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Author SHA1 Message Date
Orion Reblitz-Richardson
9ec0a2aef4 fbshipit-source-id: ba600fcd2b5cefc7621357bdeb05e24cea02e5af 2018-06-27 04:50:56 -07:00
Orion Reblitz-Richardson
edb88b5f3a
Update from Facebook (#8887) 
* 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

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* [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.
 2018-06-26 14:55:48 -07:00
sf-wind
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

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* 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

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If you copy this password, see you in SEV Review!
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* 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

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* 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
 2018-06-13 13:10:45 -07:00
Orion Reblitz-Richardson
6223bfdb1d Update from Facebook (#6692) 
* [GanH][Easy]: Add assertion to adaptive weighting layer

0 weight causes numeric instability and exploding ne

* [Easy] Add cast op before computing norm in diagnose options

As LpNorm only takes floats we add a manual casting here.

* Introduce a new caching device allocator

`cudaMalloc` and `cudaFree` calls are slow, and become slower the
more GPUs there are. Essentially, they grab a host-wide (not device-wide) lock
because GPU memory is transparently shared across all GPUs. Normally, this
isn't much of a concern since workloads allocate memory upfront, and reuse it
during later computation.

However, under some computation models (specifically, memory conserving
approaches like checkpoint-and-recompute, see
https://medium.com/@yaroslavvb/fitting-larger-networks-into-memory-583e3c758ff9)
this assumption is no longer true. In these situations, `cudaMalloc` and
`cudaFree` are common and frequent. Furthermore, in data parallel contexts,
these calls happen at nearly the same time from all GPUs worsening lock
contention.

A common solution to this problem is to add a custom allocator. In fact,
nVIDIA provides one out of the box: CUB, which Caffe2 already supports.
Unfortunately, the CUB allocator suffers from very high fragmentation. This is
primarily because it is a "buddy" allocator which neither splits nor merges
free cached blocks. Study
https://github.com/NVlabs/cub/blob/1.8.0/cub/util_allocator.cuh#L357 if you
want to convince yourself.

This diff adapts a caching allocator from the Torch codebase
https://github.com/torch/cutorch/blob/master/lib/THC/THCCachingAllocator.cpp
which does splitting and merging and ends up working really well, at least for
workloads like the checkpoint-and-recompute computation models noted above.

I simplified the implementation a little bit, made it a bit more C++-like. I
also removed a bunch of stream synchronization primitives for this diff. I
plan to add them back in subsequent diffs.

* Report reader progress in fblearner workflows

Integrate with fblearner progress reporting API and add support to report training progress from reader nodes.
If reader is constructed with batch limits, report based on finished batch vs total batch. The finished batch may be more than total batch because we evaludate if we should stop processing everytime we dequeue a split.
If no limit for the reader, report based on finished splits (Hive files) vs total splits. This is fairly accurate.

* [GanH][Diagnose]: fix plotting

1. ganh diagnose needs to set plot options
2. modifier's blob name is used for metric field can need to be fixed before
generating net

* Automatic update of fbcode/onnx to 985af3f5a0f7e7d29bc0ee6b13047e7ead9c90c8

* Make CompositeReader stops as soon as one reader finishes

Previously, CompositeReader calls all readers before stopping. It results in flaky test since the last batch may be read by different threads; resulting in dropped data.

* [dper] make sure loss is not nan

as desc.

* [rosetta2] [mobile-vision] Option to export NHWC order for RoIWarp/RoIAlign

Thanks for finding this @stzpz and @wangyanghan. Looks like NHWC is more
optimized. For OCR though it doesn't yet help since NHWC uses more mem b/w but
will soon become important.

* Intra-op parallel FC operator

Intra-op parallel FC operator

* [C2 Proto] extra info in device option

passing extra information in device option

design doc: https://fb.quip.com/yAiuAXkRXZGx

* Unregister MKL fallbacks for NCHW conversions

* Tracing for more executors

Modified Tracer to work with other executors and add more tracing

* Remove ShiftActivationDevices()

* Check for blob entry iff it is present

When processing the placeholders ops, ignore if the blob is not present in the blob_to_device.

* Internalize use of eigen tensor

Move use of eigen tensor out of the header file so we don't get template partial specialization errors when building other libraries.

* feature importance for transformed features.

* - Fix unused parameter warnings

The changes in this diff comments out unused parameters.
This will allow us to enable -Wunused-parameter as error.

#accept2ship

* add opencv dependencies to caffe2

The video input op requires additional opencv packages. This is to add them to
cmake so that it can build

* Add clip_by_value option in gradient clipping

Add clip_by_value option in gradient clipping

when the value is bigger than max or smaller than min, do the clip

* std::round compat
 2018-04-17 23:36:40 -07:00
Orion Reblitz-Richardson
1d5780d42c Remove Apache headers from source. 
* LICENSE file contains details, so removing from individual source files.
 2018-03-27 13:10:18 -07:00
Xiaolong Wang
4bb73b8361 [GanH] Weighting Layers: Adaptive/Constant/Homotopy 
use case: to weight multiple losses (real values) as a single composite loss for
optimization
 2018-03-20 13:34:22 -07:00