mirror of
https://github.com/zebrajr/pytorch.git
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3058700f7f
17 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
PyTorch MergeBot
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b5594f7df0 |
Revert "Use missing-prototypes in torch_cpu (#103725)"
This reverts commit
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cyy
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716b3b893d |
Use missing-prototypes in torch_cpu (#103725)
This PR enables Wmissing-prototypes in torch_cpu except some generated cpp files and the mps and metal backends. Pull Request resolved: https://github.com/pytorch/pytorch/pull/103725 Approved by: https://github.com/albanD |
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Nikita Shulga
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a9b0a921d5 |
Disable avoid-non-const-global-variables lint check (#62008)
Summary: As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH` All changes but the ones to `.clang-tidy` are generated using following script: ``` for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008 Reviewed By: driazati, r-barnes Differential Revision: D29838584 Pulled By: malfet fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13 |
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Nikita Shulga
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4cb534f92e |
Make PyTorch code-base clang-tidy compliant (#56892)
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
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Jane Xu
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71ca600af9 |
Renaming CAFFE2_API to TORCH_API (#49496)
Summary: Since caffe2 and torch have been consolidated, CAFFE2_API should be merged with TORCH_API. Addresses a TODO. Manually edited some references of the removed `CAFFE2_API`: * `CONTRIBUTING.md` * `caffe2/proto/CMakeLists.txt` * `cmake/ProtoBuf.cmake` * `c10/macros/Export.h` * `torch/csrc/WindowsTorchApiMacro.h` Pull Request resolved: https://github.com/pytorch/pytorch/pull/49496 Reviewed By: malfet, samestep Differential Revision: D25600726 Pulled By: janeyx99 fbshipit-source-id: 7e068d959e397ac183c097d7e9a9afeca5ddd782 |
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Tristan Rice
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b0bd35ff13 |
caffe2/event: allow multiple errors such as when cancelled (#31335)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/31335 When an error occurs in a net we end up cancelling all the async ops. If one error occurs it's highly likely other errors will occur as well. Typically we see: 1. SendOp failed due to a network error 2. async scheduling cancels all other ops via `SetFinished("Cancelled");` 3. Another SendOp fails due to a network error and crashes the process when the exception is thrown. This changes caffe2 ops to allow failing twice. Test Plan: buck test //caffe2/caffe2:caffe2_test_cpu Reviewed By: andrewwdye Differential Revision: D19106548 fbshipit-source-id: 4b7882258a240894cc16d061a563c83a3214d3d9 |
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sf-wind
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5b86c3af4a
|
Update from facebook (#8384)
* [fix] fixup the bias multiplier data access issue Hotfix for failues in conv_transpose * [D2][Easy]: lint regularizer lint with black * [GanH]: Split mu in adaptive weight for diagnose * [Dper] Add the ability to split FC weights into multiple smaller ones * fix SumReduceLikeOp for empty blob as desc. * add ctc_greedy_decoder for caffe2 ctc_greedy_decoder same as tf's * Update event callback handling Allow multiple callbacks per event * Add WeightedSum layer The motivation is to do weighted sum in HoNet/crossnet, in the next diff, I'll replace model.Add with model.WeightedSum in honet: https://fburl.com/f4rmolg2 crossnet: https://fburl.com/v7awn8se, https://fburl.com/63filbnm * Replicate DAG's behavior Some callers expect RunAsync to block, replicate that behavior in case of explicit 'dag' net type * [dper] layernorm layer as title * Override dag, async_dag, async_polling Overriding dag, async_dag and async_polling with