Linting changes from pre-commit & yapf

.clang-format

@@ -1 +1 @@
-BasedOnStyle: Google 
+BasedOnStyle: Google

.coveragerc

@@ -19,11 +19,11 @@ exclude_lines =
     if False:
     if __name__ == .__main__.:
     pass
-    
+
 omit =
     # Omit files that cannot be tested
     dace/jupyter.py
-    
+
     # Omit deprecated files
     dace/frontend/tensorflow/__init__.py
     dace/frontend/tensorflow/tensorflow.py

.github/citation_format_authors.py

@@ -3,7 +3,7 @@
 Parse the authors file and print for CITATION.cff
 """
 with open("AUTHORS", "r") as f:
-  content = f.readlines()
+    content = f.readlines()
 
 for i, l in enumerate(content[4:]):
     if l == "\n":
@@ -18,5 +18,3 @@
     last_names = ' '.join(names[1:])
     text = f"- family-names: {last_names}\n  given-names: {first_name}"
     print(text)
-
-

.github/workflows/hardware_test.yml

@@ -22,4 +22,4 @@ jobs:
         # Due to an internal bug in the Xilinx tools, where the current datetime is passed as an integer
         # and overflowed in the year 2022, run the RTL FPGA tests pretending like it's January 1st 2021.
         faketime -f "@2021-01-01 00:00:00" pytest -n auto --tb=short -m "rtl_hardware"
-        killall -9 xsim xsimk || true
+        killall -9 xsim xsimk || true

.github/workflows/heterogeneous-ci.yml

@@ -40,7 +40,7 @@ jobs:
       run: |
         papi_avail
 
-    - name: Run parallel pytest 
+    - name: Run parallel pytest
       run: |
         source ~/.venv/bin/activate # activate venv
         export DACE_cache=unique
@@ -55,7 +55,7 @@ jobs:
         source ~/.venv/bin/activate # activate venv
         ./tests/mpi_test.sh
 
-        
+
     - name: Test MPI with pytest
       run: |
         export NOSTATUSBAR=1
@@ -64,7 +64,7 @@ jobs:
         export DACE_cache=unique
         source ~/.venv/bin/activate # activate venv
         mpirun -n 2 coverage run --source=dace --parallel-mode -m pytest -x --with-mpi --tb=short -m "mpi"
-    
+
     - name: Test ScaLAPACK PBLAS with pytest
       run: |
         export NOSTATUSBAR=1

.github/workflows/pyFV3-ci.yml

@@ -33,7 +33,7 @@ jobs:
               submodules: 'recursive'
               path: 'pyFV3'
       - uses: actions/checkout@v4
-        with: 
+        with:
             path: 'dace'
             submodules: 'recursive'
       - name: Set up Python ${{ matrix.python-version }}
@@ -46,9 +46,9 @@ jobs:
           sudo apt-get install -y libopenmpi-dev libboost-all-dev
           gcc --version
       # Because Github doesn't allow us to do a git checkout in code
-      # we use a trick to checkout DaCe first (not using the external submodule)    
+      # we use a trick to checkout DaCe first (not using the external submodule)
       # install the full suite via requirements_dev, then re-install the correct DaCe
-      - name: Install Python packages 
+      - name: Install Python packages
         run: |
           python -m pip install --upgrade pip wheel setuptools
           pip install -e ./pyFV3[develop]

.github/workflows/release.sh

@@ -16,4 +16,3 @@ python -m build --sdist
 
 # Upload to PyPI
 twine upload dist/*
-

.gitignore

@@ -195,6 +195,3 @@ _build/
 
 # Ignoring the test junk
 _all_tests/
-
-
-

.gitmodules

@@ -13,4 +13,4 @@
 	url = https://github.com/spcl/dace-webclient.git
 [submodule "dace/external/rtllib"]
 	path = dace/external/rtllib
-	url = https://github.com/carljohnsen/rtllib.git 
+	url = https://github.com/carljohnsen/rtllib.git

AUTHORS

@@ -1,4 +1,4 @@
-# This is the official list of DaCe authors (individuals or organizations) for 
+# This is the official list of DaCe authors (individuals or organizations) for
 # copyright purposes.
 
 ETH Zurich

CITATION.cff

@@ -1,6 +1,6 @@
 cff-version: 1.2.0
 title: "DaCe - Data Centric Parallel Programming"
-message: "Please cite as" 
+message: "Please cite as"
 # this can be parsed from the AUTHORS file using .github/citation_format_authors.py
 authors:
   - family-names: Ben-Nun
@@ -64,7 +64,7 @@ preferred-citation:
   year: "2019"
   type: conference-paper
   collection-title: "Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis"
-  conference: 
+  conference:
     name: "SC '19"
   authors:
     - family-names: Ben-Nun

CONTRIBUTING.md

@@ -8,7 +8,7 @@ development and the code review process.
 
