Coarsening -> Simplification

.github/workflows/general-ci.yml

@@ -12,7 +12,7 @@ jobs:
     strategy:
       matrix:
         python-version: [3.7,'3.10']
-        coarsening: [0,1,autoopt]
+        simplify: [0,1,autoopt]
 
     steps:
     - uses: actions/checkout@v2
@@ -43,12 +43,12 @@ jobs:
         export DACE_testing_serialization=1
         export DACE_cache=unique
         export DACE_optimizer_interface=" "
-        if [ "${{ matrix.coarsening }}" = "autoopt" ]; then
-            export DACE_optimizer_automatic_dataflow_coarsening=1
+        if [ "${{ matrix.simplify }}" = "autoopt" ]; then
+            export DACE_optimizer_automatic_simplification=1
             export DACE_optimizer_autooptimize=1
             echo "Auto-optimization heuristics"
         else
-            export DACE_optimizer_automatic_dataflow_coarsening=${{ matrix.coarsening }}
+            export DACE_optimizer_automatic_simplification=${{ matrix.simplify }}
         fi
         pytest -n auto --cov-report=xml --cov=dace --tb=short -m "not gpu and not verilator and not tensorflow and not mkl and not sve and not papi and not mlir and not lapack and not fpga and not mpi"
         codecov
@@ -59,12 +59,12 @@ jobs:
         export DACE_testing_serialization=1
         export DACE_cache=unique
         export DACE_optimizer_interface=" "
-        if [ "${{ matrix.coarsening }}" = "autoopt" ]; then
-            export DACE_optimizer_automatic_dataflow_coarsening=1
+        if [ "${{ matrix.simplify }}" = "autoopt" ]; then
+            export DACE_optimizer_automatic_simplification=1
             export DACE_optimizer_autooptimize=1
             echo "Auto-optimization heuristics"
         else
-            export DACE_optimizer_automatic_dataflow_coarsening=${{ matrix.coarsening }}
+            export DACE_optimizer_automatic_simplification=${{ matrix.simplify }}
         fi
         pytest -n 1 --cov-report=xml --cov=dace --tb=short -m "lapack"
         codecov
@@ -74,7 +74,7 @@ jobs:
         export NOSTATUSBAR=1
         export DACE_testing_serialization=1
         export DACE_cache=single
-        export DACE_optimizer_automatic_dataflow_coarsening=${{ matrix.coarsening }}
+        export DACE_optimizer_automatic_simplification=${{ matrix.simplify }}
         export PYTHON_BINARY="coverage run --source=dace --parallel-mode"
         ./tests/polybench_test.sh
         ./tests/xform_test.sh

README.md

@@ -110,7 +110,7 @@ FPGA programming:
 SDFG interactive transformation:
  * `DACE_optimizer_transform_on_call` (default: False): Uses the transformation command line interface every time a `@dace` function is called.
  * `DACE_optimizer_interface` (default: `dace.transformation.optimizer.SDFGOptimizer`): Controls the SDFG optimization process if `transform_on_call` is enabled. By default, uses the transformation command line interface.
- * `DACE_optimizer_automatic_dataflow_coarsening` (default: True): If False, skips automatic dataflow coarsening in the Python frontend (see transformations tutorial for more information).
+ * `DACE_optimizer_automatic_simplification` (default: True): If False, skips automatic simplification in the Python frontend (see transformations tutorial for more information).
 
 Profiling:
  * `DACE_profiling` (default: False): Enables profiling measurement of the DaCe program runtime in milliseconds. Produces a log file and prints out median runtime.

dace/config_schema.yml

@@ -86,12 +86,12 @@ required:
                 title: Save intermediate SDFGs
                 description: Save SDFG files after every transformation.
 
-            automatic_dataflow_coarsening:
+            automatic_simplification:
                 type: bool
                 default: true
-                title: Automatic dataflow coarsening
+                title: Automatic SDFG simplification
                 description: >
-                    Automatically performs dataflow coarsening on programs.
+                    Automatically performs SDFG simplification on programs.
 
             detect_control_flow:
                 type: bool

dace/frontend/python/README.md

@@ -44,9 +44,9 @@ There is also upcoming support for NumPy ufuncs. You may preview ufunc support w
 
 ## Known Issues
 
-### Issues when automatic dataflow coarsening is enabled
+### Issues when automatic simplification is enabled
 
-When automatic dataflow coarsening is enabled, SDFGs created using the
+When automatic simplification is enabled, SDFGs created using the
 Python-Frontend are automatically transformed using:
 - InlineSDFG
 - EndStateElimination
@@ -67,4 +67,4 @@ ranges, leading to RW/WR/WW dependencies, InlineSDFG and StateFusion may violate
 - When there are sequential dependencies between statements due to updating a loop variable,
 StateFusion may erroneously lead to concurrent execution of those statements (see [#315](https://github.com/spcl/dace/issues/315)).
 
