spaCy/spacy/strings.pyx

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# cython: infer_types=True
# cython: profile=False
cimport cython
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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from contextlib import contextmanager
from typing import Iterator, List, Optional
from libc.stdint cimport uint32_t
from libc.string cimport memcpy
from murmurhash.mrmr cimport hash32, hash64
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from preshed.maps cimport map_clear
import srsly
from .typedefs cimport hash_t
from . import util
from .errors import Errors
from .symbols import IDS as SYMBOLS_BY_STR
from .symbols import NAMES as SYMBOLS_BY_INT
# Not particularly elegant, but this is faster than `isinstance(key, numbers.Integral)`
cdef inline bint _try_coerce_to_hash(object key, hash_t* out_hash):
try:
out_hash[0] = key
return True
except: # no-cython-lint
return False
def get_string_id(key):
"""Get a string ID, handling the reserved symbols correctly. If the key is
already an ID, return it.
This function optimises for convenience over performance, so shouldn't be
used in tight loops.
"""
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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cdef hash_t str_hash
if isinstance(key, str):
if len(key) == 0:
return 0
symbol = SYMBOLS_BY_STR.get(key, None)
if symbol is not None:
return symbol
else:
chars = key.encode("utf8")
return hash_utf8(chars, len(chars))
elif _try_coerce_to_hash(key, &str_hash):
# Coerce the integral key to the expected primitive hash type.
# This ensures that custom/overloaded "primitive" data types
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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# such as those implemented by numpy are not inadvertently used
# downsteam (as these are internally implemented as custom PyObjects
# whose comparison operators can incur a significant overhead).
return str_hash
else:
# TODO: Raise an error instead
return key
cpdef hash_t hash_string(str string) except 0:
chars = string.encode("utf8")
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return hash_utf8(chars, len(chars))
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cdef hash_t hash_utf8(char* utf8_string, int length) nogil:
return hash64(utf8_string, length, 1)
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cdef uint32_t hash32_utf8(char* utf8_string, int length) nogil:
return hash32(utf8_string, length, 1)
cdef str decode_Utf8Str(const Utf8Str* string):
cdef int i, length
if string.s[0] < sizeof(string.s) and string.s[0] != 0:
return string.s[1:string.s[0]+1].decode("utf8")
elif string.p[0] < 255:
return string.p[1:string.p[0]+1].decode("utf8")
else:
i = 0
length = 0
while string.p[i] == 255:
i += 1
length += 255
length += string.p[i]
i += 1
return string.p[i:length + i].decode("utf8")
cdef Utf8Str* _allocate(Pool mem, const unsigned char* chars, uint32_t length) except *:
cdef int n_length_bytes
cdef int i
cdef Utf8Str* string = <Utf8Str*>mem.alloc(1, sizeof(Utf8Str))
if length < sizeof(string.s):
string.s[0] = <unsigned char>length
memcpy(&string.s[1], chars, length)
return string
elif length < 255:
string.p = <unsigned char*>mem.alloc(length + 1, sizeof(unsigned char))
string.p[0] = length
memcpy(&string.p[1], chars, length)
return string
else:
i = 0
n_length_bytes = (length // 255) + 1
string.p = <unsigned char*>mem.alloc(length + n_length_bytes, sizeof(unsigned char))
for i in range(n_length_bytes-1):
string.p[i] = 255
string.p[n_length_bytes-1] = length % 255
memcpy(&string.p[n_length_bytes], chars, length)
return string
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cdef class StringStore:
"""Look up strings by 64-bit hashes.
DOCS: https://spacy.io/api/stringstore
"""
def __init__(self, strings=None, freeze=False):
"""Create the StringStore.
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strings (iterable): A sequence of unicode strings to add to the store.
"""
self.mem = Pool()
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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self._non_temp_mem = self.mem
self._map = PreshMap()
if strings is not None:
for string in strings:
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self.add(string, allow_transient=False)
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def __getitem__(self, object string_or_id):
"""Retrieve a string from a given hash, or vice versa.
string_or_id (bytes, str or uint64): The value to encode.
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Returns (str / uint64): The value to be retrieved.
