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