async_scheduling * Name the thread pools Caffe thread pools currently inherit the thread names from the thread that starts them, which can be misleading. Give them an explicit name instead. * [Caffe2] FilleOp should support int64_t dimensions Change argument type to int64_t for shape argument of FillerOp (used in ConstantFill, XavierFill, etc) * Remove caffe2/caffe2/contrib/torch/ It's not used anywhere and depends on old lua torch that conflicts with Aten. Given PT1 it's not relevant any more (though it was nice and clever code!) #accept2ship * Fix linearWarmup multiplier check The multiplier needs to be non-negative, not strictly positive. * Revert D3314316 This is after 2 years and we do not seem to have a use case for this one, so for the sake of clean API design we should potentially remove this. This would allow us to potentially pass in arguments to optionally construct an object, although it is indeed a little bit unclear how we can reuse existing objects if constructor arguments are passed in. In any case, we may want to remove this dangling feature. * Speedup generate proposals by partial_sort. Speedup generate proposals by partial_sort. FACEBOOK: - Saw speed improvement for training with this op. - Yanghan benchmarked the op on a small dataset and see consistent 100% improvement on speed (6ms -> 3ms) on 420 input resolution. See next diff for details. * More parallel processing friendly for CPP version of GenerateProposals. More parallel processing friendly for CPP version of GenerateProposals. * [DT] [43/n] Lift stop conditions inside reader code back to flow control 1. Split multi_reader function into local_reader and remote_reader 2. Lifted stop conditions inside Limiter back to flow control 3. Split epoch flow building logic into 3 cases: - single machine (1 reader, 1 trainer on trainer0 node, no PS) - (1 reader + 1 trainer) on trainer0 node, has PS - multiple readers, readers do not share nodes with trainers, might have PS or not * Resolve conflicts for torch/_thnn/utils.py * [Caffe2] Handle image decoding errors Image decoding errors can make the whole training fail. This diff is to handle them 1.Catch imdecode exceptions and check if decoded image has zero columns or rows. This is counted as decoding errors. 2.Replace the image with empty in case of error 3.Count the number of errors and throw runtime exception if the rate reaches given number The empty image data is kept. It might introduce noise in the training data. * Update MKL exporter to IDEEP ops TSIA * [Caffe2] GlobalInit is thread safe, fixing the comment With the mutex and lock, GlobalInit is thread safe. Update the comments. * Back out "Add support for generating ATen files during fbcode build" Original commit changeset: 28970ddba353 @override-unit-failures (Note: this ignores all push blocking failures!) * [DT]: fix predictor save similar to D6610058, here we add the fix for distributed online training * Remove net_singlethread_async_gpu.cc Closes https://github.com/caffe2/caffe2/pull/2528 This removes net_singlethread_async_gpu.cc as part of our effort to clean CUDAContext and the net executors. * Inline DFS task execution Add a DFS inline task execution mode in executor * Add c10 folder to fbcode This adds the c10 folder and its test cases to fbcode. Build flags are mostly taken from aten. * add dependencies for online trainer Add some dependencies so that the online model can use DataPipeline and PredictionTransform operators Relevent post: https://fb.intern.facebook.com/groups/1324375037655677/permalink/1740993462660497/ * Resolve conflicts for tools/jit/gen_jit_dispatch.py * [Fix] sparse regularization in distributed training * Support advanced pooling options in sum processor * support advanced pooling options in sum processor * remove redundant code * support attention in sum processor * Improve shard logging in net tracing code Make it handle arbitrary shard ids instead of just one digit ids. * [Caffe2] Call GlobalInit in predictor only in mobile FACEBOOK: Calling GlobalInit long after the program starts may not be safe. There are issues if the following happens: User does not call GlobalInit and