| commit | edb88b5f3af03718b443d015f195faa1832ce95b | [log] [tgz] |
|---|---|---|
| author | Orion Reblitz-Richardson <orionr@gmail.com> | Tue Jun 26 14:55:48 2018 -0700 |
| committer | GitHub <noreply@github.com> | Tue Jun 26 14:55:48 2018 -0700 |
| tree | 4e03f52c1cd17f63a4a55ca6c71a7f951f6fd994 | |
| parent | 055f527242bb68d68e5e3da6040f2698af5a2003 [diff] |
Update from Facebook (#8887) * add opencl + fpga context adds an opencl context inside caffe2/fb which can be used for fpga access * [Caffe2] Force tensor inference checks to be triggered during testing We've started to rely on TensorInference functions more for different analysis. This diff ensures that the TensorInference function's result matches what is expected from the definition of the operator. * Enable building //caffe2:torch with @mode/opt In @mode/opt, python runs out of a PAR, which breaks a lot of assumptions in the code about where templates/ folders live relative to __file__. Rather than introduce hacks with parutil, I simply turn template_path into a parameter for all the relevant functions and thread it through from the top level. * [Caffe2] Fix cost models for DotProduct and Div. Update Tensor Inference for dot product As title. DotProduct states that output is a 1-D tensor (https://caffe2.ai/docs/operators-catalogue.html#dotproduct) though code suggests it is either 0- or 1-D depending on inputs. TensorInference defined to support implementation. * [SG-MoE] Add an option to make the experts NOT as components * [nomnigraph] Rename and fixup convertToNeuralNetOperator API This will make things a bit cleaner * no longer symlink THNN.h and THCUNN.h * forced decoder network (onnx export) Closes https://github.com/pytorch/translate/pull/95 Add networks in ensemble_export.py to create a forced decoding network from PyTorch NMT checkpoints. This network takes an arbitrary numberized (source, target) pair and returns the model score for the translation, including penalties. Vocabulary reduction networks are also supported, but note that target indices which are not in the possible_translation_tokens generated for the source input will be trea * Revert schema change to fix production models Revert schema change to fix production models * MockLogDeviceReader - rebase on FIX # Goal 1), Build a make_mock_log_device_reader using make_mock_reader 2), Replace the real log_device_reader here: https://fburl.com/raihwf1p # Log by D8151734 Real log_device_reader: ``` I0529 20:29:05.373108 954994 tensor.h:839] Tensor print_net/log of type std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >. Dims: (): read_net/ParseOpenTrainingRow:0 I0529 20:29:05.373244 954994 tensor.h:839] Tensor read_net/ParseOpenTrainin * [C2/D2][1/n]: Nonnegative-Constrained Optimization -- log barrier implement log barrier as a regularization method * Add teacher weight screening. Add teacher weight sceening according to teacher labels. If teacher label is zero, we do not use the distill loss in the objective function. * Add NormalizerContext See task for more detail. This implementation is a copy of what exists for RegularizerContext except for how the parameters are defined in the model_definition thrift file. I'll try an alternative implementation which overrides the default arguments of functions instead like for argscopes in tensorflow. https://github.com/pytorch/pytorch/compare/master...MaximeBoucher:update-from-facebook-0939578c068c?expand=1 * Adding cosine similarity option in dot processor Add pairwise cosine similarity option in dot product. Add an option to concate dot product and cosine similarity. Add test cases. * [nomnigraph][redo] Concat elim for sparseNN Same as D7962948, which was reverted because Operator Schema was not defined * [pytorch] Revert pytorch/pytorch#7918 'Release GIL when copying to shared memory', breaks ASAN Revert this pytorch diff that breaks ASAN when running Filament in dev mode; in opt mode it gives "bad file descriptor" errors. Looks like a race when copying tensors to shared memory in multiple mp.Queue's (which spawn separate threads). https://github.com/pytorch/pytorch/pull/7918/files * [nomnigraph][mobile] Enable nomnigraph by default, use -Oz on nomnigraph related code to reduce code size enables nomnigraph and reduces codesize * [Warmup] Allow both offline incremental training and online training Change plan name on saving side and reading side to support both training type This diff depends on D8128530 and D8168651. * Revert D7802642: [Warmup] Allow both offline incremental training and online training This reverts commit afc213cf9b36cecf75333a788391c4d09f4afccc @bypass-lint An infra SEV is better than not reverting this diff. If you copy this password, see you in SEV Review! @cause_a_sev_many_files * Add legacy grad logic to fix div op on old graphs. Add legacy grad logic to fix div op on old graphs. * Correctly propagate operator failures Propagate errors from operators that throw exceptions and return false * Revert D8374829: [caffe2][nomnigraph][redo] Concat elim for sparseNN This reverts commit 6dda028c463e54bb5c32188bbbe9202107e188a5 @bypass-lint An infra SEV is better than not reverting this diff. If you copy this password, see you in SEV Review! @cause_a_sev_many_files * [Caffe2] Added extra_info to core.DeviceOption(), enforced extra_info to be inherited in scope.DeviceScope extra_info is a newly defined field in DeviceOption proto. This diff added extra_info to the core.DeviceOption(). And, In scope.DeviceScope(), this diff enforce the new scope to inherit the extra_info from old scope. * [opt] hgdirsync wasn't enabled, merge diverged code Here's the damage, P59732616 basically xplat was left behind but had the change from assert to CAFFE_ENFORCE * OMP parallelism over RoIs for RoIAlign op Simpler to parallelize over RoIs. Shouldn't affect other uses as it relies on the number of OMP threads set during startup. PR: https://github.com/pytorch/pytorch/pull/8562 * Use int64_t for shape in FillOps to avoid overflow of int32 * Implement Rotated RoIAlign op Based on Rotated RPNs as explained in https://arxiv.org/abs/1703.01086. The idea is simple - orientation/angle is added as an RPN anchor parameter and then the angle is further regressed similar to bbox coords. There are some additional changes related to NMS and IoU, but besides that it's a direct extension to Faster-RCNN. Further details in https://fb.quip.com/sZHlA1iMfWPZ. RoIs are represented in [center_x, center_y, width, height, angle] format. `angle` repre * Rotated RoIAlign op CUDA forward implementation CUDA forward impl for D8415490 * RoIAlignRotated op CUDA backward pass implementation TSIA * All remaining fixes to eliminate process_github.sh Most of this diff has already been reviewed separately, except for the parts relating to _thnn/utils.py and _utils._internal.py remove skipIf(True, 'Fbcode') line from process_github.sh replace sed of cpp file with #ifdef to control cudnnDestroy use undo sync-time deletion of .gitattributes, remove process_github.sh switch to using _utils._internal rather than try-import-except This diff also fixes the open-source bug where rebuilds have * Back out "Revert D7802642: [Warmup] Allow both offline incremental training and online training" Original commit changeset: 7707d2efe60e The original diff is backout becuase the online trainer package is backed out. This code would only work with new online trainer package * [easy] improve error log in adagrad op as title * re-allow use of thnn_h_path This fixes cffi usage in OSS * [4/4] [tum] paralyzing layerNorm for GPU full sync as title * add compile=False to pytorch tests, remove hack with pyc * Add shape and type inference for RowWiseArgMax operator See title * Revert D8515341: Back out "Revert D7802642: [Warmup] Allow both offline incremental training and online training" This reverts commit 78167eeef0af16b60f72c82f9dcdda9b41b4dcbd @bypass-lint An infra SEV is better than not reverting this diff. If you copy this password, see you in SEV Review! @cause_a_sev_many_files * [fix-flaky-test] mock_hive_reader_test flaky, because GlobalCounter collects local counts intervally # Problem `MockHiveReader` uses `GlobalCounter` to limit `max_examples`. GlobalCounter on server node collect local counts from worker nodes every 1 sec. This 1 sec delay makes it impossible to limit exactly to the `max_examples`, it will definitely exceed `max_examples`. # Plan Given, ``` Expected num_examples = max_examples + num_examples/sec (Read Speed) x 1 sec (GlobalCounter Sync Int * [Caffe2] Fix FCGradient cost inference. Prevent overflow in cost inference FCGradient missed a factor 2 in the `num_outputs == 3` case. Overflow was occurring with flop calculation for FC. Changed types to `uint64_t` to prevent future problems. * Fix binary ops with empty inputs Fix binary ops with empty inputs * Support the filling of input blob with provided data as title for Biz Integrity case * Back out "Revert D8515341: Back out "Revert D7802642: [Warmup] Allow both offline incremental training and online training"" Original commit changeset: 30c55dd38816 Original diff is reverted due to introducing bad integration test. Fixed the integration test. * [c2][easy] improve pack ops error loggings as desc. * Add ShapeTypeInference for LpNorm operator As desc * Shard test_nn to reduce runtime for each test target Closes https://github.com/pytorch/pytorch/pull/8793 The current test_nn would time out and be disabled in GreenWarden, and we need to have an