Store activations in Docs when save_activations is enabled (#11002)

* Store activations in Doc when `store_activations` is enabled

This change adds the new `activations` attribute to `Doc`. This
attribute can be used by trainable pipes to store their activations,
probabilities, and guesses for downstream users.

As an example, this change modifies the `tagger` and `senter` pipes to
add an `store_activations` option. When this option is enabled, the
probabilities and guesses are stored in `set_annotations`.

* Change type of `store_activations` to `Union[bool, List[str]]`

When the value is:

- A bool: all activations are stored when set to `True`.
- A List[str]: the activations named in the list are stored

* Formatting fixes in Tagger

* Support store_activations in spancat and morphologizer

* Make Doc.activations type visible to MyPy

* textcat/textcat_multilabel: add store_activations option

* trainable_lemmatizer/entity_linker: add store_activations option

* parser/ner: do not currently support returning activations

* Extend tagger and senter tests

So that they, like the other tests, also check that we get no
activations if no activations were requested.

* Document `Doc.activations` and `store_activations` in the relevant pipes

* Start errors/warnings at higher numbers to avoid merge conflicts

Between the master and v4 branches.

* Add `store_activations` to docstrings.

* Replace store_activations setter by set_store_activations method

Setters that take a different type than what the getter returns are still
problematic for MyPy. Replace the setter by a method, so that type inference
works everywhere.

* Use dict comprehension suggested by @svlandeg

* Revert "Use dict comprehension suggested by @svlandeg"

This reverts commit 6e7b958.

* EntityLinker: add type annotations to _add_activations

* _store_activations: make kwarg-only, remove doc_scores_lens arg

* set_annotations: add type annotations

* Apply suggestions from code review

Co-authored-by: Sofie Van Landeghem <[email protected]>

* TextCat.predict: return dict

* Make the `TrainablePipe.store_activations` property a bool

This means that we can also bring back `store_activations` setter.

* Remove `TrainablePipe.activations`

We do not need to enumerate the activations anymore since `store_activations` is
`bool`.

* Add type annotations for activations in predict/set_annotations

* Rename `TrainablePipe.store_activations` to `save_activations`

* Error E1400 is not used anymore

This error was used when activations were still `Union[bool, List[str]]`.

* Change wording in API docs after store -> save change

* docs: tag (save_)activations as new in spaCy 4.0

* Fix copied line in morphologizer activations test

* Don't train in any test_save_activations test

* Rename activations

- "probs" -> "probabilities"
- "guesses" -> "label_ids", except in the edit tree lemmatizer, where
  "guesses" -> "tree_ids".

* Remove unused W400 warning.

This warning was used when we still allowed the user to specify
which activations to save.

* Formatting fixes

Co-authored-by: Sofie Van Landeghem <[email protected]>

* Replace "kb_ids" by a constant

* spancat: replace a cast by an assertion

* Fix EOF spacing

* Fix comments in test_save_activations tests

* Do not set RNG seed in activation saving tests

* Revert "spancat: replace a cast by an assertion"

This reverts commit 0bd5730.

Co-authored-by: Sofie Van Landeghem <[email protected]>

Author: danieldk

spacy/pipeline/edit_tree_lemmatizer.py

@@ -7,7 +7,7 @@
 
 import srsly
 from thinc.api import Config, Model, SequenceCategoricalCrossentropy
-from thinc.types import Floats2d, Ints1d, Ints2d
+from thinc.types import ArrayXd, Floats2d, Ints1d
 
 from ._edit_tree_internals.edit_trees import EditTrees
 from ._edit_tree_internals.schemas import validate_edit_tree
@@ -21,6 +21,9 @@
 from .. import util
 
