feat[next][dace]: DaCe support for temporaries

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

@@ -22,6 +22,7 @@
 from dace.codegen.compiled_sdfg import CompiledSDFG
 from dace.sdfg import utils as sdutils
 from dace.transformation.auto import auto_optimize as autoopt
+from dace.transformation.interstate import RefineNestedAccess
 
 import gt4py.next.allocators as next_allocators
 import gt4py.next.iterator.ir as itir
@@ -71,7 +72,7 @@ def preprocess_program(
     lift_mode: itir_transforms.LiftMode,
     unroll_reduce: bool = False,
 ):
-    return itir_transforms.apply_common_transforms(
+    node = itir_transforms.apply_common_transforms(
         program,
         common_subexpression_elimination=False,
         force_inline_lambda_args=True,
@@ -80,6 +81,21 @@ def preprocess_program(
         unroll_reduce=unroll_reduce,
     )
 
+    if isinstance(node, itir_transforms.global_tmps.FencilWithTemporaries):
+        fencil_definition = node.fencil
+        tmps = node.tmps
+
+    elif isinstance(node, itir.FencilDefinition):
+        fencil_definition = node
+        tmps = []
+
+    else:
+        raise TypeError(
+            f"Expected 'FencilDefinition' or 'FencilWithTemporaries', got '{type(program).__name__}'."
+        )
+
+    return fencil_definition, tmps
+
 
 def get_args(sdfg: dace.SDFG, args: Sequence[Any]) -> dict[str, Any]:
     sdfg_params: Sequence[str] = sdfg.arg_names
@@ -160,6 +176,7 @@ def get_stride_args(
 def get_cache_id(
     build_type: str,
     build_for_gpu: bool,
+    lift_mode: itir_transforms.LiftMode,
     program: itir.FencilDefinition,
     arg_types: Sequence[ts.TypeSpec],
     column_axis: Optional[common.Dimension],
@@ -185,6 +202,7 @@ def offset_invariants(offset):
         for arg in (
             build_type,
             build_for_gpu,
+            lift_mode,
             program,
             *arg_types,
             column_axis,
@@ -272,17 +290,17 @@ def build_sdfg_from_itir(
         sdfg.validate()
         return sdfg
 
-    # TODO(edopao): As temporary fix until temporaries are supported in the DaCe Backend force
-    #                `lift_more` to `FORCE_INLINE` mode.
-    lift_mode = itir_transforms.LiftMode.FORCE_INLINE
     arg_types = [type_translation.from_value(arg) for arg in args]
 
     # visit ITIR and generate SDFG
-    program = preprocess_program(program, offset_provider, lift_mode)
-    sdfg_genenerator = ItirToSDFG(arg_types, offset_provider, column_axis)
+    program, tmps = preprocess_program(program, offset_provider, lift_mode)
+    sdfg_genenerator = ItirToSDFG(arg_types, offset_provider, tmps, column_axis)
     sdfg = sdfg_genenerator.visit(program)
     if sdfg is None:
         raise RuntimeError(f"Visit failed for program {program.id}.")
+    elif tmps:
+        # This pass is needed to avoid transformation errors in SDFG inlining, because temporaries are using offsets
+        sdfg.apply_transformations_repeated(RefineNestedAccess)
 
     for nested_sdfg in sdfg.all_sdfgs_recursive():
         if not nested_sdfg.debuginfo:
@@ -338,7 +356,9 @@ def run_dace_iterator(program: itir.FencilDefinition, *args, **kwargs):
 
     arg_types = [type_translation.from_value(arg) for arg in args]
 
-    cache_id = get_cache_id(build_type, on_gpu, program, arg_types, column_axis, offset_provider)
+    cache_id = get_cache_id(
+        build_type, on_gpu, lift_mode, program, arg_types, column_axis, offset_provider
+    )
     if build_cache is not None and cache_id in build_cache:
         # retrieve SDFG program from build cache
         sdfg_program = build_cache[cache_id]

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

@@ -19,8 +19,12 @@
 import gt4py.eve as eve
 from gt4py.next import Dimension, DimensionKind, type_inference as next_typing
 from gt4py.next.common import NeighborTable
-from gt4py.next.iterator import ir as itir, type_inference as itir_typing
-from gt4py.next.iterator.ir import Expr, FunCall, Literal, SymRef
+from gt4py.next.iterator import (
+    ir as itir,
+    transforms as itir_transforms,
+    type_inference as itir_typing,
+)
+from gt4py.next.iterator.ir import Expr, FunCall, Literal, Sym, SymRef
 from gt4py.next.type_system import type_specifications as ts, type_translation
 
 from .itir_to_tasklet import (
@@ -36,6 +40,7 @@
 from .utility import (
     add_mapped_nested_sdfg,
     as_dace_type,
+    as_scalar_type,
     connectivity_identifier,
     create_memlet_at,
     create_memlet_full,
@@ -44,6 +49,7 @@
     flatten_list,
     get_sorted_dims,
     map_nested_sdfg_symbols,
+    new_array_symbols,
     unique_name,
     unique_var_name,
 )
@@ -154,12 +160,14 @@ def __init__(
         self,
         param_types: list[ts.TypeSpec],
         offset_provider: dict[str, NeighborTable],
+        tmps: list[itir_transforms.global_tmps.Temporary],
         column_axis: Optional[Dimension] = None,
     ):
         self.param_types = param_types
         self.column_axis = column_axis
         self.offset_provider = offset_provider
         self.storage_types = {}
+        self.tmps = tmps
 
     def add_storage(
         self,
@@ -189,6 +197,70 @@ def add_storage(
             raise NotImplementedError()
         self.storage_types[name] = type_
 
