mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-21 13:44:15 +08:00
5bcbbf537327f6e8328289c25a3a453a2444d984
22 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
| 9945fd7253 |
Drop unused imports from caffe2/python (#49980)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/49980 From ``` ./python/libcst/libcst codemod remove_unused_imports.RemoveUnusedImportsWithGlean --no-format caffe2/ ``` Test Plan: Standard sandcastle tests Reviewed By: xush6528 Differential Revision: D25727359 fbshipit-source-id: c4f60005b10546423dc093d31d46deb418352286 |
|||
| 27c7158166 |
Remove __future__ imports for legacy Python2 supports (#45033)
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38 |
|||
| 2f61aca17b |
Skip DataIO tests relying on LevelDB if compiled without it (#42169)
Summary: Found while trying to get RocM Caffe2 job green Pull Request resolved: https://github.com/pytorch/pytorch/pull/42169 Reviewed By: seemethere Differential Revision: D22791896 Pulled By: malfet fbshipit-source-id: 9df6233876aec5ead056365499bab970aa7e8bdc |
|||
| f326045b37 |
Fix typos, via a Levenshtein-type corrector (#31523)
Summary: Should be non-semantic. Uses https://en.wikipedia.org/wiki/Wikipedia:Lists_of_common_misspellings/For_machines to find likely typos, with https://github.com/bwignall/typochecker to help automate the checking. Uses an updated version of the tool used in https://github.com/pytorch/pytorch/pull/30606 . Pull Request resolved: https://github.com/pytorch/pytorch/pull/31523 Differential Revision: D19216749 Pulled By: mrshenli fbshipit-source-id: 7fd489cb9a77cd7e4950c1046f925d57524960ea |
|||
| 415b17e81c |
Fix for flaky caffe2 dataio test (test_time_limit_reader_with_short_limit) (#27592)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/27592 The caffe2 data reader test `test_time_limit_reader_with_short_limit` is flaky as-written because it places an upper bound on how much can be read, but under stress it is possible for fewer records to be read. The fix is to make the assertion check a fuzzy/range check rather than exact equality, since there's not a straightforward way to precisely test a timer-based feature. ghstack-source-id: 91543898 Test Plan: `buck test mode/dev-tsan //caffe2/caffe2/python:dataio_test-2.7 -- --stress-runs 20` -> P117156924 (with fix, 100% pass) P117158750 - without fix, lots of failures in this test Reviewed By: boryiingsu Differential Revision: D17816775 fbshipit-source-id: 2ab0d3304fbd9c9806d37a4fe2912c840616db61 |
|||
| 4fee532de6 |
Pass loop_over optional parameter for cached reader properly. (#21929)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/21929 Just need to pass `loop_over` argument properly. Reviewed By: noname01 Differential Revision: D15885401 fbshipit-source-id: f1928277262a80e5b41f4c4f3945c2f378a4e233 |
|||
| 0aa7407dd0 |
Rearrange stopping condition in CompositeReader (#20062)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/20062 Previously, the batch counter is incremented even if none of the readers has data. In this diff, 1) Limiter is applied to the last reader so that the batch counter is not incremented unless the first N-1 readers have data 2) The stop blob of the last reader as the stop blob of the task so that it's checked before the counter is incremented Reviewed By: xianjiec Differential Revision: D15099761 fbshipit-source-id: 47ed6c728118fe453cf57ac3457085867939485b |
|||
| edb88b5f3a |
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. |
|||
| b875fb281c |
Update from facebook (#7451)
* [bootcamp] Improve "Shape" operator to support axes specification To improve .shape operator of Caffe2 to support x.shape(tensor, axes), which takes an optional int array "axes" as input. For example, x.shape(tensor, [1, 0]) will return the dimension for axis 1 and 0 following the specified order. For current version, "axes" input allows duplications and can have arbitrary length. * Back out "Add barrier net that runs before training nets" Original commit changeset: b373fdc9c30f. Need additional changes to some callers to support barrier failures. * Change warning to verbose log to reduce log spam The `LOG(WARNING)` was a bit spammy for regular use so lets just make it a `VLOG`. * Extract the shared code from different caffe2_benchmark binaries The OSS benchmark and Internal benchmark will share most functions in the benchmark. * Support MFR in sequence training As titled. * Make knowledge distillation work with using logged prediction feature as teacher label. 