Changes and improvements to how we initialize transformer modules from pretrained models

.github/workflows/ci.yml

@@ -152,6 +152,8 @@ jobs:
       run: |
         git clone https://github.com/allenai/allennlp-models.git
         cd allennlp-models
+        # TODO: remove this
+        git checkout transformer-init
         pip install --upgrade --upgrade-strategy eager -e . -r dev-requirements.txt
 
     - name: Run models tests

CHANGELOG.md

@@ -11,13 +11,17 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Use `dist_reduce_sum` in distributed metrics.
 - Allow Google Cloud Storage paths in `cached_path` ("gs://...").
+- Renamed `nn.util.load_state_dict()` to `read_state_dict` to avoid confusion with `torch.nn.Module.load_state_dict()`.
+- `TransformerModule.from_pretrained_module` now only accepts a pretrained model ID (e.g. "bert-base-case") instead of
+  an actual `torch.nn.Module`. Other parameters to this method have changed as well.
 - Print the first batch to the console by default.
 - Renamed `sanity_checks` to `confidence_checks` (`sanity_checks` is deprecated and will be removed in AllenNLP 3.0).
 
 ### Added
 
 - Added `TaskSuite` base class and command line functionality for running [`checklist`](https://github.com/marcotcr/checklist) test suites, along with implementations for `SentimentAnalysisSuite`, `QuestionAnsweringSuite`, and `TextualEntailmentSuite`. These can be found in the `allennlp.confidence_checks.task_checklists` module.
 - Added `allennlp diff` command to compute a diff on model checkpoints, analogous to what `git diff` does on two files.
+- Added `nn.util.distributed_device()` helper function.
 - Added `allennlp.nn.util.load_state_dict` helper function.
 - Added a way to avoid downloading and loading pretrained weights in modules that wrap transformers
   such as the `PretrainedTransformerEmbedder` and `PretrainedTransformerMismatchedEmbedder`.

allennlp/commands/diff.py

@@ -19,7 +19,7 @@
 
 from allennlp.commands.subcommand import Subcommand
 from allennlp.common.file_utils import cached_path
-from allennlp.nn.util import load_state_dict
+from allennlp.nn.util import read_state_dict
 
 
 logger = logging.getLogger(__name__)
@@ -249,10 +249,10 @@ def _get_checkpoint_path(checkpoint: str) -> str:
 def _diff(args: argparse.Namespace):
     checkpoint_1_path = _get_checkpoint_path(args.checkpoint1)
     checkpoint_2_path = _get_checkpoint_path(args.checkpoint2)
-    checkpoint_1 = load_state_dict(
+    checkpoint_1 = read_state_dict(
         checkpoint_1_path, strip_prefix=args.strip_prefix_1, strict=False
     )
-    checkpoint_2 = load_state_dict(
+    checkpoint_2 = read_state_dict(
         checkpoint_2_path, strip_prefix=args.strip_prefix_2, strict=False
     )
     for step in checkpoint_diff(checkpoint_1, checkpoint_2, args.scale, args.threshold):

allennlp/common/testing/distributed_test.py

@@ -61,12 +61,19 @@ def run_distributed_test(
 
     func: `Callable`
         `func` needs to be global for spawning the processes, so that it can be pickled.
+
+    start_method: `Optional[str]`, optional (default = `None`)
+        The start method to use for starting the workers. Defaults to "spawn" for GPU
+        processes and fork otherwise.
     """
     device_ids = device_ids or [-1, -1]
     check_for_gpu(device_ids)
     # "fork" start method is the default and should be preferred, except when we're
     # running the tests on GPU, in which case we need to use "spawn".
-    start_method = "spawn" if any(x >= 0 for x in device_ids) else "fork"
+    if "start_method" in kwargs:
+        start_method = kwargs.pop("start_method")
+    else:
+        start_method = "spawn" if any(x >= 0 for x in device_ids) else "fork"
     nprocs = world_size = len(device_ids)
     mp.start_processes(
         init_process,

allennlp/common/util.py

@@ -509,6 +509,18 @@ def is_distributed() -> bool:
     return dist.is_available() and dist.is_initialized()
 
