* Fix handling of empty batches in SumReduceDimsOp
As titled
* Deferrable async_scheduling finishRun fix
Proper order of finishing run operations in deferrable_async_scheduling net
* Simplify exception handling in async_scheduling
Simplify exception handling, no need to busy wait, thread that processes the
last task can finish the run
* [C2]worker_coordinator_memorize_worker_ids
As titled. This is related to T28689868, where the number of blobs we want to create is equal to the number of worker ids
* Add unit test for nets with no type set
* Ignore total length argument in sympolic_pad_packed_sequence
1- There was a mistake in the code that total_length was added to the wrong symbolic function (pack_padded_sequence) instead of (pad_packed_sequence)
2- No need to throw an exception if total_length is given since it is only used to enable data_parallel training on multi-gpus and doesn't have anything to do with onnx export, so just ignore it. https://fburl.com/tk4gciqp
* Add support for MKLDNN to async_scheduling
Just add MKLDNN as a possible CPU option to async_scheduling's pool function
* [AuFL][ensemble] support branch output for prediction
This diff supports using predictions from different branches and thus enables model ensembling (not fully independent).
* Fix a bug in add_loss in layer_model_helper
As titled.
* Support lradaption for adam
1.lr adaption operator
2.apply to dense adam
* Perf tweaks for async_scheduling
Restore single pool option + remove unnecessary (no-ops) calls
* add quantization to SparseSimdAdagradOp
add a bunch of quantization signatures to SparseSimdAdagradOp, implementations to come next
* [sr] [codemod] Change all SR callsites to use new API
@allow-large-files
This diff refactors all callsites of SR to use the slightly changed API introduced in the diff below. Really what this means is that you need to include the correct header. Also if you were using `ClientFactory::newFactory` you need to not prefix it with `ClientFactory::`.
```
cd ~/fbsource/fbcode
find ./ -type f -exec sed -i -e 's:#include "servicerouter/client/cpp2/ClientFactory.h":#include "servicerouter/client/cpp2/ServiceRouter.h":' -e 's:#include <servicerouter/client/cpp2/ClientFactory.h>:#include <servicerouter/client/cpp2/ServiceRouter.h>:' -e 's/ClientFactory::newFactory(/newFactory(/g' {} \;
```
Also manually fixed spots that couldn't be done automatically (or broke because they depended on transitive includes).
* Back out "Fix handling of empty batches in SumReduceDimsOp"
Original commit changeset: 282da1730cc2 This commit is blocking the
Github->fbcode sync, which really needs to get merged ASAP. D7881937 which this
diff depends on will be reverted in the sync D7990948 which causes this to
break. The sync diff cannot be patched with this reversion because it must be
landed against base revision 5c8c099 , and D7881937 must not be included in the
sync diff because it is breaking GPU tests that are not available in sandcastle
: https://ci.pytorch.org/jenkins/job/caffe2-builds/job/py2-cuda8.0-cudnn6-ubuntu16.04-test/3638/console
for one example.
* Add the flow to support operator benchmark
1) generate model with the operator 2) upload to everstore 3) generate model spec into json file 4) start running the benchmark
* [tum][gpu] Connect DPM trainer with flow and unit tests
This diff:
- Fix some small bugs for Yiming's recent changes to parallelizer, so it suits real use cases.
- Add correct tags to the TUM code, so we can do data parallel transform
- pass extra info when instantiation.
- add unit test for using DPM in TUM model
After this diff, we can do simple box, multi-gpu fully-sync trainer for TUM in Fblearner workflow, but may still need to do speed benchmarking.
* w/o normalized lradaption for adam dense only
The previous lr adaption includes a normalization step when performing the dot product operation. This is not exactly same as what is proposed in the paper. I add normalization as an option. Without it, the operator performs exactly what the paper proposed. With the option, we add the normalization step
* [fb] Use SharedPromise in DeferrableAsyncSchedulingNet
This code is to simplify DeferrableAsyncSchedulingNet by removing condition
variable + small fixes
* [tum] implement cuda sparseLengthsMean and LengthsMean
as title
* Adding an optional parameter to allow use of protobufs in InferShapesAndTypes function.
Adding an optional parameter to allow use of protobufs in InferShapesAndTypes function.
