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.
* fix type annotation in docs
* only restore entities after loss calculation
* restore entities of sample in initialization
* rename overfitting function
* fix EL scorer
* Relax test
* fix formatting
* Update spacy/pipeline/entity_linker.py
Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com>
* rename to _ensure_ents
* further rename
* allow for scorer to be None
---------
Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com>
The 'direct' option in 'spacy download' is supposed to only download from our model releases repository. However, users were able to pass in a relative path, allowing download from arbitrary repositories. This meant that a service that sourced strings from user input and which used the direct option would allow users to install arbitrary packages.
* TextCatParametricAttention.v1: set key transform dimensions
This is necessary for tok2vec implementations that initialize
lazily (e.g. curated transformers).
* Add lazily-initialized tok2vec to simulate transformers
Add a lazily-initialized tok2vec to the tests and test the current
textcat models with it.
Fix some additional issues found using this test.
* isort
* Add `test.` prefix to `LazyInitTok2Vec.v1`
The doc/token extension serialization tests add extensions that are not
serializable with pickle. This didn't cause issues before due to the
implicit run order of tests. However, test ordering has changed with
pytest 8.0.0, leading to failed tests in test_language.
Update the fixtures in the extension serialization tests to do proper
teardown and remove the extensions.
macOS now uses port 5000 for the AirPlay receiver functionality, so this
test will always fail on a macOS desktop (unless AirPlay receiver
functionality is disabled like in CI).
* Add spacy.TextCatParametricAttention.v1
This layer provides is a simplification of the ensemble classifier that
only uses paramteric attention. We have found empirically that with a
sufficient amount of training data, using the ensemble classifier with
BoW does not provide significant improvement in classifier accuracy.
However, plugging in a BoW classifier does reduce GPU training and
inference performance substantially, since it uses a GPU-only kernel.
* Fix merge fallout
* Add TextCatReduce.v1
This is a textcat classifier that pools the vectors generated by a
tok2vec implementation and then applies a classifier to the pooled
representation. Three reductions are supported for pooling: first, max,
and mean. When multiple reductions are enabled, the reductions are
concatenated before providing them to the classification layer.
This model is a generalization of the TextCatCNN model, which only
supports mean reductions and is a bit of a misnomer, because it can also
be used with transformers. This change also reimplements TextCatCNN.v2
using the new TextCatReduce.v1 layer.
* Doc fixes
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence
* Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy
* Add back a test for TextCatCNN.v2
* Replace TextCatCNN in pipe configurations and templates
* Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor
* Add last reduction (`use_reduce_last`)
* Remove non-working TextCatCNN Netlify redirect
* Revert layer changes for the quickstart
* Revert one more quickstart change
* Remove unused import
* Fix docstring
* Fix setting name in error message
---------
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update `TextCatBOW` to use the fixed `SparseLinear` layer
A while ago, we fixed the `SparseLinear` layer to use all available
parameters: https://github.com/explosion/thinc/pull/754
This change updates `TextCatBOW` to `v3` which uses the new
`SparseLinear_v2` layer. This results in a sizeable improvement on a
text categorization task that was tested.
While at it, this `spacy.TextCatBOW.v3` also adds the `length_exponent`
option to make it possible to change the hidden size. Ideally, we'd just
have an option called `length`. But the way that `TextCatBOW` uses
hashes results in a non-uniform distribution of parameters when the
length is not a power of two.
* Replace TexCatBOW `length_exponent` parameter by `length`
We now round up the length to the next power of two if it isn't
a power of two.
* Remove some tests for TextCatBOW.v2
* Fix missing import
* add language extensions for norwegian nynorsk and faroese
* update docstring for nn/examples.py
* use relative imports
* add fo and nn tokenizers to pytest fixtures
* add unittests for fo and nn and fix bug in nn
* remove module docstring from fo/__init__.py
* add comments about example sentences' origin
* add license information to faroese data credit
* format unittests using black
* add __init__ files to test/lang/nn and tests/lang/fo
* fix import order and use relative imports in fo/__nit__.py and nn/__init__.py
* Make the tests a bit more compact
* Add fo and nn to website languages
* Add note about jul.
* Add "jul." as exception
---------
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update Tokenizer.explain for special cases with whitespace
Update `Tokenizer.explain` to skip special case matches if the exact
text has not been matched due to intervening whitespace.
Enable fuzzy `Tokenizer.explain` tests with additional whitespace
normalization.
* Add unit test for special cases with whitespace, xfail fuzzy tests again
* Fix displacy span stacking.
* Format. Remove counter.
* Remove test files.
* Add unit test. Refactor to allow for unit test.
* Fix off-by-one error in tests.
