Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10929
Workspace classes methods were missing on the Python side.
Being able to write the New Checkpoint Framework with more control of the workspace and cleaner implementation.
Added
- ws.feed_blob(name, arr)
- ws.remove_blob(name)
Reviewed By: mraway
Differential Revision: D9486867
fbshipit-source-id: ea02d2e3a39d716a5a3da0482f57d4ac4c893763
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9643
Current map interface assumes float data type, which is not always correct.
Reviewed By: kennyhorror
Differential Revision: D8455784
fbshipit-source-id: b94a31267760f7f97c15aa4b03008affc347fd10
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9637
Adding a method to run plan in background. The intended use is to run BlueWhale's data reading & preprocessing net in background while the GPU is training.
Reviewed By: MisterTea
Differential Revision: D8906439
fbshipit-source-id: b1c73ca7327e2d87a8f873924e05ab3d161a3f1e
Summary:
The goal of this PR is to add an infrastructure; to convert(hipify) CUDA ops into [HIP](https://github.com/ROCm-Developer-Tools/HIP) ops , at **compile** time.
Note that HIP ops, which are portable c++ code, can run on AMD and NVIDIA platform.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9322
Differential Revision: D8884707
Pulled By: bddppq
fbshipit-source-id: dabc6319546002c308c10528238e6684f7aef0f8
* 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
Providing Python API to fetch Int8 tensors.
data, scale. zero_point = workspace.FetchInt8Blob(blob_name)
now returns a tuple if the blob contains a Int8TensorCPU
'data' = int8 data array
'scale' = fake quantization scale
'zero_point' = fake quantization offset
Although FetchBlob shares back-end implmentation with FetchInt8Blob, we raise
error to prevent unexpected behavior of the same method
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:
Reason for this change:
(1) Setting/Getting default gpu id doesn't seem to be used at all.
(2) It actually is confusing compared to the CUDA_VISIBLE_DEVICES options etc.
(3) When setting cuda_gpu_id=-1 in the CUDAContext arg, it used to use the
default gpu id but probably we should use the current gpu - so that the caller
will be able to control the device placement.
One use case is for TensorRT - if we have a custom callback layer, then it would
be easier for TRT or whatever caller to set the running device.
Reviewed By: dzhulgakov
Differential Revision: D6740357
fbshipit-source-id: 2ea710e434b10220d5a198e31c93847304636863
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:
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: Allow the use of apply_transform() in the python API
Reviewed By: bwasti
Differential Revision: D5530483
fbshipit-source-id: 61a6d36fe125c89629fdeea040a717c453d84417
Summary:
Last time I used uuid filled into OperatorDef. And operator_tracebacks was populated using traceback.extract_stack. There were several issues with this approach:
1. A random field in OperatorDef breaks workflows relying on memoization, i.e. when computation is skipped based on already computed result before.
2. Adding one more field revealed RNNs being non forward compatible wrt to new fields in there. prototxt format seems to not allow forward compatibility (thanks jamesr66a for the investigation!). For RNNs we need to swtich them to a more resilient approach. azzolini's proposed change to OperatorDef / NetDef would allow that by just nesting NetDef dirrectly inside OperatorDef without need for extra serialization.
3. traceback.extract_stack is very slow when executable is on a remote filesystem. It does one or more os.stat for each frame on the stack. For some cases it ended up being up to 15 extra minutes on model construction.
In this diff I use a different approach which should fix all those problems above.
1.2. are solved by not adding a new field at all. Instead I report operator idx wrt to a net it runs in. Thanks akyrola and dzhulgakov for the idea. Downside here is that operator list manipulation breaks the logic and separately created ops are not covered at all.
3. I solved this by operating on raw frames without using traceback and inspect modules which end up doing a lot of file system calls. See function extract_stacktace in core.py with additional comments.
Reviewed By: dzhulgakov
Differential Revision: D5286285
fbshipit-source-id: 626dd0f5f6b8b1d86bd6bf519078b122f43ddcaa
Summary:
a few issues:
1. Randomization hurts memoization
1. Even if we make it non random, then we can get key colisions when loading it back.
2. RNNs use prototxt for step net and apparently its not forward compatible like normal protobuf is
I am thinking of a better less invasive solution now.
Reviewed By: jamesr66a
Differential Revision: D5272118
fbshipit-source-id: ab577fad04fbfc632e1fceffa923377a0d3da1be
Summary: This is going to show a python Caffe2 user where a failed operator was created. Motivation for having this information not right in protobuf is to avoid having it too verboose and keep ability to read protobufs of a net after a simple print() call.
Reviewed By: jamesr66a
Differential Revision: D5226047
fbshipit-source-id: 7edfe850e05a2ec209577142aa3368664a57a108
Summary:
fixing missing future package issue.
Recently we found some of our users does not have future module support. So we might need a try/catch wrapper around all past import
Reviewed By: Yangqing
Differential Revision: D5183547
fbshipit-source-id: 262fdf2940ee1be4454bf0b0abb9e6a0f1a0ee82
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: Add a RandomFailureOp and handling to elastic data parallel model of the status code
Reviewed By: andrewwdye
Differential Revision: D5065936
fbshipit-source-id: 24224f9ea414ee535c9e90cc28add5189354b0ef
Summary:
This is from discussion with dzhulgakov : as a step towards revisiting the
core.Net autonaming, we will first guard against accidental overwrites of
existing networks in the workspace.
ajtulloch since we are doing Predictors in mobile, this should be safe right?
azzolini - I assume this would be safe, but would love to get your approval.
akyrola - would this hurt xray?
