Summary:
As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH`
All changes but the ones to `.clang-tidy` are generated using following script:
```
for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008
Reviewed By: driazati, r-barnes
Differential Revision: D29838584
Pulled By: malfet
fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13795
Add ability for dot string generation for a single subgraph and python bindings (which is pretty useful for model exploration in Python)
Restructure DotGenerator class a bit to make it easy to implement this feature
Reviewed By: bwasti
Differential Revision: D13010512
fbshipit-source-id: 825665438394b7e6968ab6da167b477af82a7b62
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14074
expose inducesEdges and addNode to python's NNSubgraph. This make it easy to manually construct a NNSubgraph in python
Reviewed By: bwasti
Differential Revision: D13092885
fbshipit-source-id: a94ed0b318162e27e3a4b5a4954eb6d169da7405
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13206
Add has device option for checking if a node has a device option set
Reviewed By: bwasti
Differential Revision: D12815365
fbshipit-source-id: 58477df93777f470cfb30cd75f02a659a7017b7c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13132
Expose more of the C++ API to python
Reviewed By: duc0
Differential Revision: D10855086
fbshipit-source-id: 98cc89bc72ef91ed1c59c1a19688e047765cf90b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12681
- Get rid of NodeMatchCriteria as a template parameter, which was too generic. So MatchNode<NodeMatchCriteria> becomes MatchNode<GraphType>, and MatchStore stores the predicate on GraphType::NodeRef.
- Similarly, get rid of NNNodeMatchCriteria
Now one can just pass in a function pointer NodeRef -> bool to NNMatchNode constructor directly like this
mg.createNode(is<Relu>)
- Merge static utilities in SubgraphMatcher class into MatchGraph class
- Rename MatchNode to MatchPredicate
Change use cases and tests to make it work
Reviewed By: ZolotukhinM
Differential Revision: D10386907
fbshipit-source-id: 43874bd154e3d7c29ce07b4b74eca8a7a9f3078a
Summary: Add a mapping for conversion -- this will help with debugging as well but is directly used by the TUI stacked on top of this
Reviewed By: duc0
Differential Revision: D10396130
fbshipit-source-id: cdd39278f0ed563bb828b1aebbbd228f486d89c8
Summary:
Simple additions that make it vastly easier to use nomnigraph in
python
Reviewed By: duc0
Differential Revision: D10383027
fbshipit-source-id: 441a883b84d4c53cca4f9c6fcc70e58692b8f782
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11418
Several improvements that aim to make the APIs more straightforward to use
- Get rid of helper methods subgraph and nonTerminal . Users now should create a NNMatchGraph directly via graph's createNode and createEdge API
- Get rid of operatorSubgraph helper method
- invertGraphTraversal flag applies to both the match graph and the scanned graph. This allows user to create match graph in the same direction as the scanned graph, thus reduce confusion.
- additional parameters of matchNode (count, includeInSubgraph, nonTerminal) are removed from the constructors and moved into setter methods. (We no longer enforce that MatchNode is immutable but this helps improve code clarity).
- Tests are updated to reflect the changes
Follow up changes:
- Possibly clean up the tests further. This change aims to minimally modify the unit tests.
- Help a validity check that enforce the current limitation of the match graph (single source node), and throws if the match graph does not satisfy the criteria.
- Have the single source node be detected automatically and callers just need to pass in the matchGraph instead of the source node reference.
Differential Revision: D9732565
fbshipit-source-id: ae8320e2bc89b867f6bb4b1c1aad635f4b219fa1
Summary: Closing the gap a bit on API, allowing users to go NetDef -> nomnigraph -> NetDef in python now
Reviewed By: duc0
Differential Revision: D9670495
fbshipit-source-id: 6497518ffc05a186deb0d657e06317980d39ddd5
Summary: Adds basic nomnigraph python bindings for quickly playing with the graphs.
Reviewed By: duc0
Differential Revision: D9441936
fbshipit-source-id: fd70f8ea279b28c766e40f124008800acd94bddd