 
+ActivationsT = Dict[str, Union[List[Floats2d], List[Ints1d]]]
+
+
 default_model_config = """
 [model]
 @architectures = "spacy.Tagger.v2"
@@ -49,6 +52,7 @@
         "overwrite": False,
         "top_k": 1,
         "scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
+        "save_activations": False,
     },
     default_score_weights={"lemma_acc": 1.0},
 )
@@ -61,6 +65,7 @@ def make_edit_tree_lemmatizer(
     overwrite: bool,
     top_k: int,
     scorer: Optional[Callable],
+    save_activations: bool,
 ):
     """Construct an EditTreeLemmatizer component."""
     return EditTreeLemmatizer(
@@ -72,6 +77,7 @@ def make_edit_tree_lemmatizer(
         overwrite=overwrite,
         top_k=top_k,
         scorer=scorer,
+        save_activations=save_activations,
     )
 
 
@@ -91,6 +97,7 @@ def __init__(
         overwrite: bool = False,
         top_k: int = 1,
         scorer: Optional[Callable] = lemmatizer_score,
+        save_activations: bool = False,
     ):
         """
         Construct an edit tree lemmatizer.
@@ -102,6 +109,7 @@ def __init__(
             frequency in the training data.
         overwrite (bool): overwrite existing lemma annotations.
         top_k (int): try to apply at most the k most probable edit trees.
+        save_activations (bool): save model activations in Doc when annotating.
         """
         self.vocab = vocab
         self.model = model
@@ -116,6 +124,7 @@ def __init__(
 
         self.cfg: Dict[str, Any] = {"labels": []}
         self.scorer = scorer
+        self.save_activations = save_activations
 
     def get_loss(
         self, examples: Iterable[Example], scores: List[Floats2d]
@@ -144,21 +153,24 @@ def get_loss(
 
         return float(loss), d_scores
 
-    def predict(self, docs: Iterable[Doc]) -> List[Ints2d]:
+    def predict(self, docs: Iterable[Doc]) -> ActivationsT:
         n_docs = len(list(docs))
         if not any(len(doc) for doc in docs):
             # Handle cases where there are no tokens in any docs.
             n_labels = len(self.cfg["labels"])
-            guesses: List[Ints2d] = [
+            guesses: List[Ints1d] = [
+                self.model.ops.alloc((0,), dtype="i") for doc in docs
+            ]
+            scores: List[Floats2d] = [
                 self.model.ops.alloc((0, n_labels), dtype="i") for doc in docs
             ]
             assert len(guesses) == n_docs
-            return guesses
+            return {"probabilities": scores, "tree_ids": guesses}
         scores = self.model.predict(docs)
         assert len(scores) == n_docs
         guesses = self._scores2guesses(docs, scores)
         assert len(guesses) == n_docs
-        return guesses
+        return {"probabilities": scores, "tree_ids": guesses}
 
     def _scores2guesses(self, docs, scores):
         guesses = []
@@ -186,8 +198,13 @@ def _scores2guesses(self, docs, scores):
 
         return guesses
 
-    def set_annotations(self, docs: Iterable[Doc], batch_tree_ids):
+    def set_annotations(self, docs: Iterable[Doc], activations: ActivationsT):
+        batch_tree_ids = activations["tree_ids"]
         for i, doc in enumerate(docs):
+            if self.save_activations:
+                doc.activations[self.name] = {}
+                for act_name, acts in activations.items():
+                    doc.activations[self.name][act_name] = acts[i]
             doc_tree_ids = batch_tree_ids[i]
             if hasattr(doc_tree_ids, "get"):
                 doc_tree_ids = doc_tree_ids.get()

spacy/pipeline/entity_linker.py

@@ -1,5 +1,7 @@
-from typing import Optional, Iterable, Callable, Dict, Union, List, Any
-from thinc.types import Floats2d
+from typing import Optional, Iterable, Callable, Dict, Sequence, Union, List, Any
+from typing import cast
+from numpy import dtype
+from thinc.types import Floats1d, Floats2d, Ints1d, Ragged
 from pathlib import Path
 from itertools import islice
 import srsly
@@ -21,6 +23,11 @@
 from .. import util
 from ..scorer import Scorer
 
+
+ActivationsT = Dict[str, Union[List[Ragged], List[str]]]
+
+KNOWLEDGE_BASE_IDS = "kb_ids"
+
 # See #9050
 BACKWARD_OVERWRITE = True
 
@@ -57,6 +64,7 @@
         "scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},
         "use_gold_ents": True,
         "threshold": None,
+        "save_activations": False,
     },
     default_score_weights={
         "nel_micro_f": 1.0,
@@ -79,6 +87,7 @@ def make_entity_linker(
     scorer: Optional[Callable],
     use_gold_ents: bool,
     threshold: Optional[float] = None,
+    save_activations: bool,
 ):
     """Construct an EntityLinker component.
 
