Summary:
When output blob names are specified while load_all=1, output blob names are ignored. However, this behavior is not documented. In this diff, we just disallow users to provide blob names when load_all=1.
See discussion at https://fb.workplace.com/groups/1405155842844877/permalink/2714909788536136/
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19133
Reviewed By: dzhulgakov
Differential Revision: D14883698
Pulled By: chandlerzuo
fbshipit-source-id: 6e4171e36c4ccc4f857e79da98b858a06b7d8ad6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19083
As we have discussed, there are too many of AdjustBatch ops and they incur reallocation overhead and affects the performance. We will eliminate these ops by
- inling the input adjust batch op into Glow
- inling the output adjust batch op into OnnxifiOp and do that only conditionally.
This is the C2 part of the change and requires change from Glow side to work e2e.
Reviewed By: rdzhabarov
Differential Revision: D14860582
fbshipit-source-id: ac2588b894bac25735babb62b1924acc559face6
Summary:
Almost there, feel free to review.
these c10 operators are exported to _caffe2 domain.
TODO:
- [x] let the onnx checker pass
- [x] test tensor list as argument
- [x] test caffe2 backend and converter
- [x] check the c10 schema can be exported to onnx
- [x] refactor the test case to share some code
- [x] fix the problem in ONNX_ATEN_FALLBACK
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18210
Reviewed By: zrphercule
Differential Revision: D14600916
Pulled By: houseroad
fbshipit-source-id: 2592a75f21098fb6ceb38c5d00ee40e9e01cd144
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18155
- Make a python decorator caffe2_flaky for caffe2 operator unit tests.
- The environment variable CAFFE2_RUN_FLAKY_TESTS are now used to mark flaky test mode
During test run,
- If flaky tests mode are on, only flaky tests are run
- If flaky tests mode are off, only non-flaky tests are run
Mark ctc_beam_search_decoder_op_test as flaky
Reviewed By: ezyang, salexspb
Differential Revision: D14468816
fbshipit-source-id: dceb4a48daeb5437ad9cc714bef3343e9761f3a4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18129
A lot of tensor interference function assume the operator passes the schema.
So call Verity to make sure this is actually the case.
Created diff before to add checking in Concat (https://github.com/pytorch/pytorch/pull/17110), but I encountered lot more places where this is assumed (for example ElementwiseOpShapeInference)
Reviewed By: mdschatz
Differential Revision: D14503933
fbshipit-source-id: cf0097b8c3e4beb1cded6b61e092a6adee4b8fcb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18084
data_strategy parameter was not used in some of unit tests for optimizers
Reviewed By: hyuen
Differential Revision: D14487830
fbshipit-source-id: d757cd06aa2965f4c0570a4a18ba090b98820ef4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18036
- Add macros to export c10 cuda operators to caffe2 frontend
- Instead of having a separate caffe2 registry for the c10 operator wrappers, use the existing caffe2 registries
Reviewed By: ezyang
Differential Revision: D14467495
fbshipit-source-id: 7715ed2e38d2bbe16f1446ae82c17193a3fabcb9
Summary:
Observed the test `TestGroupConvolution.test_group_convolution` to fail with the following error:
```
Falsifying example: test_group_convolution(self=<caffe2.python.operator_test.group_conv_test.TestGroupConvolution testMethod=test_group_convolution>, stride=3, pad=0, kernel=5, size=8, group=4, input_channels_per_group=7, output_channels_per_group=8, batch_size=2, order='NHWC', engine='', use_bias=False, gc=, dc=[, device_type: 1])
You can reproduce this example by temporarily adding reproduce_failure('3.59.1', b'AAAA') as a decorator on your test case
```
This example generated by hypothesis has `group=2, order='NHWC' and dc=[, device_type: 1])`.
I think this example should be skipped.
I have mimicked the change corresponding to [PR#13554](https://github.com/pytorch/pytorch/pull/13554) to skip this example.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17715
Differential Revision: D14346642
Pulled By: ezyang
fbshipit-source-id: b1f1fef09f625fdb43d31c7213854e61a96381ba
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16723
Removed obsolete argument correct_transform_coords in bbox_transform op.
* It was only for backward compatibility. We should not have models using it now.
Differential Revision: D13937430
fbshipit-source-id: 504bb066137ce408c12dc9dcc2e0a513bad9b7ee
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16691
Previous diffs already introduced a macro that registers caffe2 CPU kernels with c10.
This now also registers the CUDA kernels with it.
Reviewed By: bwasti
Differential Revision: D13901619
fbshipit-source-id: c15e5b7081ff10e5219af460779b88d6e091a6a6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16643
The test was disabled in D13908117 because it conflicted with another diff that was about to land.
Now fixed the merge conflict and re-landing it.
Reviewed By: ezyang
Differential Revision: D13911775
fbshipit-source-id: b790f1c3a3f207916eea41ac93bc104d011f629b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16548
With this macro, a caffe2 operator can now directly be registered with c10.
No need to write custom wrapper kernels anymore.
Differential Revision: D13877076
fbshipit-source-id: e56846238c5bb4b1989b79855fd44d5ecf089c9c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16676
This op is used for changing batch size (first dimension) of the tensor.
Reviewed By: bertmaher, ipiszy
Differential Revision: D13929200
fbshipit-source-id: 4f2c3faec072d468be8301bf00c80d33adb3b5b3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16785
There's no EIGEN engine implemented for DeformConv but unit test was checking it.
Reviewed By: BIT-silence
Differential Revision: D13967306
fbshipit-source-id: e29c19f59f5700fc0501c59f45d60443b87ffedc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16478
This diff includes an example registration of a caffe2 op in torch. A previous attempt ran into a static initialization order bug.
Reviewed By: smessmer
Differential Revision: D13854304
fbshipit-source-id: ec463ce2272126d08a5163d1599361ee5b718bbc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16630
two PRs landed concurrently - enforcing tensor constraints and refactoring c10. Since it's not a prod code - disable test and I'll let Sebastian to fix it properly.
Reviewed By: ezyang
Differential Revision: D13908117
fbshipit-source-id: 381c5626078b794afa1fc7a95cb1ea529650424c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16246
The op schema says it returns multiple values, so let's actually return multiple values instead of one tuple.
For some reason, this did work when called from python (probably some auto-unpacking),
but once called from JIT, it segfaulted. This diff fixes that.
Reviewed By: dzhulgakov
Differential Revision: D13780147
fbshipit-source-id: fe94f82f4c53b7454f77c4484fca4ac9dc444475
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16374
this fixes the original attempt in OSS (adds to CMake and python build files)
Reviewed By: smessmer
Differential Revision: D13821061
fbshipit-source-id: 82f0dade0145fd04bdf8e3cb3954b5790e918162
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16350
Example usage of the new caffe2 integration
Reviewed By: smessmer
Differential Revision: D13408546
fbshipit-source-id: 87240ca7f48d653a70241d243aa0eb25efa67611
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16335
group conv is not implemented with EIGEN engine so this diff disables related tests
Reviewed By: jamesr66a
Differential Revision: D13807204
fbshipit-source-id: 41f6de43da40882f57e64474520e185733caefb7
Summary:
bypass-lint
- Change all Caffe2 builds to use setup.py instead of cmake
- Add a -cmake- Caffe2 build configuration that uses cmake and only builds cpp
- Move skipIfCI logic from onnx test scripts to the rest of CI logic
- Removal of old PYTHONPATH/LD_LIBRARY_PATH/etc. env management
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15917
Reviewed By: orionr
Differential Revision: D13637583
Pulled By: pjh5
fbshipit-source-id: c5c5639db0251ba12b6e4b51b2ac3b26a8953153
Summary:
This is follow up on #13945 where we had to turn off some TRT tests because some ops were not ready to accept ONNX opset 9+ models. This PR fixes Reshape.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15380
Differential Revision: D13649825
Pulled By: houseroad
fbshipit-source-id: b72e62803de5b63cc001c3fe4b3bf64dfa996e94
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15865
factored out code used in tests for operators Add, Mul and Sub
into two new methods: a first one to generate the test vectors, a second
one to run the actual tests given a caffe2 and python operator.
Reviewed By: houseroad
Differential Revision: D13526955
fbshipit-source-id: 8970ba5a1305ca19a54a14b51816d4a19f19d678
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15553
Add unit test and implementation of NHWC layout for Resize operator.
