Adds compute follows data check to KernelDispatcher

    - Added checks for compute follows data compliance to
      kernel compilation.
    - Removed support for __get_item__ in KernelDispatcher
    - Address review comments.

numba_dpex/core/exceptions.py

@@ -215,6 +215,12 @@ def __init__(self, kernel_name, *, usmarray_argnum_list) -> None:
                 f"usm_ndarray arguments {usmarray_args} were not allocated "
                 "on the same queue."
             )
+        else:
+            self.message = (
+                f'Execution queue for kernel "{kernel_name}" could '
+                "be deduced using compute follows data programming model. The "
+                "kernel has no USMNdArray argument."
+            )
         super().__init__(self.message)
 
 

numba_dpex/experimental/kernel_dispatcher.py

@@ -17,18 +17,12 @@
 from numba_dpex import config, spirv_generator
 from numba_dpex.core.descriptor import dpex_kernel_target
 from numba_dpex.core.exceptions import (
-    InvalidKernelLaunchArgsError,
+    ExecutionQueueInferenceError,
     UnsupportedKernelArgumentError,
 )
-from numba_dpex.core.kernel_interface.indexers import NdRange, Range
 from numba_dpex.core.pipelines import kernel_compiler
 from numba_dpex.core.types import DpnpNdArray
 
-_KernelLauncherLowerResult = namedtuple(
-    "_KernelLauncherLowerResult",
-    ["sig", "fndesc", "library", "call_helper"],
-)
-
 _KernelModule = namedtuple("_KernelModule", ["kernel_name", "kernel_bitcode"])
 
 _KernelCompileResult = namedtuple(
@@ -38,6 +32,43 @@
 
 
 class _KernelCompiler(_FunctionCompiler):
+    """A special compiler class used to compile numba_dpex.kernel decorated
+    functions.
+    """
+
+    def _check_queue_equivalence_of_args(
+        self, py_func_name: str, args: [types.Type, ...]
+    ):
+        """Evaluates if all DpnpNdArray arguments passed to a kernel function
+        has the same DpctlSyclQueue type.
+
+        Args:
+            py_func_name (str): Name of the kernel that is being evaluated
+            args (types.Type, ...]): List of numba inferred types for each
+            argument passed to the kernel
+
+        Raises:
+            ExecutionQueueInferenceError: If all DpnpNdArray were not allocated
+            on the same dpctl.SyclQueue
+            ExecutionQueueInferenceError: If there were not DpnpNdArray
+            arguments passed to the kernel.
+        """
+        common_queue = None
+
+        for arg in args:
+            if isinstance(arg, DpnpNdArray):
+                if common_queue is None:
+                    common_queue = arg.queue
+                elif common_queue != arg.queue:
+                    raise ExecutionQueueInferenceError(
+                        kernel_name=py_func_name, usmarray_argnum_list=[]
+                    )
+
+        if common_queue is None:
+            raise ExecutionQueueInferenceError(
+                kernel_name=py_func_name, usmarray_argnum_list=None
+            )
+
     def _compile_to_spirv(
         self, kernel_library, kernel_fndesc, kernel_targetctx
     ):
@@ -156,9 +187,6 @@ def __init__(
         targetoptions["experimental"] = True
 
         self._kernel_name = pyfunc.__name__
-        self._range = None
-        self._ndrange = None
-
         self.typingctx = self.targetdescr.typing_context
         self.targetctx = self.targetdescr.target_context
 
@@ -185,7 +213,7 @@ def __init__(
         self._cache = NullCache()
         compiler_class = self._impl_kinds[impl_kind]
         self._impl_kind = impl_kind
-        self._compiler = compiler_class(
+        self._compiler: _KernelCompiler = compiler_class(
             pyfunc, self.targetdescr, targetoptions, locals, pipeline_class
         )
         self._cache_hits = Counter()
@@ -265,9 +293,14 @@ def cb_llvm(dur):
                 )
                 with ev.trigger_event("numba_dpex:compile", data=ev_details):
                     try:
+                        self._compiler._check_queue_equivalence_of_args(
+                            self._kernel_name, args
+                        )
                         kcres: _KernelCompileResult = self._compiler.compile(
                             args, return_type
                         )
+                    except ExecutionQueueInferenceError as eqie:
+                        raise eqie
                     except errors.ForceLiteralArg as e:
 
