Summary:
As per discussion in https://www.prod.facebook.com/groups/184236721951559/permalink/354591931582703/, KaimingHe pointed out that scaling LR is not same as scaling Loss, since LR scaling will affect the weight decay (which is implemented by modifying the gradient, which thus is not yet correctly 'averaged'). Actually prigoyal tried to convince me earlier that loss scaling is the way to go, but I was then not convinved :/.
So this diff removes the LR scaling parameter passed by data_parallel_model and instead passes a loss_scale parameter to the model creation function. Unfortunately, this will break all existing code that uses the data parallel model. But that is not only a bad thing, since it will bring awareness to this change. I will inform in the FB groups about this.
In this diff I modified all my models to work correctly.
Reviewed By: Yangqing
Differential Revision: D4507002
fbshipit-source-id: 16c7221663282f71a1b754b34de0c8ccd5c2ca90
Summary:
This learns Shakespeare and then generates samples one character at a time. We want this to be an example of using our LSTM and RNNs in general.
Now it takes 4ms to run the training net on current parameters (with batch size = 1). I don't have data on how much each operator takes yet. But overal python loop doesn't seem to influence much - with 1000 fake iterations in run_net it took 4s for each iteration as expected.
Future work:
* fixing convergence for batching
* profiling on operator level
* trying it out with GPUs
* benchmarking against existing char-rnn implementations
* stacking lstms (one lstm is different from two, one needs to take care of scoping)
Reviewed By: urikz
Differential Revision: D4430612
fbshipit-source-id: b36644fed9844683f670717d57f8527c25ad285c
Summary:
This diff brings us to roughly par with Torch on ResNet memory usage. On batch size 32, Resnet-50 took 7497MiB, after this 5010 MiB. This will thus allow us to handle 64 images / GPU, or 256 images / 4 GPUs.
In addition, I added a special argument to DagNet that causes it to run only one thread for the first iteration. This is needed since there are allocations on the first iteration's backward pass due to gradient sharing, and this will cause NCCL to deadlock.
The sharing of gradient buffers requires inferring which gradients can share memory (i.e that they are not used concurrently). Previous memonger code uses topological sort, but rbgirshick showed that it does not work with tree-like models. Thus, I wrote a new optimization algorithm based on DFS. It takes about 0.25 secs / GPU on resnet-50, so is clearly fast enough.
Module data_parallel_model supports this feature natively.
Reviewed By: prigoyal
Differential Revision: D4363209
fbshipit-source-id: 73b11e7610438098bb11bff0af8075ab0cf2c0f1
Summary: For some reason I had been disabling the exhaustive search heuristic for cudnn for xray/resnet trainers. On BigBasin, this gives 10% perf boost. On BigSur maybe 5%.
Reviewed By: prigoyal
Differential Revision: D4338654
fbshipit-source-id: 3974dd612f5d4f4dc8b2febccb59664d3f276c3e
Summary:
It gives a significant perf boost to do the parameter update inside MomentumSGD, instead of with a separate WeightedSum op.
To ensure backwards compatibility, I made it a separate op.
Also added an unit test.
Reviewed By: prigoyal
Differential Revision: D4262446
fbshipit-source-id: 38e7ee6d7677b398658ac7fe9b7a59b569e033f4
Summary:
This examples writes a LMDB database of image data and labels (random). Then it reads them using Caffe2's TensorProtosDBINput and validates the checksums match. This example shows how to coerce image data into TensorProtos and be happy.
Before there was no clear example how to create databases for Caffe2.
Differential Revision: D4263614
fbshipit-source-id: 21e08066899095b4efcc2d23dbc3ede81e75914a
Summary:
When refactoring data parallel model, the division of LR by number of devices was dropped, and thus we ended up effectively multiplying gradients by the number of devices. Thus, we need to scale the LR by 1/numgpus.
Created a test to confirm that data_parallel_model produces exactly same results on different number of gpus, given the total batch size.
Reviewed By: prigoyal
Differential Revision: D4248907
fbshipit-source-id: af21ede113e6ac25f12c556de298cb18974548be
Summary: Just noticed that I had duplicate code in the example imagenet trainer. Removed the function.
Differential Revision: D4223070
fbshipit-source-id: 443a9401bf7e425f7a3a13a44c9d0f7e21e72303
