Rebase on curated-transformers 2.0.0

.github/workflows/test.yml

@@ -3,7 +3,7 @@ name: Test
 on: [push, pull_request, workflow_call]
 
 env:
- hf-transformers-pip: transformers[sentencepiece]>=3.4.0,<4.32.0
+ hf-transformers-pip: transformers[sentencepiece]>=4.39.0,<4.40.0
 
 jobs:
  validate:

requirements.txt

@@ -1,5 +1,6 @@
-curated-transformers>=0.1.0,<0.2.0
-curated-tokenizers>=0.0.9,<0.1.0
+curated-transformers>=2.0.0.dev1,<3.0.0
+curated-tokenizers>=0.9.2,<1.0.0
+fsspec>=2023.5.0
 spacy>=4.0.0.dev2,<5.0.0
 thinc>=9.0.0.dev4,<9.1.0
 srsly

setup.cfg

@@ -1,5 +1,5 @@
 [metadata]
-version = 0.2.2
+version = 1.0.0.dev0
 description = Curated transformer models for spaCy pipelines
 url = https:/explosion/spacy-curated-transformers
 author = Explosion
@@ -12,10 +12,11 @@ long_description_content_type = text/markdown
 [options]
 zip_safe = true
 include_package_data = true
-python_requires = >=3.6
+python_requires = >=3.8
 install_requires =
- curated-transformers>=0.1.0,<0.2.0
- curated-tokenizers>=0.0.9,<0.1.0
+ curated-transformers>=2.0.0.dev1,<3.0.0
+ curated-tokenizers>=0.9.2,<1.0.0
+ fsspec>=2023.5.0
  spacy>=4.0.0.dev2,<5.0.0
  thinc>=9.0.0.dev4,<9.1.0
  torch>=1.12.0
@@ -56,6 +57,7 @@ thinc_model_loaders =
  spacy-curated-transformers.HFTransformerEncoderLoader.v1 = spacy_curated_transformers.models:build_hf_transformer_encoder_loader_v1
  spacy-curated-transformers.HFPieceEncoderLoader.v1 = spacy_curated_transformers.tokenization:build_hf_piece_encoder_loader_v1
  spacy-curated-transformers.PyTorchCheckpointLoader.v1 = spacy_curated_transformers.models:build_pytorch_checkpoint_loader_v1
+ spacy-curated-transformers.PyTorchCheckpointLoader.v2 = spacy_curated_transformers.models:build_pytorch_checkpoint_loader_v2
  spacy-curated-transformers.SentencepieceLoader.v1 = spacy_curated_transformers.tokenization:build_sentencepiece_encoder_loader_v1
  spacy-curated-transformers.WordpieceLoader.v1 = spacy_curated_transformers.tokenization:build_wordpiece_encoder_loader_v1
 

spacy_curated_transformers/models/architectures.py

@@ -3,15 +3,21 @@
 from typing import Any, Callable, List, Optional, Tuple, Union, cast
 