* Move feature_to_index to FeatureSpec.feature_to_index
move feature_to_index to FeatureSpec.feature_to_index to avoid override other fields
* [Caffe2] Rename bytes_moved to bytes_written
Just a rename in preparation for supporting bytes_read.
* [c2] fix ReduceFrontSumOp for empty case by setting 0
otherwise, it may use the results from last iteration when it's empty batch.
* [Caffe2] [Int8] Improve Intel CPU performance
* [Easy] Improve PrependDim op logging
as titled
* DBFileReader expand db_path using os.path.expanduser(..)
Since there are a lot of possible use cases of `DBFileReader` to read from user home path, like `~/local/sample.db`, I want to save people's trouble of calling `os.path.expanduser(db_path)` themselves.
* [Caffe2] Add bytes_read to cost structure
We're adding analytical read bytes to cost functions. This extends the structure accordingly for all CostInference defined operators.
Additionally, some small bug fixes were performed:
1) Cost functions now extract type information of operands instead of assuming float
* Fix sleef on aarch64 for hhvm
@bypass-lint
Rename flag
* Remove duplicated part in caffe2/ideep/operators/conv_op.cc
should be sync error
* Rename test helper function test_adagrad_sparse_helper to adagrad_sparse_test_helper to avoid confusing pytest
* Change Same as input type deduction to work for ops with multiple outputs
* change InferBlobShapesAndTypes definition to take vector ot pointers instead of unique_ptr. The function doesn't own the objects, so no need to pass smart pointers and that prevents calling the function with existing object, since the caller has to create unique_ptr, i.e. copy an existing object just to create the pointer
* switching order of std::move<unique_ptr> and uniqur_ptr.get
* adding comma
Adding NUMA awareness through numa_node_id in DeviceOption. Blobs of operators
with numa_node_id are allocated on corr. memory banks, using CPU pools with
NUMA affinity set to run operators.
Summary:
Commonly, net observers attach operator observers at construction. This diff separates the logic into a base class to inherit from.
Closes https://github.com/caffe2/caffe2/pull/1806
Reviewed By: salexspb
Differential Revision: D6808623
Pulled By: mdschatz
fbshipit-source-id: 75ef0eea913ef30943541c829c0a976965f42736
Summary: Adding support for DLPack tensors to Python op
Reviewed By: Yangqing
Differential Revision: D6577702
fbshipit-source-id: e14ef213fcdb2930ffe164667971a92aa8db503c
Summary:
Implemented syntactic sugar for the following constructs:
- `x.Gather(y)` can now be written as `x[y]`
- `x.Slice(start, end)` can now be written as `x[start:end]`
For slicing, `start` and/or `end` can be omitted iff `x` is one-dimensional (i.e. a vector). That is, `vector[start:]`, `vector[:end]` and `vector[:]` will work. Doesn't work for higher-dimensional tensors because to emit the start/end indices we need to know the rank of the tensor (since `Slice` requires one entry per dimension of the tensor).
Also added a `getProto()` function so that I could test that the generated code is as expected (i.e. that the syntactic sugar does not affect the structure of the output).
Reviewed By: zdevito
Differential Revision: D6605864
fbshipit-source-id: 786359713a13314c24be2fc07e01486c507404ef
Summary:
Adds the ability for a script function to call another and adds the extern function to register an external Caffe2 Net that can be called by the script.
Closes https://github.com/caffe2/caffe2/pull/1591
Reviewed By: jamesr66a
Differential Revision: D6515877
Pulled By: zdevito
fbshipit-source-id: b893d9e4bacd7389b550ac8a37ad7974b95de749
Summary: Experimental code that allows you to write C2 NetDefs directly using python-like syntax. This includes the ability to write native control-flow (if, while) and have it turn into IfOp and WhileOp
Reviewed By: jamesr66a, dzhulgakov
Differential Revision: D6123298
fbshipit-source-id: 25fc078b5769be61ac7fb3aa9a7c95bd88dccc30
Summary: Today when PythonOp throws an exception, we log the error and fail the op. Later we assert that the op/net/plan succeeds and throw with a generic message. The user must ttail the logs to find the real error. Instead, align with exception handling from other ops - throw directly. This will include full context of the exception in the error message.
