added transformers_config for passing arguments to the transformer (#268

)

* added transformers config

* changed def config to include new transformers config

* fixed quotationmarks in config

* removed wierd symbol

* added attention in data classes

* fixed keyerror

* ibid

* added pass of config to forward

* ibid

* fix for init

* fixed tensors in forward

* removed default for attention and added to_doc fix for attn

* reformatted to black (accidentally reformatted via. autopep8)

* added def to transformerdata

* bugfixes - don't get why this does not use the default argument here though

* removed default trfconfig from trfmodel

* updated dummy transformer

* fixed tests

* added Tok2VecTransformer.v2

* changed typing

* fixed type hint

* fixed name of transformer_tok2vec_v2, added def

* fixed default config to match name change

* remove ds

* renamed transformers_config

Co-authored-by: KennethEnevoldsen <[email protected]>

spacy_transformers/architectures.py

@@ -13,7 +13,7 @@ def transformer_listener_tok2vec_v1(
 ) -> Model[List[Doc], List[Floats2d]]:
     """Create a 'TransformerListener' layer, which will connect to a Transformer
     component earlier in the pipeline.
-     
+
     The layer takes a list of Doc objects as input, and produces a list of
     2d arrays as output, with each array having one row per token. Most spaCy
     models expect a sublayer with this signature, making it easy to connect them
@@ -46,7 +46,7 @@ def transformer_listener_tok2vec_v1(
 def transformer_tok2vec_v1(
     name: str,
     get_spans,
-    tokenizer_config,
+    tokenizer_config: dict,
     pooling: Model[Ragged, Floats2d],
     grad_factor: float = 1.0,
 ) -> Model[List[Doc], List[Floats2d]]:
@@ -74,6 +74,42 @@ def transformer_tok2vec_v1(
     )
 
 
+@registry.architectures.register("spacy-transformers.Tok2VecTransformer.v2")
+def transformer_tok2vec_v2(
+    name: str,
+    get_spans,
+    tokenizer_config: dict,
+    transformer_config: dict,
+    pooling: Model[Ragged, Floats2d],
+    grad_factor: float = 1.0,
+) -> Model[List[Doc], List[Floats2d]]:
+    """Use a transformer as a "Tok2Vec" layer directly. This does not allow
+    multiple components to share the transformer weights, and does not allow
+    the transformer to set annotations into the `Doc` object, but it's a
+    simpler solution if you only need the transformer within one component.
+
+    get_spans (Callable[[List[Doc]], List[List[Span]]]): A function to extract
+        spans from the batch of Doc objects. See the "TransformerModel" layer
+        for details.
+    tokenizer_config (dict): Settings to pass to the transformers tokenizer.
+    transformers_config (dict): Settings to pass to the transformers forward pass
+        of the transformer.
+    pooling (Model[Ragged, Floats2d]): A reduction layer used to calculate
+        the token vectors based on zero or more wordpiece vectors. If in doubt,
+        mean pooling (see `thinc.layers.reduce_mean`) is usually a good choice.
+     grad_factor (float): Reweight gradients from the component before passing
+        them to the transformer. You can set this to 0 to "freeze" the transformer
+        weights with respect to the component, or to make it learn more slowly.
+        Leaving it at 1.0 is usually fine.
+    """
+    return chain(
+        TransformerModel(name, get_spans, tokenizer_config, transformer_config),
+        split_trf_batch(),
+        trfs2arrays(pooling, grad_factor),
+    )
+
+
+
 registry.architectures.register(
     "spacy-transformers.TransformerModel.v1", func=TransformerModel
 )

spacy_transformers/data_classes.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass
-from typing import Optional, List, Dict, Any
+from typing import Optional, List, Dict, Any, Tuple
 import torch
 import numpy
 from transformers.tokenization_utils import BatchEncoding
@@ -155,11 +155,14 @@ class TransformerData:
     wordpieces: WordpieceBatch
     tensors: List[FloatsXd]
     align: Ragged
+    attention: Optional[Tuple[FloatsXd, ...]] = None
 
     @classmethod
     def empty(cls) -> "TransformerData":
         align = Ragged(numpy.zeros((0,), dtype="i"), numpy.zeros((0,), dtype="i"))
-        return cls(wordpieces=WordpieceBatch.empty(), tensors=[], align=align)
+        return cls(
+            wordpieces=WordpieceBatch.empty(), tensors=[], align=align, attention=None
+        )
 
     @classmethod
     def zeros(cls, length: int, width: int, *, xp=numpy) -> "TransformerData":
@@ -247,6 +250,7 @@ class FullTransformerBatch:
     wordpieces: WordpieceBatch
     tensors: List[torch.Tensor]
     align: Ragged
+    attention: Optional[Tuple[torch.Tensor]] = None
     cached_doc_data: Optional[List[TransformerData]] = None
 
     @classmethod
@@ -259,6 +263,7 @@ def empty(cls, nr_docs) -> "FullTransformerBatch":
             wordpieces=WordpieceBatch.empty(),
             tensors=[],
             align=align,
+            attention=None,
             cached_doc_data=doc_data,
         )
 
