on the left below). For fine-tuning, a single linear layer is added on top of the pre-trained network to train the model on labeled data of audio downstream tasks such as speech recognition, speech translation and audio classification (i.e. diagram on the right below). XLS-R shows impressive improvements over previous state-of-the-art results on both speech recognition, speech translation and speaker/language identification, cf. with Table 3-6, Table 7-10, and Table 11-12 respectively of the official paper. Setup -------------- In this blog, we will give an in-detail explanation of how XLS-R - more specifically the pre-trained checkpoint Wav2Vec2-XLS-R-300M - can be fine-tuned for ASR. For demonstration purposes, we fine-tune the model on the low resource ASR dataset of Common Voice that contains only ca. 4h of validated training data. XLS-R is fine-tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence-to-sequence problems, such as ASR and handwriting recognition. I highly recommend reading the well-written blog post Sequence Modeling with CTC (2017) by Awni Hannun. Before we start, let\'s install datasets and transformers. Also, we need the torchaudio to load audio files and jiwer to evaluate our fine-tuned model using the word error rate (WER) metric \( {}^1 \). python !pip install datasets==1.18.3 !pip install transformers==4.11.3 !pip install huggingface_hub==0.1 !pip install torchaudio !pip install librosa !pip install jiwer We strongly suggest to upload your training checkpoints directly to the Hugging Face Hub while training. The Hugging Face Hub has integrated version control so you can be sure that no model checkpoint is getting lost during training. To do so you have to store your authentication token from the Hugging Face website (sign up here if you haven\'t already!) python from huggingface_hub import notebook_login notebook_login() Print Output: bash Login successful Your token has been saved to /root/.huggingface/token Then you need to install Git-LFS to upload your model checkpoints: bash apt install git-lfs ------------------------------------------------------------------------ \( {}^1 \) In the paper, the model was evaluated using the phoneme error rate (PER), but by far the most common metric in ASR is the word error rate (WER). To keep this notebook as general as possible we decided to evaluate the model using WER. Prepare Data, Tokenizer, Feature Extractor ------------------------------------------ ASR models transcribe speech to text, which means that we both need a feature extractor that processes the speech signal to the model\'s input format, e.g. a feature vector, and a tokenizer that processes the model\'s output format to text. In 🤗 Transformers, the XLS-R model is thus accompanied by both a tokenizer, called Wav2Vec2CTCTokenizer, and a feature extractor, called Wav2Vec2FeatureExtractor. Let\'s start by creating the tokenizer to decode the predicted output classes to the output transcription. | [
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Accelerating PyTorch distributed fine-tuning with Intel technologies | juliensimon | November 19, 2021 | accelerating-pytorch | guide | https://huggingface.co/blog/accelerating-pytorch | Setting up a distributed job with oneCCL Three steps are required to run a distributed training job: * List the nodes of the training cluster, * Define environment variables, * Modify the training script. Listing the nodes of the training cluster On the master instance, in transformers/examples/pytorch/text-classification, we create a text file named hostfile. This file stores the names of the nodes in the cluster (IP addresses would work too). The first line should point to the master instance. Here's my file: ip-172-31-28-17.ec2.internal ip-172-31-30-87.ec2.internal ip-172-31-29-11.ec2.internal ip-172-31-20-77.ec2.internal Defining environment variables Next, we need to set some environment variables on the master node, most notably its IP address. You can find more information on oneCCL variables in the documentation. for nic in eth0 eib0 hib0 enp94s0f0; do master_addr=$(ifconfig $nic 2>/dev/null | grep netmask | awk '{print $2}'| cut -f2 -d:) if [ "$master_addr" ]; then break fi done export MASTER_ADDR=$master_addr source /home/ec2-user/anaconda3/envs/transformer/lib/python3.7/site-packages/torch_ccl-1.3.0+43f48a1-py3.7-linux-x86_64.egg/torch_ccl/env/setvars.sh export LD_LIBRARY_PATH=/home/ec2-user/anaconda3/envs/transformer/lib/python3.7/site-packages/torch_ccl-1.3.0+43f48a1-py3.7-linux-x86_64.egg/:$LD_LIBRARY_PATH export LD_PRELOAD="${CONDA_PREFIX}/lib/libtcmalloc.so:${CONDA_PREFIX}/lib/libiomp5.so" export CCL_WORKER_COUNT=4 export CCL_WORKER_AFFINITY="0,1,2,3,32,33,34,35" export CCL_ATL_TRANSPORT=ofi export ATL_PROGRESS_MODE=0 Modifying the training script The following changes have already been applied to our training script (run_glue.py) in order to enable distributed training. You would need to apply similar changes when using your own training code. * Import the torch_cclpackage. * Receive the address of the master node and the local rank of the node in the cluster. +import torch_ccl + import datasets import numpy as np from datasets import load_dataset, load_metric @@ -47,7 +49,7 @@ from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. -check_min_version("4.13.0.dev0") +# check_min_version("4.13.0.dev0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") @@ -191,6 +193,17 @@ def main(): # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. + # add local rank for cpu-dist + sys.argv.append("--local_rank") + sys.argv.append(str(os.environ.get("PMI_RANK", -1))) + + # ccl specific environment variables + if "ccl" in sys.argv: + os.environ["MASTER_ADDR"] = os.environ.get("MASTER_ADDR", "127.0.0.1") + os.environ["MASTER_PORT"] = "29500" + os.environ["RANK"] = str(os.environ.get("PMI_RANK", -1)) + os.environ["WORLD_SIZE"] = str(os.environ.get("PMI_SIZE", -1)) + parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): Setup is now complete. Let's scale our training job to 2 nodes and 4 nodes. | [
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Accelerating PyTorch distributed fine-tuning with Intel technologies | juliensimon | November 19, 2021 | accelerating-pytorch | guide | https://huggingface.co/blog/accelerating-pytorch | Conclusion In this post, you've learned how to build a distributed training cluster based on Intel CPUs and performance libraries, and how to use this cluster to speed up fine-tuning jobs. Indeed, transfer learning is putting CPU training back into the game, and you should definitely consider it when designing and building your next deep learning workflows. Thanks for reading this long post. I hope you found it informative. Feedback and questions are welcome at [email protected]. Until next time, keep learning! Julien | [
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Accelerating PyTorch distributed fine-tuning with Intel technologies | juliensimon | November 19, 2021 | accelerating-pytorch | guide | https://huggingface.co/blog/accelerating-pytorch | Launching a single-node job To get a baseline, let's launch a single-node job running the run_glue.py script in transformers/examples/pytorch/text-classification. This should work on any of the instances, and it's a good sanity check before proceeding to distributed training. python run_glue.py \ --model_name_or_path bert-base-cased --task_name mrpc \ --do_train --do_eval --max_seq_length 128 \ --per_device_train_batch_size 32 --learning_rate 2e-5 --num_train_epochs 3 \ --output_dir /tmp/mrpc/ --overwrite_output_dir True This job takes 7 minutes and 46 seconds. Now, let's set up distributed jobs with oneCCL and speed things up! | [
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Accelerating PyTorch distributed fine-tuning with Intel technologies | juliensimon | November 19, 2021 | accelerating-pytorch | guide | https://huggingface.co/blog/accelerating-pytorch | # Accelerating PyTorch distributed fine-tuning with Intel technologies For all their amazing performance, state of the art deep learning models often take a long time to train. In order to speed up training jobs, engineering teams rely on distributed training, a divide-and-conquer technique where clustered servers each keep a copy of the model, train it on a subset of the training set, and exchange results to converge to a final model. Graphical Processing Units (GPUs) have long been the de facto choice to train deep learning models. However, the rise of transfer learning is changing the game. Models are now rarely trained from scratch on humungous datasets. Instead, they are frequently fine-tuned on specific (and smaller) datasets, in order to build specialized models that are more accurate than the base model for particular tasks. As these training jobs are much shorter, using a CPU-based cluster can prove to be an interesting option that keeps both training time and cost under control. | [
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Accelerating PyTorch distributed fine-tuning with Intel technologies | juliensimon | November 19, 2021 | accelerating-pytorch | guide | https://huggingface.co/blog/accelerating-pytorch | Using Intel performance libraries To leverage AVX-512 and VNNI in PyTorch, Intel has designed the Intel extension for PyTorch. This software library provides out of the box speedup for training and inference, so we should definitely install it. When it comes to distributed training, the main performance bottleneck is often networking. Indeed, the different nodes in the cluster need to periodically exchange model state information to stay in sync. As transformers are large models with billions of parameters (sometimes much more), the volume of information is significant, and things only get worse as the number of nodes increase. Thus, it's important to use a communication library optimized for deep learning. In fact, PyTorch includes the torch.distributed package, which supports different communication backends. Here, we'll use the Intel oneAPI Collective Communications Library (oneCCL), an efficient implementation of communication patterns used in deep learning (all-reduce, etc.). You can learn about the performance of oneCCL versus other backends in this PyTorch blog post. Now that we're clear on building blocks, let's talk about the overall setup of our training cluster. | [
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Accelerating PyTorch distributed fine-tuning with Intel technologies | juliensimon | November 19, 2021 | accelerating-pytorch | guide | https://huggingface.co/blog/accelerating-pytorch | Installing dependencies Here are the steps we will follow: * Install Intel toolkits, * Install the Anaconda distribution, * Create a new conda environment, * Install PyTorch and the Intel extension for PyTorch, * Compile and install oneCCL, * Install the transformers library. It looks like a lot, but there's nothing complicated. Here we go! Installing Intel toolkits First, we download and install the Intel OneAPI base toolkit as well as the AI toolkit. You can learn about them on the Intel website. wget https://registrationcenter-download.intel.com/akdlm/irc_nas/18236/l_BaseKit_p_2021.4.0.3422_offline.sh sudo bash l_BaseKit_p_2021.4.0.3422_offline.sh wget https://registrationcenter-download.intel.com/akdlm/irc_nas/18235/l_AIKit_p_2021.4.0.1460_offline.sh sudo bash l_AIKit_p_2021.4.0.1460_offline.sh Installing Anaconda Then, we download and install the Anaconda distribution. wget https://repo.anaconda.com/archive/Anaconda3-2021.05-Linux-x86_64.sh sh Anaconda3-2021.05-Linux-x86_64.sh Creating a new conda environment We log out and log in again to refresh paths. Then, we create a new conda environment to keep things neat and tidy. yes | conda create -n transformer python=3.7.9 -c anaconda eval "$(conda shell.bash hook)" conda activate transformer yes | conda install pip cmake Installing PyTorch and the Intel extension for PyTorch Next, we install PyTorch 1.9 and the Intel extension toolkit. Versions must match. yes | conda install pytorch==1.9.0 cpuonly -c pytorch pip install torch_ipex==1.9.0 -f https://software.intel.com/ipex-whl-stable Compiling and installing oneCCL Then, we install some native dependencies required to compile oneCCL. sudo yum -y update sudo yum install -y git cmake3 gcc gcc-c++ Next, we clone the oneCCL repository, build the library and install it. Again, versions must match. source /opt/intel/oneapi/mkl/latest/env/vars.sh git clone https://github.com/intel/torch-ccl.git cd torch-ccl git checkout ccl_torch1.9 git submodule sync git submodule update --init --recursive python setup.py install cd .. Installing the transformers library Next, we install the transformers library and dependencies required to run GLUE tasks. pip install transformers datasets yes | conda install scipy scikit-learn Finally, we clone a fork of the transformersrepository containing the example we're going to run. git clone https://github.com/kding1/transformers.git cd transformers git checkout dist-sigopt We're done! Let's run a single-node job. | [
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Accelerating PyTorch distributed fine-tuning with Intel technologies | juliensimon | November 19, 2021 | accelerating-pytorch | guide | https://huggingface.co/blog/accelerating-pytorch | Setting up our cluster In this demo, I'm using Amazon EC2 instances running Amazon Linux 2 (c6i.16xlarge, 64 vCPUs, 128GB RAM, 25Gbit/s networking). Setup will be different in other environments, but steps should be very similar. Please keep in mind that you will need 4 identical instances, so you may want to plan for some sort of automation to avoid running the same setup 4 times. Here, I will set up one instance manually, create a new Amazon Machine Image (AMI) from this instance, and use this AMI to launch three identical instances. From a networking perspective, we will need the following setup: * Open port 22 for ssh access on all instances for setup and debugging. * Configure password-less ssh between the master instance (the one you'll launch training from) and all other instances (master included). * Open all TCP ports on all instances for oneCCL communication inside the cluster. Please make sure NOT to open these ports to the external world. AWS provides a convenient way to do this by only allowing connections from instances running a particular security group. Here's how my setup looks. Now, let's provision the first instance manually. I first create the instance itself, attach the security group above, and add 128GB of storage. To optimize costs, I have launched it as a spot instance. Once the instance is up, I connect to it with ssh in order to install dependencies. | [
