Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71007
A string copy at Line 417 is currently consuming 125,749,287,000 cycles/day. I suspect the issue is with a copy-on-return, but we can experiment with introducing a reference in the middle to see if that produces a good savings without changing the interface.
Reference
```
["Inline caffe2::ArgumentHelper::GetSingleArgument @ caffe2/caffe2/utils/proto_utils.cc:417"]
```
Test Plan: Sandcastle
Reviewed By: xw285cornell
Differential Revision: D33478883
fbshipit-source-id: e863e359c0c718fcd0d52fd4b3c7858067de0670
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64285
With C++14 heterogeneous ordered container lookup, it is no longer necessary to create a `std::string` in order to look up elements of a `CaffeMap` keyed by std::string. Accordingly, this diff reworks the argument-getting operator functions to avoid that in favor of `c10::string_view`.
ghstack-source-id: 137139818
ghstack-source-id: 137139818
Test Plan: buildsizebot iOS apps -- code size win. less strings is probably marginally good for perf but this only happens at setup time anyway.
Reviewed By: dzhulgakov
Differential Revision: D26826676
fbshipit-source-id: ee653b14dc2c528bae8c90f0fc6a7a419cbca1d6
Summary:
Add a rule to wrap proto_utils.h and depend on that, rather than
relying on a glob which violates package boundaries.
Reviewed By: igorsugak
Differential Revision: D29273453
fbshipit-source-id: 08f198a03d06ee2fdf61f5dbe1d0087db22aec8b
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
Summary:
This guards some deprecated usages of the Protobuf API behind an `#ifdef` (this is how onnx does it as well)
](https://our.intern.facebook.com/intern/diff/27803121/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56186
Pulled By: driazati
Reviewed By: bertmaher, dzhulgakov
Differential Revision: D27803121
fbshipit-source-id: 2d3a348ec1ab9879a0d8f2dff17c5444fd4baf2c
Summary:
ATT, so that the shape inference works for a model with only distributed parts.
Previously, we rely on a full_predictor net to do shape inference. For very large models, the full_predictor net won't be generated, so we have to do shape inference based on distributed parts. Surprisingly, the PredictorCall op does tensor name mapping so it has to have shape inference func supported.
Test Plan: Added unittests.
Reviewed By: khabinov
Differential Revision: D27250956
fbshipit-source-id: 3ebd36ba1eb020bb5d00358cffb8f038a6a996e8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47023
DeviceType pretty clearly only needs 1 byte. DeviceIndex only needs 1 byte given that machines don't have anywhere near 255 GPUs in them as far as I know.
ghstack-source-id: 116901430
Test Plan: Existing tests, added assertion to catch if my assumption about DeviceIndex is incorrect
Reviewed By: dzhulgakov
Differential Revision: D24605460
fbshipit-source-id: 7c9a89027fcf8eebd623b7cdbf6302162c981cd2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45979
For some reason, sometime we cannot write out the debug files. This shouldn't block the whole service. Hence, we opt in to error out instead of throw error.
Test Plan: Run net_runner test at `/` and observe error being printed out but the test passes.
Reviewed By: ipiszy
Differential Revision: D24165081
fbshipit-source-id: a4e1d0479d54d741e615e3a00b3003f512394fd4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41606
The previous diff (D22220798 (59294fbbb9) and D22220797) was recently reverted (D22492356 (28291d3cf8), D22492355) because of a bug associated with the op AsyncIf. The AsyncIf op has net_defs as args and the SSA rewriting didn't take that into account. It has a special path for the op If, but not for AsyncIf. Several changes I made to fix the bug:
1) Add op AsyncIf to the special path for If op in SSA rewriting
2) clear inputs/outputs of the netdefs that are args in If/AsyncIf ops because they're no longer valid
3) revert renamed inputs/outputs in the arg netdefs that are in the external_outputs in the parent netdef
2) and 3) are existing bugs in the `SsaRewrite` function that were just never exposed before.
The algorithm for `RemoveOpsByType` is the same as in my previous diff D22220798 (59294fbbb9). The only new changes in this diff are in `onnx::SsaRewrite` and a few newly added unit tests.
(Note: this ignores all push blocking failures!)
Reviewed By: yinghai
Differential Revision: D22588652
fbshipit-source-id: ebb68ecd1662ea2bae14d4be8f61a75cd8b7e3e6
Summary: ATenOp should go away, but before it does it's important to understand what's going inside of it. We already log `arguments`, but it's rather hard to parse in scuba as its a list, not a dictionary. Let's extract operator name explicitly so that grouping works well
Test Plan: unittest
Reviewed By: ngimel
Differential Revision: D21057966
fbshipit-source-id: 86be7cca39055620477a28bd5d8ab29e8edd2ff9
Summary:
Does the same things as D19658565 but for Caffe2 models.