initFacebook after program starts User sets a flag manually: https://fburl.com/mcsumw7d User calls OSS predictor. OSS predictor calls GlobalInit GlobalInit calls initFacebook initFacebook resets all flags: https://fburl.com/tolszha1 Thus, the user manually set flags are overwritten This would happen anytime GlobalInit is called long after the program starts. I suppose the intention of the user in this case is not to call GlobalInit throughout the program, but use Caffe2 regardless (is that desired?) But adding GlobalInit in the OSS predictor would automatically call GlobalInit when using Caffe2. This issue doesn't exist in mobile, since initFacebook is not called on mobile. For now, guard the GlobalInit in predictor for mobile only. May want to ensure the GlobalInit is always called at the start of the program. @[3501714:kutta] has seen weird issues when not calling GlobalInit at the start of the program on server side. He has made some progress on this. * resolve conflicts for caffe2/core/logging_is_google_glog.h and test/test_torch.py * Add empty fix for SumLikeReduceOp Add empty fix for SumLikeReduceOp * Revert D7962948: [caffe2][nomnigraph] Concat elim for sparseNN This reverts commit f7f434dc5c34ca6058b9765d2ef615453d2276a9 @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 * Remove Declarations.yaml * Include common.h * Change std::stoi to caffe2::stoi * Add thread_name.cc to the CMake file * No need to subtract 1. Fix test segfaults * Fix NetTest, ObserverTest Fix tests (cherry picked from commit 3767e66c3f365596cba3d46d3e7322c933a0ab41) * CTCGreedyDecoderOp only has CPU implementation, test should only run on CPU * Add a variable to avoid conversion resizing issue * [fix] fixup the bias multiplier data access issue Hotfix for failues in conv_transpose * [D2][Easy]: lint regularizer lint with black * [GanH]: Split mu in adaptive weight for diagnose * [Dper] Add the ability to split FC weights into multiple smaller ones * fix SumReduceLikeOp for empty blob as desc. * add ctc_greedy_decoder for caffe2 ctc_greedy_decoder same as tf's * Update event callback handling Allow multiple callbacks per event * Add WeightedSum layer The motivation is to do weighted sum in HoNet/crossnet, in the next diff, I'll replace model.Add with model.WeightedSum in honet: https://fburl.com/f4rmolg2 crossnet: https://fburl.com/v7awn8se, https://fburl.com/63filbnm * Replicate DAG's behavior Some callers expect RunAsync to block, replicate that behavior in case of explicit 'dag' net type * [dper] layernorm layer as title * Override dag, async_dag, async_polling Overriding dag, async_dag and async_polling with async_scheduling * Name the thread pools Caffe thread pools currently inherit the thread names from the thread that starts them, which can be misleading. Give them an explicit name instead. * [Caffe2] FilleOp should support int64_t dimensions Change argument type to int64_t for shape argument of FillerOp (used in ConstantFill, XavierFill, etc) * Remove caffe2/caffe2/contrib/torch/ It's not used anywhere and depends on old lua torch that conflicts with Aten. Given PT1 it's not relevant any more (though it was nice and clever code!) #accept2ship * Fix linearWarmup multiplier check The multiplier needs to be non-negative, not strictly positive. * Revert D3314316 This is after 2 years and we do not seem to have a use case for this one, so for the sake of clean API design we should potentially remove this. This would allow us to potentially pass in arguments to optionally construct an object, although it is indeed a little bit unclear how we can reuse existing objects if constructor arguments are passed in. In any case, we may want to remove this dangling feature. * Speedup generate proposals by partial_sort. Speedup generate proposals by partial_sort. FACEBOOK: - Saw speed improvement for training with this op. - Yanghan benchmarked the op on a small dataset and see consistent 100% improvement on speed (6ms -> 3ms) on 420 input resolution. See next diff for details. * More parallel processing friendly for CPP version of GenerateProposals. More parallel processing friendly for CPP version of