Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49486
Remove code for Python 3.5 and lower.
There's more that can be removed/modernised, but sticking mainly to redundant version checks here, to keep the diff/PR smaller.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46579
Reviewed By: zou3519
Differential Revision: D24453571
Pulled By: ezyang
fbshipit-source-id: c2cfcf05d6c5f65df64d89c331692c9aec09248e
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:
Just a tiny fix to make debugging easier (output errors to stderr and include in the exception message)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25809
Reviewed By: zrphercule
Differential Revision: D17329957
Pulled By: houseroad
fbshipit-source-id: 0d73dd9f62c735fbc5096e6a7c0e5f58e4cd90ae
Summary:
This PR adds support for torch.rand export in the PyTorch ONNX exporter. There are other generator ops that need to be supported for export and they will added in subsequent PRs. This op is needed with priority for a model on our end.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20559
Differential Revision: D15379653
Pulled By: houseroad
fbshipit-source-id: d590db04a4cbb256c966f4010a9361ab8eb3ade3
Summary:
We updated the description of upsample_op in onnx: https://github.com/onnx/onnx/pull/1467
Therefore, we need to support the new upsample_op in caffe2-onnx backend as well.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13272
Reviewed By: houseroad
Differential Revision: D12833656
Pulled By: zrphercule
fbshipit-source-id: 21af5282abaae12d2d044e4018a2b152aff79917
Summary:
If block is missing control inputs when do caffe2 net execution, this PR add them back and remove the un-SSA semantics
jamesr66a
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12224
Differential Revision: D10135408
Pulled By: wanchaol
fbshipit-source-id: 746c870bde54ed4ca627167361db1b3f36cd235c
Summary:
Requires https://github.com/onnx/onnx/pull/1377
This PR makes it so that slices with dynamic boundary values can be exported from pytorch and run in caffe2 via ONNX.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11255
Differential Revision: D9790216
Pulled By: jamesr66a
fbshipit-source-id: 6adfcddc5788df4d34d7ca98341077140402a3e2
Summary:
Fixes the issue discussed in #10838. `hidden_size` should be the last dimension regardless if we're in ONNX or PyTorch.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11368
Differential Revision: D9734814
Pulled By: soumith
fbshipit-source-id: 7f69947a029964e092c7b88d1d79b188a417bf5f
Summary:
And let Gemm conversion to inspect the input `C` to try converting to FC.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9870
Reviewed By: houseroad
Differential Revision: D9013198
Pulled By: bddppq
fbshipit-source-id: b4c509cfccca238262e1c406b004e66cef256321
Summary:
Fix the problem if caffe2 works with old version of onnx
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9284
Reviewed By: yinghai
Differential Revision: D8773894
Pulled By: houseroad
fbshipit-source-id: 99b5a962099f854edc85a2ea815cb88c82a6e175
* Added support to run ONNX Upsample operator (mode=nearest) in Caffe2
* adding error checks to upsample
* adding error checks to upsample
* adding error checks to upsample
* changing to np.isclose
* Revert onnx submodule update
* still fixing
* Skip some tests to unbreak CI
* Pass the opset_version to run_node
* Remove the stale check_graph call, caffe2_net_to_onnx_model will invoke check_model
* [GanH][Easy]: Add assertion to adaptive weighting layer
0 weight causes numeric instability and exploding ne
* [Easy] Add cast op before computing norm in diagnose options
As LpNorm only takes floats we add a manual casting here.
* Introduce a new caching device allocator
`cudaMalloc` and `cudaFree` calls are slow, and become slower the
more GPUs there are. Essentially, they grab a host-wide (not device-wide) lock
because GPU memory is transparently shared across all GPUs. Normally, this
isn't much of a concern since workloads allocate memory upfront, and reuse it
during later computation.
However, under some computation models (specifically, memory conserving
approaches like checkpoint-and-recompute, see
https://medium.com/@yaroslavvb/fitting-larger-networks-into-memory-583e3c758ff9)
this assumption is no longer true. In these situations, `cudaMalloc` and
`cudaFree` are common and frequent. Furthermore, in data parallel contexts,
these calls happen at nearly the same time from all GPUs worsening lock
contention.
A common solution to this problem is to add a custom allocator. In fact,
nVIDIA provides one out of the box: CUB, which Caffe2 already supports.
Unfortunately, the CUB allocator suffers from very high fragmentation. This is
primarily because it is a "buddy" allocator which neither splits nor merges
free cached blocks. Study
https://github.com/NVlabs/cub/blob/1.8.0/cub/util_allocator.cuh#L357 if you
want to convince yourself.
This diff adapts a caching allocator from the Torch codebase
https://github.com/torch/cutorch/blob/master/lib/THC/THCCachingAllocator.cpp
which does splitting and merging and ends up working really well, at least for
workloads like the checkpoint-and-recompute computation models noted above.
I simplified the implementation a little bit, made it a bit more C++-like. I
also removed a bunch of stream synchronization primitives for this diff. I
plan to add them back in subsequent diffs.
* Report reader progress in fblearner workflows
Integrate with fblearner progress reporting API and add support to report training progress from reader nodes.
If reader is constructed with batch limits, report based on finished batch vs total batch. The finished batch may be more than total batch because we evaludate if we should stop processing everytime we dequeue a split.
If no limit for the reader, report based on finished splits (Hive files) vs total splits. This is fairly accurate.
* [GanH][Diagnose]: fix plotting
1. ganh diagnose needs to set plot options
2. modifier's blob name is used for metric field can need to be fixed before
generating net
* Automatic update of fbcode/onnx to 985af3f5a0f7e7d29bc0ee6b13047e7ead9c90c8
* Make CompositeReader stops as soon as one reader finishes
Previously, CompositeReader calls all readers before stopping. It results in flaky test since the last batch may be read by different threads; resulting in dropped data.
* [dper] make sure loss is not nan
as desc.
* [rosetta2] [mobile-vision] Option to export NHWC order for RoIWarp/RoIAlign
Thanks for finding this @stzpz and @wangyanghan. Looks like NHWC is more
optimized. For OCR though it doesn't yet help since NHWC uses more mem b/w but
will soon become important.
* Intra-op parallel FC operator
Intra-op parallel FC operator
* [C2 Proto] extra info in device option
passing extra information in device option
design doc: https://fb.quip.com/yAiuAXkRXZGx
* Unregister MKL fallbacks for NCHW conversions
* Tracing for more executors
Modified Tracer to work with other executors and add more tracing
* Remove ShiftActivationDevices()
* Check for blob entry iff it is present
When processing the placeholders ops, ignore if the blob is not present in the blob_to_device.
* Internalize use of eigen tensor
Move use of eigen tensor out of the header file so we don't get template partial specialization errors when building other libraries.
* feature importance for transformed features.
* - Fix unused parameter warnings
The changes in this diff comments out unused parameters.
This will allow us to enable -Wunused-parameter as error.
#accept2ship
* add opencv dependencies to caffe2
The video input op requires additional opencv packages. This is to add them to
cmake so that it can build
* Add clip_by_value option in gradient clipping
Add clip_by_value option in gradient clipping
when the value is bigger than max or smaller than min, do the clip
* std::round compat