From investigation https://fb.quip.com/PbgsAEmoJVuf the model id that predictor uses and the model id saved inside the model don't match. Common reason is recurring fluent2 jobs but there are others.
Since model_id from predictor is what the rest of datasets use, it's way more useful imho. I've considered adding both ids, but it'd require additional piping and I don't think it's that useful.
Test Plan: unittests added
Reviewed By: houseroad
Differential Revision: D20630599
fbshipit-source-id: 3e6d0cb0b6f8c8b6ae5935138f55ae7a2ff60653
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34321
Mostly cosmetic as we can infer the shape anyway. It can remove a lot of the noise in the log though.
Note that weight sharing doesn't work yet. I'll add another diff to address this.
Reviewed By: houseroad
Differential Revision: D20290841
fbshipit-source-id: fe6f9b60d05dbe150af15b5d9d7a69fd902e12cc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18194
Add a util method to cleanup external inputs and outputs from a NetDef
The following conditions will be met after the modification
- No duplicate external inputs
- No duplicate external outputs
- Going through list of ops in order, all op inputs must be outputs
from other ops, or registered as external inputs.
- All external outputs must be outputs of some operators.
Reviewed By: ZolotukhinM
Differential Revision: D14528589
fbshipit-source-id: c8d82fda1946aa3696abcbec869a4a8bb22f09b6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15316
This starts cleaning up the files in c10 according to the module structure we decided on.
Move to c10/util:
- Half.h, Half-inl.h, Half.cpp, bitcasts.h
Move to c10/core:
- Device.h, Device.cpp
- DeviceType.h, DeviceType.cpp
i-am-not-moving-c2-to-c10
Reviewed By: dzhulgakov
Differential Revision: D13498493
fbshipit-source-id: dfcf1c490474a12ab950c72ca686b8ad86428f63
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14918
When ProtoBuf-Lite is in use, ProtoDebugString just calls SerializeAsString.
This produces binary output, which is not a very suitable "debug" string.
Specifically, we've observed it causing problems when calling code tries to
add the debug string to a Java exception message (which requires valid UTF-8).
Now, we replace all non-ASCII bytes with "?".
This is not a very fast implementation, but generating debug strings shouldn't
be a performance-sensitive operation in any application.
Reviewed By: dzhulgakov
Differential Revision: D13385540
fbshipit-source-id: 8868172baf20efaf53fecf7d666a6980f59b64f5
Summary:
We lost HIP support in last refactoring 620ece2668
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13400
Differential Revision: D12868211
Pulled By: bddppq
fbshipit-source-id: 72dbfda105b826bee28ddf480e88fca7d63f93d8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13114
Using one thread pool creator for all device types
Reviewed By: manojkris, wesolwsk
Differential Revision: D10851533
fbshipit-source-id: 32ca51d7932ba7faa8137df26315f52ecb4c6157
Summary:
There are still a few work to be done:
- Move logging and unify AT_WARN with LOG(ERROR).
- A few header files are still being plumbed through, need cleaning.
- caffe2::EnforceNotMet aliasing is not done yet.
- need to unify the macros. See c10/util/Exception.h
This is mainly a codemod and not causing functional changes. If you find your job failing and trace back to this diff, usually it can be fixed by the following approaches:
(1) add //caffe2/c10:c10 to your dependency (or transitive dependency).
(2) change objects such as at::Error, at::Optional to the c10 namespace.
(3) change functions to the c10 namespace. Especially, caffe2::MakeString is not overridden by the unified c10::str function. Nothing else changes.
Please kindly consider not reverting this diff - it involves multiple rounds of rebasing and the fix is usually simple. Contact jiayq@ or AI Platform Dev for details.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12354
Reviewed By: orionr
Differential Revision: D10238910
Pulled By: Yangqing
fbshipit-source-id: 7794d5bf2797ab0ca6ebaccaa2f7ebbd50ff8f32
Summary:
Original commit changeset: f5614a5d2607
D9986213 is causing Multifeed Aggregator a [huge performance different](https://our.intern.facebook.com/intern/ads/analyze_canary/412951953278781781/) and is blocking aggregator push since last Friday night: https://fburl.com/feedtools/b6izvwjz
We need to land this revert ASAP to unblock aggregator push.
