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config.json

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+{
+  "_name_or_path": "experiments/relik-reader-large-lr-0.0001-half/2024-03-02/12-14-56/wandb/latest-run/files/hf_model/relik-large-nyt/",
+  "activation": "gelu",
+  "add_entity_embedding": null,
+  "additional_special_symbols": 24,
+  "additional_special_symbols_types": 0,
+  "architectures": [
+    "RelikReaderREModel"
+  ],
+  "auto_map": {
+    "AutoModel": "modeling_relik_dev.RelikReaderREModel"
+  },
+  "default_reader_class": null,
+  "entity_type_loss": null,
+  "linears_hidden_size": 512,
+  "model_type": "relik-reader",
+  "num_layers": null,
+  "threshold": 0.912111759185791,
+  "torch_dtype": "float32",
+  "training": true,
+  "transformer_model": "microsoft/deberta-v3-large",
+  "transformers_version": "4.33.3",
+  "use_last_k_layers": 1
+}

configuration_relik.py

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+from typing import Optional
+
+from transformers import AutoConfig
+from transformers.configuration_utils import PretrainedConfig
+
+
+class RelikReaderConfig(PretrainedConfig):
+    model_type = "relik-reader"
+
+    def __init__(
+        self,
+        transformer_model: str = "microsoft/deberta-v3-base",
+        additional_special_symbols: int = 101,
+        additional_special_symbols_types: Optional[int] = 0,
+        num_layers: Optional[int] = None,
+        activation: str = "gelu",
+        linears_hidden_size: Optional[int] = 512,
+        use_last_k_layers: int = 1,
+        threshold: Optional[float] = 0.5,
+        entity_type_loss: bool = False,
+        add_entity_embedding: bool = None,
+        training: bool = False,
+        default_reader_class: Optional[str] = None,
+        **kwargs
+    ) -> None:
+        self.transformer_model = transformer_model
+        self.additional_special_symbols = additional_special_symbols
+        self.additional_special_symbols_types = additional_special_symbols_types
+        self.num_layers = num_layers
+        self.activation = activation
+        self.linears_hidden_size = linears_hidden_size
+        self.use_last_k_layers = use_last_k_layers
+        self.entity_type_loss = entity_type_loss
+        self.add_entity_embedding = (
+            True
+            if add_entity_embedding is None and entity_type_loss
+            else add_entity_embedding
+        )
+        self.training = training
+        self.threshold = threshold
+        self.default_reader_class = default_reader_class
+        super().__init__(**kwargs)
+
+
+AutoConfig.register("relik-reader", RelikReaderConfig)

modeling_relik_dev.py

@@ -0,0 +1,1130 @@
+from typing import Any, Dict, Optional
+
+import torch
+from transformers import AutoModel, PreTrainedModel
+from transformers.activations import ClippedGELUActivation, GELUActivation
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_utils import PoolerEndLogits
+
+from .configuration_relik import RelikReaderConfig
+
+torch.set_float32_matmul_precision('medium')
+
+def get_mention_features(
+    features: torch.Tensor,
+    starts: torch.Tensor,
+    ends: torch.Tensor,
+    batch_size: int,
+) -> torch.Tensor:
+    # Step 1: Create the mask for the spans
+    start_positions = starts.nonzero(as_tuple=True)
+    end_positions = ends.nonzero(as_tuple=True)
+    averaged_features = []
+    for batch_idx, (start, end) in enumerate(zip(start_positions[1], end_positions[1])):
+        # Select the batch where the span is located
+        batch_id = start_positions[0][batch_idx]
+        # Extract features for the span
+        span_features = features[batch_id, start:end+1]
+        # Compute the average of the features
+        span_avg = span_features.mean(dim=0)
+        averaged_features.append(span_avg)
+
+    averaged_features = torch.stack(averaged_features, dim=0)
+
+    # use torch.nn.utils.rnn.pad_sequence and split to pad and split the features to batch_size
+    averaged_features = torch.nn.utils.rnn.pad_sequence(
+        torch.split(averaged_features, torch.sum(starts, dim=1), dim=0),
+    )
+    return averaged_features
+
+def custom_margin_ranking_loss(scores, labels, margin=1.0):
+    """
+    Custom implementation of margin ranking loss for imbalanced positive/negative scores.
+    
+    Parameters:
+    - scores: Tensor containing the scores for each example.