option to split it up in order to pass the stress test. Right now GreenWarden roughly allows running 100 test cases in test_nn before timing out, and here we have an option to divide test_nn into 30 shards (with ~40 tests in each shard) to allow for some test suite growth in the future. * Change default caffe2_streams_per_gpu to 1 * Remove IN_SANDCASTLE from common.py and test_nn.py We prefer to disable the failing tests through Sandcastle UI instead. * Add a new class for an updated prof_dag.proto This diff contains: - An updated prof_dag.proto that contains blob profiles. - A class to deserialize this information (serialization is in a follow up diff) - Update to separate profiling information from NeuralNet (and use it as part of the class above). - Unit tests * Lambdarank for SparseNN This diff adds a lambda_rank_layer for SparseNN. changes include 1) Adds support for multi sessions in c2 op 2) Adds support for two different loss functions in c2 op 3) Unit tests for op * Revert D8586950: Back out "Revert D8515341: Back out "Revert D7802642: [Warmup] Allow both offline incremental training and online training"" This reverts commit 012220ed63eccc35659a57b31d16a3625da6317b @bypass-lint An infra SEV is better than not reverting this diff. If you copy this password, see you in SEV Review! @cause_a_sev_many_files * [easy] A few fixups to multithread predictor benchmark (1) support perf on T6 server (2) remove dead code * fix a bug about the map size as title * Fix reduce sum on in-place case. Fix reduce sum on in-place case. * [Warmup] Reland reverted diff Allow both offline incremental training and online training Closes https://github.com/pytorch/pytorch/pull/8827 fix net transform integration test. Allow offline and online trainer to coexist D7802642. * Add StoreHandlerNotAvailableException Add an exception for a store that is not available or has been deleted. * Use exception handling for fault tolerance, missing KV store Remove status blobs to communication ops so that exceptions propagate on failure. * [C2/D2][2/n]: Nonnegative-Constrained Optimization -- bounded grad proj for simple bounded constrained optimization, incl non-negative box constraints. * [GanH]: Adaptive Weighting with More Estimations With implemented postivity optimization, we now learn adaptive weights with different parameterizations. This improves parameter estimation and training stability. * Revert some changes for landing * Remove AutoNoGIL in StorageSharing * Temporarily disable net_tests * Revert "[Caffe2] Force tensor inference checks to be triggered during testing" This reverts commit 67ef05c22b2f71b4a489695384932f968384a2a4. * Revert "Fix reduce sum on in-place case." This reverts commit 6cb8a8e1b3db7b6d20941b0053e3f3836068eb64. * Revert "Revert "Fix reduce sum on in-place case."" This reverts commit 130a257c0893dc09f4bd6e6a45d112261807fd2c.
PyTorch is a Python package that provides two high-level features:
You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed.
We are in an early-release beta. Expect some adventures and rough edges.
| System | 2.7 | 3.5 |
|---|---|---|
| Linux CPU | ||
| Linux GPU | ||
| Windows GPU | — |
See also the ci.pytorch.org HUD
At a granular level, PyTorch is a library that consists of the following components:
Usually one uses PyTorch either as:
Elaborating further:
If you use NumPy, then you have used Tensors (a.k.a ndarray).
PyTorch provides Tensors that can live either on the CPU or the GPU, and accelerate compute by a huge amount.
We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs such as slicing, indexing, math operations, linear algebra, reductions. And they are fast!
PyTorch has a unique way of building neural networks: using and replaying a tape recorder.
Most frameworks such as TensorFlow, Theano, Caffe and CNTK have a static view of the world. One has to build a neural network, and reuse the same structure again and again. Changing the way the network behaves means that one has to start from scratch.
With PyTorch, we use a technique called reverse-mode auto-differentiation, which allows you to change the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes from several research papers on this topic, as well as current and past work such as torch-autograd, autograd, Chainer, etc.
While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date. You get the best of speed and flexibility for your crazy research.
PyTorch is not a Python binding into a monolithic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use NumPy / SciPy / scikit-learn etc. You can write your new neural network layers in Python itself, using your favorite libraries and use packages such as Cython and Numba. Our goal is to not reinvent the wheel where appropriate.