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Accelerating PyTorch distributed fine-tuning with Intel technologies | juliensimon | November 19, 2021 | accelerating-pytorch | guide | https://huggingface.co/blog/accelerating-pytorch | Running a distributed job with oneCCL On the master node, I use mpirunto launch a 2-node job: -np (number of processes) is set to 2 and -ppn (process per node) is set to 1. Hence, the first two nodes in hostfile will be selected. mpirun -f hostfile -np 2 -ppn 1 -genv I_MPI_PIN_DOMAIN=[0xfffffff0] \ -genv OMP_NUM_THREADS=28 python run_glue.py \ --model_name_or_path distilbert-base-uncased --task_name mrpc \ --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 32 \ --learning_rate 2e-5 --num_train_epochs 3 --output_dir /tmp/mrpc/ \ --overwrite_output_dir True --xpu_backend ccl --no_cuda True Within seconds, a job starts on the first two nodes. The job completes in 4 minutes and 39 seconds, a 1.7x speedup. Setting -np to 4 and launching a new job, I now see one process running on each node of the cluster. Training completes in 2 minutes and 36 seconds, a 3x speedup. One last thing. Changing --task_name to qqp, I also ran the Quora Question Pairs GLUE task, which is based on a much larger dataset (over 400,000 training samples). The fine-tuning times were: * Single-node: 11 hours 22 minutes, * 2 nodes: 6 hours and 38 minutes (1.71x), * 4 nodes: 3 hours and 51 minutes (2.95x). It looks like the speedup is pretty consistent. Feel free to keep experimenting with different learning rates, batch sizes and oneCCL settings. I'm sure you can go even faster! | [
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Accelerating PyTorch distributed fine-tuning with Intel technologies | juliensimon | November 19, 2021 | accelerating-pytorch | guide | https://huggingface.co/blog/accelerating-pytorch | Using Intel servers For best performance, we will use Intel servers based on the Ice Lake architecture, which supports hardware features such as Intel AVX-512 and Intel Vector Neural Network Instructions (VNNI). These features accelerate operations typically found in deep learning training and inference. You can learn more about them in this presentation (PDF). All three major cloud providers offer virtual machines powered by Intel Ice Lake CPUs: - Amazon Web Services: Amazon EC2 M6i and C6i instances. - Azure: Dv5/Dsv5-series, Ddv5/Ddsv5-series and Edv5/Edsv5-series virtual machines. - Google Cloud Platform: N2 Compute Engine virtual machines. Of course, you can also use your own servers. If they are based on the Cascade Lake architecture (Ice Lake's predecessor), they're good to go as Cascade Lake also includes AVX-512 and VNNI. | [
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Accelerating PyTorch distributed fine-tuning with Intel technologies | juliensimon | November 19, 2021 | accelerating-pytorch | guide | https://huggingface.co/blog/accelerating-pytorch | What this post is about In this post, you will learn how to accelerate PyTorch training jobs by distributing them on a cluster of Intel Xeon Scalable CPU servers, powered by the Ice Lake architecture and running performance-optimized software libraries. We will build the cluster from scratch using virtual machines, and you should be able to easily replicate the demo on your own infrastructure, either in the cloud or on premise. Running a text classification job, we will fine-tune a BERT model on the MRPC dataset (one of the tasks included in the GLUE benchmark). The MRPC dataset contains 5,800 sentence pairs extracted from news sources, with a label telling us whether the two sentences in each pair are semantically equivalent. We picked this dataset for its reasonable training time, and trying other GLUE tasks is just a parameter away. Once the cluster is up and running, we will run a baseline job on a single server. Then, we will scale it to 2 servers and 4 servers and measure the speed-up. Along the way, we will cover the following topics: * Listing the required infrastructure and software building blocks, * Setting up our cluster, * Installing dependencies, * Running a single-node job, * Running a distributed job. Let's get to work! | [
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Introducing the Data Measurements Tool: an Interactive Tool for Looking at Datasets | sasha | November 29, 2021 | data-measurements-tool | research | https://huggingface.co/blog/data-measurements-tool | Descriptive Statistics To look at the surface characteristics of the dataset This begins to answer questions like “What kind of language is in this dataset? How diverse is it?” - The dataset vocabulary size and word distribution, for both open- and closed-class words. - The dataset label distribution and information about class (im)balance. - The mean, median, range, and distribution of instance lengths. - The number of duplicates in the dataset and how many times they are repeated. You can use these widgets to check whether what is most and least represented in the dataset make sense for the goals of the dataset. These measurements are intended to inform whether the dataset can be useful in capturing a variety of contexts or if what it captures is more limited, and to measure how ''balanced'' the labels and instance lengths are. You can also use these widgets to identify outliers and duplicates you may want to remove. | [
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Introducing the Data Measurements Tool: an Interactive Tool for Looking at Datasets | sasha | November 29, 2021 | data-measurements-tool | research | https://huggingface.co/blog/data-measurements-tool | What is the status of 🤗 Data Measurements Tool development? We currently present the alpha version (v0) of the tool, demonstrating its usefulness on a handful of popular English-language datasets (e.g. SQuAD, imdb, C4, ...) available on the Dataset Hub, with the functionalities described above. The words that we selected for nPMI visualization are a subset of identity terms that came up frequently in the datasets that we were working with. In coming weeks and months, we will be extending the tool to: - Cover more languages and datasets present in the 🤗 Datasets library. - Provide support for user-provided datasets and iterative dataset building. - Add more features and functionalities to the tool itself. For example, we will make it possible to add your own terms for the nPMI visualization so you can pick the words that matter most to you. | [
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Introducing the Data Measurements Tool: an Interactive Tool for Looking at Datasets | sasha | November 29, 2021 | data-measurements-tool | research | https://huggingface.co/blog/data-measurements-tool | Why have we created this tool? Thoughtful curation and analysis of Machine Learning datasets is often overlooked in AI development. Current norms for “big data” in AI (Luccioni et al., 2021, Dodge et al., 2021) include using data scraped from various websites, with little or no attention paid to concrete measurements of what the different data sources represent, nor the nitty-gritty details of how they may influence what a model learns. Although dataset annotation approaches can help to curate datasets that are more in line with a developer’s goals, the methods for “measuring” different aspects of these datasets are fairly limited (Sambasivan et al., 2021). A new wave of research in AI has called for a fundamental paradigm shift in how the field approaches ML datasets (Paullada et al., 2020, Denton et al., 2021). This includes defining fine-grained requirements for dataset creation from the start (Hutchinson et al., 2021), curating datasets in light of problematic content and bias concerns (Yang et al., 2020, Prabhu and Birhane, 2020), and making explicit the values inherent in dataset construction and maintenance (Scheuerman et al., 2021, Birhane et al., 2021). Although there is general agreement that dataset development is a task that people from many different disciplines should be able to inform, in practice there is often a bottleneck in interfacing with the raw data itself, which tends to require complex coding skills in order to analyze and query the dataset. Despite this, there are few tools openly available to the public to enable people from different disciplines to measure, interrogate, and compare datasets. We aim to help fill this gap. We learn and build from recent tools such as Know Your Data and Data Quality for AI, as well as research proposals for dataset documentation such as Vision and Language Datasets (Ferraro et al., 2015), Datasheets for Datasets (Gebru et al, 2018), and Data Statements (Bender & Friedman 2019). The result is an open-source library for dataset measurements, and an accompanying no-code interface for detailed dataset analysis. | [
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Introducing the Data Measurements Tool: an Interactive Tool for Looking at Datasets | sasha | November 29, 2021 | data-measurements-tool | research | https://huggingface.co/blog/data-measurements-tool | Comparison statistics This begins to answer questions like “What kinds of topics, biases, and associations are in this dataset?” - Embedding clusters to pinpoint any clusters of similar language in the dataset. Taking in the diversity of text represented in a dataset can be challenging when it is made up of hundreds to hundreds of thousands of sentences. Grouping these text items based on a measure of similarity can help users gain some insights into their distribution. We show a hierarchical clustering of the text fields in the dataset based on a Sentence-Transformer model and a maximum dot product single-linkage criterion. To explore the clusters, you can: - hover over a node to see the 5 most representative examples (deduplicated) - enter an example in the text box to see which leaf clusters it is most similar to - select a cluster by ID to show all of its examples - The normalized pointwise mutual information (nPMI) between word pairs in the dataset, which may be used to identify problematic stereotypes. You can use this as a tool in dealing with dataset “bias”, where here the term “bias” refers to stereotypes and prejudices for identity groups along the axes of gender and sexual orientation. We will add further terms in the near future. | [
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Introducing the Data Measurements Tool: an Interactive Tool for Looking at Datasets | sasha | November 29, 2021 | data-measurements-tool | research | https://huggingface.co/blog/data-measurements-tool | Distributional Statistics To measure the language patterns in the dataset This begins to answer questions like “How does the language behave in this dataset?” - Adherence to Zipf’s law, which provides measurements of how closely the distribution over words in the dataset fits to the expected distribution of words in natural language. You can use this to figure out whether your dataset represents language as it tends to behave in the natural world or if there are things that are more unnatural about it. If you’re someone who enjoys optimization, then you can view the alpha value this widget calculates as a value to get as close as possible to 1 during dataset development. Further details on alpha values following Zipf’s law in different languages is available here. In general, an alpha greater than 2 or a minimum rank greater than 10 (take with a grain of salt) means that your distribution is relatively unnatural for natural language. This can be a sign of mixed artefacts in the dataset, such as HTML markup. You can use this information to clean up your dataset or to guide you in determining how further language you add to the dataset should be distributed. | [
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Introducing the Data Measurements Tool: an Interactive Tool for Looking at Datasets | sasha | November 29, 2021 | data-measurements-tool | research | https://huggingface.co/blog/data-measurements-tool | Dataset Basics For a high-level overview of the dataset This begins to answer questions like “What is this dataset? Does it have missing items?”. You can use this as “sanity checks” that the dataset you’re working with is as you expect it to be. - A description of the dataset (from the Hugging Face Hub) - Number of missing values or NaNs | [
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Introducing the Data Measurements Tool: an Interactive Tool for Looking at Datasets | sasha | November 29, 2021 | data-measurements-tool | research | https://huggingface.co/blog/data-measurements-tool | Acknowledgements Thank you to Thomas Wolf for initiating this work, as well as other members of the 🤗 team (Quentin, Lewis, Sylvain, Nate, Julien C., Julien S., Clément, Omar, and many others!) for their help and support. | [
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Introducing the Data Measurements Tool: an Interactive Tool for Looking at Datasets | sasha | November 29, 2021 | data-measurements-tool | research | https://huggingface.co/blog/data-measurements-tool | # Introducing the 🤗 Data Measurements Tool: an Interactive Tool for Looking at Datasets tl;dr: We made a tool you can use online to build, measure, and compare datasets. Click to access the 🤗 Data Measurements Tool here. ----- As developers of a fast-growing unified repository for Machine Learning datasets (Lhoest et al. 2021), the 🤗 Hugging Face team has been working on supporting good practices for dataset documentation (McMillan-Major et al., 2021). While static (if evolving) documentation represents a necessary first step in this direction, getting a good sense of what is actually in a dataset requires well-motivated measurements and the ability to interact with it, dynamically visualizing different aspects of interest. To this end, we introduce an open-source Python library and no-code interface called the 🤗 Data Measurements Tool, using our Dataset and Spaces Hubs paired with the great Streamlit tool. This can be used to help understand, build, curate, and compare datasets. | [
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Introducing the Data Measurements Tool: an Interactive Tool for Looking at Datasets | sasha | November 29, 2021 | data-measurements-tool | research | https://huggingface.co/blog/data-measurements-tool | What is the 🤗 Data Measurements Tool? The Data Measurements Tool (DMT) is an interactive interface and open-source library that lets dataset creators and users automatically calculate metrics that are meaningful and useful for responsible data development. | [
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Introducing the Data Measurements Tool: an Interactive Tool for Looking at Datasets | sasha | November 29, 2021 | data-measurements-tool | research | https://huggingface.co/blog/data-measurements-tool | What can I learn using the 🤗 Data Measurements Tool? | [
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Introducing the Data Measurements Tool: an Interactive Tool for Looking at Datasets | sasha | November 29, 2021 | data-measurements-tool | research | https://huggingface.co/blog/data-measurements-tool | When can I use the 🤗 Data Measurements Tool? The 🤗 Data Measurements Tool can be used iteratively for exploring one or more existing NLP datasets, and will soon support iterative development of datasets from scratch. It provides actionable insights informed by research on datasets and responsible dataset development, allowing users to hone in on both high-level information and specific items. | [