+    - labels: Tensor containing labels (1 for positive, 0 for negative examples).
+    - margin: Desired margin between positive and negative scores.
+    
+    Returns:
+    - loss: Computed loss value.
+    """
+    # Separate scores into positive and negative based on labels
+    positive_scores = scores[labels == 1]
+    negative_scores = scores[labels == 0]
+    
+    # Compute all pairs of positive-negative scores
+    pos_neg_diff = positive_scores.unsqueeze(1) - negative_scores.unsqueeze(0)
+    
+    # Calculate loss for all positive-negative pairs
+    loss_components = torch.clamp(margin - pos_neg_diff, min=0)
+    
+    # Average loss across all comparisons
+    loss = torch.mean(loss_components)
+    
+    return loss
+
+def split_and_process(tensor, projector_start, projector_end):
+    # Split the tensor along the last dimension
+    half = tensor.shape[-1] // 2
+    tensor_start, tensor_end = tensor[..., :half], tensor[..., half:]
+    
+    # Apply the linear layers
+    tensor_start_processed = projector_start(tensor_start)
+    tensor_end_processed = projector_end(tensor_end)
+    
+    return tensor_start_processed, tensor_end_processed
+
+def get_mention_features_vectorized(features, starts, ends, batch_size):
+    # Create a range tensor that matches the size of the longest span
+    max_length = (ends - starts).max() + 1
+    range_tensor = torch.arange(max_length).to(features.device)
+
+    # Expand starts and range_tensor to compute a mask for each position in each span
+    expanded_starts = starts.unsqueeze(-1)  # Adding an extra dimension for broadcasting
+    expanded_ends = ends.unsqueeze(-1)
+    range_mask = (range_tensor < (expanded_ends - expanded_starts + 1))
+
+    # Use the mask to select features, handling variable-length sequences with padding
+    span_lengths = (expanded_ends - expanded_starts).squeeze(-1) + 1
+    max_span_length = span_lengths.max()
+    padded_features = torch.zeros((batch_size, max_span_length, features.size(-1)), device=features.device)
+    
+    for i in range(batch_size):
+        span = features[i, starts[i]:ends[i]+1]
+        padded_features[i, :span.size(0)] = span
+
+    # Compute the mean of features for each span, using the mask for correct averaging
+    span_means = (padded_features * range_mask.unsqueeze(-1)).sum(dim=1) / range_mask.sum(dim=1, keepdim=True)
+
+    return span_means
+
+def random_half_tensor_dropout(tensor, dropout_prob=0.5, is_training=True):
+    """
+    Applies dropout to either the first half or the second half of the tensor with a specified probability.
+    Dropout is only applied during training.
+
+    Args:
+    tensor (torch.Tensor): The input tensor.
+    dropout_prob (float): The probability of dropping out half of the tensor.
+    is_training (bool): If True, apply dropout; if False, do not apply dropout.
+
+    Returns:
+    torch.Tensor: The tensor after applying dropout.
+    """
+    assert 0 <= dropout_prob <= 1, "Dropout probability must be in the range [0, 1]"
+
+    if is_training:
+        # Size of the last dimension
+        last_dim_size = tensor.size(-1)
+
+        # Calculate the index for splitting the tensor into two halves
+        split_index = last_dim_size // 2
+
+        # Generate a random number and compare it with the dropout probability
+        if torch.rand(1).item() < dropout_prob:
+            # Randomly choose to drop the first half or the second half
+            if torch.rand(1).item() < 0.5:
+                # Set the first half to zero
+                tensor[..., :split_index] = 0
+            else:
+                # Set the second half to zero
+                tensor[..., split_index:] = 0
+
+    return tensor
+
+class RelikReaderSample:
+    def __init__(self, **kwargs):
+        super().__setattr__("_d", {})
+        self._d = kwargs
+
+    def __getattribute__(self, item):
+        return super(RelikReaderSample, self).__getattribute__(item)
+
+    def __getattr__(self, item):
+        if item.startswith("__") and item.endswith("__"):
+            # this is likely some python library-specific variable (such as __deepcopy__ for copy)
+            # better follow standard behavior here
+            raise AttributeError(item)
+        elif item in self._d:
+            return self._d[item]
+        else:
+            return None
+
+    def __setattr__(self, key, value):
+        if key in self._d:
+            self._d[key] = value
+        else:
+            super().__setattr__(key, value)
+
+
+activation2functions = {
+    "relu": torch.nn.ReLU(),
+    "gelu": GELUActivation(),
+    "gelu_10": ClippedGELUActivation(-10, 10),
+}
+
+
+class PoolerEndLogitsBi(PoolerEndLogits):
+    def __init__(self, config: PretrainedConfig):
+        super().__init__(config)
