Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14197
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13642
Previously we pass in a patially initialized Tensor to Deserialize and it will fill
it with the result of deserialization of a tensor proto. Now we want it to return
a Tensor directly since it's just a shared pointer to TensorImpl.
Reviewed By: dzhulgakov
Differential Revision: D12874357
fbshipit-source-id: 12b80a763375da23cfa64a74d6bc186d8d03b94f
Summary:
Hi guys,
I'd like to build Caffe2 with more supported options in Windows with Microsoft Visual Studios.
This is the first pull request.
Running scripts/build_windows_shared.bat is able to build Caffe2 with both CMAKE_BUILD_TYPE=Debug and CMAKE_BUILD_TYPE=Release with Visual Studio 14 2015.
CUDA is 9.0, cudnn is 7.0.5, glog, gflags and lmdb are supported on my system.
Python is 3.5, Detectron works from python interface as well.
It was even possible to debug detectron code and step into caffe2_gpu.dll with pdbs built.
What is disappointing, that c10/experimental ops don't build with this Visual Studio generator, I added special option INCLUDE_EXPERIMENTAL_C10_OPS (default ON) to deal with it in build_windows_shared.bat.
After this pull request the next step is to add Visual Studio 2017 support in the script.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13550
Reviewed By: ezyang
Differential Revision: D13042597
Pulled By: orionr
fbshipit-source-id: f313f909f599cd582a1d000eff766eef3a9fc4fc
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/14005
the partial initialization of tensor is no longer supported, we need to fix multiple places
Reviewed By: hl475
Differential Revision: D13078206
fbshipit-source-id: a1be2bd2a9f573db54e1366a0d7a17cc2e0db0c9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13495
If the user has one data file to put in, the data is filled up in every iteration,
which actually flushes the caches. The retrieved latency is larger than the latency
when the caches are warm. Instead of doing that, we should only rely on wipe_cache
variable to wipe the caches.
The change is to skip filling the data if the input only has one size and it is
not the first iteration
Reviewed By: hl475
Differential Revision: D12897946
fbshipit-source-id: ee54ed09b8ec85fcefe930858420b90d494ad972
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12347
add sleep time between net and operator runs, and between each iteration.
Reviewed By: sf-wind
Differential Revision: D10209308
fbshipit-source-id: 9a42b47e1fdc14b42dba6bb3ff048fe8e2934615
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12848
Updated all non-test uses of protobuf::MessageLite::SerializeAsString to call
SerializeAsString_EnforceCheck so that the return value is checked and can
throw an exception if failing.
Most of the affected code was called from classes derived from BlobSerializeBase.
Didn't touch most tests and ENFORCE calls because they usually do checks
anyway.
Original commit changeset: c0760e73ecc7
Reviewed By: dzhulgakov
Differential Revision: D10453456
fbshipit-source-id: d2f2b7b4578e721924354149f08f627c7e3bf070
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12799
Updated all non-test uses of protobuf::MessageLite::SerializeAsString to call
SerializeAsString_EnforceCheck so that the return value is checked and can
throw an exception if failing.
Most of the affected code was called from classes derived from BlobSerializeBase.
Didn't touch most tests and ENFORCE calls because they usually do checks
anyway.
Reviewed By: ezyang
Differential Revision: D10416438
fbshipit-source-id: cb842e3e26b0918829d71267a375d4dd40600d58
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12714
This is a short change to enable c10 namespace in caffe2. We did not enable
it before due to gflags global variable confusion, but it should have been
mostly cleaned now. Right now, the plan on record is that namespace caffe2 and
namespace aten will fully be supersets of namespace c10.
Most of the diff is codemod, and only two places of non-codemod is in caffe2/core/common.h, where
```
using namespace c10;
```
is added, and in Flags.h, where instead of creating aliasing variables in c10 namespace, we directly put it in the global namespace to match gflags (and same behavior if gflags is not being built with).
Reviewed By: dzhulgakov
Differential Revision: D10390486
fbshipit-source-id: 5e2df730e28e29a052f513bddc558d9f78a23b9b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12433
To test 3d conv, we need to pass lists in spec argument. We also don't want to set use_cudnn=True which is the default in brew.