@@ -312,11 +317,16 @@ def split_by_doc(self) -> List[TransformerData]:
             doc_tokens = self.wordpieces[start:end]
             doc_align = self.align[start_i:end_i]
             doc_align.data = doc_align.data - prev_tokens
+            if self.attention:
+                attn = [torch2xp(t[start:end]) for t in self.attention]
+            else:
+                attn = None
             outputs.append(
                 TransformerData(
                     wordpieces=doc_tokens,
                     tensors=[torch2xp(t[start:end]) for t in self.tensors],
                     align=doc_align,
+                    attention=attn,
                 )
             )
             prev_tokens += doc_tokens.input_ids.size

spacy_transformers/layers/transformer_model.py

@@ -15,7 +15,7 @@
 
 
 def TransformerModel(
-    name: str, get_spans: Callable, tokenizer_config: dict
+    name: str, get_spans: Callable, tokenizer_config: dict = {}, transformer_config: dict = {}
 ) -> Model[List[Doc], FullTransformerBatch]:
     """
     get_spans (Callable[[List[Doc]], List[Span]]):
@@ -25,6 +25,7 @@ def TransformerModel(
         overlap, and you can also omit sections of the Doc if they are not
         relevant.
     tokenizer_config (dict): Settings to pass to the transformers tokenizer.
+    transformer_config (dict): Settings to pass to the transformers forward pass.
     """
 
     return Model(
@@ -38,6 +39,7 @@ def TransformerModel(
             "get_spans": get_spans,
             "name": name,
             "tokenizer_config": tokenizer_config,
+            "transformer_config": transformer_config,
             "set_transformer": set_pytorch_transformer,
             "has_transformer": False,
             "flush_cache_chance": 0.0,
@@ -75,7 +77,8 @@ def init(model: Model, X=None, Y=None):
         return
     name = model.attrs["name"]
     tok_cfg = model.attrs["tokenizer_config"]
-    tokenizer, transformer = huggingface_from_pretrained(name, tok_cfg)
+    trf_cfg = model.attrs["transformer_config"]
+    tokenizer, transformer = huggingface_from_pretrained(name, tok_cfg, trf_cfg)
     model.attrs["tokenizer"] = tokenizer
     model.attrs["set_transformer"](model, transformer)
     # Call the model with a batch of inputs to infer the width
@@ -89,26 +92,23 @@ def init(model: Model, X=None, Y=None):
         for doc_spans in nested_spans:
             flat_spans.extend(doc_spans)
         token_data = huggingface_tokenize(
-            model.attrs["tokenizer"],
-            [span.text for span in flat_spans]
+            model.attrs["tokenizer"], [span.text for span in flat_spans]
         )
         wordpieces = WordpieceBatch.from_batch_encoding(token_data)
         align = get_alignment(
-            flat_spans,
-            wordpieces.strings, model.attrs["tokenizer"].all_special_tokens
+            flat_spans, wordpieces.strings, model.attrs["tokenizer"].all_special_tokens
         )
         wordpieces, align = truncate_oversize_splits(
             wordpieces, align, tokenizer.model_max_length
         )
     else:
         texts = ["hello world", "foo bar"]
-        token_data = huggingface_tokenize(
-            model.attrs["tokenizer"],
-            texts
-        )
+        token_data = huggingface_tokenize(model.attrs["tokenizer"], texts)
         wordpieces = WordpieceBatch.from_batch_encoding(token_data)
     model.layers[0].initialize(X=wordpieces)
     tensors = model.layers[0].predict(wordpieces)
+    if trf_cfg["output_attentions"] is True:
+        tensors = tensors[:-1]  # remove attention
     t_i = find_last_hidden(tensors)
     model.set_dim("nO", tensors[t_i].shape[-1])
 
@@ -118,6 +118,7 @@ def forward(
 ) -> Tuple[FullTransformerBatch, Callable]:
     tokenizer = model.attrs["tokenizer"]
     get_spans = model.attrs["get_spans"]
+    trf_config = model.attrs["transformer_config"]
     transformer = model.layers[0]
 
     nested_spans = get_spans(docs)
@@ -142,8 +143,17 @@ def forward(
     tensors, bp_tensors = transformer(wordpieces, is_train)
     if "logger" in model.attrs:
         log_gpu_memory(model.attrs["logger"], "after forward")
+    if ("output_attentions" in trf_config) and (trf_config["output_attentions"] is True):
+        attn = tensors[-1]
+        tensors = tensors[:-1]
+    else:
+        attn = None
     output = FullTransformerBatch(
-        spans=nested_spans, wordpieces=wordpieces, tensors=tensors, align=align
+        spans=nested_spans,
+        wordpieces=wordpieces,
+        tensors=tensors,
+        align=align,
+        attention=attn,
     )
     if "logger" in model.attrs:
         log_gpu_memory(model.attrs["logger"], "return from forward")

spacy_transformers/pipeline_component.py

@@ -30,6 +30,7 @@
 @architectures = "spacy-transformers.TransformerModel.v1"
 name = "roberta-base"
 tokenizer_config = {"use_fast": true}
+transformer_config = {"output_attentions": false}
 