GenerateProposals. * [DT] [43/n] Lift stop conditions inside reader code back to flow control 1. Split multi_reader function into local_reader and remote_reader 2. Lifted stop conditions inside Limiter back to flow control 3. Split epoch flow building logic into 3 cases: - single machine (1 reader, 1 trainer on trainer0 node, no PS) - (1 reader + 1 trainer) on trainer0 node, has PS - multiple readers, readers do not share nodes with trainers, might have PS or not * Resolve conflicts for torch/_thnn/utils.py * [Caffe2] Handle image decoding errors Image decoding errors can make the whole training fail. This diff is to handle them 1.Catch imdecode exceptions and check if decoded image has zero columns or rows. This is counted as decoding errors. 2.Replace the image with empty in case of error 3.Count the number of errors and throw runtime exception if the rate reaches given number The empty image data is kept. It might introduce noise in the training data. * Update MKL exporter to IDEEP ops TSIA * [Caffe2] GlobalInit is thread safe, fixing the comment With the mutex and lock, GlobalInit is thread safe. Update the comments. * Back out "Add support for generating ATen files during fbcode build" Original commit changeset: 28970ddba353 @override-unit-failures (Note: this ignores all push blocking failures!) * [DT]: fix predictor save similar to D6610058, here we add the fix for distributed online training * Remove net_singlethread_async_gpu.cc Closes https://github.com/caffe2/caffe2/pull/2528 This removes net_singlethread_async_gpu.cc as part of our effort to clean CUDAContext and the net executors. * Inline DFS task execution Add a DFS inline task execution mode in executor * Add c10 folder to fbcode This adds the c10 folder and its test cases to fbcode. Build flags are mostly taken from aten. * add dependencies for online trainer Add some dependencies so that the online model can use DataPipeline and PredictionTransform operators Relevent post: https://fb.intern.facebook.com/groups/1324375037655677/permalink/1740993462660497/ * Resolve conflicts for tools/jit/gen_jit_dispatch.py * [Fix] sparse regularization in distributed training * Support advanced pooling options in sum processor * support advanced pooling options in sum processor * remove redundant code * support attention in sum processor * Improve shard logging in net tracing code Make it handle arbitrary shard ids instead of just one digit ids. * [Caffe2] Call GlobalInit in predictor only in mobile FACEBOOK: Calling GlobalInit long after the program starts may not be safe. There are issues if the following happens: User does not call GlobalInit and initFacebook after program starts User sets a flag manually: https://fburl.com/mcsumw7d User calls OSS predictor. OSS predictor calls GlobalInit GlobalInit calls initFacebook initFacebook resets all flags: https://fburl.com/tolszha1 Thus, the user manually set flags are overwritten This would happen anytime GlobalInit is called long after the program starts. I suppose the intention of the user in this case is not to call GlobalInit throughout the program, but use Caffe2 regardless (is that desired?) But adding GlobalInit in the OSS predictor would automatically call GlobalInit when using Caffe2. This issue doesn't exist in mobile, since initFacebook is not called on mobile. For now, guard the GlobalInit in predictor for mobile only. May want to ensure the GlobalInit is always called at the start of the program. @[3501714:kutta] has seen weird issues when not calling GlobalInit at the start of the program on server side. He has made some progress on this. * resolve conflicts for caffe2/core/logging_is_google_glog.h and test/test_torch.py * Add empty fix for SumLikeReduceOp Add empty fix for SumLikeReduceOp * Revert D7962948: [caffe2][nomnigraph] Concat elim for sparseNN This reverts commit f7f434dc5c34ca6058b9765d2ef615453d2276a9 @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 * Remove Declarations.yaml * Include common.h * Change std::stoi to caffe2::stoi * Add thread_name.cc to the CMake file * No need to subtract 1. Fix test segfaults * Fix NetTest, ObserverTest Fix tests (cherry picked