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Getting Started with Hugging Face Transformers for IPUs with Optimum | internetoftim | November 30, 2021 | graphcore-getting-started | partnerships, graphcore, guide | https://huggingface.co/blog/graphcore-getting-started | Getting the data A very simple way to get datasets is to use the Hugging Face Datasets library, which makes it easy for developers to download and share datasets on the Hugging Face hub. It also has pre-built data versioning based on git and git-lfs, so you can iterate on updated versions of the data by just pointing to the same repo. Here, the dataset comes with the training and validation files, and dataset configs to help facilitate which inputs to use in each model execution phase. The argument --dataset_name==squad points to SQuAD v1.1 on the Hugging Face Hub. You could also provide your own CSV/JSON/TXT training and evaluation files as long as they follow the same format as the SQuAD dataset or another question-answering dataset in Datasets library. | [
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Getting Started with Hugging Face Transformers for IPUs with Optimum | internetoftim | November 30, 2021 | graphcore-getting-started | partnerships, graphcore, guide | https://huggingface.co/blog/graphcore-getting-started | Clone Optimum Graphcore Repository The Optimum Graphcore repository contains the sample code for using Optimum models in IPU. You should clone the repository and change the directory to the example/question-answering folder which contains the IPU implementation of BERT. $ git clone https://github.com/huggingface/optimum-graphcore.git $ cd optimum-graphcore/examples/question-answering Now, we will use run_qa.py to fine-tune the IPU implementation of BERT on the SQUAD1.1 dataset. | [
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Getting Started with Hugging Face Transformers for IPUs with Optimum | internetoftim | November 30, 2021 | graphcore-getting-started | partnerships, graphcore, guide | https://huggingface.co/blog/graphcore-getting-started | Resources for Optimum Transformers on IPU Systems * Optimum-Graphcore: SQuAD Examples * Graphcore Hugging Face Models & Datasets * GitHub Tutorial: BERT Fine-tuning on IPU using Hugging Face transformers * Graphcore Developer Portal * Graphcore GitHub * Graphcore SDK Containers on Docker Hub | [
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Getting Started with Hugging Face Transformers for IPUs with Optimum | internetoftim | November 30, 2021 | graphcore-getting-started | partnerships, graphcore, guide | https://huggingface.co/blog/graphcore-getting-started | Set up PopTorch for the IPU PopTorch is part of the Poplar SDK. It provides functions that allow PyTorch models to run on the IPU with minimal code changes. You can create and activate a PopTorch environment following the guide Setting up PyTorch for the IPU: $ virtualenv -p python3 ~/workspace/poptorch_env $ source ~/workspace/poptorch_env/bin/activate $ pip3 install -U pip $ pip3 install /opt/gc/poplar_sdk-ubuntu_18_04-2.3.0+774-b47c577c2a/poptorch-<sdk-version>.whl | [
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Getting Started with Hugging Face Transformers for IPUs with Optimum | internetoftim | November 30, 2021 | graphcore-getting-started | partnerships, graphcore, guide | https://huggingface.co/blog/graphcore-getting-started | # Getting Started with Hugging Face Transformers for IPUs with Optimum Transformer models have proven to be extremely efficient on a wide range of machine learning tasks, such as natural language processing, audio processing, and computer vision. However, the prediction speed of these large models can make them impractical for latency-sensitive use cases like conversational applications or search. Furthermore, optimizing their performance in the real world requires considerable time, effort and skills that are beyond the reach of many companies and organizations. Luckily, Hugging Face has introduced Optimum, an open source library which makes it much easier to reduce the prediction latency of Transformer models on a variety of hardware platforms. In this blog post, you will learn how to accelerate Transformer models for the Graphcore Intelligence Processing Unit (IPU), a highly flexible, easy-to-use parallel processor designed from the ground up for AI workloads. | [
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Getting Started with Hugging Face Transformers for IPUs with Optimum | internetoftim | November 30, 2021 | graphcore-getting-started | partnerships, graphcore, guide | https://huggingface.co/blog/graphcore-getting-started | Getting started with IPUs and Optimum Let’s use BERT as an example to help you get started with using Optimum and IPUs. In this guide, we will use an IPU-POD16 system in Graphcloud, Graphcore’s cloud-based machine learning platform and follow PyTorch setup instructions found in Getting Started with Graphcloud. Graphcore’s Poplar SDK is already installed on the Graphcloud server. If you have a different setup, you can find the instructions that apply to your system in the PyTorch for the IPU: User Guide. | [
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Getting Started with Hugging Face Transformers for IPUs with Optimum | internetoftim | November 30, 2021 | graphcore-getting-started | partnerships, graphcore, guide | https://huggingface.co/blog/graphcore-getting-started | Set up the Poplar SDK Environment You will need to run the following commands to set several environment variables that enable Graphcore tools and Poplar libraries. On the latest system running Poplar SDK version 2.3 on Ubuntu 18.04, you can find in the folder /opt/gc/poplar_sdk-ubuntu_18_04-2.3.0+774-b47c577c2a/. You would need to run both enable scripts for Poplar and PopART (Poplar Advanced Runtime) to use PyTorch: $ cd /opt/gc/poplar_sdk-ubuntu_18_04-2.3.0+774-b47c577c2a/ $ source poplar-ubuntu_18_04-2.3.0+774-b47c577c2a/enable.sh $ source popart-ubuntu_18_04-2.3.0+774-b47c577c2a/enable.sh | [
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Getting Started with Hugging Face Transformers for IPUs with Optimum | internetoftim | November 30, 2021 | graphcore-getting-started | partnerships, graphcore, guide | https://huggingface.co/blog/graphcore-getting-started | Run a sample to fine-tune BERT on SQuAD1.1 The run_qa.py script only works with models that have a fast tokenizer (backed by the 🤗 Tokenizers library), as it uses special features of those tokenizers. This is the case for our BERT model, and you should pass its name as the input argument to --model_name_or_path. In order to use the IPU, Optimum will look for the ipu_config.json file from the path passed to the argument --ipu_config_name. $ python3 run_qa.py \ --ipu_config_name=./ \ --model_name_or_path bert-base-uncased \ --dataset_name squad \ --do_train \ --do_eval \ --output_dir output \ --overwrite_output_dir \ --per_device_train_batch_size 2 \ --per_device_eval_batch_size 2 \ --learning_rate 6e-5 \ --num_train_epochs 3 \ --max_seq_length 384 \ --doc_stride 128 \ --seed 1984 \ --lr_scheduler_type linear \ --loss_scaling 64 \ --weight_decay 0.01 \ --warmup_ratio 0.1 \ --output_dir /tmp/debug_squad/ | [
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Getting Started with Hugging Face Transformers for IPUs with Optimum | internetoftim | November 30, 2021 | graphcore-getting-started | partnerships, graphcore, guide | https://huggingface.co/blog/graphcore-getting-started | Optimum Meets Graphcore IPU Through this partnership between Graphcore and Hugging Face, we are now introducing BERT as the first IPU-optimized model. We will be introducing many more of these IPU-optimized models in the coming months, spanning applications such as vision, speech, translation and text generation. Graphcore engineers have implemented and optimized BERT for our IPU systems using Hugging Face transformers to help developers easily train, fine-tune and accelerate their state-of-the-art models. | [
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Getting Started with Hugging Face Transformers for IPUs with Optimum | internetoftim | November 30, 2021 | graphcore-getting-started | partnerships, graphcore, guide | https://huggingface.co/blog/graphcore-getting-started | Loading the pretrained model and tokenizer To turn words into tokens, this script will require a fast tokenizer. It will show an error if you didn't pass one. For reference, here's the list of supported tokenizers. # Tokenizer check: this script requires a fast tokenizer. if not isinstance(tokenizer, PreTrainedTokenizerFast): raise ValueError("This example script only works for models that have a fast tokenizer. Checkout the big table of models "at https://huggingface.co/transformers/index.html#supported-frameworks to find the model types that meet this " "requirement" ) The argument --model_name_or_path==bert-base-uncased`` loads the [bert-base-uncased](https://huggingface.co/bert-base-uncased) model implementation available in the Hugging Face Hub. From the Hugging Face Hub description: "*BERT base model (uncased): Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English.*" | [
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Getting Started with Hugging Face Transformers for IPUs with Optimum | internetoftim | November 30, 2021 | graphcore-getting-started | partnerships, graphcore, guide | https://huggingface.co/blog/graphcore-getting-started | A closer look at Optimum-Graphcore | [
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Getting Started with Hugging Face Transformers for IPUs with Optimum | internetoftim | November 30, 2021 | graphcore-getting-started | partnerships, graphcore, guide | https://huggingface.co/blog/graphcore-getting-started | Training and Validation You can now use theIPUTrainerclass available in Optimum to leverage the entire Graphcore software and hardware stack, and train your models in IPUs with minimal code changes. Thanks to Optimum, you can plug-and-play state of the art hardware to train your state of the art models. <kbd> <img src="assets/38_getting_started_graphcore/graphcore_1.png"> </kbd> In order to train and validate the BERT model, you can pass the arguments--do_trainand--do_evalto therun_qa.pyscript. After executing the script with the hyper-parameters above, you should see the following training and validation results: "epoch": 3.0, "train_loss": 0.9465060763888888, "train_runtime": 368.4015, "train_samples": 88524, "train_samples_per_second": 720.877, "train_steps_per_second": 2.809 The validation step yields the following results: * eval metrics *** epoch = 3.0 eval_exact_match = 80.6623 eval_f1 = 88.2757 eval_samples = 10784 ``` You can see the rest of the IPU BERT implementation in the Optimum-Graphcore: SQuAD Examples. | [
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Getting Started with Hugging Face Transformers for IPUs with Optimum | internetoftim | November 30, 2021 | graphcore-getting-started | partnerships, graphcore, guide | https://huggingface.co/blog/graphcore-getting-started | Install Optimum Graphcore Now that your environment has all the Graphcore Poplar and PopTorch libraries available, you need to install the latest 🤗 Optimum Graphcore package in this environment. This will be the interface between the 🤗 Transformers library and Graphcore IPUs. Please make sure that the PopTorch virtual environment you created in the previous step is activated. Your terminal should have a prefix showing the name of the poptorch environment like below: (poptorch_env) user@host:~/workspace/poptorch_env$ pip3 install optimum[graphcore] optuna | [
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Introducing Snowball Fight ☃️, our First ML-Agents Environment | ThomasSimonini | December 2, 2021 | snowball-fight | research, rl | https://huggingface.co/blog/snowball-fight | Be part of the conversation: join our discord server! If you're using ML-Agents or interested in Deep Reinforcement Learning and want to be part of the conversation, you can join our discord server. We just added two channels (and we'll add more in the future): - Deep Reinforcement Learning - ML-Agents Our discord is the place where you can exchange about Hugging Face, NLP, Deep RL, and more! It's also in this discord that we'll announce all our new environments and features in the future. | [
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Introducing Snowball Fight ☃️, our First ML-Agents Environment | ThomasSimonini | December 2, 2021 | snowball-fight | research, rl | https://huggingface.co/blog/snowball-fight | # Introducing Snowball Fight ☃️, our First ML-Agents Environment We're excited to share our first custom Deep Reinforcement Learning environment: Snowball Fight 1vs1 🎉. Snowball Fight is a game made with Unity ML-Agents, where you shoot snowballs against a Deep Reinforcement Learning agent. The game is hosted on Hugging Face Spaces. 👉 You can play it online here In this post, we'll cover the ecosystem we are working on for Deep Reinforcement Learning researchers and enthusiasts that use Unity ML-Agents. | [
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Introducing Snowball Fight ☃️, our First ML-Agents Environment | ThomasSimonini | December 2, 2021 | snowball-fight | research, rl | https://huggingface.co/blog/snowball-fight | What's next? In the coming weeks and months, we will be extending the ecosystem by: - Writing some technical tutorials on ML-Agents. - Working on a Snowball Fight 2vs2 version, where the agents will collaborate in teams using MA-POCA, a new Deep Reinforcement Learning algorithm that trains cooperative behaviors in a team. - And we're building new custom environments that will be hosted in Hugging Face. | [
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Introducing Snowball Fight ☃️, our First ML-Agents Environment | ThomasSimonini | December 2, 2021 | snowball-fight | research, rl | https://huggingface.co/blog/snowball-fight | Unity ML-Agents at Hugging Face The Unity Machine Learning Agents Toolkit is an open source library that allows you to build games and simulations with Unity game engine to serve as environments for training intelligent agents. With this first step, our goal is to build an ecosystem on Hugging Face for Deep Reinforcement Learning researchers and enthusiasts that uses ML-Agents, with three features. 1. Building and sharing custom environments. We are developing and sharing exciting environments to experiment with new problems: snowball fights, racing, puzzles... All of them will be open source and hosted on the Hugging Face's Hub. 2. Allowing you to easily host your environments, save models and share them on the Hugging Face Hub. We have already published the Snowball Fight training environment here, but there will be more to come! 3. You can now easily host your demos on Spaces and showcase your results quickly with the rest of the ecosystem. | [
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Introducing Snowball Fight ☃️, our First ML-Agents Environment | ThomasSimonini | December 2, 2021 | snowball-fight | research, rl | https://huggingface.co/blog/snowball-fight | Conclusion We're excited to see what you're working on with ML-Agents and how we can build features and tools that help you to empower your work. Don't forget to join our discord server to be alerted of the new features. | [
