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Update test_statistic.py to run on Iris Xe #1494

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Merged
vlad-perevezentsev merged 7 commits into from
Jul 24, 2023
Merged

Update test_statistic.py to run on Iris Xe #1494

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f1ca919
Update dpnp_median
vlad-perevezentsev Jul 21, 2023
6665b9f
Update dpnp_cov
vlad-perevezentsev Jul 21, 2023
b79acb1
Update test_statistics
vlad-perevezentsev Jul 21, 2023
7c5c6c1
Move get_ret_type_and_func to dpnp_algo_utils
vlad-perevezentsev Jul 24, 2023
4eac725
Merge master into fix_test_statistics
vlad-perevezentsev Jul 24, 2023
70b38f3
Call get_default_floating_type without <>
vlad-perevezentsev Jul 24, 2023
618f2fb
Fix remarks
vlad-perevezentsev Jul 24, 2023
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16 changes: 14 additions & 2 deletions 16 dpnp/backend/kernels/dpnp_krnl_statistics.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1330,9 +1330,21 @@ void func_map_init_statistics(func_map_t &fmap)
eft_DBL, (void *)dpnp_median_default_c<double, double>};

fmap[DPNPFuncName::DPNP_FN_MEDIAN_EXT][eft_INT][eft_INT] = {
eft_DBL, (void *)dpnp_median_ext_c<int32_t, double>};
get_default_floating_type<>(),
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(void *)dpnp_median_ext_c<
int32_t, func_type_map_t::find_type<get_default_floating_type<>()>>,
get_default_floating_type<std::false_type>(),
(void *)dpnp_median_ext_c<
int32_t, func_type_map_t::find_type<
get_default_floating_type<std::false_type>()>>};
fmap[DPNPFuncName::DPNP_FN_MEDIAN_EXT][eft_LNG][eft_LNG] = {
eft_DBL, (void *)dpnp_median_ext_c<int64_t, double>};
get_default_floating_type<>(),
(void *)dpnp_median_ext_c<
int64_t, func_type_map_t::find_type<get_default_floating_type<>()>>,
get_default_floating_type<std::false_type>(),
(void *)dpnp_median_ext_c<
int64_t, func_type_map_t::find_type<
get_default_floating_type<std::false_type>()>>};
fmap[DPNPFuncName::DPNP_FN_MEDIAN_EXT][eft_FLT][eft_FLT] = {
eft_FLT, (void *)dpnp_median_ext_c<float, float>};
fmap[DPNPFuncName::DPNP_FN_MEDIAN_EXT][eft_DBL][eft_DBL] = {
Expand Down
8 changes: 5 additions & 3 deletions 8 dpnp/dpnp_algo/dpnp_algo_statistics.pxi
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Expand Up @@ -265,8 +265,12 @@ cpdef utils.dpnp_descriptor dpnp_median(utils.dpnp_descriptor array1):

array1_obj = array1.get_array()

cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(array1_obj, kernel_data)
cdef DPNPFuncType return_type = ret_type_and_func[0]
cdef custom_statistic_1in_1out_func_ptr_t func = < custom_statistic_1in_1out_func_ptr_t > ret_type_and_func[1]

cdef utils.dpnp_descriptor result = utils.create_output_descriptor((1,),
kernel_data.return_type,
return_type,
None,
device=array1_obj.sycl_device,
usm_type=array1_obj.usm_type,
Expand All @@ -277,8 +281,6 @@ cpdef utils.dpnp_descriptor dpnp_median(utils.dpnp_descriptor array1):
cdef c_dpctl.SyclQueue q = <c_dpctl.SyclQueue> result_sycl_queue
cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref()

cdef custom_statistic_1in_1out_func_ptr_t func = <custom_statistic_1in_1out_func_ptr_t > kernel_data.ptr

# stub for interface support
cdef shape_type_c axis
cdef Py_ssize_t axis_size = 0
Expand Down
8 changes: 7 additions & 1 deletion 8 dpnp/dpnp_utils/dpnp_algo_utils.pxd
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Expand Up @@ -28,7 +28,7 @@
from libcpp cimport bool as cpp_bool

from dpnp.dpnp_algo cimport shape_type_c
from dpnp.dpnp_algo.dpnp_algo cimport DPNPFuncName, DPNPFuncType
from dpnp.dpnp_algo.dpnp_algo cimport DPNPFuncData, DPNPFuncName, DPNPFuncType


cpdef checker_throw_runtime_error(function_name, message)
Expand Down Expand Up @@ -162,3 +162,9 @@ cdef tuple get_common_usm_allocation(dpnp_descriptor x1, dpnp_descriptor x2)
"""
Get common USM allocation in the form of (sycl_device, usm_type, sycl_queue)
"""