Reviewed By: Yangqing, akyrola
Differential Revision: D6359684
fbshipit-source-id: 85133ba6562759607a3971449120647cbacce946
Summary: Pass the list of observers to rnnExecutor_ and attach them to operators
Reviewed By: akyrola
Differential Revision: D6279655
fbshipit-source-id: 086dde1bf6edbfb36082d6b4de33ec41f0bbefab
Summary:
Implementation of polling async net executor.
Notes:
- New net executor async_polling - schedules CPU and GPU ops asynchronously, uses single polling thread
- Events: update to Caffe2 events to support async CPU events, adding new methods:
Query() - non-blocking checking of event states: INITIALIZED -> RECORDED -> SUCCESS/FAILED
ErrorMessage() - when operation runs asynchronously and fails calling this on event will give error message
- Tasks: using existing DAGNet's algorithm to compute CPU and GPU chains, a separate task for each chain
- Polling: using single thread to query state of events - for CPU tasks atomically queries task state, for GPU task - uses cudaEventQuery; using Event
- Scheduling of CPU ops: using global thread pools
- Scheduling of GPU ops: using GPU thread pool per GPU device
Reviewed By: dzhulgakov
Differential Revision: D5985110
fbshipit-source-id: a9de7fcbb71d046a3aa1b573072b89a65dfeee8c
Summary: observer framework can now be used in python + a small writeup of how to use it. this is D6035393 with a fix for ct-scan
Reviewed By: salexspb
Differential Revision: D6066380
fbshipit-source-id: 896c4c580d4387240b81ac2dbbc43db51d4bfeb9
Summary: observer framework can now be used in python + a small writeup of how to use it
Reviewed By: sf-wind
Differential Revision: D6035393
fbshipit-source-id: 4563cf0203095fa979bb2160621cd16dd22ff830
Summary: observer framework can now be used in python + a small writeup of how to use it
Reviewed By: salexspb
Differential Revision: D5905002
fbshipit-source-id: e40ec24a55e08fb73beea9b4f3b68e71fc66ffb1
Summary:
Useful for figuring out with people which version they built with. We can just ask for --caffe2_version gflag or get core.build_options from python.
Also adds CMAKE_INSTALL_RPATH_USE_LINK_PATH - without it wasn't building on my Mac. How should it be tested?
Closes https://github.com/caffe2/caffe2/pull/1271
Reviewed By: bddppq
Differential Revision: D5940750
Pulled By: dzhulgakov
fbshipit-source-id: 45b4c94f67e79346a10a65b34f40fd258295dad1
Summary:
Also add the ability to mark an argument as required.
Added a string constant `OpSchema::Arg_IsTest` for `is_test` arg.
If users define the `is_test` argument with `ArgIsTest(...)`, then it automatically becomes required argument, in the meanwhile user can still use `Arg("is_test", ...)` to define an optional `is_test` argument.
Reviewed By: akyrola
Differential Revision: D5812391
fbshipit-source-id: eaaba50d027813a8012389edc6c459de23c3c728
Summary:
Implemented ApplyTransformIfFaster
Determine if a transform is faster, then return whichever net is better.
Reviewed By: bwasti
Differential Revision: D5534535
fbshipit-source-id: 509943205b0c454bf30fb01343ac4e88d1441c39
Summary: This diff replaces the main of the memonger for dag algorithm _compute_blob_recycling_for_dag with a c++ implementation.
Reviewed By: akyrola
Differential Revision: D5544219
fbshipit-source-id: 9f868880c8d0eb997ad3dd39433f9d0b9216d303
Summary: Allow the use of apply_transform() in the python API
Reviewed By: bwasti
Differential Revision: D5530483
fbshipit-source-id: 61a6d36fe125c89629fdeea040a717c453d84417
Summary:
Nothing gets changed - this would allow us to more easily deal with build
systems. Also now everything that is MKL related lives under mkl/.
Reviewed By: dzhulgakov
Differential Revision: D5505157
fbshipit-source-id: ddb2e6ac290a146a7cb495da23bb0e5b5594bd2a
Summary:
This is needed for us to do more fine grained dispatch based on CPU arch, so
I figured we should just add it. Can help Dima and Misha doing optimization
I think?
Reviewed By: dzhulgakov
Differential Revision: D5477444
fbshipit-source-id: 48aaf8bd799e9755493cd51c793ceec080a8846c
