* 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
* 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
* span finder integrated into spacy from experimental
* black
* isort
* black
* default spankey constant
* black
* Update spacy/pipeline/spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* rename
* rename
* max_length and min_length as Optional[int] and strict checking
* black
* mypy fix for integer type infinity
* revert line order
* implement all comparison operators for inf int
* avoid two for loops over all docs by not precomputing
* interleave thresholding with span creation
* black
* revert to not interleaving (relized its faster)
* black
* Update spacy/errors.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* update dosctring
* enforce that the gold and predicted documents have the same text
* new error for ensuring reference and predicted texts are the same
* remove todo
* adjust test
* black
* handle misaligned tokenization
* return correct variable
* failing overfit test
* only use a single spans_key like in spancat
* black
* remove debug lines
* typo
* remove comment
* remove near duplicate reduntant method
* use the 'spans_key' variable name everywhere
* Update spacy/pipeline/span_finder.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* flaky test fix suggestion, hand set bias terms
* only test suggester and test result exhaustively
* make it clear that the span_finder_suggester is more general (not specific to span_finder)
* Update spacy/tests/pipeline/test_span_finder.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Apply suggestions from code review
* remove question comment
* move preset_spans_suggester test to spancat tests
* Add docs and unify default configs for spancat and span finder
* Add `allow_overlap=True` to span finder scorer
* Fix offset bug in set_annotations
* Ignore labels in span finder scorer
* Format
* Add span_finder to quickstart template
* Move settings to self.cfg, store min/max unset as None
* Remove debugging
* Update docstrings and docs
* Update spacy/pipeline/span_finder.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Fix imports
---------
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* avoid nesting then flattening
* mypy fix
* Apply suggestions from code review
* Add type for indices
* Run full matrix for mypy
* Add back modified type: ignore
* Revert "Run full matrix for mypy"
This reverts commit e218873d04.
---------
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Convert Candidate from Cython to Python class.
* Format.
* Fix .entity_ typo in _add_activations() usage.
* Change type for mentions to look up entity candidates for to SpanGroup from Iterable[Span].
* Update docs.
* Update spacy/kb/candidate.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update doc string of BaseCandidate.__init__().
* Update spacy/kb/candidate.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Rename Candidate to InMemoryCandidate, BaseCandidate to Candidate.
* Adjust Candidate to support and mandate numerical entity IDs.
* Format.
* Fix docstring and docs.
* Update website/docs/api/kb.mdx
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Rename alias -> mention.
* Refactor Candidate attribute names. Update docs and tests accordingly.
* Refacor Candidate attributes and their usage.
* Format.
* Fix mypy error.
* Update error code in line with v4 convention.
* Reverse erroneous changes during merge.
* Update return type in EL tests.
* Re-add Candidate to setup.py.
* Format updated docs.
---------
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Make empty_kb() configurable.
* Format.
* Update docs.
* Be more specific in KB serialization test.
* Update KB serialization tests. Update docs.
* Remove doc update for batched candidate generation.
* Fix serialization of subclassed KB in tests.
* Format.
* Update docstring.
* Update docstring.
* Switch from pickle to json for custom field serialization.
* Improve the correctness of _parse_patch
* If there are no more actions, do not attempt to make further
transitions, even if not all states are final.
* Assert that the number of actions for a step is the same as
the number of states.
* Reimplement distillation with oracle cut size
The code for distillation with an oracle cut size was not reimplemented
after the parser refactor. We did not notice, because we did not have
tests for this functionality. This change brings back the functionality
and adds this to the parser tests.
* Rename states2actions to _states_to_actions for consistency
* Test distillation max cuts in NER
* Mark parser/NER tests as slow
* Typo
* Fix invariant in _states_diff_to_actions
* Rename _init_batch -> _init_batch_from_teacher
* Ninja edit the ninja edit
* Check that we raise an exception when we pass the incorrect number or actions
* Remove unnecessary get
Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
* Write out condition more explicitly
---------
Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
* Try to fix doc.copy
* Set dev version
* Make vocab always own lexemes
* Change version
* Add SpanGroups.copy method
* Fix set_annotations during Parser.update
* Fix dict proxy copy
* Upd version
* Fix copying SpanGroups
* Fix set_annotations in parser.update
* Fix parser set_annotations during update
* Revert "Fix parser set_annotations during update"
This reverts commit eb138c89ed.
