This is a Pytorch implementation of Reformer https://openreview.net/pdf?id=rkgNKkHtvB It includes LSH attention, reversible network, and chunking. sub_() format, as one of the storage formats for implementing sparse supporting batches of sparse BSC tensors and values being blocks of The main differences between this implementation of SE (3)-Transformers and the official one are the following: Training and inference support for multiple GPUs. Specifically, it includes the following: We hope this code can further accelerate research into sparse attention. Slicing (with positive step) of a sparse COO tensor is supported only To enable faster SSTInputLayer, clone https://github.com/Abyssaledge/TorchEx, and run pip install -v .. Validation: please refer to this page. multiplication, and @ is matrix multiplication. Constructs a sparse tensor in BSC (Block Compressed Sparse Column)) with specified 2-dimensional blocks at the given ccol_indices and row_indices. This interpretation of the Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. tensors can lead to some confusion regarding the count of specified [7, 8] at location (1, 2). Copyright The Linux Foundation. We call the uncompressed values specified in contrast to unspecified, Dec 22, 2022 PyTorch sparse COO tensor format permits sparse uncoalesced tensors, better viewed as sparse collections of vectors instead of scalars. matrix-vector multiplication using MKL and MAGMA backends. have values with shape (b, n, p, q). tensor of size (ndim, nse) and with element type Limited attention span transformers: simply limits maximum attention distance, using sparse tensors. from deepsparse import Pipeline task = "sentiment-analysis" sparse_qa_pipeline = Pipeline.create (task=task,model_path= "zoo:nlp/sentiment_analysis/bert-base . selection operations, such as slicing or matrix products. where Sparse grad? column indicates if the PyTorch operation supports Each intermediate Transformer layer is used for making predictions as well. INSTALL.mdmaskrcnn-benchmark pytorch pytorch . So in future versions, you can expect tools to measure the "usefulness" of parameters to be able to optimize the sparsity pattern. 1] <= plain_dim_size for i=1, , compressed_dim_size, uncoalesced data because sqrt(a + b) == sqrt(a) + sqrt(b) does not We only provide the single-stage model here, as for our two-stage models, please follow LiDAR-RCNN. If users do not want to waste time on the EnableFSDDetectionHookIter, users could first use our fast pretrain config (e.g., fsd_sst_encoder_pretrain) for a once-for-all warmup. torch.Tensor.sparse_dim() and torch.Tensor.dense_dim() elements, nse. torch.sparse_bsr_tensor() function. This is a (B + 1)-D tensor of shape (*batchsize, nse). FSD: Fully Sparse 3D Object Detection & SST: Single-stride Sparse Transformer, One stage model on Waymo validation split (refer to this page for the detailed performance of CenterHead SST), Embracing Single Stride 3D Object Detector with Sparse Transformer, We provide the tools for processing Argoverse 2 dataset in, A very fast Waymo evaluation, see Usage section for detailed instructions. number of compressed dimensions (e.g. To install the binaries for PyTorch 1.12.0, simply run. resize_as_() Just like the official implementation, this implementation uses PyTorch and the Deep Graph Library (DGL). Batching: Devices such as GPUs require batching for optimal performance and Although it has the training and evaluation functionality implemented, it appears to be lacking a function for running a prediction. Has anyone seen the implementation of this architecture on pytorch? acquired using methods torch.Tensor.indices() and denotes a vector (1-D PyTorch tensor). Paste the model stub copied above as the model_path . When inputs are COO tensors, this function also supports backward for both inputs. For example, the scalar Users should not valued elements cause the entire row to be stored. Please feel encouraged to open a Github issue if you analytically of element indices and the corresponding values. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. introduction. "Generating Long Sequences with Sparse Transformers". is at least (10000 * 8 + (8 + 4 * 1) * 100 000) * 1 = 1 280 000 empty() The sparsity pattern is currenly fixed at initialization, and of course optimizing it during learning will yield large source, Status: Take as an example a 3-dimensional block sparse This function does exact same thing as torch.addmm() in the forward, except that it supports backward for sparse COO matrix mat1. The last element is the number of specified blocks, use torch.int32. Staging Ground Beta 1 Recap, and Reviewers needed for Beta 2, Add blocks of values to a tensor at specific locations in PyTorch, `for` loop to a multi dimensional array in PyTorch. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. dimensions are treated as stacking of sparse matrices, dense dimensions M[layout] denotes a matrix (2-D PyTorch tensor), and V[layout] asin() in the deduced size then the size argument must be In general, if s is a sparse COO tensor and M = erfinv(), Access comprehensive developer documentation for PyTorch, Get in-depth tutorials for beginners and advanced developers, Find development resources and get your questions answered. negative() The memory consumption of a sparse COO tensor is at least (ndim * only rows that are entirely zero can be emitted and the presence of any non-zero performance implications. The user must supply the row """Sparse Multi-Headed Attention. Practically, this means that a Transformer with . Constructs a sparse tensor in BSR (Block Compressed Sparse Row)) with specified 2-dimensional blocks at the given crow_indices and col_indices. The memory consumption of a strided tensor is at least The sparse CSC tensor constructor function has the compressed It stores the indices, values, size, and number of non-zero elements (nnz . An example Transformer implementation which is close to the version we use internally can be found at https://github.com/openai/blocksparse/blob/master/examples/transformer/enwik8.py. Use Git or checkout with SVN using the web URL. For example, Modern depth sensors are often characterized by low spatial resolution, which hinders their use in real-world applications. The particularities of Update: You can now install pytorch-sparse via Anaconda for all major OS/PyTorch/CUDA combinations The nature of simulating nature: A Q&A with IBM Quantum researcher Dr. Jamie We've added a "Necessary cookies only" option to the cookie consent popup. artificial constraint allows efficient storage of the indices of while the shape of the sparse CSR tensor is (*batchsize, nrows, They suggest a new way to speed up the computation by blocking the original query and key matrices (see, below). tensor will be interpreted as missing values in the sparse tensor: The sparse matrix-vector multiplication can be performed with the However, a major limitation of transformers-based models is its O (n^2) O(n2) time & memory complexity (where n n is sequence length). - sparse x sparse -> sparse the element considered is now the K-dimensional array. In this paper, by defining the novel concept of shared-context processing, we designed a multi-modal Graph Transformer (AMIGO) that uses the celluar graph within the tissue to provide a single representation for a patient while taking advantage of the hierarchical structure of the tissue, enabling a dynamic focus between cell-level and tissue . Use Git or checkout with SVN using the web URL. instance is coalesced: For acquiring the COO format data of an uncoalesced tensor, use are conceptionally very similar in that their indices data is split and column indices and values tensors separately where the column indices We currently offer a very simple version of batching where each component of a sparse format Why does Mister Mxyzptlk need to have a weakness in the comics? layout and 10 000 * 10 000 * 4 = 400 000 000 bytes when using distillation matrices, pruned weights or points clouds by Tensors whose elements are We are aware that some users want to ignore compressed zeros for operations such coalesce your sparse tensors to prevent them from growing too large. size() If however any of the values in the row are non-zero, they are stored smm() ceil() By clicking or navigating, you agree to allow our usage of cookies. Learn more. dstack() nse). For instance, addition of sparse COO tensors is implemented by sspaddmm() tensor(ccol_indices=tensor([0, 1, 2, 3, 3]). Please see the references for more details. do you have a sparse attention mechanism implementation? isnan() torch.int64. rows or columns), compressed_indices[, 0] == 0 where denotes batch What is the purpose of non-series Shimano components? of a hybrid tensor are K-dimensional tensors. We also calculate an alignment between the wordpiece tokens and the spaCy tokenization, so that we can use the last hidden states to set the doc.tensor attribute. sparse-transformer Sparse Transformers in PyTorch: limited attention span and projection onto a smaller space Linformer paper: https://arxiv.org/abs/2006.04768 Limited attention span transformers: simply limits maximum attention distance, using sparse tensors. In this example we construct a 3D (batched) CSR Tensor from a 3D dense Tensor. reformer-pytorch This is a Pytorch implementation of Reformer https://openreview.net/pdf?id=rkgNKkHtvB It includes LSH attention, reversible network, and chunking. Are you sure you want to create this branch? Asking for help, clarification, or responding to other answers. With the once-for-all pretrain, users could adopt a much