                         def folded(args, kws):
@@ -283,40 +316,11 @@ def folded(args, kws):
                 return kcres.kernel_module
 
     def __getitem__(self, args):
-        """Square-bracket notation for configuring the global_range and
-        local_range settings when launching a kernel on a SYCL queue.
-
-        When a Python function decorated with the @kernel decorator,
-        is invoked it creates a KernelLauncher object. Calling the
-        KernelLauncher objects ``__getitem__`` function inturn clones the object
-        and sets the ``global_range`` and optionally the ``local_range``
-        attributes with the arguments passed to ``__getitem__``.
-
-        Args:
-            args (tuple): A tuple of tuples that specify the global and
-            optionally the local range for the kernel execution. If the
-            argument is a two-tuple of tuple, then it is assumed that both
-            global and local range options are specified. The first entry is
-            considered to be the global range and the second the local range.
-
-            If only a single tuple value is provided, then the kernel is
-            launched with only a global range and the local range configuration
-            is decided by the SYCL runtime.
-
-        Returns:
-            KernelLauncher: A clone of the KernelLauncher object, but with the
-            global_range and local_range attributes initialized.
+        """Square-bracket notation for configuring launch arguments is not
+        supported.
         """
 
-        if isinstance(args, Range):
-            self._range = args
-        elif isinstance(args, NdRange):
-            self._ndrange = args
-        else:
-            # FIXME: Improve error message
-            raise InvalidKernelLaunchArgsError(kernel_name=self._kernel_name)
-
-        return self
+        raise NotImplementedError
 
     def __call__(self, *args, **kw_args):
         """Functor to launch a kernel."""

numba_dpex/tests/experimental/test_exec_queue_inference.py

@@ -0,0 +1,90 @@
+# SPDX-FileCopyrightText: 2023 Intel Corporation
+#
+# SPDX-License-Identifier: Apache-2.0
+
+
+import dpctl
+import dpnp
+import pytest
+
+import numba_dpex.experimental as exp_dpex
+from numba_dpex import Range
+from numba_dpex.core.exceptions import ExecutionQueueInferenceError
+
+
+@exp_dpex.kernel(
+    release_gil=False,
+    no_compile=True,
+    no_cpython_wrapper=True,
+    no_cfunc_wrapper=True,
+)
+def add(a, b, c):
+    c[0] = b[0] + a[0]
+
+
+def test_successful_execution_queue_inference():
+    """
+    Tests if KernelDispatcher successfully infers the execution queue for the
+    kernel.
+    """
+
+    q = dpctl.SyclQueue()
+    a = dpnp.ones(100, sycl_queue=q)
+    b = dpnp.ones_like(a, sycl_queue=q)
+    c = dpnp.zeros_like(a, sycl_queue=q)
+    r = Range(100)
+
+    # FIXME: This test fails unexpectedly if the NUMBA_CAPTURED_ERRORS is set
+    # to "new_style"
+    try:
+        exp_dpex.call_kernel(add, r, a, b, c)
+    except:
+        pytest.fail("Unexpected error when calling kernel")
+
+    assert c[0] == b[0] + a[0]
+
+
+def test_execution_queue_inference_error():
+    """
+    Tests if KernelDispatcher successfully raised ExecutionQueueInferenceError
+    when dpnp.ndarray arguments do not share the same dpctl.SyclQueue
+    instance.
+    """
+
+    q1 = dpctl.SyclQueue()
+    q2 = dpctl.SyclQueue()
+    a = dpnp.ones(100, sycl_queue=q1)
+    b = dpnp.ones_like(a, sycl_queue=q2)
+    c = dpnp.zeros_like(a, sycl_queue=q1)
+    r = Range(100)
+
+    from numba.core import config
+
+    current_captured_error_style = config.CAPTURED_ERRORS
+    config.CAPTURED_ERRORS = "new_style"
+
+    with pytest.raises(ExecutionQueueInferenceError):
+        exp_dpex.call_kernel(add, r, a, b, c)
+
+    config.CAPTURED_ERRORS = current_captured_error_style
+
+
+def test_error_when_no_array_args():
+    """
+    Tests if KernelDispatcher successfully raised ExecutionQueueInferenceError
+    when no dpnp.ndarray arguments were passed to a kernel.
+    """
+    a = 1
+    b = 2
+    c = 3
+    r = Range(100)
+
+    from numba.core import config
+
+    current_captured_error_style = config.CAPTURED_ERRORS
+    config.CAPTURED_ERRORS = "new_style"
+
+    with pytest.raises(ExecutionQueueInferenceError):
+        exp_dpex.call_kernel(add, r, a, b, c)
+
+    config.CAPTURED_ERRORS = current_captured_error_style