cdef (DPNPFuncType, void *) get_ret_type_and_func(x1_obj, DPNPFuncData kernel_data)
"""
Get the corresponding return type and function pointer based on the
capability of the allocated input array device for the integer types.
"""
10 changes: 10 additions & 0 deletions 10 dpnp/dpnp_utils/dpnp_algo_utils.pyx
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Expand Up @@ -663,6 +663,16 @@ cdef tuple get_common_usm_allocation(dpnp_descriptor x1, dpnp_descriptor x2):
return (common_sycl_queue.sycl_device, common_usm_type, common_sycl_queue)


cdef (DPNPFuncType, void *) get_ret_type_and_func(x1_obj, DPNPFuncData kernel_data):
if dpnp.issubdtype(x1_obj.dtype, dpnp.integer) and not x1_obj.sycl_device.has_aspect_fp64:
return_type = kernel_data.return_type_no_fp64
func = kernel_data.ptr_no_fp64
else:
return_type = kernel_data.return_type
func = kernel_data.ptr
return return_type, func


cdef class dpnp_descriptor:
def __init__(self, obj, dpnp_descriptor orig_desc=None):
""" Initialze variables """
Expand Down
9 changes: 9 additions & 0 deletions 9 dpnp/dpnp_utils/dpnp_utils_statistics.py
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Expand Up @@ -59,6 +59,11 @@ def _get_2dmin_array(x, dtype):
It casts to another dtype, if the input array differs from requested one.

"""
# dtype map for devices without double precision type support
dtype_map = {
dpnp.float64: dpnp.float32,
dpnp.complex128: dpnp.complex64,
}
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if x.ndim == 0:
x = x.reshape((1, 1))
Expand All @@ -69,6 +74,10 @@ def _get_2dmin_array(x, dtype):
x = x.T

if x.dtype != dtype:
fp64 = x.sycl_device.has_aspect_fp64
# TODO: remove when dpctl.result_type() is fixed
if not fp64:
dtype = dtype_map[dtype.type]
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x = dpnp.astype(x, dtype)
return x

Expand Down
20 changes: 5 additions & 15 deletions 20 dpnp/linalg/dpnp_algo_linalg.pyx
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Expand Up @@ -79,16 +79,6 @@ ctypedef c_dpctl.DPCTLSyclEventRef(*custom_linalg_2in_1out_func_ptr_t)(c_dpctl.D
const c_dpctl.DPCTLEventVectorRef)


cdef (DPNPFuncType, void *) get_ret_type_and_func(x1_obj, DPNPFuncData kernel_data):
if dpnp.issubdtype(x1_obj.dtype, dpnp.integer) and not x1_obj.sycl_device.has_aspect_fp64:
return_type = kernel_data.return_type_no_fp64
func = kernel_data.ptr_no_fp64
else:
return_type = kernel_data.return_type
func = kernel_data.ptr
return return_type, func


cpdef utils.dpnp_descriptor dpnp_cholesky(utils.dpnp_descriptor input_):
size_ = input_.shape[-1]

Expand Down Expand Up @@ -206,7 +196,7 @@ cpdef tuple dpnp_eig(utils.dpnp_descriptor x1):

x1_obj = x1.get_array()

cdef (DPNPFuncType, void *) ret_type_and_func = get_ret_type_and_func(x1_obj, kernel_data)
cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(x1_obj, kernel_data)
cdef DPNPFuncType return_type = ret_type_and_func[0]
cdef custom_linalg_2in_1out_func_ptr_t func = < custom_linalg_2in_1out_func_ptr_t > ret_type_and_func[1]

Expand Down Expand Up @@ -252,7 +242,7 @@ cpdef utils.dpnp_descriptor dpnp_eigvals(utils.dpnp_descriptor input):

input_obj = input.get_array()

cdef (DPNPFuncType, void *) ret_type_and_func = get_ret_type_and_func(input_obj, kernel_data)
cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(input_obj, kernel_data)
cdef DPNPFuncType return_type = ret_type_and_func[0]
cdef custom_linalg_1in_1out_with_size_func_ptr_t_ func = < custom_linalg_1in_1out_with_size_func_ptr_t_ > ret_type_and_func[1]

Expand Down Expand Up @@ -291,7 +281,7 @@ cpdef utils.dpnp_descriptor dpnp_inv(utils.dpnp_descriptor input):

input_obj = input.get_array()

cdef (DPNPFuncType, void *) ret_type_and_func = get_ret_type_and_func(input_obj, kernel_data)
cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(input_obj, kernel_data)
cdef DPNPFuncType return_type = ret_type_and_func[0]
cdef custom_linalg_1in_1out_func_ptr_t func = < custom_linalg_1in_1out_func_ptr_t > ret_type_and_func[1]

