Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64244
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64040
In operator cost inference functions, in many places we are using sizeof(x.data_type()). Since data_type() returns a 32 bit integer from [this enum](https://www.internalfb.com/code/fbsource/[15e7ffe4073cf08c61077c7c24a4839504b964a2]/fbcode/caffe2/caffe2/proto/caffe2.proto?lines=20), we are basically always getting 4 for sizeof(x.data_type()) no matter what actual data type x has. Big thanks to Jack Langman for specifically pointing to this bug.
We would instead use the size in bytes based on actual data type.
Test Plan:
Added unit tests BatchMatMulMemCostTest:
buck test //caffe2/caffe2/fb/fbgemm:batch_matmul_op_test -- BatchMatMulMemCostTest
Extended existing unit test test_columnwise_concat for different data types:
buck test //caffe2/caffe2/python/operator_test:concat_op_cost_test -- test_columnwise_concat
Reviewed By: CrazySherman
Differential Revision: D30656698
fbshipit-source-id: d42c0c9a0c5b0ddc5dba39e4994f1f85a5e618bf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64040
In operator cost inference functions, in many places we are using sizeof(x.data_type()). Since data_type() returns a 32 bit integer from [this enum](https://www.internalfb.com/code/fbsource/[15e7ffe4073cf08c61077c7c24a4839504b964a2]/fbcode/caffe2/caffe2/proto/caffe2.proto?lines=20), we are basically always getting 4 for sizeof(x.data_type()) no matter what actual data type x has. Big thanks to Jack Langman for specifically pointing to this bug.
We would instead use the size in bytes based on actual data type.
Test Plan:
Added unit tests BatchMatMulMemCostTest:
buck test //caffe2/caffe2/fb/fbgemm:batch_matmul_op_test -- BatchMatMulMemCostTest
Extended existing unit test test_columnwise_concat for different data types:
buck test //caffe2/caffe2/python/operator_test:concat_op_cost_test -- test_columnwise_concat
Differential Revision: D30561459
fbshipit-source-id: 976fa5167097a35af548498480001aafd7851d93
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44840
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44762
Move CostInferenceForFCGradient to fc_inference.cc/h to be used in multiple .cc files.
Test Plan: CI
Reviewed By: qizzzh
Differential Revision: D23714877
fbshipit-source-id: d27f33e270a93b0e053f2af592dc4a24e35526cd
* 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
