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Aug 4, 2021
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Linalg to desc 1 #813

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9 changes: 6 additions & 3 deletions dpnp/dpnp_algo/dpnp_algo_statistics.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -138,6 +138,7 @@ cpdef dparray dpnp_cov(dparray array1):


cpdef dparray _dpnp_max(dparray input, _axis_, output_shape):
cdef dparray_shape_type input_shape = input.shape
cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(input.dtype)

cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_MAX, param1_type, param1_type)
Expand All @@ -157,7 +158,7 @@ cpdef dparray _dpnp_max(dparray input, _axis_, output_shape):
axis_.push_back(shape_it)
axis_size = len(axis)

func(input.get_data(), result.get_data(), < size_t * > input._dparray_shape.data(), input.ndim, < size_t * > axis_.data(), axis_size)
func(input.get_data(), result.get_data(), < size_t * > input_shape.data(), input.ndim, < size_t * > axis_.data(), axis_size)

dpnp_array = dpnp.array(result, dtype=input.dtype)
dpnp_result_array = dpnp_array.reshape(output_shape)
Expand Down Expand Up @@ -194,6 +195,7 @@ cpdef dparray dpnp_max(dparray input, axis):


cpdef dparray _dpnp_mean(dparray input):
cdef dparray_shape_type input_shape = input.shape
cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(input.dtype)

cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_MEAN, param1_type, param1_type)
Expand All @@ -207,7 +209,7 @@ cpdef dparray _dpnp_mean(dparray input):
cdef dparray_shape_type axis
cdef Py_ssize_t axis_size = 0

func(input.get_data(), result.get_data(), < size_t * > input._dparray_shape.data(), input.ndim, < size_t * > axis.data(), axis_size)
func(input.get_data(), result.get_data(), < size_t * > input_shape.data(), input.ndim, < size_t * > axis.data(), axis_size)

return result

Expand Down Expand Up @@ -336,6 +338,7 @@ cpdef dparray dpnp_median(utils.dpnp_descriptor array1):


cpdef dparray _dpnp_min(dparray input, _axis_, output_shape):
cdef dparray_shape_type input_shape = input.shape
cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(input.dtype)

cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_MIN, param1_type, param1_type)
Expand All @@ -357,7 +360,7 @@ cpdef dparray _dpnp_min(dparray input, _axis_, output_shape):
axis_.push_back(shape_it)
axis_size = len(axis)

func(input.get_data(), result.get_data(), < size_t * > input._dparray_shape.data(), input.ndim, < size_t * > axis_.data(), axis_size)
func(input.get_data(), result.get_data(), < size_t * > input_shape.data(), input.ndim, < size_t * > axis_.data(), axis_size)

dpnp_array = dpnp.array(result, dtype=input.dtype)
dpnp_result_array = dpnp_array.reshape(output_shape)
Expand Down
54 changes: 26 additions & 28 deletions dpnp/linalg/dpnp_algo_linalg.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@ and the rest of the library
"""

import dpnp
from dpnp.dpnp_utils cimport *
cimport dpnp.dpnp_utils as utils
from dpnp.dpnp_algo cimport *
from dpnp.dparray cimport dparray, dparray_shape_type
import numpy
Expand Down Expand Up @@ -63,24 +63,19 @@ ctypedef void(*custom_linalg_1in_3out_shape_t)(void * , void * , void * , void *
ctypedef void(*custom_linalg_2in_1out_func_ptr_t)(void * , void * , void * , size_t )


cpdef dparray dpnp_cholesky(dparray input):
if input.dtype == dpnp.int32 or input.dtype == dpnp.int64:
input_ = input.astype(dpnp.float64)
else:
input_ = input

