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3 Commits
| Author | SHA1 | Message | Date | |
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Sebastian Meßmer
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49f8581745
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Update from facebook (#7855)
* [mpscnn] MPSCNNChannelShuffle
att
* [Easy] Adding tags as an argument to the functional layer
Without it "tags" would be added as an argument to the operator.
The change here is based on the assumption that there is no operator that takes "tags" as an argument.
* Fix locally_connected_op schema check.
Fix locally_connected_op schema check.
* [C2] Add TypeAndShape inference for few more operators
As desc
* [c2] Shape inference should support 0 as dimension
Tensors can have 0 in their dimension.
* Make MockHiveReader loop over and support max_examples
Replace DatasetReader with RandomDatasetReader.
So that Mock Hive Reader can simulate a large data input using a small sample file as source.
* Utility function to wipe cache between benchmark runs
Caffe2 benchmark does not wipe out cache between runs, and this potentially creates an unrealistically optimistic picture of performance. This diff adds utility function to wipe out the cache.
* Allow caffe2 GlobalInit to be invoked multiple times
Allow caffe2 GlobalInit to be invoked multiple times. Will re-parse gflags and update logging levels on successive invocations, but will not re-run init functions or perform other one-time initialization.
* Add Caffe2 GlobalInitIsCalledGuard to base net and operator classes
Warn if caffe2's GlobalInit function has not been invoked before creating an operator or net object. This is based on discussion here: https://fb.quip.com/kqGIAbmK7vNG
* Rethrow current exception on failure
Rethrow current exception instead of copy constructing a new one on op failure.
* Make `clone()` return subclass of List/Struct
`clone()` is not working correctly when we subclass those classes
* Wipe the cache before the net run
the util function is copied from D7409424
will rebase once D7409424 is landed.
* [Caffe2] [Mobile] Support utils/cast.h::GetCastDataType with LITE_PROTO builds
* Correct includes
async_polling include -> async_base include
* Prepare execution flags for executor migration
Making async_scheduling aware of underlying net type to prepare for executor
migration
* Add operator level observers into async executor
Adding operator level observers into RunAsync operators' calls
* Cleanup TEST_Benchmark
Remove duplicate code and provide default implementation in NetBase
* [C2] Fix type and shape inference for binary comparison ops
As desc.
* Add GlobalInit to predictor to ensure initialization is always done before prediction
FACEBOOK:
Redo D7651453 the correct way.
Now use a static variable for the arguments passed to GLog
* Remove spammy log message
This method is currently used in various places inside Caffe itself.
* Disable events for operators inside a chain
We don't need to use events in operators within a chain because the chain is
always scheduled on a single stream, keeping only first and last event for
scheduling purposes
* Ensure correct finish run order
In rare cases we might call finishRun and trigger net's destruction while
another worker is still holding shared_ptr to a thread pool, that can cause
thread pool destruction from within a worker thread in case no other nets are
using the pool. This diff fixes the order of calling finishRun and also changes
pool() to return raw pointer to keep pool's ownership within the net
* Reduce unnecessary polling
Make sure we don't waste CPU by polling operators that we can set an efficient
callbacks on
* Squash commit of syncing
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bddppq
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f94ae3ba1d
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Update from facebook (#7696)
* Fix handling of empty batches in SumReduceDimsOp As titled * Deferrable async_scheduling finishRun fix Proper order of finishing run operations in deferrable_async_scheduling net * Simplify exception handling in async_scheduling Simplify exception handling, no need to busy wait, thread that processes the last task can finish the run * [C2]worker_coordinator_memorize_worker_ids As titled. This is related to T28689868, where the number of blobs we want to create is equal to the number of worker ids * Add unit test for nets with no type set * Ignore total length argument in sympolic_pad_packed_sequence 1- There was a mistake in the code that total_length was added to the wrong symbolic function (pack_padded_sequence) instead of (pad_packed_sequence) 2- No need to throw an exception if total_length is given since it is only used to enable data_parallel training on multi-gpus and doesn't have anything to do with onnx export, so just ignore it. https://fburl.com/tk4gciqp * Add support for MKLDNN to async_scheduling Just add MKLDNN as a