Also, add pragma parallel loop to old NCHWC layout.
Reviewed By: jspark1105
Differential Revision: D13540762
fbshipit-source-id: eebf252bf0d1efdff180a171d804181045f100a5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15625
3D group conv (both NCHW and NHWC layout) was not correct.
Added group=2 in test_1d_convolution and test_3d_convolution in conv_test
Reviewed By: protonu
Differential Revision: D13562099
fbshipit-source-id: 586e8a7574a2764f2a3b559db6c2415b3ab90453
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15632
Just formatting and a few lints.
Reviewed By: yinghai
Differential Revision: D13562403
fbshipit-source-id: c56f8ee61f68cdaccc0828a764ff729454f68259
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15588
Use NHWC2NCHW or NCHW2NHWC functions which is easier to understand compared to code using transpose and generalizable to non-2D convolutions.
Reviewed By: csummersea
Differential Revision: D13557674
fbshipit-source-id: c4fdb8850503ea58f6b17b188513ae2b29691ec0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15082
We didn't have unit test for low-precision rowwise adagrad
Reviewed By: chocjy
Differential Revision: D13300732
fbshipit-source-id: 46e7bdfc82c5a6855eeb6f653c0a96b0b3a20546
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15389
SparseLengthsMean was generating uninitialized data for empty inputs (lengths == 0). We should return zeros.
The unit tests were also not covering this special case which is fixed by this diff.
Reviewed By: salexspb
Differential Revision: D13515970
fbshipit-source-id: 3c35265638f64f13f0262cee930c94f8628005da
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15174
Previously, Caffe2 maintained a separate per-thread per-device
current logical CUDA stream ID. In this PR, we switch Caffe2 over
to using c10::Stream to manage the current stream, and also
manage the allocation of cudaStream_t objects.
This results in a slight behavior change: previously, Caffe2
would have been willing to allocate an arbitrary number of
CUDA streams, depending on how high the logical stream IDs
went. The c10::Stream pool has a fixed number of streams, once
you exceed it, it wraps around.
Reviewed By: dzhulgakov
Differential Revision: D13451550
fbshipit-source-id: da6cf33ee026932a2d873835f6e090f7b8a7d8dc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15110
support casting to string on CPU
Reviewed By: intermilan
Differential Revision: D13429381
fbshipit-source-id: b737a1ba1237b10f692d5c42b42a544b94ba9fd1
Summary:
This pull request contains changes for:
1. Added MIOpen RNN API miopenGetRNNLayerBiasSize and miopenGetRNNLayerParamSize.
2. Fixed usage of API miopenGetRNNLayerParam.
3. Modifying the RNN test to run using MIOpen engine.
Differential Revision: D13355699
Pulled By: bddppq
fbshipit-source-id: 6f750657f8049c5446eca893880b397804120b69
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13756
This implements general Gather operator for arbitrary axis, sharing the code with BatchGather.
- CPU gather & batch gather logic is now shared through caffe2::gather_helper, for any axis.
- Shared CUDA kernel moved to gather_op.cuh, for any axis.
- Gradients of axis > 0 delegate to BatchGatherGradientOp which now has axis argument.
- BatchGatherOp doc strings updated to have correct rank (q + (r -1)) and output.
- Added tests for axis == 2.
GatherOp supports index wrapping for axis == 0 by default, which was earlier for ONNX.
This diff also extends it to work in Cuda kernel. Added "wrap_indices" argument which specifies
wheather this wrapping should be done; set it to true if you'd like wrapping for any axis.
TBD: Update gradients to support negative indices (separate diff).
TBD: Once we have operator versioning, we'd like to update GatherOp to NOT support axis 0 wrapping
by default, but rather do it only if wrap_indices is set.
Reviewed By: dzhulgakov
Differential Revision: D12983815
fbshipit-source-id: 8add9d67b47fe8c5ba7a335f581ca0530b205cd7
Summary:
Goal of this PR is to unify cuda and hip device types in caffe2 python front end.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14221
Differential Revision: D13148564
Pulled By: bddppq
fbshipit-source-id: ef9bd2c7d238200165f217097ac5727e686d887b
Summary:
This pull request contains changes for:
1. Removing ConvTranspose related changes from caffe2/operators/hip/conv_op_miopen.cc
2. Adding the file caffe2/operators/hip/conv_transpose_op_miopen.cc
3. Modifying the tests to run convTranspose op using MIOpen engine
Differential Revision: D13055099
Pulled By: bddppq
fbshipit-source-id: ca284f8f9a073005b22013c375cc958257815865
Summary: Currently Lambdarank applies exponential emphasis on relevance, i.e., g=2^rel when calculating dcg, this diff adds options that supports g=rel in the loss function.
Reviewed By: itomatik
Differential Revision: D9891514
fbshipit-source-id: 64730d467a665670edd37e6dc1c077987991d1a8
Summary:
Add a markdown document summarizing the coverage of serialized operator tests. This currently only takes into account what has been covered by the tests with respect to the entire registry of c2 operators.
Next, we will break down the coverage by which operators have unit tests associated with them, which have hypothesis tests, and which have tests more specifically calling assertReferenceChecks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13703
Reviewed By: dzhulgakov
Differential Revision: D12970810
Pulled By: ajyu
fbshipit-source-id: 4f0cd057b1cf734371333e24d26cbab630a170e1
Summary:
I was hitting this error:
caffe2/caffe2/operators/stats_put_ops.h:66:25: runtime error: 9.22337e+18 is outside the range of representable values of type 'long'
So, the assignment from int64_t to float loses some precision and because of that we overflow.
Reproduced this issue with this diff D12945013
Reviewed By: mlappelbaum, jdshi-fb
Differential Revision: D12927086
fbshipit-source-id: 7eae7fe25ab49d5ac15279335bd5b1fa89d6e683
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12733
Conv in NHWC layout only works for 2D images. This has been a pain point when implementing quantized 3D convolution because we need NHWC layout for best performance (note that NHWC layout in general gives better performance in CPU not just for quantized operators). For example, our quantized ops have a functionality to measure quantized error operator by operator but this needs running a shadow fp32 operator, but this is not easy when there's no 3D conv in NHWC layout is available (currently we're doing layout conversion on the fly for the shadow fp32 operator which is error prone). Some of Caffe2 frameworks like brew generates error when we try to create a 3D conv op in NHWC layout. This was also a blocker for using aibench because aibench is using brew.
i-am-not-moving-c2-to-c10
Reviewed By: houseroad
Differential Revision: D10333829
fbshipit-source-id: 2d203ee1db833cd3f9d39353219e3894b46c4389
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13554
D10233252 broke ROCM test.
We don't have group conv in NHWC for hip yet and this diff omits related tests.
Reviewed By: hyuen
Differential Revision: D12917880
fbshipit-source-id: 9baf36a8cb061ee8cf393b2c438a2d1460ce5cd8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12428
Group conv in NHWC layout was enabled in CPU after D7547497.
In D7547497, unit test of group conv in NHWC layout in CPU was enabled in group_conv_test.py but not in conv_test.py . This diff also enables it in conv_test.py .
Reviewed By: BIT-silence
Differential Revision: D10233252
fbshipit-source-id: aeeaf3eedc60e1cf6321b5a1dbe6a561e3aacbde
Summary:
Essentially makes cuDNN to think of those kernels like of Nx1 ones.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12902
Reviewed By: BIT-silence
Differential Revision: D10852862
Pulled By: soumith
fbshipit-source-id: 7416cf6d131177340d21cbf1d42c1daa6c7cad8c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12843
This adds a cuda implementation for the UpsampleBilinearOp and UpsampleBilinearGradientOp.
The CUDA code is based off of the corresponding ResizeNearest operators but with bilinear interpolation logic taken from the CPU implementation.
Reviewed By: houseroad
Differential Revision: D10453776
fbshipit-source-id: b29ac330b72465974ddb27c0587bca590773fdec
Summary:
This is mostly for reusing all the cudnn test cases in our python operator_tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12278
Differential Revision: D10842592
Pulled By: bddppq
fbshipit-source-id: 4b3ed91fca64ff02060837b3270393bc2f9a9898
Summary:
TSIA - we want to deprecate numba in fbcode when moving to new compiler tiers.
Converted the old test to a non-numba regular python op test.
