Add flake8 eradicate

.flake8

@@ -13,9 +13,12 @@ per-file-ignores =
     __init__.py: F403
     # module level import not at top of file
     target.py: E402
+    # config should containe code lines examples in comment
+    docs/source/conf.py: E800
 exclude =
     .git,
     __pycache__,
     _version.py,
+    versioneer.py,
     lowerer.py,
     parfor.py

.pre-commit-config.yaml

@@ -35,6 +35,7 @@ repos:
     rev: 3.9.2
     hooks:
     -   id: flake8
+        additional_dependencies: [flake8-eradicate]
 -   repo: https://github.com/koalaman/shellcheck-precommit
     rev: v0.8.0
     hooks:

numba_dpex/core/itanium_mangler.py

@@ -184,9 +184,6 @@ def mangle_identifier(ident, template_params="", *, abi_tags=(), uid=None):
 
     This treats '.' as '::' in C++.
     """
-    # if uid is not None:
-    # Add uid to abi-tags
-    #    abi_tags = (f"v{uid}", *abi_tags)
     parts = [_len_encoded(_escape_string(x)) for x in ident.split(".")]
     enc_abi_tags = list(map(mangle_abi_tag, abi_tags))
     extras = template_params + "".join(enc_abi_tags)

numba_dpex/core/kernel_interface/dispatcher.py

@@ -488,7 +488,7 @@ def __call__(self, *args):
 
         # TODO: return event that calls wait if no reference to the object if
         # it is possible
-        # event = exec_queue.submit(
+        # event = exec_queue.submit( # noqa: E800
         exec_queue.submit(
             sycl_kernel,
             packer.unpacked_args,

numba_dpex/core/parfors/parfor_lowerer.py

@@ -414,7 +414,7 @@ def _lower_parfor_as_kernel(self, lowerer, parfor):
         flags.error_model = "numpy"
 
         # Can't get here unless
-        # flags.set('auto_parallel', ParallelOptions(True))
+        # flags.set('auto_parallel', ParallelOptions(True)) # noqa: E800
         index_var_typ = typemap[parfor.loop_nests[0].index_variable.name]
 
         # index variables should have the same type, check rest of indices

numba_dpex/core/parfors/reduction_helper.py

@@ -41,7 +41,7 @@ def _allocate_partial_reduction_arrays(
         # Get the type of the reduction variable.
         redvar_typ = lowerer.fndesc.typemap[red_name]
 
-        # redarrvar_typ is type(partial_sum)
+        # redarrvar_typ is type(partial_sum) # noqa: E800 help understanding
         redarrvar_typ = self._redtyp_to_redarraytype(redvar_typ, inputArrayType)
         reddtype = redarrvar_typ.dtype
         redarrdim = redarrvar_typ.ndim
@@ -63,7 +63,7 @@ def _allocate_partial_reduction_arrays(
             name="tot_work",
         )
 
-        # global_size_mod = tot_work%work_group_size
+        # global_size_mod = tot_work%work_group_size # noqa: E800 help understanding
         ir_expr = ir.Expr.binop(
             operator.mod, total_work_var, work_group_size_var, loc
         )
@@ -232,7 +232,7 @@ def __init__(
         loop_body = copy.copy(parfor_node.loop_body)
         remove_dels(loop_body)
 
-        # parfor_dim = len(parfor_node.loop_nests)
+        # parfor_dim = len(parfor_node.loop_nests) # noqa: E800 help understanding
         loop_indices = [
             loop_nest.index_variable.name
             for loop_nest in parfor_node.loop_nests

numba_dpex/core/targets/dpjit_target.py

@@ -38,8 +38,11 @@ def init(self):
         self.is32bit = utils.MACHINE_BITS == 32
         self._internal_codegen = JITCPUCodegen("numba.exec")
         self.lower_extensions = {}
+        # TODO: initialize nrt once switched to nrt from drt. Most likely we
+        # call it somewhere. Double check.
+        # https://github.com/IntelPython/numba-dpex/issues/1175
         # Initialize NRT runtime
-        # rtsys.initialize(self)
+        # rtsys.initialize(self) # noqa: E800
         self.refresh()
 
     @cached_property

numba_dpex/decorators.py

@@ -159,7 +159,7 @@ def dpjit(*args, **kws):
     # FIXME: When trying to use dpex's target context, overloads do not work
     # properly. We will turn on dpex target once the issue is fixed.
 
