algo module. optimize imports a bit (#741)

Author: shssf

dpnp/dpnp_algo/dpnp_algo.pyx

@@ -33,6 +33,7 @@ and the rest of the library
 """
 
 from libc.time cimport time, time_t
+import dpnp
 import dpnp.config as config
 import numpy
 
@@ -111,7 +112,7 @@ cpdef dparray dpnp_array(obj, dtype=None):
     if not cpython.PySequence_Check(obj):
         raise TypeError(f"DPNP array(): Unsupported non-sequence obj={type(obj)}")
 
-    obj_shape, elem_dtype = get_shape_dtype(obj)
+    obj_shape, elem_dtype = utils.get_shape_dtype(obj)
     if dtype is not None:
         """ Set type from parameter. result might be empty array """
         result = dparray(obj_shape, dtype=dtype)
@@ -122,7 +123,7 @@ cpdef dparray dpnp_array(obj, dtype=None):
         else:
             result = dparray(obj_shape, dtype=elem_dtype)
 
-    copy_values_to_dparray(result, obj)
+    utils.copy_values_to_dparray(result, obj)
 
     return result
 
@@ -241,9 +242,9 @@ cpdef dparray dpnp_matmul(dparray in_array1, dparray in_array2, dparray out=None
 
     if out is not None:
         if out.dtype != result_type:
-            checker_throw_value_error('matmul', 'out.dtype', out.dtype, result_type)
+            utils.checker_throw_value_error('matmul', 'out.dtype', out.dtype, result_type)
         if out.shape != shape_result:
-            checker_throw_value_error('matmul', 'out.shape', out.shape, shape_result)
+            utils.checker_throw_value_error('matmul', 'out.shape', out.shape, shape_result)
         result = out
     else:
         result = dparray(shape_result, dtype=result_type)
@@ -302,7 +303,7 @@ cpdef DPNPFuncType dpnp_dtype_to_DPNPFuncType(dtype):
     elif dtype in [numpy.bool, numpy.bool_, 'bool', '?']:
         return DPNP_FT_BOOL
     else:
-        checker_throw_type_error("dpnp_dtype_to_DPNPFuncType", dtype)
+        utils.checker_throw_type_error("dpnp_dtype_to_DPNPFuncType", dtype)
 
 cpdef dpnp_DPNPFuncType_to_dtype(size_t type):
     """
@@ -322,7 +323,7 @@ cpdef dpnp_DPNPFuncType_to_dtype(size_t type):
     elif type == <size_t > DPNP_FT_BOOL:
         return numpy.bool
     else:
-        checker_throw_type_error("dpnp_DPNPFuncType_to_dtype", type)
+        utils.checker_throw_type_error("dpnp_DPNPFuncType_to_dtype", type)
 
 
 cdef dparray call_fptr_1out(DPNPFuncName fptr_name, result_shape, result_dtype):
@@ -373,23 +374,23 @@ cdef dparray call_fptr_2in_1out(DPNPFuncName fptr_name, object x1_obj, object x2
 
     cdef dparray x1_dparray, x2_dparray
 
-    common_type = find_common_type(x1_obj, x2_obj)
+    common_type = utils.find_common_type(x1_obj, x2_obj)
 
     if x1_obj_is_dparray:
         x1_dparray = x1_obj
     else:
         x1_dparray = dparray((1,), dtype=common_type)
-        copy_values_to_dparray(x1_dparray, (x1_obj,))
+        utils.copy_values_to_dparray(x1_dparray, (x1_obj,))
 
     if x2_obj_is_dparray:
         x2_dparray = x2_obj
     else:
         x2_dparray = dparray((1,), dtype=common_type)
-        copy_values_to_dparray(x2_dparray, (x2_obj,))
+        utils.copy_values_to_dparray(x2_dparray, (x2_obj,))
 
     x1_shape = x1_dparray.shape
     x2_shape = x2_dparray.shape
-    result_shape = get_common_shape(x1_shape, x2_shape)
+    result_shape = utils.get_common_shape(x1_shape, x2_shape)
 
     # Convert string type names (dparray.dtype) to C enum DPNPFuncType
     cdef DPNPFuncType x1_c_type = dpnp_dtype_to_DPNPFuncType(x1_dparray.dtype)
@@ -401,7 +402,7 @@ cdef dparray call_fptr_2in_1out(DPNPFuncName fptr_name, object x1_obj, object x2
     # TODO: apply parameters out and dtype after reafactoring fmap (required 4th level nesting)
 
