Summary:
Enforce that blobs don't mix between operators on different GPUs or CPU/GPU. Add test.
+ Fix memonger when no namescope is provided.
Reviewed By: asaadaldien
Differential Revision: D5644708
fbshipit-source-id: 0cb361efd6361b6e2138462584bab6b4de039b5d
Summary:
Here is my example:
For static RNN timestep is created as a part of param_init_net. Before DPM assumed that it is CUDA blob by default and it participated in broadcasting causing Copy on line 798 to fail. No device mapping is correct for this blob.
Reviewed By: akyrola
Differential Revision: D5631716
fbshipit-source-id: 28c3eb17ecc3080c95c41d69a60bf7262d3907d4
Summary: Added Nesterov momentum as an option for BMUF and corresponding tests
Reviewed By: asaadaldien
Differential Revision: D5599888
fbshipit-source-id: 30819c9e689347c8b75daddc7444bea9f54193ae
Summary: as promised, a separate diff for dpm changes I made in experimental code
Reviewed By: pietern
Differential Revision: D5551304
fbshipit-source-id: 9013aeab6c388b1c415ffb2e36fb8dd6b8cf90b0
Summary:
In Python 3x dictionary values aren't a list and can't be concatenated to a list
this diff should fix that.
Reviewed By: andrewwdye
Differential Revision: D5576724
fbshipit-source-id: c60441857ceceb9c4a71122d2db5e9abad6d3fc2
Summary: It is common mistake to create test/validation model with init_params=True. When its param_init_net is run, it will overwrite training models' params, and with DPM, those won't be synchronized to all GPUs. I don't want to make this an assertion yet, since it might break people's trainers (it is ok to have init_params=True if you never run the param_init_net...).
Reviewed By: asaadaldien
Differential Revision: D5509963
fbshipit-source-id: 63b1a16ec0af96e3790e226850f6e0e64689143f
Summary:
As per rushabhmshah99 request: he wants to append a pre-trained model (without training that) to the model.
So added data_parallel_model.ConvertNetForDevice() to enable that. The unit test shows example how to use this with
AppendNet, and I also added a blurb to the function.
Differential Revision: D5503335
fbshipit-source-id: b2a5db5c1739dc97f46dd0d7606ed555d99255b8
Summary: Use romain-intel's ContextFactory to create common worlds from existing common worlds, thus bypassing KV store completely. Changed data_parallel_model to automatically find if there is already a CW we can work. CreateCommonWorldOp takes optional second parameter, which is existing CW.
Reviewed By: andrewwdye
Differential Revision: D5494956
fbshipit-source-id: 5f7a840bcd5fe4ea756fafeacc746bc2cf5078b0
Summary: Allowing CPU device scope instead of enforcing no device scope in data_parallel_model and data_parallel_rendevous.
Reviewed By: akyrola
Differential Revision: D5440492
fbshipit-source-id: bcd4344d64c710ea50ec8a65e3e9d102e35c66ea
Summary: Added function _RunComparison to data_parallel_model that checks if all shards in a given rendevous have the same value for a given blob_name
Reviewed By: wesolwsk
Differential Revision: D5394164
fbshipit-source-id: c2b07d0f8d5846fa9887d53b0be091a8c057f106
Summary:
Added device scope checks to data_parallel_model and data_parallel_rendevous
Added test to check that checks are working correctly to data_parallel_model_test
Fixed device_scope error in test_synchronization_barrier
Reviewed By: akyrola
Differential Revision: D5403936
fbshipit-source-id: 849c1cd7452692efbc5ef74d2d60ede090c9c017
Summary: replace params with sp, otherwise it will report an empty list
Reviewed By: akyrola
Differential Revision: D5382716
fbshipit-source-id: 34d8e6ee00cbe1718702e3d1f23ea12f8d65063e
Summary: Add synchronization barrier API with configurable timeout. Users can call Synchronize() to join variable length execution before resuming multi-machine communication steps, i.e., resuming distributed training iterations after validation on a single machine.
Reviewed By: akyrola
Differential Revision: D5348387
fbshipit-source-id: 5826da10e6a60c50394c36c7cf47624f10191d11
Summary: Huge improvement in my tests, and it does not really hurt either.
Reviewed By: wesolwsk
Differential Revision: D5374925
fbshipit-source-id: c96a4ed2ca653120a82233c0037cbfded8a2d2a1
Summary:
Otherwise was always added to main net instead of param_init_net when
desired (i.e. initial param sync)
Closes https://github.com/caffe2/caffe2/pull/894
Differential Revision: D5367451
Pulled By: akyrola
fbshipit-source-id: 3d82be6da687c736bd15f4852dbd272266eb4811
Summary: This was broken in a previous diff, fixing it to use model device type.