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

cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(input_.dtype)

cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_CHOLESKY, param1_type, param1_type)

result_type = dpnp_DPNPFuncType_to_dtype( < size_t > kernel_data.return_type)
cdef dparray result = dparray(input_.shape, dtype=result_type)
# ceate result array with type given by FPTR data
cdef utils.dpnp_descriptor result = utils.create_output_descriptor(input_.shape, kernel_data.return_type, None)

cdef custom_linalg_1in_1out_with_2size_func_ptr_t_ func = <custom_linalg_1in_1out_with_2size_func_ptr_t_ > kernel_data.ptr

func(input_.get_data(), result.get_data(), input.size, size_)
func(input_.get_data(), result.get_data(), input_.size, size_)

return result

Expand Down Expand Up @@ -108,30 +103,31 @@ cpdef dparray dpnp_cond(dparray input, p):
return ret


cpdef dparray dpnp_det(dparray input):
cpdef utils.dpnp_descriptor dpnp_det(utils.dpnp_descriptor input):
cdef dparray_shape_type input_shape = input.shape
cdef size_t n = input.shape[-1]
cdef size_t size_out = 1
if input.ndim != 2:
output_shape = tuple((list(input.shape))[:-2])
for i in range(len(output_shape)):
size_out *= output_shape[i]

cdef dparray_shape_type result_shape = (size_out,)
if size_out > 1:
result_shape = output_shape

cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(input.dtype)

cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_DET, param1_type, param1_type)

result_type = dpnp_DPNPFuncType_to_dtype( < size_t > kernel_data.return_type)
cdef dparray result = dparray(size_out, dtype=result_type)
# ceate result array with type given by FPTR data
cdef utils.dpnp_descriptor result = utils.create_output_descriptor(result_shape, kernel_data.return_type, None)

cdef custom_linalg_1in_1out_func_ptr_t func = <custom_linalg_1in_1out_func_ptr_t > kernel_data.ptr

func(input.get_data(), result.get_data(), < size_t * > input._dparray_shape.data(), input.ndim)
func(input.get_data(), result.get_data(), < size_t * > input_shape.data(), input.ndim)

if size_out > 1:
dpnp_result = result.reshape(output_shape)
return dpnp_result
else:
return result
return result


cpdef tuple dpnp_eig(dparray x1):
Expand All @@ -154,17 +150,16 @@ cpdef tuple dpnp_eig(dparray x1):
return (res_val, res_vec)


cpdef dparray dpnp_eigvals(dparray input):
cpdef utils.dpnp_descriptor dpnp_eigvals(utils.dpnp_descriptor input):
cdef dparray_shape_type input_shape = input.shape

cdef size_t size = 0 if input_shape.empty() else input_shape.front()

cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(input.dtype)
cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_EIGVALS, param1_type, param1_type)

result_type = dpnp_DPNPFuncType_to_dtype( < size_t > kernel_data.return_type)

cdef dparray res_val = dparray((size,), dtype=result_type)
# ceate result array with type given by FPTR data
cdef utils.dpnp_descriptor res_val = utils.create_output_descriptor((size,), kernel_data.return_type, None)

cdef custom_linalg_1in_1out_with_size_func_ptr_t_ func = <custom_linalg_1in_1out_with_size_func_ptr_t_ > kernel_data.ptr
# call FPTR function
Expand All @@ -175,6 +170,8 @@ cpdef dparray dpnp_eigvals(dparray input):

cpdef dparray dpnp_inv(dparray input_):
cdef dparray input = input_.astype(dpnp.float64)
cdef dparray_shape_type input_shape = input.shape

cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(input.dtype)

cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_INV, param1_type, param1_type)
Expand All @@ -183,23 +180,24 @@ cpdef dparray dpnp_inv(dparray input_):

cdef custom_linalg_1in_1out_func_ptr_t func = <custom_linalg_1in_1out_func_ptr_t > kernel_data.ptr

func(input.get_data(), result.get_data(), < size_t * > input._dparray_shape.data(), input.ndim)
func(input.get_data(), result.get_data(), < size_t * > input_shape.data(), input.ndim)