Reviewed By: xw285cornell
Differential Revision: D10519910
fbshipit-source-id: 0e9188a6d0fc159100f0db704b106fbfde3c5833
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12736
This updates UpsampleBilinearOp and UpsampleBilinearGradientOp to support scales to bring it inline with ResizeNearestOp https://github.com/pytorch/pytorch/pull/12720.
Reviewed By: houseroad
Differential Revision: D10416228
fbshipit-source-id: f339b7e06979c9c566afb4cee64a2d939b352957
Summary: Added 2 years ago in D3665603, never used, kill it.
Reviewed By: ezyang
Differential Revision: D10421336
fbshipit-source-id: 1b027a9ef2b71d0dd2c572cd4338bc8e046320d8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12382
implement fp16-> (uint8 + scale and bias in fp32)
this is similar to fp32 rowwise quantization
we could have done scale and bias in fp16 but not too motivated since we are not saving much and those datatypes have to be converted to fp32 to process since x86 doesn't support half float operations anyways
Reviewed By: csummersea
Differential Revision: D10220463
fbshipit-source-id: 6c382026de881f03798c2e5fc43abfc80f84ea1f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12390
Introduce a no op optimizer for when we don't want updates to happen, but don't want to affect downstream processes.
Reviewed By: mlappelbaum
Differential Revision: D10209812
fbshipit-source-id: 2af4ebc0fb42e78ea851c3a9f4860f3d224037b6
Summary:
Changes in this PR:
1. Intermediate Docker image is shared from build stage to test stage through ECR, in order to fix the Caffe2 flaky CUDA tests.
2. There are ~7 Caffe2 operator tests that are only flaky in `caffe2_py2_gcc4_8_ubuntu14_04_test` on CPU. Disabling those tests on that config only, which is okay to do because we are still running those tests in other test jobs.
After this PR is merged, CircleCI will be running on master automatically, and will be running on PRs if the author rebased their PR onto the newest master (which we will ask all the authors to do when we switch off Jenkins for Linux).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12389
Differential Revision: D10224267
Pulled By: yf225
fbshipit-source-id: dd1a90a425c3d13b870d3d328cb301eee2e6e2cd
Summary:
Original commit changeset: f5614a5d2607
D9986213 is causing Multifeed Aggregator a [huge performance different](https://our.intern.facebook.com/intern/ads/analyze_canary/412951953278781781/) and is blocking aggregator push since last Friday night: https://fburl.com/feedtools/b6izvwjz
We need to land this revert ASAP to unblock aggregator push.
Reviewed By: orionr
Differential Revision: D10123245
fbshipit-source-id: d83da8e00a1250f5d09811a0a587c127e377aab2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11349
Special case BatchGather and BatchGatherGradient for block_size=1. This makes BatchGather 3-4X faster and BatchGatherGradient 10X for this case.
Reviewed By: jspark1105, ilia-cher
Differential Revision: D7218043
fbshipit-source-id: ea12042239a8adc92b9efcbd0b66e354fb43f4c7
Summary:
Followup to [the serialized test framework](https://github.com/pytorch/pytorch/pull/10594)
Round 1 for refactoring tests, starting alphabetically. I added some functionality, so I wanted to send out some of these initial changes sooner.
I'm skipping all tests that don't explicitly call assertReferenceChecks. Some tests directly call np.allclose, and others are simply TestCase (rather than HypothesisTestCase).
1. Start alphabetically producing serialized outputs for test functions, annotating those we want to include with `serialized_test_util.given`. So far I've only added one test per operator, but this already does seem to add quite a few tests.
2. Add functionality to allow us to generate outputs using pytest by adding pytest argument options. This allows us to skip adding a `__main__` function to quite a few tests.
3. Catch any exceptions generating the gradient operator and skip serializing/reading it, since certain operators don't have gradients.
4. Add functionality to better handle jagged array inputs, which numpy doesn't handle very well. We simply explicitly do the conversion to dtype=object.
5. Make only one file per test function, rather than 4, to reduce the number of files in the github repo.
I also noticed that there is some hypothesis handling that makes `serialized_test_util.given` not compatible with adding more hypothesis decorators on top. For example, there are tests that do
```
settings(...)
given(...)
def test_my_stuff(...)
```
But there is a hypothesis handler that explicitly checks that `given` is called below `settings`, so we cannot refactor this to `serialized_test_util.given`. I've just avoided decorating these kinds of tests for now, I hope that's alright.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11350
Reviewed By: houseroad
Differential Revision: D9693857
Pulled By: ajyu
fbshipit-source-id: a9b4279afbe51c90cf2025c5ac6b2db2111f4af7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11413
LengthsTileOp was implemented using a sequence of device memcopies initiated on the CPU. This was very slow. I changed it to use a kernel. TUM benchmark QPS improved from 13k QPS to 20k QPS as a result.
Reviewed By: manojkris, xianjiec
Differential Revision: D9724988
fbshipit-source-id: 2f98c697730982734d7c6a26d0b6967310d49900
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10974
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10291
This new operator will do the following:
Given a LENGTHS vector and n_splits, output a "split" LENGTHS vector where:
1. Each length in input vector is split into n_splits values (thus output vector should have LENGTHS.size(0) * n_splits elements)
2. The new lengths in output should be evenly split, and if the length is not divisible by n_splits, then order new values in descending order. (e.g. n_splits = 3, length = 5 -> 2 2 1)
3. If n_splits > some element in the array, its split elements will contain 0s. (e.g. n_splits = 3, length = 2 - > 1 1 0)
Reviewed By: bddppq, chocjy
Differential Revision: D9013119
fbshipit-source-id: 82bf3371ec08c41fc3379177f0007afc142e0d84
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10888
Add cuda version of SpatialBNOp also optimize SpatialBN on CPU
Reviewed By: houseroad
Differential Revision: D9512435
fbshipit-source-id: 6f828c88d56d30dc9a2f98a297a161c35cc511b1
Summary:
This PR adds all PyTorch and Caffe2 job configs to CircleCI.
Steps for the CircleCI mini-trial:
- [ ] Make sure this PR passes Jenkins CI and fbcode internal tests
- [x] Approve this PR
- [ ] Ask CircleCI to turn up the number of build machines
- [ ] Land this PR so that the new `.circleci/config.yml` will take effect
Several Caffe2 tests are flaky on CircleCI machines and hence skipped when running on CircleCI. A proper fix for them will be worked on after a successful mini-trial.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11264
Differential Revision: D9656793
Pulled By: yf225
fbshipit-source-id: 7832e90018f3dff7651489c04a179d6742168fe1
Summary:
Generate serialized test inputs/outputs/backward graphs of tests inside `caffe2/python/operator_test` that call assertSerializedOperatorCheck(). Tests should be decorated with serialized_test.collect_tests.given_and_seeded to run hypothesis tests that are actually random and a single fixed seeded hypothesis tests.
To use:
1. Refactor your test to be a SerializedTestCase
1a. Decorate it with given_and_seeded
1b. Call testWithArgs in main
2. Run your test with -g to generate the output. Check it in.
3. Subsequent runs of the test without generating the output will check against the checked in test case.
Details:
Run your test with `python caffe2/python/operator_test/[your_test].py -g`
Outputs are in `caffe2/python/serialized_test/data`. The operator tests outputs are in a further subdirectory `operator_test`, to allow for other tests in the future (model zoo tests?)
Currently, we've only refactored weighted_sum_test to use this, but in the next diff, we'll refactor as many as possible. The directory structure may also change as usually there are multiple tests in a single file, so we may create more structure to account for that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10594
Reviewed By: ezyang
Differential Revision: D9370359
Pulled By: ajyu
fbshipit-source-id: 2ce77389cd8bcc0255d3bccd61569833e545ede8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10955
Add GPU version of HardSigmoid Op to Caffe2. Updated test file to
include GPU tests.
Reviewed By: enosair
Differential Revision: D9499353
fbshipit-source-id: fcb51902063d0c3e4b10354533a8a42cf827c545
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10439
Update Im2Col related to make preparation for group conv in NHWC order.
Reviewed By: houseroad
Differential Revision: D9285344
fbshipit-source-id: 1377b0243acb880d2ad9cf73084529a787dcb97d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10395
Order switch ops (NCHW2NHWC and NHWC2NCHW) were only supporting 2D images.