Reviewed By: llyfacebook, csummersea
Differential Revision: D10234315
fbshipit-source-id: 96a39992a97e020d6e9dac103e6d64df0cc1020b
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12029
In order to remove New() function in StaticContext(to remove StaticContext) and converge to the Allocator design, we'll first change the return type of New to at::DataPtr.
Reviewed By: ezyang
Differential Revision: D9889990
fbshipit-source-id: 3257c763530b987025f428741bdd2e089d11bad4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12279
By some reason if we don't write to the wipe buffer, it doesn't really wipe out everything from caches in x86.
We also need to wipe out cache after initializing input blobs.
Reviewed By: Maratyszcza
Differential Revision: D10161211
fbshipit-source-id: c34414dd8b83947805010d7d57e4134d56de1430
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
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12250
We use caffe2::int8::Int8TensorCPU for quantized tensor with uint8_t element type.
Reviewed By: llyfacebook
Differential Revision: D10121216
fbshipit-source-id: b63cd3a75f87e043cc3c83de4f3520b6ffbf1d07
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11414
caffe2::Blob can be stored in an IValue. This is a precondition for caffe2 to switch from Blob to IValue.
Reviewed By: ezyang
Differential Revision: D9731326
fbshipit-source-id: 462a39d2d9ab6f85b99b1670848c6976a3de417c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12043
Re-trying D9979976, this time with all call sites fixed.
D9979976 got reverted because there was a call site that wasn't covered by sandcastle it seems.
I fixed it and used 'grep' to ensure there aren't any more call sites in fbsource.
Reviewed By: ezyang
Differential Revision: D10026392
fbshipit-source-id: cd341514a8e53a40147ea0ee3e52f63bb6444157
Summary: The controller you requested could not be found. Original commit changeset: 2ea17724e223
Differential Revision:
D10026321
Ninja: stable broken
fbshipit-source-id: faf87cb7cc0f78c2c10d4aa6fceea279cd27acd6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11923
This is pre-work to allow moving Blob to ATen/core, which cannot depend on caffe2 anymore.
(1) Removing the Blob -> Tensor dependency allows us to move Blob to ATen/core and use it inside IValue without having to wait for the Tensor merge to be complete.
(2) In the final Blob design, we want it to be a very small class that doesn't have any special treatment for Tensor (or to be more correct, doesn't allow storing Tensor anymore), so this is anyhow the direction we want to go.
This changes call sites that will have to be moved to IValue later, but they cannot be moved to IValue directly, because for that, IValue first needs to be able to store Blob, which in turn first needs this diff and some other changes coming up in future diffs.
Codemods:
$ codemod --extensions h,hpp,c,cpp,cc "([a-zA-Z0-9_]+)\\.IsTensorType\\(" "BlobIsTensorType(\\1, "
$ codemod --extensions h,hpp,c,cpp,cc "([a-zA-Z0-9_]+)->IsTensorType\\(" "BlobIsTensorType(*\\1, "
$ codemod --extensions h,hpp,c,cpp,cc "([a-zA-Z0-9_]+)\\.GetMutableTensor\\(" "BlobGetMutableTensor(\\1, "
$ codemod --extensions h,hpp,c,cpp,cc "([a-zA-Z0-9_]+)->GetMutableTensor\\(" "BlobGetMutableTensor(*\\1, "
It is, however, not only these codemods because regex based refactoring was only able to match a small amount of the call sites. To catch more, I wouldn've needed a AST aware tool like clangr, which I didn't figure out how to use.
Reviewed By: ezyang
Differential Revision: D9979976
fbshipit-source-id: 2ea17724e223b5b73b44f99362727759ca689e61
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11817
Blob::Serialize() and Blob::Deserialize() are now free functions SerializeBlob(), DeserializeBlob() instead.
This takes away access to Blob internals from them and makes future refactorings easier.