1) Add loading raw dense feature as teacher label. 2) Optional calibration function for teacher label 3) Add teacher label into generic unit test 4) Deprecated TTSN workflow version using feature_options to config teacher label * [C2/CUDA]: unjoined cross entropy sigmoid as desc * Add async_scheduling executor into deferrable_net_exec_test Add async_scheduling into tests and fix some exception cases * Fix Event disabled error When disabling event in RNN ops make sure we don't call Finish on disabled event from op's RunAsync * cuda ensure cpu output op can handle both TensorCPU and TensorCUDA as desc. * [C2 Core] Infer input device option in C2 hypothesis_test checkers Improve how we default input blob device options. Previously it defaults as where op lives but it is not necessarily the case. For example: CopyCPUToGPU * [C2 Op]SplitByLengthsOp CPU/GPU implementation [C2 Op]SplitByLengthsOp CPU/GPU implementation * fix undefined symbol error not sure why we're getting undefined symbol even with link_whole = True Need to figure out why but need this workaround for now * Add tools in DAIPlayground platform to help debugging models Add additional tools to allow Plauground override individual method defined in AnyExp. This will allow user to create module that specificly change certain default method behavior. An example included in this diff is deactivating test model and checkpointing. When debugging any model problems, switching off components helps me quickly narrow down the location of the bug. The technique is extensively used in task T27038712 (Steady memory increase in EDPM, eventually resulting in gloo/cuda.cu:34: out of memory) * add shape and type inference for int8 conversion operator * Fix flaky test for group_norm Fix flaky test for group_norm * Fix group_norm_op_test flaky Fix group_norm_op_test flaky * Implementation of composite learning rate policy In many state-of-the-arts deep learning works, people use a simple trick to schedule the learning rate: use a fixed learning rate until error plateaus and then switch to a different fixed learning rate, and so on. In this diff, we implemented a simple version of the composite learning rate. The user gives a set of learning rates policies and corresponding iteration nums, and the optimizer will change the learning rate policy based on the number of iterations so far. For example, the user give two learning rate policies, one is FixedLearningRate and PolyLearningRate, with an iteration number of 1k. Then the first 1k iteration, we use FixedLearningRate. For the following iterations, we use PolyLearningRate. * Split two use cases of CachedReader into two classes, DBFileReader and CachedReader # Use Cases: 1). input: DB file -> output: DatasetReader. Use DBFileReader. 2). input: Reader -> build cache DB file -> output: DatasetReader. Use CachedReader. # Changes to CachedReader: 1). Move db_path to the constructor. Because in mock reader. cache will always be built ahead. # Changes to tests: 1). Make a separate TestCase class for CachedReader and DBFileReader. 2). Make it possible to add more test functions by adding setUp, tearDown and _make_temp_path. 3). Make delete db_path more general. `db_path` could be a file for `log_file_db`, but could also be a directory for `leveldb`. * Back out "On Mobile phones, call GlobalInit with no arguments in predictor in case we need to perform initialization" Original commit changeset: 4489c6133f11 * Fix LARS bug Fixed a bug in the LARS implementation which caused all subsequent blobs not using LARS to have the LARS learning rate multiplier applied to them. * [tum] support sparse init & add uniformFill option as title * Propagate exception for async nets Capture the exception when an exception is thrown in async nets and re-throw it after wait(). This allows exceptions to be propagated up to the caller. This diff was a part of D7752068. We split the diff so that C2 core files changes are in a separate diff. * Automatic update of fbcode/onnx to 69894f207dfcd72d1e70497d387201cec327efbc Previous import was 403ccfbd0161c38f0834413d790bad0874afbf9a Included changes: - **[69894f2](https://github.com/onnx/onnx/commit/69894f2)**: Use op schema.all tensor types in random like definitions (#865) <Scott McKay> - **[b9d6b90](https://github.com/onnx/onnx/commit/b9d6b90)**: Clarify random like operators (#846) <Scott McKay> - **[fc6b5fb](https://github.com/onnx/onnx/commit/fc6b5fb)**: Refactor shape inference implementation (#855) <anderspapitto> - **[b7d8dc8](https://github.com/onnx/onnx/commit/b7d8dc8)**: fix cmake warning message (#863) <Eric S. Yu> - **[f585c5d](https://github.com/onnx/onnx/commit/f585c5d)**: add pytorch-operator test for tile (#831) <Wenhao Hu> - **[993fe70](https://github.com/onnx/onnx/commit/993fe70)**: add install step (#832) <Eric S. Yu> - **[68bc26c](https://github.com/onnx/onnx/commit/68bc26c)**: add type inference for traditional ml ops except classifier ops. (#857) <Ke Zhang> - **[9cc0cda](https://github.com/onnx/onnx/commit/9cc0cda)**: fix string representation of scalar types (#858) <G. Ramalingam> - **[1078925](https://github.com/onnx/onnx/commit/1078925)**: fix y in pow test case to scalar (#852) <Wenhao Hu> - **[c66fb6f](https://github.com/onnx/onnx/commit/c66fb6f)**: Add some math function shape inference (#845) <anderspapitto> - **[ff667d1](https://github.com/onnx/onnx/commit/ff667d1)**: Refactor return type and docs for ONNXIFI_BACKEND_DIRECTX_ID (#853) <Marat Dukhan> - **[11c6876](https://github.com/onnx/onnx/commit/11c6876)**: clear initializer names when clear initializer (#849) <Wenhao Hu> - **[73c34ae](https://github.com/onnx/onnx/commit/73c34ae)**: Clarify FeatureVectorizer description. (#843) <Scott McKay> - **[1befb9b](https://github.com/onnx/onnx/commit/1befb9b)**: Remove useless text in docs (#850) <Lu Fang> - **[e84788f](https://github.com/onnx/onnx/commit/e84788f)**: Fix SELU attributes' default values (#839) <Lu Fang> - **[ebac046](https://github.com/onnx/onnx/commit/ebac046)**: Add tile test case (#823) <Wenhao Hu> - **[8b7a925](https://github.com/onnx/onnx/commit/8b7a925)**: a few more shape inference functions (#772) <anderspapitto> - **[9718f42](https://github.com/onnx/onnx/commit/9718f42)**: Make the coefficient non optional for LinearClassifier (#836) <Jaliya Ekanayake> - **[ef083d0](https://github.com/onnx/onnx/commit/ef083d0)**: Add save_tensor and load_tensor functions for Protos (#770) <Lu Fang> - **[45ceb55](https://github.com/onnx/onnx/commit/45ceb55)**: Check if CMAKE_BUILD_TYPE set before project(). (#812) <Sergii Dymchenko> - **[4b3d2b0](https://github.com/onnx/onnx/commit/4b3d2b0)**: [WIP] reenable shape inference tests (#834) <anderspapitto> - **[22d17ee](https://github.com/onnx/onnx/commit/22d17ee)**: RNN tests: LSTM, GRU, SimpleRNN (#739) <Peyman Manikashani> - **[de65b95](https://github.com/onnx/onnx/commit/de65b95)**: dimension denotation (#443) <Tian Jin> - **[eccc76e](https://github.com/onnx/onnx/commit/eccc76e)**: fix field number issue in onnx operator proto and enable its build (#829) <Ke Zhang> - **[d582beb](https://github.com/onnx/onnx/commit/d582beb)**: disable shape inference test to unbreak ci (#830) <Lu Fang> - **[485b787](https://github.com/onnx/onnx/commit/485b787)**: function proto for composite op. (#802) <Ke Zhang> - **[cd58928](https://github.com/onnx/onnx/commit/cd58928)**: specify defaults for attributes of Affine op (#820) <G. Ramalingam> - **[7ee2cf9](https://github.com/onnx/onnx/commit/7ee2cf9)**: merge the dummy backend back into the main one (#743) <anderspapitto> - **[1c03a5a](https://github.com/onnx/onnx/commit/1c03a5a)**: [Proposal] ONNX Interface for Framework Integration (previously ONNX Backend API) header and docs (#551) <Marat Dukhan> - **[3769a98](https://github.com/onnx/onnx/commit/3769a98)**: Rename real model test case from VGG-16 to ZFNet (#821) <Lu Fang> * [C2]ReluN Op relu n op. tf reference: https://www.tensorflow.org/api_docs/python/tf/nn/relu6 * Call destructor when assigning a blob value * Add executor overrides Add executor overrides flag to enable migration to async_scheduling executor * Add barrier net that runs before training nets - attempt #2 Add a synchonize barrier net that is run before training nets. With this net, shards that are faster will wait for other shards before start training. This reduce chances of the faster shards timing out during GLOO AllReduce. Removed explicit data_parallel_model.py.synchronize call in holmes workflow. This change was landed previously but caused errors for some EDPM workflows - See https://fb.facebook.com/groups/1426530000692545/permalink/1906766366002237/ - because EDPM assumes any call to CreateOrCloneCommonWorld and Gloo ops are wrapped in exception handlers but in this case exception thrown in the barrier init net is not handled. To address this issue, we add _CreateOrCloneCommonWorld to the param_init_net instead of a new barrier init net. Since errors for param_init_net run is handled gracefully and re-rendezvous, it should fixes the problem. * Handle empty nets in async_scheduling Make sure we don't get stuck on empty nets * use CUDA_ARCH for conditional compile * [C2 fix] infer function for ensure_cpu_output_op * Update group_norm test to reduce flaky test * Fix lr_multiplier for GPU |