* Revert "Fix set_annotations in parser.update"
This reverts commit c6df0eafd0.
* Fix set_annotations during parser update
* Inc version
* Handle final states in get_oracle_sequence
* Inc version
* Try to fix parser training
* Inc version
* Fix
* Inc version
* Fix parser oracle
* Inc version
* Inc version
* Fix transition has_gold
* Inc version
* Try to use real histories, not oracle
* Inc version
* Upd parser
* Inc version
* WIP on rewrite parser
* WIP refactor parser
* New progress on parser model refactor
* Prepare to remove parser_model.pyx
* Convert parser from cdef class
* Delete spacy.ml.parser_model
* Delete _precomputable_affine module
* Wire up tb_framework to new parser model
* Wire up parser model
* Uncython ner.pyx and dep_parser.pyx
* Uncython
* Work on parser model
* Support unseen_classes in parser model
* Support unseen classes in parser
* Cleaner handling of unseen classes
* Work through tests
* Keep working through errors
* Keep working through errors
* Work on parser. 15 tests failing
* Xfail beam stuff. 9 failures
* More xfail. 7 failures
* Xfail. 6 failures
* cleanup
* formatting
* fixes
* pass nO through
* Fix empty doc in update
* Hackishly fix resizing. 3 failures
* Fix redundant test. 2 failures
* Add reference version
* black formatting
* Get tests passing with reference implementation
* Fix missing prints
* Add missing file
* Improve indexing on reference implementation
* Get non-reference forward func working
* Start rigging beam back up
* removing redundant tests, cf #8106
* black formatting
* temporarily xfailing issue 4314
* make flake8 happy again
* mypy fixes
* ensure labels are added upon predict
* cleanup remnants from merge conflicts
* Improve unseen label masking
Two changes to speed up masking by ~10%:
- Use a bool array rather than an array of float32.
- Let the mask indicate whether a label was seen, rather than
unseen. The mask is most frequently used to index scores for
seen labels. However, since the mask marked unseen labels,
this required computing an intermittent flipped mask.
* Write moves costs directly into numpy array (#10163)
This avoids elementwise indexing and the allocation of an additional
array.
Gives a ~15% speed improvement when using batch_by_sequence with size
32.
* Temporarily disable ner and rehearse tests
Until rehearse is implemented again in the refactored parser.
* Fix loss serialization issue (#10600)
* Fix loss serialization issue
Serialization of a model fails with:
TypeError: array(738.3855, dtype=float32) is not JSON serializable
Fix this using float conversion.
* Disable CI steps that require spacy.TransitionBasedParser.v2
After finishing the refactor, TransitionBasedParser.v2 should be
provided for backwards compat.
* Add back support for beam parsing to the refactored parser (#10633)
* Add back support for beam parsing
Beam parsing was already implemented as part of the `BeamBatch` class.
This change makes its counterpart `GreedyBatch`. Both classes are hooked
up in `TransitionModel`, selecting `GreedyBatch` when the beam size is
one, or `BeamBatch` otherwise.
* Use kwarg for beam width
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Avoid implicit default for beam_width and beam_density
* Parser.{beam,greedy}_parse: ensure labels are added
* Remove 'deprecated' comments
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Parser `StateC` optimizations (#10746)
* `StateC`: Optimizations
Avoid GIL acquisition in `__init__`
Increase default buffer capacities on init
Reduce C++ exception overhead
* Fix typo
* Replace `set::count` with `set::find`
* Add exception attribute to c'tor
* Remove unused import
* Use a power-of-two value for initial capacity
Use default-insert to init `_heads` and `_unshiftable`
* Merge `cdef` variable declarations and assignments
* Vectorize `example.get_aligned_parses` (#10789)
* `example`: Vectorize `get_aligned_parse`
Rename `numpy` import
* Convert aligned array to lists before returning
* Revert import renaming
* Elide slice arguments when selecting the entire range
* Tagger/morphologizer alignment performance optimizations (#10798)
* `example`: Unwrap `numpy` scalar arrays before passing them to `StringStore.__getitem__`
* `AlignmentArray`: Use native list as staging buffer for offset calculation
* `example`: Vectorize `get_aligned`
* Hoist inner functions out of `get_aligned`
* Replace inline `if..else` clause in assignment statement
* `AlignmentArray`: Use raw indexing into offset and data `numpy` arrays
* `example`: Replace array unique value check with `groupby`
* `example`: Correctly exclude tokens with no alignment in `_get_aligned_vectorized`
Simplify `_get_aligned_non_vectorized`
* `util`: Update `all_equal` docstring
* Explicitly use `int32_t*`
* Restore C CPU inference in the refactored parser (#10747)
* Bring back the C parsing model
The C parsing model is used for CPU inference and is still faster for
CPU inference than the forward pass of the Thinc model.