This diff generalizes them to 1D and 3D, and also add a unit test we didn't have.
Reviewed By: protonu
Differential Revision: D9261177
fbshipit-source-id: 56e7ec54c9a8fb71781ac1336f3f28cf024b4bda
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10390
Fixed a bug in box_with_nms_limit where it may produce more bounding boxes than specified.
* The original code first finds the threshold for the boxes at the 'detectons_per_im' position, and filters out boxes lower than the threshold.
* In some cases that there are multiple boxes have the same threshold, the op will return more boxes than 'detectons_per_im'.
Reviewed By: wat3rBro
Differential Revision: D9252726
fbshipit-source-id: 63f40829bcd275cb181692bc7547c384cee01499
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10389
Added some unit test for box_with_nms_limit_op.
Reviewed By: wat3rBro
Differential Revision: D9237860
fbshipit-source-id: 2d65744bd387314071b68d2a0c934289fc64a731
Summary:
This operator implements b (1/2/4/8) bit stochastic quantization of a floating
matrix in a row-wise fashion. 8/b floating values are concatenated to a byte
and returned in uint8 tensor. PR: https://github.com/pytorch/pytorch/pull/8629
Reviewed By: harouwu
Differential Revision: D8493264
fbshipit-source-id: 01f64066568a1e5a2b87c6d2134bd31cdf119c02
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9905
This diff improves lars operator in Caffe2 by applying clipping to the computed learning rate
Reviewed By: pjh5
Differential Revision: D9020606
fbshipit-source-id: b579f1d628113c09366feac9406002f1ef4bd54f
Summary:
The goal of this PR is to update the hip files to reflect relevant changes in cuda source files.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9826
Differential Revision: D9032840
Pulled By: bddppq
fbshipit-source-id: 504e55c46308eebfee3c9a7beea1f294fe03470f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9747
Currently the ctc_greedy_decoder op initializes the `merge_repeated` argument only if it has been provided by the user. Change to initialize in all cases.
Reviewed By: houseroad
Differential Revision: D8963635
fbshipit-source-id: 18955c7c26a77d9d7f5137e4dec085252ffabfeb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9581
Mostly to simplify code. Should also improve performance but order switch ops
don't take much time anyway.
Reviewed By: viswanathgs
Differential Revision: D8909766
fbshipit-source-id: 17a302d5bf4aba2755d88223fc01a41fd72c5919
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9643
Current map interface assumes float data type, which is not always correct.
Reviewed By: kennyhorror
Differential Revision: D8455784
fbshipit-source-id: b94a31267760f7f97c15aa4b03008affc347fd10
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9598
The "max_length" should be passed to UnPackSegmentsOp if "max_length" is given when calling PackSegmentsOp.
Reviewed By: jerryzh168
Differential Revision: D8919799
fbshipit-source-id: 8c97aa717b69177b8a5d5d56892817d488853840
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9594
When the input vector is a zero vector, the previous GPU code will give Nan in backward. We fix this.
Reviewed By: pjh5
Differential Revision: D8849732
fbshipit-source-id: 87b1fb1ee05dfdb0d43bcbe67e36f15896fe1706
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9403
In BBoxTransform and GenerateProposal ops, clip_boxes makes sure the bbox fits
within the images. For rotated boxes, this doesn't always make sense as there
could be multiple ways to clip a rotated box within an image boundary.
Moreover, clipping to a horizontal box means we leave out pixels of interest
potentially. Therefore, we clip only boxes with angle almost equal to 0 (with a
specified `angle_thresh` tolerance).
Reviewed By: pjh5
Differential Revision: D8828588
fbshipit-source-id: 39c1eafdb5d39d383780faa0a47e76149145e50c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9299
Onnx has ReduceL1 and ReduceL2 operators that would facilitate this, so allow pytorch to export those and allow caffe2 to run them.
I only implemented this on CPU so far.
Reviewed By: pjh5
Differential Revision: D8757381
fbshipit-source-id: 68afc9e2f90042a70929b73ace05a499b5c670c7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9385
The operator transform dense features to sparse features by bucketizing. Only the feature in indices tensor will be transformed and output.
Reviewed By: bddppq
Differential Revision: D8820351
fbshipit-source-id: a66cae546b870c6b2982ac20641f198334f2e853
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/8999
Closes https://github.com/pytorch/pytorch/pull/8999
Implemented the WRgrad optimizer operator for dense (base case as well as the case with additional output for effective learning rate and update value) and sparse case.
Reviewed By: pjh5
Differential Revision: D8627933
fbshipit-source-id: a63cde46c04bcc6b428ab5f77a4b3b2beb66c046
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9056
Closes https://github.com/pytorch/pytorch/pull/9056
Updates bbox_transform for rotated boxes with angle info to normalize the
predicted angle to be within [angle_bound_lo, angle_bound_hi] range.
Reviewed By: pjh5
Differential Revision: D8706240
fbshipit-source-id: f3ee834cf362736136e285f0f8f0c063af94a879
Summary:
Breaking this out of https://github.com/pytorch/pytorch/pull/8338
Use a local version of `np.rot90` with an `axes` argument, since we don't have NumPy 1.12.0 in all of the test environments. Caffe2 conda2-ubuntu16.04, for example, fails. Generally, it seems better to not require a NumPy bump just for this test.
cc mingzhe09088
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9267
Reviewed By: mingzhe09088
Differential Revision: D8767819
Pulled By: orionr
fbshipit-source-id: c51a6295d58366eba06e4e55e3f1ffaa8af96975
Summary:
Closes https://github.com/pytorch/pytorch/pull/9048
max_length argument helps fix the shape of the output to be N * max_length * D, where N is the batch_size, D is the feature_dim.
Reviewed By: bddppq
Differential Revision: D8702782
fbshipit-source-id: e30555608fee1c4a61cc95922f4a71c7f54903af
Summary:
Closes https://github.com/pytorch/pytorch/pull/9072
Use FixedDivisor in Reduce and Broadcast CUDA kernels
Reviewed By: houseroad
Differential Revision: D8710243
fbshipit-source-id: 6f1da12234898594a1be8c979d942aa515832aeb
Summary:
Closes https://github.com/pytorch/pytorch/pull/8933
spatialBN implementation cannot deal with empty batch, this diff tries to enable zero batch setting:
during training, when batch_size = 0:
in forward, output's saved_mean and saved_var are zeros.
in backward, the gradient for SCALE_GRAD and BIAS_GRAD are zeros.
Reviewed By: pjh5
Differential Revision: D8644699
fbshipit-source-id: 599ea687329d68699c987e05f56f409f4e729d1c
* 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.
* [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
In my use case, in the backward propogate pass, the reshape need to
change a [0] tensor into [0,0] shaped tensor. The original implementation would
cause out of index issue. This diff fix this problem.
* Update elementwise ops to support numpy style boradcast
Update elementwise ops to support numpy style boradcast
* Fix sqrt_op
* Fix compare ops
* Fix gradient test
* Fix optimizer legacy broadcast
* Fix legacy broadcast for elementwise ops
* Skip flaky test
* Fix eigen simple binary op
* Fix attention test
* Fix rnn test
* Fix LSTM test
* Fix tan grad
* Fix schema check
* Adding instance weight to batch distill loss
as title
* add bfloat 16-31
added bfloat 16-31 and their respective unit tests
* [CUDA9] Upgrade - fbcode
CUDA9 upgrade diff D5654023 has been out for a while thanks to Pieter. But with time growing it's becoming quite hard to rebase, because of the symlinks and auto-generated build/config files in tp2. Break D5654023 into two diffs, one touching tp2 config files, and another one touching fbcode TARGETS file (adding nvcc flag). These two should be a bit easier to rebase (for detailed procedure see "Test Plan").
This diff can only be committed if:
1. CUDA 9 rpm is rolled out fleet-wide (TBD)
2. NVidia driver 390.40 is rolled out fleet-wide (done)
3. Upgrade CUDA 9.1, cudnn 7.1, nccl 2.1 (done)
4. Make sure all dependents are built (done)
5. Test all C2 operators, PyTorch (see test plan)
* Share intermediate int32 buffer across Conv ops
Adding a known type
* [C2 fix] infer function for ensure_cpu_output_op
this is adding the missing device funtion for ensure_cpu_output_op
* [int8] Add blob serializer/deserializer for Int8TensorCPU
To export to logfiledb
* [nomnigraph] Add try catch block to optimization passes in predictor
This will catch failures that happen in the optimization pass.
