feat[next][dace]: make canonical representation of field domain optio…

…nal (GridTools#1476)

Baseline implementation of DaCe backend was reordering the dimensions in
field domain based on alphabetical order. This is the canonical
representation of field domain, and provides the advantage of not
requiring regenerating the SDFG for different memory layouts of field
arguments. Besides, the code for accessing a field is simple, because
all field domains are assumed to follow the same layout.

However, the canonical representation poses an obstacle to the
realization of module-level SDFGs, because it requires an additional
conversion step of all array arguments before calling the SDFG.
Therefore, we make the canonical representation optional. Note that this
change should not have any performance impact, because the real memory
layout of field arrays is not modified.

src/gt4py/next/program_processors/runners/dace_iterator/__init__.py

@@ -42,38 +42,37 @@
     cp = None
 
 
-def get_sorted_dim_ranges(domain: common.Domain) -> Sequence[common.FiniteUnitRange]:
-    assert common.Domain.is_finite(domain)
-    sorted_dims = get_sorted_dims(domain.dims)
-    return [domain.ranges[dim_index] for dim_index, _ in sorted_dims]
-
-
 """ Default build configuration in DaCe backend """
 _build_type = "Release"
-
-
-def convert_arg(arg: Any, sdfg_param: str):
-    if common.is_field(arg):
-        # field domain offsets are not supported
-        non_zero_offsets = [
-            (dim, dim_range)
-            for dim, dim_range in zip(arg.domain.dims, arg.domain.ranges)
-            if dim_range.start != 0
-        ]
-        if non_zero_offsets:
-            dim, dim_range = non_zero_offsets[0]
-            raise RuntimeError(
-                f"Field '{sdfg_param}' passed as array slice with offset {dim_range.start} on dimension {dim.value}."
-            )
-        sorted_dims = get_sorted_dims(arg.domain.dims)
-        ndim = len(sorted_dims)
-        dim_indices = [dim_index for dim_index, _ in sorted_dims]
-        if isinstance(arg.ndarray, np.ndarray):
-            return np.moveaxis(arg.ndarray, range(ndim), dim_indices)
-        else:
-            assert cp is not None and isinstance(arg.ndarray, cp.ndarray)
-            return cp.moveaxis(arg.ndarray, range(ndim), dim_indices)
-    return arg
+_default_on_gpu = False
+_default_use_field_canonical_representation = False
+
+
+def convert_arg(arg: Any, sdfg_param: str, use_field_canonical_representation: bool):
+    if not common.is_field(arg):
+        return arg
+    # field domain offsets are not supported
+    non_zero_offsets = [
+        (dim, dim_range)
+        for dim, dim_range in zip(arg.domain.dims, arg.domain.ranges)
+        if dim_range.start != 0
+    ]
+    if non_zero_offsets:
+        dim, dim_range = non_zero_offsets[0]
+        raise RuntimeError(
+            f"Field '{sdfg_param}' passed as array slice with offset {dim_range.start} on dimension {dim.value}."
+        )
+    if not use_field_canonical_representation:
+        return arg.ndarray
+    # the canonical representation requires alphabetical ordering of the dimensions in field domain definition
+    sorted_dims = get_sorted_dims(arg.domain.dims)
+    ndim = len(sorted_dims)
+    dim_indices = [dim_index for dim_index, _ in sorted_dims]
+    if isinstance(arg.ndarray, np.ndarray):
+        return np.moveaxis(arg.ndarray, range(ndim), dim_indices)
+    else:
+        assert cp is not None and isinstance(arg.ndarray, cp.ndarray)
+        return cp.moveaxis(arg.ndarray, range(ndim), dim_indices)
 
 
 def preprocess_program(
@@ -107,9 +106,14 @@ def preprocess_program(
     return fencil_definition, tmps
 
 
-def get_args(sdfg: dace.SDFG, args: Sequence[Any]) -> dict[str, Any]:
+def get_args(
+    sdfg: dace.SDFG, args: Sequence[Any], use_field_canonical_representation: bool
+) -> dict[str, Any]:
     sdfg_params: Sequence[str] = sdfg.arg_names
-    return {sdfg_param: convert_arg(arg, sdfg_param) for sdfg_param, arg in zip(sdfg_params, args)}
+    return {
+        sdfg_param: convert_arg(arg, sdfg_param, use_field_canonical_representation)
+        for sdfg_param, arg in zip(sdfg_params, args)
+    }
 
 
 def _ensure_is_on_device(
@@ -162,8 +166,13 @@ def get_stride_args(
                 raise ValueError(
                     f"Stride ({stride_size} bytes) for argument '{sym}' must be a multiple of item size ({value.itemsize} bytes)."
                 )
-            stride_args[str(sym)] = stride
-
+            if isinstance(sym, dace.symbol):
+                assert sym.name not in stride_args
+                stride_args[str(sym)] = stride
+            elif sym != stride:
+                raise RuntimeError(
+                    f"Expected stride {arrays[name].strides} for arg {name}, got {value.strides}."
+                )
     return stride_args
 
