* [GanH]: two_task_discriminator
as titled
and adding label smooth
* [Dper2] Simplified UI options needed for blob magnitude visualization
* [GanH]: fix tags
as titled
* Added type and shape inference for GatherRange operator
This helps with type / shape inference when using this operator in layers.
Also just a nice to have in general.
* Demonstrate Caffe2 exception handling with StoreHandlerTimeoutError in Python
We'd like to catch and recover from certain Caffe2 net exceptions. Use this diff to demonstrate a pattern of registering a pybind exception mapping and catching in Pythonusing caffe2::StoreHandlerTimeoutException.
* Bind Gloo IoException to IoError in Python
Allow peer failure handling and recovery using an exception based mechanism. This diff registers gloo::IoException with pybind.
* [GanH]: add label smoothing to softmax with loss
as titled
* [C2] Enable LARS in Adagrad and hook it to DPER
* [DPER] Don't pass LayerModelHelper in create_trainer_nodes
Since we're planning to get rid of it eventually and I want to get access to
NetDef only interface ASAP - I'm looking towards removing all references to
LMH, where we don't really need them.
* fix bugs in LambdaRankNdcgOp
the loss and gradient in LambdaRankNdcgOp are incorrect. The loss should be negative log of probs instead of log.
* Restrict thread pool on iOS to only big cores
Historically, iPhones exposed only one type of cores, and Caffe2 thread pool used all of them.
However, iPhone 8/iPhone X exposes 2 big + 4 LITTLE cores. As our thread pool doesn't support work stealing or other forms of load balancing, fast cores end up waiting for the slow ones, and it may be better to restrict execution to only 2 fast cores, like we do on Android.
* Remove SparseLength Sum/WeightedSum/Mean operators with fp16 engine
Remove SparseLength Sum/WeightedSum/Mean operators with fp16 engine
* make clang happy and get fewer warnings
make clang happy and get fewer warnings
* [Personalization] Support add_output_schema() in layer_model_helper
Problem:
Currently the output_schema of sparse_nn can only be set once. https://fburl.com/efth5zer.
Solution:
For flexibility, we want to add fields to output_schema incrementally.
Plan:
Wrap the change of `model._output_schema` into a new function `add_output_schema()` for adding additional output_schema.
Callsite:
The add_output_schema() should be called instead at https://fburl.com/efth5zer
Reference:
The newly added `add_output_schema()` will be similar to `add_loss()` in https://fburl.com/t2ii8njh
After D6953547 some of the blobs were no longer impacted by uint8 quanitzation,
but they would still generate operators expecting uint8 inputs and thus fail.
This diff is adding a temporal hack to avoid doing this quantization when layer
is not quantized.
Will fix it with switching to Net rewriting instead.
Summary:
In some cases we were doing quantization even we we should not. This diff is
preventing this from happening.
Reviewed By: rayleichen
Differential Revision: D6953547
fbshipit-source-id: 7c65baaf969e5e1bddb68ca8182f4f3b43f2431d
Summary: Updates `sparse_lookup.py` for the new fused 8-bit rowwise quantization. Mostly just changing the same files as the original diffs (D5753626 and D5761202). I know very little about this code here so please let me know if this is safe, also in terms of migration away from the non-fused storage.
Reviewed By: kennyhorror
Differential Revision: D6710784
fbshipit-source-id: 185f147af52a094a937ba631b0351225e660d205
Summary: add Test for SparseLookup with PositionWeighted.
Reviewed By: kennyhorror
Differential Revision: D6771612
fbshipit-source-id: b4b3bfd514f366f579b4192643330ae73843d4f9
Summary:
so that user can use 'WeightedSum' pooling method when there is mix of id list feature and id score list features.
- it's still intuitive to have "WeightedSum" for id list, and we do not need to introduce new "UnWeightedSum" etc.
Reviewed By: chocjy
Differential Revision: D6369270
fbshipit-source-id: 722fa08d1a7986bc6ecf4c7cb02bbae0825bcab4
Summary:
To achive this, I modified the blob name scheme defined in a layer.
Before it was scope/fc_w and scope/fc_w_auto_0 (if there is another fc
within the same scope).
Now I change it to scope/fc/w and scope/fc_auto_0/w.
That is, we rely on the uniqueness of the scoped layer name to define
names for blobs.
I also overwrote the create_param method in LayerModelHelper to let it
use the resolved name for blobs given the sharingparameter context.
There are some details such as making the initializer more structured
that I need to finalize.
Reviewed By: kennyhorror
Differential Revision: D5435132
fbshipit-source-id: a0525f5ea0977e255dd5ea765b38913f5951d455
Summary: Adding pooling option as None, and SparseLookup will gather the embedding for each id.