 import torch
-from curated_transformers.models.albert import AlbertConfig, AlbertEncoder
-from curated_transformers.models.bert import BertConfig, BertEncoder
-from curated_transformers.models.curated_transformer import (
- CuratedEncoderT,
- CuratedTransformer,
+from curated_transformers.layers import Activation, AttentionMask
+from curated_transformers.models import (
+ ALBERTConfig,
+ ALBERTEncoder,
+ BERTConfig,
+ BERTEncoder,
+ CamemBERTEncoder,
+ EncoderModule,
+ FromHFHub,
+ ModelOutput,
+ RoBERTaConfig,
+ RoBERTaEncoder,
+ XLMREncoder,
 )
-from curated_transformers.models.hf_util import convert_pretrained_model_for_encoder
-from curated_transformers.models.output import PyTorchTransformerOutput
-from curated_transformers.models.roberta import RobertaConfig, RobertaEncoder
+from fsspec.implementations.local import LocalFileSystem
 from spacy.tokens import Doc
 from spacy.util import SimpleFrozenDict
 from thinc.api import (
@@ -125,33 +131,34 @@ def build_albert_transformer_model_v1(
  Optional listener to wrap. Only used when replacing listeners
  in downstream components.
  """
- config = AlbertConfig(
+ config = ALBERTConfig(
  embedding_width=embedding_width,
  hidden_width=hidden_width,
  intermediate_width=intermediate_width,
- num_attention_heads=num_attention_heads,
- num_hidden_groups=num_hidden_groups,
- num_hidden_layers=num_hidden_layers,
+ n_attention_heads=num_attention_heads,
+ n_hidden_groups=num_hidden_groups,
+ n_hidden_layers=num_hidden_layers,
  attention_probs_dropout_prob=attention_probs_dropout_prob,
  hidden_dropout_prob=hidden_dropout_prob,
- hidden_act=hidden_act,
- vocab_size=vocab_size,
- type_vocab_size=type_vocab_size,
- max_position_embeddings=max_position_embeddings,
+ activation=Activation(hidden_act),
+ n_pieces=vocab_size,
+ n_types=type_vocab_size,
+ n_positions=max_position_embeddings,
  model_max_length=model_max_length,
  layer_norm_eps=layer_norm_eps,
- padding_idx=padding_idx,
  )
 
  if torchscript:
  transformer = _torchscript_encoder(
  model_max_length=model_max_length, padding_idx=padding_idx
  )
  else:
- encoder = AlbertEncoder(config)
+ encoder = ALBERTEncoder(config)
  transformer = _pytorch_encoder(
  encoder,
  hidden_width=hidden_width,
+ model_max_length=model_max_length,
+ padding_idx=padding_idx,
  mixed_precision=mixed_precision,
  grad_scaler_config=grad_scaler_config,
  )
@@ -233,32 +240,33 @@ def build_bert_transformer_model_v1(
  Optional listener to wrap. Only used when replacing listeners
  in downstream components.
  """
- config = BertConfig(
+ config = BERTConfig(
  embedding_width=hidden_width,
  hidden_width=hidden_width,
  intermediate_width=intermediate_width,
- num_attention_heads=num_attention_heads,
- num_hidden_layers=num_hidden_layers,
+ n_attention_heads=num_attention_heads,
+ n_hidden_layers=num_hidden_layers,
  attention_probs_dropout_prob=attention_probs_dropout_prob,
  hidden_dropout_prob=hidden_dropout_prob,
- hidden_act=hidden_act,
- vocab_size=vocab_size,
- type_vocab_size=type_vocab_size,
- max_position_embeddings=max_position_embeddings,
+ activation=Activation(hidden_act),
+ n_pieces=vocab_size,
+ n_types=type_vocab_size,
+ n_positions=max_position_embeddings,
  model_max_length=model_max_length,
  layer_norm_eps=layer_norm_eps,
- padding_idx=padding_idx,
  )
 
  if torchscript:
  transformer = _torchscript_encoder(
  model_max_length=model_max_length, padding_idx=padding_idx
  )
  else:
- encoder = BertEncoder(config)
+ encoder = BERTEncoder(config)
  transformer = _pytorch_encoder(
  encoder,
  hidden_width=hidden_width,
+ model_max_length=model_max_length,
+ padding_idx=padding_idx,
  mixed_precision=mixed_precision,
  grad_scaler_config=grad_scaler_config,
  )
@@ -340,32 +348,33 @@ def build_camembert_transformer_model_v1(
  Optional listener to wrap. Only used when replacing listeners
  in downstream components.
  """
- config = RobertaConfig(
+ config = RoBERTaConfig(
  embedding_width=hidden_width,
  hidden_width=hidden_width,
  intermediate_width=intermediate_width,
- num_attention_heads=num_attention_heads,
- num_hidden_layers=num_hidden_layers,
+ n_attention_heads=num_attention_heads,
+ n_hidden_layers=num_hidden_layers,
  attention_probs_dropout_prob=attention_probs_dropout_prob,
  hidden_dropout_prob=hidden_dropout_prob,
- hidden_act=hidden_act,
- vocab_size=vocab_size,
- type_vocab_size=type_vocab_size,
- max_position_embeddings=max_position_embeddings,
+ activation=Activation(hidden_act),
+ n_pieces=vocab_size,
+ n_types=type_vocab_size,
+ n_positions=max_position_embeddings,
  model_max_length=model_max_length,
  layer_norm_eps=layer_norm_eps,
- padding_idx=padding_idx,
  )
 
