Summary: this diff adds optimizer into param_info, and the associated implementations for modelhelper and brew to set optimizer for each individual parameter.
Reviewed By: kennyhorror
Differential Revision: D5385432
fbshipit-source-id: 5d682f9d1ab077e04a5d76a24d71470f4e64fc92
Summary:
salexspb This fixes a major perf issue (40% boost on alexnet end-to-end perf) in the multi-precision SGD optimizer - it was causing repeated cudaMalloc / cudaFree calls during training iterations due to the changing size of the `grad` blob as it moved from fp16 <-> fp32.
Closes https://github.com/caffe2/caffe2/pull/797
Differential Revision: D5246978
Pulled By: salexspb
fbshipit-source-id: ec3d7ef18445e19eaf5aac908d0a7bcd5957eb60
Summary:
Add add_weight_decay to optimizer + test.
In D5142973 I accidentally removed weight decay from resnet50 trainer, so this restores it.
Reviewed By: asaadaldien
Differential Revision: D5173594
fbshipit-source-id: c736d8955eddff151632ae6be11afde0883f7531
Summary:
Recent diff introduced a duplicate parameter to the model, which would hurt the performance and also affect correctness (duplicate momentum updates, for example). We unfortunately had no checks for duplicate params, outside of data_parallel_model, which fortunately brought this into our attention.
But it is better to have a Validate() function in model_helper, and call that before adding gradient ops and querying for parameters. Added to brew_test calls as well.
Reviewed By: kennyhorror
Differential Revision: D5163458
fbshipit-source-id: 35692e8bfcc359d4e8bc73e6f2358659f6e45ceb
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: Fix an issue where the parameter is not created in param_init_net, or net, and then we secondarily look at which device op outputs the gradient. This did not work if the gradient was a GradientSlice.
Reviewed By: harouwu
Differential Revision: D5153102
fbshipit-source-id: 20eae660ea32e5a9ea484bf93c04c8f8c71a51ed
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:
hankun is using the optimizer, but having mixed set of of GPU and CPU operators. Currently this won't work with optimizer since it adds optimizers for all parameters in the current device scope. But we can actually infer the device that a param belongs to by looking at the device option in the param_init_net.
Added a test as well.
Reviewed By: salexspb
Differential Revision: D5133652
fbshipit-source-id: ad8689d75ac1f5c78981bae1b6978fe91e40ef0f
Summary:
This is going to unblock Nvidia in their work on adding fp16
support to Caffe2. I discussed this with kennyhorror before to make
sure this fits into his work on parameter sharing.
Reviewed By: kennyhorror
Differential Revision: D5127797
fbshipit-source-id: 4db155d320b1862570c23b77c4252bdacbf2296f
Summary: mutex is only supported on CPU. need to make sure mutex and following atomicIter are both on CPU. This is critical for gpu SparseNN training
Differential Revision: D5093184
fbshipit-source-id: 021e6ba699a3208449fa4761cad6b0ec4544957e
Summary:
In transfer learning, parameter initialized from pretrained model might require
a different learning rate than otherwise initialized. To this end, here we
implement a python solution where `base_learning_rate` is scaled by `scale`,
which is in turn set by `scale_learning_rate`; Alternatively, we can achieve
same effect by rewriting the LearningRate operator in C++
Reviewed By: kennyhorror
Differential Revision: D4992827
fbshipit-source-id: 8d7e87a61c95b3eb8ef733ec436f4060e865c0ac
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:
1. Adds a function to return auxiliary parameters for each optimizer. This function can be used to serialize the optimizers so that they can be recovered.
2. Fixes the bug that the iteration blob is not incremented by one in each iteration. Suppose there are k parameters using the adam learning rate optimizer, the iteration blob is incremented by k based on the original implementation.
Reviewed By: azzolini
Differential Revision: D4872397
fbshipit-source-id: d86711feedda2ba83af5f2a18141b06a6a473733
Summary:
The current optimizer code in c2/python has the following issues:
(1) the optimizers in sgd.py cannot config per param-blob optimizer;
(2) sgd.py is a bad file name. optimizer.py is a better name;
(3) layer_model_helper.py has another set of optimizer code (which supports per param-blob optimizer)
This diff did the following
(1) create optimizer objects so that we can config per param-blob optimizer and that are also compatible to the existing optimizer code
(2) the new optimizer code are much more modulized
(3) move the optimizer code to file with better name (optimizer.py)
(4) replace the optimizer imports in the existing code
will do in next diffs
(1) optimizers with structured parameters for dper2
(2) get rid of the optimizer code in layer_model_helper.py
Reviewed By: salexspb
Differential Revision: D4609013
fbshipit-source-id: 2e2d6dfa8685d10498f89069157453d9feca3f27