* Use C sgemm provided by the Ops implementation
* Make tb_framework module Cython, merge in C forward implementation
* TransitionModel: raise in backprop returned from forward_cpu
* Re-enable greedy parse test
* Return transition scores when forward_cpu is used
* Apply suggestions from code review
Import `Model` from `thinc.api`
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Use relative imports in tb_framework
* Don't assume a default for beam_width
* We don't have a direct dependency on BLIS anymore
* Rename forwards to _forward_{fallback,greedy_cpu}
* Require thinc >=8.1.0,<8.2.0
* tb_framework: clean up imports
* Fix return type of _get_seen_mask
* Move up _forward_greedy_cpu
* Style fixes.
* Lower thinc lowerbound to 8.1.0.dev0
* Formatting fix
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Reimplement parser rehearsal function (#10878)
* Reimplement parser rehearsal function
Before the parser refactor, rehearsal was driven by a loop in the
`rehearse` method itself. For each parsing step, the loops would:
1. Get the predictions of the teacher.
2. Get the predictions and backprop function of the student.
3. Compute the loss and backprop into the student.
4. Move the teacher and student forward with the predictions of
the student.
In the refactored parser, we cannot perform search stepwise rehearsal
anymore, since the model now predicts all parsing steps at once.
Therefore, rehearsal is performed in the following steps:
1. Get the predictions of all parsing steps from the student, along
with its backprop function.
2. Get the predictions from the teacher, but use the predictions of
the student to advance the parser while doing so.
3. Compute the loss and backprop into the student.
To support the second step a new method, `advance_with_actions` is
added to `GreedyBatch`, which performs the provided parsing steps.
* tb_framework: wrap upper_W and upper_b in Linear
Thinc's Optimizer cannot handle resizing of existing parameters. Until
it does, we work around this by wrapping the weights/biases of the upper
layer of the parser model in Linear. When the upper layer is resized, we
copy over the existing parameters into a new Linear instance. This does
not trigger an error in Optimizer, because it sees the resized layer as
a new set of parameters.
* Add test for TransitionSystem.apply_actions
* Better FIXME marker
Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
* Fixes from Madeesh
* Apply suggestions from Sofie
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Remove useless assignment
Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Rename some identifiers in the parser refactor (#10935)
* Rename _parseC to _parse_batch
* tb_framework: prefix many auxiliary functions with underscore
To clearly state the intent that they are private.
* Rename `lower` to `hidden`, `upper` to `output`
* Parser slow test fixup
We don't have TransitionBasedParser.{v1,v2} until we bring it back as a
legacy option.
* Remove last vestiges of PrecomputableAffine
This does not exist anymore as a separate layer.
* ner: re-enable sentence boundary checks
* Re-enable test that works now.
* test_ner: make loss test more strict again
* Remove commented line
* Re-enable some more beam parser tests
* Remove unused _forward_reference function
* Update for CBlas changes in Thinc 8.1.0.dev2
Bump thinc dependency to 8.1.0.dev3.
* Remove references to spacy.TransitionBasedParser.{v1,v2}
Since they will not be offered starting with spaCy v4.
* `tb_framework`: Replace references to `thinc.backends.linalg` with `CBlas`
* dont use get_array_module (#11056) (#11293)
Co-authored-by: kadarakos <kadar.akos@gmail.com>
* Move `thinc.extra.search` to `spacy.pipeline._parser_internals` (#11317)
* `search`: Move from `thinc.extra.search`
Fix NPE in `Beam.__dealloc__`
* `pytest`: Add support for executing Cython tests
Move `search` tests from thinc and patch them to run with `pytest`
* `mypy` fix
* Update comment
* `conftest`: Expose `register_cython_tests`
* Remove unused import
* Move `argmax` impls to new `_parser_utils` Cython module (#11410)
* Parser does not have to be a cdef class anymore
This also fixes validation of the initialization schema.