* Caffe2: avoid static initialization order fiasco for CAFFE_ENFORCE
CAFFE_ENFORCE uses strack trace fetcher. Which is currently a
global static variable. If at static initialization time CAFFE_ENFORCE
is used, this is a SIOF. Recently CAFFE_ENFORCE was added into init
functions registration, so we started to see this.
Meyers singleton is going to provide safety here. If stacktrace
fetcher was not registered yet, it will just use a dummy one.
* NUMA support in SparseNN CPU benchmark
Adding support for NUMA in SparseNN CPU benchmark
* [mobile-roofline] Add logging needed for roofline model
This should be all that's needed
* Let the operators using the same input if the operators are not chained
or else, we have to change the input data dims
* fix null-pointer-use UBSAN errors in in reshape_op.h
* revert previous fix on input blob name
as title
* Adding flag to let MineHardNegative automatically extract single value from dict
Model exporter requires the output of the model to be a struct. This makes it convenient to use those models directly in MineHardNegative by allow automatic extraction of the single element of dict, which is a common use case.
* Reverting change that broke internal tests back to OSS compatible state
* [mpscnn] MPSCNNChannelShuffle
att
* [Easy] Adding tags as an argument to the functional layer
Without it "tags" would be added as an argument to the operator.
The change here is based on the assumption that there is no operator that takes "tags" as an argument.
* Fix locally_connected_op schema check.
Fix locally_connected_op schema check.
* [C2] Add TypeAndShape inference for few more operators
As desc
* [c2] Shape inference should support 0 as dimension
Tensors can have 0 in their dimension.
* Make MockHiveReader loop over and support max_examples
Replace DatasetReader with RandomDatasetReader.
So that Mock Hive Reader can simulate a large data input using a small sample file as source.
* Utility function to wipe cache between benchmark runs
Caffe2 benchmark does not wipe out cache between runs, and this potentially creates an unrealistically optimistic picture of performance. This diff adds utility function to wipe out the cache.
* Allow caffe2 GlobalInit to be invoked multiple times
Allow caffe2 GlobalInit to be invoked multiple times. Will re-parse gflags and update logging levels on successive invocations, but will not re-run init functions or perform other one-time initialization.
* Add Caffe2 GlobalInitIsCalledGuard to base net and operator classes
Warn if caffe2's GlobalInit function has not been invoked before creating an operator or net object. This is based on discussion here: https://fb.quip.com/kqGIAbmK7vNG
* Rethrow current exception on failure
Rethrow current exception instead of copy constructing a new one on op failure.
* Make `clone()` return subclass of List/Struct
`clone()` is not working correctly when we subclass those classes
* Wipe the cache before the net run
the util function is copied from D7409424
will rebase once D7409424 is landed.
* [Caffe2] [Mobile] Support utils/cast.h::GetCastDataType with LITE_PROTO builds
* Correct includes
async_polling include -> async_base include
* Prepare execution flags for executor migration
Making async_scheduling aware of underlying net type to prepare for executor
migration
* Add operator level observers into async executor
Adding operator level observers into RunAsync operators' calls
* Cleanup TEST_Benchmark
Remove duplicate code and provide default implementation in NetBase
* [C2] Fix type and shape inference for binary comparison ops
As desc.
* Add GlobalInit to predictor to ensure initialization is always done before prediction
FACEBOOK:
Redo D7651453 the correct way.
Now use a static variable for the arguments passed to GLog
* Remove spammy log message
This method is currently used in various places inside Caffe itself.
* Disable events for operators inside a chain
We don't need to use events in operators within a chain because the chain is
always scheduled on a single stream, keeping only first and last event for
scheduling purposes
* Ensure correct finish run order
In rare cases we might call finishRun and trigger net's destruction while
another worker is still holding shared_ptr to a thread pool, that can cause
thread pool destruction from within a worker thread in case no other nets are
using the pool. This diff fixes the order of calling finishRun and also changes
pool() to return raw pointer to keep pool's ownership within the net
* Reduce unnecessary polling
Make sure we don't waste CPU by polling operators that we can set an efficient
callbacks on
* Squash commit of syncing 9506eeb from github to fbcode
Patch xplat buck fix
add virtual destructor to OptimizationPass
add virtual destructor to OptimizationPass
build fixes for sync
build fixes for sync
* Fix net tracing
Fix net tracing from async_scheduling
* Fix logging
* Fix handling of empty batches in SumReduceDimsOp
As titled
* Deferrable async_scheduling finishRun fix
Proper order of finishing run operations in deferrable_async_scheduling net
* Simplify exception handling in async_scheduling
Simplify exception handling, no need to busy wait, thread that processes the
last task can finish the run
* [C2]worker_coordinator_memorize_worker_ids
As titled. This is related to T28689868, where the number of blobs we want to create is equal to the number of worker ids
* Add unit test for nets with no type set
* Ignore total length argument in sympolic_pad_packed_sequence
1- There was a mistake in the code that total_length was added to the wrong symbolic function (pack_padded_sequence) instead of (pad_packed_sequence)
2- No need to throw an exception if total_length is given since it is only used to enable data_parallel training on multi-gpus and doesn't have anything to do with onnx export, so just ignore it. https://fburl.com/tk4gciqp
* Add support for MKLDNN to async_scheduling
Just add MKLDNN as a possible CPU option to async_scheduling's pool function
* [AuFL][ensemble] support branch output for prediction
This diff supports using predictions from different branches and thus enables model ensembling (not fully independent).
* Fix a bug in add_loss in layer_model_helper
As titled.
* Support lradaption for adam
1.lr adaption operator
2.apply to dense adam
* Perf tweaks for async_scheduling
Restore single pool option + remove unnecessary (no-ops) calls
* add quantization to SparseSimdAdagradOp
add a bunch of quantization signatures to SparseSimdAdagradOp, implementations to come next
* [sr] [codemod] Change all SR callsites to use new API
@allow-large-files
This diff refactors all callsites of SR to use the slightly changed API introduced in the diff below. Really what this means is that you need to include the correct header. Also if you were using `ClientFactory::newFactory` you need to not prefix it with `ClientFactory::`.
```
cd ~/fbsource/fbcode
find ./ -type f -exec sed -i -e 's:#include "servicerouter/client/cpp2/ClientFactory.h":#include "servicerouter/client/cpp2/ServiceRouter.h":' -e 's:#include <servicerouter/client/cpp2/ClientFactory.h>:#include <servicerouter/client/cpp2/ServiceRouter.h>:' -e 's/ClientFactory::newFactory(/newFactory(/g' {} \;
```
Also manually fixed spots that couldn't be done automatically (or broke because they depended on transitive includes).
* Back out "Fix handling of empty batches in SumReduceDimsOp"
Original commit changeset: 282da1730cc2 This commit is blocking the
Github->fbcode sync, which really needs to get merged ASAP. D7881937 which this
diff depends on will be reverted in the sync D7990948 which causes this to
break. The sync diff cannot be patched with this reversion because it must be
landed against base revision 5c8c099 , and D7881937 must not be included in the
sync diff because it is breaking GPU tests that are not available in sandcastle
: https://ci.pytorch.org/jenkins/job/caffe2-builds/job/py2-cuda8.0-cudnn6-ubuntu16.04-test/3638/console
for one example.
* Add the flow to support operator benchmark
1) generate model with the operator 2) upload to everstore 3) generate model spec into json file 4) start running the benchmark
* [tum][gpu] Connect DPM trainer with flow and unit tests
This diff:
- Fix some small bugs for Yiming's recent changes to parallelizer, so it suits real use cases.
- Add correct tags to the TUM code, so we can do data parallel transform
- pass extra info when instantiation.
- add unit test for using DPM in TUM model
After this diff, we can do simple box, multi-gpu fully-sync trainer for TUM in Fblearner workflow, but may still need to do speed benchmarking.
* w/o normalized lradaption for adam dense only
The previous lr adaption includes a normalization step when performing the dot product operation. This is not exactly same as what is proposed in the paper. I add normalization as an option. Without it, the operator performs exactly what the paper proposed. With the option, we add the normalization step
* [fb] Use SharedPromise in DeferrableAsyncSchedulingNet
This code is to simplify DeferrableAsyncSchedulingNet by removing condition
variable + small fixes
* [tum] implement cuda sparseLengthsMean and LengthsMean
as title
* Adding an optional parameter to allow use of protobufs in InferShapesAndTypes function.