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"""
cdef hash_t str_hash
cdef Utf8Str* utf8str = NULL
if isinstance(string_or_id, str):
if len(string_or_id) == 0:
return 0
# Return early if the string is found in the symbols LUT.
symbol = SYMBOLS_BY_STR.get(string_or_id, None)
if symbol is not None:
return symbol
else:
return hash_string(string_or_id)
elif isinstance(string_or_id, bytes):
return hash_utf8(string_or_id, len(string_or_id))
elif _try_coerce_to_hash(string_or_id, &str_hash):
if str_hash == 0:
return ""
elif str_hash < len(SYMBOLS_BY_INT):
return SYMBOLS_BY_INT[str_hash]
else:
utf8str = <Utf8Str*>self._map.get(str_hash)
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if utf8str is NULL:
raise KeyError(Errors.E018.format(hash_value=string_or_id))
else:
return decode_Utf8Str(utf8str)
else:
# TODO: Raise an error instead
utf8str = <Utf8Str*>self._map.get(string_or_id)
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if utf8str is NULL:
raise KeyError(Errors.E018.format(hash_value=string_or_id))
else:
return decode_Utf8Str(utf8str)
def as_int(self, key):
"""If key is an int, return it; otherwise, get the int value."""
if not isinstance(key, str):
return key
else:
return self[key]
def as_string(self, key):
"""If key is a string, return it; otherwise, get the string value."""
if isinstance(key, str):
return key
else:
return self[key]
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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def __len__(self) -> int:
"""The number of strings in the store.
RETURNS (int): The number of strings in the store.
"""
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return self.keys.size() + self._transient_keys.size()
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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@contextmanager
def memory_zone(self, mem: Optional[Pool] = None) -> Pool:
"""Begin a block where all resources allocated during the block will
be freed at the end of it. If a resources was created within the
memory zone block, accessing it outside the block is invalid.
Behaviour of this invalid access is undefined. Memory zones should
not be nested.
The memory zone is helpful for services that need to process large
volumes of text with a defined memory budget.
"""
if mem is None:
mem = Pool()
self.mem = mem
yield mem
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for key in self._transient_keys:
map_clear(self._map.c_map, key)
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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self._transient_keys.clear()
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self.mem = self._non_temp_mem
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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def add(self, string: str, allow_transient: Optional[bool] = None) -> int:
"""Add a string to the StringStore.
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string (str): The string to add.
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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allow_transient (bool): Allow the string to be stored in the 'transient'
map, which will be flushed at the end of the memory zone. Strings
encountered during arbitrary text processing should be added
with allow_transient=True, while labels and other strings used
internally should not.
RETURNS (uint64): The string's hash value.
"""
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if allow_transient is None:
allow_transient = self.mem is not self._non_temp_mem
cdef hash_t str_hash
if isinstance(string, str):
if string in SYMBOLS_BY_STR:
return SYMBOLS_BY_STR[string]
string = string.encode("utf8")
str_hash = hash_utf8(string, len(string))
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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self._intern_utf8(string, len(string), &str_hash, allow_transient)
elif isinstance(string, bytes):
if string in SYMBOLS_BY_STR:
return SYMBOLS_BY_STR[string]
str_hash = hash_utf8(string, len(string))
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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self._intern_utf8(string, len(string), &str_hash, allow_transient)
else:
raise TypeError(Errors.E017.format(value_type=type(string)))
return str_hash
def __len__(self):
"""The number of strings in the store.
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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if string in SYMBOLS_BY_STR:
return SYMBOLS_BY_STR[string]
else:
return self._intern_str(string, allow_transient)
RETURNS (int): The number of strings in the store.
"""
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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return self.keys.size() + self._transient_keys.size()
def __contains__(self, string_or_id not None):
"""Check whether a string or ID is in the store.
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string_or_id (str or int): The string to check.
RETURNS (bool): Whether the store contains the string.
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"""
cdef hash_t str_hash
if isinstance(string_or_id, str):
if len(string_or_id) == 0:
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return True
elif string_or_id in SYMBOLS_BY_STR:
return True
str_hash = hash_string(string_or_id)
elif _try_coerce_to_hash(string_or_id, &str_hash):
pass
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else:
# TODO: Raise an error instead
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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if self._map.get(string_or_id) is not NULL:
return True
else:
return False
if str_hash < len(SYMBOLS_BY_INT):
return True
else:
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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if self._map.get(str_hash) is not NULL:
return True
else:
return False
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def __iter__(self):
"""Iterate over the strings in the store, in order.
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YIELDS (str): A string in the store.
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"""
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cdef int i
cdef hash_t key
for i in range(self.keys.size()):
key = self.keys[i]
utf8str = <Utf8Str*>self._map.get(key)
yield decode_Utf8Str(utf8str)
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for i in range(self._transient_keys.size()):
key = self._transient_keys[i]
utf8str = <Utf8Str*>self._map.get(key)
yield decode_Utf8Str(utf8str)
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def __reduce__(self):
strings = list(self)
return (StringStore, (strings,), None, None, None)
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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def values(self) -> List[int]:
"""Iterate over the stored strings hashes in insertion order.