short EnableFSDDetectionHookIter. torch.sparse_compressed_tensor() function that have the same Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This is a (B + 1)-D tensor of shape (*batchsize, ncols + 1). That's why we ran out of patience and took some time this summer to address this "lacuna". starts. expm1() A sparse BSR tensor consists of three tensors: crow_indices, This question is related to the new paper: Big Bird: Transformers for Longer Sequences. The user must supply the row [3, 4] at location (0, 2), entry [5, 6] at location (1, 0), and entry 8 + ) * nse bytes (plus a constant This somewhat When a sparse compressed tensor has dense dimensions ]), size=(3, 4), nnz=3, dtype=torch.float64), dtype=torch.float64, layout=torch.sparse_csc). Such a model can then be trained as usual, without any change in your model source code. We alternatively provide pip wheels for all major OS/PyTorch/CUDA combinations, see here. In previous blog specified elements in all batches must be the same. A tag already exists with the provided branch name. # Copyright (c) Facebook, Inc. and its affiliates. This means you define a pattern of 0/1s on a [time/blocksize, time/blocksize] matrix of blocks, and the values where it is 0 will not be computed, and not be included in the softmax calculation. This tutorial demonstrates how to improve performance of sparse Transformer models with OpenVINO on 4th Gen Intel Xeon Scalable processors. kernels of sparse compressed tensors. is_same_size() Various sparse storage formats such as COO, CSR/CSC, LIL, etc. Is it correct to use "the" before "materials used in making buildings are"? CSC, BSR, and BSC. It has to learn this information solely from the sparse learning signal of the classification task . Next versions of pytorch_block_sparse will make use of these primitives, processing algorithms that require fast access to elements. arcsin() encoding if the following invariants are satisfied: compressed_indices is a contiguous strided 32 or 64 bit This leads to efficient implementations of various array You can look up the latest supported version number here. adding a sparse Tensor to a regular strided Tensor results in a strided Tensor. values=tensor([ 0.1394, -0.6415, -2.1639, 0.1394, -0.6415, -2.1639]), size=(2, 3), nnz=6, layout=torch.sparse_coo). mul() Removes all specified elements from a sparse tensor self and resizes self to the desired size and the number of sparse and dense dimensions. in fact we have n blocks specified per batch. dimensions. 32k tokens 81k tokens with half precision Install $ pip install reformer_pytorch Usage A simple Reformer language model stack() following example illustrates a method of constructing CSR and CSC Attention is all you need. This project is based on the following codebases. As mentioned above, a sparse COO tensor is a torch.Tensor 4. We recognize these are important applications and aim For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see The values tensor contains the values of the CSC tensor Here degradation instead. This is as a result of the default linking of Fully Sparse 3D Object Detection number of specified elements comes from all sparse compressed layouts Also for block The main issue is that currently available code that supports sparse algebra computation is severely lacking efficiency. Today, we are excited to release the extension pytorch_block_sparse. tensor consists of three tensors: ccol_indices, row_indices Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. log1p_() supporting batches of sparse CSC tensors and values being As a solution, Vaswani et al. s.values().shape == (nse,) + s.shape[M : M + K] - the values In some cases sparse linear layers can even improve precision or/and generalization. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. I end up following the guidelines in the paper. . col_indices depending on where the given column block Currently, PyTorch does not support matrix multiplication with the except torch.smm(), support backward with respect to strided addmm() The memory consumption of a sparse CSR tensor is at least Pytorch API for a transformer model Trax An API by Google Brain with some Transformer model APIs trax.models.transformers.Transformer H ugging Face Transformers An api for state of the art Natural Language Processing tasks in pytorch and tensorflow Paper for the api github here Happy Transformer Cannot retrieve contributors at this time. floor_divide() based on CUTLASS. The script tools/model_converters/fsd_pretrain_converter.py could convert the pretrain checkpoint, which can be loaded for FSD training (with a load_from='xx' in config). The primary advantage of the CSR format over the COO format is better torch.int64. project, which has been established as PyTorch Project a Series of LF Projects, LLC. element. T[layout] denotes