Reviewed By: orionr
Differential Revision: D10123245
fbshipit-source-id: d83da8e00a1250f5d09811a0a587c127e377aab2
Summary:
TSIA. Right now we should basically use C10_EXPORT and C10_IMPORT for explicitly marking dllexport and dllimport, as a continued effort of the C10 unification.
This is a codemod by mechanically doing the following change:
CAFFE2_{EXPORT,IMPORT} -> C10_{EXPORT,IMPORT}
AT_CORE_{EXPORT,IMPORT} -> C10_{EXPORT,IMPORT}
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12019
Reviewed By: ezyang, teng-li
Differential Revision: D10016276
Pulled By: Yangqing
fbshipit-source-id: a420d62c43d1110105fc88f9e9076e28a3203164
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11254
Previously we use DeviceType in caffe2.proto directly, but it's an `enum` and have implicit conversion to int, which does not have type safety, e.g. we have to explicitly check for a device type is valid in event.h:
```
template <int d>
struct EventCreateFunctionRegisterer {
explicit EventCreateFunctionRegisterer(EventCreateFunction f) {
static_assert(d < MaxDeviceTypes, "");
Event::event_creator_[d] = f;
}
};
```
at::DeviceType is an `enum class`, and it does not have implicit conversion to int, and provides better type safety guarantees. In this diff we have done the following refactor(taking CPU as an example):
1. caffe2::DeviceType → caffe2::DeviceTypeProto
2. caffe2::CPU → caffe2::PROTO_CPU
3. caffe2::DeviceType = at::DeviceType
4. caffe2::CPU = at::DeviceType::CPU
codemod -d caffe2/caffe2 --extensions h,cc,cpp 'device_type\(\), ' 'device_type(), PROTO_'
+ some manual changes
In short, after this diff, in c++, caffe2::CPU refers to the at::DeviceType::CPU and the old proto caffe2::CPU will be caffe2::PROTO_CPU.
In python side, we have a temporary workaround that alias `caffe2_pb2.CPU = caffe2_pb2.PROOT_CPU` to make the change easier to review and this will be removed later.
Reviewed By: ezyang
Differential Revision: D9545704
fbshipit-source-id: 461a28a4ca74e616d3ee183a607078a717fd38a7
Summary:
This completely removes BUILD_CAFFE2 from CMake. There is still a little bit of "full build" stuff in setup.py that enables USE_CUDNN and BUILD_PYTHON, but otherwise everything should be enabled for PyTorch as well as Caffe2. This gets us a lot closer to full unification.
cc mingzhe09088, pjh5, ezyang, smessmer, Yangqing
Pull Request resolved: https://github.com/pytorch/pytorch/pull/8338
Reviewed By: mingzhe09088
Differential Revision: D9600513
Pulled By: orionr
fbshipit-source-id: 9f6ca49df35b920d3439dcec56e7b26ad4768b7d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10175
Previously, we had at::Device::Type and caffe2::DeviceType (from protobuf),
intended to help us distinguish between CPU, CUDA, etc. devices.
This replaces at::Device::Type entirely with at::DeviceType, which in turn
is a direct, 'enum class' version of the protobuf generated caffe2::DeviceType
'enum'. We can't eliminate the 'enum' because this would a pretty drastic
API change (enum is interconvertible with integers, enum class is not) but
we can make the two line up exactly and share code for, e.g., printing.
Reviewed By: Yangqing
Differential Revision: D9137156
fbshipit-source-id: 566385cd6efb1ed722b25e6f7849a910b50342ab
Summary:
Will bump up to opset 8 in another PR to match the current opset version.
Already tested through generating the models in current model zoo.
Closes https://github.com/pytorch/pytorch/pull/8854
Reviewed By: ezyang
Differential Revision: D8666437
Pulled By: houseroad
fbshipit-source-id: feffdf704dd3136aa59c0f1ff1830c14d1bd20aa
Summary:
Closes https://github.com/pytorch/pytorch/pull/8927
Closes https://github.com/pytorch/pytorch/pull/8855
- Add parameter `enable_tracing` to the Arg field of NetDef. `net_async_tracing` will only enable Tracer for Net instances that have this field set (unless the command line argument also include the net name).
- Append a unique id to the json profiling result file because there could be multiple instances of the same net running.