@@ -97,6 +106,7 @@ def make_entity_linker(
         component must provide entity annotations.
     threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the threshold,
         prediction is discarded. If None, predictions are not filtered by any threshold.
+    save_activations (bool): save model activations in Doc when annotating.
     """
 
     if not model.attrs.get("include_span_maker", False):
@@ -128,6 +138,7 @@ def make_entity_linker(
         scorer=scorer,
         use_gold_ents=use_gold_ents,
         threshold=threshold,
+        save_activations=save_activations,
     )
 
 
@@ -164,6 +175,7 @@ def __init__(
         scorer: Optional[Callable] = entity_linker_score,
         use_gold_ents: bool,
         threshold: Optional[float] = None,
+        save_activations: bool = False,
     ) -> None:
         """Initialize an entity linker.
 
@@ -212,6 +224,7 @@ def __init__(
         self.scorer = scorer
         self.use_gold_ents = use_gold_ents
         self.threshold = threshold
+        self.save_activations = save_activations
 
     def set_kb(self, kb_loader: Callable[[Vocab], KnowledgeBase]):
         """Define the KB of this pipe by providing a function that will
@@ -397,7 +410,7 @@ def get_loss(self, examples: Iterable[Example], sentence_encodings: Floats2d):
         loss = loss / len(entity_encodings)
         return float(loss), out
 
-    def predict(self, docs: Iterable[Doc]) -> List[str]:
+    def predict(self, docs: Iterable[Doc]) -> ActivationsT:
         """Apply the pipeline's model to a batch of docs, without modifying them.
         Returns the KB IDs for each entity in each doc, including NIL if there is
         no prediction.
@@ -410,13 +423,20 @@ def predict(self, docs: Iterable[Doc]) -> List[str]:
         self.validate_kb()
         entity_count = 0
         final_kb_ids: List[str] = []
-        xp = self.model.ops.xp
+        ops = self.model.ops
+        xp = ops.xp
+        docs_ents: List[Ragged] = []
+        docs_scores: List[Ragged] = []
         if not docs:
-            return final_kb_ids
+            return {KNOWLEDGE_BASE_IDS: final_kb_ids, "ents": docs_ents, "scores": docs_scores}
         if isinstance(docs, Doc):
             docs = [docs]
-        for i, doc in enumerate(docs):
+        for doc in docs:
+            doc_ents: List[Ints1d] = []
+            doc_scores: List[Floats1d] = []
             if len(doc) == 0:
+                docs_scores.append(Ragged(ops.alloc1f(0), ops.alloc1i(0)))
+                docs_ents.append(Ragged(xp.zeros(0, dtype="uint64"), ops.alloc1i(0)))
                 continue
             sentences = [s for s in doc.sents]
             # Looping through each entity (TODO: rewrite)
@@ -439,14 +459,32 @@ def predict(self, docs: Iterable[Doc]) -> List[str]:
                 if ent.label_ in self.labels_discard:
                     # ignoring this entity - setting to NIL
                     final_kb_ids.append(self.NIL)
+                    self._add_activations(
+                        doc_scores=doc_scores,
+                        doc_ents=doc_ents,
+                        scores=[0.0],
+                        ents=[0],
+                    )
                 else:
                     candidates = list(self.get_candidates(self.kb, ent))
                     if not candidates:
                         # no prediction possible for this entity - setting to NIL
                         final_kb_ids.append(self.NIL)
+                        self._add_activations(
+                            doc_scores=doc_scores,
+                            doc_ents=doc_ents,
+                            scores=[0.0],
+                            ents=[0],
+                        )
                     elif len(candidates) == 1 and self.threshold is None:
                         # shortcut for efficiency reasons: take the 1 candidate
                         final_kb_ids.append(candidates[0].entity_)
+                        self._add_activations(
+                            doc_scores=doc_scores,
+                            doc_ents=doc_ents,
+                            scores=[1.0],
+                            ents=[candidates[0].entity_],
+                        )
                     else:
                         random.shuffle(candidates)
                         # set all prior probabilities to 0 if incl_prior=False
@@ -479,27 +517,48 @@ def predict(self, docs: Iterable[Doc]) -> List[str]:
                             if self.threshold is None or scores.max() >= self.threshold
                             else EntityLinker.NIL
                         )
+                        self._add_activations(
+                            doc_scores=doc_scores,
+                            doc_ents=doc_ents,
+                            scores=scores,
+                            ents=[c.entity for c in candidates],
+                        )
+            self._add_doc_activations(
+                docs_scores=docs_scores,
+                docs_ents=docs_ents,
+                doc_scores=doc_scores,
+                doc_ents=doc_ents,
+            )
         if not (len(final_kb_ids) == entity_count):
             err = Errors.E147.format(
                 method="predict", msg="result variables not of equal length"
             )
             raise RuntimeError(err)
-        return final_kb_ids
+        return {KNOWLEDGE_BASE_IDS: final_kb_ids, "ents": docs_ents, "scores": docs_scores}
 