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Training CodeParrot 🦜 from Scratch | lvwerra | December 8, 2021 | codeparrot | guide, research, nlp | https://huggingface.co/blog/codeparrot | Summary In this short blog post we walked through all the steps involved for training a large GPT-2 model called CodeParrot 🦜 for code generation. Using 🤗 Accelerate we built a training script with less than 200 lines of code that we can effortlessly scale across many GPUs. With that you can now train your own GPT-2 model! This post gives a brief overview of CodeParrot 🦜, but if you are interested in diving deeper into how to pretrain this models, we recommend reading its dedicated chapter in the upcoming book on Transformers and NLP. This chapter provides many more details around building custom datasets, design considerations when training a new tokenizer, and architecture choice. | [
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Training CodeParrot 🦜 from Scratch | lvwerra | December 8, 2021 | codeparrot | guide, research, nlp | https://huggingface.co/blog/codeparrot | Results Let's start slow and check if we can prompt the model to write a function to return the size of a file given its path: Prompt: Python def get_files_size(filename): Completion: Python """ Returns the file size of a file. :param filepath: The path to the file. :type filepath: str :return: The file size. :rtype: int """ return os.path.getsize(filepath) So far so good, but let's take this a step further and try something a bit more interesting. One of the less exciting aspects of building production ready code is writing unittest. Let's see if we can use CodeParrot to do this for us! Prompt: Python def is_even(value): """Returns True if value is an even number.""" return value % 2 == 0 # setup unit tests for is_even import unittest Completion: Python class TestIsEven(unittest.TestCase): """Tests for is_even function.""" def test_is_even(self): """Test is_even function.""" self.assertTrue(is_even(0)) self.assertTrue(is_even(1)) self.assertTrue Although the test condition itself does not look quite right the model has built all the boilerplate code for us. This alone could already be quite useful and save us a couple of minutes time! But what we really want to do is build better transformer models. Let's use CodeParrot to help us do that: Prompt: Python from transformers import AutoTokenizer, AutoModelForSequenceClassification # build a BERT classifier Completion: Python tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased') Looking at a few examples is nice but by now you probably want to try it yourself. We prepared a few online demos just for that! | [
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Training CodeParrot 🦜 from Scratch | lvwerra | December 8, 2021 | codeparrot | guide, research, nlp | https://huggingface.co/blog/codeparrot | Demos You can play with the models in two demos we added to Hugging Face Spaces. With the first you can quickly generate code with the model and with the second you can highlight your code with the model to spot bugs! - Code Generation - Code Highlighting You can also directly use the models from the transformers library: Python from transformers import pipeline pipe = pipeline('text-generation', model='lvwerra/codeparrot') pipe('def hello_world():') | [
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Training CodeParrot 🦜 from Scratch | lvwerra | December 8, 2021 | codeparrot | guide, research, nlp | https://huggingface.co/blog/codeparrot | Implementing the Training Loop We train with the 🤗 Accelerate library which allows us to scale the training from our laptop to a multi-GPU machine without changing a single line of code. We just create an accelerator and do some argument housekeeping: Python accelerator = Accelerator() acc_state = {str(k): str(v) for k, v in accelerator.state.__dict__.items()} parser = HfArgumentParser(TrainingArguments) args = parser.parse_args() args = Namespace(**vars(args), **acc_state) samples_per_step = accelerator.state.num_processes * args.train_batch_size set_seed(args.seed) We are now ready to train! Let's use the huggingface_hub client library to clone the repository with the new tokenizer and model. We will checkout to a new branch for this experiment. With that setup, we can run many experiments in parallel and in the end we just merge the best one into the main branch. Python # Clone model repository if accelerator.is_main_process: hf_repo = Repository(args.save_dir, clone_from=args.model_ckpt) # Checkout new branch on repo if accelerator.is_main_process: hf_repo.git_checkout(run_name, create_branch_ok=True) We can directly load the tokenizer and model from the local repository. Since we are dealing with big models we might want to turn on gradient checkpointing to decrease the GPU memory footprint during training. Python # Load model and tokenizer model = AutoModelForCausalLM.from_pretrained(args.save_dir) if args.gradient_checkpointing: model.gradient_checkpointing_enable() tokenizer = AutoTokenizer.from_pretrained(args.save_dir) Next up is the dataset. We make training simpler with a dataset that yields examples with a fixed context size. To not waste too much data (some samples are too short or too long) we can concatenate many examples with an EOS token and then chunk them. The more sequences we prepare together, the smaller the fraction of tokens we discard (the grey ones in the previous figure). Since we want to stream the dataset instead of preparing everything in advance we use an IterableDataset. The full dataset class looks as follows: Python class ConstantLengthDataset(IterableDataset): def __init__( self, tokenizer, dataset, infinite=False, seq_length=1024, num_of_sequences=1024, chars_per_token=3.6 ): self.tokenizer = tokenizer self.concat_token_id = tokenizer.bos_token_id self.dataset = dataset self.seq_length = seq_length self.input_characters = seq_length * chars_per_token * num_of_sequences self.epoch = 0 self.infinite = infinite def __iter__(self): iterator = iter(self.dataset) more_examples = True while more_examples: buffer, buffer_len = [], 0 while True: if buffer_len >= self.input_characters: break try: buffer.append(next(iterator)["content"]) buffer_len += len(buffer[-1]) except StopIteration: if self.infinite: iterator = iter(self.dataset) self.epoch += 1 logger.info(f"Dataset epoch: {self.epoch}") else: more_examples = False break tokenized_inputs = self.tokenizer(buffer, truncation=False)["input_ids"] all_token_ids = [] for tokenized_input in tokenized_inputs: all_token_ids.extend(tokenized_input + [self.concat_token_id]) for i in range(0, len(all_token_ids), self.seq_length): input_ids = all_token_ids[i : i + self.seq_length] if len(input_ids) == self.seq_length: yield torch.tensor(input_ids) Texts in the buffer are tokenized in parallel and then concatenated. Chunked samples are then yielded until the buffer is empty and the process starts again. If we set infinite=True the dataset iterator restarts at its end. Python def create_dataloaders(args): ds_kwargs = {"streaming": True} train_data = load_dataset(args.dataset_name_train, split="train", streaming=True) train_data = train_data.shuffle(buffer_size=args.shuffle_buffer, seed=args.seed) valid_data = load_dataset(args.dataset_name_valid, split="train", streaming=True) train_dataset = ConstantLengthDataset(tokenizer, train_data, infinite=True, seq_length=args.seq_length) valid_dataset = ConstantLengthDataset(tokenizer, valid_data, infinite=False, seq_length=args.seq_length) train_dataloader = DataLoader(train_dataset, batch_size=args.train_batch_size) eval_dataloader = DataLoader(valid_dataset, batch_size=args.valid_batch_size) return train_dataloader, eval_dataloader train_dataloader, eval_dataloader = create_dataloaders(args) Before we start training we need to set up the optimizer and learning rate schedule. We don’t want to apply weight decay to biases and LayerNorm weights so we use a helper function to exclude those. Python def get_grouped_params(model, args, no_decay=["bias", "LayerNorm.weight"]): params_with_wd, params_without_wd = [], [] for n, p in model.named_parameters(): if any(nd in n for nd in no_decay): params_without_wd.append(p) else: params_with_wd.append(p) return [{"params": params_with_wd, "weight_decay": args.weight_decay}, {"params": params_without_wd, "weight_decay": 0.0},] optimizer = AdamW(get_grouped_params(model, args), lr=args.learning_rate) lr_scheduler = get_scheduler(name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps, num_training_steps=args.max_train_steps,) A big question that remains is how all the data and models will be distributed across several GPUs. This sounds like a complex task but actually only requires a single line of code with 🤗 Accelerate. Python model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader) Under the hood it'll use DistributedDataParallel, which means a batch is sent to each GPU worker which has its own copy of the model. There the gradients are computed and then aggregated to update the model on each worker. We also want to evaluate the model from time to time on the validation set so let’s write a function to do just that. This is done automatically in a distributed fashion and we just need to gather all the losses from the workers. We also want to report the perplexity. Python def evaluate(args): model.eval() losses = [] for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(batch, labels=batch) loss = outputs.loss.repeat(args.valid_batch_size) losses.append(accelerator.gather(loss)) if args.max_eval_steps > 0 and step >= args.max_eval_steps: break loss = torch.mean(torch.cat(losses)) try: perplexity = torch.exp(loss) except OverflowError: perplexity = float("inf") return loss.item(), perplexity.item() We are now ready to write the main training loop. It will look pretty much like a normal PyTorch training loop. Here and there you can see that we use the accelerator functions rather than native PyTorch. Also, we push the model to the branch after each evaluation. Python # Train model model.train() completed_steps = 0 for step, batch in enumerate(train_dataloader, start=1): loss = model(batch, labels=batch, use_cache=False).loss loss = loss / args.gradient_accumulation_steps accelerator.backward(loss) if step % args.gradient_accumulation_steps == 0: accelerator.clip_grad_norm_(model.parameters(), 1.0) optimizer.step() lr_scheduler.step() optimizer.zero_grad() completed_steps += 1 if step % args.save_checkpoint_steps == 0: eval_loss, perplexity = evaluate(args) accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save) if accelerator.is_main_process: hf_repo.push_to_hub(commit_message=f"step {step}") model.train() if completed_steps >= args.max_train_steps: break When we call wait_for_everyone() and unwrap_model() we make sure that all workers are ready and any model layers that have been added by prepare() earlier are removed. We also use gradient accumulation and gradient clipping that are easily implemented. Lastly, after training is complete we run a last evaluation and save the final model and push it to the hub. Python # Evaluate and save the last checkpoint logger.info("Evaluating and saving model after training") eval_loss, perplexity = evaluate(args) log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity}) accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save) if accelerator.is_main_process: hf_repo.push_to_hub(commit_message="final model") Done! That's all the code to train a full GPT-2 model from scratch with as little as 150 lines. We did not show the imports and logs of the scripts to make the code a little bit more compact. Now let's actually train it! With this code we trained models for our upcoming book on Transformers and NLP: a 110M and 1.5B parameter GPT-2 model. We used a 16 x A100 GPU machine to train these models for 1 day and 1 week, respectively. Enough time to get a coffee and read a book or two! | [
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Training CodeParrot 🦜 from Scratch | lvwerra | December 8, 2021 | codeparrot | guide, research, nlp | https://huggingface.co/blog/codeparrot | Evaluation This is still relatively short training time for pretraining but we can already observe good downstream performance as compared to similar models. We evaluated the models on OpenAI's HumanEval benchmark that was introduced in the Codex paper. It measures the performance of code generation models on almost 200 coding challenges. Note that we trained CodeParrot on roughly 25-30B tokens whereas GPT-neo was trained on 300B tokens and Codex on 300B (GPT-3 checkpoint) + 100B (code) tokens. So you definitely get a good bang for your buck here. But what is this pass@k metric exactly? Simply put it measures the probability of at least one program passing the unittest of a programming problem given k candidate generations from the model. Naturally, the metric increases with k as more candidates can potentially solve the coding challenge. You can use the metric with datasets: Python from datasets import load_metric code_eval = datasets.load_metric("code_eval") test_cases = ["assert add(2,3)==5"] candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]] pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2]) print(pass_at_k) # -> {'pass@1': 0.5, 'pass@2': 1.0} You can also load OpenAI's HumanEval dataset with datasets: Python from datasets import load_dataset dataset = load_dataset("openai_humaneval") Now let's have a look at what we get after anxiously waiting for so long! | [
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Training CodeParrot 🦜 from Scratch | lvwerra | December 8, 2021 | codeparrot | guide, research, nlp | https://huggingface.co/blog/codeparrot | Initializing the Tokenizer and Model First we need a tokenizer. Let's train one specifically on code so it splits code tokens well. We can take an existing tokenizer (e.g. GPT-2) and directly train it on our own dataset with the train_new_from_iterator() method. We then push it to the Hub. Note that we omit imports, arguments parsing and logging from the code examples to keep the code blocks compact. But you'll find the full code including preprocessing and downstream task evaluation here. Python # Iterator for Training def batch_iterator(batch_size=10): for _ in tqdm(range(0, args.n_examples, batch_size)): yield [next(iter_dataset)["content"] for _ in range(batch_size)] # Base tokenizer tokenizer = GPT2Tokenizer.from_pretrained("gpt2") base_vocab = list(bytes_to_unicode().values()) # Load dataset dataset = load_dataset("lvwerra/codeparrot-clean", split="train", streaming=True) iter_dataset = iter(dataset) # Training and saving new_tokenizer = tokenizer.train_new_from_iterator(batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab) new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub) Learn more about tokenizers and how to build them in the Hugging Face course. See that inconspicuous streaming=True argument? This small change has a big impact: instead of downloading the full (50GB) dataset this will stream individual samples as needed saving a lot of disk space! Checkout the Hugging Face course for more information on streaming. Now, we initialize a new model. We’ll use the same hyperparameters as GPT-2 large (1.5B parameters) and adjust the embedding layer to fit our new tokenizer also adding some stability tweaks. The scale_attn_by_layer_idx flag makes sure we scale the attention by the layer id and reorder_and_upcast_attn mainly makes sure that we compute the attention in full precision to avoid numerical issues. We push the freshly initialized model to the same repo as the tokenizer. Python # Load codeparrot tokenizer trained for Python code tokenization tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name) # Configuration config_kwargs = {"vocab_size": len(tokenizer), "scale_attn_by_layer_idx": True, "reorder_and_upcast_attn": True} # Load model with config and push to hub config = AutoConfig.from_pretrained('gpt2-large', **config_kwargs) model = AutoModelForCausalLM.from_config(config) model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub) Now that we have an efficient tokenizer and a freshly initialized model we can start with the actual training loop. | [
