197 行
4.6 KiB
Cython
197 行
4.6 KiB
Cython
# Translated from the reference implementation
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# at https://github.com/veorq/SipHash
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cimport cython
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from libc.stdlib cimport (
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free,
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malloc,
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)
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import numpy as np
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from numpy cimport (
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import_array,
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ndarray,
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uint8_t,
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uint32_t,
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uint64_t,
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)
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import_array()
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from pandas._libs.util cimport is_nan
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@cython.boundscheck(False)
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def hash_object_array(
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ndarray[object] arr, str key, str encoding="utf8"
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) -> np.ndarray[np.uint64]:
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"""
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Parameters
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----------
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arr : 1-d object ndarray of objects
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key : hash key, must be 16 byte len encoded
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encoding : encoding for key & arr, default to 'utf8'
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Returns
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-------
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1-d uint64 ndarray of hashes.
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Raises
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------
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TypeError
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If the array contains mixed types.
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Notes
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-----
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Allowed values must be strings, or nulls
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mixed array types will raise TypeError.
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"""
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cdef:
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Py_ssize_t i, n
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uint64_t[::1] result
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bytes data, k
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uint8_t *kb
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uint64_t *lens
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char **vecs
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char *cdata
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object val
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list datas = []
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k = <bytes>key.encode(encoding)
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kb = <uint8_t *>k
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if len(k) != 16:
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raise ValueError(
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f"key should be a 16-byte string encoded, got {k} (len {len(k)})"
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)
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n = len(arr)
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# create an array of bytes
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vecs = <char **>malloc(n * sizeof(char *))
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lens = <uint64_t*>malloc(n * sizeof(uint64_t))
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for i in range(n):
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val = arr[i]
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if isinstance(val, bytes):
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data = <bytes>val
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elif isinstance(val, str):
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data = <bytes>val.encode(encoding)
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elif val is None or is_nan(val):
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# null, stringify and encode
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data = <bytes>str(val).encode(encoding)
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elif isinstance(val, tuple):
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# GH#28969 we could have a tuple, but need to ensure that
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# the tuple entries are themselves hashable before converting
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# to str
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hash(val)
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data = <bytes>str(val).encode(encoding)
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else:
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raise TypeError(
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f"{val} of type {type(val)} is not a valid type for hashing, "
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"must be string or null"
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)
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lens[i] = len(data)
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cdata = data
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# keep the references alive through the end of the
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# function
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datas.append(data)
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vecs[i] = cdata
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result = np.empty(n, dtype=np.uint64)
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with nogil:
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for i in range(n):
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result[i] = low_level_siphash(<uint8_t *>vecs[i], lens[i], kb)
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free(vecs)
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free(lens)
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return result.base # .base to retrieve underlying np.ndarray
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cdef inline uint64_t _rotl(uint64_t x, uint64_t b) nogil:
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return (x << b) | (x >> (64 - b))
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cdef inline uint64_t u8to64_le(uint8_t* p) nogil:
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return (<uint64_t>p[0] |
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<uint64_t>p[1] << 8 |
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<uint64_t>p[2] << 16 |
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<uint64_t>p[3] << 24 |
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<uint64_t>p[4] << 32 |
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<uint64_t>p[5] << 40 |
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<uint64_t>p[6] << 48 |
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<uint64_t>p[7] << 56)
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cdef inline void _sipround(uint64_t* v0, uint64_t* v1,
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uint64_t* v2, uint64_t* v3) nogil:
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v0[0] += v1[0]
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v1[0] = _rotl(v1[0], 13)
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v1[0] ^= v0[0]
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v0[0] = _rotl(v0[0], 32)
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v2[0] += v3[0]
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v3[0] = _rotl(v3[0], 16)
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v3[0] ^= v2[0]
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v0[0] += v3[0]
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v3[0] = _rotl(v3[0], 21)
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v3[0] ^= v0[0]
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v2[0] += v1[0]
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v1[0] = _rotl(v1[0], 17)
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v1[0] ^= v2[0]
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v2[0] = _rotl(v2[0], 32)
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@cython.cdivision(True)
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cdef uint64_t low_level_siphash(uint8_t* data, size_t datalen,
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uint8_t* key) nogil:
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cdef uint64_t v0 = 0x736f6d6570736575ULL
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cdef uint64_t v1 = 0x646f72616e646f6dULL
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cdef uint64_t v2 = 0x6c7967656e657261ULL
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cdef uint64_t v3 = 0x7465646279746573ULL
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cdef uint64_t b
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cdef uint64_t k0 = u8to64_le(key)
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cdef uint64_t k1 = u8to64_le(key + 8)
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cdef uint64_t m
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cdef int i
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cdef uint8_t* end = data + datalen - (datalen % sizeof(uint64_t))
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cdef int left = datalen & 7
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cdef int left_byte
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cdef int cROUNDS = 2
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cdef int dROUNDS = 4
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b = (<uint64_t>datalen) << 56
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v3 ^= k1
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v2 ^= k0
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v1 ^= k1
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v0 ^= k0
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while (data != end):
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m = u8to64_le(data)
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v3 ^= m
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for i in range(cROUNDS):
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_sipround(&v0, &v1, &v2, &v3)
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v0 ^= m
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data += sizeof(uint64_t)
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for i in range(left-1, -1, -1):
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b |= (<uint64_t>data[i]) << (i * 8)
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v3 ^= b
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for i in range(cROUNDS):
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_sipround(&v0, &v1, &v2, &v3)
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v0 ^= b
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v2 ^= 0xff
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for i in range(dROUNDS):
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_sipround(&v0, &v1, &v2, &v3)
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b = v0 ^ v1 ^ v2 ^ v3
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return b
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