dpnp_result = result.reshape(input.shape)
return dpnp_result


cpdef dparray dpnp_matrix_rank(dparray input):
cpdef utils.dpnp_descriptor dpnp_matrix_rank(utils.dpnp_descriptor input):
cdef dparray_shape_type input_shape = input.shape
cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(input.dtype)

cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_MATRIX_RANK, param1_type, param1_type)

result_type = dpnp_DPNPFuncType_to_dtype( < size_t > kernel_data.return_type)
cdef dparray result = dparray((1,), dtype=result_type)
# ceate result array with type given by FPTR data
cdef utils.dpnp_descriptor result = utils.create_output_descriptor((1,), kernel_data.return_type, None)

cdef custom_linalg_1in_1out_func_ptr_t func = <custom_linalg_1in_1out_func_ptr_t > kernel_data.ptr

func(input.get_data(), result.get_data(), < size_t * > input._dparray_shape.data(), input.ndim)
func(input.get_data(), result.get_data(), < size_t * > input_shape.data(), input.ndim)

return result

Expand Down
52 changes: 27 additions & 25 deletions dpnp/linalg/dpnp_iface_linalg.py
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Expand Up @@ -88,13 +88,17 @@ def cholesky(input):
matrix object if `input` is a matrix object.
"""

if not use_origin_backend(input):
if not isinstance(input, dparray):
pass
elif input.shape[-1] != input.shape[-2]:
x1_desc = dpnp.get_dpnp_descriptor(input)
if x1_desc:
if x1_desc.shape[-1] != x1_desc.shape[-2]:
pass
else:
return dpnp_cholesky(input)
if input.dtype == dpnp.int32 or input.dtype == dpnp.int64:
# TODO memory copy. needs to move into DPNPC
input_ = dpnp.get_dpnp_descriptor(input.astype(dpnp.float64))
else:
input_ = x1_desc
return dpnp_cholesky(input_).get_pyobj()

return call_origin(numpy.linalg.cholesky, input)

Expand Down Expand Up @@ -140,11 +144,11 @@ def det(input):
det : (...) array_like
Determinant of `input`.
"""
is_input_dparray = isinstance(input, dparray)

if not use_origin_backend(input) and is_input_dparray:
if input.shape[-1] == input.shape[-2]:
result_obj = dpnp_det(input)
x1_desc = dpnp.get_dpnp_descriptor(input)
if x1_desc:
if x1_desc.shape[-1] == x1_desc.shape[-2]:
result_obj = dpnp_det(x1_desc).get_pyobj()
result = dpnp.convert_single_elem_array_to_scalar(result_obj)

return result
Expand Down Expand Up @@ -188,11 +192,10 @@ def eigvals(input):
real for real matrices.
"""

is_input_dparray = isinstance(input, dparray)

if (not use_origin_backend(input) and is_input_dparray):
if (input.size > 0):
return dpnp_eigvals(input)
x1_desc = dpnp.get_dpnp_descriptor(input)
if x1_desc:
if x1_desc.size > 0:
return dpnp_eigvals(x1_desc).get_pyobj()

return call_origin(numpy.linalg.eigvals, input)

Expand Down Expand Up @@ -276,18 +279,17 @@ def matrix_rank(input, tol=None, hermitian=False):

"""

is_input_dparray = isinstance(input, dparray)

if not use_origin_backend(input) and is_input_dparray:
x1_desc = dpnp.get_dpnp_descriptor(input)
if x1_desc:
if tol is not None:
checker_throw_value_error("matrix_rank", "tol", type(tol), None)
if hermitian is not False:
checker_throw_value_error("matrix_rank", "hermitian", hermitian, False)

result_obj = dpnp_matrix_rank(input)
result = dpnp.convert_single_elem_array_to_scalar(result_obj)

return result
pass
elif hermitian:
pass
else:
result_obj = dpnp_matrix_rank(x1_desc).get_pyobj()
result = dpnp.convert_single_elem_array_to_scalar(result_obj)
return result

return call_origin(numpy.linalg.matrix_rank, input, tol, hermitian)

Expand Down