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Training CodeParrot 🦜 from Scratch | lvwerra | December 8, 2021 | codeparrot | guide, research, nlp | https://huggingface.co/blog/codeparrot | Creating a Large Dataset of Source Code The first thing we need is a large training dataset. With the goal to train a Python code generation model, we accessed the GitHub dump available on Google's BigQuery and filtered for all Python files. The result is a 180 GB dataset with 20 million files (available here). After initial training experiments, we found that the duplicates in the dataset severely impacted the model performance. Further investigating the dataset we found that: - 0.1% of the unique files make up 15% of all files - 1% of the unique files make up 35% of all files - 10% of the unique files make up 66% of all files You can learn more about our findings in this Twitter thread. We removed the duplicates and applied the same cleaning heuristics found in the Codex paper. Codex is the model behind CoPilot and is a GPT-3 model fine-tuned on GitHub code. The cleaned dataset is still 50GB big and available on the Hugging Face Hub: codeparrot-clean. With that we can setup a new tokenizer and train a model. | [
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Training CodeParrot 🦜 from Scratch | lvwerra | December 8, 2021 | codeparrot | guide, research, nlp | https://huggingface.co/blog/codeparrot | # Training CodeParrot 🦜 from Scratch In this blog post we'll take a look at what it takes to build the technology behind GitHub CoPilot, an application that provides suggestions to programmers as they code. In this step by step guide, we'll learn how to train a large GPT-2 model called CodeParrot 🦜, entirely from scratch. CodeParrot can auto-complete your Python code - give it a spin here. Let's get to building it from scratch! | [
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Perceiver IO: a scalable, fully-attentional model that works on any modality | nielsr | December 15, 2021 | perceiver | research, guide, nlp, audio, cv | https://huggingface.co/blog/perceiver | Footnotes 1 Note that in the official paper, the authors used a two-layer MLP to generate the output logits, which was omitted here for brevity. ↩ | [
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Perceiver IO: a scalable, fully-attentional model that works on any modality | nielsr | December 15, 2021 | perceiver | research, guide, nlp, audio, cv | https://huggingface.co/blog/perceiver | Introduction The Transformer, originally introduced by Vaswani et al. in 2017, caused a revolution in the AI community, initially improving state-of-the-art (SOTA) results in machine translation. In 2018, BERT was released, a Transformer encoder-only model that crushed the benchmarks of natural language processing (NLP), most famously the GLUE benchmark. Not long after that, AI researchers started to apply the idea of BERT to other domains. To name a few examples: * Wav2Vec2 by Facebook AI illustrated that the architecture could be extended to audio * the Vision Transformer (ViT) by Google AI showed that the architecture works really well for vision * most recently the Video Vision transformer (ViViT), also by Google AI, applied the architecture to video. In all of these domains, state-of-the-art results were improved dramatically, thanks to the combination of this powerful architecture with large-scale pre-training. However, there's an important limitation to the architecture of the Transformer: due to its self-attention mechanism, it scales very poorly in both compute and memory. In every layer, all inputs are used to produce queries and keys, for which a pairwise dot product is computed. Hence, it is not possible to apply self-attention on high-dimensional data without some form of preprocessing. Wav2Vec2, for example, solves this by employing a feature encoder to turn a raw waveform into a sequence of time-based features. The Vision Transformer (ViT) divides an image into a sequence of non-overlapping patches, which serve as "tokens". The Video Vision Transformer (ViViT) extracts non-overlapping, spatio-temporal “tubes” from a video, which serve as "tokens". To make the Transformer work on a particular modality, one typically discretizes it to a sequence of tokens to make it work. | [
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Perceiver IO: a scalable, fully-attentional model that works on any modality | nielsr | December 15, 2021 | perceiver | research, guide, nlp, audio, cv | https://huggingface.co/blog/perceiver | Perceiver for images Now that we've seen how to apply the Perceiver to perform text classification, it is straightforward to apply the Perceiver to do image classification. The only difference is that we'll provide a different preprocessor to the model, which will embed the image inputs. The Perceiver authors actually tried out 3 different ways of preprocessing: - flattening the pixel values, applying a convolutional layer with kernel size 1 and adding learned absolute 1D position embeddings. - flattening the pixel values and adding fixed 2D Fourier position embeddings. - applying a 2D convolutional + maxpool layer and adding fixed 2D Fourier position embeddings. Each of these are implemented in the Transformers library, and called PerceiverForImageClassificationLearned, PerceiverForImageClassificationFourier and PerceiverForImageClassificationConvProcessing respectively. They only differ in their configuration of PerceiverImagePreprocessor. Let's take a closer look at PerceiverForImageClassificationLearned. It initializes a PerceiverModel as follows: python from torch import nn from transformers import PerceiverModel from transformers.models.perceiver.modeling_perceiver import PerceiverImagePreprocessor, PerceiverClassificationDecoder class PerceiverForImageClassificationLearned(nn.Module): def __init__(self, config): super().__init__(config) self.perceiver = PerceiverModel( config, input_preprocessor=PerceiverImagePreprocessor( config, prep_type="conv1x1", spatial_downsample=1, out_channels=256, position_encoding_type="trainable", concat_or_add_pos="concat", project_pos_dim=256, trainable_position_encoding_kwargs=dict(num_channels=256, index_dims=config.image_size ** 2), ), decoder=PerceiverClassificationDecoder( config, num_channels=config.d_latents, trainable_position_encoding_kwargs=dict(num_channels=config.d_latents, index_dims=1), use_query_residual=True, ), ) One can see that PerceiverImagePreprocessor is initialized with prep_type = "conv1x1" and that one adds arguments for the trainable position encodings. So how does this preprocessor work in detail? Suppose that one provides a batch of images to the model. Let's say one applies center cropping to a resolution of 224 and normalization of the color channels first, such that the inputs are of shape (batch_size, num_channels, height, width) = (batch_size, 3, 224, 224). One can use PerceiverImageProcessor for this, as follows: python from transformers import PerceiverImageProcessor import requests from PIL import Image processor = PerceiverImageProcessor.from_pretrained("deepmind/vision-perceiver") url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) inputs = processor(image, return_tensors="pt").pixel_values PerceiverImagePreprocessor (with the settings defined above) will first apply a convolutional layer with kernel size (1, 1) to turn the inputs into a tensor of shape (batch_size, 256, 224, 224) - hence increasing the channel dimension. It will then place the channel dimension last - so now one has a tensor of shape (batch_size, 224, 224, 256). Next, it flattens the spatial (height + width) dimensions such that one has a tensor of shape (batch_size, 50176, 256). Next, it concatenates it with trainable 1D position embeddings. As the dimensionality of the position embeddings is defined to be 256 (see the num_channels argument above), one is left with a tensor of shape (batch_size, 50176, 512). This tensor will be used for the cross-attention operation with the latents. The authors use 512 latents for all image models, and set the dimensionality of the latents to 1024. Hence, the latents are a tensor of shape (batch_size, 512, 1024) - assuming we add a batch dimension. The cross-attention layer takes the queries of shape (batch_size, 512, 1024) and keys + values of shape (batch_size, 50176, 512) as input, and produces a tensor that has the same shape as the queries, so outputs a new tensor of shape (batch_size, 512, 1024). Next, a block of 6 self-attention layers is applied repeatedly (8 times), to produce final hidden states of the latents of shape (batch_size, 512, 1024). To turn these into classification logits, PerceiverClassificationDecoder is used, which works similarly to the one for text classification: it uses the latents as keys + values, and uses trainable position embeddings of shape (batch_size, 1, num_labels) as queries. The output of the cross-attention operation is a tensor of shape (batch_size, 1, num_labels), which is squeezed to have classification logits of shape (batch_size, num_labels). The Perceiver authors show that the model is capable of achieving strong results compared to models designed primarily for image classification (such as ResNet or ViT). After large-scale pre-training on JFT, the model that uses conv+maxpool preprocessing (PerceiverForImageClassificationConvProcessing) achieves 84.5 top-1 accuracy on ImageNet. Remarkably, PerceiverForImageClassificationLearned, the model that only employs a 1D fully learned position encoding, achieves a top-1 accuracy of 72.7 despite having no privileged information about the 2D structure of images. | [
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Perceiver IO: a scalable, fully-attentional model that works on any modality | nielsr | December 15, 2021 | perceiver | research, guide, nlp, audio, cv | https://huggingface.co/blog/perceiver | TLDR We've added Perceiver IO to Transformers, the first Transformer-based neural network that works on all kinds of modalities (text, images, audio, video, point clouds,...) and combinations thereof. Take a look at the following Spaces to view some examples: - predicting optical flow between images - classifying images. We also provide several notebooks. Below, you can find a technical explanation of the model. | [
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Perceiver IO: a scalable, fully-attentional model that works on any modality | nielsr | December 15, 2021 | perceiver | research, guide, nlp, audio, cv | https://huggingface.co/blog/perceiver | Conclusion In this blog post, we went over the architecture of Perceiver IO, an extension of the Perceiver by Google Deepmind, and showed its generality of handling all kinds of modalities. The big advantage of the Perceiver is that the compute and memory requirements of the self-attention mechanism don't depend on the size of the inputs and outputs, as the bulk of compute happens in a latent space (a not-too large set of vectors). Despite its task-agnostic architecture, the model is capabable of achieving great results on modalities such as language, vision, multimodal data, and point clouds. In the future, it might be interesting to train a single (shared) Perceiver encoder on several modalities at the same time, and use modality-specific preprocessors and postprocessors. As Karpathy puts it, it may well be that this architecture can unify all modalities into a shared space, with a library of encoders/decoders. Speaking of a library, the model is available in HuggingFace Transformers as of today. It will be exciting to see what people build with it, as its applications seem endless! | [
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Perceiver IO: a scalable, fully-attentional model that works on any modality | nielsr | December 15, 2021 | perceiver | research, guide, nlp, audio, cv | https://huggingface.co/blog/perceiver | Appendix The implementation in HuggingFace Transformers is based on the original JAX/Haiku implementation which can be found here. The documentation of the Perceiver IO model in HuggingFace Transformers is available here. Tutorial notebooks regarding the Perceiver on several modalities can be found here. | [