+        self.dense_1 = torch.nn.Linear(config.hidden_size, 2)
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        start_states: Optional[torch.FloatTensor] = None,
+        start_positions: Optional[torch.LongTensor] = None,
+        p_mask: Optional[torch.FloatTensor] = None,
+    ) -> torch.FloatTensor:
+        if p_mask is not None:
+            p_mask = p_mask.unsqueeze(-1)
+        logits = super().forward(
+            hidden_states,
+            start_states,
+            start_positions,
+            p_mask,
+        )
+        return logits
+
+
+class RelikReaderSpanModel(PreTrainedModel):
+    config_class = RelikReaderConfig
+
+    def __init__(self, config: RelikReaderConfig, *args, **kwargs):
+        super().__init__(config)
+        # Transformer model declaration
+        self.config = config
+        self.transformer_model = (
+            AutoModel.from_pretrained(self.config.transformer_model)
+            if self.config.num_layers is None
+            else AutoModel.from_pretrained(
+                self.config.transformer_model, num_hidden_layers=self.config.num_layers
+            )
+        )
+        self.transformer_model.resize_token_embeddings(
+            self.transformer_model.config.vocab_size
+            + self.config.additional_special_symbols,
+            pad_to_multiple_of=8,
+        )
+
+        self.activation = self.config.activation
+        self.linears_hidden_size = self.config.linears_hidden_size
+        self.use_last_k_layers = self.config.use_last_k_layers
+
+        # named entity detection layers
+        self.ned_start_classifier = self._get_projection_layer(
+            self.activation, last_hidden=2, layer_norm=False
+        )
+        self.ned_end_classifier = PoolerEndLogits(self.transformer_model.config)
+
+        # END entity disambiguation layer
+        self.ed_projector = self._get_projection_layer(self.activation, last_hidden = 2*self.linears_hidden_size, hidden=2*self.linears_hidden_size)
+
+        self.training = self.config.training
+
+        # criterion
+        self.criterion = torch.nn.CrossEntropyLoss()
+
+    def _get_projection_layer(
+        self,
+        activation: str,
+        last_hidden: Optional[int] = None,
+        hidden: Optional[int] = None,
+        input_hidden=None,
+        layer_norm: bool = True,
+    ) -> torch.nn.Sequential:
+        head_components = [
+            torch.nn.Dropout(0.1),
+            torch.nn.Linear(
+                (
+                    self.transformer_model.config.hidden_size * self.use_last_k_layers
+                    if input_hidden is None
+                    else input_hidden
+                ),
+                self.linears_hidden_size if hidden is None else hidden,
+            ),
+            activation2functions[activation],
+            torch.nn.Dropout(0.1),
+            torch.nn.Linear(
+                self.linears_hidden_size if hidden is None else hidden,
+                self.linears_hidden_size if last_hidden is None else last_hidden,
+            ),
+        ]
+
+        if layer_norm:
+            head_components.append(
+                torch.nn.LayerNorm(
+                    self.linears_hidden_size if last_hidden is None else last_hidden,
+                    self.transformer_model.config.layer_norm_eps,
+                )
+            )
+
+        return torch.nn.Sequential(*head_components)
+
+    def _mask_logits(self, logits: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+        mask = mask.unsqueeze(-1)
+        if next(self.parameters()).dtype == torch.float16:
+            logits = logits * (1 - mask) - 65500 * mask
+        else:
+            logits = logits * (1 - mask) - 1e30 * mask
+        return logits
+
+    def _get_model_features(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        token_type_ids: Optional[torch.Tensor],
+    ):
+        model_input = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "output_hidden_states": self.use_last_k_layers > 1,
+        }
+
+        if token_type_ids is not None:
+            model_input["token_type_ids"] = token_type_ids
+
+        model_output = self.transformer_model(**model_input)
+
+        if self.use_last_k_layers > 1:
+            model_features = torch.cat(
+                model_output[1][-self.use_last_k_layers :], dim=-1
+            )
+        else:
+            model_features = model_output[0]
+
+        return model_features
+
+    def compute_ned_end_logits(
+        self,
+        start_predictions,
+        start_labels,
+        model_features,
+        prediction_mask,
+        batch_size,
+    ) -> Optional[torch.Tensor]:
+        # todo: maybe when constraining on the spans,
+        #  we should not use a prediction_mask for the end tokens.