-Temporary workaround: Disable the automatic dataflow coarsening flag in the configuration file `.dace.conf`.
+Temporary workaround: Disable the automatic simplification pass flag in the configuration file `.dace.conf`.

dace/frontend/python/newast.py

@@ -138,14 +138,14 @@ def parse_dace_program(name: str,
                        argtypes: Dict[str, data.Data],
                        constants: Dict[str, Any],
                        closure: SDFGClosure,
-                       coarsen: Optional[bool] = None,
+                       simplify: Optional[bool] = None,
                        save=True) -> SDFG:
     """ Parses a `@dace.program` function into an SDFG.
         :param src_ast: The AST of the Python program to parse.
         :param visitor: A ProgramVisitor object returned from 
                         ``preprocess_dace_program``.
         :param closure: An object that contains the @dace.program closure.
-        :param coarsen: If True, dataflow coarsening will be performed.
+        :param simplify: If True, simplification pass will be performed.
         :param save: If True, saves source mapping data for this SDFG.
         :return: A 2-tuple of SDFG and its reduced (used) closure.
     """
@@ -158,7 +158,7 @@ def parse_dace_program(name: str,
                              scope_arrays=argtypes,
                              scope_vars={},
                              closure=closure,
-                             coarsen=coarsen)
+                             simplify=simplify)
 
     sdfg, _, _, _ = visitor.parse_program(preprocessed_ast.preprocessed_ast.body[0])
     sdfg.set_sourcecode(preprocessed_ast.src, 'python')
@@ -959,7 +959,7 @@ def __init__(self,
                  closure: SDFGClosure = None,
                  nested: bool = False,
                  tmp_idx: int = 0,
-                 coarsen: Optional[bool] = None):
+                 simplify: Optional[bool] = None):
         """ ProgramVisitor init method
 
         Arguments:
@@ -972,7 +972,7 @@ def __init__(self,
             scope_arrays {Dict[str, data.Data]} -- Scope arrays
             scope_vars {Dict[str, str]} -- Scope variables
             closure {SDFGClosure} -- The closure of this program
-            coarsen {bool} -- Whether to apply dataflow coarsening after parsing nested dace programs
+            simplify {bool} -- Whether to apply simplification pass after parsing nested dace programs
 
         Keyword Arguments:
             nested {bool} -- True, if SDFG is nested (default: {False})
@@ -991,7 +991,7 @@ def __init__(self,
         self.globals = global_vars
         self.closure = closure
         self.nested = nested
-        self.coarsen = coarsen
+        self.simplify = simplify
 
         # Keeps track of scope arrays, numbers, variables and accesses
         self.scope_arrays = OrderedDict()
@@ -3248,7 +3248,7 @@ def _parse_sdfg_call(self, funcname: str, func: Union[SDFG, SDFGConvertible], no
 
                 if isinstance(fcopy, DaceProgram):
                     fcopy.signature = copy.deepcopy(func.signature)
-                    sdfg = fcopy.to_sdfg(*fargs, **fkwargs, coarsen=self.coarsen, save=False)
+                    sdfg = fcopy.to_sdfg(*fargs, **fkwargs, simplify=self.simplify, save=False)
                 else:
                     sdfg = fcopy.__sdfg__(*fargs, **fkwargs)
 

dace/frontend/python/parser.py

@@ -181,7 +181,7 @@ def _auto_optimize(self, sdfg: SDFG, symbols: Dict[str, int] = None) -> SDFG:
         from dace.transformation.auto import auto_optimize as autoopt
         return autoopt.auto_optimize(sdfg, self.device, symbols=symbols)
 
-    def to_sdfg(self, *args, coarsen=None, save=False, validate=False, use_cache=False, **kwargs) -> SDFG:
+    def to_sdfg(self, *args, simplify=None, save=False, validate=False, use_cache=False, **kwargs) -> SDFG:
         """ Parses the DaCe function into an SDFG. """
         if use_cache:
             # Update global variables with current closure
@@ -204,7 +204,7 @@ def to_sdfg(self, *args, coarsen=None, save=False, validate=False, use_cache=Fal
                 entry = self._cache.get(cachekey)
                 return entry.sdfg
 