Adding an optional parameter to allow use of protobufs in InferShapesAndTypes function.
* Move feature_to_index to FeatureSpec.feature_to_index
move feature_to_index to FeatureSpec.feature_to_index to avoid override other fields
* [Caffe2] Rename bytes_moved to bytes_written
Just a rename in preparation for supporting bytes_read.
* [c2] fix ReduceFrontSumOp for empty case by setting 0
otherwise, it may use the results from last iteration when it's empty batch.
* [Caffe2] [Int8] Improve Intel CPU performance
* [Easy] Improve PrependDim op logging
as titled
* DBFileReader expand db_path using os.path.expanduser(..)
Since there are a lot of possible use cases of `DBFileReader` to read from user home path, like `~/local/sample.db`, I want to save people's trouble of calling `os.path.expanduser(db_path)` themselves.
* [Caffe2] Add bytes_read to cost structure
We're adding analytical read bytes to cost functions. This extends the structure accordingly for all CostInference defined operators.
Additionally, some small bug fixes were performed:
1) Cost functions now extract type information of operands instead of assuming float
* Fix sleef on aarch64 for hhvm
@bypass-lint
Rename flag
* Remove duplicated part in caffe2/ideep/operators/conv_op.cc
should be sync error
* Rename test helper function test_adagrad_sparse_helper to adagrad_sparse_test_helper to avoid confusing pytest
* [bootcamp] Improve "Shape" operator to support axes specification
To improve .shape operator of Caffe2 to support x.shape(tensor, axes), which takes an optional int array "axes" as input. For example, x.shape(tensor, [1, 0]) will return the dimension for axis 1 and 0 following the specified order. For current version, "axes" input allows duplications and can have arbitrary length.
* Back out "Add barrier net that runs before training nets"
Original commit changeset: b373fdc9c30f. Need additional changes to some callers to support barrier failures.
* Change warning to verbose log to reduce log spam
The `LOG(WARNING)` was a bit spammy for regular use so lets just make it a `VLOG`.
* Extract the shared code from different caffe2_benchmark binaries
The OSS benchmark and Internal benchmark will share most functions in the benchmark.
* Support MFR in sequence training
As titled.
* Make knowledge distillation work with using logged prediction feature as teacher label.
1) Add loading raw dense feature as teacher label.
2) Optional calibration function for teacher label
3) Add teacher label into generic unit test
4) Deprecated TTSN workflow version using feature_options to config teacher label
* [C2/CUDA]: unjoined cross entropy sigmoid
as desc
* Add async_scheduling executor into deferrable_net_exec_test
Add async_scheduling into tests and fix some exception cases
* Fix Event disabled error
When disabling event in RNN ops make sure we don't call Finish on disabled
event from op's RunAsync
* cuda ensure cpu output op can handle both TensorCPU and TensorCUDA
as desc.
* [C2 Core] Infer input device option in C2 hypothesis_test checkers
Improve how we default input blob device options.
Previously it defaults as where op lives but it is not necessarily the case.
For example:
CopyCPUToGPU
* [C2 Op]SplitByLengthsOp CPU/GPU implementation
[C2 Op]SplitByLengthsOp CPU/GPU implementation
* fix undefined symbol error
not sure why we're getting undefined symbol even with link_whole = True
Need to figure out why but need this workaround for now
* Add tools in DAIPlayground platform to help debugging models
Add additional tools to allow Plauground override individual method defined in AnyExp. This will allow user to create module that specificly change certain default method behavior. An example included in this diff is deactivating test model and checkpointing. When debugging any model problems, switching off components helps me quickly narrow down the location of the bug. The technique is extensively used in task T27038712 (Steady memory increase in EDPM, eventually resulting in gloo/cuda.cu:34: out of memory)
* add shape and type inference for int8 conversion operator
* Fix flaky test for group_norm
Fix flaky test for group_norm
* Fix group_norm_op_test flaky
Fix group_norm_op_test flaky
* Implementation of composite learning rate policy
In many state-of-the-arts deep learning works, people use a simple trick to
schedule the learning rate: use a fixed learning rate until error plateaus
and then switch to a different fixed learning rate, and so on. In this diff,
we implemented a simple version of the composite learning rate. The user gives
a set of learning rates policies and corresponding iteration nums, and the
optimizer will change the learning rate policy based on the number of iterations so far.
For example, the user give two learning rate policies, one is FixedLearningRate
and PolyLearningRate, with an iteration number of 1k. Then the first 1k iteration,
we use FixedLearningRate. For the following iterations, we use PolyLearningRate.
* Split two use cases of CachedReader into two classes, DBFileReader and CachedReader
# Use Cases:
1). input: DB file -> output: DatasetReader.
Use DBFileReader.
2). input: Reader -> build cache DB file -> output: DatasetReader.
Use CachedReader.
# Changes to CachedReader:
1). Move db_path to the constructor.
Because in mock reader. cache will always be built ahead.
# Changes to tests:
1). Make a separate TestCase class for CachedReader and DBFileReader.
2). Make it possible to add more test functions by adding setUp, tearDown and _make_temp_path.
3). Make delete db_path more general. `db_path` could be a file for `log_file_db`, but could also be a directory for `leveldb`.
* Back out "On Mobile phones, call GlobalInit with no arguments in predictor in case we need to perform initialization"
Original commit changeset: 4489c6133f11
* Fix LARS bug
Fixed a bug in the LARS implementation which caused all subsequent blobs not using LARS to have the LARS learning rate multiplier applied to them.
* [tum] support sparse init & add uniformFill option
as title
* Propagate exception for async nets
Capture the exception when an exception is thrown in async nets and re-throw it after wait(). This allows exceptions to be propagated up to the caller.
This diff was a part of D7752068. We split the diff so that C2 core files changes are in a separate diff.