+        #  at least we should not during training imo
+        start_positions = start_labels if self.training else start_predictions
+        start_positions_indices = (
+            torch.arange(start_positions.size(1), device=start_positions.device)
+            .unsqueeze(0)
+            .expand(batch_size, -1)[start_positions > 0]
+        ).to(start_positions.device)
+
+        if len(start_positions_indices) > 0:
+            expanded_features = model_features.repeat_interleave(
+                torch.sum(start_positions > 0, dim=-1), dim=0
+            )
+            expanded_prediction_mask = prediction_mask.repeat_interleave(
+                torch.sum(start_positions > 0, dim=-1), dim=0
+            )
+            end_logits = self.ned_end_classifier(
+                hidden_states=expanded_features,
+                start_positions=start_positions_indices,
+                p_mask=expanded_prediction_mask,
+            )
+
+            return end_logits
+
+        return None
+
+    def compute_classification_logits(
+        self,
+        model_features,
+        special_symbols_mask,
+        prediction_mask,
+        batch_size,
+        start_positions=None,
+        end_positions=None,
+        attention_mask=None,
+    ) -> torch.Tensor:
+        if start_positions is None or end_positions is None:
+            start_positions = torch.zeros_like(prediction_mask)
+            end_positions = torch.zeros_like(prediction_mask)
+
+        model_ed_features = self.ed_projector(model_features)
+
+        model_ed_features[start_positions > 0][:, model_ed_features.shape[-1] // 2:] = model_ed_features[end_positions > 0][
+            :, :model_ed_features.shape[-1] // 2
+        ]
+
+        # computing ed features
+        classes_representations = torch.sum(special_symbols_mask, dim=1)[0].item()
+        special_symbols_mask_start = special_symbols_mask.roll(1, 1)
+        special_symbols_mask_start[:, :2] = torch.tensor([True, False], device=special_symbols_mask.device).expand_as(
+            special_symbols_mask_start[:, :2]
+        )
+
+        special_symbols_mask_end = special_symbols_mask.roll(-1, 1)
+        cumsum = special_symbols_mask_end.cumsum(dim=1)
+        # Identify the second True in each row (where cumulative sum equals 2)
+        special_symbols_mask_end[cumsum == 2] = False
+        special_symbols_mask_end[:, [0, -1]] = torch.tensor([True, False], device=special_symbols_mask.device).expand_as(
+            special_symbols_mask_end[:, [0, -1]]
+        )
+        # first padding token in 
+        last_token_ent = attention_mask.sum(1) - 2
+        special_symbols_mask_end[torch.arange(special_symbols_mask_end.shape[0], device=special_symbols_mask_end.device), last_token_ent] = True
+
+
+        special_symbols_representation_start = model_ed_features[special_symbols_mask_start][:,:model_ed_features.shape[-1] // 2].view(
+            batch_size, classes_representations, -1
+        )
+        special_symbols_representation_end = model_ed_features[special_symbols_mask_end][:,model_ed_features.shape[-1] // 2:].view(
+            batch_size, classes_representations, -1
+        )
+        # special_symbols_representation_start = self.ed_special_tokens_projector_start(special_symbols_representation_start)
+        # special_symbols_representation_end = self.ed_special_tokens_projector_end(special_symbols_representation_end)
+
+        special_symbols_representation = torch.cat(
+            [special_symbols_representation_start, special_symbols_representation_end, special_symbols_representation_end, special_symbols_representation_start], dim=-1
+        )
+        model_ed_features = torch.cat(
+            [model_ed_features, model_ed_features], dim=-1
+        )
+        
+        logits = torch.bmm(
+            model_ed_features,
+            torch.permute(special_symbols_representation, (0, 2, 1)),
+        )
+
+        logits = self._mask_logits(logits, prediction_mask)
+
+        return logits
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        token_type_ids: Optional[torch.Tensor] = None,
+        prediction_mask: Optional[torch.Tensor] = None,
+        special_symbols_mask: Optional[torch.Tensor] = None,
+        start_labels: Optional[torch.Tensor] = None,
+        end_labels: Optional[torch.Tensor] = None,
+        use_predefined_spans: bool = False,
+        *args,
+        **kwargs,
+    ) -> Dict[str, Any]:
+        batch_size, seq_len = input_ids.shape
+
+        model_features = self._get_model_features(