Reviewed By: kittipatv
Differential Revision: D5421667
fbshipit-source-id: 1e8e2b550893ff3869dab12f8eb1fe24a063c3d5
Summary:
Segment based Ops requires increasing seg id, and without gap. Lengths based Ops does not
have this requirements.
Otherpooling methods, e.g., LogExpMean does not have Lengths based Ops available yet.
Differential Revision: D5019165
fbshipit-source-id: ab01a220e10d4ed9fa2162939579d346607f905e
Summary: Somehow, feed-non-ranking training data usually have this type of column. Add option to support it.
Reviewed By: xianjiec, kennyhorror
Differential Revision: D4773960
fbshipit-source-id: 5a7ef4618a070e04f3cd8ddfcbf2b7441c00d92d
Summary:
Add distributed training to dper2 and keep the dper1 working.
* Created a ModelDelegator to wrap ModelHelper and LayerModelHelper to mitigate the difference.
* To get the average length for sparse feature, I extracted some information in feature_processor. There should be some better way to do it after we have new compute_meta.
* metric right now only runs on the first trainer.
* The model is saved correctly for evaluation. But I'm still not sure how to handle the weights for adagrad.
Reviewed By: kennyhorror
Differential Revision: D4767745
fbshipit-source-id: 0559d264827a7fd9327071e8367d1e84a936bea9
Summary: sum processor and sqrt pooling is to mimic the DoubleHelix model.
Differential Revision: D4678413
fbshipit-source-id: fc1ccfe3c92c540ce5914dfd8ff1a040805c48db
Summary:
previously fp16 type was supported in SparseLengthsSum operator, now it
works in all other segment operator as well.
Reviewed By: dzhulgakov
Differential Revision: D4624312
fbshipit-source-id: c9d72110e3762167270bb088405eaf9c56e88493
Summary: Another part of making DPER compatible with half-floats. This diffs adds supoprt of fp16 to segment reduction operators used in DPER.
Reviewed By: dzhulgakov
Differential Revision: D4587560
fbshipit-source-id: 0ae10648a7286a820bffaee802464dd9464584bc
Summary:
First part of adding half-floats support to DPER 2.0. Let's add an option use_half_floats to enable converting some weights of the model from fp32 to fp16 before saving it to predictor models parts. For now it's for SparseLookup layer's embeddings. All conversion is done after training is finished and saved models are ready to be used on remote predictors as-is (they will be stored compacted in memory). New fp16 blobs are saved to the model instead of original ones, under the same names, so we don't modify MetaNetDef at all.
Next steps:
1) support on delivery side -- operators working with these blobs should support both float and float16 input types
2) benchmark performance to make sure there is no regression
a) of serialization
b) of delivery
3) support realtime training (I'm thinking about adding new pre-publishing net which will be executed each time the realtime trainer stops to publish a new snapshot)
Depends on D4567304
Reviewed By: kennyhorror
Differential Revision: D4571710
fbshipit-source-id: 19967a17d3bd84878d66e8c0ed8c5342bf38d979
Summary:
Remove the use of `NextName` in layer model helper, so that the same function return `model_helper` that should construct identical `Net`, when under the same NameScope.
The `NextScopedBlob` should only take effect when there is real name conflicting, otherwise it returns ScopedBlobReference.
This is critical for parameter blobs. In long run, we need to be able to specify parameter blobs more explicitly. (kennyhorror is working on this). This solution works in short term for e.g., two tower sparse nn models.
Reviewed By: kennyhorror
Differential Revision: D4555423
fbshipit-source-id: 2c4b99a61392e5d51aa878f7346466a8f14be187
Summary:
We want to implement request only net and to do this we decided to split the work into two parts. The first part will propagate required metadata and the second part will cut the nets properly.
This diff is to propagate request_only metadata across the layers.
A few notes about implementation:
- Each layer contains a field request_only which can be set based on the input_record. If all the scalars from the input_record are marked request_only we mark a layer as request_only;
- Sparse-To-Dense layer sets request_only metadata;
- SigridTransformation and SparseLookup layers propagate request_only status;
- As for now we join request_only and other sparse features together in input_record, but ideally we may want to separate this, because request_only should be served separately;
Reviewed By: xianjiec
Differential Revision: D4259505
fbshipit-source-id: db8a30ef92cba84f1a843981b9dde3a8b9633608
Summary: adding more methods to the layer representation. The corresponding implementation in DPER is: https://fburl.com/563869364
Differential Revision: D4256583
fbshipit-source-id: 91326b7bb9e960a5bc70b5a13812fce90054eceb