  if torchscript:
  transformer = _torchscript_encoder(
  model_max_length=model_max_length, padding_idx=padding_idx
  )
  else:
- encoder = RobertaEncoder(config)
+ encoder = CamemBERTEncoder(config)
  transformer = _pytorch_encoder(
  encoder,
  hidden_width=hidden_width,
+ model_max_length=model_max_length,
+ padding_idx=padding_idx,
  mixed_precision=mixed_precision,
  grad_scaler_config=grad_scaler_config,
  )
@@ -447,32 +456,33 @@ def build_roberta_transformer_model_v1(
  Optional listener to wrap. Only used when replacing listeners
  in downstream components.
  """
- config = RobertaConfig(
+ config = RoBERTaConfig(
  embedding_width=hidden_width,
  hidden_width=hidden_width,
  intermediate_width=intermediate_width,
- num_attention_heads=num_attention_heads,
- num_hidden_layers=num_hidden_layers,
+ n_attention_heads=num_attention_heads,
+ n_hidden_layers=num_hidden_layers,
  attention_probs_dropout_prob=attention_probs_dropout_prob,
  hidden_dropout_prob=hidden_dropout_prob,
- hidden_act=hidden_act,
- vocab_size=vocab_size,
- type_vocab_size=type_vocab_size,
- max_position_embeddings=max_position_embeddings,
+ activation=Activation(hidden_act),
+ n_pieces=vocab_size,
+ n_types=type_vocab_size,
+ n_positions=max_position_embeddings,
  model_max_length=model_max_length,
  layer_norm_eps=layer_norm_eps,
- padding_idx=padding_idx,
  )
 
  if torchscript:
  transformer = _torchscript_encoder(
  model_max_length=model_max_length, padding_idx=padding_idx
  )
  else:
- encoder = RobertaEncoder(config)
+ encoder = RoBERTaEncoder(config)
  transformer = _pytorch_encoder(
  encoder,
  hidden_width=hidden_width,
+ model_max_length=model_max_length,
+ padding_idx=padding_idx,
  mixed_precision=mixed_precision,
  grad_scaler_config=grad_scaler_config,
  )
@@ -554,32 +564,33 @@ def build_xlmr_transformer_model_v1(
  Optional listener to wrap. Only used when replacing listeners
  in downstream components.
  """
- config = RobertaConfig(
+ config = RoBERTaConfig(
  embedding_width=hidden_width,
  hidden_width=hidden_width,
  intermediate_width=intermediate_width,
- num_attention_heads=num_attention_heads,
- num_hidden_layers=num_hidden_layers,
+ n_attention_heads=num_attention_heads,
+ n_hidden_layers=num_hidden_layers,
  attention_probs_dropout_prob=attention_probs_dropout_prob,
  hidden_dropout_prob=hidden_dropout_prob,
- hidden_act=hidden_act,
- vocab_size=vocab_size,
- type_vocab_size=type_vocab_size,
- max_position_embeddings=max_position_embeddings,
+ activation=Activation(hidden_act),
+ n_pieces=vocab_size,
+ n_types=type_vocab_size,
+ n_positions=max_position_embeddings,
  model_max_length=model_max_length,
  layer_norm_eps=layer_norm_eps,
- padding_idx=padding_idx,
  )
 
  if torchscript:
  transformer = _torchscript_encoder(
  model_max_length=model_max_length, padding_idx=padding_idx
  )
  else:
- encoder = RobertaEncoder(config)
+ encoder = XLMREncoder(config)
  transformer = _pytorch_encoder(
  encoder,
  hidden_width=hidden_width,
+ model_max_length=model_max_length,
+ padding_idx=padding_idx,
  mixed_precision=mixed_precision,
  grad_scaler_config=grad_scaler_config,
  )
@@ -620,8 +631,6 @@ def build_transformer_model_v1(
  transformer: TorchTransformerModelT,
  piece_encoder: Tok2PiecesModelT,
 ) -> TransformerModelT:
- # FIXME: do we want to make `remove_bos_eos` configurable as well or
- # is it always the same post-processing?
  layers = [
  with_non_ws_tokens(
  chain(piece_encoder, with_spans(transformer), remove_bos_eos())
@@ -668,8 +677,10 @@ def transformer_model_init(
 