+            input_ids, attention_mask, token_type_ids
+        )
+
+        ned_start_labels = None
+
+        # named entity detection if required
+        if use_predefined_spans:  # no need to compute spans
+            ned_start_logits, ned_start_probabilities, ned_start_predictions = (
+                None,
+                None,
+                torch.clone(start_labels)
+                if start_labels is not None
+                else torch.zeros_like(input_ids),
+            )
+            ned_end_logits, ned_end_probabilities, ned_end_predictions = (
+                None,
+                None,
+                torch.clone(end_labels)
+                if end_labels is not None
+                else torch.zeros_like(input_ids),
+            )
+
+            ned_start_predictions[ned_start_predictions > 0] = 1
+            ned_end_predictions[ned_end_predictions > 0] = 1
+
+        else:  # compute spans
+            # start boundary prediction
+            ned_start_logits = self.ned_start_classifier(model_features)
+            ned_start_logits = self._mask_logits(ned_start_logits, prediction_mask)
+            ned_start_probabilities = torch.softmax(ned_start_logits, dim=-1)
+            ned_start_predictions = ned_start_probabilities.argmax(dim=-1)
+
+            # end boundary prediction
+            ned_start_labels = (
+                torch.zeros_like(start_labels) if start_labels is not None else None
+            )
+
+            if ned_start_labels is not None:
+                ned_start_labels[start_labels == -100] = -100
+                ned_start_labels[start_labels > 0] = 1
+
+            ned_end_logits = self.compute_ned_end_logits(
+                ned_start_predictions,
+                ned_start_labels,
+                model_features,
+                prediction_mask,
+                batch_size,
+            )
+
+            if ned_end_logits is not None:
+                ned_end_probabilities = torch.softmax(ned_end_logits, dim=-1)
+                ned_end_predictions = torch.argmax(ned_end_probabilities, dim=-1)
+            else:
+                ned_end_logits, ned_end_probabilities = None, None
+                ned_end_predictions = ned_start_predictions.new_zeros(batch_size)
+
+            # flattening end predictions
+            #   (flattening can happen only if the
+            #   end boundaries were not predicted using the gold labels)
+            if not self.training and ned_end_logits is not None:
+                flattened_end_predictions = torch.zeros_like(ned_start_predictions)
+
+                row_indices, start_positions = torch.where(ned_start_predictions > 0)
+                ned_end_predictions[ned_end_predictions<start_positions] = start_positions[ned_end_predictions<start_positions]
+
+                end_spans_repeated = (row_indices + 1)* seq_len + ned_end_predictions
+                cummax_values, _ = end_spans_repeated.cummax(dim=0)
+
+                end_spans_repeated = (end_spans_repeated > torch.cat((end_spans_repeated[:1], cummax_values[:-1])))
+                end_spans_repeated[0] = True
+
+                ned_start_predictions[row_indices[~end_spans_repeated], start_positions[~end_spans_repeated]] = 0
+
+                row_indices, start_positions, ned_end_predictions = row_indices[end_spans_repeated], start_positions[end_spans_repeated], ned_end_predictions[end_spans_repeated]
+
+                flattened_end_predictions[row_indices, ned_end_predictions] = 1
+
+                total_start_predictions, total_end_predictions = ned_start_predictions.sum(), flattened_end_predictions.sum()
+
+                assert (
+                    total_start_predictions == 0
+                    or total_start_predictions == total_end_predictions
+                ), (
+                    f"Total number of start predictions = {total_start_predictions}. "
+                    f"Total number of end predictions = {total_end_predictions}"
+                )
+                ned_end_predictions = flattened_end_predictions
+            else:
+                ned_end_predictions = torch.zeros_like(ned_start_predictions)
+
+        start_position, end_position = (
+            (start_labels, end_labels)
+            if self.training
+            else (ned_start_predictions, ned_end_predictions)
+        )
+
+        # Entity disambiguation
+        ed_logits = self.compute_classification_logits(
+            model_features,
+            special_symbols_mask,