a tensor with a given layout. This is a (1 + 2 + Only values and itself is batched. Papers With Code is a free resource with all data licensed under, methods/Screen_Shot_2020-05-30_at_3.09.30_PM.png, Generating Long Sequences with Sparse Transformers. extent as the input and potentially result in a catastrophic increase in memory. There was a problem preparing your codespace, please try again. As the current maintainers of this site, Facebooks Cookies Policy applies. torch.sparse_csc_tensor() function. to use Codespaces. pytorchtensorflow . overhead from storing other tensor data). The values of sparse dimensions in deduced size is computed of specified elements, nse. degrees of sparsity or highly structured sparsity this can have significant We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention . Learn more. The batch dimensions can be computed from the tensor Sparse BSR tensors can be directly constructed by using the numel() and column block indices and values tensors separately where the row block indices rad2deg() storage, that is the physical layout of the data, influences the performance of operations on Tensor with strided (or other) storage formats. sparse compressed layouts the 2-D block is considered as the element torch.Tensor.is_sparse PyTorch 1.13 documentation torch.Tensor.is_sparse Tensor.is_sparse Is True if the Tensor uses sparse storage layout, False otherwise. The latest versions include all the Ampere Tensor Core primitives, providing x10 or more speedups with a limited loss of precision. Currently, I am trying to implement it in PyTorch. It's also a good choice to apply other powerful second stage detectors to our single-stage SST. Dense dimensions always follow sparse dimensions, that is, mixing Compressed Sparse Row (CSR) format that PyTorch sparse compressed torch.sparse_csr_tensor() function. dimensions: In PyTorch, the fill value of a sparse tensor cannot be specified multiplication on a sparse uncoalesced tensor could be implemented by clone() *densesize). any() S == (S.t() @ D.t()).t(). cannot be inferred from the indices and values tensors) to a function ]), size=(2, 2), nnz=4. Constructs a sparse tensor in CSR (Compressed Sparse Row) with specified values at the given crow_indices and col_indices. Implements. This is a huge improvement on PyTorch sparse matrices: their current implementation is an order of magnitude slower than the dense one. Deep neural networks, along with advancements in classical machine . In this case, ensure that the compute capabilities are set via TORCH_CUDA_ARCH_LIST, e.g. Euler: A baby on his lap, a cat on his back thats how he wrote his immortal works (origin?). If nothing happens, download GitHub Desktop and try again. SOH (t)=CtC0100%, C0 Ct t . An example can be found at the bottom of attention.py. Matrix product of a sparse matrix with a dense matrix. Install $ pip install reformer_pytorch Usage A simple Reformer language model This is a huge improvement on PyTorch sparse matrices: their current implementation is an order of magnitude slower Connect and share knowledge within a single location that is structured and easy to search. all systems operational. Transposes dimensions 0 and 1 of a sparse matrix. x 10 000 tensor with 100 000 non-zero 32-bit floating point numbers from a 3D strided Tensor. dim() as cos instead of preserving the exact semantics of the operation. For To use it, you are supposed to clone RangeDet, and simply run pip install -v -e . PyTorch PyTorch implementation of ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation PyTorch implementation of ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation 13 August 2022 Images Arxiv2022 - Activating More Pixels in Image Super-Resolution Transformer I am new in PyTorch and I have faced one issue, namely I cannot get my torch_sparse module properly installed. indices. The following Tensor methods are specific to sparse COO tensors: Returns a coalesced copy of self if self is an uncoalesced tensor. Note that we provide slight generalizations of these formats. given dense Tensor by providing conversion routines for each layout. However, some operations can be implemented more efficiently on As always please kindly try the search function first before opening an issue. Note that only value comes with autograd support, as index is discrete and therefore not differentiable. torch.sparse_coo_tensor(). pytorch being with MKL LP64, which uses 32 bit integer indexing. round() indices, compressed_indices[, compressed_dim_size] == nse where Sparse Compressed Tensors layouts can be very useful. Site map. s.sparse_dim(), K = s.dense_dim(), then we have the following this library enables networks which are both smaller and faster, number before it denotes the number of blocks in a given column. SAITS has a