 
+def is_global_primary() -> bool:
+    """
+    Checks if the distributed process group is the global primary (rank = 0).
+    If the distributed process group is not available or has not been initialized,
+    this trivially returns `True`.
+    """
+    if not is_distributed():
+        return True
+    else:
+        return dist.get_rank() == 0
+
+
 def sanitize_wordpiece(wordpiece: str) -> str:
     """
     Sanitizes wordpieces from BERT, RoBERTa or ALBERT tokenizers.

allennlp/models/model.py

@@ -335,7 +335,7 @@ def _load(
 
         # Load state dict. We pass `strict=False` so PyTorch doesn't raise a RuntimeError
         # if the state dict is missing keys because we handle this case below.
-        model_state = util.load_state_dict(weights_file, cuda_device=cuda_device)
+        model_state = util.read_state_dict(weights_file, cuda_device=cuda_device)
         missing_keys, unexpected_keys = model.load_state_dict(model_state, strict=False)
 
         # Modules might define a class variable called `authorized_missing_keys`,

allennlp/modules/backbones/vilbert_backbone.py

@@ -7,7 +7,12 @@
 from allennlp.data.fields.text_field import TextFieldTensors
 from allennlp.data.vocabulary import Vocabulary
 from allennlp.modules.backbones.backbone import Backbone
-from allennlp.modules.transformer import BiModalEncoder, ImageFeatureEmbeddings, Embeddings
+from allennlp.modules.transformer import (
+    BiModalEncoder,
+    ImageFeatureEmbeddings,
+    TransformerEmbeddings,
+    TransformerPooler,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -23,7 +28,7 @@ class VilbertBackbone(Backbone):
     def __init__(
         self,
         vocab: Vocabulary,
-        text_embeddings: Embeddings,
+        text_embeddings: TransformerEmbeddings,
         image_embeddings: ImageFeatureEmbeddings,
         encoder: BiModalEncoder,
         pooled_output_dim: int,
@@ -36,7 +41,6 @@ def __init__(
         self.text_embeddings = text_embeddings
         self.image_embeddings = image_embeddings
         self.encoder = encoder
-        from allennlp.modules.transformer import TransformerPooler
 
         self.t_pooler = TransformerPooler(encoder.hidden_size1, pooled_output_dim)
         self.v_pooler = TransformerPooler(encoder.hidden_size2, pooled_output_dim)
@@ -66,44 +70,7 @@ def from_huggingface_model_name(
         image_fixed_layer: int,
         fusion_method: str = "sum",
     ):
-        from transformers import AutoModel
-
-        transformer = AutoModel.from_pretrained(model_name)
-
-        from copy import deepcopy
-
-        text_embeddings = deepcopy(transformer.embeddings)
-
-        # Albert (and maybe others?) has this "embedding_size", that's different from "hidden_size".
-        # To get them to the same dimensionality, it uses a linear transform after the embedding
-        # layer, which we need to pull out and copy here.
-        if hasattr(transformer.config, "embedding_size"):
-            config = transformer.config
-
-            from transformers.models.albert.modeling_albert import AlbertModel
-
-            if isinstance(transformer, AlbertModel):
-                linear_transform = deepcopy(transformer.encoder.embedding_hidden_mapping_in)
-            else:
-                logger.warning(
-                    "Unknown model that uses separate embedding size; weights of the linear "
-                    f"transform will not be initialized.  Model type is: {transformer.__class__}"
-                )
-                linear_transform = torch.nn.Linear(config.embedding_dim, config.hidden_dim)
-
-            # We can't just use torch.nn.Sequential here, even though that's basically all this is,
-            # because Sequential doesn't accept *inputs, only a single argument.
-
-            class EmbeddingsShim(torch.nn.Module):
-                def __init__(self, embeddings: torch.nn.Module, linear_transform: torch.nn.Module):
-                    super().__init__()
-                    self.linear_transform = linear_transform
-                    self.embeddings = embeddings
-
-                def forward(self, *inputs, **kwargs):
-                    return self.linear_transform(self.embeddings(*inputs, **kwargs))
-
-            text_embeddings = EmbeddingsShim(text_embeddings, linear_transform)
+        text_embeddings = TransformerEmbeddings.from_pretrained_module(model_name)
 
         image_embeddings = ImageFeatureEmbeddings(
             feature_size=image_feature_dim,
@@ -112,7 +79,7 @@ def forward(self, *inputs, **kwargs):
         )
 
