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KsanaKozlovaKsanaKozlova
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add allclose function (#770)
* add allclose function
1 parent 06ca5e9 commit ee35bf4

7 files changed

Lines changed: 155 additions & 0 deletions

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‎dpnp/backend/include/dpnp_iface.hpp‎

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@@ -116,6 +116,20 @@ void dpnp_memory_memcpy_c(void* dst, const void* src, size_t size_in_bytes);
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template <typename _DataType, typename _ResultType>
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INP_DLLEXPORT void dpnp_all_c(const void* array, void* result, const size_t size);
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/**
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* @ingroup BACKEND_API
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* @brief Test whether all array elements along a given axis evaluate to True.
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*
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* @param [in] array1_in First input array.
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* @param [in] array2_in Second input array.
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* @param [out] result1 Output array.
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* @param [in] size Number of input elements in `array`.
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* @param [in] rtol_val The relative tolerance parameter.
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* @param [in] atol_val The absolute tolerance parameter.
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*/
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template <typename _DataType1, typename _DataType2, typename _ResultType>
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INP_DLLEXPORT void dpnp_allclose_c(const void* array1_in, const void* array2_in, void* result1, const size_t size, double rtol, double atol);
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/**
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* @ingroup BACKEND_API
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* @brief Test whether any array element along a given axis evaluates to True.

‎dpnp/backend/include/dpnp_iface_fptr.hpp‎

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@@ -61,6 +61,7 @@ enum class DPNPFuncName : size_t
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DPNP_FN_ABSOLUTE, /**< Used in numpy.absolute() implementation */
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DPNP_FN_ADD, /**< Used in numpy.add() implementation */
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DPNP_FN_ALL, /**< Used in numpy.all() implementation */
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DPNP_FN_ALLCLOSE, /**< Used in numpy.allclose() implementation */
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DPNP_FN_ANY, /**< Used in numpy.any() implementation */
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DPNP_FN_ARANGE, /**< Used in numpy.arange() implementation */
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DPNP_FN_ARCCOS, /**< Used in numpy.arccos() implementation */

‎dpnp/backend/kernels/dpnp_krnl_logic.cpp‎

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@@ -71,6 +71,52 @@ void dpnp_all_c(const void* array1_in, void* result1, const size_t size)
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event.wait();
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}
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template <typename _DataType1, typename _DataType2, typename _ResultType>
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class dpnp_allclose_c_kernel;
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template <typename _DataType1, typename _DataType2, typename _ResultType>
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void dpnp_allclose_c(const void* array1_in, const void* array2_in, void* result1, const size_t size, double rtol_val, double atol_val)
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{
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if (!array1_in || !result1)
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{
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return;
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}
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const _DataType1* array1 = reinterpret_cast<const _DataType1*>(array1_in);
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const _DataType2* array2 = reinterpret_cast<const _DataType2*>(array2_in);
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_ResultType* result = reinterpret_cast<_ResultType*>(result1);
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result[0] = true;
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if (!size)
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{
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return;
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}
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cl::sycl::event event;
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cl::sycl::range<1> gws(size);
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auto kernel_parallel_for_func = [=](cl::sycl::id<1> global_id) {
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size_t i = global_id[0];
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if (std::abs(array1[i] - array2[i]) > (atol_val + rtol_val * std::abs(array2[i]))){
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result[0]= false;
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}
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};
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auto kernel_func = [&](cl::sycl::handler& cgh) {
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cgh.parallel_for<class dpnp_allclose_c_kernel<_DataType1, _DataType2, _ResultType>>(gws, kernel_parallel_for_func);
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};
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event = DPNP_QUEUE.submit(kernel_func);
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event.wait();
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}
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template <typename _DataType, typename _ResultType>
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class dpnp_any_c_kernel;
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@@ -121,6 +167,23 @@ void func_map_init_logic(func_map_t& fmap)
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fmap[DPNPFuncName::DPNP_FN_ALL][eft_FLT][eft_FLT] = {eft_FLT, (void*)dpnp_all_c<float, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALL][eft_DBL][eft_DBL] = {eft_DBL, (void*)dpnp_all_c<double, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_INT][eft_INT] = {eft_BLN, (void*)dpnp_allclose_c<int, int, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_LNG][eft_INT] = {eft_BLN, (void*)dpnp_allclose_c<long, int, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_FLT][eft_INT] = {eft_BLN, (void*)dpnp_allclose_c<float, int, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_DBL][eft_INT] = {eft_BLN, (void*)dpnp_allclose_c<double, int, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_INT][eft_LNG] = {eft_BLN, (void*)dpnp_allclose_c<int, long, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_LNG][eft_LNG] = {eft_BLN, (void*)dpnp_allclose_c<long, long, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_FLT][eft_LNG] = {eft_BLN, (void*)dpnp_allclose_c<float, long, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_DBL][eft_LNG] = {eft_BLN, (void*)dpnp_allclose_c<double, long, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_INT][eft_FLT] = {eft_BLN, (void*)dpnp_allclose_c<int, float, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_LNG][eft_FLT] = {eft_BLN, (void*)dpnp_allclose_c<long, float, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_FLT][eft_FLT] = {eft_BLN, (void*)dpnp_allclose_c<float, float, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_DBL][eft_FLT] = {eft_BLN, (void*)dpnp_allclose_c<double, float, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_INT][eft_DBL] = {eft_BLN, (void*)dpnp_allclose_c<int, double, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_LNG][eft_DBL] = {eft_BLN, (void*)dpnp_allclose_c<long, double, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_FLT][eft_DBL] = {eft_BLN, (void*)dpnp_allclose_c<float, double, bool>};
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fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_DBL][eft_DBL] = {eft_BLN, (void*)dpnp_allclose_c<double, double, bool>};
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fmap[DPNPFuncName::DPNP_FN_ANY][eft_BLN][eft_BLN] = {eft_BLN, (void*)dpnp_any_c<bool, bool>};
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fmap[DPNPFuncName::DPNP_FN_ANY][eft_INT][eft_INT] = {eft_INT, (void*)dpnp_any_c<int, bool>};
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fmap[DPNPFuncName::DPNP_FN_ANY][eft_LNG][eft_LNG] = {eft_LNG, (void*)dpnp_any_c<long, bool>};

