Add fill_zeros to private array

numba_dpex/experimental/_kernel_dpcpp_spirv_overloads/_private_array_overloads.py

@@ -9,6 +9,7 @@
 
 import llvmlite.ir as llvmir
 from llvmlite.ir.builder import IRBuilder
+from numba.core import cgutils
 from numba.core.typing.npydecl import parse_dtype as _ty_parse_dtype
 from numba.core.typing.npydecl import parse_shape as _ty_parse_shape
 from numba.core.typing.templates import Signature
@@ -31,9 +32,13 @@
     inline="always",
 )
 def _intrinsic_private_array_ctor(
-    ty_context, ty_shape, ty_dtype  # pylint: disable=unused-argument
+    ty_context,  # pylint: disable=unused-argument
+    ty_shape,
+    ty_dtype,
+    ty_fill_zeros,
 ):
     require_literal(ty_shape)
+    require_literal(ty_fill_zeros)
 
     ty_array = USMNdArray(
         dtype=_ty_parse_dtype(ty_dtype),
@@ -42,7 +47,7 @@ def _intrinsic_private_array_ctor(
         addrspace=AddressSpace.PRIVATE,
     )
 
-    sig = ty_array(ty_shape, ty_dtype)
+    sig = ty_array(ty_shape, ty_dtype, ty_fill_zeros)
 
     def codegen(
         context: DpexExpKernelTypingContext,
@@ -52,11 +57,18 @@ def codegen(
     ):
         shape = args[0]
         ty_shape = sig.args[0]
+        ty_fill_zeros = sig.args[-1]
         ty_array = sig.return_type
 
         ary = make_spirv_generic_array_on_stack(
             context, builder, ty_array, ty_shape, shape
         )
+
+        if ty_fill_zeros.literal_value:
+            cgutils.memset(
+                builder, ary.data, builder.mul(ary.itemsize, ary.nitems), 0
+            )
+
         return ary._getvalue()  # pylint: disable=protected-access
 
     return (
@@ -74,6 +86,7 @@ def codegen(
 def ol_private_array_ctor(
     shape,
     dtype,
+    fill_zeros=False,
 ):
     """Overload of the constructor for the class
     class:`numba_dpex.kernel_api.PrivateArray`.
@@ -88,8 +101,9 @@ def ol_private_array_ctor(
     def ol_private_array_ctor_impl(
         shape,
         dtype,
+        fill_zeros=False,
     ):
         # pylint: disable=no-value-for-parameter
-        return _intrinsic_private_array_ctor(shape, dtype)
+        return _intrinsic_private_array_ctor(shape, dtype, fill_zeros)
 
     return ol_private_array_ctor_impl

numba_dpex/kernel_api/private_array.py

@@ -7,7 +7,7 @@
 kernel function.
 """
 
-from numpy import ndarray
+import numpy as np
 
 
 class PrivateArray:
@@ -16,10 +16,13 @@ class PrivateArray:
     inside kernel work item.
     """
 
-    def __init__(self, shape, dtype) -> None:
+    def __init__(self, shape, dtype, fill_zeros=False) -> None:
         """Creates a new PrivateArray instance of the given shape and dtype."""
 
-        self._data = ndarray(shape=shape, dtype=dtype)
+        if fill_zeros:
+            self._data = np.zeros(shape=shape, dtype=dtype)
+        else:
+            self._data = np.empty(shape=shape, dtype=dtype)
 
     def __getitem__(self, idx_obj):
         """Returns the value stored at the position represented by idx_obj in

numba_dpex/tests/experimental/test_private_array.py

@@ -23,6 +23,30 @@ def private_array_kernel(item: Item, a):
         a[i] += p[j]
 
 
+def private_array_kernel_fill_true(item: Item, a):
+    i = item.get_linear_id()
+    p = PrivateArray(10, a.dtype, fill_zeros=True)
+
+    for j in range(10):
+        p[j] = j * j
+
+    a[i] = 0
+    for j in range(10):
+        a[i] += p[j]
+
+
+def private_array_kernel_fill_false(item: Item, a):
+    i = item.get_linear_id()
+    p = PrivateArray(10, a.dtype, fill_zeros=False)
+
+    for j in range(10):
+        p[j] = j * j
+
+    a[i] = 0
+    for j in range(10):
+        a[i] += p[j]
+
+
 def private_2d_array_kernel(item: Item, a):
     i = item.get_linear_id()
     p = PrivateArray(shape=(5, 2), dtype=a.dtype)
@@ -36,7 +60,13 @@ def private_2d_array_kernel(item: Item, a):
 
 
 @pytest.mark.parametrize(
-    "kernel", [private_array_kernel, private_2d_array_kernel]
+    "kernel",
+    [
+        private_array_kernel,
+        private_array_kernel_fill_true,
+        private_array_kernel_fill_false,
+        private_2d_array_kernel,
+    ],
 )
 @pytest.mark.parametrize(
     "call_kernel, decorator",