         encoder = BiModalEncoder.from_pretrained_module(
-            pretrained_module=transformer,
+            model_name,
             num_hidden_layers2=image_num_hidden_layers,
             hidden_size2=image_hidden_size,
             num_attention_heads2=image_num_attention_heads,
@@ -126,6 +93,7 @@ def forward(self, *inputs, **kwargs):
             fixed_layer1=text_fixed_layer,
             fixed_layer2=image_fixed_layer,
         )
+
         return cls(
             vocab=vocab,
             text_embeddings=text_embeddings,

allennlp/modules/transformer/__init__.py

@@ -123,9 +123,12 @@ def forward(self, token_ids: torch.LongTensor, mask: torch.BoolTensor):
 ```
 """
 
+from allennlp.modules.transformer.layer_norm import LayerNorm
 from allennlp.modules.transformer.positional_encoding import SinusoidalPositionalEncoding
-
-from allennlp.modules.transformer.transformer_module import TransformerModule
+from allennlp.modules.transformer.transformer_module import (
+    TransformerModule,
+    DistributedLoadingStrategy,
+)
 from allennlp.modules.transformer.transformer_embeddings import (
     Embeddings,
     TransformerEmbeddings,

allennlp/modules/transformer/bimodal_attention.py

@@ -118,10 +118,12 @@ def forward(
         input_tensor2,
         attention_mask1=None,
         attention_mask2=None,
-        co_attention_mask=None,
+        co_attention_mask=None,  # TODO: is this flag necessary?
         use_co_attention_mask=False,
     ):
         """
+        # Parameters
+
         input_tensor1 : `torch.Tensor`
             Shape `batch_size x seq_len1 x hidden_dim1`
             where `seq_len1` can be the sequence length
@@ -143,7 +145,6 @@ def forward(
             if you know which words correspond to which regions in the image,
             this mask can be applied to limit the attention given the bias.
         use_co_attention_mask : `bool`
-            # TODO: is this flag necessary?
             Whether to use co_attention_mask or not, default = `False`.
         """
 

allennlp/modules/transformer/bimodal_connection_layer.py

@@ -31,7 +31,7 @@ def forward(self, hidden_states1, input_tensor1, hidden_states2, input_tensor2):
 
 class BiModalConnectionLayer(TransformerModule, FromParams):
 
-    _huggingface_mapping = {"biAttention": "bimodal_attention", "biOutput": "bimodal_output"}
+    _pretrained_mapping = {"biAttention": "bimodal_attention", "biOutput": "bimodal_output"}
 
     def __init__(
         self,

allennlp/modules/transformer/bimodal_encoder.py

@@ -1,14 +1,16 @@
-from typing import Optional, Dict, List, Union
+from typing import Optional, List, TYPE_CHECKING
+
 import torch
 
 from allennlp.common import FromParams
-
 from allennlp.modules.util import replicate_layers
-
 from allennlp.modules.transformer.transformer_layer import TransformerLayer
 from allennlp.modules.transformer.bimodal_connection_layer import BiModalConnectionLayer
 from allennlp.modules.transformer.transformer_module import TransformerModule
 
+if TYPE_CHECKING:
+    from transformers.configuration_utils import PretrainedConfig
+
 
 class BiModalEncoder(TransformerModule, FromParams):
     """
@@ -46,8 +48,9 @@ class BiModalEncoder(TransformerModule, FromParams):
     in_batch_pairs: `bool` (default = `False`)
     """
 
-    _huggingface_mapping = {"layer": "layers1"}
-    _relevant_module = "encoder"
+    _pretrained_mapping = {"layer": "layers1"}
+    _pretrained_relevant_module = ["encoder", "bert.encoder"]
+    _pretrained_allow_missing = [r"^layers2\..*", r"^c_layer\..*"]
 
     def __init__(
         self,
@@ -243,93 +246,14 @@ def forward(
         )
 
     @classmethod
-    def _get_input_arguments(
-        cls,
-        pretrained_module: torch.nn.Module,
-        source="huggingface",
-        mapping: Optional[Dict[str, str]] = None,
-        **kwargs,
-    ):
-        """
-        The `pretrained_module` only supplies one of the modalities.
-        """
-        submodules = cls._get_mapped_submodules(pretrained_module, source, mapping)
-
+    def _from_config(cls, config: "PretrainedConfig", **kwargs):
         final_kwargs = {}
-