* Add back spacy.TransitionBasedParser.v2
* Fix a rename that was missed in #10878.
So that rehearsal tests pass.
* Remove module from setup.py that got added during the merge
* Bring back support for `update_with_oracle_cut_size` (#12086)
* Bring back support for `update_with_oracle_cut_size`
This option was available in the pre-refactor parser, but was never
implemented in the refactored parser. This option cuts transition
sequences that are longer than `update_with_oracle_cut` size into
separate sequences that have at most `update_with_oracle_cut`
transitions. The oracle (gold standard) transition sequence is used to
determine the cuts and the initial states for the additional sequences.
Applying this cut makes the batches more homogeneous in the transition
sequence lengths, making forward passes (and as a consequence training)
much faster.
Training time 1000 steps on de_core_news_lg:
- Before this change: 149s
- After this change: 68s
- Pre-refactor parser: 81s
* Fix a rename that was missed in #10878.
So that rehearsal tests pass.
* Apply suggestions from @shadeMe
* Use chained conditional
* Test with update_with_oracle_cut_size={0, 1, 5, 100}
And fix a git that occurs with a cut size of 1.
* Fix up some merge fall out
* Update parser distillation for the refactor
In the old parser, we'd iterate over the transitions in the distill
function and compute the loss/gradients on the go. In the refactored
parser, we first let the student model parse the inputs. Then we'll let
the teacher compute the transition probabilities of the states in the
student's transition sequence. We can then compute the gradients of the
student given the teacher.
* Add back spacy.TransitionBasedParser.v1 references
- Accordion in the architecture docs.
- Test in test_parse, but disabled until we have a spacy-legacy release.
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
Co-authored-by: svlandeg <svlandeg@github.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Co-authored-by: kadarakos <kadar.akos@gmail.com>
* Add `TrainablePipe.{distill,get_teacher_student_loss}`
This change adds two methods:
- `TrainablePipe::distill` which performs a training step of a
student pipe on a teacher pipe, giving a batch of `Doc`s.
- `TrainablePipe::get_teacher_student_loss` computes the loss
of a student relative to the teacher.
The `distill` or `get_teacher_student_loss` methods are also implemented
in the tagger, edit tree lemmatizer, and parser pipes, to enable
distillation in those pipes and as an example for other pipes.
* Fix stray `Beam` import
* Fix incorrect import
* Apply suggestions from code review
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* TrainablePipe.distill: use `Iterable[Example]`
* Add Pipe.is_distillable method
* Add `validate_distillation_examples`
This first calls `validate_examples` and then checks that the
student/teacher tokens are the same.
* Update distill documentation
* Add distill documentation for all pipes that support distillation
* Fix incorrect identifier
* Apply suggestions from code review
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Add comment to explain `is_distillable`
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Handle docs with no entities
If a whole batch contains no entities it won't make it to the model, but
it's possible for individual Docs to have no entities. Before this
commit, those Docs would cause an error when attempting to concatenate
arrays because the dimensions didn't match.
It turns out the process of preparing the Ragged at the end of the span
maker forward was a little different from list2ragged, which just uses
the flatten function directly. Letting list2ragged do the conversion
avoids the dimension issue.
This did not come up before because in NEL demo projects it's typical
for data with no entities to be discarded before it reaches the NEL
component.
This includes a simple direct test that shows the issue and checks it's
resolved. It doesn't check if there are any downstream changes, so a
more complete test could be added. A full run was tested by adding an
example with no entities to the Emerson sample project.
* Add a blank instance to default training data in tests
Rather than adding a specific test, since not failing on instances with
no entities is basic functionality, it makes sense to add it to the
default set.
* Fix without modifying architecture
If the architecture is modified this would have to be a new version, but
this change isn't big enough to merit that.