 
 def _pytorch_encoder(
- encoder: CuratedEncoderT,
+ encoder: EncoderModule,
  hidden_width: int,
+ padding_idx: int,
+ model_max_length: int,
  *,
  mixed_precision: bool = False,
  grad_scaler_config: dict = SimpleFrozenDict(),
@@ -682,11 +693,11 @@ def _pytorch_encoder(
  grad_scaler_config["enabled"] = mixed_precision
 
  model = PyTorchWrapper_v2(
- CuratedTransformer(encoder),
+ encoder,
  convert_inputs=partial(
  _convert_inputs,
- max_model_seq_len=encoder.max_seq_len,
- padding_idx=encoder.padding_idx,
+ max_model_seq_len=model_max_length,
+ padding_idx=padding_idx,
  ),
  convert_outputs=_convert_outputs,
  mixed_precision=mixed_precision,
@@ -735,18 +746,19 @@ def _convert_inputs(
  span_len = span.shape[0]
  Xt[i, :span_len] = span
  Xt = xp2torch(Xt)
+ mask = AttentionMask(Xt.ne(padding_idx))
 
  def convert_from_torch_backward(d_inputs: Any):
  # No gradients for the inputs.
  return [ops.alloc1f(x.shape[0]) for x in X]
 
- output = ArgsKwargs(args=(Xt,), kwargs={})
+ output = ArgsKwargs(args=(Xt, mask), kwargs={})
  return output, convert_from_torch_backward
 
 
 def _convert_outputs(
  model: Model,
- inputs_outputs: Tuple[TorchTransformerInT, PyTorchTransformerOutput],
+ inputs_outputs: Tuple[TorchTransformerInT, ModelOutput],
  is_train: bool,
 ) -> Tuple[TorchTransformerOutT, Callable[[List[List[Floats2d]]], ArgsKwargs]]:
  model_inputs, model_outputs = inputs_outputs
@@ -790,19 +802,40 @@ def build_pytorch_checkpoint_loader_v1(*, path: Path) -> Callable[
  TorchTransformerModelT,
 ]:
  """Construct a callback that initializes a supported transformer
- model with weights from a PyTorch checkpoint.
+ model with weights from a PyTorch or SafeTensors checkpoint.
 
  path (Path):
  Path to the PyTorch checkpoint.
  """
+ return build_pytorch_checkpoint_loader_v2(path=path.parent)
+
+
+def build_pytorch_checkpoint_loader_v2(*, path: Path) -> Callable[
+ [TorchTransformerModelT, Optional[List[Doc]], Optional[List[Doc]]],
+ TorchTransformerModelT,
+]:
+ """Construct a callback that initializes a supported transformer
+ model with weights from a PyTorch or SafeTensors checkpoint.
+
+ path (Path):
+ Path to the directory containing the checkpoint.
+ """
 
  def load(model, X=None, Y=None):
- encoder = model.shims[0]._model
  device = get_torch_default_device()
- params = torch.load(path, map_location=device)
- params = convert_pretrained_model_for_encoder(encoder, params)
- encoder.load_state_dict(params)
- encoder.to(device)
+ encoder = model.shims[0]._model
+ assert isinstance(encoder, FromHFHub)
+ from_fsspec = type(encoder).from_fsspec
+
+ # We can discard the previously initialized model entirely
+ # and use the Curated Transformers API to load it from the
+ # hub.
+ model.shims[0]._model = None
+ del encoder
+
+ fs = LocalFileSystem()
+ encoder = from_fsspec(fs=fs, model_path=path, device=device)
+ model.shims[0]._model = encoder
  return model
 
  return load