     # Create result array
-    cdef dparray result = create_output_array(result_shape, kernel_data.return_type, out)
+    cdef dparray result = utils.create_output_array(result_shape, kernel_data.return_type, out)
 
     """ Call FPTR function """
     cdef fptr_2in_1out_t func = <fptr_2in_1out_t > kernel_data.ptr
@@ -411,7 +412,7 @@ cdef dparray call_fptr_2in_1out(DPNPFuncName fptr_name, object x1_obj, object x2
     return result
 
 # this is replacement for "call_fptr_2in_1out". original function must be deleted after transotion.
-cdef dparray call_fptr_2in_1out_new(DPNPFuncName fptr_name, dpnp_descriptor x1_obj, dpnp_descriptor x2_obj,
+cdef dparray call_fptr_2in_1out_new(DPNPFuncName fptr_name, utils.dpnp_descriptor x1_obj, utils.dpnp_descriptor x2_obj,
                                     object dtype=None, dparray out=None, object where=True):
     # Convert string type names (dparray.dtype) to C enum DPNPFuncType
     cdef DPNPFuncType x1_c_type = dpnp_dtype_to_DPNPFuncType(x1_obj.dtype)
@@ -423,8 +424,8 @@ cdef dparray call_fptr_2in_1out_new(DPNPFuncName fptr_name, dpnp_descriptor x1_o
     # Create result array
     cdef dparray_shape_type x1_shape = x1_obj.shape
     cdef dparray_shape_type x2_shape = x2_obj.shape
-    cdef dparray_shape_type result_shape = get_common_shape(x1_shape, x2_shape)
-    cdef dparray result = create_output_array(result_shape, kernel_data.return_type, out)
+    cdef dparray_shape_type result_shape = utils.get_common_shape(x1_shape, x2_shape)
+    cdef dparray result = utils.create_output_array(result_shape, kernel_data.return_type, out)
 
     """ Call FPTR function """
     cdef fptr_2in_1out_t func = <fptr_2in_1out_t > kernel_data.ptr

dpnp/dpnp_algo/dpnp_algo_arraycreation.pyx

@@ -32,13 +32,7 @@ and the rest of the library
 
 """
 
-
-import dpnp
-import numpy
-
-from dpnp.dpnp_utils cimport *
-from dpnp.dpnp_algo cimport *
-
+# NO IMPORTs here. All imports must be placed into main "dpnp_algo.pyx" file
 
 __all__ += [
     "dpnp_copy",

dpnp/dpnp_algo/dpnp_algo_bitwise.pyx

@@ -32,9 +32,7 @@ and the rest of the library
 
 """
 
-
-from dpnp.dpnp_utils cimport *
-
+# NO IMPORTs here. All imports must be placed into main "dpnp_algo.pyx" file
 
 __all__ += [
     "dpnp_bitwise_and",

dpnp/dpnp_algo/dpnp_algo_counting.pyx

@@ -32,10 +32,7 @@ and the rest of the library
 
 """
 
-
-import numpy
-from dpnp.dpnp_utils cimport *
-
+# NO IMPORTs here. All imports must be placed into main "dpnp_algo.pyx" file
 
 __all__ += [
     "dpnp_count_nonzero"

dpnp/dpnp_algo/dpnp_algo_indexing.pyx

@@ -32,11 +32,7 @@ and the rest of the library
 
 """
 
-
-import numpy
-from dpnp.dpnp_utils cimport *
-from dpnp.dpnp_iface_counting import count_nonzero
-
+# NO IMPORTs here. All imports must be placed into main "dpnp_algo.pyx" file
 
 __all__ += [
     "dpnp_choose",
@@ -169,7 +165,7 @@ cpdef tuple dpnp_nonzero(dparray in_array1):
     res_count = in_array1.ndim
 
     # have to go through array one extra time to count size of result arrays
-    res_size = count_nonzero(in_array1)
+    res_size = dpnp.count_nonzero(in_array1)
 
     cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(in_array1.dtype)
 
@@ -185,7 +181,7 @@ cpdef tuple dpnp_nonzero(dparray in_array1):
 
         res_list.append(res_arr)
 