|||
| 664fe34e0a |
[Caffe2][fbcode=>GH sync] Update from facebook 4323b18ce13c (#7116)
* [fix] Re-enable events in RNN ops We have earlier added event disabling in RNN ops as back then we didn't use events, with current use cases this is no longer true (https://fburl.com/8vd0lp8y) * use ops with cude impl * Revert D7729695: [caffe2][fix] Re-enable events in RNN ops This reverts commit 4b215c7496fb724656ff4c776933a15bdbbcde5e @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 * [observer] Clean up observer_config.h #accept2ship * [1/n] Refactor dataio_test.py Replace code duplication with a common function * Add barrier net that runs before training nets Add a synchonize barrier net that is run before training nets. With this net, shards that are faster will wait for other shards before start training. This reduce chances of the faster shards timing out during GLOO AllReduce. Removed explicit data_parallel_model.py.synchronize call in holmes workflow. Similar change in speech/asr_training workflow will come in another diff. * Support the dnnlowp backend in caffe2_benchmark This is for SHARE operator latency evaluation * Migrate integral_image_op to main caffe2 migrate integral_image_op(GPU version) given by https://fburl.com/yvqezigi to caffe2/caffe2/operators and implement its CPU version. Write up a test using the hypothesis_test mechanism * [pos_disc, fbcode] Implement unjoined lr loss As explained in https://our.intern.facebook.com/intern/wiki/Model_Based_Calibration/, when the dataset is an joined data set, where labels might change later, we need to use unjoined logloss. The implementation is almost the same as in Sigrid (https://fburl.com/1trngsls), where loss = y (log(p) - log(1-p)) + (1-y)(log(1-p)) = xy - (1-y)x - (1-y)log(1+exp(-x)) For x < 0, to ensure stability and avoid overflow, we reformulate the above exp as loss = xy - (1-y)x - (1-y)x + (1-y)log(1+exp(x)) = xy + (1-y)log(1+exp(x)) Then the final expression becomes loss = xy + (y - 1) x (x >= 0) - (1 - y) log(1 + exp(x - 2 x (x >= 0))) where y is the true label, x is the dot product and p = logistic(x). This kind of implementation is align with the current implementation of the original cross entropy in https://phabricator.intern.facebook.com/diffusion/FBS/browse/master/fbcode/caffe2/caffe2/operators/cross_entropy_op.cc;0bae3b5d0f825897c5e0dd0ff10f489d7271bf25$7-13 * Keep the array to fix the conflict * [C2] Compute Adagrad effective LR The AdagradWithLR op outputs an extra blob which is contains the average effective learning rate across all weights in this blob. * Open-source extractMetaNetDef & runGlobalInitialization, add new Predictor constructor from db file, and add run_map_outputs 1. Open-source extractMetaNetDef and runGlobalInitialization, for use in 2. new Predictor constructor from db file. 3. Add new run function that returns outputs as TensorMap * Disable eigen cpu Disable eigen cpu in transpose and reduce * Introduce request_only/object_only property of ModelLayer by default this is False * A simple TC Caffe2 benchmark We can run tunner, get MappingOptions and then use them to compare against cuBLAS currently broken due to LLVM issues. How to run: hg checkout eec1ab31b59c03b8deded1c755a9abaf8c45be01 add D7401202 add D7434625 add D7506031 add D7540728 buck run @mode/dev-nosan tc/tc/benchmarks_python:caffe2_benchmark * Move Caffe2 feature_maps_ops to open source Need feature maps operators in open source project facebookresearch/BlueWhale * Manually fix the conflicts in channel shuffle op * Fix the inconsistency between different gh and fbcode * Skip Adagrad GPU Test (Because some gpu implementation is missing) * Fix another test to make sure it won't run on gpu when implementation is not available yet |
|||
| 6223bfdb1d |
Update from Facebook (#6692)