Reviewed By: ezyang
Differential Revision: D9882726
fbshipit-source-id: 3251ebd4b53fc12f5e6924a6e4a8db3846ab3729
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11254
Previously we use DeviceType in caffe2.proto directly, but it's an `enum` and have implicit conversion to int, which does not have type safety, e.g. we have to explicitly check for a device type is valid in event.h:
```
template <int d>
struct EventCreateFunctionRegisterer {
explicit EventCreateFunctionRegisterer(EventCreateFunction f) {
static_assert(d < MaxDeviceTypes, "");
Event::event_creator_[d] = f;
}
};
```
at::DeviceType is an `enum class`, and it does not have implicit conversion to int, and provides better type safety guarantees. In this diff we have done the following refactor(taking CPU as an example):
1. caffe2::DeviceType → caffe2::DeviceTypeProto
2. caffe2::CPU → caffe2::PROTO_CPU
3. caffe2::DeviceType = at::DeviceType
4. caffe2::CPU = at::DeviceType::CPU
codemod -d caffe2/caffe2 --extensions h,cc,cpp 'device_type\(\), ' 'device_type(), PROTO_'
+ some manual changes
In short, after this diff, in c++, caffe2::CPU refers to the at::DeviceType::CPU and the old proto caffe2::CPU will be caffe2::PROTO_CPU.
In python side, we have a temporary workaround that alias `caffe2_pb2.CPU = caffe2_pb2.PROOT_CPU` to make the change easier to review and this will be removed later.
Reviewed By: ezyang
Differential Revision: D9545704
fbshipit-source-id: 461a28a4ca74e616d3ee183a607078a717fd38a7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10976
The app can run in XCode with the benchmark metrics collected.
It can also run when building with buck
Reviewed By: llyfacebook
Differential Revision: D9546755
fbshipit-source-id: 60ad0112946f8cf57138417f6838a58ed6d2c90f
Summary: Update all the caller for the new interface
Reviewed By: highker
Differential Revision: D9323167
fbshipit-source-id: a39335ceb402db0719f5f2314085ba9a81380308
Summary:
This was used as a convenient way for us to convert c1 models. Now that conversion is more or less done, we should probably require any users who need to convert c1 models to explicitly install c1. This PR removes the explicit c1 proto (which was copied from c1) in favor of explicit installation.
Note that caffe_translator would still work properly, only difference is that now users need to install c1 separately.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10380
Differential Revision: D9267981
Pulled By: Yangqing
fbshipit-source-id: a6ce5d9463e6567976da83f2d08b2c3d94d14390
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9939
Pull Request resolved: https://github.com/facebookresearch/weakly-supervised-action-detection/pull/13
Pull Request resolved: https://github.com/pytorch/translate/pull/166
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9125
Closes https://github.com/pytorch/pytorch/pull/9125
Use inheritance for polymorphism, and remove template parameter
This is to change the templating in call sites, the core implementations will change later
Before Caffe2 Tensor class was compile-time fixed to bind to a particular device/context. With this change, we're making it a runtime property (stored inside the tensor), but preserve the same semantics. For example, one has to specify device type in order to create a Tensor - there are no uninitialized tensors. More specifically the changes are:
1. We added an extra argument *DeviceType* to most of the constructors of the tensor, e.g. (Tensor(DeviceType type)),
2. Semantics of constructor Tensor(const Tensor<SrcContext>& src, ContextForCopy* context); is changed, in this constructor, the second context is passed in to enable us to call the templated Copy function, it could be in a different context as source and target previously, now we'll enforce that the context should have same device type as src, if it is provided.
3. To preserve 'get-or-construct' semantics of Blob, we added specialized getter Blob::GetMutableTensor that verifies both that Blob contains a Tensor and that it's of a correct type
4. Specifically, Tensor type is not default-constructible any more (as we don't have unknown device tensors) and thus some of the code handling STL containers needs to change
Note: Some changes are postponed just to keep this diff a bit smaller. Please see `TODO`s.
