Summary:
There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports:
```2to3 -f future -w caffe2```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033
Reviewed By: seemethere
Differential Revision: D23808648
Pulled By: bugra
fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
Summary:
data_workers.py provides a really nice, easy way to run background threads for data input. Unfortunately, it's restrictive, the output of the fetcher function has to be a numpy array.
I pulled out that core nice thread management into parallel_workers, and updated the classes data_workers to extend those classes. The main change was refactoring out most of the queue handling logic into QueueManager.
This way parallel_workers can be used to manage background threads without having to use the queue for output.
Reviewed By: akyrola
Differential Revision: D5538626
fbshipit-source-id: f382cc43f800ff90840582a378dc9b86ac05b613
Summary: Data workers test timeouts randomly (very seldom), and looks like the reason is that we call FeedBlob in a thread (eneuque-thread), and first time that is called, it will call workspace.CreateBlob() -- which is not thread safe. Fix this by initializing the scratch blobs explicitly.
Reviewed By: panshen1
Differential Revision: D5292426
fbshipit-source-id: d7dad68f3ccc636c60bd82b2527f00f20da298b5
Summary: Based on jay-mahadeokar's code, add a test for input order consistency to data workers.
Reviewed By: jay-mahadeokar
Differential Revision: D5096887
fbshipit-source-id: efd226343f81e9a0157ec89d4588f1eee8a78549
Summary:
Add a parameter dont_rebatch to data_workers. This disables batching of input from fetcher to equal-batch size chunks. This is not desired with RNNs where with longer sequence length we might want to have smaller batches etc.
For some reason the graceful-shutdown test interfered with other tests, so I removed it.
Reviewed By: jay-mahadeokar
Differential Revision: D4988549
fbshipit-source-id: cbab46d77c948f2e293e79e6eb538dde17d800ee
Summary: Now you can call coordinator.stop_coordinator("train") to stop the train model's data input and release its memory.
Reviewed By: rpenggithub
Differential Revision: D4955014
fbshipit-source-id: c1bc3ec67337b94aff8ea9b306c3b4158eeef42c
Summary:
Mysterious deadlocks after epoch has finished have occured randomly but quite frequently recently for myself, vigneshr and others. Looking at a stack trace of vigneshr's job (P57129798), I noticed a couple of threads were calling BlobsQueue.blockingWrite (or something like that). That call stucks when the caffe2/c++ side queue is at capacity (we use capacity of 4 with data workers). So in cases when this call was just being made while the script was to be terminated, the thread did not close and the whole process did not close either (not completely sure why that is since thread is a daemon thread, but this might be a flow-related issue since we run inside a flow container).
This is quite easy to fix: just call CloseBlobsQueue() when terminating the process. I modified coordinator.stop() and wait_for_finish() to return a status code based on whether threads that were joined actually closed within the 1.0sec timeout. This allowed creating an unit test to test for this issue. Before my change, the unit test failed.
Reviewed By: pietern
Differential Revision: D4619638
fbshipit-source-id: d96314ca783977517274fc7aadf8db4ee5636bdf
Summary:
A couple of more misc changes:
- allow starting the coordinator multiple times -- this makes data parallel programming easier
- make the fetcher id a global sequence, before each gpu had same ids for workers
- my flow jobs got stuck when joining the fetcher threads. I think there is actually a memory fencing problem with the is_active boolean. But I am too tired to add proper condition variables there. Instead just add timeout to join(). It is needed anyway since some i/o thread could get blocked.
Differential Revision: D4333381
fbshipit-source-id: 88226c8a9c9a5e05d771360a502a2ba21a6b9d76
Summary:
Xray sampler (originally by ajtulloch) and prigoyal's resnet trainer use variants of the threaded data input where worker threads put stuff into a python queue that is drained by an enqueuer thread that dumps those batches to a Caffe2 queue, that is then drained by the net's DequeueBlobs operator.
There is a lot of boilerplate, which is also quite complicated.
This diff is an attempt to generalize that general stuff under a new module "data_workers" (name could be improved). Basically you pass it a function that is able to return chunks of data (usually data + labels).
I also created a module 'everstore_data_input' which generalizes everstore-origin data input with preprocessing function (image augmentation , for example). See how I refactored sampler.py for the usage.
Next we could create fetcher function for Laser data.
Differential Revision: D4297667
fbshipit-source-id: 8d8a863b177784ae13940730a27dc76cd1dd3dac