* Automatic update of fbcode/onnx to 69894f207dfcd72d1e70497d387201cec327efbc
Previous import was 403ccfbd0161c38f0834413d790bad0874afbf9a
Included changes:
- **[69894f2](https://github.com/onnx/onnx/commit/69894f2)**: Use op schema.all tensor types in random like definitions (#865) <Scott McKay>
- **[b9d6b90](https://github.com/onnx/onnx/commit/b9d6b90)**: Clarify random like operators (#846) <Scott McKay>
- **[fc6b5fb](https://github.com/onnx/onnx/commit/fc6b5fb)**: Refactor shape inference implementation (#855) <anderspapitto>
- **[b7d8dc8](https://github.com/onnx/onnx/commit/b7d8dc8)**: fix cmake warning message (#863) <Eric S. Yu>
- **[f585c5d](https://github.com/onnx/onnx/commit/f585c5d)**: add pytorch-operator test for tile (#831) <Wenhao Hu>
- **[993fe70](https://github.com/onnx/onnx/commit/993fe70)**: add install step (#832) <Eric S. Yu>
- **[68bc26c](https://github.com/onnx/onnx/commit/68bc26c)**: add type inference for traditional ml ops except classifier ops. (#857) <Ke Zhang>
- **[9cc0cda](https://github.com/onnx/onnx/commit/9cc0cda)**: fix string representation of scalar types (#858) <G. Ramalingam>
- **[1078925](https://github.com/onnx/onnx/commit/1078925)**: fix y in pow test case to scalar (#852) <Wenhao Hu>
- **[c66fb6f](https://github.com/onnx/onnx/commit/c66fb6f)**: Add some math function shape inference (#845) <anderspapitto>
- **[ff667d1](https://github.com/onnx/onnx/commit/ff667d1)**: Refactor return type and docs for ONNXIFI_BACKEND_DIRECTX_ID (#853) <Marat Dukhan>
- **[11c6876](https://github.com/onnx/onnx/commit/11c6876)**: clear initializer names when clear initializer (#849) <Wenhao Hu>
- **[73c34ae](https://github.com/onnx/onnx/commit/73c34ae)**: Clarify FeatureVectorizer description. (#843) <Scott McKay>
- **[1befb9b](https://github.com/onnx/onnx/commit/1befb9b)**: Remove useless text in docs (#850) <Lu Fang>
- **[e84788f](https://github.com/onnx/onnx/commit/e84788f)**: Fix SELU attributes' default values (#839) <Lu Fang>
- **[ebac046](https://github.com/onnx/onnx/commit/ebac046)**: Add tile test case (#823) <Wenhao Hu>
- **[8b7a925](https://github.com/onnx/onnx/commit/8b7a925)**: a few more shape inference functions (#772) <anderspapitto>
- **[9718f42](https://github.com/onnx/onnx/commit/9718f42)**: Make the coefficient non optional for LinearClassifier (#836) <Jaliya Ekanayake>
- **[ef083d0](https://github.com/onnx/onnx/commit/ef083d0)**: Add save_tensor and load_tensor functions for Protos (#770) <Lu Fang>
- **[45ceb55](https://github.com/onnx/onnx/commit/45ceb55)**: Check if CMAKE_BUILD_TYPE set before project(). (#812) <Sergii Dymchenko>
- **[4b3d2b0](https://github.com/onnx/onnx/commit/4b3d2b0)**: [WIP] reenable shape inference tests (#834) <anderspapitto>
- **[22d17ee](https://github.com/onnx/onnx/commit/22d17ee)**: RNN tests: LSTM, GRU, SimpleRNN (#739) <Peyman Manikashani>
- **[de65b95](https://github.com/onnx/onnx/commit/de65b95)**: dimension denotation (#443) <Tian Jin>
- **[eccc76e](https://github.com/onnx/onnx/commit/eccc76e)**: fix field number issue in onnx operator proto and enable its build (#829) <Ke Zhang>
- **[d582beb](https://github.com/onnx/onnx/commit/d582beb)**: disable shape inference test to unbreak ci (#830) <Lu Fang>
- **[485b787](https://github.com/onnx/onnx/commit/485b787)**: function proto for composite op. (#802) <Ke Zhang>
- **[cd58928](https://github.com/onnx/onnx/commit/cd58928)**: specify defaults for attributes of Affine op (#820) <G. Ramalingam>
- **[7ee2cf9](https://github.com/onnx/onnx/commit/7ee2cf9)**: merge the dummy backend back into the main one (#743) <anderspapitto>
- **[1c03a5a](https://github.com/onnx/onnx/commit/1c03a5a)**: [Proposal] ONNX Interface for Framework Integration (previously ONNX Backend API) header and docs (#551) <Marat Dukhan>
- **[3769a98](https://github.com/onnx/onnx/commit/3769a98)**: Rename real model test case from VGG-16 to ZFNet (#821) <Lu Fang>
* [C2]ReluN Op
relu n op.
tf reference: https://www.tensorflow.org/api_docs/python/tf/nn/relu6
* Call destructor when assigning a blob value
* Add executor overrides
Add executor overrides flag to enable migration to async_scheduling executor
* Add barrier net that runs before training nets - attempt #2
Add a synchonize barrier net that is run before training nets. With this net, shards that are faster will wait for other shards before start training. This reduce chances of the faster shards timing out during GLOO AllReduce.
Removed explicit data_parallel_model.py.synchronize call in holmes workflow.
This change was landed previously but caused errors for some EDPM workflows - See https://fb.facebook.com/groups/1426530000692545/permalink/1906766366002237/ - because EDPM assumes any call to CreateOrCloneCommonWorld and Gloo ops are wrapped in exception handlers but in this case exception thrown in the barrier init net is not handled.
To address this issue, we add _CreateOrCloneCommonWorld to the param_init_net instead of a new barrier init net. Since errors for param_init_net run is handled gracefully and re-rendezvous, it should fixes the problem.
* Handle empty nets in async_scheduling
Make sure we don't get stuck on empty nets
* use CUDA_ARCH for conditional compile
* [C2 fix] infer function for ensure_cpu_output_op
* Update group_norm test to reduce flaky test
* Fix lr_multiplier for GPU
* [fix] Re-enable events in RNN ops
We have earlier added event disabling in RNN ops as back then we didn't use
events, with current use cases this is no longer true
(https://fburl.com/8vd0lp8y)
* use ops with cude impl
* Revert D7729695: [caffe2][fix] Re-enable events in RNN ops
This reverts commit 4b215c7496fb724656ff4c776933a15bdbbcde5e
@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
* [observer] Clean up observer_config.h
#accept2ship
* [1/n] Refactor dataio_test.py
Replace code duplication with a common function
* Add barrier net that runs before training nets
Add a synchonize barrier net that is run before training nets. With this net, shards that are faster will wait for other shards before start training. This reduce chances of the faster shards timing out during GLOO AllReduce.
Removed explicit data_parallel_model.py.synchronize call in holmes workflow. Similar change in speech/asr_training workflow will come in another diff.
* Support the dnnlowp backend in caffe2_benchmark
This is for SHARE operator latency evaluation
* Migrate integral_image_op to main caffe2
migrate integral_image_op(GPU version) given by https://fburl.com/yvqezigi
to caffe2/caffe2/operators and implement its CPU version. Write up a test
using the hypothesis_test mechanism
* [pos_disc, fbcode] Implement unjoined lr loss
As explained in https://our.intern.facebook.com/intern/wiki/Model_Based_Calibration/, when the dataset is an joined data set, where labels might change later, we need to use unjoined logloss.
The implementation is almost the same as in Sigrid (https://fburl.com/1trngsls), where
loss = y (log(p) - log(1-p)) + (1-y)(log(1-p)) = xy - (1-y)x - (1-y)log(1+exp(-x))
For x < 0, to ensure stability and avoid overflow, we reformulate the above exp as
loss = xy - (1-y)x - (1-y)x + (1-y)log(1+exp(x)) = xy + (1-y)log(1+exp(x))
Then the final expression becomes
loss = xy + (y - 1) x (x >= 0) - (1 - y) log(1 + exp(x - 2 x (x >= 0)))
where y is the true label, x is the dot product and p = logistic(x).
This kind of implementation is align with the current implementation of the original cross entropy in
https://phabricator.intern.facebook.com/diffusion/FBS/browse/master/fbcode/caffe2/caffe2/operators/cross_entropy_op.cc;0bae3b5d0f825897c5e0dd0ff10f489d7271bf25$7-13
* Keep the array to fix the conflict
* [C2] Compute Adagrad effective LR
The AdagradWithLR op outputs an extra blob which is contains the average effective learning rate across all weights in this blob.
* Open-source extractMetaNetDef & runGlobalInitialization, add new Predictor constructor from db file, and add run_map_outputs
1. Open-source extractMetaNetDef and runGlobalInitialization, for use in
2. new Predictor constructor from db file.
3. Add new run function that returns outputs as TensorMap
* Disable eigen cpu
Disable eigen cpu in transpose and reduce
* Introduce request_only/object_only property of ModelLayer
by default this is False
* A simple TC Caffe2 benchmark
We can run tunner, get MappingOptions and then use them to
compare against cuBLAS
currently broken due to LLVM issues. How to run:
hg checkout eec1ab31b59c03b8deded1c755a9abaf8c45be01
add D7401202
add D7434625
add D7506031
add D7540728
buck run @mode/dev-nosan tc/tc/benchmarks_python:caffe2_benchmark
* Move Caffe2 feature_maps_ops to open source
Need feature maps operators in open source project facebookresearch/BlueWhale
* Manually fix the conflicts in channel shuffle op
* Fix the inconsistency between different gh and fbcode
* Skip Adagrad GPU Test (Because some gpu implementation is missing)
* Fix another test to make sure it won't run on gpu when implementation is not available yet
* Add moments op in caffe2
* Use rsqrtf in float for group_norm
* Add docs for default behavior when axes is not provided.
* Update group_norm_op by using Eigen::sqrt on CPU
* Add full impl of GroupNorm
* Fix comments in math.h
* Remove unsed buffers
* Add #include <array> in gpu version
* Remove unused moments_buffer_
* Make inverse std to be a template.
* Add detailed comments
DEPTHWISE_3x3 engine provides an optimized implementation of depthwise 3x3 convolution, e.g. for ShuffleNet, MobileNets
Implementations exist for CPU (generic), ARM CPU, and CUDA GPU.
Originally developed by @ajtulloch
* Track checkpoint performance in scuba
As title.
* [C2/CUDA]: fix cross entropy sigmoid with logits
when adding log_d_trick, I forgot to add it to the cuda impl; this diff fixes
it.