-    def set_annotations(self, docs: Iterable[Doc], kb_ids: List[str]) -> None:
+    def set_annotations(self, docs: Iterable[Doc], activations: ActivationsT) -> None:
         """Modify a batch of documents, using pre-computed scores.
 
         docs (Iterable[Doc]): The documents to modify.
-        kb_ids (List[str]): The IDs to set, produced by EntityLinker.predict.
+        activations (ActivationsT): The activations used for setting annotations, produced
+                                 by EntityLinker.predict.
 
         DOCS: https://spacy.io/api/entitylinker#set_annotations
         """
+        kb_ids = cast(List[str], activations[KNOWLEDGE_BASE_IDS])
         count_ents = len([ent for doc in docs for ent in doc.ents])
         if count_ents != len(kb_ids):
             raise ValueError(Errors.E148.format(ents=count_ents, ids=len(kb_ids)))
         i = 0
         overwrite = self.cfg["overwrite"]
-        for doc in docs:
+        for j, doc in enumerate(docs):
+            if self.save_activations:
+                doc.activations[self.name] = {}
+                for act_name, acts in activations.items():
+                    if act_name != KNOWLEDGE_BASE_IDS:
+                        # We only copy activations that are Ragged.
+                        doc.activations[self.name][act_name] = cast(Ragged, acts[j])
+
             for ent in doc.ents:
                 kb_id = kb_ids[i]
                 i += 1
@@ -598,3 +657,32 @@ def rehearse(self, examples, *, sgd=None, losses=None, **config):
 
     def add_label(self, label):
         raise NotImplementedError
+
+    def _add_doc_activations(
+        self,
+        *,
+        docs_scores: List[Ragged],
+        docs_ents: List[Ragged],
+        doc_scores: List[Floats1d],
+        doc_ents: List[Ints1d],
+    ):
+        if not self.save_activations:
+            return
+        ops = self.model.ops
+        lengths = ops.asarray1i([s.shape[0] for s in doc_scores])
+        docs_scores.append(Ragged(ops.flatten(doc_scores), lengths))
+        docs_ents.append(Ragged(ops.flatten(doc_ents), lengths))
+
+    def _add_activations(
+        self,
+        *,
+        doc_scores: List[Floats1d],
+        doc_ents: List[Ints1d],
+        scores: Sequence[float],
+        ents: Sequence[int],
+    ):
+        if not self.save_activations:
+            return
+        ops = self.model.ops
+        doc_scores.append(ops.asarray1f(scores))
+        doc_ents.append(ops.asarray1i(ents, dtype="uint64"))