possible CPU option to async_scheduling's pool function * [AuFL][ensemble] support branch output for prediction This diff supports using predictions from different branches and thus enables model ensembling (not fully independent). * Fix a bug in add_loss in layer_model_helper As titled. * Support lradaption for adam 1.lr adaption operator 2.apply to dense adam * Perf tweaks for async_scheduling Restore single pool option + remove unnecessary (no-ops) calls * add quantization to SparseSimdAdagradOp add a bunch of quantization signatures to SparseSimdAdagradOp, implementations to come next * [sr] [codemod] Change all SR callsites to use new API @allow-large-files This diff refactors all callsites of SR to use the slightly changed API introduced in the diff below. Really what this means is that you need to include the correct header. Also if you were using `ClientFactory::newFactory` you need to not prefix it with `ClientFactory::`. ``` cd ~/fbsource/fbcode find ./ -type f -exec sed -i -e 's:#include "servicerouter/client/cpp2/ClientFactory.h":#include "servicerouter/client/cpp2/ServiceRouter.h":' -e 's:#include <servicerouter/client/cpp2/ClientFactory.h>:#include <servicerouter/client/cpp2/ServiceRouter.h>:' -e 's/ClientFactory::newFactory(/newFactory(/g' {} \; ``` Also manually fixed spots that couldn't be done automatically (or broke because they depended on transitive includes). * Back out "Fix handling of empty batches in SumReduceDimsOp" Original commit changeset: 282da1730cc2 This commit is blocking the Github->fbcode sync, which really needs to get merged ASAP. D7881937 which this diff depends on will be reverted in the sync D7990948 which causes this to break. The sync diff cannot be patched with this reversion because it must be landed against base revision 5c8c099 , and D7881937 must not be included in the sync diff because it is breaking GPU tests that are not available in sandcastle : https://ci.pytorch.org/jenkins/job/caffe2-builds/job/py2-cuda8.0-cudnn6-ubuntu16.04-test/3638/console for one example. * Add the flow to support operator benchmark 1) generate model with the operator 2) upload to everstore 3) generate model spec into json file 4) start running the benchmark * [tum][gpu] Connect DPM trainer with flow and unit tests This diff: - Fix some small bugs for Yiming's recent changes to parallelizer, so it suits real use cases. - Add correct tags to the TUM code, so we can do data parallel transform - pass extra info when instantiation. - add unit test for using DPM in TUM model After this diff, we can do simple box, multi-gpu fully-sync trainer for TUM in Fblearner workflow, but may still need to do speed benchmarking. * w/o normalized lradaption for adam dense only The previous lr adaption includes a normalization step when performing the dot product operation. This is not exactly same as what is proposed in the paper. I add normalization as an option. Without it, the operator performs exactly what the paper proposed. With the option, we add the normalization step * [fb] Use SharedPromise in DeferrableAsyncSchedulingNet This code is to simplify DeferrableAsyncSchedulingNet by removing condition variable + small fixes * [tum] implement cuda sparseLengthsMean and LengthsMean as title * Adding an optional parameter to allow use of protobufs in InferShapesAndTypes function. Adding an optional parameter to allow use of protobufs in InferShapesAndTypes function. * Move feature_to_index to FeatureSpec.feature_to_index move feature_to_index to FeatureSpec.feature_to_index to avoid override other fields * [Caffe2] Rename bytes_moved to bytes_written Just a rename in preparation for supporting bytes_read. * [c2] fix ReduceFrontSumOp for empty case by setting 0 otherwise, it may use the results from last iteration when it's empty batch. * [Caffe2] [Int8] Improve Intel CPU performance * [Easy] Improve PrependDim op logging as titled * DBFileReader expand db_path using os.path.expanduser(..) Since there are a lot of possible use cases of `DBFileReader` to read from user home path, like `~/local/sample.db`, I want to save people's trouble of calling `os.path.expanduser(db_path)` themselves. * [Caffe2] Add bytes_read to cost structure We're adding analytical read bytes to cost functions. This extends the structure accordingly for all CostInference defined operators. Additionally, some small bug fixes were performed: 1) Cost functions now extract type information of operands instead of assuming float * Fix sleef on aarch64 for hhvm @bypass-lint Rename flag * Remove duplicated part in caffe2/ideep/operators/conv_op.cc should be sync error * Rename test helper function test_adagrad_sparse_helper to adagrad_sparse_test_helper to avoid confusing pytest |
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Paul Jesse Hellemn
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b875fb281c
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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 |