Summary:
- Created the random fourier features layer
- Generated a unit test to test the random fourier features layer is built correctly
- Inspired by the paper [[ https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf | Random Features for Large-Scale Kernel Machines]]
Reviewed By: chocjy
Differential Revision: D5318105
fbshipit-source-id: c3885cb5ad1358853d4fc13c780fec3141609176
Summary: Layer for LastNWindowCollector op. We need this since it's an in-place operator.
Reviewed By: chocjy
Differential Revision: D4981772
fbshipit-source-id: ec85dbf247d0944db422ad396771fa9308650883
Summary:
Layer to allow model to follow different paths for each instantiation context and join later. Together with tagging system cleanup (this is a separate issue), this should reduce the need to write a layer to differentiate between context.
Re: tagging system clean up, we should make exclusion more explicit: EXCLUDE_FROM_<CONTEXT>. This would simplify instation code. TRAIN_ONLY should become a set of all EXCLUDE_FROM_*, except EXCLUDE_FROM_TRAIN.
Reviewed By: kennyhorror
Differential Revision: D4964949
fbshipit-source-id: ba6453b0deb92d1989404efb9d86e1ed25297202
Summary: Current eval nets contain loss operators; see example: https://fburl.com/6otbe0n7, which is unnecessary. This diff is to remove them from the eval net.
Differential Revision: D4934589
fbshipit-source-id: 1ba96c20a3a7ef720414acb4124002fb54cabfc7
Summary: A layer that takes raw ids as inputs and outputs the indices which can be used as labels. The mapping will be stored with the model.
Reviewed By: kittipatv
Differential Revision: D4902556
fbshipit-source-id: 647db47b0362142cdba997effa2ef7a5294c84ee
Summary: `not field` calls `__len__()`, causing the field to appear to be missing even when it's not
Differential Revision: D4910587
fbshipit-source-id: bc2b2fadab96571ae43c4af97b30e50c084437af
Summary:
Currently, the functional layer infers the output types and shapes by running the operator once.
But in cases where special input data are needed to run the operator, the inferrence may fail.
This diff allows the caller to manually specify the output types and shapes if the auto infererence may fail.
Reviewed By: kennyhorror
Differential Revision: D4864003
fbshipit-source-id: ba242586ea384f76d745b29a450497135717bdcc
Summary: Having to pack the input to schema doesn't make much sense since the structure is not recognized by operators anyway.
Differential Revision: D4895686
fbshipit-source-id: df78884ed331f7bd0c69db4f86c682c52829ec76
Summary: Perform gather on the whole record. This will be used for negative random sampling.
Reviewed By: kennyhorror
Differential Revision: D4882430
fbshipit-source-id: 19e20f7307064755dc4140afb5ba47a699260289
Summary:
multiple places broken, blocking the push :(
- fix the weighted training for ads and feeds
- fix the publishing if no exporter model is selected
- fix the feeds retrieval evaluation
- added the default config for retrieval workflows. plan to use for flow test (in next diff)
- clean up not used code
- smaller hash size for faster canary test
Reviewed By: chocjy
Differential Revision: D4817829
fbshipit-source-id: e3d407314268b6487c22b1ee91f158532dda8807
Summary: This layer will be used to sample negative labels for sampled softmax.
Differential Revision: D4773444
fbshipit-source-id: 605a979c09d07531293dd9472da9d2fa7439c619
Summary:
This diff is adding eval nets to layer model helper. It should be useful for
the cases when train/eval nets need some extra input (usually some supervision)
for train/eval. For example various sampled layers, etc.
Differential Revision: D4769453
fbshipit-source-id: 7a8ec7024051eab73b8869ec21e20b5f10fd9acb
Summary:
`SamplingTrain` layer is a wrapper around another layer subclassing `SamplingTrainableMixin`. When initiated in the training context, `SamplingTrain` produces sparse output of the wrapped layer. Output can be paired with `indices` to create Map schema. When initiated in prediction context, the full output of the wrap layer is produced.
This is liked the SampledFC function in model helper, https://fburl.com/gi9g1awh, with the ability to initiated in both trainig and prediction context.
I'd like to get consensus whether we should introduce the `SamplingTrain` layer and the accompaying mixin. This can probably be accomplished in some other way, but I think this is not too bad.
Reviewed By: xianjiec
Differential Revision: D4689887
fbshipit-source-id: 7be8a52d82f3a09a053378146262df1047ab26a8
Summary:
currently the output schema and blobs are names as "field_i" which is
bad for debugging. This diff allows us to specify output names.
Reviewed By: kennyhorror
Differential Revision: D4744949
fbshipit-source-id: 8ac4d3c75cacbb4c9b5f55793ac969fe1cf20467
Summary: For some embedding task, we don't want to include bias term in embedding computation.
Reviewed By: xianjiec
Differential Revision: D4689620
fbshipit-source-id: 4168584681d30c0eaa1d17ceaf68edda11924644
Summary: Some operators, e.g., SoftmaxWithLoss, returns scalar-typed tensor. This would allow us to use those ops without having to write layer manually.
Reviewed By: xianjiec, kennyhorror
Differential Revision: D4703982
fbshipit-source-id: f33969971c57fc037c9b44adb37af1caba4084b6
Summary:
This diff is trying to address one of the concerns that Xianjie have had - requirements create a layer for all operators and attach pass shapes and other info around.
The basic idea of the diff:
1. Try to create a layer with a given name, but if it's not available try to fallback on operator with that name (that is expected to have no parameters).
2. For all operators that we're adding through this functional style of creation - try to use C2 Shape/Type inference logic to get output type. If we fail to get - it just return untyped record and expect user to annotate it when it's really needed.
Reviewed By: xianjiec
Differential Revision: D4408771
fbshipit-source-id: aced7487571940d726424269970df0eb62670c39