* [GanH][Easy]: Add assertion to adaptive weighting layer 0 weight causes numeric instability and exploding ne * [Easy] Add cast op before computing norm in diagnose options As LpNorm only takes floats we add a manual casting here. * Introduce a new caching device allocator `cudaMalloc` and `cudaFree` calls are slow, and become slower the more GPUs there are. Essentially, they grab a host-wide (not device-wide) lock because GPU memory is transparently shared across all GPUs. Normally, this isn't much of a concern since workloads allocate memory upfront, and reuse it during later computation. However, under some computation models (specifically, memory conserving approaches like checkpoint-and-recompute, see https://medium.com/@yaroslavvb/fitting-larger-networks-into-memory-583e3c758ff9) this assumption is no longer true. In these situations, `cudaMalloc` and `cudaFree` are common and frequent. Furthermore, in data parallel contexts, these calls happen at nearly the same time from all GPUs worsening lock contention. A common solution to this problem is to add a custom allocator. In fact, nVIDIA provides one out of the box: CUB, which Caffe2 already supports. Unfortunately, the CUB allocator suffers from very high fragmentation. This is primarily because it is a "buddy" allocator which neither splits nor merges free cached blocks. Study https://github.com/NVlabs/cub/blob/1.8.0/cub/util_allocator.cuh#L357 if you want to convince yourself. This diff adapts a caching allocator from the Torch codebase https://github.com/torch/cutorch/blob/master/lib/THC/THCCachingAllocator.cpp which does splitting and merging and ends up working really well, at least for workloads like the checkpoint-and-recompute computation models noted above. I simplified the implementation a little bit, made it a bit more C++-like. I also removed a bunch of stream synchronization primitives for this diff. I plan to add them back in subsequent diffs. * Report reader progress in fblearner workflows Integrate with fblearner progress reporting API and add support to report training progress from reader nodes. If reader is constructed with batch limits, report based on finished batch vs total batch. The finished batch may be more than total batch because we evaludate if we should stop processing everytime we dequeue a split. If no limit for the reader, report based on finished splits (Hive files) vs total splits. This is fairly accurate. * [GanH][Diagnose]: fix plotting 1. ganh diagnose needs to set plot options 2. modifier's blob name is used for metric field can need to be fixed before generating net * Automatic update of fbcode/onnx to 985af3f5a0f7e7d29bc0ee6b13047e7ead9c90c8 * Make CompositeReader stops as soon as one reader finishes Previously, CompositeReader calls all readers before stopping. It results in flaky test since the last batch may be read by different threads; resulting in dropped data. * [dper] make sure loss is not nan as desc. * [rosetta2] [mobile-vision] Option to export NHWC order for RoIWarp/RoIAlign Thanks for finding this @stzpz and @wangyanghan. Looks like NHWC is more optimized. For OCR though it doesn't yet help since NHWC uses more mem b/w but will soon become important. * Intra-op parallel FC operator Intra-op parallel FC operator * [C2 Proto] extra info in device option passing extra information in device option design doc: https://fb.quip.com/yAiuAXkRXZGx * Unregister MKL fallbacks for NCHW conversions * Tracing for more executors Modified Tracer to work with other executors and add more tracing * Remove ShiftActivationDevices() * Check for blob entry iff it is present When processing the placeholders ops, ignore if the blob is not present in the blob_to_device. * Internalize use of eigen tensor Move use of eigen tensor out of the header file so we don't get template partial specialization errors when building other libraries. * feature importance for transformed features. * - Fix unused parameter warnings The changes in this diff comments out unused parameters. This will allow us to enable -Wunused-parameter as error. #accept2ship * add opencv dependencies to caffe2 The video input op requires additional opencv packages. This is to add them to cmake so that it can build * Add clip_by_value option in gradient clipping Add clip_by_value option in gradient clipping when the value is bigger than max or smaller than min, do the clip * std::round compat |
|||
| ef8f556212 |
[Caffe2] Changes done inside Facebook (#6378)