 [transformer.model.get_spans]
 @span_getters = "spacy-transformers.strided_spans.v1"
@@ -143,7 +144,9 @@ def add_listener(self, listener: TransformerListener, component_name: str) -> No
         if self.model.has_dim("nO") and listener.has_dim("nO") is None:
             listener.set_dim("nO", self.model.get_dim("nO"))
 
-    def remove_listener(self, listener: TransformerListener, component_name: str) -> bool:
+    def remove_listener(
+        self, listener: TransformerListener, component_name: str
+    ) -> bool:
         """Remove a listener for a downstream component. Usually internals."""
         if component_name in self.listener_map:
             if listener in self.listener_map[component_name]:
@@ -167,7 +170,10 @@ def find_listeners(self, component) -> None:
         names = ("*", self.name)
         if isinstance(getattr(component, "model", None), Model):
             for node in component.model.walk():
-                if isinstance(node, TransformerListener) and node.upstream_name in names:
+                if (
+                    isinstance(node, TransformerListener)
+                    and node.upstream_name in names
+                ):
                     self.add_listener(node, component.name)
 
     def __call__(self, doc: Doc) -> Doc:
@@ -296,7 +302,8 @@ def accumulate_gradient(d_trf_datas: List[TransformerData]):
             nonlocal d_tensors
             for i, d_trf_data in enumerate(d_trf_datas):
                 for d_tensor in d_trf_data.tensors:
-                    losses[self.name] += float((d_tensor ** 2).sum())  # type: ignore
+                    # type: ignore
+                    losses[self.name] += float((d_tensor ** 2).sum())
                 if i >= len(d_tensors):
                     d_tensors.append(d_trf_data.tensors)
                 else:
@@ -389,7 +396,7 @@ def from_disk(
         def load_model(p):
             p = Path(p).absolute()
             tokenizer, transformer = huggingface_from_pretrained(
-                p, self.model.attrs["tokenizer_config"]
+                p, self.model.attrs["tokenizer_config"], self.model.attrs["transformer_config"]
             )
             self.model.attrs["tokenizer"] = tokenizer
             self.model.attrs["set_transformer"](self.model, transformer)

spacy_transformers/tests/test_model_wrapper.py

@@ -28,7 +28,9 @@ def name(request):
 
 @pytest.fixture(scope="session")
 def trf_model(name):
-    model = TransformerModel(name, get_doc_spans, {"use_fast": True})
+    model = TransformerModel(
+        name, get_doc_spans, {"use_fast": True}, {"output_attentions": False}
+    )
     model.initialize()
     return model
 

spacy_transformers/tests/util.py

@@ -116,7 +116,11 @@ def _forward(model, tokens, is_train):
             tensors.append(torch.zeros(*shape))
         return tensors, lambda d_tensors: tokens
 
-    return Model("dummy-transformer", _forward, attrs={"width": width, "depth": depth})
+    return Model(
+        "dummy-transformer",
+        _forward,
+        attrs={"width": width, "depth": depth},
+    )
 
 
 def DummyTransformer(
@@ -132,6 +136,7 @@ def DummyTransformer(
             "tokenizer": DummyTokenizer(),
             "grad_factor": 1.0,
             "flush_cache_chance": 0.0,
+            "transformer_config": {}
         },
         dims={"nO": width},
     )

spacy_transformers/util.py

@@ -1,5 +1,6 @@
 from typing import List, Dict, Union
 from pathlib import Path
+from functools import partial
 import random
 from transformers import AutoModel, AutoTokenizer
 from transformers.tokenization_utils import BatchEncoding
@@ -16,20 +17,24 @@
 # fmt: on
 
 
-def huggingface_from_pretrained(source: Union[Path, str], config: Dict):
+def huggingface_from_pretrained(
+    source: Union[Path, str], tok_config: Dict, trf_config: Dict
+):
     """Create a Huggingface transformer model from pretrained weights. Will
     download the model if it is not already downloaded.
 
     source (Union[str, Path]): The name of the model or a path to it, such as
         'bert-base-cased'.
-    config (dict): Settings to pass to the tokenizer.
+    tok_config (dict): Settings to pass to the tokenizer.
+    trf_config (dict): Settings to pass to the transformer.
     """
     if hasattr(source, "absolute"):
         str_path = str(source.absolute())
     else:
         str_path = source
-    tokenizer = AutoTokenizer.from_pretrained(str_path, **config)
+    tokenizer = AutoTokenizer.from_pretrained(str_path, **tok_config)
     transformer = AutoModel.from_pretrained(str_path)
+    transformer.forward = partial(transformer.forward, **trf_config)
     ops = get_current_ops()
     if isinstance(ops, CupyOps):
         transformer.cuda()