Summary:
+ Add ArgMin support to Caffe2 to PyTorch converter
+ Using hypothesis to parameterize different conditions for test
Test Plan: buck test //caffe2/torch/fb/model_transform/c2_convert:c2_pt_converter_test
Reviewed By: houseroad
Differential Revision: D25016203
fbshipit-source-id: 94489fcf1ed3183ec96f9796a5b4fb348fbde5bc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48240
Adds the support for converting the SparseLengthsSum4BitRowwiseSparse operator from caffe2 to pytorch as a part of c2_pt_converter
Test Plan:
Added a unit tested
buck test //caffe2/torch/fb/model_transform/c2_convert:c2_pt_converter_test
Tests Passed :
https://our.intern.facebook.com/intern/testinfra/testrun/2251799856412296
Reviewed By: houseroad
Differential Revision: D25067833
fbshipit-source-id: 45cbc331ca35bee27e083714e65a1e87a2a2d2e0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48407
T79817692: Fused8BitRowwiseQuantizedToFloat operator support for c2_pt_converter.
Also refactored some repeated code from the existing test functions. (Initial commit only has refactoring.)
Test Plan: buck test //caffe2/torch/fb/model_transform/c2_convert:c2_pt_converter_test
Reviewed By: bugra
Differential Revision: D25069936
fbshipit-source-id: 72f6a845a1b4639b9542c6b230c8cd74b06bc5a0
Summary: Added the MatMul operator for caffe2
Test Plan: buck test //caffe2/torch/fb/model_transform/c2_convert:c2_pt_converter_test
Reviewed By: bugra
Differential Revision: D24920937
fbshipit-source-id: 7ba09ba0439cb9bd15d6a41fd8ff1a86d8d11437
Summary:
There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports:
```2to3 -f future -w caffe2```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033
Reviewed By: seemethere
Differential Revision: D23808648
Pulled By: bugra
fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
* 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
* 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
Summary:
Change log
- Support rectangle cropping, where height and width of clip cropping can be set separately. This is useful when most video resolution is non-square, such as 240p, 360p and 480p where width is significantly larger than height.
- Comparisons of training on ucf101 between using 112x112 croppings and using 112x144 cropping.
- https://fburl.com/i0rw6y1k
- Support 14 multi-cropping per video clip at testing stage to improve classification accuracy. Take left-top, central-top, right-top, left-bottom, central-bottom, right-bottom and central-central croppings as well as their mirrorings. In total, 14 croppings.
- Comparisons on the same model trained on UCF-101. Use 1 clip per video
- RGB. f41014306, w/o Vs f41014868, w/ multi-cropping: `0.64099 Vs 0.65796`
- OF. f41014889, w/o Vs f41014913, w/ multi-cropping: `0.65796 Vs 0.67624`
- Support color jittering and color lighting on RGB data for training data augmentation.
- Comparisons of training on ucf101 from scratch with and without color jittering and lighting:
- https://fburl.com/k69zatul
Reviewed By: HengCV
Differential Revision: D6962620
fbshipit-source-id: 9b43478945874142727fea351ee04417218e6606
Summary:
Main changes:
1. Move reader creation to Brew in order to be consistent and avoid a wild use of param_init_net
2. Use optimizers for training function, avoid manual optimizer construction
3. Add MLP mode (a default)
4. Fix a bunch of too verbose comments and add a bit of new explanations
Closes https://github.com/caffe2/caffe2/pull/1760
Differential Revision: D6749059
Pulled By: salexspb
fbshipit-source-id: 9dfbbb2d9772a74a0300c2e404a92e791f7cc593
Summary:
Adding if and while control ops to brew, also adding unit tests
Note: unlike net_builder where we can figure which blobs are external and which ones are local to subnets, here in brew we need to use external_blobs param explicitly to point at external blobls
Reviewed By: harouwu
Differential Revision: D6440508
fbshipit-source-id: c920f0af84b77ccb2d8462ffc7567bb1908c844a
Summary: Quick fix for unit test broken by D6454290. This is my fault for approving while the tests covering the single callsite were broken.
Reviewed By: goldsborough
Differential Revision: D6466566
fbshipit-source-id: 2683be3d6bb184286e64fbde3e572946e39030c7
Summary:
While working on layer normalization for LSTMs I encountered an issue where the layer norm parameters (which are the scale/gain and bias/shift from the paper) were not registered in the model for `brew.layer_norm`. salexspb explained that this is because it was using the `init_net_param` API instead of `create_param`. This diff fixes this.
While fixing I noticed that I noticed that `brew.layer_norm` actually had a bug where it was multiplying with the bias instead of adding it. Another issue was that the function giving the scale and bias a shape of `[1]`, however the paper (https://arxiv.org/pdf/1607.06450.pdf) specifies that, like for batch norm, there is one scale and bias parameter per neuron, i.e. the shape should be `[1, axis_dimension]`. The API now takes an explicit `dim_in` parameter (also more consistent with other normalization functions in that module) so that this can be specified. See tests for how this now looks.