-        final_kwargs["num_hidden_layers1"] = len(submodules["layers1"])
-
-        final_kwargs["hidden_size1"] = submodules["layers1.0.attention.self.query"].in_features
-        final_kwargs["num_attention_heads1"] = submodules[
-            "layers1.0.attention.self"
-        ].num_attention_heads
-        final_kwargs["attention_dropout1"] = submodules["layers1.0.attention.self.dropout"].p
-        final_kwargs["hidden_dropout1"] = submodules["layers1.0.attention.output.dropout"].p
-        final_kwargs["intermediate_size1"] = submodules["layers1.0.intermediate.dense"].out_features
-        final_kwargs["activation"] = submodules["layers1.0.intermediate"].intermediate_act_fn
-
+        final_kwargs["num_hidden_layers1"] = config.num_hidden_layers
+        final_kwargs["hidden_size1"] = config.hidden_size
+        final_kwargs["num_attention_heads1"] = config.num_attention_heads
+        final_kwargs["attention_dropout1"] = config.attention_probs_dropout_prob
+        final_kwargs["hidden_dropout1"] = config.hidden_dropout_prob
+        final_kwargs["intermediate_size1"] = config.intermediate_size
+        final_kwargs["activation"] = config.hidden_act
         final_kwargs.update(**kwargs)
-
-        return final_kwargs
-
-    def _load_from_pretrained_module(
-        self,
-        pretrained_module: torch.nn.Module,
-        source="huggingface",
-        mapping: Optional[Dict[str, str]] = None,
-        ignore_absent_parameters: Optional[List] = None,
-    ):
-        if source == "huggingface":
-            ignore_absent_parameters = ["layers2", "c_layer"]
-        super()._load_from_pretrained_module(
-            pretrained_module, source, mapping, ignore_absent_parameters
-        )
-
-    @classmethod
-    def from_pretrained_module(  # type: ignore
-        cls,
-        pretrained_module: Union[str, torch.nn.Module],
-        num_hidden_layers2: int,
-        hidden_size2: int,
-        combined_hidden_size: int,
-        intermediate_size2: int,
-        num_attention_heads2: int,
-        combined_num_attention_heads: int,
-        attention_dropout2: float,
-        hidden_dropout2: float,
-        biattention_id1: List[int],
-        biattention_id2: List[int],
-        fixed_layer1: int,
-        fixed_layer2: int,
-        fast_mode: bool = False,
-        with_coattention: bool = True,
-        in_batch_pairs: bool = False,
-        source="huggingface",
-        mapping: Optional[Dict[str, str]] = None,
-        # **kwargs,
-    ):
-        """
-        The `pretrained_module` only supplies one of the modalities.
-        """
-        pretrained_module = cls.get_relevant_module(
-            pretrained_module, source=source, mapping=mapping
-        )
-        final_kwargs = {}
-        final_kwargs.update(cls._get_input_arguments(pretrained_module, source, mapping))
-        final_kwargs["num_hidden_layers2"] = num_hidden_layers2
-        final_kwargs["hidden_size2"] = hidden_size2
-        final_kwargs["combined_hidden_size"] = combined_hidden_size
-        final_kwargs["intermediate_size2"] = intermediate_size2
-        final_kwargs["num_attention_heads2"] = num_attention_heads2
-        final_kwargs["combined_num_attention_heads"] = combined_num_attention_heads
-        final_kwargs["attention_dropout2"] = attention_dropout2
-        final_kwargs["hidden_dropout2"] = hidden_dropout2
-        final_kwargs["biattention_id1"] = biattention_id1
-        final_kwargs["biattention_id2"] = biattention_id2
-        final_kwargs["fixed_layer1"] = fixed_layer1
-        final_kwargs["fixed_layer2"] = fixed_layer2
-        final_kwargs["fast_mode"] = fast_mode
-        final_kwargs["with_coattention"] = with_coattention
-        final_kwargs["in_batch_pairs"] = in_batch_pairs
-
-        return super().from_pretrained_module(pretrained_module, source, mapping, **final_kwargs)
+        return cls(**final_kwargs)

allennlp/modules/transformer/layer_norm.py

@@ -0,0 +1,7 @@
+import torch
+
+from allennlp.modules.transformer.transformer_module import TransformerModule
+
+
+class LayerNorm(torch.nn.LayerNorm, TransformerModule):
+    _pretrained_mapping = {"gamma": "weight", "beta": "bias"}