Reviewed By: asaadaldien
Differential Revision: D5356005
fbshipit-source-id: a4fcc932bae772076b57625a5fcc0d38eb702cc9
Summary:
This add CollectivesConcurrencyControl class to mange creating common context and cyclic controls to execute GLOO collectivces
and refactors AllReduce and _AddDistributedParamterSync to use it
Reviewed By: akyrola
Differential Revision: D5335795
fbshipit-source-id: 5084e0a65cdb989cd949be3868b77a680561022d
Summary: Combine _AddDistributedParameterSync() and _SyncParams() into a single function to broadcast across distributes machines and all local GPU simultaneously. This is similar to how calls to Allreduce has already optimized using the functionalities of Gloo. All the refactoring work is contained in data_parallel_model.py.
Reviewed By: akyrola, andrewwdye
Differential Revision: D5329277
fbshipit-source-id: 4407b88980cf396f2e0f994d796294fa79fd39ed
Summary: - One line fix for loading saved checkpoint when using Parallelize_GPU_BMUF
Reviewed By: asaadaldien
Differential Revision: D5315254
fbshipit-source-id: a20ba6438c8e6b2ef44b65270c1d3f9ab645ded0
Summary:
Refactor data_parallel_model all_reduce and broadcast methods to work for
a given parameter set not only gradients and reuse them for BMUF distributed
implementation.
Add a distributed test (multiprocessing) to BMUF.
Reviewed By: akyrola
Differential Revision: D5267083
fbshipit-source-id: 8dcc7527d0a755b903d693d8071585f0b54d3403
Summary:
CPU -version of data parallel model. Great thing is that now we can run data_parallel_model_test in Sandcastle (as it does not have GPUs).
Pretty simple change, really. I did not change all variable names with "gpu" in them, to reduce risk (and being a bit lazy). Can improve later.
Reviewed By: wesolwsk
Differential Revision: D5277350
fbshipit-source-id: 682e0c5f9f4ce94a8f5bd089905b0f8268bd2210
Summary: Don't want to assert since it can be useful to sometimes create models that are not run (for example, unit tests).
Reviewed By: pietern
Differential Revision: D5258905
fbshipit-source-id: f1beee0605bfef235ed0f23f7e78259109720254
Summary:
Make it easier for users by returning from ExtractPredictorNet the list of blobs that must be saved/exported to run a predictor net. Added a test for ExtractPredictorNet
Codemod.
Reviewed By: asaadaldien
Differential Revision: D5176097
fbshipit-source-id: b1af42132459487b8d94fcdde0e4c514da608243
Summary:
This diff is the first step in the effort for refactoring all parameters. As a first step - I'm merging concept of params and computed_params, that is going
to be based on tags instead (in the first version it's still using old data structs to store all the BlobReferences).
Renaming computed_params to non-trainable/non-backprop params should be done is some other diff.
Reviewed By: salexspb
Differential Revision: D5171159
fbshipit-source-id: 68031ca779f053fb266a7c4a2e5b482a3bd9c832
Summary:
This diff is the first step in the effort for refactoring all paramters. As a
first step - I'm merging concept of params and computed_params, that is going
to be based on tags instead (in the first version it's still using old data
structs to store all the BlobReferences).
Renaming computed_params to non-trainable/non-backprop params should be done is
some other diff.
Reviewed By: salexspb
Differential Revision: D5119830
fbshipit-source-id: 2001090a37346eb12abbb234e13e727c288eb8a7
Summary:
Adds support for generating and training pfp16 models. Added SGD optimizer for multi-precision trainers and a new callback to data_parallel_model in order to help multi-precision models keep their different copies of parameters in sync during training.
Closes https://github.com/caffe2/caffe2/pull/697
Differential Revision: D5159712
Pulled By: salexspb
fbshipit-source-id: 60a889494d2e2f4df1d720331e19f638c5eb95cc
Summary:
Currently we can get into broken situations when some nodes working on computation detectChanges() faster than others, thus only some of the nodes start doing next iteration of training. This is an inconsistent state. To prevent this to happen, now each node sets a "re-rendezvous flag" and that is allreduced after each iteration. Once all agnodes agree, re-rendezvous will be done.
Also noticed that min_shards=1 does not work because data parallel model assumed num_shards>1 when rendezvous is not None. Fixed that.