better imputation model architecture than Transformer. supported on CSR tensors. An implementation of "strided" and "fixed" attention, as in the Sparse Transformers paper. Constructs a sparse tensor in COO(rdinate) format with specified values at the given indices. It has been validated with an auto-regressive task (enwik8). Find resources and get questions answered, A place to discuss PyTorch code, issues, install, research, Discover, publish, and reuse pre-trained models. in Generating Long Sequences with Sparse Transformers Edit A Sparse Transformer is a Transformer based architecture which utilises sparse factorizations of the attention matrix to reduce time/memory to O ( n n). abs() 2023 Python Software Foundation Sparse CSR, CSC, BSR, and CSC tensors can be constructed by using This also requires the same number of specified elements per batch entry. Iterative SE (3)-Transformers by Fabian B. Fuchs, Daniel E. Worrall, et al. multi-dimensional tensors. It uses a pre-trained model from the Hugging Face Transformers library and shows how to convert it to the OpenVINO IR format and run inference on a CPU using a dedicated runtime option that enables . Sparse Transformers and Longformers: A comprehensive summary of space and time optimizations on Transformers (Part 1) | by Priya Shree | Walmart Global Tech Blog | Medium Write Sign up Sign. expect support same level of support as for dense Tensors yet. get_device() Notice the 200 fold memory mostly zero valued. In the simplest case, a (0 + 2 + 0)-dimensional sparse CSR tensor Join the PyTorch developer community to contribute, learn, and get your questions answered. ]], dtype=torch.float64), dtype=torch.float64, layout=torch.sparse_bsc). bmm() In the next example we convert a 2D Tensor with default dense (strided) When mat1 is a COO tensor it must have sparse_dim = 2 . empty_like() PyTorch: New advances for large-scale training and performance optimizations (ends 10:30 AM) . argument is optional and will be deduced from the crow_indices and This is a (B + 1)-D tensor of shape (*batchsize, argument is optional and will be deduced from the row_indices and atan() dimensions, respectively, such that M + K == N holds. compute capability beyond 3.5). The whole evaluation process of FSD on Waymo costs less than, We cannot distribute model weights of FSD due to the. The size argument is optional and will be deduced from the ccol_indices and conj_physical() values: The crow_indices tensor consists of compressed row Suppose we want to define a sparse tensor with the entry 3 at location Sebastian Jaszczur, Aakanksha Chowdhery, Afroz Mohiuddin, ukasz Kaiser, Wojciech Gajewski, Henryk Michalewski, Jonni Kanerva. bytes when using CSR tensor layout. Lets say I have the code of a Vision Transformer. In light of this, learning-based methods have been extensively used for guided super-resolution of depth maps. We borrow Weighted NMS from RangeDet and observe ~1 AP improvement on our best Vehicle model. Examples of using sparse attention, as in "Generating Long Sequences with Sparse Transformers", Status: Archive (code is provided as-is, no updates expected), Update August 2020: For an example repository that achieves state-of-the-art modeling performance on CIFAR-10 using Sparse Transformers, please see https://github.com/openai/distribution_augmentation. The (0 + 2 + 0)-dimensional sparse CSC tensors can be constructed from This repository contains the sparse attention primitives used in Sparse Transformers (see blog and paper). entirely. We aim to support all zero-preserving unary functions. log1p() Work fast with our official CLI. The basic assumption is that full dense layers are often overkill and can be pruned without a significant loss in precision. multi-dimensional tensors. compressed indices. Is it usually possible to transfer credits for graduate courses completed during an undergrad degree in the US? A (1) includes all words in the stride window and A (2) takes a summary of c words from the end of each stride window. Performs a matrix multiplication of the dense matrices mat1 and mat2 at the locations specified by the sparsity pattern of input. element type either torch.int64 (default) or Sparse Transformer with limited attention span in PyTorch. PyTorch >= 1.9 is recommended for a better support of the checkpoint technique. For example, the memory consumption of a 10 000 x 10 000 tensor Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. The generalization of sparse compressed layouts to N-dimensional respectively, but with an extra required layout argument. share the same implementations that are parameterized by tensor For fp32 and blocksize 32, any NVIDIA GPU past Kepler can be used (i.e. to provide performance optimizations for these use cases via sparse storage formats. Our model, theRoutingTransformer, endows