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Perceiver IO: a scalable, fully-attentional model that works on any modality | nielsr | December 15, 2021 | perceiver | research, guide, nlp, audio, cv | https://huggingface.co/blog/perceiver | Perceiver for multimodal autoencoding The authors also use the Perceiver for multimodal autoencoding. The goal of multimodal autoencoding is to learn a model that can accurately reconstruct multimodal inputs in the presence of a bottleneck induced by an architecture. The authors train the model on the Kinetics-700 dataset, in which each example consists of a sequence of images (i.e. frames), audio and a class label (one of 700 possible labels). This model is also implemented in HuggingFace Transformers, and available as PerceiverForMultimodalAutoencoding. For brevity, I will omit the code of defining this model, but important to note is that it uses PerceiverMultimodalPreprocessor to prepare the inputs for the model. This preprocessor will first use the respective preprocessor for each modality (image, audio, label) separately. Suppose one has a video of 16 frames of resolution 224x224 and 30,720 audio samples, then the modalities are preprocessed as follows: - The images - actually a sequence of frames - of shape (batch_size, 16, 3, 224, 224) are turned into a tensor of shape (batch_size, 50176, 243) using PerceiverImagePreprocessor. This is a “space to depth” transformation, after which fixed 2D Fourier position embeddings are concatenated. - The audio has shape (batch_size, 30720, 1) and is turned into a tensor of shape (batch_size, 1920, 401) using PerceiverAudioPreprocessor (which concatenates fixed Fourier position embeddings to the raw audio). - The class label of shape (batch_size, 700) is turned into a tensor of shape (batch_size, 1, 700) using PerceiverOneHotPreprocessor. In other words, this preprocessor just adds a dummy time (index) dimension. Note that one initializes the class label with a tensor of zeros during evaluation, so as to let the model act as a video classifier. Next, PerceiverMultimodalPreprocessor will pad the preprocessed modalities with modality-specific trainable embeddings to make concatenation along the time dimension possible. In this case, the modality with the highest channel dimension is the class label (it has 700 channels). The authors enforce a minimum padding size of 4, hence each modality will be padded to have 704 channels. They can then be concatenated, hence the final preprocessed input is a tensor of shape (batch_size, 50176 + 1920 + 1, 704) = (batch_size, 52097, 704). The authors use 784 latents, with a dimensionality of 512 for each latent. Hence, the latents have shape (batch_size, 784, 512). After the cross-attention, one again has a tensor of the same shape (as the latents act as queries). Next, a single block of 8 self-attention layers (each of which has 8 attention heads) is applied to update the embeddings of the latents. Next, there is PerceiverMultimodalDecoder, which will first create output queries for each modality separately. However, as it is not possible to decode an entire video in a single forward pass, the authors instead auto-encode in chunks. Each chunk will subsample certain index dimensions for every modality. Let's say we process the video in 128 chunks, then the decoder queries will be produced as follows: - For the image modality, the total size of the decoder query is 16x3x224x224 = 802,816. However, when auto-encoding the first chunk, one subsamples the first 802,816/128 = 6272 values. The shape of the image output query is (batch_size, 6272, 195) - the 195 comes from the fact that fixed Fourier position embeddings are used. - For the audio modality, the total input has 30,720 values. However, one only subsamples the first 30720/128/16 = 15 values. Hence, the shape of the audio query is (batch_size, 15, 385). Here, the 385 comes from the fact that fixed Fourier position embeddings are used. - For the class label modality, there's no need to subsample. Hence, the subsampled index is set to 1. The shape of the label output query is (batch_size, 1, 1024). One uses trainable position embeddings (of size 1024) for the queries. Similarly to the preprocessor, PerceiverMultimodalDecoder pads the different modalities to the same number of channels, to make concatenation of the modality-specific queries possible along the time dimension. Here, the class label has again the highest number of channels (1024), and the authors enforce a minimum padding size of 2, hence every modality will be padded to have 1026 channels. After concatenation, the final decoder query has shape (batch_size, 6272 + 15 + 1, 1026) = (batch_size, 6288, 1026). This tensor produces queries in the cross-attention operation, while the latents act as keys and values. Hence, the output of the cross-attention operation is a tensor of shape (batch_size, 6288, 1026). Next, PerceiverMultimodalDecoder employs a linear layer to reduce the output channels to get a tensor of shape (batch_size, 6288, 512). Finally, there is PerceiverMultimodalPostprocessor. This class postprocesses the output of the decoder to produce an actual reconstruction of each modality. It first splits up the time dimension of the decoder output according to the different modalities: (batch_size, 6272, 512) for image, (batch_size, 15, 512) for audio and (batch_size, 1, 512) for the class label. Next, the respective postprocessors for each modality are applied: - The image post processor (which is called PerceiverProjectionPostprocessor in Transformers) simply turns the (batch_size, 6272, 512) tensor into a tensor of shape (batch_size, 6272, 3) - i.e. it projects the final dimension to RGB values. - PerceiverAudioPostprocessor turns the (batch_size, 15, 512) tensor into a tensor of shape (batch_size, 240). - PerceiverClassificationPostprocessor simply takes the first (and only index), to get a tensor of shape (batch_size, 700). So now one ends up with tensors containing the reconstruction of the image, audio and class label modalities respectively. As one auto-encodes an entire video in chunks, one needs to concatenate the reconstruction of each chunk to have a final reconstruction of an entire video. The figure below shows an example: Above: original video (left), reconstruction of the first 16 frames (right). Video taken from the UCF101 dataset. Below: reconstructed audio (taken from the paper). Top 5 predicted labels for the video above. By masking the class label, the Perceiver becomes a video classifier. With this approach, the model learns a joint distribution across 3 modalities. The authors do note that because the latent variables are shared across modalities and not explicitly allocated between them, the quality of reconstructions for each modality is sensitive to the weight of its loss term and other training hyperparameters. By putting stronger emphasis on classification accuracy, they are able to reach 45% top-1 accuracy while maintaining 20.7 PSNR (peak signal-to-noise ratio) for video. | [
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Perceiver IO: a scalable, fully-attentional model that works on any modality | nielsr | December 15, 2021 | perceiver | research, guide, nlp, audio, cv | https://huggingface.co/blog/perceiver | Perceiver for optical flow The authors show that it's straightforward to make the Perceiver also work on optical flow, which is a decades-old problem in computer vision, with many broader applications. For an introduction to optical flow, I refer to this blog post. Given two images of the same scene (e.g. two consecutive frames of a video), the task is to estimate the 2D displacement for each pixel in the first image. Existing algorithms are quite hand-engineered and complex, however with the Perceiver, this becomes relatively simple. The model is implemented in the Transformers library, and available as PerceiverForOpticalFlow. It is implemented as follows: python from torch import nn from transformers import PerceiverModel from transformers.models.perceiver.modeling_perceiver import PerceiverImagePreprocessor, PerceiverOpticalFlowDecoder class PerceiverForOpticalFlow(nn.Module): def __init__(self, config): super().__init__(config) fourier_position_encoding_kwargs_preprocessor = dict( num_bands=64, max_resolution=config.train_size, sine_only=False, concat_pos=True, ) fourier_position_encoding_kwargs_decoder = dict( concat_pos=True, max_resolution=config.train_size, num_bands=64, sine_only=False ) image_preprocessor = PerceiverImagePreprocessor( config, prep_type="patches", spatial_downsample=1, conv_after_patching=True, conv_after_patching_in_channels=54, temporal_downsample=2, position_encoding_type="fourier", # position_encoding_kwargs fourier_position_encoding_kwargs=fourier_position_encoding_kwargs_preprocessor, ) self.perceiver = PerceiverModel( config, input_preprocessor=image_preprocessor, decoder=PerceiverOpticalFlowDecoder( config, num_channels=image_preprocessor.num_channels, output_image_shape=config.train_size, rescale_factor=100.0, use_query_residual=False, output_num_channels=2, position_encoding_type="fourier", fourier_position_encoding_kwargs=fourier_position_encoding_kwargs_decoder, ), ) As one can see, PerceiverImagePreprocessor is used as preprocessor (i.e. to prepare the 2 images for the cross-attention operation with the latents) and PerceiverOpticalFlowDecoder is used as decoder (i.e. to decode the final hidden states of the latents to an actual predicted flow). For each of the 2 frames, the authors extract a 3 x 3 patch around each pixel, leading to 3 x 3 x 3 = 27 values for each pixel (as each pixel also has 3 color channels). The authors use a training resolution of (368, 496). If one stacks 2 frames of size (368, 496) of each training example on top of each other, the inputs to the model are of shape (batch_size, 2, 27, 368, 496). The preprocessor (with the settings defined above) will first concatenate the frames along the channel dimension, leading to a tensor of shape (batch_size, 368, 496, 54) - assuming one also moves the channel dimension to be last. The authors explain in their paper (page 8) why concatenation along the channel dimension makes sense. Next, the spatial dimensions are flattened, leading to a tensor of shape (batch_size, 368*496, 54) = (batch_size, 182528, 54). Then, position embeddings (each of which have dimensionality 258) are concatenated, leading to a final preprocessed input of shape (batch_size, 182528, 322). These will be used to perform cross-attention with the latents. The authors use 2048 latents for the optical flow model (yes, 2048!), with a dimensionality of 512 for each latent. Hence, the latents have shape (batch_size, 2048, 512). After the cross-attention, one again has a tensor of the same shape (as the latents act as queries). Next, a single block of 24 self-attention layers (each of which has 16 attention heads) are applied to update the embeddings of the latents. To decode the final hidden states of the latents to an actual predicted flow, PerceiverOpticalFlowDecoder simply uses the preprocessed inputs of shape (batch_size, 182528, 322) as queries for the cross-attention operation. Next, these are projected to a tensor of shape (batch_size, 182528, 2). Finally, one rescales and reshapes this back to the original image size to get a predicted flow of shape (batch_size, 368, 496, 2). The authors claim state-of-the-art results on important benchmarks including Sintel and KITTI when training on AutoFlow, a large synthetic dataset of 400,000 annotated image pairs. The video below shows the predicted flow on 2 examples. Optical flow estimation by Perceiver IO. The colour of each pixel shows the direction and speed of motion estimated by the model, as indicated by the legend on the right. | [
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Perceiver IO: a scalable, fully-attentional model that works on any modality | nielsr | December 15, 2021 | perceiver | research, guide, nlp, audio, cv | https://huggingface.co/blog/perceiver | Perceiver for text Suppose that one wants to apply the Perceiver to perform text classification. As the memory and time requirements of the Perceiver's self-attention mechanism don't depend on the size of the inputs, one can directly provide raw UTF-8 bytes to the model. This is beneficial, as familar Transformer-based models (like BERT and RoBERTa) all employ some form of explicit tokenization, such as WordPiece, BPE or SentencePiece, which may be harmful. For a fair comparison to BERT (which uses a sequence length of 512 subword tokens), the authors used input sequences of 2048 bytes. Let's say one also adds a batch dimension, then the inputs to the model are of shape (batch_size, 2048). The inputs contain the byte IDs (similar to the input_ids of BERT) for a single piece of text. One can use PerceiverTokenizer to turn a text into a sequence of byte IDs, padded up to a length of 2048: python from transformers import PerceiverTokenizer tokenizer = PerceiverTokenizer.from_pretrained("deepmind/language-perceiver") text = "hello world" inputs = tokenizer(text, padding="max_length", return_tensors="pt").input_ids In this case, one provides PerceiverTextPreprocessor as preprocessor to the model, which will take care of embedding the inputs (i.e. turn each byte ID into a corresponding vector), as well as adding absolute position embeddings. As decoder, one provides PerceiverClassificationDecoder to the model (which will turn the last hidden states of the latents into classification logits). No postprocessor is required. In other words, a Perceiver model for text classification (which is called PerceiverForSequenceClassification in HuggingFace Transformers) is implemented as follows: python from torch import nn from transformers import PerceiverModel from transformers.models.perceiver.modeling_perceiver import PerceiverTextPreprocessor, PerceiverClassificationDecoder class PerceiverForSequenceClassification(nn.Module): def __init__(self, config): super().__init__(config) self.perceiver = PerceiverModel( config, input_preprocessor=PerceiverTextPreprocessor(config), decoder=PerceiverClassificationDecoder( config, num_channels=config.d_latents, trainable_position_encoding_kwargs=dict(num_channels=config.d_latents, index_dims=1), use_query_residual=True, ), ) One can already see here that the decoder is initialized with trainable position encoding arguments. Why is that? Well, let's take a look in detail at how Perceiver IO works. At initialization, PerceiverModel internally defines a set of latent variables, as follows: python from torch import nn self.latents = nn.Parameter(torch.randn(config.num_latents, config.d_latents)) In the Perceiver IO paper, one uses 256 latents, and sets the dimensionality of the latents to 1280. If one also adds a batch dimension, the Perceiver has latents of shape (batch_size, 256, 1280). First, the preprocessor (which one provides at initialization) will take care of embedding the UTF-8 byte IDs to embedding vectors. Hence, PerceiverTextPreprocessor will turn the inputs of shape (batch_size, 2048) to a tensor of shape (batch_size, 2048, 768) - assuming that each byte ID is turned into a vector of size 768 (this is determined by the d_model attribute of PerceiverConfig). After this, Perceiver IO applies cross-attention between the latents (which produce queries) of shape (batch_size, 256, 1280) and the preprocessed inputs (which produce keys and values) of shape (batch_size, 2048, 768). The output of this initial cross-attention operation is a tensor that has the same shape as the queries (which are the latents, in this case). In other words, the output of the cross-attention operation is of shape (batch_size, 256, 1280). Next, a (repeatable) block of self-attention layers is applied to update the representations of the latents. Note that these don't depend on the length of the inputs (i.e. the bytes) one provided, as these were only used during the cross-attention operation. In the Perceiver IO paper, a single block of 26 self-attention layers (each of which has 8 attention heads) were used to update the representations of the latents of the text model. Note that the output after these 26 self-attention layers still has the same shape as what one initially provided as input to the encoder: (batch_size, 256, 1280). These are also called the "last hidden states" of the latents. This is very similar to the "last hidden states" of the tokens one provides to BERT. Ok, so now one has final hidden states of shape (batch_size, 256, 1280). Great, but one actually wants to turn these into classification logits of shape (batch_size, num_labels). How can we make the Perceiver output these? This is handled by PerceiverClassificationDecoder. The idea is very similar to what was done when mapping the inputs to the latent space: one uses cross-attention. But now, the latent variables will produce keys and values, and one provides a tensor of whatever shape we'd like - in this case we'll provide a tensor of shape (batch_size, 1, num_labels) which will act as queries (the authors refer to these as "decoder queries", because they are used in the decoder). This tensor will be randomly initialized at the beginning of training, and trained end-to-end. As one can see, one just provides a dummy sequence length dimension of 1. Note that the output of a QKV attention layer always has the same shape as the shape of the queries - hence the decoder will output a tensor of shape (batch_size, 1, num_labels). The decoder then simply squeezes this tensor to have shape (batch_size, num_labels) and boom, one has classification logits1. Great, isn't it? The Perceiver authors also show that it is straightforward to pre-train the Perceiver for masked language modeling, similar to BERT. This model is also available in HuggingFace Transformers, and called PerceiverForMaskedLM. The only difference with PerceiverForSequenceClassification is that it doesn't use PerceiverClassificationDecoder as decoder, but rather PerceiverBasicDecoder, to decode the latents to a tensor of shape (batch_size, 2048, 1280). After this, a language modeling head is added, which turns it into a tensor of shape (batch_size, 2048, vocab_size). The vocabulary size of the Perceiver is only 262, namely the 256 UTF-8 byte IDs, as well as 6 special tokens. By pre-training the Perceiver on English Wikipedia and C4, the authors show that it is possible to achieve an overall score of 81.8 on GLUE after fine-tuning. | [
