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a183db3cef
* 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 commiteb138c89ed. * Revert "Fix set_annotations in parser.update" This reverts commitc6df0eafd0. * 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>
310 lines
17 KiB
Plaintext
310 lines
17 KiB
Plaintext
---
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title: Legacy functions and architectures
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teaser: Archived implementations available through spacy-legacy
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source: spacy/legacy
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---
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The [`spacy-legacy`](https://github.com/explosion/spacy-legacy) package includes
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outdated registered functions and architectures. It is installed automatically
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as a dependency of spaCy, and provides backwards compatibility for archived
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functions that may still be used in projects.
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You can find the detailed documentation of each such legacy function on this
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page.
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## Architectures {id="architectures"}
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These functions are available from `@spacy.registry.architectures`.
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### spacy.Tok2Vec.v1 {id="Tok2Vec_v1"}
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The `spacy.Tok2Vec.v1` architecture was expecting an `encode` model of type
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`Model[Floats2D, Floats2D]` such as `spacy.MaxoutWindowEncoder.v1` or
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`spacy.MishWindowEncoder.v1`.
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> #### Example config
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>
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> ```ini
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> [model]
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> @architectures = "spacy.Tok2Vec.v1"
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>
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> [model.embed]
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> @architectures = "spacy.CharacterEmbed.v1"
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> # ...
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>
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> [model.encode]
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> @architectures = "spacy.MaxoutWindowEncoder.v1"
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> # ...
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> ```
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Construct a tok2vec model out of two subnetworks: one for embedding and one for
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encoding. See the
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["Embed, Encode, Attend, Predict"](https://explosion.ai/blog/deep-learning-formula-nlp)
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blog post for background.
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| Name | Description |
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| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `embed` | Embed tokens into context-independent word vector representations. For example, [CharacterEmbed](/api/architectures#CharacterEmbed) or [MultiHashEmbed](/api/architectures#MultiHashEmbed). ~~Model[List[Doc], List[Floats2d]]~~ |
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| `encode` | Encode context into the embeddings, using an architecture such as a CNN, BiLSTM or transformer. For example, [MaxoutWindowEncoder.v1](/api/legacy#MaxoutWindowEncoder_v1). ~~Model[Floats2d, Floats2d]~~ |
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| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
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### spacy.MaxoutWindowEncoder.v1 {id="MaxoutWindowEncoder_v1"}
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The `spacy.MaxoutWindowEncoder.v1` architecture was producing a model of type
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`Model[Floats2D, Floats2D]`. Since `spacy.MaxoutWindowEncoder.v2`, this has been
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changed to output type `Model[List[Floats2d], List[Floats2d]]`.
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> #### Example config
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>
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> ```ini
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> [model]
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> @architectures = "spacy.MaxoutWindowEncoder.v1"
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> width = 128
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> window_size = 1
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> maxout_pieces = 3
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> depth = 4
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> ```
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Encode context using convolutions with maxout activation, layer normalization
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and residual connections.
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| Name | Description |
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| --------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `width` | The input and output width. These are required to be the same, to allow residual connections. This value will be determined by the width of the inputs. Recommended values are between `64` and `300`. ~~int~~ |
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| `window_size` | The number of words to concatenate around each token to construct the convolution. Recommended value is `1`. ~~int~~ |
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| `maxout_pieces` | The number of maxout pieces to use. Recommended values are `2` or `3`. ~~int~~ |
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| `depth` | The number of convolutional layers. Recommended value is `4`. ~~int~~ |
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| **CREATES** | The model using the architecture. ~~Model[Floats2d, Floats2d]~~ |
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### spacy.MishWindowEncoder.v1 {id="MishWindowEncoder_v1"}
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The `spacy.MishWindowEncoder.v1` architecture was producing a model of type
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`Model[Floats2D, Floats2D]`. Since `spacy.MishWindowEncoder.v2`, this has been
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changed to output type `Model[List[Floats2d], List[Floats2d]]`.
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> #### Example config
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>
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> ```ini
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> [model]
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> @architectures = "spacy.MishWindowEncoder.v1"
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> width = 64
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> window_size = 1
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> depth = 4
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> ```
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Encode context using convolutions with
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[`Mish`](https://thinc.ai/docs/api-layers#mish) activation, layer normalization
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and residual connections.