from commit 3767e66c3f365596cba3d46d3e7322c933a0ab41) * CTCGreedyDecoderOp only has CPU implementation, test should only run on CPU * Add a variable to avoid conversion resizing issue * Remove the code per soumith's comments * Remove the code per soumith's comments * Remove blank lines in the end of file * Resolve conflicts for torch/_thnn/utils.py * Update MKL exporter to IDEEP ops TSIA * Back out "Add support for generating ATen files during fbcode build" Original commit changeset: 28970ddba353 @override-unit-failures (Note: this ignores all push blocking failures!) * add dependencies for online trainer Add some dependencies so that the online model can use DataPipeline and PredictionTransform operators Relevent post: https://fb.intern.facebook.com/groups/1324375037655677/permalink/1740993462660497/ * Resolve conflicts for tools/jit/gen_jit_dispatch.py * Support advanced pooling options in sum processor * support advanced pooling options in sum processor * remove redundant code * support attention in sum processor * resolve conflicts for caffe2/core/logging_is_google_glog.h and test/test_torch.py * Revert D7962948: [caffe2][nomnigraph] Concat elim for sparseNN This reverts commit f7f434dc5c34ca6058b9765d2ef615453d2276a9 @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 * Remove Declarations.yaml * Include common.h * Change std::stoi to caffe2::stoi * [caffe2] uprade IDEEP and hotfix for conv op accuracy issue (#8364) * [IDEEP] Upgrade IDEEP version Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com> * [IDEEP] Fix accuracy issue in conv op Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com> * Fix build error due to lack of src in CMakeLists Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com> * Remove the code per soumith's comments * [ONNX] Add an ATen fallback pathway for ONNX export (#8273) * ATen fallback for ONNX export * Move to enum * Fix model test * Add comment * Address comments BC interface * Remove imaginary file (#8415) * [Caffe2] Enable AMD/MIOPEN ops for Caffe2 (#8306) * Add hip support for caffe2 core * Add MIOPEN header/wrapper to caffe2 core * Add HIP device into caffe2 PB * top level makefile change for rocm/hip * makefile scaffolding for AMD/RocM/HIP * Makefile scafodding for AMD/RocM/HIP; add makefile/utility for HIP files * caffe2 PB update for AMD/ROCM HIP device * Add AMD/RocM/Thrust dependency * HIP threadpool update * Fix makefile macro * makefile fix: duplicate test/binary name * makefile clean-up * makefile clean-up * add HIP operator registry * add utilities for hip device * Add USE_HIP to config summary * makefile fix for BUILD_TEST * merge latest * Fix indentation * code clean-up * Guard builds without HIP and use the same cmake script as PyTorch to find HIP * Setup rocm environment variables in build.sh (ideally should be done in the docker images) * setup locale * set HIP_PLATFORM * Revert "set HIP_PLATFORM" This reverts commit 8ec58db2b390c9259220c49fa34cd403568300ad. * continue the build script environment variables mess * HCC_AMDGPU_TARGET * Cleanup the mess, has been fixed in the lastest docker images * Assign protobuf field hip_gpu_id a new field number for backward compatibility * change name to avoid conflict * Fix duplicated thread pool flag * Refactor cmake files to not add hip includes and libs globally * Fix the wrong usage of environment variables detection in cmake * Add MIOPEN CNN operators * Revert "Add MIOPEN CNN operators" This reverts commit 6e89ad4385b5b8967a7854c4adda52c012cee42a. * Add MIOPEN pooling operator * Add MIOPEN activation operator * Add MIOPEN softmax operator * Add MIOPEN spatial batch norm operator * Add MIOPEN loacl response normalization operator * Add MIOPEN conv operator * Clean-up LRN ops * enable fp16 in MIOPEN pool ops * Enable fp16 for MIOPEN relu op * Enable fp16 for MIOPEN spatial batch norm op * code clean-up * revert float16 support * Create Caffe2 python binding for AMD/ROCM/HIP * Add op fallback for HIP operator * add hip src/test files in cmake * exclude hip src/test files * fix python binding for hip backend * fix MIOPEN pooling op workspace * hack to compile miopen operators * fix