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Perceiver IO: a scalable, fully-attentional model that works on any modality | nielsr | December 15, 2021 | perceiver | research, guide, nlp, audio, cv | https://huggingface.co/blog/perceiver | # Perceiver IO: a scalable, fully-attentional model that works on any modality | [
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Perceiver IO: a scalable, fully-attentional model that works on any modality | nielsr | December 15, 2021 | perceiver | research, guide, nlp, audio, cv | https://huggingface.co/blog/perceiver | The Perceiver The Perceiver aims to solve this limitation by employing the self-attention mechanism on a set of latent variables, rather than on the inputs. The inputs (which could be text, image, audio, video) are only used for doing cross-attention with the latents. This has the advantage that the bulk of compute happens in a latent space, where compute is cheap (one typically uses 256 or 512 latents). The resulting architecture has no quadratic dependence on the input size: the Transformer encoder only depends linearly on the input size, while latent attention is independent of it. In a follow-up paper, called Perceiver IO, the authors extend this idea to let the Perceiver also handle arbitrary outputs. The idea is similar: one only uses the outputs for doing cross-attention with the latents. Note that I'll use the terms "Perceiver" and "Perceiver IO" interchangeably to refer to the Perceiver IO model throughout this blog post. In the following section, we look in a bit more detail at how Perceiver IO actually works by going over its implementation in HuggingFace Transformers, a popular library that initially implemented Transformer-based models for NLP, but is now starting to implement them for other domains as well. In the sections below, we explain in detail - in terms of shapes of tensors - how the Perceiver actually pre and post processes modalities of any kind. All Perceiver variants in HuggingFace Transformers are based on the PerceiverModel class. To initialize a PerceiverModel, one can provide 3 additional instances to the model: - a preprocessor - a decoder - a postprocessor. Note that each of these are optional. A preprocessor is only required in case one hasn't already embedded the inputs (such as text, image, audio, video) themselves. A decoder is only required in case one wants to decode the output of the Perceiver encoder (i.e. the last hidden states of the latents) into something more useful, such as classification logits or optical flow. A postprocessor is only required in case one wants to turn the output of the decoder into a specific feature (this is only required when doing auto-encoding, as we will see further). An overview of the architecture is depicted below. The Perceiver architecture. In other words, the inputs (which could be any modality, or a combination thereof) are first optionally preprocessed using a preprocessor. Next, the preprocessed inputs perform a cross-attention operation with the latent variables of the Perceiver encoder. In this operation, the latent variables produce queries (Q), while the preprocessed inputs produce keys and values (KV). After this operation, the Perceiver encoder employs a (repeatable) block of self-attention layers to update the embeddings of the latents. The encoder will finally produce a tensor of shape (batch_size, num_latents, d_latents), containing the last hidden states of the latents. Next, there's an optional decoder, which can be used to decode the final hidden states of the latents into something more useful, such as classification logits. This is done by performing a cross-attention operation, in which trainable embeddings are used to produce queries (Q), while the latents are used to produce keys and values (KV). Finally, there's an optional postprocessor, which can be used to postprocess the decoder outputs to specific features. Let's start off by showing how the Perceiver is implemented to work on text. | [
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Perceiver IO: a scalable, fully-attentional model that works on any modality | nielsr | December 15, 2021 | perceiver | research, guide, nlp, audio, cv | https://huggingface.co/blog/perceiver | Other applications of the Perceiver Note that there are no limits on the applications of the Perceiver! In the original Perceiver paper, the authors showed that the architecture can be used to process 3D point clouds – a common concern for self-driving cars equipped with Lidar sensors. They trained the model on ModelNet40, a dataset of point clouds derived from 3D triangular meshes spanning 40 object categories. The model was shown to achieve a top-1 accuracy of 85.7 % on the test set, competing with PointNet++, a highly specialized model that uses extra geometric features and performs more advanced augmentations. The authors also used the Perceiver to replace the original Transformer in AlphaStar, the state-of-the-art reinforcement learning system for the complex game of StarCraft II. Without tuning any additional parameters, the authors observed that the resulting agent reached the same level of performance as the original AlphaStar agent, reaching an 87% win-rate versus the Elite bot after behavioral cloning on human data. It is important to note that the models currently implemented (such as PerceiverForImageClassificationLearned, PerceiverForOpticalFlow) are just examples of what you can do with the Perceiver. Each of these are different instances of PerceiverModel, just with a different preprocessor and/or decoder (and optionally, a postprocessor as is the case for multimodal autoencoding). People can come up with new preprocessors, decoders and postprocessors to make the model solve different problems. For instance, one could extend the Perceiver to perform named-entity recognition (NER) or question-answering similar to BERT, audio classification similar to Wav2Vec2 or object detection similar to DETR. | [
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Gradio joins Hugging Face! | abidlabs | December 21, 2021 | gradio-joins-hf | community, open-source-collab | https://huggingface.co/blog/gradio-joins-hf | # Gradio is joining Hugging Face! Gradio is joining Hugging Face! By acquiring Gradio, a machine learning startup, Hugging Face will be able to offer users, developers, and data scientists the tools needed to get to high level results and create better models and tools... Hmm, paragraphs about acquisitions like the one above are so common that an algorithm could write them. In fact, one did!! This first paragraph was written with the Acquisition Post Generator, a machine learning demo on Hugging Face Spaces. You can run it yourself in your browser: provide the names of any two companies and you'll get a reasonable-sounding start to an article announcing their acquisition! The Acquisition Post Generator was built using our open-source Gradio library -- it is just one of our recent collaborations with Hugging Face. And I'm excited to announce that these collaborations are culminating in... 🥁 Hugging Face's acquisition of Gradio (so yes, that first paragraph might have been written by an algorithm but it's true!) As one of the founders of Gradio, I couldn't be more excited about the next step in our journey. I still remember clearly how we started in 2019: as a PhD student at Stanford, I struggled to share a medical computer vision model with one of my collaborators, who was a doctor. I needed him to test my machine learning model, but he didn't know Python and couldn't easily run the model on his own images. I envisioned a tool that could make it super simple for machine learning engineers to build and share demos of computer vision models, which in turn would lead to better feedback and more reliable models 🔁 I recruited my talented housemates Ali Abdalla, Ali Abid, and Dawood Khan to release the first version of Gradio in 2019. We steadily expanded to cover more areas of machine learning including text, speech, and video. We found that it wasn't just researchers who needed to share machine learning models: interdisciplinary teams in industry, from startups to public companies, were building models and needed to debug them internally or showcase them externally. Gradio could help with both. Since we first released the library, more than 300,000 demos have been built with Gradio. We couldn't have done this without our community of contributors, our supportive investors, and the amazing Ahsen Khaliq who joined our company this year. Demos and GUIs built with Gradio give the power of machine learning to more and more people because they allow non-technical users to access, use, and give feedback on models. And our acquisition by Hugging Face is the next step in this ongoing journey of accessibility. Hugging Face has already radically democratized machine learning so that any software engineer can use state-of-the-art models with a few lines of code. By working together with Hugging Face, we're taking this even further so that machine learning is accessible to literally anyone with an internet connection and a browser. With Hugging Face, we are going to keep growing Gradio and make it the best way to share your machine learning model with anyone, anywhere 🚀 In addition to the shared mission of Gradio and Hugging Face, what delights me is the team that we are joining. Hugging Face's remarkable culture of openness and innovation is well-known. Over the past few months, I've gotten to know the founders as well: they are wonderful people who genuinely care about every single person at Hugging Face and are willing to go to bat for them. On behalf of the entire Gradio team, we couldn't be more thrilled to be working with them to build the future of machine learning 🤗 Also: we are hiring!! ❤️ | [
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Active Learning with AutoNLP and Prodigy | abhishek | December 23, 2021 | autonlp-prodigy | research, partnerships, nlp | https://huggingface.co/blog/autonlp-prodigy | # Active Learning with AutoNLP and Prodigy Active learning in the context of Machine Learning is a process in which you iteratively add labeled data, retrain a model and serve it to the end user. It is an endless process and requires human interaction for labeling/creating the data. In this article, we will discuss how to use AutoNLP and Prodigy to build an active learning pipeline. | [
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Active Learning with AutoNLP and Prodigy | abhishek | December 23, 2021 | autonlp-prodigy | research, partnerships, nlp | https://huggingface.co/blog/autonlp-prodigy | Active Learning The dataset we used did have categories but it didn't have labels for entity recognition. So, we decided to use Prodigy to label the dataset for another task: named entity recognition. Once you have Prodigy installed, you can simply run: $ prodigy ner.manual bbc blank:en BBC_News_Train.csv --label PERSON,ORG,PRODUCT,LOCATION Let's look at the different values: * bbc is the dataset that will be created by Prodigy. * blank:en is the spaCy tokenizer being used. * BBC_News_Train.csv is the dataset that will be used for labeling. * PERSON,ORG,PRODUCT,LOCATION is the list of labels that will be used for labeling. Once you run the above command, you can go to the prodigy web interface (usually at localhost:8080) and start labelling the dataset. Prodigy interface is very simple, intuitive and easy to use. The interface looks like the following: All you have to do is select which entity you want to label (PERSON, ORG, PRODUCT, LOCATION) and then select the text that belongs to the entity. Once you are done with one document, you can click on the green button and Prodigy will automatically provide you with next unlabelled document. Using Prodigy, we started labelling the dataset. When we had around 20 samples, we trained a model using AutoNLP. Prodigy doesn't export the data in AutoNLP format, so we wrote a quick and dirty script to convert the data into AutoNLP format: python import json import spacy from prodigy.components.db import connect db = connect() prodigy_annotations = db.get_dataset("bbc") examples = ((eg["text"], eg) for eg in prodigy_annotations) nlp = spacy.blank("en") dataset = [] for doc, eg in nlp.pipe(examples, as_tuples=True): try: doc.ents = [doc.char_span(s["start"], s["end"], s["label"]) for s in eg["spans"]] iob_tags = [f"{t.ent_iob_}-{t.ent_type_}" if t.ent_iob_ else "O" for t in doc] iob_tags = [t.strip("-") for t in iob_tags] tokens = [str(t) for t in doc] temp_data = { "tokens": tokens, "tags": iob_tags } dataset.append(temp_data) except: pass with open('data.jsonl', 'w') as outfile: for entry in dataset: json.dump(entry, outfile) outfile.write('\n') This will provide us with a JSONL file which can be used for training a model using AutoNLP. The steps will be same as before except we will select Token Classification task when creating the AutoNLP project. Using the initial data we had, we trained a model using AutoNLP. The best model had an accuracy of around 86% with 0 precision and recall. We knew the model didn't learn anything. It's pretty obvious, we had only around 20 samples. After labelling around 70 samples, we started getting some results. The accuracy went up to 92%, precision was 0.52 and recall around 0.42. We were getting some results, but still not satisfactory. In the following image, we can see how this model performs on an unseen sample. As you can see, the model is struggling. But it's much better than before! Previously, the model was not even able to predict anything in the same text. At least now, it's