* Enable Cython<->Python bindings for `Pipe` and `TrainablePipe` methods
* `pipes_with_nvtx_range`: Skip hooking methods whose signature cannot be ascertained
When loading pipelines from a config file, the arguments passed to individual pipeline components is validated by `pydantic` during init. For this, the validation model attempts to parse the function signature of the component's c'tor/entry point so that it can check if all mandatory parameters are present in the config file.
When using the `models_and_pipes_with_nvtx_range` as a `after_pipeline_creation` callback, the methods of all pipeline components get replaced by a NVTX range wrapper **before** the above-mentioned validation takes place. This can be problematic for components that are implemented as Cython extension types - if the extension type is not compiled with Python bindings for its methods, they will have no signatures at runtime. This resulted in `pydantic` matching the *wrapper's* parameters with the those in the config and raising errors.
To avoid this, we now skip applying the wrapper to any (Cython) methods that do not have signatures.
After the precomputable affine table of shape [nB, nF, nO, nP] is
computed, padding with shape [1, nF, nO, nP] is assigned to the first
row of the precomputed affine table. However, when we are indexing the
precomputed table, we get a row of shape [nF, nO, nP]. CuPy versions
before 10.0 cannot paper over this shape difference.
This change fixes compatibility with CuPy < 10.0 by squeezing the first
dimension of the padding before assignment.
* precompute_hiddens/Parser: do not look up CPU ops
`get_ops("cpu")` is quite expensive. To avoid this, we want to cache the
result as in #11068. However, for 3.x we do not want to change the ABI.
So we avoid the expensive lookup by using NumpyOps. This should have a
minimal impact, since `get_ops("cpu")` was only used when the model ops
were `CupyOps`. If the ops are `AppleOps`, we are still passing through
the correct BLAS implementation.
* _NUMPY_OPS -> NUMPY_OPS
* `TrainablePipe`: Add NVTX range decorator
* Annotate `TrainablePipe` subclasses with NVTX ranges
* Export function signature to allow introspection of args in tests
* Revert "Annotate `TrainablePipe` subclasses with NVTX ranges"
This reverts commit d8684f7372.
* Revert "Export function signature to allow introspection of args in tests"
This reverts commit f4405ca3ad.
* Revert "`TrainablePipe`: Add NVTX range decorator"
This reverts commit 26536eb6b8.
* Add `spacy.pipes_with_nvtx_range` pipeline callback
* Show warnings for all missing user-defined pipe functions that need to be annotated
Fix imports, typos
* Rename `DEFAULT_ANNOTATABLE_PIPE_METHODS` to `DEFAULT_NVTX_ANNOTATABLE_PIPE_METHODS`
Reorder import
* Walk model nodes directly whilst applying NVTX ranges
Ignore pipe method wrapper when applying range
The `forward` of `precomputable_biaffine` performs matrix multiplication
and then `vstack`s the result with padding. This creates a temporary
array used for the output of matrix concatenation.
This change avoids the temporary by pre-allocating an array that is
large enough for the output of matrix multiplication plus padding and
fills the array in-place.
This gave me a small speedup (a bit over 100 WPS) on de_core_news_lg on
M1 Max (after changing thinc-apple-ops to support in-place gemm as BLIS
does).
* Fix TODO about typing
Fix was simple: just request an array2f.
* Add type ignore
Maxout has a more restrictive type than the residual layer expects (only
Floats2d vs any Floats).
* Various cleanup
This moves a lot of lines around but doesn't change any functionality.
Details:
1. use `continue` to reduce indentation
2. move sentence doc building inside conditional since it's otherwise
unused
3. reduces some temporary assignments
* Parser: use C saxpy/sgemm provided by the Ops implementation
This is a backport of https://github.com/explosion/spaCy/pull/10747
from the parser refactor branch. It eliminates the explicit calls
to BLIS, instead using the saxpy/sgemm provided by the Ops
implementation.
This allows us to use Accelerate in the parser on M1 Macs (with
an updated thinc-apple-ops).