Reviewed By: andrewwdye
Differential Revision: D5156282
fbshipit-source-id: f2ccbd8ad13ed37f7813ff8ad1080d963d0d17e3
Summary:
Fixes some performance issues when `broadcast_computed_params=True` is passed to Parallelize_GPU. Enabled via the same `use_nccl` flag as AllReduce
Closes https://github.com/caffe2/caffe2/pull/630
Differential Revision: D5149828
Pulled By: akyrola
fbshipit-source-id: 12c9714c7fa078811f1cde61c8523dca8f7f968f
Summary:
This diff does two things:
- add supports for optimizer to data_parallel_model. User can supply optimizer_builder_fun instead of param_update_builder_fun. The latter is called for each GPU separately with proper namescope and devicescope, while optimizer builder only is called once and adds optimizes to the whole model.
- use MomentumSGDUpdate instead of MomentumSGD + WeightedSum. This bring major perf benefits.
Changes resnet50 trainer to use optimizer.
This relies on D5133652
Reviewed By: dzhulgakov
Differential Revision: D5142973
fbshipit-source-id: 98e1114f5fae6c657314b3296841ae2dad0dc0e2
Summary:
See discussion at https://github.com/caffe2/caffe2/pull/633#issuecomment-303536902
Tested with a TitanX (Pascal) and a TitanZ (Kepler) with this access pattern.
```
Checking GPU(s) for support of peer to peer memory access...
> Peer access from TITAN X (Pascal) (GPU0) -> GeForce GTX TITAN Z (GPU1) : No
> Peer access from TITAN X (Pascal) (GPU0) -> GeForce GTX TITAN Z (GPU2) : No
> Peer access from GeForce GTX TITAN Z (GPU1) -> TITAN X (Pascal) (GPU0) : No
> Peer access from GeForce GTX TITAN Z (GPU1) -> GeForce GTX TITAN Z (GPU2) : Yes
> Peer access from GeForce GTX TITAN Z (GPU2) -> TITAN X (Pascal) (GPU0) : No
> Peer access from GeForce GTX TITAN Z (GPU2) -> GeForce GTX TITAN Z (GPU1) : Yes
```
All combinations pass:
* `0,1`
* `0,2`
* `1,2`
* `0,1,2`
Closes https://github.com/caffe2/caffe2/pull/659
Differential Revision: D5148779
Pulled By: akyrola
fbshipit-source-id: 6263edfe8b36623983f1946b5c3f4a3fef415a45
Summary:
This was hardcoded at 4 before but should be made
configurable. Can be kept low for big MLPs and higher for convnets.
Reviewed By: akyrola
Differential Revision: D5126138
fbshipit-source-id: 713ee8bbeb243b7de1479808fd6398d397e0b49a
Summary: When two or more blobs are gathered by the same indices blob in a data parallel model, we used to concatenate multiple times and re-write to the same indices blob. This leads to illegal memory access at times because the gradientslice indices blob is longer than its corresponding gradientslice values blob. This diff adds a check in order to avoid this.
Reviewed By: akyrola
Differential Revision: D5116817
fbshipit-source-id: 1c086d092eb6d48926d600f9408f578f5ddc41c7
Summary:
Address KaimingHe's comments in D5093689 about same blob being initialized twice causing internal consistency check to fail. Also I noticed that my new test for test_checkpoint_params was completely botched due to an indentatino issue (it did not actually execute any test). So this fixes that as well.
Modified the test to add a duplicate param initializer, so that this bug is tested for.
Reviewed By: KaimingHe
Differential Revision: D5101304
fbshipit-source-id: 72f343035c1b4953e7bb9a1a1c171cf05d3ead26
Summary:
If Predictor Exporter save_to_db is called in CUDAContext, a failure occurs since the following FeedBlob() tries to store a string (meta data), but for CUDA blobs we assume they are tensors.
+ fix a typo in data_parallel_model that I bumped on.
Reviewed By: asaadaldien
Differential Revision: D5099837
fbshipit-source-id: 69d01b35a9a1816bf083f13d8a6ce88e1f5aecb7
Summary:
Major improvements. Before we only synced "params" and "computed params" of model after initialization and after loading a checkpoint. But actually we want to sync all blobs that are generated in the param_init_net. For example the _momentum blobs were missed by the previous implementation and had to be manually included in checkpoint finalization.
I also added GetCheckpointParams() to data_parallel_model because it is now fully general. Also added a unit test.
Reviewed By: andrewwdye
Differential Revision: D5093689
fbshipit-source-id: 8154ded0c73cd6a0f54ee024dc5f2c6826ed7e42
Summary: Add a RandomFailureOp and handling to elastic data parallel model of the status code
Reviewed By: andrewwdye
Differential Revision: D5065936
fbshipit-source-id: 24224f9ea414ee535c9e90cc28add5189354b0ef