Reviewed By: ezyang, houseroad
Differential Revision: D9024330
fbshipit-source-id: e0b8295d2dc6ebe2963383ded5af799ad17164ba
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9891
Add an argument to benchmark binary to specify the seconds to sleep before the run and after the warmup.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9880
Reviewed By: llyfacebook
Differential Revision: D9014254
Pulled By: sf-wind
fbshipit-source-id: d5566186c8ed768f1e170e9266c5f2d6077391e0
Summary:
Pull Request resolved: https://github.com/facebookresearch/weakly-supervised-action-detection/pull/13
Pull Request resolved: https://github.com/pytorch/translate/pull/166
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9125
Closes https://github.com/pytorch/pytorch/pull/9125
Use inheritance for polymorphism, and remove template parameter
This is to change the templating in call sites, the core implementations will change later
Before Caffe2 Tensor class was compile-time fixed to bind to a particular device/context. With this change, we're making it a runtime property (stored inside the tensor), but preserve the same semantics. For example, one has to specify device type in order to create a Tensor - there are no uninitialized tensors. More specifically the changes are:
1. We added an extra argument *DeviceType* to most of the constructors of the tensor, e.g. (Tensor(DeviceType type)),
2. Semantics of constructor Tensor(const Tensor<SrcContext>& src, ContextForCopy* context); is changed, in this constructor, the second context is passed in to enable us to call the templated Copy function, it could be in a different context as source and target previously, now we'll enforce that the context should have same device type as src, if it is provided.
3. To preserve 'get-or-construct' semantics of Blob, we added specialized getter Blob::GetMutableTensor that verifies both that Blob contains a Tensor and that it's of a correct type
4. Specifically, Tensor type is not default-constructible any more (as we don't have unknown device tensors) and thus some of the code handling STL containers needs to change
Note: Some changes are postponed just to keep this diff a bit smaller. Please see `TODO`s.
Reviewed By: xw285cornell
Differential Revision: D8121878
fbshipit-source-id: 4a5e9a677ba4ac82095df959851a054c81eccf81
* 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
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* 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
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* [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
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* [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.
* Adding instance weight to batch distill loss
as title
* add bfloat 16-31
added bfloat 16-31 and their respective unit tests
* [CUDA9] Upgrade - fbcode
CUDA9 upgrade diff D5654023 has been out for a while thanks to Pieter. But with time growing it's becoming quite hard to rebase, because of the symlinks and auto-generated build/config files in tp2. Break D5654023 into two diffs, one touching tp2 config files, and another one touching fbcode TARGETS file (adding nvcc flag). These two should be a bit easier to rebase (for detailed procedure see "Test Plan").
This diff can only be committed if:
1. CUDA 9 rpm is rolled out fleet-wide (TBD)
2. NVidia driver 390.40 is rolled out fleet-wide (done)
3. Upgrade CUDA 9.1, cudnn 7.1, nccl 2.1 (done)
4. Make sure all dependents are built (done)
5. Test all C2 operators, PyTorch (see test plan)
* Share intermediate int32 buffer across Conv ops
Adding a known type
* [C2 fix] infer function for ensure_cpu_output_op
this is adding the missing device funtion for ensure_cpu_output_op
* [int8] Add blob serializer/deserializer for Int8TensorCPU
To export to logfiledb
* [nomnigraph] Add try catch block to optimization passes in predictor
This will catch failures that happen in the optimization pass.
* Caffe2: avoid static initialization order fiasco for CAFFE_ENFORCE
CAFFE_ENFORCE uses strack trace fetcher. Which is currently a
global static variable. If at static initialization time CAFFE_ENFORCE
is used, this is a SIOF. Recently CAFFE_ENFORCE was added into init
functions registration, so we started to see this.
Meyers singleton is going to provide safety here. If stacktrace
fetcher was not registered yet, it will just use a dummy one.
* NUMA support in SparseNN CPU benchmark
Adding support for NUMA in SparseNN CPU benchmark
* [mobile-roofline] Add logging needed for roofline model
This should be all that's needed
* Let the operators using the same input if the operators are not chained
or else, we have to change the input data dims
* fix null-pointer-use UBSAN errors in in reshape_op.h
* revert previous fix on input blob name
as title
* Adding flag to let MineHardNegative automatically extract single value from dict
Model exporter requires the output of the model to be a struct. This makes it convenient to use those models directly in MineHardNegative by allow automatic extraction of the single element of dict, which is a common use case.
* Reverting change that broke internal tests back to OSS compatible state
* [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 9506eeb from github to fbcode
Patch xplat buck fix
add virtual destructor to OptimizationPass
add virtual destructor to OptimizationPass
build fixes for sync
build fixes for sync
* Fix net tracing
Fix net tracing from async_scheduling
* Fix logging
* 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