Expand Down Expand Up @@ -472,7 +462,7 @@ cpdef tuple dpnp_qr(utils.dpnp_descriptor x1, str mode):

x1_obj = x1.get_array()

cdef (DPNPFuncType, void *) ret_type_and_func = get_ret_type_and_func(x1_obj, kernel_data)
cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(x1_obj, kernel_data)
cdef DPNPFuncType return_type = ret_type_and_func[0]
cdef custom_linalg_1in_3out_shape_t func = < custom_linalg_1in_3out_shape_t > ret_type_and_func[1]

Expand Down Expand Up @@ -525,7 +515,7 @@ cpdef tuple dpnp_svd(utils.dpnp_descriptor x1, cpp_bool full_matrices, cpp_bool

x1_obj = x1.get_array()

cdef (DPNPFuncType, void *) ret_type_and_func = get_ret_type_and_func(x1_obj, kernel_data)
cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(x1_obj, kernel_data)
cdef DPNPFuncType return_type = ret_type_and_func[0]
cdef custom_linalg_1in_3out_shape_t func = < custom_linalg_1in_3out_shape_t > ret_type_and_func[1]

Expand Down
27 changes: 14 additions & 13 deletions 27 tests/test_statistics.py
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@@ -1,37 +1,37 @@
import numpy
import pytest
from numpy.testing import assert_allclose

import dpnp

from .helper import get_all_dtypes


@pytest.mark.parametrize(
"type",
[numpy.float64, numpy.float32, numpy.int64, numpy.int32],
ids=["float64", "float32", "int64", "int32"],
"dtype", get_all_dtypes(no_none=True, no_bool=True, no_complex=True)
)
@pytest.mark.parametrize("size", [2, 4, 8, 16, 3, 9, 27, 81])
def test_median(type, size):
a = numpy.arange(size, dtype=type)
def test_median(dtype, size):
a = numpy.arange(size, dtype=dtype)
ia = dpnp.array(a)

np_res = numpy.median(a)
dpnp_res = dpnp.median(ia)

numpy.testing.assert_allclose(dpnp_res, np_res)
assert_allclose(dpnp_res, np_res)


@pytest.mark.usefixtures("allow_fall_back_on_numpy")
@pytest.mark.parametrize("axis", [0, 1, -1, 2, -2, (1, 2), (0, -2)])
def test_max(axis):
a = numpy.arange(768, dtype=numpy.float64).reshape((4, 4, 6, 8))
dtype = dpnp.default_float_type()
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a = numpy.arange(768, dtype=dtype).reshape((4, 4, 6, 8))
ia = dpnp.array(a)

np_res = numpy.max(a, axis=axis)
dpnp_res = dpnp.max(ia, axis=axis)

numpy.testing.assert_allclose(dpnp_res, np_res)
assert_allclose(dpnp_res, np_res)


@pytest.mark.usefixtures("allow_fall_back_on_numpy")
Expand Down Expand Up @@ -71,16 +71,17 @@ def test_max(axis):
],
)
def test_nanvar(array):
a = numpy.array(array)
dtype = dpnp.default_float_type()
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a = numpy.array(array, dtype=dtype)
ia = dpnp.array(a)
for ddof in range(a.ndim):
expected = numpy.nanvar(a, ddof=ddof)
result = dpnp.nanvar(ia, ddof=ddof)
numpy.testing.assert_array_equal(expected, result)
assert_allclose(expected, result, rtol=1e-06)

expected = numpy.nanvar(a, axis=None, ddof=0)
result = dpnp.nanvar(ia, axis=None, ddof=0)
numpy.testing.assert_array_equal(expected, result)
assert_allclose(expected, result, rtol=1e-06)


@pytest.mark.usefixtures("allow_fall_back_on_numpy")
Expand All @@ -99,7 +100,7 @@ def test_bincount_minlength(self, array, minlength):

expected = numpy.bincount(np_a, minlength=minlength)
result = dpnp.bincount(dpnp_a, minlength=minlength)
numpy.testing.assert_array_equal(expected, result)
assert_allclose(expected, result)

@pytest.mark.parametrize(
"array", [[1, 2, 2, 1, 2, 4]], ids=["[1, 2, 2, 1, 2, 4]"]
Expand All @@ -115,7 +116,7 @@ def test_bincount_weights(self, array, weights):

expected = numpy.bincount(np_a, weights=weights)
result = dpnp.bincount(dpnp_a, weights=weights)
numpy.testing.assert_array_equal(expected, result)
assert_allclose(expected, result)


@pytest.mark.parametrize(
Expand Down
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