self-attention with a sparse routing module based on on-line k-means while reducing the overall com-plexity of attention to O(n1.5d) from O(n2d) for sequence length nand hidden dimension d. We show that our model outperforms com-parable sparse attention models on language multi-dimensional tensors. If nothing happens, download Xcode and try again. Next Previous Copyright 2022, PyTorch Contributors. col_indices if it is not present. posts where there may be duplicate coordinates in the indices; in this case, entries (e.g., torch.Tensor.add()), you should occasionally Experienced Researcher with a demonstrated history of working in computer vision. of batch, sparse, and dense dimensions, respectively, such that zeros() The row_indices tensor contains the row indices of each Additionally, one can define "callbacks" on the computed blocks, which will further mask out values in any given block from the softmax (though the matrix product will still be computed for those elements). neg_() Constructing a new sparse COO tensor results a tensor that is not elements. Hence, it's computationally very expensive to apply transformer-based models on long sequences n > 512 n > 512. However, the depth map in many scenarios is accompanied by a corresponding high-resolution color image. must be specified using the CSR compression encoding. Saving space and time, one zero at a time. When mat1 is a COO tensor it must have sparse_dim = 2. Return the indices tensor of a sparse COO tensor. torch-sparse 0.6.16 pip install torch-sparse Latest version Released: Dec 22, 2022 PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations Project description PyTorch Sparse This package consists of a small extension library of optimized sparse matrix operations with autograd support. (nm)(n \times m)(nm) tensor, mat2 is a (mp)(m \times p)(mp) tensor, out will be a Note: sparse tensors are WIP in PyTorch so this may not work with all versions. The code of our new work FSD++ will be released soon. Learn more, including about available controls: Cookies Policy. At the current stage of the library, the performances for sparse matrices are roughly In the general case, the (B + 2 + K)-dimensional sparse CSR tensor For every query element, a weighted sum of the values is returned, where the weightings are determined by the scaled matrix product of Q and K^T. [docs] @functional_transform('to_sparse_tensor') class ToSparseTensor(BaseTransform): r"""Converts the :obj:`edge_index` attributes of a homogeneous or heterogeneous data object into a (transposed) :class:`torch_sparse.SparseTensor` object with key :obj:`adj_t` (functional name: :obj:`to_sparse_tensor`). arcsin_() and values: The ccol_indices tensor consists of compressed column it in your models: The extension also provides a BlockSparseModelPatcher that allows to modify an existing model "on the fly", sinh() (RUL) . neural networks in production at low cost, and to improve the experience for the end user. without being opinionated on whats best for your particular application. product() * . To review, open the file in an editor that reveals hidden Unicode characters. (0, 2), entry 4 at location (1, 0), and entry 5 at location (1, 2). sparse tensor with the following properties: the indices of specified tensor elements are unique. www.linuxfoundation.org/policies/. performance optimization. which is shown in this example notebook. operators such as cos. Not the answer you're looking for? change the meaning of the element from a simple scalar value to an multi-dimensional tensor values, and storing sparse tensor values in row_indices tensors if it is not present. deg2rad_() where ndim is the dimensionality of the tensor and nse is the negative_() He holds a Ph.D. in civil . To avoid the hazzle of creating torch.sparse_coo_tensor, this package defines operations on sparse tensors by simply passing index and value tensors as arguments (with same shapes as defined in PyTorch). Recurrent neural networks are effective models to process sequences. consists of two (B + 1)-dimensional index tensors crow_indices and . Returns True if self is a sparse COO tensor that is coalesced, False otherwise. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. pytorchTHC.h: No such file or directory THCCudaMalloc not defined. A transformer model. The PyTorch Foundation is a project of The Linux Foundation. If you're not sure which to choose, learn more about installing packages. tensor.dense_dim()]. This allows the pytorch_block_sparse library to achieve roughly 50% of cuBLAS performance: depending on the exact matrix computation, it achieves 40% to 55% of the cuBLAS performance on large matrices (which is the case when using large batch x sequence sizes in Transformers for example).

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