able to figure out that Bruce and David are names. Thus, we continued. We labelled a few more samples. Please note that, in each iteration, our dataset is getting bigger. All we are doing is uploading the new dataset to AutoNLP and let it do the rest. After labelling around ~150 samples, we started getting some good results. The accuracy went up to 95.7%, precision was 0.64 and recall around 0.76. Let's take a look at how this model performs on the same unseen sample. WOW! This is amazing! As you can see, the model is now performing extremely well! Its able to detect many entities in the same text. The precision and recall were still a bit low and thus we continued labeling even more data. After labeling around ~250 samples, we had the best results in terms of precision and recall. The accuracy went up to ~95.9% and precision and recall were 0.73 and 0.79 respectively. At this point, we decided to stop labelling and end the experimentation process. The following graph shows how the accuracy of best model improved as we added more samples to the dataset: Well, it's a well known fact that more relevant data will lead to better models and thus better results. With this experimentation, we successfully created a model that can not only classify the entities in the news articles but also categorize them. Using tools like Prodigy and AutoNLP, we invested our time and effort only to label the dataset (even that was made simpler by the interface prodigy offers). AutoNLP saved us a lot of time and effort: we didn't have to figure out which models to use, how to train them, how to evaluate them, how to tune the parameters, which optimizer and scheduler to use, pre-processing, post-processing etc. We just needed to label the dataset and let AutoNLP do everything else. We believe with tools like AutoNLP and Prodigy it's very easy to create data and state-of-the-art models. And since the whole process requires almost no coding at all, even someone without a coding background can create datasets which are generally not available to the public, train their own models using AutoNLP and share the model with everyone else in the community (or just use them for their own research / business). We have open-sourced the best model created using this process. You can try it here. The labelled dataset can also be downloaded here. Models are only state-of-the-art because of the data they are trained on. | [
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Active Learning with AutoNLP and Prodigy | abhishek | December 23, 2021 | autonlp-prodigy | research, partnerships, nlp | https://huggingface.co/blog/autonlp-prodigy | Dataset Now begins the most interesting part of this article. After looking at a lot of datasets and different types of problems, we stumbled upon BBC News Classification dataset on Kaggle. This dataset was used in an inclass competition and can be accessed here. Let's take a look at this dataset: As we can see this is a classification dataset. There is a Text column which is the text of the news article and a Category column which is the class of the article. Overall, there are 5 different classes: business, entertainment, politics, sport & tech. Training a multi-class classification model on this dataset using AutoNLP is a piece of cake. Step 1: Download the dataset. Step 2: Open AutoNLP and create a new project. Step 3: Upload the training dataset and choose auto-splitting. Step 4: Accept the pricing and train your models. Please note that in the above example, we are training 15 different multi-class classification models. AutoNLP pricing can be as low as $10 per model. AutoNLP will select the best models and do hyperparameter tuning for you on its own. So, now, all we need to do is sit back, relax and wait for the results. After around 15 minutes, all models finished training and the results are ready. It seems like the best model scored 98.67% accuracy! So, we are now able to classify the articles in the dataset with an accuracy of 98.67%! But wait, we were talking about active learning and Prodigy. What happened to those? 🤔 We did use Prodigy as we will see soon. We used it to label this dataset for the named entity recognition task. Before starting the labeling part, we thought it would be cool to have a project in which we are not only able to detect the entities in news articles but also categorize them. That's why we built this classification model on existing labels. | [
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Active Learning with AutoNLP and Prodigy | abhishek | December 23, 2021 | autonlp-prodigy | research, partnerships, nlp | https://huggingface.co/blog/autonlp-prodigy | AutoNLP AutoNLP is a framework created by Hugging Face that helps you to build your own state-of-the-art deep learning models on your own dataset with almost no coding at all. AutoNLP is built on the giant shoulders of Hugging Face's transformers, datasets, inference-api and many other tools. With AutoNLP, you can train SOTA transformer models on your own custom dataset, fine-tune them (automatically) and serve them to the end-user. All models trained with AutoNLP are state-of-the-art and production-ready. At the time of writing this article, AutoNLP supports tasks like binary classification, regression, multi class classification, token classification (such as named entity recognition or part of speech), question answering, summarization and more. You can find a list of all the supported tasks here. AutoNLP supports languages like English, French, German, Spanish, Hindi, Dutch, Swedish and many more. There is also support for custom models with custom tokenizers (in case your language is not supported by AutoNLP). | [
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Active Learning with AutoNLP and Prodigy | abhishek | December 23, 2021 | autonlp-prodigy | research, partnerships, nlp | https://huggingface.co/blog/autonlp-prodigy | Prodigy Prodigy is an annotation tool developed by Explosion (the makers of spaCy). It is a web-based tool that allows you to annotate your data in real time. Prodigy supports NLP tasks such as named entity recognition (NER) and text classification, but it's not limited to NLP! It supports Computer Vision tasks and even creating your own tasks! You can try the Prodigy demo: here. Note that Prodigy is a commercial tool. You can find out more about it here. We chose Prodigy as it is one of the most popular tools for labeling data and is infinitely customizable. It is also very easy to setup and use. | [
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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | Usage best practices When using generative models, most of the time you want to configure or customize your prediction to fit your needs, for example by using beam search, configuring the max or min length of the generated sequence, or adjust the temperature to reduce repetition. The Transformers library provides different strategies and kwargs to do this, the Hugging Face Inference toolkit offers the same functionality using the parameters attribute of your request payload. Below you can find examples on how to generate text without parameters, with beam search, and using custom configurations. If you want to learn about different decoding strategies check out this blog post. | [
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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | Tutorial With this method of saving and loading models, we achieved model loading performance for GPT-J compatible with production scenarios. But we need to keep in mind that we need to align: > Align PyTorch and Transformers version when saving the model with torch.save(model,PATH) and loading the model with torch.load(PATH) to avoid incompatibility. > | [
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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | Parameterized request This is an example of a request using a custom parameter, e.g. min_length for generating at least 512 tokens. Inference-time after the first request: 38s python predictor.predict({ "inputs": "Can you please let us know more details about your ", "parameters" : { "max_length": 512, "temperature": 0.9, } }) | [
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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | Few-Shot example (advanced) This is an example of how you could eos_token_id to stop the generation on a certain token, e.g. \n ,. or | [
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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | Conclusion We successfully managed to deploy GPT-J, a 6 billion parameter language model created by EleutherAI, using Amazon SageMaker. We reduced the model load time from 3.5 minutes down to 8 seconds to be able to run scalable, reliable inference. Remember that using torch.save() and torch.load() can create incompatibility issues. If you want to learn more about scaling out your Amazon SageMaker Endpoints check out my other blog post: “MLOps: End-to-End Hugging Face Transformers with the Hub & SageMaker Pipelines”. --- Thanks for reading! If you have any question, feel free to contact me, through Github, or on the forum. You can also connect with me on Twitter or LinkedIn. | [
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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | Deploy GPT-J as Amazon SageMaker Endpoint To deploy our Amazon SageMaker Endpoint we are going to use the Amazon SageMaker Python SDK and the HuggingFaceModel class. The snippet below uses the get_execution_role which is only available inside Amazon SageMaker Notebook Instances or Studio. If you want to deploy a model outside of it check the documentation. The model_uri defines the location of our GPT-J model artifact. We are going to use the publicly available one provided by us. python from sagemaker.huggingface import HuggingFaceModel import sagemaker # IAM role with permissions to create endpoint role = sagemaker.get_execution_role() # public S3 URI to gpt-j artifact model_uri="s3://huggingface-sagemaker-models/transformers/4.12.3/pytorch/1.9.1/gpt-j/model.tar.gz" # create Hugging Face Model Class huggingface_model = HuggingFaceModel( model_data=model_uri, transformers_version='4.12.3', pytorch_version='1.9.1', py_version='py38', role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.g4dn.xlarge' #'ml.p3.2xlarge' # ec2 instance type ) If you want to use your own model.tar.gz just replace the model_uri with your S3 Uri. The deployment should take around 3-5 minutes. | [
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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | Run predictions We can run predictions using the predictor instances created by our .deploy method. To send a request to our endpoint we use the predictor.predict with our inputs. python predictor.predict({ "inputs": "Can you please let us know more details about your " }) If you want to customize your predictions using additional kwargs like min_length, check out “Usage best practices” below. | [
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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | Create model.tar.gz for the Amazon SageMaker real-time endpoint Since we can load our model quickly and run inference on it let’s deploy it to Amazon SageMaker. There are two ways you can deploy transformers to Amazon SageMaker. You can either “Deploy a model from the Hugging Face Hub” directly or “Deploy a model with model_data stored on S3”. Since we are not using the default Transformers method we need to go with the second option and deploy our endpoint with the model stored on S3. For this, we need to create a model.tar.gz artifact containing our model weights and additional files we need for inference, e.g. tokenizer.json. We provide uploaded and publicly accessible model.tar.gz artifacts, which can be used with the HuggingFaceModel to deploy GPT-J to Amazon SageMaker. See “Deploy GPT-J as Amazon SageMaker Endpoint” on how to use them. If you still want or need to create your own model.tar.gz, e.g. because of compliance guidelines, you can use the helper script convert_gpt.py for this purpose, which creates the model.tar.gz and uploads it to S3. bash # clone directory git clone https://github.com/philschmid/amazon-sagemaker-gpt-j-sample.git # change directory to amazon-sagemaker-gpt-j-sample cd amazon-sagemaker-gpt-j-sample # create and upload model.tar.gz pip3 install -r requirements.txt python3 convert_gptj.py --bucket_name {model_storage} The convert_gpt.py should print out an S3 URI similar to this. s3://hf-sagemaker-inference/gpt-j/model.tar.gz. | [
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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | key: hugging face tweet:""" predictor.predict({ 'inputs': prompt, "parameters" : { "max_length": int(len(prompt) + max_generated_token_length), "temperature": float(temperature), "eos_token_id": int(tokenizer.convert_tokens_to_ids(end_sequence)), "return_full_text":False } }) --- To delete your endpoint you can run. python predictor.delete_endpoint() | [
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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | key: startups tweet: Startups shouldn’t worry about how to put out fires, they should worry about how to start them. | [
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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | for few-shot predictions. Below is a few-shot example for generating tweets for keywords. Inference-time after the first request: 15-45s python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B") end_sequence="###" temperature=4 max_generated_token_length=25 prompt= """key: markets tweet: Take feedback from nature and markets, not from people. | [
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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | Save GPT-J using torch.save To create our torch.load() compatible model file we load GPT-J using Transformers and the from_pretrained method, and then save it with torch.save(). python from transformers import AutoTokenizer,GPTJForCausalLM import torch # load fp 16 model model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16) # save model with torch.save torch.save(model, "gptj.pt") Now we are able to load our GPT-J model with torch.load() to run predictions. python from transformers import pipeline import torch # load model model = torch.load("gptj.pt") # load tokenizer tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B") # create pipeline gen = pipeline("text-generation",model=model,tokenizer=tokenizer,device=0) # run prediction gen("My Name is philipp") #[{'generated_text': 'My Name is philipp k. and I live just outside of Detroit.... --- | [
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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | Beam search request This is an example of a request using beam search with 5 beams. Inference-time after the first request: 3.3s python predictor.predict({ "inputs": "Can you please let us know more details about your ", "parameters" : { "num_beams": 5, } }) | [
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