 
@@ -221,12 +230,15 @@ def get_sdfg_args(sdfg: dace.SDFG, *args, check_args: bool = False, **kwargs) ->
         sdfg:               The SDFG for which we want to get the arguments.
     """
     offset_provider = kwargs["offset_provider"]
-    on_gpu = kwargs.get("on_gpu", False)
+    on_gpu = kwargs.get("on_gpu", _default_on_gpu)
+    use_field_canonical_representation = kwargs.get(
+        "use_field_canonical_representation", _default_use_field_canonical_representation
+    )
 
     neighbor_tables = filter_neighbor_tables(offset_provider)
     device = dace.DeviceType.GPU if on_gpu else dace.DeviceType.CPU
 
-    dace_args = get_args(sdfg, args)
+    dace_args = get_args(sdfg, args, use_field_canonical_representation)
     dace_field_args = {n: v for n, v in dace_args.items() if not np.isscalar(v)}
     dace_conn_args = get_connectivity_args(neighbor_tables, device)
     dace_shapes = get_shape_args(sdfg.arrays, dace_field_args)
@@ -261,6 +273,7 @@ def build_sdfg_from_itir(
     load_sdfg_from_file: bool = False,
     cache_id: Optional[str] = None,
     save_sdfg: bool = True,
+    use_field_canonical_representation: bool = True,
 ) -> dace.SDFG:
     """Translate a Fencil into an SDFG.
 
@@ -275,6 +288,7 @@ def build_sdfg_from_itir(
         load_sdfg_from_file: Allows to read the SDFG from file, instead of generating it, for debug only.
         cache_id:            The id of the cache entry, used to disambiguate stored sdfgs.
         save_sdfg:           If `True`, the default the SDFG is stored as a file and can be loaded, this allows to skip the lowering step, requires `load_sdfg_from_file` set to `True`.
+        use_field_canonical_representation: If `True`,  assume that the fields dimensions are sorted alphabetically.
 
     Notes:
         Currently only the `FORCE_INLINE` liftmode is supported and the value of `lift_mode` is ignored.
@@ -292,7 +306,9 @@ def build_sdfg_from_itir(
 
     # visit ITIR and generate SDFG
     program, tmps = preprocess_program(program, offset_provider, lift_mode)
-    sdfg_genenerator = ItirToSDFG(arg_types, offset_provider, tmps, column_axis)
+    sdfg_genenerator = ItirToSDFG(
+        arg_types, offset_provider, tmps, use_field_canonical_representation, column_axis
+    )
     sdfg = sdfg_genenerator.visit(program)
     if sdfg is None:
         raise RuntimeError(f"Visit failed for program {program.id}.")
@@ -343,9 +359,12 @@ def run_dace_iterator(program: itir.FencilDefinition, *args, **kwargs):
     build_cache = kwargs.get("build_cache", None)
     compiler_args = kwargs.get("compiler_args", None)  # `None` will take default.
     build_type = kwargs.get("build_type", "RelWithDebInfo")
-    on_gpu = kwargs.get("on_gpu", False)
+    on_gpu = kwargs.get("on_gpu", _default_on_gpu)
     auto_optimize = kwargs.get("auto_optimize", True)
     lift_mode = kwargs.get("lift_mode", itir_transforms.LiftMode.FORCE_INLINE)
+    use_field_canonical_representation = kwargs.get(
+        "use_field_canonical_representation", _default_use_field_canonical_representation
+    )
     # ITIR parameters
     column_axis = kwargs.get("column_axis", None)
     offset_provider = kwargs["offset_provider"]
@@ -374,6 +393,7 @@ def run_dace_iterator(program: itir.FencilDefinition, *args, **kwargs):
             load_sdfg_from_file=load_sdfg_from_file,
             cache_id=cache_id,
             save_sdfg=save_sdfg,
+            use_field_canonical_representation=use_field_canonical_representation,
         )
 
         sdfg.build_folder = compilation_cache._session_cache_dir_path / ".dacecache"