Reviewed By: jhcross
Differential Revision: D6454290
fbshipit-source-id: fc00ca614de3190c40ab743e8984bec9e85fb58c
Summary: Updated brew SpatialBN to use initializers similar to other brew ops such as conv and fc instead of initilaizing all of its parameters itself within the brew call.
Reviewed By: asaadaldien
Differential Revision: D5840359
fbshipit-source-id: 9f3d688d4957605eaf7ecd2488bc26bfb1da3f78
Summary: Allow the GEMMs in the FC/FCGradient Op to do FP16 compute instead of FP32 if the appropriate op flag is set.
Reviewed By: asaadaldien
Differential Revision: D5839777
fbshipit-source-id: 8051daedadf72bf56c298c1cf830b019b7019f43
Summary: The cudnn version of the DropoutOp was taking a significant (and unwarranted) amount of time in our RNN training. Further investigation showed that setting the cudnn dropout descriptors was an extremely expensive operation (https://pxl.cl/99nT), much more so than the dropout operation itself. This diff adds to the DropoutCell the option to disable cudnn. The non-cudnn version uses a raw curand call that elides all of the expensive descriptor setting.
Reviewed By: jmp84, akyrola
Differential Revision: D5972022
fbshipit-source-id: 6325ec5d6569f8b94d776cbb2554cc8ddb28f699
Summary: PR 1175 caused a build error because gemmBatched was only under a specific #ifdef. Now put it outside the #ifdef, and things work.
Reviewed By: asaadaldien
Differential Revision: D5834868
fbshipit-source-id: 072a64c8f4b259ff7504104121766115b46b8aa0
Summary: Implement a brew wrapper for the LayerNorm op. This adds the scalar weight and bias terms to the op.
Reviewed By: jmp84
Differential Revision: D5595836
fbshipit-source-id: 467b2e1158b0c454a149d4b26c47719826e98752
Summary:
Add support for TensorCore convolution and gemm on Volta hardware.
Currently built on top of #1055
Closes https://github.com/caffe2/caffe2/pull/1056
Differential Revision: D5604068
Pulled By: Yangqing
fbshipit-source-id: 100f67e26ed5fabb1dbb31dcd77f7ecb84de4ee7
Summary: This allows users to add an arbitrary of additional outputs to ImageInputOp. These are populated by reading additional TensorProto values from the TensorProtos from the DBReader, and converting them into Tensors. Similar to labels, only ints and floats are supported, and multiple values are supported.
Reviewed By: panshen1
Differential Revision: D5502019
fbshipit-source-id: 5a8b61b3a8549272a112e8e02cd613d8f9a271ba
Summary:
Given the parameter init_params=False, Weight Blob(*_w) and Bias Blob (*_b) should be suppressed in model.param_init_net. Without this fix, the init_params=False doesn't take effect in brew.conv as it does in brew.fc or other ops. This issue is the root cause of #790 [https://github.com/caffe2/caffe2/pull/790].
Closes https://github.com/caffe2/caffe2/pull/824
Reviewed By: harouwu
Differential Revision: D5276676
Pulled By: akyrola
fbshipit-source-id: 8f7088a8e1976658f67e027223e555375b3a2392
Summary:
Add a helper function for parametric op ElementwiseLinear
The typical syntax is model.ElementwiseLinear(input, output, dimension)
Reviewed By: harouwu, akyrola
Differential Revision: D5114152
fbshipit-source-id: 8e8c691f824f518ae510a72ab0c12de1b018f3b5
Summary:
This diff is creating new type of Initializer - ExternalInitializer. This
initializer is supposed to be used in cases when the parameter blob is already
expected to exist in the workspace.
Reviewed By: dzhulgakov
Differential Revision: D5171322
fbshipit-source-id: d27861f0f80afdea93c235d49f63da19adccc92c
Summary:
This diff is the first step in the effort for refactoring all parameters. As a first step - I'm merging concept of params and computed_params, that is going
to be based on tags instead (in the first version it's still using old data structs to store all the BlobReferences).
Renaming computed_params to non-trainable/non-backprop params should be done is some other diff.
Reviewed By: salexspb
Differential Revision: D5171159
fbshipit-source-id: 68031ca779f053fb266a7c4a2e5b482a3bd9c832
Summary:
This diff is the first step in the effort for refactoring all paramters. As a
first step - I'm merging concept of params and computed_params, that is going
to be based on tags instead (in the first version it's still using old data
structs to store all the BlobReferences).
Renaming computed_params to non-trainable/non-backprop params should be done is
some other diff.
Reviewed By: salexspb
Differential Revision: D5119830
fbshipit-source-id: 2001090a37346eb12abbb234e13e727c288eb8a7
Summary:
Adds support for generating and training pfp16 models. Added SGD optimizer for multi-precision trainers and a new callback to data_parallel_model in order to help multi-precision models keep their different copies of parameters in sync during training.
Closes https://github.com/caffe2/caffe2/pull/697
Differential Revision: D5159712
Pulled By: salexspb
fbshipit-source-id: 60a889494d2e2f4df1d720331e19f638c5eb95cc
