…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>

* [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 facebookarchive/caffe2#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 facebookarchive/caffe2#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 8ec58db.

* 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 6e89ad4.

* 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 56020cb.

* 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