 We follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html), with a few notable exceptions:
 
-* **"Power Features"**: We like Python power features. We use them both externally (to make the Python/Numpy frontend easy to use) and internally. Use of any Python power feature that increases productivity is allowed. 
+* **"Power Features"**: We like Python power features. We use them both externally (to make the Python/Numpy frontend easy to use) and internally. Use of any Python power feature that increases productivity is allowed.
     * **Note on compatibility**: DaCe currently supports Python versions 3.6 and above, please make sure that the feature is supported in those versions, and if not ([dataclasses](https://docs.python.org/3/library/dataclasses.html) for example), please make sure to add a backwards-compatibility dependency. [Example](https://github.com/spcl/dace/blob/205d7c911a74e507d2fcbcc4b6cb5819b026648a/setup.py#L71)
 * **Type Hints**: New functions must include proper Python [typing information](https://docs.python.org/3/library/typing.html), in order to support type checking and smoother development.
 * **Importing classes and functions directly**: This is disallowed, with the exception of directly importing the following main graph components (which are heavily reused throughout the framework): `SDFG, SDFGState, Memlet, InterstateEdge`.
@@ -63,7 +63,7 @@ to install the `git` hooks for this repository.
 
 ## Tests
 
-We use [pytest](https://www.pytest.org/) for our testing infrastructure. All tests under the `tests/` folder 
+We use [pytest](https://www.pytest.org/) for our testing infrastructure. All tests under the `tests/` folder
 (and any subfolders within) are automatically read and run. The files must be under the right subfolder
 based on the component being tested (e.g., `tests/sdfg/` for IR-related tests), and must have the right
 suffix: either `*_test.py` or `*_cudatest.py`. See [pytest.ini](https://github.com/spcl/dace/blob/main/pytest.ini)
@@ -105,4 +105,3 @@ Use Python docstrings for function/class/file documentation. Since we use [Sphin
 
 File documentation is mostly one or a few sentences documenting the purpose and elements
 in a file. If your file contains more than one class/function, which share a general theme that should be documented, include a docstring for the Python file containing a general explanation (and ideally an example of the topic). [Example](dace/codegen/control_flow.py)
-

README.md

@@ -12,24 +12,24 @@
 _Decoupling domain science from performance optimization._
 
 DaCe is a [fast](https://nbviewer.org/github/spcl/dace/blob/main/tutorials/benchmarking.ipynb) parallel programming
-framework that takes code in Python/NumPy and other programming languages, and maps it to high-performance 
-**CPU, GPU, and FPGA** programs, which can be optimized to achieve state-of-the-art. Internally, DaCe 
-uses the Stateful DataFlow multiGraph (SDFG) *data-centric intermediate 
-representation*: A transformable, interactive representation of code based on 
+framework that takes code in Python/NumPy and other programming languages, and maps it to high-performance
+**CPU, GPU, and FPGA** programs, which can be optimized to achieve state-of-the-art. Internally, DaCe
+uses the Stateful DataFlow multiGraph (SDFG) *data-centric intermediate
+representation*: A transformable, interactive representation of code based on
 data movement.
-Since the input code and the SDFG are separate, it is possible to optimize a 
-program without changing its source, so that it stays readable. On the other 
-hand, transformations are customizable and user-extensible, so they can be written 
+Since the input code and the SDFG are separate, it is possible to optimize a
+program without changing its source, so that it stays readable. On the other
+hand, transformations are customizable and user-extensible, so they can be written
 once and reused in many applications.
-With data-centric parallel programming, we enable **direct knowledge transfer** 
+With data-centric parallel programming, we enable **direct knowledge transfer**
 of performance optimization, regardless of the application or the target processor.
 
 DaCe generates high-performance programs for:
  * Multi-core CPUs (tested on Intel, IBM POWER9, and ARM with SVE)
  * NVIDIA GPUs and AMD GPUs (with HIP)
  * Xilinx and Intel FPGAs
 
-DaCe can be written inline in Python and transformed in the command-line/Jupyter 
+DaCe can be written inline in Python and transformed in the command-line/Jupyter
 Notebooks or SDFGs can be interactively modified using our [Visual Studio Code extension](https://marketplace.visualstudio.com/items?itemName=phschaad.sdfv).
 