include path for MIOPEN ops * Fix include path * Add HIP math utilities * Fix path for HIP math utils * cmake fix * Cmake fix / hipcc for hip files * suppress hipcc warning * cmake fix /replcae USE_HIP with USE_ROCM * revert LoadHIP.cmake change * fix include for thrust/cub-hip * include path fix for conversion.h * Updated with latest upstream changes * clang format fixes * Context_hip updates * Fixed typo in rocblas handle get function * Updated hipified math utils * Updated math hip test util * Updated context hip test * Updated common_hip * Updated net async dag for HIP * Added MIOPEN in operator hip test * fix * C2 dependencies clean-up * fix include path for building custom protobuf * Decouple miopen pool op and conv_pool_op base * cmake refactor * fix operator_hip_test * move all hip/miopen ops files into caffe2/operators/hip * sanitize cmake * permission issue * remove extra parenthesis * remove artifact from resolving merge conflict * cont. sanitize cmake files * fix syntax error * sanitize conversion.h * . * Revert "." This reverts commit 56020cb0e996a31ae27bf1f8f491955ed0b121b9. * clang-format * Enable some reduce operators' ONNX backend tests (#8418) * fix old comment to point to the right file (#8416) * Stop pinning nccl version. (#8421) Signed-off-by: Edward Z. Yang <ezyang@fb.com> * Expose logsumexp docs and mark log_sum_exp in distributions for internal use (#8428) * Enable some of the ONNX backend test on broadcasting (#8423) * Enable some of the ONNX backend test on broadcasting * enable gemm broadcast * Expose proto utils and ONNX (#8073) * Expose proto utils and ONNX from PyTorch libcaffe2.so * Try to use protobuf from _C.so * Fix ONNX proto header include * Adjust order of imports for ONNX until nanopb goes away * Set and use ONNX_NAMESPACE for PyTorch builds * Show protobuf summary for all builds * Add ONNX_NAMESPACE for cpp_build * Statically link libprotobuf.a into libtorch.so * Set ONNX_NAMESPACE on Windows build * Move core/dispatch up as well * Add /MD flag for Windows build of _C * Potential Windows fix for ONNX and protobuf * Add direct linkage from _C to ONNX on Windows * Only include protobuf wrapper for PyTorch * Pass extra_compile_args to _nvrtc ext build * Remove installation of .a files * Rebase creates some weird situations, revert them manually * Remove more weird changes due to rebase * Need to add thread_name.cc after merge |
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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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Orion Reblitz-Richardson
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1d5780d42c |
Remove Apache headers from source.
* LICENSE file contains details, so removing from individual source files. |
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Ilia Cherniavskii
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25e7f8ab28 |
Fix event synchronization logic
Summary: Fix logic in operator's event synchronization: Record might be called after async CPU op calls SetFinished Reviewed By: azzolini Differential Revision: D7003277 fbshipit-source-id: 4d77d6619c6403e71ba45fbaaf78e939982452b6 |
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Yangqing Jia
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91d76f5dbd |
Reapply Windows fix
Summary:
Last fix was uncommitted due to a bug in internal build (CAFFE2_API causing error). This one re-applies it as well as a few more, especially enabling gtest.
Earlier commit message: Basically, this should make windows {static_lib, shared_lib} * {static_runtime, shared_runtime} * {cpu, gpu} work other than gpu shared_lib, which willyd kindly pointed out a symbol limit problem. A few highlights:
(1) Updated newest protobuf.
(2) use protoc dllexport command to ensure proper symbol export for windows.
(3) various code updates to make sure that C2 symbols are properly shown
(4) cmake file changes to make build proper
(5) option to choose static runtime and shared runtime similar to protobuf
(6) revert to visual studio 2015 as current cuda and msvc 2017 do not play well together.
(7) enabled gtest and fixed testing bugs.