Performance of the de_core_news_lg pipe:
BLIS 0.7.0, no thinc-apple-ops: 6385 WPS
BLIS 0.7.0, thinc-apple-ops: 36455 WPS
BLIS 0.9.0, no thinc-apple-ops: 19188 WPS
BLIS 0.9.0, thinc-apple-ops: 36682 WPS
This PR, thinc-apple-ops: 38726 WPS
Performance of the de_core_news_lg pipe (only tok2vec -> parser):
BLIS 0.7.0, no thinc-apple-ops: 13907 WPS
BLIS 0.7.0, thinc-apple-ops: 73172 WPS
BLIS 0.9.0, no thinc-apple-ops: 41576 WPS
BLIS 0.9.0, thinc-apple-ops: 72569 WPS
This PR, thinc-apple-ops: 87061 WPS
* Require thinc >=8.1.0,<8.2.0
* Lower thinc lowerbound to 8.1.0.dev0
* Use best CPU ops for CBLAS when the parser model is on the GPU
* Fix another unguarded cblas() call
* Fix: use ops as a shorthand for self.model.ops
Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
* Make changes to typing
* Correction
* Format with black
* Corrections based on review
* Bumped Thinc dependency version
* Bumped blis requirement
* Correction for older Python versions
* Update spacy/ml/models/textcat.py
Co-authored-by: Daniël de Kok <me@github.danieldk.eu>
* Corrections based on review feedback
* Readd deleted docstring line
Co-authored-by: Daniël de Kok <me@github.danieldk.eu>
* Tagger: use unnormalized probabilities for inference
Using unnormalized softmax avoids use of the relatively expensive exp function,
which can significantly speed up non-transformer models (e.g. I got a speedup
of 27% on a German tagging + parsing pipeline).
* Add spacy.Tagger.v2 with configurable normalization
Normalization of probabilities is disabled by default to improve
performance.
* Update documentation, models, and tests to spacy.Tagger.v2
* Move Tagger.v1 to spacy-legacy
* docs/architectures: run prettier
* Unnormalized softmax is now a Softmax_v2 option
* Require thinc 8.0.14 and spacy-legacy 3.0.9
* Partial fix of entity linker batching
* Add import
* Better name
* Add `use_gold_ents` option, docs
* Change to v2, create stub v1, update docs etc.
* Fix error type
Honestly no idea what the right type to use here is.
ConfigValidationError seems wrong. Maybe a NotImplementedError?
* Make mypy happy
* Add hacky fix for init issue
* Add legacy pipeline entity linker
* Fix references to class name
* Add __init__.py for legacy
* Attempted fix for loss issue
* Remove placeholder V1
* formatting
* slightly more interesting train data
* Handle batches with no usable examples
This adds a test for batches that have docs but not entities, and a
check in the component that detects such cases and skips the update step
as thought the batch were empty.
* Remove todo about data verification
Check for empty data was moved further up so this should be OK now - the
case in question shouldn't be possible.
* Fix gradient calculation
The model doesn't know which entities are not in the kb, so it generates
embeddings for the context of all of them.
However, the loss does know which entities aren't in the kb, and it
ignores them, as there's no sensible gradient.
This has the issue that the gradient will not be calculated for some of
the input embeddings, which causes a dimension mismatch in backprop.
That should have caused a clear error, but with numpyops it was causing
nans to happen, which is another problem that should be addressed
separately.
This commit changes the loss to give a zero gradient for entities not in
the kb.
* add failing test for v1 EL legacy architecture
* Add nasty but simple working check for legacy arch
* Clarify why init hack works the way it does
* Clarify use_gold_ents use case
* Fix use gold ents related handling
* Add tests for no gold ents and fix other tests
* Use aligned ents function (not working)
This doesn't actually work because the "aligned" ents are gold-only. But
if I have a different function that returns the intersection, *then*
this will work as desired.
* Use proper matching ent check
This changes the process when gold ents are not used so that the
intersection of ents in the pred and gold is used.
* Move get_matching_ents to Example
* Use model attribute to check for legacy arch
* Rename flag
* bump spacy-legacy to lower 3.0.9
Co-authored-by: svlandeg <svlandeg@github.com>
* Auto-format code with black
* add black requirement to dev dependencies and pin to 22.x
* ignore black dependency for comparison with setup.cfg
Co-authored-by: explosion-bot <explosion-bot@users.noreply.github.com>
Co-authored-by: svlandeg <svlandeg@github.com>
* Use Vectors.shape rather than Vectors.data.shape
* Use Vectors.size rather than Vectors.data.size
* Add Vectors.to_ops to move data between different ops
* Add documentation for Vector.to_ops