 ## [For more information, see the documentation](https://spcldace.readthedocs.io/en/latest/)
@@ -53,10 +53,10 @@ def myprogram(a):
     return np.sum(a)
 ```
 
-Calling `myprogram` with any NumPy array or GPU array (e.g., PyTorch, Numba, CuPy) will 
-generate data-centric code, compile, and run it. From here on out, you can 
-_optimize_ (interactively or automatically), _instrument_, and _distribute_ 
-your code. The code creates a shared library (DLL/SO file) that can readily 
+Calling `myprogram` with any NumPy array or GPU array (e.g., PyTorch, Numba, CuPy) will
+generate data-centric code, compile, and run it. From here on out, you can
+_optimize_ (interactively or automatically), _instrument_, and _distribute_
+your code. The code creates a shared library (DLL/SO file) that can readily
 be used in any C ABI compatible language (C/C++, FORTRAN, etc.).
 
 For more information on how to use DaCe, see the [samples](samples) or tutorials below:
@@ -93,4 +93,3 @@ DaCe is an open-source project. We are happy to accept Pull Requests with your c
 License
 -------
 DaCe is published under the New BSD license, see [LICENSE](LICENSE).
-

dace/__init__.py

@@ -30,6 +30,7 @@
 # Hack that enables using @dace as a decorator
 # See https://stackoverflow.com/a/48100440/6489142
 class DaceModule(sys.modules[__name__].__class__):
+
     def __call__(self, *args, **kwargs):
         return function(*args, **kwargs)
 

dace/builtin_hooks.py

@@ -82,12 +82,12 @@ def profile(
 def _make_filter_function(filter: Optional[Union[str, Callable[[Any], bool]]],
                           with_attr: bool = True) -> Callable[[Any], bool]:
     """
-    Internal helper that makes a filtering function. 
-      
+    Internal helper that makes a filtering function.
+
       * If nothing is given, the filter always returns True.
       * If a string is given, performs wildcard matching.
       * If a callable is given, use predicate directly.
-    
+
 
     :param filter: The filter to use.
     :param with_attr: If True, uses the ``name`` attribute for testing strings.
@@ -121,7 +121,7 @@ def instrument(itype: 'InstrumentationType',
 
     .. code-block:: python
 
-        with dace.instrument(dace.InstrumentationType.GPU_Events, 
+        with dace.instrument(dace.InstrumentationType.GPU_Events,
                              filter='*add??') as profiler:
             some_program(...)
             # ...
@@ -148,6 +148,7 @@ def instrument(itype: 'InstrumentationType',
     filter_func = _make_filter_function(filter)
 
     class Instrumenter:
+
         def __init__(self):
             self.reports: List[InstrumentationReport] = []
 
@@ -212,15 +213,15 @@ def sample(a: dace.float64, b: dace.float64):
 
         with dace.instrument_data(dace.DataInstrumentationType.Save, filter='a??'):
             result_ab = sample(a, b)
-        
+
         # Optionally, get the serialized data containers
         dreport = sdfg.get_instrumented_data()
         assert dreport.keys() == {'arr'}  # dreport['arr'] is now the internal ``arr``
 
         # Reload latest instrumented data (can be customized if ``restore_from`` is given)
         with dace.instrument_data(dace.DataInstrumentationType.Restore, filter='a??'):
             result_cd = sample(c, d)  # where ``c, d`` are different from ``a, b``
-        
+
         assert numpy.allclose(result_ab, result_cd)
 
 
@@ -243,6 +244,7 @@ def sample(a: dace.float64, b: dace.float64):
     filter_func = _make_filter_function(filter, with_attr=False)
 
     class DataInstrumenter:
+
         @contextmanager
         def __call__(self, sdfg: 'SDFG'):
             for n, _ in sdfg.all_nodes_recursive():
@@ -269,6 +271,7 @@ def __call__(self, sdfg: 'SDFG'):
     if ditype == DataInstrumentationType.Restore:
         # Restore data into compiled SDFG
         class DataRestoreHook:
+
             @contextmanager
             def __call__(self, csdfg: 'CompiledSDFG', args: Tuple[Any, ...]):
                 # Restore data from requested data report

dace/cli/fcdc.py

@@ -8,6 +8,7 @@
 import shutil
 from dace.frontend.fortran import fortran_parser
 
+
 def main():
     # Command line options parser
     parser = argparse.ArgumentParser(description='Fortran to SDFG command-line transpiler.')

dace/cli/sdfg_diff.py

@@ -21,7 +21,6 @@
 from dace.sdfg.state import ControlFlowBlock
 import dace.serialize
 
-
 DiffableT = Union[ControlFlowBlock, nd.Node, MultiConnectorEdge[mlt.Memlet], Edge[InterstateEdge]]
 DiffSetsT = Tuple[Set[str], Set[str], Set[str]]
 
@@ -69,7 +68,7 @@ def _print_diff(sdfg_A: dace.SDFG, sdfg_B: dace.SDFG, diff_sets: DiffSetsT) -> N