-    result = _object_to_tuple(res_list)
+    result = utils._object_to_tuple(res_list)
 
     return result
 

dpnp/dpnp_algo/dpnp_algo_linearalgebra.pyx

@@ -32,9 +32,7 @@ and the rest of the library
 
 """
 
-from dpnp.dpnp_utils cimport *
-cimport numpy
-
+# NO IMPORTs here. All imports must be placed into main "dpnp_algo.pyx" file
 
 __all__ += [
     "dpnp_dot",
@@ -78,7 +76,7 @@ cpdef dparray dpnp_dot(dparray in_array1, dparray in_array2):
     # vector
     # test: pytest tests/third_party/cupy/linalg_tests/test_product.py::TestProduct::test_dot_vec1 -v -s
     if size1 != size2:
-        raise checker_throw_runtime_error("dpnp_dot", "input vectors must be of equal size")
+        utils.checker_throw_runtime_error("dpnp_dot", "input vectors must be of equal size")
 
     # convert string type names (dparray.dtype) to C enum DPNPFuncType
     cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(in_array1.dtype)

dpnp/dpnp_algo/dpnp_algo_logic.pyx

@@ -32,13 +32,7 @@ and the rest of the library
 
 """
 
-
-import dpnp
-import numpy
-
-from dpnp.dpnp_utils cimport *
-from dpnp.dpnp_algo cimport *
-
+# NO IMPORTs here. All imports must be placed into main "dpnp_algo.pyx" file
 
 __all__ += [
     "dpnp_all",

dpnp/dpnp_algo/dpnp_algo_manipulation.pyx

@@ -32,9 +32,7 @@ and the rest of the library
 
 """
 
-
-from dpnp.dpnp_utils cimport *
-
+# NO IMPORTs here. All imports must be placed into main "dpnp_algo.pyx" file
 
 __all__ += [
     "dpnp_atleast_2d",
@@ -98,21 +96,21 @@ cpdef dpnp_copyto(dparray dst, dparray src, where=True):
 
 
 cpdef dparray dpnp_expand_dims(dparray in_array, axis):
-    axis_tuple = _object_to_tuple(axis)
+    axis_tuple = utils._object_to_tuple(axis)
     result_ndim = len(axis_tuple) + in_array.ndim
 
     if len(axis_tuple) == 0:
         axis_ndim = 0
     else:
         axis_ndim = max(-min(0, min(axis_tuple)), max(0, max(axis_tuple))) + 1
 
-    axis_norm = _object_to_tuple(normalize_axis(axis_tuple, result_ndim))
+    axis_norm = utils._object_to_tuple(utils.normalize_axis(axis_tuple, result_ndim))
 
     if axis_ndim - len(axis_norm) > in_array.ndim:
-        checker_throw_axis_error("dpnp_expand_dims", "axis", axis, axis_ndim)
+        utils.checker_throw_axis_error("dpnp_expand_dims", "axis", axis, axis_ndim)
 
     if len(axis_norm) > len(set(axis_norm)):
-        checker_throw_value_error("dpnp_expand_dims", "axis", axis, "no repeated axis")
+        utils.checker_throw_value_error("dpnp_expand_dims", "axis", axis, "no repeated axis")
 
     shape_list = []
     axis_idx = 0
@@ -123,7 +121,7 @@ cpdef dparray dpnp_expand_dims(dparray in_array, axis):
             shape_list.append(in_array.shape[axis_idx])
             axis_idx = axis_idx + 1
 
-    shape = _object_to_tuple(shape_list)
+    shape = utils._object_to_tuple(shape_list)
     cdef dparray result = dpnp.copy(in_array).reshape(shape)
 
     return result
@@ -195,15 +193,15 @@ cpdef dparray dpnp_squeeze(dparray in_array, axis):
             if in_array.shape[i] != 1:
                 shape_list.append(in_array.shape[i])
     else:
-        axis_norm = _object_to_tuple(normalize_axis(_object_to_tuple(axis), in_array.ndim))
+        axis_norm = utils._object_to_tuple(utils.normalize_axis(utils._object_to_tuple(axis), in_array.ndim))
         for i in range(in_array.ndim):
             if i in axis_norm:
                 if in_array.shape[i] != 1:
-                    checker_throw_value_error("dpnp_squeeze", "axis", axis, "axis has size not equal to one")
+                    utils.checker_throw_value_error("dpnp_squeeze", "axis", axis, "axis has size not equal to one")
             else:
                 shape_list.append(in_array.shape[i])
 