- Dump json profling file regularly instead of just when the Tracer object is destroyed
Reviewed By: ilia-cher
Differential Revision: D8372378
fbshipit-source-id: 8adc9d59f48b67456beed2e3a88235c298fdfd01
* Expose proto utils and ONNX from PyTorch libcaffe2.so
* Try to use protobuf from _C.so
* Fix ONNX proto header include
* Adjust order of imports for ONNX until nanopb goes away
* Set and use ONNX_NAMESPACE for PyTorch builds
* Show protobuf summary for all builds
* Add ONNX_NAMESPACE for cpp_build
* Statically link libprotobuf.a into libtorch.so
* Set ONNX_NAMESPACE on Windows build
* Move core/dispatch up as well
* Add /MD flag for Windows build of _C
* Potential Windows fix for ONNX and protobuf
* Add direct linkage from _C to ONNX on Windows
* Only include protobuf wrapper for PyTorch
* Pass extra_compile_args to _nvrtc ext build
* Remove installation of .a files
* Add hip support for caffe2 core
* Add MIOPEN header/wrapper to caffe2 core
* Add HIP device into caffe2 PB
* top level makefile change for rocm/hip
* makefile scaffolding for AMD/RocM/HIP
* Makefile scafodding for AMD/RocM/HIP; add makefile/utility for HIP files
* caffe2 PB update for AMD/ROCM HIP device
* Add AMD/RocM/Thrust dependency
* HIP threadpool update
* Fix makefile macro
* makefile fix: duplicate test/binary name
* makefile clean-up
* makefile clean-up
* add HIP operator registry
* add utilities for hip device
* Add USE_HIP to config summary
* makefile fix for BUILD_TEST
* merge latest
* Fix indentation
* code clean-up
* Guard builds without HIP and use the same cmake script as PyTorch to find HIP
* Setup rocm environment variables in build.sh (ideally should be done in the docker images)
* setup locale
* set HIP_PLATFORM
* Revert "set HIP_PLATFORM"
This reverts commit 8ec58db2b390c9259220c49fa34cd403568300ad.
* continue the build script environment variables mess
* HCC_AMDGPU_TARGET
* Cleanup the mess, has been fixed in the lastest docker images
* Assign protobuf field hip_gpu_id a new field number for backward compatibility
* change name to avoid conflict
* Fix duplicated thread pool flag
* Refactor cmake files to not add hip includes and libs globally
* Fix the wrong usage of environment variables detection in cmake
* Add MIOPEN CNN operators
* Revert "Add MIOPEN CNN operators"
This reverts commit 6e89ad4385b5b8967a7854c4adda52c012cee42a.
* Resolve merge conflicts
* .
* Update GetAsyncNetHIPThreadPool
* Enable BUILD_CAFFE2 in pytorch build
* Unifiy USE_HIP and USE_ROCM
* always check USE_ROCM
* .
* remove unrelated change
* move all core hip files to separate subdirectory
* .
* .
* recurse glob core directory
* .
* correct include
* .
* Add hip support for caffe2 core
* Add MIOPEN header/wrapper to caffe2 core
* Add HIP device into caffe2 PB
* top level makefile change for rocm/hip
* makefile scaffolding for AMD/RocM/HIP
* Makefile scafodding for AMD/RocM/HIP; add makefile/utility for HIP files
* caffe2 PB update for AMD/ROCM HIP device
* Add AMD/RocM/Thrust dependency
* HIP threadpool update
* Fix makefile macro
* makefile fix: duplicate test/binary name
* makefile clean-up
* makefile clean-up
* add HIP operator registry
* add utilities for hip device
* Add USE_HIP to config summary
* makefile fix for BUILD_TEST
* merge latest
* Fix indentation
* code clean-up
* Guard builds without HIP and use the same cmake script as PyTorch to find HIP
* Setup rocm environment variables in build.sh (ideally should be done in the docker images)
* setup locale
* set HIP_PLATFORM
* Revert "set HIP_PLATFORM"
This reverts commit 8ec58db2b390c9259220c49fa34cd403568300ad.
* continue the build script environment variables mess
* HCC_AMDGPU_TARGET
* Cleanup the mess, has been fixed in the lastest docker images
* Assign protobuf field hip_gpu_id a new field number for backward compatibility
* change name to avoid conflict
* Fix duplicated thread pool flag
* Refactor cmake files to not add hip includes and libs globally
* Fix the wrong usage of environment variables detection in cmake
* Add MIOPEN CNN operators
* Revert "Add MIOPEN CNN operators"
This reverts commit 6e89ad4385b5b8967a7854c4adda52c012cee42a.
