Merge pull request #1148 from IntelPython/feature/range_ndrange_type_…

…support

Adds Range and NdRange as supported types in numba_dpex.dpjit.

numba_dpex/core/datamodel/models.py

@@ -14,6 +14,8 @@
     DpctlSyclEvent,
     DpctlSyclQueue,
     DpnpNdArray,
+    NdRangeType,
+    RangeType,
     USMNdArray,
 )
 
@@ -195,6 +197,39 @@ def __init__(self, dmm, fe_type):
         super(SyclEventModel, self).__init__(dmm, fe_type, members)
 
 
+class RangeModel(StructModel):
+    """The native data model for a
+    numba_dpex.core.kernel_interface.indexers.Range PyObject.
+    """
+
+    def __init__(self, dmm, fe_type):
+        members = [
+            ("ndim", types.int64),
+            ("dim0", types.int64),
+            ("dim1", types.int64),
+            ("dim2", types.int64),
+        ]
+        super(RangeModel, self).__init__(dmm, fe_type, members)
+
+
+class NdRangeModel(StructModel):
+    """The native data model for a
+    numba_dpex.core.kernel_interface.indexers.NdRange PyObject.
+    """
+
+    def __init__(self, dmm, fe_type):
+        members = [
+            ("ndim", types.int64),
+            ("gdim0", types.int64),
+            ("gdim1", types.int64),
+            ("gdim2", types.int64),
+            ("ldim0", types.int64),
+            ("ldim1", types.int64),
+            ("ldim2", types.int64),
+        ]
+        super(NdRangeModel, self).__init__(dmm, fe_type, members)
+
+
 def _init_data_model_manager() -> datamodel.DataModelManager:
     """Initializes a DpexKernelTarget-specific data model manager.
 
@@ -249,3 +284,8 @@ def _init_data_model_manager() -> datamodel.DataModelManager:
 
 # Register the DpctlSyclEvent type
 register_model(DpctlSyclEvent)(SyclEventModel)
+# Register the RangeType type
+register_model(RangeType)(RangeModel)
+
+# Register the NdRangeType type
+register_model(NdRangeType)(NdRangeModel)

numba_dpex/core/kernel_interface/indexers.py

@@ -4,6 +4,11 @@
 
 from collections.abc import Iterable
 
+from llvmlite import ir as llvmir
+from numba.core import cgutils, errors, types
+from numba.core.datamodel import default_manager
+from numba.extending import intrinsic, overload
+
 
 class Range(tuple):
     """A data structure to encapsulate a single kernel launch parameter.
@@ -18,6 +23,8 @@ class Range(tuple):
     the behavior of `sycl::range`.
     """
 
+    UNDEFINED_DIMENSION = -1
+
     def __new__(cls, dim0, dim1=None, dim2=None):
         """Constructs a 1, 2, or 3 dimensional range.
 
@@ -74,6 +81,50 @@ def size(self):
         else:
             return self[0]
 
+    @property
+    def ndim(self) -> int:
+        """Returns the rank of a Range object.
+
+        Returns:
+            int: Number of dimensions in the Range object
+        """
+        return len(self)
+
+    @property
+    def dim0(self) -> int:
+        """Return the extent of the first dimension for the Range object.
+
+        Returns:
+            int: Extent of first dimension for the Range object
+        """
+        return self[0]
+
+    @property
+    def dim1(self) -> int:
+        """Return the extent of the second dimension for the Range object.
+
+        Returns:
+            int: Extent of second dimension for the Range object or -1 for 1D
+            Range
+        """
+        try:
+            return self[1]
+        except IndexError:
+            return Range.UNDEFINED_DIMENSION
+
+    @property
+    def dim2(self) -> int:
+        """Return the extent of the second dimension for the Range object.
+
+        Returns:
+            int: Extent of second dimension for the Range object or -1 for 1D or
+            2D Range
+        """
+        try:
+            return self[2]
+        except IndexError:
+            return Range.UNDEFINED_DIMENSION
+
 