Earlier PR is #1793
Closes https://github.com/caffe2/caffe2/pull/1827
Differential Revision: D6832086
Pulled By: Yangqing
fbshipit-source-id: 85f86e9a992ee5c53c70b484b761c9d6aed721df
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Vladimir Chalyshev
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8c02674964 |
Revert D6817719: [caffe2][PR] Better support for windows
Summary: This reverts commit d286264fccc72bf90a2fcd7da533ecca23ce557e 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 Differential Revision: D6817719 fbshipit-source-id: 8fe0ad7aba75caaa4c3cac5e0a804ab957a1b836 |
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Yangqing Jia
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8aa8eaabb1 |
Better support for windows
Summary:
Basically, this should make windows {static_lib, shared_lib} * {static_runtime, shared_runtime} * {cpu, gpu} work. A few highlights:
(1) Updated newest protobuf.
(2) use protoc dllexport command to ensure proper symbol export.
(3) various code updates to make sure that C2 symbols are properly shown
(4) cmake file changes to make build proper
(5) option to choose static runtime and shared runtime similar to protobuf
(6) revert to visual studio 2015 as current cuda and msvc 2017 do not play well together.
Closes https://github.com/caffe2/caffe2/pull/1793
Reviewed By: dzhulgakov
Differential Revision: D6817719
Pulled By: Yangqing
fbshipit-source-id: d286264fccc72bf90a2fcd7da533ecca23ce557e
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Ilia Cherniavskii
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1149b9bbb5 |
Polling async net executor
Summary: Implementation of polling async net executor. Notes: - New net executor async_polling - schedules CPU and GPU ops asynchronously, uses single polling thread - Events: update to Caffe2 events to support async CPU events, adding new methods: Query() - non-blocking checking of event states: INITIALIZED -> RECORDED -> SUCCESS/FAILED ErrorMessage() - when operation runs asynchronously and fails calling this on event will give error message - Tasks: using existing DAGNet's algorithm to compute CPU and GPU chains, a separate task for each chain - Polling: using single thread to query state of events - for CPU tasks atomically queries task state, for GPU task - uses cudaEventQuery; using Event - Scheduling of CPU ops: using global thread pools - Scheduling of GPU ops: using GPU thread pool per GPU device Reviewed By: dzhulgakov Differential Revision: D5985110 fbshipit-source-id: a9de7fcbb71d046a3aa1b573072b89a65dfeee8c |
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Yangqing Jia
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8286ce1e3a |
Re-license to Apache
Summary: Closes https://github.com/caffe2/caffe2/pull/1260 Differential Revision: D5906739 Pulled By: Yangqing fbshipit-source-id: e482ba9ba60b5337d9165f28f7ec68d4518a0902 |
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Yangqing Jia
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0b363fd9de |
Add event as a first-class citizen of the OperatorBase interface.
Summary: This adds Event as a new member object to OperatorBase, hence allowing us to do async computation more easily. Will send a fix for proper RunAsync() for SimpleNet. In principle this should have no functionality change yet - the only difference is that async_dag net now delegates to the operators for holding the event objects. Reviewed By: harouwu Differential Revision: D5668627 fbshipit-source-id: 55f994074be6b85d6c66f09795dcbe2b93aba300 |
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Yangqing Jia
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5d24a4eeef |
Early design for a general Event abstraction cross-devices.
Summary: There are ad-hoc efforts on avoiding excessive device synchronizations, such as async_dag, singlethread_async, etc. This diff aims to provide an early design for a general Event class, that can achieve the following: (1) It is device agnostic, essentially using a vtable to do cross device record, wait and synchronization. (2) Created new functions WaitEvent and Record in the Context class for interacting with Events. (3) Exposed the corresponding WaitEvent and Record functions in the OperatorBase class as well. An example use case is that, after potential future refactoring, one can achieve a real async execution per operator by running op.WaitEvent(previous_event); op.RunAsync(); op.RecordEvent(this_op_event); and the next op can do next_op.WaitEvent(this_op_event); Right now, I changed async_dag net implementation so that it uses the general event design. The old Event class is assimilated to the general Event class and the old Stream class is now essentially taken over by the Context class itself. Reviewed By: harouwu Differential Revision: D5648463 fbshipit-source-id: 58bd84d06e4a9977b0b835110ddb2f18be3b7cbc |