* fix unit test for sqrt op From the error logging: [idx, grad, grad_estimate] are: [[ 146. 0.5 0.45776367] [ 147. 0.5 0.45776367] The gradient == 0.5 is correct, which means the SqrtOp and its gradient is doing right job. (Because y = sqrt(x), loss = y^2/2 = x/2, and then d(loss)/dx = 1/2 = 0.5; ) The test failed because of numerical problem of grad_estimate (in unit test). It can be because the step_size is small, and float precision is not high (when there are multiple elements in the tensor, we do sum(y^2) to compute loss) This diff - increase the step size, and also move the test cases to be further away from 0 (where sqrt(x) is not well defined) to be safe :) - also clean up, and merge the test case for inplace Vs. non-inplace Tested with: `CAFFE2_HYPOTHESIS_PROFILE=debug ai_bt caffe2/caffe2/python/operator_test:elementwise_ops_test -- "test_sqrt"` * CompositeReader & CompositeReaderBuilder A new type of reader gluing multiple readers together. * Back out "Revert D7394363: [GanH]: Log D Trick for Cross Entropy with Sigmoid" Original commit changeset: 9325a4356dbe * [dai][WIP] convert params to int8 on ps before sending to trainer Add float->uint8 conversion in addition to float->fp16 conversion in model_saver. * [easy] improve unit test for sparse length sum ops as desc. #accept2ship * Update GitHub upstream to 771fcb3455cbfe69c2abcc4cb3bd7ef92d59af24 * move sparse hash unique ops to OOS and add unit tests - move the SparseHash version to OOS, since 'sparsehash' is already deps of caffe2 OOS: https://fburl.com/arssw4n1 - The 'SparseHash' engine is also being used in OOS, so the SparseHash version shall be in OOS to reduce confusion: https://fburl.com/o5ea7ah2 - fix the CUDA UniqueOp for the case when batch is empty. - add unit test * group_norm_op for caffe2 This is the cuda op for Group Normalization (GN): https://arxiv.org/abs/1803.08494 This code implements GN in one op that computes Y=gamma * (X-mu) / sigma + beta and also its gradients. It is expected to have minimal memory consumption (similar to the BN op), without creating new blobs if GN were implemented as several ops (e.g., reshape, norm_mean/std, affine_channel). * Resubmit D7405233: disappeared in D7464958 OOS publish causes the op missing -- however, test was still there * [c2] add sparse hash engine for cuda unique op The SparseHash version of UniqueOp copy input tensor to CPU, and make use of sparse hash map to get unique output, and then copy back to GPU. * [dper][gpu] enable unit testing gpu trainer for sparse nn to debug the GPU trainer using mock data in unit test. make it easier to develop GPU trainer for new models. * Reuse Gloo context for Synchronize() calls Previously we were creating (and leaking) the Gloo context on each call to Synchronize(). Now only run the common world op and create the barrier net once, then run the barrier net on each Synchronize() call. Since timeout is associated with the Gloo context, assert that the timeout is fixed instead of trying to handle the complexity of multiple timeouts (and associated contexts). * [GanH/WGAN][1/n]: add FC param clipping as titled * [mobile] minimizing changes between caffe2_benchmark and speed_benchmark * [GanH]: enable diagnose within model avoid finding blob names but to directly enable inside the model * Add `net_transformer_fun` option to DPM This callback allows for various transformations to be made to the model after gradient operators have been added. The immediate motivation for this is to allow transformations such has "checkpoint-and-recompute" which allow trading off memory for additional compute. Adding several callbacks like this has made DPM's API less than ideal at this stage. However, I could not find any reasonable alternative. * [DT] [33/n] Compile flow task groups task groups need to compiled in order to pickle the object in fblearner. However I also changed the Job's compile function as creating new object is not necessary. * Initial commit for sparse_normalize vectorization and benchmark * [GanH]: LB Calibration for JSD as titled * Tracing event in async executor Adding event tracing through TRACE_EVENT macro in async executor * [Resubmit] D7409751 Reseting book-keeping blobs when the reservoir is reset D7409751 got lost in D7464958 * Visualizing realtime weights values we want to visualize the weights values as optimizer is iterating. This diff supports to visual the weights at an assigned index. Currently, we assume the blob to be 2 dimensional. * [GanH][Easy]: Fix Homotopy Weighting apparantely, there was a bug in homotopy weight (alpha, beta) update * [c2] move sparse hash unique op out of oss so that oss do not need to depend on google hash map. * Get rid of std::round as it's not supported on Android * Revert changes on setup.py * Skip shaky test on Dataio * fix |
|||
| 1d5780d42c |
Remove Apache headers from source.