* fix unit test for sqrt op
From the error logging:
[idx, grad, grad_estimate] are:
[[ 146. 0.5 0.45776367]
[ 147. 0.5 0.45776367]
The gradient == 0.5 is correct, which means the SqrtOp and its gradient is doing right job. (Because y = sqrt(x), loss = y^2/2 = x/2, and then d(loss)/dx = 1/2 = 0.5; )
The test failed because of numerical problem of grad_estimate (in unit test). It can be because the step_size is small, and float precision is not high (when there are multiple elements in the tensor, we do sum(y^2) to compute loss)
This diff
- increase the step size, and also move the test cases to be further away from 0 (where sqrt(x) is not well defined) to be safe :)
- also clean up, and merge the test case for inplace Vs. non-inplace
Tested with:
`CAFFE2_HYPOTHESIS_PROFILE=debug ai_bt caffe2/caffe2/python/operator_test:elementwise_ops_test -- "test_sqrt"`
* CompositeReader & CompositeReaderBuilder
A new type of reader gluing multiple readers together.
* Back out "Revert D7394363: [GanH]: Log D Trick for Cross Entropy with Sigmoid"
Original commit changeset: 9325a4356dbe
* [dai][WIP] convert params to int8 on ps before sending to trainer
Add float->uint8 conversion in addition to float->fp16 conversion in model_saver.
* [easy] improve unit test for sparse length sum ops
as desc.
#accept2ship
* Update GitHub upstream to 771fcb3455
* move sparse hash unique ops to OOS and add unit tests
- move the SparseHash version to OOS, since 'sparsehash' is already deps of caffe2 OOS: https://fburl.com/arssw4n1
- The 'SparseHash' engine is also being used in OOS, so the SparseHash version shall be in OOS to reduce confusion: https://fburl.com/o5ea7ah2
- fix the CUDA UniqueOp for the case when batch is empty.
- add unit test
* group_norm_op for caffe2
This is the cuda op for Group Normalization (GN): https://arxiv.org/abs/1803.08494
This code implements GN in one op that computes Y=gamma * (X-mu) / sigma + beta and also its gradients. It is expected to have minimal memory consumption (similar to the BN op), without creating new blobs if GN were implemented as several ops (e.g., reshape, norm_mean/std, affine_channel).
* Resubmit D7405233: disappeared in D7464958
OOS publish causes the op missing -- however, test was still there
* [c2] add sparse hash engine for cuda unique op
The SparseHash version of UniqueOp copy input tensor to CPU, and make use of sparse hash map to get unique output, and then copy back to GPU.
* [dper][gpu] enable unit testing gpu trainer for sparse nn
to debug the GPU trainer using mock data in unit test.
make it easier to develop GPU trainer for new models.
* Reuse Gloo context for Synchronize() calls
Previously we were creating (and leaking) the Gloo context on each call to Synchronize(). Now only run the common world op and create the barrier net once, then run the barrier net on each Synchronize() call. Since timeout is associated with the Gloo context, assert that the timeout is fixed instead of trying to handle the complexity of multiple timeouts (and associated contexts).
* [GanH/WGAN][1/n]: add FC param clipping
as titled
* [mobile] minimizing changes between caffe2_benchmark and speed_benchmark
* [GanH]: enable diagnose within model
avoid finding blob names but to directly enable inside the model
* Add `net_transformer_fun` option to DPM
This callback allows for various transformations to be made to the
model after gradient operators have been added. The immediate motivation for
this is to allow transformations such has "checkpoint-and-recompute" which
allow trading off memory for additional compute.
Adding several callbacks like this has made DPM's API less than ideal at this
stage. However, I could not find any reasonable alternative.
* [DT] [33/n] Compile flow task groups
task groups need to compiled in order to pickle the object in fblearner. However I also changed the Job's compile function as creating new object is not necessary.
* Initial commit for sparse_normalize vectorization and benchmark
* [GanH]: LB Calibration for JSD
as titled
* Tracing event in async executor
Adding event tracing through TRACE_EVENT macro in async executor
* [Resubmit] D7409751 Reseting book-keeping blobs when the reservoir is reset
D7409751 got lost in D7464958
* Visualizing realtime weights values
we want to visualize the weights values as optimizer is iterating. This diff supports to visual the weights at an assigned index.
Currently, we assume the blob to be 2 dimensional.
* [GanH][Easy]: Fix Homotopy Weighting
apparantely, there was a bug in homotopy weight (alpha, beta) update
* [c2] move sparse hash unique op out of oss
so that oss do not need to depend on google hash map.
* Get rid of std::round as it's not supported on Android
* Revert changes on setup.py
* Skip shaky test on Dataio
* fix
