Summary:
prigoyal sharply noticed a bug in the Resnet models: we have not been checkpointing, nor synchronizing between gpus, the moving average and variance computed by the SpatialBN ops. Particularly the first problen is serious, since models starting from checkpoint would have started from a null-state for SpatialBN. Not synchronizing with the data parallel model is less tragic since each GPU should see very similar data.
Thus I propose keeping track of "computed params", i.e params that are computed from data but not optimized. I don't know if there are other examples, but SpatialBN's moving avg and var definitely are one.
- I modified the checkpointign for xray model to store those blobs + also ensure the synchronization of those blobs
- I modified data parallel model to broadcast those params from gpu0. I first tried averaging, but hit some NCCL deadlocks ... :(
Differential Revision: D4281265
fbshipit-source-id: 933311afeec4b7e9344a13cf2d38aa939c50ac31
Summary:
With parameter server, sparse features are updated on the parameter server.
Local update for sparse features are disabled. But that logic is removed in
D4144922. This diff is to add this logic back in a slightly different way.
Previously, in trainer_example, I did that in a hacky way just avoid adding
sparse weight to model.params. It will still generate grad, but will not add
optimization operators. At the same time, it is always registered directly in
the sparse_mapping, so the parameter server is aware of this parameter.
But with the new change for ParameterInfo. I can not do it in that way anymore.
Because the param registry and params are bind together in ParameterInfo.
For dper, there is a option in dper model helper to disable all of the sparse
parameter optimizer.
To combine these two together, I directly changed the ModelHelperBase in this
diff. It is not quite ideal. It is better to do it in Layer. But to fix the old
one, this seems to be more reasonable place to cover both cases.
With this diff, there is no spike anymore. So probably this is the root cause
for the convergence issue we have seen in D4144922. It explains that why the
model can recover, which is because adagrad decays local learning rate and
local updates cause less change.
Reviewed By: dzhulgakov
Differential Revision: D4229684
fbshipit-source-id: da1241d43d7c52cbf13560f9bb83e09897d8d56f
Summary:
This consists of a series of diffs for implementing Multi-task learning.
This diff is to
1. save model;
2. support MT learning in evaluator
3. add unittest.
model after merging (saved model): https://our.intern.facebook.com/intern/graphviz/?paste=56793140
Reviewed By: xianjiec
Differential Revision: D4123316
fbshipit-source-id: 225bf8616962ec08f4f1ef85729c1e94ba7c373a
Summary: This is so they don't generate spurious warning messages in the logs
Reviewed By: dzhulgakov
Differential Revision: D4205610
fbshipit-source-id: f764b51565430f4057898ab929372bc7943e0495