Reviewed By: dzhulgakov
Differential Revision: D4897725
fbshipit-source-id: aa41271927ad6671f07a53b9505283623f8c49e5
Summary:
Add cudnn v6 support, including testing support for dilated convolution.
Add a check to ensure that the versions of cuDNN used to compile Caffe2 and run it are compatible
Closes https://github.com/caffe2/caffe2/pull/85
Reviewed By: bwasti
Differential Revision: D4387690
Pulled By: Yangqing
fbshipit-source-id: 312960134398dd4afe6ee0c01cdc160046c904e8
Summary: Removed Model API because no one {seems to,should} be using it
Reviewed By: Yangqing
Differential Revision: D4575126
fbshipit-source-id: 174d39e9aa46750f1fae8295f7e1e5452559af33
Summary:
.In Tutorial, I found it not correct when calling Model(). After that changing, It works.
Closes https://github.com/caffe2/caffe2/pull/148
Reviewed By: bwasti
Differential Revision: D4556894
Pulled By: Yangqing
fbshipit-source-id: 949a8d0496861f19869436908ffe1ef1a0f853b1
Summary:
Shape inference allows Caffe2 to compute shapes of blobs without running a model. Update InferShapesAndTypes() to accept an optional blob:dimensions map so that external input blobs do not need to be part of the workspace.
InferShapesAndTypes() in workspace.py conditionally calls the ...from_workspace or ...from_map bindings. Note I favored a small amount of code duplication here for the sake of readability. InferShapesAndTypes() in operator.cc has been refactored into mirrored entry points, invoking a common helper.
Other minor changes to address linter warnings.
Reviewed By: dzhulgakov
Differential Revision: D4524873
fbshipit-source-id: 56f863b759c016d7f23523f06fda3aa5bba22357
Summary:
Running RunNet() in python in a loop can be a performance issue if the python code is doing a lot of other processing, such as data input, because python's Global Interpreter lock (GIL) will prevent the RunNet() to be called. This can easily be fixed by making RunNet() run multiple iterations inside the C++ land. (Another way to accomplish the same thing is to use Caffe2's "execution plans", but that requires more setup).
+ fixed timing reporting in my OC workflow
+ improved one error log in data_workers.py
Sorry for piggypagging those small changes, but landing diffs currently is slow...
Reviewed By: rpenggithub
Differential Revision: D4523575
fbshipit-source-id: 039a647576efad5dd9afda74df478ac22b43c103
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: As part of PR from GitHub, "logging.basicConfig()" was added to workplace, causing havoc with existing logger configurations. It should not be here. Thanks rbgirshick for reporting.
Reviewed By: kdub0
Differential Revision: D4346077
fbshipit-source-id: 084ddcbfe6354bdaf5c97a42086c0bd36ec4629c
Summary:
The exception in FeedBlob causes many tests to fail.
Instead of exception, we log a warning message and move on.
Feeding a float64 blob should not cause any issue.
Closes https://github.com/caffe2/caffe2/pull/57
Reviewed By: bwasti
Differential Revision: D4343135
Pulled By: Yangqing
fbshipit-source-id: cd1144b94c9883fcbd8bdcd78f9f93a67debc0a6
Summary:
When refactoring data parallel model, the division of LR by number of devices was dropped, and thus we ended up effectively multiplying gradients by the number of devices. Thus, we need to scale the LR by 1/numgpus.
Created a test to confirm that data_parallel_model produces exactly same results on different number of gpus, given the total batch size.
Reviewed By: prigoyal
Differential Revision: D4248907
fbshipit-source-id: af21ede113e6ac25f12c556de298cb18974548be
Summary:
Previously DPER was quite broken - we couldn't change loaders on the fly because serialized model had blob names hard-coded, e.g. "nn_loader/dense". In fact, the tests worked only by accident as both trainer and evaluator used the same loader type.
This diff does the following:
1) when writing out model, remap input blobs to be 'inputs/<field_name>'
2) when loading eval model, remap them back to the current loader
This diff uses Net.input_schema() for convenience, in particular the schema format is implicitly serialized in input blobs names. From our discussion with Andrey this type of hardcoding is actually acceptible since the schema of HiveReader on python side is inferred via the same string-parsing procedure
It also modifies model saving a bit so that we don't pollute global namespace with shape_provider net.
Overall code in mlp.py is pretty terrible. But I'd leave refactoring to xianjiec as a part of Layers migration.
Reviewed By: xianjiec
Differential Revision: D4218902
fbshipit-source-id: 6cd19f0343ec1be6ddaa3581512e61879957749e
Summary:
Recurrent developer-issue is that they pass numpy arrays with FeedBlob but forget that python float is actually double. Cuda ops in caffe2 don't allow doubles.
Thus, I think we should reject incorrect types already at the FeedBlob() when device option is CUDA.
Added test.
Is this too strong?
Reviewed By: ajtulloch
Differential Revision: D4208153
fbshipit-source-id: 364b057a2a37b5d4b95de4e59faebdab724bb0ed