PyTorch is designed to be intuitive, linear in thought and easy to use. When you execute a line of code, it gets executed. There isn't an asynchronous view of the world. When you drop into a debugger, or receive error messages and stack traces, understanding them is straightforward. The stack trace points to exactly where your code was defined. We hope you never spend hours debugging your code because of bad stack traces or asynchronous and opaque execution engines.
PyTorch has minimal framework overhead. We integrate acceleration libraries such as Intel MKL and NVIDIA (cuDNN, NCCL) to maximize speed. At the core, its CPU and GPU Tensor and neural network backends (TH, THC, THNN, THCUNN) are written as independent libraries with a C99 API. They are mature and have been tested for years.
Hence, PyTorch is quite fast – whether you run small or large neural networks.
The memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives. We've written custom memory allocators for the GPU to make sure that your deep learning models are maximally memory efficient. This enables you to train bigger deep learning models than before.
Writing new neural network modules, or interfacing with PyTorch's Tensor API was designed to be straightforward and with minimal abstractions.
You can write new neural network layers in Python using the torch API or your favorite NumPy-based libraries such as SciPy.
If you want to write your layers in C/C++, we provide a convenient extension API that is efficient and with minimal boilerplate. There is no wrapper code that needs to be written. You can see a tutorial here and an example here.
Commands to install from binaries via Conda or pip wheels are on our website:
If you are installing from source, we highly recommend installing an Anaconda environment. You will get a high-quality BLAS library (MKL) and you get a controlled compiler version regardless of your Linux distro.
Once you have Anaconda installed, here are the instructions.
If you want to compile with CUDA support, install
If you want to disable CUDA support, export environment variable NO_CUDA=1. Other potentially useful environment variables may be found in setup.py.
If you want to build on Windows, Visual Studio 2017 and NVTX are also needed.
On Linux
export CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" # [anaconda root directory] # Install basic dependencies conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing conda install -c mingfeima mkldnn # Add LAPACK support for the GPU conda install -c pytorch magma-cuda80 # or magma-cuda90 if CUDA 9
On macOS
export CMAKE_PREFIX_PATH=[anaconda root directory] conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing
On Windows
conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing
git clone --recursive https://github.com/pytorch/pytorch cd pytorch
On Linux
python setup.py install
On macOS
MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install
On Windows
set "VS150COMNTOOLS=C:\Program Files (x86)\Microsoft Visual Studio\2017\Enterprise\VC\Auxiliary\Build" set CMAKE_GENERATOR=Visual Studio 15 2017 Win64 set DISTUTILS_USE_SDK=1 REM The following line is needed for Python 2.7, but the support for it is very experimental. set MSSdk=1 call "%VS150COMNTOOLS%\vcvarsall.bat" x64 -vcvars_ver=14.11 python setup.py install
Dockerfile is supplied to build images with cuda support and cudnn v7. Build as usual
docker build -t pytorch -f docker/pytorch/Dockerfile .
You can also pull a pre-built docker image from Docker Hub and run with nvidia-docker, but this is not currently maintained and will pull PyTorch 0.2.
nvidia-docker run --rm -ti --ipc=host pytorch/pytorch:latest
Please note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g. for multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you should increase shared memory size either with --ipc=host or --shm-size command line options to nvidia-docker run.
Installation instructions and binaries for previous PyTorch versions may be found on our website.
Three pointers to get you started:
PyTorch has a 90 day release cycle (major releases). Its current state is Beta, we expect no obvious bugs. Please let us know if you encounter a bug by filing an issue.
We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.
If you plan to contribute new features, utility functions or extensions to the core, please first open an issue and discuss the feature with us. Sending a PR without discussion might end up resulting in a rejected PR, because we might be taking the core in a different direction than you might be aware of.
PyTorch is a community driven project with several skillful engineers and researchers contributing to it.
PyTorch is currently maintained by Adam Paszke, Sam Gross, Soumith Chintala and Gregory Chanan with major contributions coming from 10s of talented individuals in various forms and means. A non-exhaustive but growing list needs to mention: Trevor Killeen, Sasank Chilamkurthy, Sergey Zagoruyko, Adam Lerer, Francisco Massa, Alykhan Tejani, Luca Antiga, Alban Desmaison, Andreas Kopf, James Bradbury, Zeming Lin, Yuandong Tian, Guillaume Lample, Marat Dukhan, Natalia Gimelshein, Christian Sarofeen, Martin Raison, Edward Yang, Zachary Devito.
Note: this project is unrelated to hughperkins/pytorch with the same name. Hugh is a valuable contributor in the Torch community and has helped with many things Torch and PyTorch.