+    def add_storage_for_temporaries(
+        self, node_params: list[Sym], defs_state: dace.SDFGState, program_sdfg: dace.SDFG
+    ) -> dict[str, str]:
+        symbol_map: dict[str, TaskletExpr] = {}
+        # The shape of temporary arrays might be defined based on scalar values passed as program arguments.
+        # Here we collect these values in a symbol map.
+        tmp_ids = set(tmp.id for tmp in self.tmps)
+        for sym in node_params:
+            if sym.id not in tmp_ids and sym.kind != "Iterator":
+                name_ = str(sym.id)
+                type_ = self.storage_types[name_]
+                assert isinstance(type_, ts.ScalarType)
+                symbol_map[name_] = SymbolExpr(name_, as_dace_type(type_))
+
+        tmp_symbols: dict[str, str] = {}
+        for tmp in self.tmps:
+            tmp_name = str(tmp.id)
+
+            # We visit the domain of the temporary field, passing the set of available symbols.
+            assert isinstance(tmp.domain, itir.FunCall)
+            self.node_types.update(itir_typing.infer_all(tmp.domain))
+            domain_ctx = Context(program_sdfg, defs_state, symbol_map)
+            tmp_domain = self._visit_domain(tmp.domain, domain_ctx)
+
+            # We build the FieldType for this temporary array.
+            dims: list[Dimension] = []
+            for dim, _ in tmp_domain:
+                dims.append(
+                    Dimension(
+                        value=dim,
+                        kind=(
+                            DimensionKind.VERTICAL
+                            if self.column_axis is not None and self.column_axis.value == dim
+                            else DimensionKind.HORIZONTAL
+                        ),
+                    )
+                )
+            assert isinstance(tmp.dtype, str)
+            type_ = ts.FieldType(dims=dims, dtype=as_scalar_type(tmp.dtype))
+            self.storage_types[tmp_name] = type_
+
+            # N.B.: skip generation of symbolic strides and just let dace assign default strides, for now.
+            # Another option, in the future, is to use symbolic strides and apply auto-tuning or some heuristics
+            # to assign optimal stride values.
+            tmp_shape, _ = new_array_symbols(tmp_name, len(dims))
+            tmp_offset = [
+                dace.symbol(unique_name(f"{tmp_name}_offset{i}")) for i in range(len(dims))
+            ]
+            _, tmp_array = program_sdfg.add_array(
+                tmp_name, tmp_shape, as_dace_type(type_.dtype), offset=tmp_offset, transient=True
+            )
+
+            # Loop through all dimensions to visit the symbolic expressions for array shape and offset.
+            # These expressions are later mapped to interstate symbols.
+            for (_, (begin, end)), offset_sym, shape_sym in zip(
+                tmp_domain,
+                tmp_array.offset,
+                tmp_array.shape,
+            ):
+                tmp_symbols[str(offset_sym)] = f"0 - {begin.value}"
+                tmp_symbols[str(shape_sym)] = f"{end.value} - {begin.value}"
+
+        return tmp_symbols
+
     def get_output_nodes(
         self, closure: itir.StencilClosure, sdfg: dace.SDFG, state: dace.SDFGState
     ) -> dict[str, dace.nodes.AccessNode]:
@@ -204,7 +276,7 @@ def get_output_nodes(
     def visit_FencilDefinition(self, node: itir.FencilDefinition):
         program_sdfg = dace.SDFG(name=node.id)
         program_sdfg.debuginfo = dace_debuginfo(node)
-        last_state = program_sdfg.add_state("program_entry", True)
+        entry_state = program_sdfg.add_state("program_entry", is_start_block=True)
         self.node_types = itir_typing.infer_all(node)
 
         # Filter neighbor tables from offset providers.
@@ -214,6 +286,20 @@ def visit_FencilDefinition(self, node: itir.FencilDefinition):
         for param, type_ in zip(node.params, self.param_types):
             self.add_storage(program_sdfg, str(param.id), type_, neighbor_tables)
 
+        if self.tmps:
+            tmp_symbols = self.add_storage_for_temporaries(node.params, entry_state, program_sdfg)
+            # on the first interstate edge define symbols for shape and offsets of temporary arrays
+            last_state = program_sdfg.add_state("init_symbols_for_temporaries")
+            program_sdfg.add_edge(
+                entry_state,
+                last_state,
+                dace.InterstateEdge(
+                    assignments=tmp_symbols,
+                ),
+            )
+        else:
+            last_state = entry_state
+
         # Add connectivities as SDFG storages.
         for offset, offset_provider in neighbor_tables.items():
             scalar_kind = type_translation.get_scalar_kind(offset_provider.table.dtype)

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

@@ -51,6 +51,14 @@ def as_dace_type(type_: ts.ScalarType):
     raise ValueError(f"Scalar type '{type_}' not supported.")
 
 
+def as_scalar_type(typestr: str) -> ts.ScalarType:
+    try:
+        kind = getattr(ts.ScalarKind, typestr.upper())
+    except AttributeError:
+        raise ValueError(f"Data type {typestr} not supported.")
+    return ts.ScalarType(kind)
+
+
 def filter_neighbor_tables(offset_provider: dict[str, Any]):
     return {
         offset: table