-    # kws.update({"_target": "dpex"})
+    # kws.update({"_target": "dpex"}) # noqa: E800
 
     return decorators.jit(*args, **kws)
 

numba_dpex/dpnp_iface/arrayobj.py

@@ -234,7 +234,7 @@ def impl(
                 shape,
                 dtype=None,
                 order="C",
-                # like=None, # see issue https://github.com/IntelPython/numba-dpex/issues/998
+                # like=None, # noqa: E800 see issue https://github.com/IntelPython/numba-dpex/issues/998
                 device=None,
                 usm_type="device",
                 sycl_queue=None,
@@ -243,7 +243,7 @@ def impl(
                     shape,
                     _dtype,
                     order,
-                    # like, # see issue https://github.com/IntelPython/numba-dpex/issues/998
+                    # like, # noqa: E800 see issue https://github.com/IntelPython/numba-dpex/issues/998
                     _device,
                     _usm_type,
                     sycl_queue,

numba_dpex/examples/blacksholes_njit.py

@@ -12,11 +12,6 @@
 
 import numba_dpex as dpex
 
-# @numba.vectorize(nopython=True)
-# def cndf2(inp):
-#     out = 0.5 + 0.5 * math.erf((math.sqrt(2.0) / 2.0) * inp)
-#     return out
-
 
 @dpex.dpjit
 def blackscholes(sptprice, strike, timev, rate, volatility):
@@ -68,7 +63,6 @@ def run(iterations):
     t1 = time.time()
     put = blackscholes(sptprice, initStrike, rate, volatility, timev)
     t = time.time() - t1
-    #  print("checksum: ", sum(put))
     print(put)
     print("SELFTIMED ", t)
 

numba_dpex/numba_patches/patch_arrayexpr_tree_to_ir.py

@@ -46,14 +46,12 @@ def _ufunc_to_parfor_instr(
             g_math_assign = ir.Assign(g_math, g_math_var, loc)
             func_var_def = ir.Expr.getattr(g_math_var, "sqrt", loc)
             out_ir.append(g_math_assign)
-        #                     out_ir.append(func_var_def)
         ir_expr = ir.Expr.call(func_var, arg_vars, (), loc)
         call_typ = typemap[func_var.name].get_call_type(
             typingctx, tuple(typemap[a.name] for a in arg_vars), {}
         )
         calltypes[ir_expr] = call_typ
         el_typ = call_typ.return_type
-        # signature(el_typ, el_typ)
         out_ir.append(ir.Assign(func_var_def, func_var, loc))
         out_ir.append(ir.Assign(ir_expr, expr_out_var, loc))
 
@@ -122,7 +120,6 @@ def _arrayexpr_tree_to_ir(
                 out_ir.append(ir.Assign(ir_expr, expr_out_var, loc))
             for T in array_analysis.MAP_TYPES:
                 if isinstance(op, T):
-                    # elif isinstance(op, (np.ufunc, DUFunc)):
                     # function calls are stored in variables which are not removed
                     # op is typing_key to the variables type
                     el_typ = _ufunc_to_parfor_instr(
@@ -169,7 +166,6 @@ def _arrayexpr_tree_to_ir(
                     out_ir,
                 )
             else:
-                # assert typemap[expr.name]==el_typ
                 el_typ = var_typ
                 ir_expr = expr
             out_ir.append(ir.Assign(ir_expr, expr_out_var, loc))

numba_dpex/numba_patches/patch_mk_alloc.py

@@ -77,7 +77,7 @@ def _mk_alloc(
                 out,
             )
 
-        # g_np_var = Global(numpy)
+        # g_np_var = Global(numpy) # noqa: E800
         g_np_var = ir.Var(scope, mk_unique_var("$np_g_var"), loc)
         if typemap:
             typemap[g_np_var.name] = types.misc.Module(numpy)

numba_dpex/ocl/mathdecl.py

@@ -108,9 +108,6 @@ def resolve_degrees(self, mod):
     def resolve_radians(self, mod):
         return types.Function(Math_radians)
 
-    # def resolve_hypot(self, mod):
-    # return types.Function(Math_hypot)
-
     def resolve_copysign(self, mod):
         return types.Function(Math_copysign)
 
@@ -248,16 +245,6 @@ class Math_degrees(Math_unary):
     key = math.degrees
 
 
-# class Math_hypot(ConcreteTemplate):
-# key = math.hypot
-#     cases = [
-#         signature(types.float64, types.int64, types.int64),
-#         signature(types.float64, types.uint64, types.uint64),
-#         signature(types.float32, types.float32, types.float32),
-#         signature(types.float64, types.float64, types.float64),
-#     ]
-
-
 class Math_erf(Math_unary):
     key = math.erf
 
@@ -340,7 +327,6 @@ class Math_isinf(ConcreteTemplate):
 infer_global(math.asinh, types.Function(Math_asinh))
 infer_global(math.acosh, types.Function(Math_acosh))
 infer_global(math.atanh, types.Function(Math_atanh))
-# infer_global(math.hypot, types.Function(Math_hypot))
 infer_global(math.floor, types.Function(Math_floor))
 infer_global(math.ceil, types.Function(Math_ceil))
 infer_global(math.trunc, types.Function(Math_trunc))

numba_dpex/ocl/mathimpl.py

@@ -160,7 +160,6 @@ def function_name_to_supported_decl(name, sig):
             return None
 
     fn = _mk_fn_decl(name, sig)
-    # lower(key, *sig.args)(fn)
     lower_ocl_impl[(name, sig)] = lower(key, *sig.args)(fn)
 
 

numba_dpex/ocl/ocldecl.py

@@ -22,8 +22,6 @@
 intrinsic_attr = registry.register_attr
 intrinsic_global = registry.register_global
 