* Back out "[caffe2] Unregister MKL fallbacks for NCHW conversions"
Original commit changeset: 8918dd40205a
Will land after @jongsoo's diff https://phabricator.intern.facebook.com/D7596315 lands
* [Easy][C2] Don't add blob to external outputs from output_record if it's already external output
As desc.
* On Mobile phones, call GlobalInit with no arguments in predictor in case we need to perform initialization
FACEBOOK:
The QPL logger needs the initialization code. In the past, the initialization code is put in the pipeline calling Caffe2. However, those places become obsolete quickly, as the product teams change places to call Caffe2 from time to time. We also need to track which teams use Caffe2 so that we can put the initialization code there.
With this diff, the initialization code is put in the predictor constructor, only enabled for mobile phones. This way, we can always enable QPL logging.
Once we do this, we can check how many times Caffe2 inference is called in production, and which models are more popular in production. This way, we can prioritize our effort supporting those models.
Will clean up the old code calling the init in the product in a separate diff.
* add padding op for sparse length tensor
to pad length-based sparse tensor with padding_value
* Add conv_op with cudaconvnet engine
Add conv_op with cudaconvnet engine
* [numa] Fix simple NUMA copy benchmark
Move XavierFill into init_net and also compute BW
* call roundf (device function) instead of round (host function)
* [caffe2_benchmark][observer] Make caffe2_benchmark use its own observer
1. Add ClearGlobalNetObservers()
2. Make caffe2_benchmark use its own observer and observer_reporter
* [detectron] Use roundf instead of round in the detectron module ops
* allow K larger than number of elements in top k op
one use case is to use this op together with PackSegments for sparse tensors, where the number of elements in each slice is not statistically defined.
* add ChannelShuffle DNNLOWP op
* fixup math_cpu.cc break
* Caffe2: Enhance test for CollectAndDistributeOp
This also changes the operator and the test to use stable sort
otherwise the test will fail due to differences between the op
and the test when facing ROIs of the same score.
* Caffe2: Adjust comparator to make std::nth_element and std::sort stable
Revert the removal of std::nth_element and std::sort and adding of
std::stable_sort.
* [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
* fix unit test for sqrt op
From the error logging:
[idx, grad, grad_estimate] are:
[[ 146. 0.5 0.45776367]
[ 147. 0.5 0.45776367]
The gradient == 0.5 is correct, which means the SqrtOp and its gradient is doing right job. (Because y = sqrt(x), loss = y^2/2 = x/2, and then d(loss)/dx = 1/2 = 0.5; )
The test failed because of numerical problem of grad_estimate (in unit test). It can be because the step_size is small, and float precision is not high (when there are multiple elements in the tensor, we do sum(y^2) to compute loss)
This diff
- increase the step size, and also move the test cases to be further away from 0 (where sqrt(x) is not well defined) to be safe :)
- also clean up, and merge the test case for inplace Vs. non-inplace
Tested with:
`CAFFE2_HYPOTHESIS_PROFILE=debug ai_bt caffe2/caffe2/python/operator_test:elementwise_ops_test -- "test_sqrt"`
* CompositeReader & CompositeReaderBuilder
A new type of reader gluing multiple readers together.
* Back out "Revert D7394363: [GanH]: Log D Trick for Cross Entropy with Sigmoid"
Original commit changeset: 9325a4356dbe
* [dai][WIP] convert params to int8 on ps before sending to trainer
Add float->uint8 conversion in addition to float->fp16 conversion in model_saver.
* [easy] improve unit test for sparse length sum ops
as desc.
#accept2ship
* Update GitHub upstream to 771fcb3455
* move sparse hash unique ops to OOS and add unit tests
- move the SparseHash version to OOS, since 'sparsehash' is already deps of caffe2 OOS: https://fburl.com/arssw4n1
- The 'SparseHash' engine is also being used in OOS, so the SparseHash version shall be in OOS to reduce confusion: https://fburl.com/o5ea7ah2
- fix the CUDA UniqueOp for the case when batch is empty.
- add unit test
* group_norm_op for caffe2
This is the cuda op for Group Normalization (GN): https://arxiv.org/abs/1803.08494
This code implements GN in one op that computes Y=gamma * (X-mu) / sigma + beta and also its gradients. It is expected to have minimal memory consumption (similar to the BN op), without creating new blobs if GN were implemented as several ops (e.g., reshape, norm_mean/std, affine_channel).
* Resubmit D7405233: disappeared in D7464958
OOS publish causes the op missing -- however, test was still there
* [c2] add sparse hash engine for cuda unique op
The SparseHash version of UniqueOp copy input tensor to CPU, and make use of sparse hash map to get unique output, and then copy back to GPU.
* [dper][gpu] enable unit testing gpu trainer for sparse nn
to debug the GPU trainer using mock data in unit test.
make it easier to develop GPU trainer for new models.
* Reuse Gloo context for Synchronize() calls
Previously we were creating (and leaking) the Gloo context on each call to Synchronize(). Now only run the common world op and create the barrier net once, then run the barrier net on each Synchronize() call. Since timeout is associated with the Gloo context, assert that the timeout is fixed instead of trying to handle the complexity of multiple timeouts (and associated contexts).
* [GanH/WGAN][1/n]: add FC param clipping
as titled
* [mobile] minimizing changes between caffe2_benchmark and speed_benchmark
* [GanH]: enable diagnose within model
avoid finding blob names but to directly enable inside the model
* Add `net_transformer_fun` option to DPM
This callback allows for various transformations to be made to the
model after gradient operators have been added. The immediate motivation for
this is to allow transformations such has "checkpoint-and-recompute" which
allow trading off memory for additional compute.
Adding several callbacks like this has made DPM's API less than ideal at this
stage. However, I could not find any reasonable alternative.
* [DT] [33/n] Compile flow task groups
task groups need to compiled in order to pickle the object in fblearner. However I also changed the Job's compile function as creating new object is not necessary.
* Initial commit for sparse_normalize vectorization and benchmark
* [GanH]: LB Calibration for JSD
as titled
* Tracing event in async executor
Adding event tracing through TRACE_EVENT macro in async executor
* [Resubmit] D7409751 Reseting book-keeping blobs when the reservoir is reset
D7409751 got lost in D7464958
* Visualizing realtime weights values
we want to visualize the weights values as optimizer is iterating. This diff supports to visual the weights at an assigned index.
Currently, we assume the blob to be 2 dimensional.
* [GanH][Easy]: Fix Homotopy Weighting
apparantely, there was a bug in homotopy weight (alpha, beta) update
* [c2] move sparse hash unique op out of oss
so that oss do not need to depend on google hash map.
* Get rid of std::round as it's not supported on Android
* Revert changes on setup.py
* Skip shaky test on Dataio
* fix
* [easy] allow empty tensor in cuda relu op
The diff has not enabled unit test of empty tensor, because MLKVersion of ReluOp need extra work to support
* Make blob norm plotting work with distributed trainer when the old framework is used
This reverts commit d63266ccbc0c1390c58c2a71ae0b562fdec2fbc0
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@cause_a_sev_many_files
Added a caffe2 math sum operator so that it takes integers (only int32)
Changed the SumFloatIter to SumGenericIter so that it takes >1 types.
Added a sumElementInt operator
* Revert update on top_k_op
* Add axis to top_k_op
* Remove do { ... } while (false)
* Revert top_k op to upstream
* Add argmin and argmax ops
Add argmin and argmax ops
* Revert top_k_test to upstream
* Add argmin and argmax ops
Add argmin and argmax ops
1. support the LpNorm operator to calculate the average LpNorm by adding one more boolean argument, i.e., LpNorm(average = true) = LpNorm(x) / size of (x)
2. integrate the average option into visualization framework
* Add CollectAndDistributeFpnRpnProposalsOp for FPN support
* Adds a C++ operator equivalent to the Python op in Detectron
* Once some additional GenerateProposalsOp changes are made this will
let us support Detectron FPN models with straight Caffe2 C++ ops
* RetinaNet and segmentation models require additional work
* Remove some uses of conservativeResize
* Add notes about training and inputs/outputs to operator documentation
* Reduce Sum and Reduce Mean
* Handle reductions with empty 'axes'
* Merge codebase and simplify tesnor reduction logic
* Restructure code and add comments.
* Fix parameter to scale
* Fix parameter to scale