-    shape = _object_to_tuple(shape_list)
+    shape = utils._object_to_tuple(shape_list)
     cdef dparray result = dpnp.copy(in_array).reshape(shape)
 
     return result

dpnp/dpnp_algo/dpnp_algo_mathematical.pyx

@@ -32,11 +32,7 @@ and the rest of the library
 
 """
 
-from dpnp.dpnp_utils cimport *
-import dpnp
-import numpy
-cimport numpy
-
+# NO IMPORTs here. All imports must be placed into main "dpnp_algo.pyx" file
 
 __all__ += [
     "dpnp_absolute",
@@ -257,7 +253,7 @@ cpdef dparray dpnp_minimum(object x1_obj, object x2_obj, object dtype=None, dpar
     return call_fptr_2in_1out(DPNP_FN_MINIMUM, x1_obj, x2_obj, dtype=dtype, out=out, where=where)
 
 
-cpdef tuple dpnp_modf(dpnp_descriptor x1):
+cpdef tuple dpnp_modf(utils.dpnp_descriptor x1):
     """ Convert string type names (dparray.dtype) to C enum DPNPFuncType """
     cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(x1.dtype)
 
@@ -355,16 +351,16 @@ cpdef dparray dpnp_prod(dparray input, object axis=None, object dtype=None, dpar
     cdef dparray_shape_type input_shape = input.shape
     cdef DPNPFuncType input_c_type = dpnp_dtype_to_DPNPFuncType(input.dtype)
 
-    cdef dparray_shape_type axis_shape = _object_to_tuple(axis)
+    cdef dparray_shape_type axis_shape = utils._object_to_tuple(axis)
 
-    cdef dparray_shape_type result_shape = get_reduction_output_shape(input_shape, axis, keepdims)
-    cdef DPNPFuncType result_c_type = get_output_c_type(DPNP_FN_PROD, input_c_type, out, dtype)
+    cdef dparray_shape_type result_shape = utils.get_reduction_output_shape(input_shape, axis, keepdims)
+    cdef DPNPFuncType result_c_type = utils.get_output_c_type(DPNP_FN_PROD, input_c_type, out, dtype)
 
     """ select kernel """
     cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_PROD, input_c_type, result_c_type)
 
     """ Create result array """
-    cdef dparray result = create_output_array(result_shape, result_c_type, out)
+    cdef dparray result = utils.create_output_array(result_shape, result_c_type, out)
     cdef dpnp_reduction_c_t func = <dpnp_reduction_c_t > kernel_data.ptr
 
     """ Call FPTR interface function """
@@ -390,16 +386,16 @@ cpdef dparray dpnp_sum(dparray input, object axis=None, object dtype=None, dparr
     cdef dparray_shape_type input_shape = input.shape
     cdef DPNPFuncType input_c_type = dpnp_dtype_to_DPNPFuncType(input.dtype)
 
-    cdef dparray_shape_type axis_shape = _object_to_tuple(axis)
+    cdef dparray_shape_type axis_shape = utils._object_to_tuple(axis)
 
-    cdef dparray_shape_type result_shape = get_reduction_output_shape(input_shape, axis, keepdims)
-    cdef DPNPFuncType result_c_type = get_output_c_type(DPNP_FN_SUM, input_c_type, out, dtype)
+    cdef dparray_shape_type result_shape = utils.get_reduction_output_shape(input_shape, axis, keepdims)
+    cdef DPNPFuncType result_c_type = utils.get_output_c_type(DPNP_FN_SUM, input_c_type, out, dtype)
 
     """ select kernel """
     cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_SUM, input_c_type, result_c_type)
 
     """ Create result array """
-    cdef dparray result = create_output_array(result_shape, result_c_type, out)
+    cdef dparray result = utils.create_output_array(result_shape, result_c_type, out)
     cdef dpnp_reduction_c_t func = <dpnp_reduction_c_t > kernel_data.ptr
 
     """ Call FPTR interface function """

dpnp/dpnp_algo/dpnp_algo_searching.pyx

@@ -32,11 +32,7 @@ and the rest of the library
 
 """
 
-from libcpp.string cimport string
-import cython
-import numpy
-
-from dpnp.dpnp_utils cimport *
+# NO IMPORTs here. All imports must be placed into main "dpnp_algo.pyx" file
 
 __all__ += [
     "dpnp_argmax",