-# register_number_classes(intrinsic_global)
-
 
 @intrinsic
 class Ocl_get_global_id(ConcreteTemplate):

numba_dpex/parfor_diagnostics.py

@@ -41,7 +41,6 @@ def print_auto_offloading(self, lines):
             tmp.append([])
 
         summary = dict()
-        # region : {fused, serialized}
 
         def print_nest(fadj_, nadj_, theroot, reported, region_id):
             def print_g(fadj_, nadj_, nroot, depth):

numba_dpex/tests/core/types/test_box_unbox.py

@@ -24,7 +24,7 @@
     lambda: numpy.empty(10, dtype=numpy.float32),
     lambda: dpnp.empty(10, dtype=dpnp.float32),
     # TODO: is it possible to test USMNd array same way?
-    # lambda: dpx.USMNdArray(1, queue=None, dtype=dpx.float32),
+    # lambda: dpx.USMNdArray(1, queue=None, dtype=dpx.float32), # noqa: E800
 ]
 
 ranges = [(10,), (10, 10), (10, 10, 10)]
@@ -56,7 +56,6 @@ def unbox_box(a):
     return a
 
 
-# @numba.njit
 @dpx.dpjit
 def unbox(a):
     return None

numba_dpex/tests/debugging/test_breakpoints.py

@@ -77,13 +77,13 @@ def test_breakpoint_with_condition_by_function_argument(app, breakpoint, api):
     "breakpoint, script",
     [
         # location specified by file name and function name
-        # commands/break_file_func
+        # commands/break_file_func # noqa: E800
         ("simple_sum.py:data_parallel_sum", None),
         # location specified by function name
-        # commands/break_func
+        # commands/break_func # noqa: E800
         ("data_parallel_sum", "simple_sum.py"),
         # location specified by file name and nested function name
-        # commands/break_nested_func
+        # commands/break_nested_func # noqa: E800
         ("simple_dpex_func.py:func_sum", None),
     ],
 )
@@ -95,7 +95,7 @@ def test_breakpoint_common(app, breakpoint, script):
 @pytest.mark.parametrize(
     "breakpoint, variable_name, variable_value",
     [
-        # commands/break_conditional
+        # commands/break_conditional # noqa: E800
         (f"{simple_sum_condition_breakpoint} if i == 1", "i", "1"),
     ],
 )

numba_dpex/tests/dpjit_tests/dpnp/test_dpnp_full.py

@@ -118,7 +118,6 @@ def func(shape, fill_value):
             "Returned queue does not have the same queue as cached against the device."
         )
 
-    # dummy = dpnp.full(shape, fill_value, dtype=dtype)
     # dpnp can't cast 4294967295 into int32 and so on,
     # but we can, also numpy can, so we are using numpy here
     dummy = numpy.full(shape, fill_value, dtype=dtype)
@@ -164,7 +163,6 @@ def func(shape, fill_value, queue):
             "Returned queue does not have the same queue as the one passed to the dpnp function."
         )
 
-    # dummy = dpnp.full(shape, fill_value, dtype=dtype)
     # dpnp can't cast 4294967295 into int32 and so on,
     # but we can, also numpy can, so we are using numpy here
     dummy = numpy.full(shape, fill_value, dtype=dtype)

numba_dpex/tests/dpjit_tests/dpnp/test_dpnp_full_like.py

@@ -117,7 +117,6 @@ def func(x, fill_value):
             "Returned queue does not have the same queue as cached against the device."
         )
 
-    # dummy = dpnp.full_like(a, fill_value, dtype=dtype)
     # dpnp can't cast 4294967295 into int32 and so on,
     # but we can, also numpy can, so we are using numpy here
     dummy = numpy.full_like(a.asnumpy(), fill_value, dtype=dtype)
@@ -161,7 +160,6 @@ def func(x, fill_value, queue):
     assert c.sycl_queue == a.sycl_queue
     assert c.sycl_queue == queue
 
-    # dummy = dpnp.full_like(a, fill_value, dtype=dtype)
     # dpnp can't cast 4294967295 into int32 and so on,
     # but we can, also numpy can, so we are using numpy here
     dummy = numpy.full_like(a.asnumpy(), fill_value, dtype=dtype)

numba_dpex/tests/kernel_tests/test_usm_ndarray_interop.py

@@ -9,12 +9,6 @@
 import numba_dpex as dpex
 from numba_dpex.tests._helper import get_all_dtypes
 
-# list_of_dtype = [
-#     numpy.int32,
-#     numpy.int64,
-#     numpy.float32,
-#     numpy.float64,
-# ]
 list_of_dtype = get_all_dtypes(
     no_bool=True, no_float16=True, no_none=True, no_complex=True
 )

numba_dpex/vectorizers.py

@@ -148,7 +148,6 @@ def attempt_ravel(a):
                 return devout.reshape(outshape)
             else:
                 # Otherwise, transfer output back to host
-                # return devout.copy_to_host().reshape(outshape)
                 raise ValueError("copy_to_host() is not yet supported")
 
         elif cr.is_device_array(out):