‎dpnp/dpnp_algo/dpnp_algo.pxd‎

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@@ -36,6 +36,7 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName": # need this na
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DPNP_FN_ABSOLUTE
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DPNP_FN_ADD
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DPNP_FN_ALL
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DPNP_FN_ALLCLOSE
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DPNP_FN_ANY
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DPNP_FN_ARANGE
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DPNP_FN_ARCCOS

‎dpnp/dpnp_algo/dpnp_algo_logic.pyx‎

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@@ -36,6 +36,7 @@ and the rest of the library
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__all__ += [
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"dpnp_all",
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"dpnp_allclose",
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"dpnp_any",
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"dpnp_equal",
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"dpnp_greater",
@@ -55,6 +56,7 @@ __all__ += [
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ctypedef void(*custom_logic_1in_1out_func_ptr_t)(void * , void * , const size_t)
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ctypedef void(*custom_allclose_1in_1out_func_ptr_t)(void * , void * , void *, const size_t, double, double)
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cpdef dparray dpnp_all(dpnp_descriptor array1):
@@ -71,6 +73,21 @@ cpdef dparray dpnp_all(dpnp_descriptor array1):
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return result
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cpdef dparray dpnp_allclose(dpnp_descriptor array1, dpnp_descriptor array2, double rtol_val, double atol_val):
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cdef dparray result = dparray((1,), dtype=numpy.bool)
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cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(array1.dtype)
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cdef DPNPFuncType param2_type = dpnp_dtype_to_DPNPFuncType(array2.dtype)
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cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_ALLCLOSE, param1_type, param2_type)
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cdef custom_allclose_1in_1out_func_ptr_t func = <custom_allclose_1in_1out_func_ptr_t > kernel_data.ptr
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func(array1.get_data(), array2.get_data(), result.get_data(), array1.size, rtol_val, atol_val)
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return result
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cpdef dparray dpnp_any(dpnp_descriptor array1):
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cdef dparray result = dparray((1,), dtype=numpy.bool)
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‎dpnp/dpnp_iface_logic.py‎

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@@ -50,6 +50,7 @@
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__all__ = [
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"all",
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"allclose",
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"any",
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"equal",
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"greater",
@@ -122,6 +123,42 @@ def all(x1, axis=None, out=None, keepdims=False):
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return call_origin(numpy.all, x1, axis, out, keepdims)
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def allclose(x1, x2, rtol=1.e-5, atol=1.e-8, **kwargs):
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"""
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Returns True if two arrays are element-wise equal within a tolerance.
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For full documentation refer to :obj:`numpy.allclose`.
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Limitations
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-----------
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Parameters ``x1`` and ``x2`` are supported as either :obj:`dpnp.ndarray` or scalar.
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Keyword arguments ``kwargs`` are currently unsupported.
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Otherwise the functions will be executed sequentially on CPU.
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Input array data types are limited by supported DPNP :ref:`Data types`.
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Examples
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--------
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>>> import dpnp as np
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>>> np.allclose([1e10,1e-7], [1.00001e10,1e-8])
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>>> False
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"""
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rtol_is_scalar = dpnp.isscalar(rtol)
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atol_is_scalar = dpnp.isscalar(atol)
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x1_desc = dpnp.get_dpnp_descriptor(x1)
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x2_desc = dpnp.get_dpnp_descriptor(x2)
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if x1_desc and x2_desc and not kwargs:
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if not rtol_is_scalar or not atol_is_scalar:
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pass
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else:
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result = dpnp_allclose(x1_desc, x2_desc, rtol, atol)
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return result[0]
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return call_origin(numpy.allclose, x1, x2, rtol=rtol, atol=atol, **kwargs)
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def any(x1, axis=None, out=None, keepdims=False):
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"""
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Test whether any array element along a given axis evaluates to True.

‎tests/test_logic.py‎

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@@ -37,6 +37,28 @@ def test_all(type, shape):
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dpnp_res = ia.all()
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numpy.testing.assert_allclose(dpnp_res, np_res)
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@pytest.mark.parametrize("type",
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[numpy.float64, numpy.float32, numpy.int64, numpy.int32],
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ids=['float64', 'float32', 'int64', 'int32'])
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def test_allclose(type):
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a = numpy.random.rand(10)
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b = a + numpy.random.rand(10) * 1e-8
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dpnp_a = dpnp.array(a, dtype=type)
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dpnp_b = dpnp.array(b, dtype=type)
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np_res = numpy.allclose(a, b)
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dpnp_res = dpnp.allclose(dpnp_a, dpnp_b)
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numpy.testing.assert_allclose(dpnp_res, np_res)
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a[0] = numpy.inf
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dpnp_a = dpnp.array(a)
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np_res = numpy.allclose(a, b)
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dpnp_res = dpnp.allclose(dpnp_a, dpnp_b)
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numpy.testing.assert_allclose(dpnp_res, np_res)
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@pytest.mark.parametrize("type",
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[numpy.float64, numpy.float32, numpy.int64, numpy.int32, numpy.bool, numpy.bool_],

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