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| Name | Description |
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| ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `width` | The input and output width. These are required to be the same, to allow residual connections. This value will be determined by the width of the inputs. Recommended values are between `64` and `300`. ~~int~~ |
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| `window_size` | The number of words to concatenate around each token to construct the convolution. Recommended value is `1`. ~~int~~ |
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| `depth` | The number of convolutional layers. Recommended value is `4`. ~~int~~ |
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| **CREATES** | The model using the architecture. ~~Model[Floats2d, Floats2d]~~ |
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### spacy.HashEmbedCNN.v1 {id="HashEmbedCNN_v1"}
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Identical to [`spacy.HashEmbedCNN.v2`](/api/architectures#HashEmbedCNN) except
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using [`spacy.StaticVectors.v1`](#StaticVectors_v1) if vectors are included.
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### spacy.MultiHashEmbed.v1 {id="MultiHashEmbed_v1"}
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Identical to [`spacy.MultiHashEmbed.v2`](/api/architectures#MultiHashEmbed)
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except with [`spacy.StaticVectors.v1`](#StaticVectors_v1) if vectors are
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included.
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### spacy.CharacterEmbed.v1 {id="CharacterEmbed_v1"}
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Identical to [`spacy.CharacterEmbed.v2`](/api/architectures#CharacterEmbed)
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except using [`spacy.StaticVectors.v1`](#StaticVectors_v1) if vectors are
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included.
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### spacy.TextCatEnsemble.v1 {id="TextCatEnsemble_v1"}
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The `spacy.TextCatEnsemble.v1` architecture built an internal `tok2vec` and
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`linear_model`. Since `spacy.TextCatEnsemble.v2`, this has been refactored so
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that the `TextCatEnsemble` takes these two sublayers as input.
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> #### Example Config
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>
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> ```ini
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> [model]
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> @architectures = "spacy.TextCatEnsemble.v1"
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> exclusive_classes = false
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> pretrained_vectors = null
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> width = 64
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> embed_size = 2000
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> conv_depth = 2
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> window_size = 1
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> ngram_size = 1
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> dropout = null
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> nO = null
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> ```
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Stacked ensemble of a bag-of-words model and a neural network model. The neural
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network has an internal CNN Tok2Vec layer and uses attention.
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| Name | Description |
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| -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
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| `pretrained_vectors` | Whether or not pretrained vectors will be used in addition to the feature vectors. ~~bool~~ |
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| `width` | Output dimension of the feature encoding step. ~~int~~ |
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| `embed_size` | Input dimension of the feature encoding step. ~~int~~ |
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| `conv_depth` | Depth of the tok2vec layer. ~~int~~ |
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| `window_size` | The number of contextual vectors to [concatenate](https://thinc.ai/docs/api-layers#expand_window) from the left and from the right. ~~int~~ |
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| `ngram_size` | Determines the maximum length of the n-grams in the BOW model. For instance, `ngram_size=3`would give unigram, trigram and bigram features. ~~int~~ |
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| `dropout` | The dropout rate. ~~float~~ |
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| `nO` | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
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| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
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### spacy.TextCatCNN.v1 {id="TextCatCNN_v1"}
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Since `spacy.TextCatCNN.v2`, this architecture has become resizable, which means
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that you can add labels to a previously trained textcat. `TextCatCNN` v1 did not
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yet support that.
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> #### Example Config
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>
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> ```ini
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> [model]
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> @architectures = "spacy.TextCatCNN.v1"
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> exclusive_classes = false
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> nO = null
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>
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> [model.tok2vec]
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> @architectures = "spacy.HashEmbedCNN.v1"
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> pretrained_vectors = null
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> width = 96
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> depth = 4
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> embed_size = 2000
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> window_size = 1
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> maxout_pieces = 3
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> subword_features = true
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> ```
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A neural network model where token vectors are calculated using a CNN. The
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vectors are mean pooled and used as features in a feed-forward network. This
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architecture is usually less accurate than the ensemble, but runs faster.
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| Name | Description |
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| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
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| `tok2vec` | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~ |
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| `nO` | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
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| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
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### spacy.TextCatBOW.v1 {id="TextCatBOW_v1"}
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Since `spacy.TextCatBOW.v2`, this architecture has become resizable, which means
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that you can add labels to a previously trained textcat. `TextCatBOW` v1 did not
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yet support that.