RETURNS: A list of string hashs.
"""
cdef int i
hashes = [None] * self._keys.size()
for i in range(self._keys.size()):
hashes[i] = self._keys[i]
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transient_hashes = [None] * self._transient_keys.size()
for i in range(self._transient_keys.size()):
transient_hashes[i] = self._transient_keys[i]
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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return hashes + transient_hashes
def to_disk(self, path):
"""Save the current state to a directory.
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path (str / Path): A path to a directory, which will be created if
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it doesn't exist. Paths may be either strings or Path-like objects.
"""
path = util.ensure_path(path)
strings = sorted(self)
srsly.write_json(path, strings)
def from_disk(self, path):
"""Loads state from a directory. Modifies the object in place and
returns it.
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path (str / Path): A path to a directory. Paths may be either
strings or `Path`-like objects.
RETURNS (StringStore): The modified `StringStore` object.
"""
path = util.ensure_path(path)
strings = srsly.read_json(path)
prev = list(self)
self._reset_and_load(strings)
for word in prev:
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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self.add(word, allow_transient=False)
return self
def to_bytes(self, **kwargs):
"""Serialize the current state to a binary string.
RETURNS (bytes): The serialized form of the `StringStore` object.
"""
return srsly.json_dumps(sorted(self))
def from_bytes(self, bytes_data, **kwargs):
"""Load state from a binary string.
bytes_data (bytes): The data to load from.
RETURNS (StringStore): The `StringStore` object.
"""
strings = srsly.json_loads(bytes_data)
prev = list(self)
self._reset_and_load(strings)
for word in prev:
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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self.add(word, allow_transient=False)
return self
def _reset_and_load(self, strings):
self.mem = Pool()
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self._non_temp_mem = self.mem
self._map = PreshMap()
self.keys.clear()
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self._transient_keys.clear()
for string in strings:
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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self.add(string, allow_transient=False)
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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cdef const Utf8Str* intern_unicode(self, str py_string, bint allow_transient):
# 0 means missing, but we don't bother offsetting the index.
cdef bytes byte_string = py_string.encode("utf8")
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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return self._intern_utf8(byte_string, len(byte_string), NULL, allow_transient)
@cython.final
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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cdef const Utf8Str* _intern_utf8(self, char* utf8_string, int length, hash_t* precalculated_hash, bint allow_transient):
# TODO: This function's API/behaviour is an unholy mess...
# 0 means missing, but we don't bother offsetting the index.
cdef hash_t key = precalculated_hash[0] if precalculated_hash is not NULL else hash_utf8(utf8_string, length)
cdef Utf8Str* value = <Utf8Str*>self._map.get(key)
if value is not NULL:
return value
value = _allocate(self.mem, <unsigned char*>utf8_string, length)
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self._map.set(key, value)
if allow_transient and self.mem is not self._non_temp_mem:
Support 'memory zones' for user memory management (#13621) Add a context manage nlp.memory_zone(), which will begin memory_zone() blocks on the vocab, string store, and potentially other components. Example usage: ``` with nlp.memory_zone(): for text in nlp.pipe(texts): do_something(doc) # do_something(doc) <-- Invalid ``` Once the memory_zone() block expires, spaCy will free any shared resources that were allocated for the text-processing that occurred within the memory_zone. If you create Doc objects within a memory zone, it's invalid to access them once the memory zone is expired. The purpose of this is that spaCy creates and stores Lexeme objects in the Vocab that can be shared between multiple Doc objects. It also interns strings. Normally, spaCy can't know when all Doc objects using a Lexeme are out-of-scope, so new Lexemes accumulate in the vocab, causing memory pressure. Memory zones solve this problem by telling spaCy "okay none of the documents allocated within this block will be accessed again". This lets spaCy free all new Lexeme objects and other data that were created during the block. The mechanism is general, so memory_zone() context managers can be added to other components that could benefit from them, e.g. pipeline components. I experimented with adding memory zone support to the tokenizer as well, for its cache. However, this seems unnecessarily complicated. It makes more sense to just stick a limit on the cache size. This lets spaCy benefit from the efficiency advantage of the cache better, because we can maintain a (bounded) cache even if only small batches of documents are being processed.
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self._transient_keys.push_back(key)
else:
self.keys.push_back(key)
return value