         print('SDFGs are identical')
 
 
-def _sdfg_diff(sdfg_A: dace.SDFG, sdfg_B: dace.SDFG, eq_strategy = Union[Literal['hash', '==']]) -> DiffSetsT:
+def _sdfg_diff(sdfg_A: dace.SDFG, sdfg_B: dace.SDFG, eq_strategy=Union[Literal['hash', '==']]) -> DiffSetsT:
     all_id_elements_A: Dict[str, DiffableT] = dict()
     all_id_elements_B: Dict[str, DiffableT] = dict()
 
@@ -151,17 +150,13 @@ def main():
                         action='store_true',
                         help="If set, visualize the difference graphically",
                         default=False)
-    parser.add_argument('-o',
-                        '--output',
-                        dest='output',
-                        help="The output filename to generate",
-                        type=str)
+    parser.add_argument('-o', '--output', dest='output', help="The output filename to generate", type=str)
     parser.add_argument('-H',
                         '--hash',
                         dest='hash',
                         action='store_true',
                         help="If set, use the hash of JSON serialized properties for change checks instead of " +
-                             "Python's dictionary equivalence checks. This makes changes order sensitive.",
+                        "Python's dictionary equivalence checks. This makes changes order sensitive.",
                         default=False)
 
     args = parser.parse_args()

dace/cli/sdfv.py

@@ -16,6 +16,7 @@
 
 
 def partialclass(cls, *args, **kwds):
+
     class NewCls(cls):
         __init__ = functools.partialmethod(cls.__init__, *args, **kwds)
 
@@ -37,11 +38,8 @@ def view(sdfg: dace.SDFG, filename: Optional[Union[str, int]] = None, verbose: b
     """
     # If vscode is open, try to open it inside vscode
     if filename is None:
-        if (
-            'VSCODE_IPC_HOOK' in os.environ
-            or 'VSCODE_IPC_HOOK_CLI' in os.environ
-            or 'VSCODE_GIT_IPC_HANDLE' in os.environ
-        ):
+        if ('VSCODE_IPC_HOOK' in os.environ or 'VSCODE_IPC_HOOK_CLI' in os.environ
+                or 'VSCODE_GIT_IPC_HANDLE' in os.environ):
             suffix = '.sdfgz' if compress else '.sdfg'
             fd, filename = tempfile.mkstemp(suffix=suffix)
             sdfg.save(filename, compress=compress)
@@ -82,7 +80,7 @@ def view(sdfg: dace.SDFG, filename: Optional[Union[str, int]] = None, verbose: b
     with open(html_filename, "w") as f:
         f.write(html)
 
-    if(verbose):
+    if (verbose):
         print("File saved at %s" % html_filename)
 
     if fd is not None:
@@ -95,7 +93,7 @@ def view(sdfg: dace.SDFG, filename: Optional[Union[str, int]] = None, verbose: b
         # start the web server
         handler = partialclass(http.server.SimpleHTTPRequestHandler, directory=dirname)
         httpd = http.server.HTTPServer(('localhost', filename), handler)
-        if(verbose):
+        if (verbose):
             print(f"Serving at localhost:{filename}, press enter to stop...")
 
         # start the server in a different thread

dace/codegen/Xilinx_IP.tcl.in

@@ -6,4 +6,4 @@ set_part ${DACE_XILINX_PART_NAME}
 create_clock -period ${DACE_XILINX_INTERNAL_TARGET_CLOCK}MHz -name default
 csynth_design
 export_design -format ip_catalog
-quit
+quit

dace/codegen/codegen.py

@@ -210,9 +210,10 @@ def generate_code(sdfg: SDFG, validate=True) -> List[CodeObject]:
     targets = {'cpu': default_target(frame, sdfg)}
 
     # Instantiate the rest of the targets
-    targets.update(
-        {v['name']: k(frame, sdfg)
-         for k, v in TargetCodeGenerator.extensions().items() if v['name'] not in targets})
+    targets.update({
+        v['name']: k(frame, sdfg)
+        for k, v in TargetCodeGenerator.extensions().items() if v['name'] not in targets
+    })
 
     # Query all code generation targets and instrumentation providers in SDFG
     _get_codegen_targets(sdfg, frame)

dace/codegen/codeobject.py

@@ -16,7 +16,8 @@ class CodeObject(object):
                                          value_type=str,
                                          desc="Additional compiler argument "
                                          "variables to add to template")
-    linkable = Property(dtype=bool, desc='Should this file participate in ' 'overall linkage?')
+    linkable = Property(dtype=bool, desc='Should this file participate in '
+                        'overall linkage?')
     environments = SetProperty(str,
                                desc="Environments required by CMake to build and run this code node.",
                                default=set())