* Load the cli module lazily for spacy.info
This avoids that the `spacy` module cannot be imported when the
users chooses not to install `typer`/`requests`.
* Add test
---------
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* remove migration support form
* initial test commit
* add fixture
* add combo test
* pull out parameter example data
* fix formatting on examples
* remove unused import
* remove unncessary fmt:off instructions
* only set logger level if verbose flag is explicitly set
---------
Co-authored-by: svlandeg <svlandeg@github.com>
* Add data structures to docs
* Adjusted descriptions for more consistency
* Add _optional_ flag to parameters
* Add tests and adjust optional title key in doc
* Add title to dep visualizations
* fix typo
---------
Co-authored-by: thomashacker <EdwardSchmuhl@web.de>
* Add cli for finding locations of registered func
* fixes: naming and typing
* isort
* update naming
* remove to find-function
* remove file:// bit
* use registry name if given and exit gracefully if a registry was not found
* clean up failure msg
* specify registry_name options
* mypy fixes
* return location for internal usage
* add documentation
* more mypy fixes
* clean up example
* add section to menu
* add tests
---------
Co-authored-by: svlandeg <svlandeg@github.com>
* Update numpy build constraints for numpy 1.25
Starting in numpy 1.25 (see
https://github.com/numpy/numpy/releases/tag/v1.25.0), the numpy C API is
backwards-compatible by default.
For python 3.9+, we should be able to drop the specific numpy build
requirements and use `numpy>=1.25`, which is currently
backwards-compatible to `numpy>=1.19`.
In the future, the python <3.9 requirements could be dropped and the
lower numpy pin could correspond to the oldest supported version for the
current lower python pin.
* Turn off fail-fast
* Revert "Turn off fail-fast"
This reverts commit 4306f516bc.
* Update for python 3.6
* Fix typo
These changes add a missing call to `escape_html` in the displaCy span
renderer. Previously span-annotated tokens would be inserted into the
page markup without being escaped, resulting in potentially incorrect
rendering. When I encountered this issue, it resulted in some docs and
span underlines being superimposed on top of properly rendered docs and
span underlines near the beginning of the visualization (due to an
unescaped `<span>` tag).
* Setting up weasel branch (#12456)
* remove project-specific functionality
* remove project-specific tests
* remove project-specific schemas
* remove project-specific information in about
* remove project-specific functions in util.py
* remove project-specific error strings
* remove project-specific CLI commands
* black formatting
* restore some functions that are used beyond projects
* remove project imports
* remove imports
* remove remote_storage tests
* remove one more project unit test
* update for PR 12394
* remove get_hash and get_checksum
* remove upload_ and download_file methods
* remove ensure_pathy
* revert clumsy fingers
* reinstate E970
* feat: use weasel as spacy project command (#12473)
* feat: use weasel as spacy project command
* build: use constrained requirement for weasel
* feat: add weasel to the library requirements
* build: update weasel to new version
* build: use specific weasel tag
* build: use weasel-0.1.0rc1 from PyPI
* fix: remove weasel from requirements.txt
* fix: requirements.txt and setup.cfg need to reflect each other
* feat: remove legacy spacy project code
* bump version
* further merge fixes
* isort
---------
Co-authored-by: Basile Dura <bdura@users.noreply.github.com>
* Support custom token/lexeme attribute for vectors
* Fix imports
* Back off to ORTH without Vectors.attr
* Fallback if vectors.attr doesn't exist
* Update docs
When sourcing a component, the object from the original pipeline is added to the new pipeline as the same object. This creates a situation where there are several attributes that cannot be in sync between the original pipeline and the new pipeline at the same time for this one object:
* component.name
* component.listener_map / component.listening_components for tok2vec and transformer
When running replace_listeners on a component, the config is not updated correctly if the state of the component is incorrect for the current pipeline (in particular changes that should be applied from model.attrs["replace_listener_cfg"] as used in spacy-transformers) due to the fact that:
* find_listeners relies on component.name to set the name in the listener_map
* replace_listeners relies on listener_map to determine how to modify the configs
In addition, there are several places where pipeline components are modified and the listener map and/or internal component names aren't currently updated.
In cases where there is a component shared by two pipelines that cannot be in sync, this PR chooses to prioritize the most recently modified or initialized pipeline. There is no actual solution with the current source behavior that will make both pipelines usable, so the current pipeline is updated whenever components are added/renamed/removed or the pipeline is initialized for training.
* Use isort with Black profile
* isort all the things
* Fix import cycles as a result of import sorting
* Add DOCBIN_ALL_ATTRS type definition
* Add isort to requirements
* Remove isort from build dependencies check
* Typo