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> #### Example Config
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>
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> ```ini
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> [model]
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> @architectures = "spacy.TextCatBOW.v1"
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> exclusive_classes = false
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> ngram_size = 1
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> no_output_layer = false
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> nO = null
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> ```
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An n-gram "bag-of-words" model. This architecture should run much faster than
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the others, but may not be as accurate, especially if texts are short.
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| Name | Description |
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| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
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| `ngram_size` | Determines the maximum length of the n-grams in the BOW model. For instance, `ngram_size=3` would give unigram, trigram and bigram features. ~~int~~ |
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| `no_output_layer` | Whether or not to add an output layer to the model (`Softmax` activation if `exclusive_classes` is `True`, else `Logistic`). ~~bool~~ |
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| `nO` | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
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| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
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### spacy.TransitionBasedParser.v1 {id="TransitionBasedParser_v1"}
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Identical to
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[`spacy.TransitionBasedParser.v3`](/api/architectures#TransitionBasedParser)
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except the `use_upper` was set to `True` by default.
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## Layers {id="layers"}
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These functions are available from `@spacy.registry.layers`.
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### spacy.StaticVectors.v1 {id="StaticVectors_v1"}
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Identical to [`spacy.StaticVectors.v2`](/api/architectures#StaticVectors) except
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for the handling of tokens without vectors.
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<Infobox title="Bugs for tokens without vectors" variant="warning">
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`spacy.StaticVectors.v1` maps tokens without vectors to the final row in the
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vectors table, which causes the model predictions to change if new vectors are
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added to an existing vectors table. See more details in
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[issue #7662](https://github.com/explosion/spaCy/issues/7662#issuecomment-813925655).
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</Infobox>
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## Loggers {id="loggers"}
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These functions are available from `@spacy.registry.loggers`.
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### spacy.ConsoleLogger.v1 {id="ConsoleLogger_v1"}
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> #### Example config
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>
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> ```ini
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> [training.logger]
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> @loggers = "spacy.ConsoleLogger.v1"
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> progress_bar = true
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> ```
|
||
|
||
Writes the results of a training step to the console in a tabular format.
|
||
|
||
<Accordion title="Example console output" spaced>
|
||
|
||
```bash
|
||
$ python -m spacy train config.cfg
|
||
```
|
||
|
||
```
|
||
ℹ Using CPU
|
||
ℹ Loading config and nlp from: config.cfg
|
||
ℹ Pipeline: ['tok2vec', 'tagger']
|
||
ℹ Start training
|
||
ℹ Training. Initial learn rate: 0.0
|
||
|
||
E # LOSS TOK2VEC LOSS TAGGER TAG_ACC SCORE
|
||
--- ------ ------------ ----------- ------- ------
|
||
0 0 0.00 86.20 0.22 0.00
|
||
0 200 3.08 18968.78 34.00 0.34
|
||
0 400 31.81 22539.06 33.64 0.34
|
||
0 600 92.13 22794.91 43.80 0.44
|
||
0 800 183.62 21541.39 56.05 0.56
|
||
0 1000 352.49 25461.82 65.15 0.65
|
||
0 1200 422.87 23708.82 71.84 0.72
|
||
0 1400 601.92 24994.79 76.57 0.77
|
||
0 1600 662.57 22268.02 80.20 0.80
|
||
0 1800 1101.50 28413.77 82.56 0.83
|
||
0 2000 1253.43 28736.36 85.00 0.85
|
||
0 2200 1411.02 28237.53 87.42 0.87
|
||
0 2400 1605.35 28439.95 88.70 0.89
|
||
```
|
||
|
||
Note that the cumulative loss keeps increasing within one epoch, but should
|
||
start decreasing across epochs.
|
||
|
||
</Accordion>
|
||
|
||
| Name | Description |
|
||
| -------------- | --------------------------------------------------------- |
|
||
| `progress_bar` | Whether the logger should print the progress bar ~~bool~~ |
|
||
|
||
Logging utilities for spaCy are implemented in the
|
||
[`spacy-loggers`](https://github.com/explosion/spacy-loggers) repo, and the
|
||
functions are typically available from `@spacy.registry.loggers`.
|
||
|
||
More documentation can be found in that repo's
|
||
[readme](https://github.com/explosion/spacy-loggers/blob/main/README.md) file.
|