 class NdRange:
     """A class to encapsulate all kernel launch parameters.
@@ -169,3 +220,170 @@ def __repr__(self):
             str: str representation for NdRange class.
         """
         return self.__str__()
+
+    def __eq__(self, other):
+        if isinstance(other, NdRange):
+            return (
+                self.global_range == other.global_range
+                and self.local_range == other.local_range
+            )
+        else:
+            return False
+
+
+@intrinsic
+def _intrin_range_alloc(typingctx, ty_dim0, ty_dim1, ty_dim2, ty_range):
+    ty_retty = ty_range.instance_type
+    sig = ty_retty(
+        ty_dim0,
+        ty_dim1,
+        ty_dim2,
+        ty_range,
+    )
+
+    def codegen(context, builder, sig, args):
+        typ = sig.return_type
+        dim0, dim1, dim2, _ = args
+        range_struct = cgutils.create_struct_proxy(typ)(context, builder)
+        range_struct.dim0 = dim0
+
+        if not isinstance(sig.args[1], types.NoneType):
+            range_struct.dim1 = dim1
+        else:
+            range_struct.dim1 = llvmir.Constant(
+                llvmir.types.IntType(64), Range.UNDEFINED_DIMENSION
+            )
+
+        if not isinstance(sig.args[2], types.NoneType):
+            range_struct.dim2 = dim2
+        else:
+            range_struct.dim2 = llvmir.Constant(
+                llvmir.types.IntType(64), Range.UNDEFINED_DIMENSION
+            )
+
+        range_struct.ndim = llvmir.Constant(llvmir.types.IntType(64), typ.ndim)
+
+        return range_struct._getvalue()
+
+    return sig, codegen
+
+
+@intrinsic
+def _intrin_ndrange_alloc(
+    typingctx, ty_global_range, ty_local_range, ty_ndrange
+):
+    ty_retty = ty_ndrange.instance_type
+    sig = ty_retty(
+        ty_global_range,
+        ty_local_range,
+        ty_ndrange,
+    )
+    range_datamodel = default_manager.lookup(ty_global_range)
+
+    def codegen(context, builder, sig, args):
+        typ = sig.return_type
+
+        global_range, local_range, _ = args
+        ndrange_struct = cgutils.create_struct_proxy(typ)(context, builder)
+        ndrange_struct.ndim = llvmir.Constant(
+            llvmir.types.IntType(64), typ.ndim
+        )
+        ndrange_struct.gdim0 = builder.extract_value(
+            global_range,
+            range_datamodel.get_field_position("dim0"),
+        )
+        ndrange_struct.gdim1 = builder.extract_value(
+            global_range,
+            range_datamodel.get_field_position("dim1"),
+        )
+        ndrange_struct.gdim2 = builder.extract_value(
+            global_range,
+            range_datamodel.get_field_position("dim2"),
+        )
+        ndrange_struct.ldim0 = builder.extract_value(
+            local_range,
+            range_datamodel.get_field_position("dim0"),
+        )
+        ndrange_struct.ldim1 = builder.extract_value(
+            local_range,
+            range_datamodel.get_field_position("dim1"),
+        )
+        ndrange_struct.ldim2 = builder.extract_value(
+            local_range,
+            range_datamodel.get_field_position("dim2"),
+        )
+
+        return ndrange_struct._getvalue()
+
+    return sig, codegen
+
+
+@overload(Range)
+def _ol_range_init(dim0, dim1=None, dim2=None):
+    """Numba overload of the Range constructor to make it usable inside an
+    njit and dpjit decorated function.
+
+    """
+    from numba_dpex.core.types import RangeType
+
+    ndims = 1
+    ty_optional_dims = (dim1, dim2)
+
+    # A Range should at least have the 0th dimension populated
+    if not isinstance(dim0, types.Integer):
+        raise errors.TypingError(
+            "Expected a Range's dimension should to be an Integer value, but "
+            "encountered " + dim0.name
+        )
+
+    for ty_dim in ty_optional_dims:
+        if isinstance(ty_dim, types.Integer):
+            ndims += 1
+        elif ty_dim is not None:
+            raise errors.TypingError(
+                "Expected a Range's dimension to be an Integer value, "
+                f"but {type(ty_dim)} was provided."
+            )
+
+    ret_ty = RangeType(ndims)
+
+    def impl(dim0, dim1=None, dim2=None):
+        return _intrin_range_alloc(dim0, dim1, dim2, ret_ty)
+
+    return impl
+
+
+@overload(NdRange)
+def _ol_ndrange_init(global_range, local_range):
+    """Numba overload of the NdRange constructor to make it usable inside an
+    njit and dpjit decorated function.
+
+    """
+    from numba_dpex.core.exceptions import UnmatchedNumberOfRangeDimsError
+    from numba_dpex.core.types import NdRangeType, RangeType
+
+    if not isinstance(global_range, RangeType):
+        raise errors.TypingError(
+            "Only global range values specified as a Range are "
+            "supported inside dpjit"
+        )
+
+    if not isinstance(local_range, RangeType):
+        raise errors.TypingError(
+            "Only local range values specified as a Range are "
+            "supported inside dpjit"
+        )
+
+    if not global_range.ndim == local_range.ndim:
+        raise UnmatchedNumberOfRangeDimsError(
+            kernel_name="",
+            global_ndims=global_range.ndim,
+            local_ndims=local_range.ndim,
+        )
+
+    ret_ty = NdRangeType(global_range.ndim)
+
+    def impl(global_range, local_range):
+        return _intrin_ndrange_alloc(global_range, local_range, ret_ty)
+
+    return impl

numba_dpex/core/types/__init__.py

@@ -26,6 +26,7 @@
     uint64,
     void,
 )
+from .range_types import NdRangeType, RangeType
 from .usm_ndarray_type import USMNdArray
 
 usm_ndarray = USMNdArray
@@ -35,6 +36,8 @@
     "DpctlSyclQueue",
     "DpctlSyclEvent",
     "DpnpNdArray",
+    "RangeType",
+    "NdRangeType",
     "USMNdArray",
     "none",
     "boolean",