-        sdfg = self._parse(args, kwargs, coarsen=coarsen, save=save, validate=validate)
+        sdfg = self._parse(args, kwargs, simplify=simplify, save=save, validate=validate)
 
         if use_cache:
             # Add to cache
@@ -213,11 +213,11 @@ def to_sdfg(self, *args, coarsen=None, save=False, validate=False, use_cache=Fal
         return sdfg
 
     def __sdfg__(self, *args, **kwargs) -> SDFG:
-        return self._parse(args, kwargs, coarsen=None, save=False, validate=False)
+        return self._parse(args, kwargs, simplify=None, save=False, validate=False)
 
-    def compile(self, *args, coarsen=None, save=False, **kwargs):
+    def compile(self, *args, simplify=None, save=False, **kwargs):
         """ Convenience function that parses and compiles a DaCe program. """
-        sdfg = self._parse(args, kwargs, coarsen=coarsen, save=save)
+        sdfg = self._parse(args, kwargs, simplify=simplify, save=save)
 
         # Invoke auto-optimization as necessary
         if Config.get_bool('optimizer', 'autooptimize') or self.auto_optimize:
@@ -383,15 +383,15 @@ def __call__(self, *args, **kwargs):
 
         return result
 
-    def _parse(self, args, kwargs, coarsen=None, save=False, validate=False) -> SDFG:
+    def _parse(self, args, kwargs, simplify=None, save=False, validate=False) -> SDFG:
         """ 
         Try to parse a DaceProgram object and return the `dace.SDFG` object
         that corresponds to it.
         :param function: DaceProgram object (obtained from the ``@dace.program``
                         decorator).
         :param args: The given arguments to the function.
         :param kwargs: The given keyword arguments to the function.
-        :param coarsen: Whether to apply dataflow coarsening or not (None
+        :param simplify: Whether to apply simplification pass or not (None
                        uses configuration-defined value). 
         :param save: If True, saves the generated SDFG to 
                     ``_dacegraphs/program.sdfg`` after parsing.
@@ -403,18 +403,18 @@ def _parse(self, args, kwargs, coarsen=None, save=False, validate=False) -> SDFG
         from dace.transformation import helpers as xfh
 
         # Obtain DaCe program as SDFG
-        sdfg, cached = self._generate_pdp(args, kwargs, coarsen=coarsen)
+        sdfg, cached = self._generate_pdp(args, kwargs, simplify=simplify)
 
-        # Apply dataflow coarsening automatically
-        if not cached and (coarsen == True or
-                           (coarsen is None and Config.get_bool('optimizer', 'automatic_dataflow_coarsening'))):
+        # Apply simplification pass automatically
+        if not cached and (simplify == True or
+                           (simplify is None and Config.get_bool('optimizer', 'automatic_simplification'))):
 
             # Promote scalars to symbols as necessary
             promoted = scal2sym.promote_scalars_to_symbols(sdfg)
             if Config.get_bool('debugprint') and len(promoted) > 0:
                 print('Promoted scalars {%s} to symbols.' % ', '.join(p for p in sorted(promoted)))
 
-            sdfg.coarsen_dataflow()
+            sdfg.simplify()
 
             # Split back edges with assignments and conditions to allow richer
             # control flow detection in code generation
@@ -619,11 +619,11 @@ def load_precompiled_sdfg(self, path: str, *args, **kwargs) -> None:
         # Update SDFG cache with the SDFG and compiled version
         self._cache.add(cachekey, csdfg.sdfg, csdfg)
 
-    def _generate_pdp(self, args, kwargs, coarsen=None) -> SDFG:
+    def _generate_pdp(self, args, kwargs, simplify=None) -> SDFG:
         """ Generates the parsed AST representation of a DaCe program.
             :param args: The given arguments to the program.
             :param kwargs: The given keyword arguments to the program.
-            :param coarsen: Whether to apply dataflow coarsening when parsing 
+            :param simplify: Whether to apply simplification pass when parsing 
                            nested dace programs.
             :return: A 2-tuple of (parsed SDFG object, was the SDFG retrieved
                      from cache).
@@ -690,7 +690,12 @@ def _generate_pdp(self, args, kwargs, coarsen=None) -> SDFG:
             cached = True
         else:
             cached = False
-            sdfg = newast.parse_dace_program(self.name, parsed_ast, argtypes, self.dec_kwargs, closure, coarsen=coarsen)
+            sdfg = newast.parse_dace_program(self.name,
+                                             parsed_ast,
+                                             argtypes,
+                                             self.dec_kwargs,
+                                             closure,
+                                             simplify=simplify)
 
