* [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
Summary:
Add a function to return true if the model contains loss and retuen
false if the model doesn't include a loss.
Reviewed By: kittipatv
Differential Revision: D6982444
fbshipit-source-id: 1f63b7a1eaa3077841a0ad5d8d854b471d0aa84c
Summary: Sometimes we need to add some extra schema later
Reviewed By: sunnieshang
Differential Revision: D6951849
fbshipit-source-id: 564eb88f9250eae24869fd10ba3426e00a18af33
Summary:
This is a first attempt at completing bootcamp task T24449916. This diff contains 3 major changes:
1) Change LayerModelHelper to allow for exposing the output and parameters of any layer to metrics
2) Added a runner that allows metrics to draw arbitrary plots to a matplotlib axes object
3) Implement a metric that aggregates distributions of values in a blob over the training, and try this out in a notebook
Reviewed By: kennyhorror
Differential Revision: D6671273
fbshipit-source-id: b8961837395e89c957edbf5c7c862bdb845ccf4b
Summary: This diff enables setting model initialization seed, instead of random seed, when reproducible restults are desired.
Reviewed By: xianjiec
Differential Revision: D6642971
fbshipit-source-id: 387b1ee2ecef4f8f66570c882498fb97d7007e17
Summary:
In layer model helper, add a method `maybe_add_global_constant` to ensure
that when two global constants are added with the same name, we check if they
are actually the same (by initializer) and only add it once.
Reviewed By: kennyhorror
Differential Revision: D6537532
fbshipit-source-id: 37aa3860a2e40d81161ccdea0c50a316248be2e2
Summary: small changes as I was reading through the dper code base. all of them are nits, but somewhat helped me understanding things.
Reviewed By: xianjiec
Differential Revision: D6389380
fbshipit-source-id: 3412052e4fcba199c6ffc84c6f7ae11bf8ff6ee9
Summary: When parameter sharing is used, the model may not own the parameters. Emptying out initializer ensures that the shared model doesn't overwrite initialization.
Reviewed By: chocjy
Differential Revision: D5870362
fbshipit-source-id: f8587b84c3a13f331a3251973e8206563939606a
Summary: This is not a very generic constant
Reviewed By: volkhin
Differential Revision: D5870378
fbshipit-source-id: 59509bb48cecb52ba4a3f26b290855374547fe7e
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: - Minor fix for error message in layer model helper file
Reviewed By: chocjy
Differential Revision: D5440768
fbshipit-source-id: df47bfe68a0caa750f0d3c8def28a5585e465ee0
Summary: Add api model.add_loss(), which allows adding loss, such as optimization and regularization. See change in sparse_nn.py, in which 'model.loss = loss' is changed to 'model.add_loss(loss)'.
Reviewed By: xianjiec
Differential Revision: D5399056
fbshipit-source-id: 13b2ced4b75d129a5ee4a9b0e989606c04d2ca8b
Summary:
the FC ModelLayer needs an optimizer, also seems the catch-all
that sets a default for missing optimizers had a bug
Reviewed By: xianjiec
Differential Revision: D5048302
fbshipit-source-id: cbbf641fb9ee4f4f89c5dbb132f7837ecdbe37a5
Summary:
Adds a parameter cost estimation step before the actual training starts. The costs are later used in order to better shard the parameters across instances of the parameter server.
Things I needed to modify:
- A few changes to make ModelLayerHelper picklable
- Add support for stopping a distributed job after a number of stats reporting steps.
- Refactored run_dist_job to support collocating the reader with the trainer even when PS are present.
- Option to disable dense updates (when num_dense_servers=0).
Currently there's a huge overhead posed by having to launch a child workflow. I'll try and address next in a subsequent diff.
This is WIP because the other workflows need to be migrated as well.
I can break this down into smaller diffs if reviewers would prefer it.
Reviewed By: kennyhorror
Differential Revision: D4974752
fbshipit-source-id: 04c336acb2945f8f11324a221ffc6967818c0672
Summary: In certain situation, like in D4907916 where we insert additional step in the middle of a model, it's neccessary to keep the blob names constant across model helper so that it doesn't break communication schema.
Reviewed By: kennyhorror
Differential Revision: D4981527
fbshipit-source-id: 6b8d6d240279dd48f801cfacbaa1d320ba54d694
Summary:
rename ModelHelperBase to Model.
This is the result of running:
find . -type f -exec sed -i 's/ModelHelperBase/ModelHelper/g' {} +
We had 19 results when fbgs ModelHelperBase. Here is 20 instances because I added 1 test in model_helpers_test.py
Reviewed By: salexspb
Differential Revision: D4928337
fbshipit-source-id: bc4c12b60b90c167e717de50ea9fe17521e142e3
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:
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: Currently, we cannot have layer constant because layer params are required to have gradient and optimizer. Global constants don't cut for this because it can only be added once; therefore, a layer that add any global constant can only be used once.
Differential Revision: D4773212
fbshipit-source-id: 5b60d31f3c1602afb04b61f6d30b8e3e06ed2de3
Summary: This diff is migrating existing DPER workflows to use new metric abstractions in training.
Reviewed By: xianjiec
Differential Revision: D4656576
fbshipit-source-id: 1b3b16b390fc0757480e41df1c4214c11cd76e8a
Summary: Add SparseNN workflow for feed. I haven't fully thought about the change needed for ads, as I added a property called 'preproc_output_schema' for LayerModelHelper.
Reviewed By: xianjiec
Differential Revision: D4585796
fbshipit-source-id: 060d08f4beb928e7e7863f2e563f612c358951fb
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
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: to verify that a model only used a subset of the parameters of another model (e.g., the model doing training).
Differential Revision: D4557787
fbshipit-source-id: bd8ac96f5e78e05f6f56086db6e6ddcda36c1d37
Summary:
DPer example have been creating multiple copies of the transform config in net
defition till this moment, that resulted in the fact that I've hit the limit of
ProtoBuf (64MB) for a certain Task requests (especially visible because of the
ValidationPipeline that I was adding).
After this diff we're going to store SigridTransforms in one instance per
machine for training (or 1 instance per reading).
Difference in sizes of the plans for some simple SparseNN model ~30 MB (even including the fact that second model have validation plan as well).
TODO: Do similar logic for NNPreProc as well (it's also pretty large).
Reviewed By: dzhulgakov
Differential Revision: D4441441
fbshipit-source-id: 4452dd86a4dc49b2c7f5b7642f443aed5720b047
Summary:
this normalizes the sparse gradient, so that the "effective learning rate" of each sparse parameter will NOT be affected by the number of examples in a batch that "use" this sparse parameter.
experiment shows it help convergence (about 0.1% better train NE): https://fburl.com/1230747813683956. It's not conclusive yet, and we still need to do more experiments. But this diff adds it as an option, and does not change the default behavior, so we can get this in first.
Differential Revision: D4367283
fbshipit-source-id: 49ea80dfa9ea776ff4160e220cf6c86593521607
