Summary:
Resubmit #20698 which got messed up.
Idea is that when PyTorch is used in a custom build environment (e.g. Facebook), it's useful to track usage of various APIs centrally. This PR introduces a simple very lightweight mechanism to do so - only first invocation of a trigger point would be logged. This is significantly more lightweight than #18235 and thus we can allow to put logging in e.g. TensorImpl.
Also adds an initial list of trigger points. Trigger points are added in such a way that no static initialization triggers them, i.e. just linking with libtorch.so will not cause any logging. Further suggestions of what to log are welcomed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20745
Differential Revision: D15429196
Pulled By: dzhulgakov
fbshipit-source-id: a5e41a709a65b7ebccc6b95f93854e583cf20aca
Summary:
Merge binaries "convert_image_to_tensor" and "caffe2_benchmark" to remove the overhead of writing to/reading from Tensor file.
*TODO next: TensorProtos is another overhead. No need for de-serialization.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16875
Reviewed By: sf-wind
Differential Revision: D13997726
Pulled By: ZhizhenQin
fbshipit-source-id: 4dec17f0ebb59cf1438b9aba5421db2b41c47a9f
Summary:
xw285cornell
- To make hip files to have unique filename extension we change hip files from _hip.cc to .hip (it's the only blessing option other than .cu in hipcc 3d51a1fb01/bin/hipcc (L552)).
- Change to use host compiler to compile .cc|.cpp files. Previously we use hcc to compile them which is unnecessary
- Change the hipify script to not replace "gpu" with "hip" in the filename of the generated hipified files. Previously we do this because hcc has a bug when linking files that have same filename. We have now changed to use host linker to do linking so this is unnecessary anymore.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14036
Reviewed By: xw285cornell
Differential Revision: D13091813
Pulled By: bddppq
fbshipit-source-id: ea3d887751d8abb39d75f5d5104aa66ce66b9ee0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12274
We use caffe2::int8::Int8TensorCPU for quantized tensor with uint8_t element type.
Reviewed By: llyfacebook
Differential Revision: D10156452
fbshipit-source-id: 52cf2bedc9dbb433cd5d03f0b76723f7df6a7361
Summary:
The code that reads a blob from input files are broken. Fixing them. Also, add a binary that converts input files to blobs that can be used by Caffe2 directly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12211
Reviewed By: llyfacebook
Differential Revision: D10121845
Pulled By: sf-wind
fbshipit-source-id: 6e48bb594680bcb3186d8d43276b602041c30d3e
* [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