             # Set SDFG argument names, filtering out constants
             sdfg.arg_names = [a for a in self.argnames if a in argtypes]

dace/frontend/tensorflow/tensorflow.py

@@ -329,7 +329,7 @@ def train(
 
         print("Adding connectors")
         self.graph.fill_scope_connectors()
-        # self.graph.coarsen_dataflow(validate=False)
+        # self.graph.simplify(validate=False)
         if gpu:
             self.graph.apply_gpu_transformations()
 

dace/libraries/blas/nodes/gemv.py

@@ -832,10 +832,10 @@ def _gemTv_pblas(_A: dtype[m, n], _x: dtype[m], _y: dtype[n]):
         # in ValueError: Node type "BlockCyclicScatter" not supported for
         # promotion
         if transA:
-            sdfg = _gemTv_pblas.to_sdfg(coarsen=False)
+            sdfg = _gemTv_pblas.to_sdfg(simplify=False)
         else:
-            sdfg = _gemNv_pblas.to_sdfg(coarsen=False)
-        sdfg.coarsen_dataflow()
+            sdfg = _gemNv_pblas.to_sdfg(simplify=False)
+        sdfg.simplify()
         return sdfg
 
 

dace/sdfg/nodes.py

@@ -489,7 +489,7 @@ class NestedSDFG(CodeNode):
 
     no_inline = Property(dtype=bool,
                          desc="If True, this nested SDFG will not be inlined during "
-                         "dataflow coarsening",
+                         "simplification",
                          default=False)
 
     unique_name = Property(dtype=str, desc="Unique name of the SDFG", default="")

dace/sdfg/sdfg.py

@@ -1896,18 +1896,17 @@ def is_valid(self) -> bool:
 
     def apply_strict_transformations(self, validate=True, validate_all=False):
         """
-        This method is DEPRECATED in favor of ``coarsen_dataflow``.
+        This method is DEPRECATED in favor of ``simplify``.
         Applies safe transformations (that will surely increase the
         performance) on the SDFG. For example, this fuses redundant states
         (safely) and removes redundant arrays.
 
         B{Note:} This is an in-place operation on the SDFG.
         """
-        warnings.warn('SDFG.apply_strict_transformations is deprecated, use SDFG.coarsen_dataflow instead.',
-                      DeprecationWarning)
-        return self.coarsen_dataflow(validate, validate_all)
+        warnings.warn('SDFG.apply_strict_transformations is deprecated, use SDFG.simplify instead.', DeprecationWarning)
+        return self.simplify(validate, validate_all)
 
-    def coarsen_dataflow(self, validate=True, validate_all=False):
+    def simplify(self, validate=True, validate_all=False):
         """ Applies safe transformations (that will surely increase the
             performance) on the SDFG. For example, this fuses redundant states
             (safely) and removes redundant arrays.
@@ -1919,7 +1918,7 @@ def coarsen_dataflow(self, validate=True, validate_all=False):
         from dace.transformation.dataflow import RedundantReadSlice, RedundantWriteSlice
         from dace.sdfg import utils as sdutil
         # This is imported here to avoid an import loop
-        from dace.transformation.transformation import coarsening_transformations
+        from dace.transformation.transformation import simplification_transformations
 
         # First step is to apply multi-state inline, before any state fusion can
         # occur
@@ -1930,7 +1929,7 @@ def coarsen_dataflow(self, validate=True, validate_all=False):
                                             validate=validate,
                                             permissive=False,
                                             validate_all=validate_all)
-        self.apply_transformations_repeated(coarsening_transformations(),
+        self.apply_transformations_repeated(simplification_transformations(),
                                             validate=validate,
                                             permissive=False,
                                             validate_all=validate_all)
@@ -2141,7 +2140,7 @@ def apply_gpu_transformations(self, states=None, validate=True, validate_all=Fal
             generate GPU code.
             :note: It is recommended to apply redundant array removal
             transformation after this transformation. Alternatively,
-            you can coarsen_dataflow() after this transformation.
+            you can simplify() after this transformation.
             :note: This is an in-place operation on the SDFG.
         """
         # Avoiding import loops

dace/transformation/__init__.py

@@ -1,2 +1,2 @@
-from .transformation import (coarsening_transformations, SingleStateTransformation, MultiStateTransformation,
+from .transformation import (simplification_transformations, SingleStateTransformation, MultiStateTransformation,
                              SubgraphTransformation, ExpandTransformation)