* LICENSE file contains details, so removing from individual source files. |
|||
| bb04034bf7 |
Adding a time limit reader
Summary: ReaderWithTimeLimit() class to stop after a certain amount of time Reviewed By: boryiingsu Differential Revision: D6477623 fbshipit-source-id: 165874c9344b0c9c7e0b33e12e72e24c46669cb2 |
|||
| 067bc141c3 |
Cached reader
Summary: a wrapper around reader with persistent file cache. Reviewed By: kennyhorror Differential Revision: D6257639 fbshipit-source-id: 113296173ca18d25b86e188e0c09e3dbd830969d |
|||
| 8286ce1e3a |
Re-license to Apache
Summary: Closes https://github.com/caffe2/caffe2/pull/1260 Differential Revision: D5906739 Pulled By: Yangqing fbshipit-source-id: e482ba9ba60b5337d9165f28f7ec68d4518a0902 |
|||
| 7d482742fd |
Allow tasks/execution_steps to be cloned at runtime
Summary: Advantages of cloning the tasks/execution_steps at runtime: - Less complexity on the python side: no need to clone nets and add prefixes to blob names - Faster start-up: we had cases of complex plans that took up to 30min to be created. - Better isolation: each task cloned at runtime has its own child workspace, preventing false sharing of blobs. - Opens up possibility for dynamic scheduling: Number of threads per task can be increased on the fly, at runtime. Reviewed By: dzhulgakov Differential Revision: D5100730 fbshipit-source-id: 71b83193b135da4e6eaf2536d8fc266528e1fdcc |
|||
| 4102a79da4 |
Explicitly set should_stop_blob to False in pipeline init
Summary: This diff fixes an issue with running the same reader in the same workspace multiple times. In order to achieve correct behavior of execution step we have to explicitly initialize should_stop_blob with False. Reviewed By: kennyhorror Differential Revision: D5224410 fbshipit-source-id: 4ad2740e187b62b0a1f5612ea3eef223dcc8a799 |
|||
| 60c78d6160 |
Fixes range/xrange for Python 3
Summary: As title Differential Revision: D5151894 fbshipit-source-id: 7badce5d3122e8f2526a7170fbdcf0d0b66e2638 |
|||
| b61aaa90b6 |
Stop multi_reader if we run out of data before max_examples
Summary: Before we didn't propagate the 'out-of-data' signal if splits_per_epoch wasn't specified. Right now it's a hacky fix (just reuse ReaderWithLimit). azzolini - any suggestions of more elegant solution? I can create an extra reader that just export "is empty" signal out. Overall, I guess we need to turn global_queue into a more sustainable unittest that verifies all possible combinations - I'm still not sure it's correct :-\ Reviewed By: xianjiec Differential Revision: D4665677 fbshipit-source-id: fe44d10ee82c3383145635e67dea1d9b666e061f |
|||
| d0621a2449 |
NextScopedBlob with well-defined behavior and respect namescope
Summary: Remove the use of `NextName` in layer model helper, so that the same function return `model_helper` that should construct identical `Net`, when under the same NameScope. The `NextScopedBlob` should only take effect when there is real name conflicting, otherwise it returns ScopedBlobReference. This is critical for parameter blobs. In long run, we need to be able to specify parameter blobs more explicitly. (kennyhorror is working on this). This solution works in short term for e.g., two tower sparse nn models. Reviewed By: kennyhorror Differential Revision: D4555423 fbshipit-source-id: 2c4b99a61392e5d51aa878f7346466a8f14be187 |
|||
| 238ceab825 | fbsync. TODO: check if build files need update. |
