Summary:
`brew_test.py` is just plain broken. `core_test.py` doesn't work with pytest. `apmeter_test.py` and `top_k_test.py` don't work for CUDA builds.
Closes https://github.com/caffe2/caffe2/pull/765
Differential Revision: D5211817
Pulled By: Yangqing
fbshipit-source-id: 78ec5af35a3fa870978e4c9590210ade9e3bc5ac
Summary:
This diff plan to attack the problem where we want to just annotate device option for operators and leave Caffe2 to help us inject cross device copy functions. This feature would be useful for mixed device training and multi device training with several nets, where previously we do the heavy lifting of adding copy functions ourselves.
Ideally, this feature will happen like this:
//construct your nets first
core.InjectDeviceCopyAmongNets([train_init, train_net, ...])
My ideas are written in comments. I will update them here as well later.
Reviewed By: dzhulgakov
Differential Revision: D5134103
fbshipit-source-id: 173f7da9d1773d1c50ccdc27f1b5cd3067b04af5
Summary: Infer input and output device from OperatorDef through OperatorSchema. This is inspired by shape inference. With this feature, we can easily analysis device information for all blobs in the net in a generic way. It is really helpful for auto cross device execution.
Reviewed By: akyrola, dzhulgakov
Differential Revision: D5161065
fbshipit-source-id: ee656123112171a4ca00f2fb3f6940f32ddf3135
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 one step towards enabling python 3 build by making it be more diligent in its handling of strings.
Reviewed By: salexspb
Differential Revision: D4893083
fbshipit-source-id: 28b8adf3280e8d1f0a7dc9b0fee5ad53f2fada57
Summary: External inputs must be computed before updating the _ops_output structure, otherwise if the net to be appended outputs the external input, it is not added correctly
Differential Revision: D5013496
fbshipit-source-id: 6a83d0a6f1c63ef8ae7bec4d862c0ac2a690d47b
Summary:
When appending net A to net B, an external input of net A should not be added as
an external input of net B if net B is outputting that blob.
Reviewed By: dzhulgakov
Differential Revision: D4975921
fbshipit-source-id: a5c0ada7b96d851e57d345244d322dd93c7be8e4
Summary:
Remove the use of `NextName` in layer model helper, so that the same function return `model_helper` that should construct identical `Net`, when under the same NameScope.
The `NextScopedBlob` should only take effect when there is real name conflicting, otherwise it returns ScopedBlobReference.
This is critical for parameter blobs. In long run, we need to be able to specify parameter blobs more explicitly. (kennyhorror is working on this). This solution works in short term for e.g., two tower sparse nn models.
Reviewed By: kennyhorror
Differential Revision: D4555423
fbshipit-source-id: 2c4b99a61392e5d51aa878f7346466a8f14be187
Summary:
This is a bit large diff, sorry about it. It includes basic shape and type inference functionality, based on YQ's Schema scaffolding. I added some helper functions to make it easier to write simple translations.
Bigger refactoring was needed for ConvPoolBase so that we could use the shape inference already there in the schema.
I annotated enough operators to be able to infer forward-pass of shapes for basic convnet, and added test for that. I intend to bootcamp some annotations and annotate enough to handle Resnets fully. Need to think about gradients, if they could be annotated in an easier way.
Only shapes are now exposed to Python, types will follow later. Also the inference is not called yet anywhere but unit test.
Also I am not sure if everything is in the best location in the code, but shouldn't be hard to move stuff around.
Reviewed By: dzhulgakov
Differential Revision: D4436818
fbshipit-source-id: eebee5937ccc9ac09c245465302388a1fae6933c
Summary:
I have noticed that constructing the Xray model takes quite a while. To measure this, I wrote a benchmark script that creates a resnet-50 model on 8 gpus. This takes about 95 secs -- which is kind of annoying when you want to quickly debug stuff.
Profiling (using Python's cProfile), I was able to see that the most of the time is used in net.BlobIsDefined(), which does a linear search over external inputs and operator outputs. Thus it gets slower and slower with large nets. This can be fully optimized by keeping a separate lookup table of operator inputs and outputs (and external inputs and outputs). It is a bit annoying to keep this separate data structure, but I setup the unit tests to ensure things are doing correctly over Clones.
After the optimization, the net construction drops from 95 secs to 8.2 secs!
Reviewed By: azzolini
Differential Revision: D4288307
fbshipit-source-id: 0bb82c8bde9d86a2702b298f4aa706cba509346e
Summary:
I got a weird error about NoneType not being iterable which made me think
it was some error in the C2 core, whereas it was an error in my code.
Reviewed By: Yangqing
Differential Revision: D4192799
fbshipit-source-id: 0122f13e205c1c6a0766545f0ad6296228d3a3d9