+            prediction_mask,
+            batch_size,
+            start_position,
+            end_position,
+            attention_mask,
+        )
+        ed_probabilities = torch.softmax(ed_logits, dim=-1)
+        ed_predictions = torch.argmax(ed_probabilities, dim=-1)
+
+        # output build
+        output_dict = dict(
+            batch_size=batch_size,
+            ned_start_logits=ned_start_logits,
+            ned_start_probabilities=ned_start_probabilities,
+            ned_start_predictions=ned_start_predictions,
+            ned_end_logits=ned_end_logits,
+            ned_end_probabilities=ned_end_probabilities,
+            ned_end_predictions=ned_end_predictions,
+            ed_logits=ed_logits,
+            ed_probabilities=ed_probabilities,
+            ed_predictions=ed_predictions,
+        )
+
+        # compute loss if labels
+        if start_labels is not None and end_labels is not None and self.training:
+            # named entity detection loss
+
+            # start
+            if ned_start_logits is not None:
+                ned_start_loss = self.criterion(
+                    ned_start_logits.view(-1, ned_start_logits.shape[-1]),
+                    ned_start_labels.view(-1),
+                )
+            else:
+                ned_start_loss = 0
+
+            # end
+            if ned_end_logits is not None:
+                ned_end_labels = torch.zeros_like(end_labels)
+                ned_end_labels[end_labels == -100] = -100
+                ned_end_labels[end_labels > 0] = 1
+
+                ned_end_loss = self.criterion(
+                    ned_end_logits,
+                    (
+                        torch.arange(
+                            ned_end_labels.size(1), device=ned_end_labels.device
+                        )
+                        .unsqueeze(0)
+                        .expand(batch_size, -1)[ned_end_labels > 0]
+                    ).to(ned_end_labels.device),
+                )
+
+            else:
+                ned_end_loss = 0
+
+            # entity disambiguation loss
+            start_labels[ned_start_labels != 1] = -100
+            ed_labels = torch.clone(start_labels)
+            ed_labels[end_labels > 0] = end_labels[end_labels > 0]
+            ed_loss = self.criterion(
+                ed_logits.view(-1, ed_logits.shape[-1]),
+                ed_labels.view(-1),
+            )
+
+            output_dict["ned_start_loss"] = ned_start_loss
+            output_dict["ned_end_loss"] = ned_end_loss
+            output_dict["ed_loss"] = ed_loss
+
+            output_dict["loss"] = ned_start_loss + ned_end_loss + ed_loss
+
+        return output_dict
+
+
+class RelikReaderREModel(PreTrainedModel):
+    config_class = RelikReaderConfig
+
+    def __init__(self, config, *args, **kwargs):
+        super().__init__(config)
+        # Transformer model declaration
+        # self.transformer_model_name = transformer_model
+        self.config = config
+        self.transformer_model = (
+            AutoModel.from_pretrained(config.transformer_model)
+            if config.num_layers is None
+            else AutoModel.from_pretrained(
+                config.transformer_model, num_hidden_layers=config.num_layers
+            )
+        )
+        self.transformer_model.resize_token_embeddings(
+            self.transformer_model.config.vocab_size
+            + config.additional_special_symbols
+            + config.additional_special_symbols_types,
+            pad_to_multiple_of=8,
+        )
+
+        # named entity detection layers
+        self.ned_start_classifier = self._get_projection_layer(
+            config.activation, last_hidden=2, layer_norm=False
+        )
+
+        self.ned_end_classifier = PoolerEndLogitsBi(self.transformer_model.config)
+
+        self.relation_disambiguation_loss = (
+            config.relation_disambiguation_loss
+            if hasattr(config, "relation_disambiguation_loss")
+            else False
+        )
+
+        if self.config.entity_type_loss and self.config.add_entity_embedding:
+            input_hidden_ents = 3 * self.config.linears_hidden_size
+        else:
+            input_hidden_ents = 2 * self.config.linears_hidden_size
+
+        self.re_projector = self._get_projection_layer(
+            config.activation, input_hidden=2*self.transformer_model.config.hidden_size, hidden=input_hidden_ents, last_hidden=2*self.config.linears_hidden_size
+        )
+
+        self.re_relation_projector = self._get_projection_layer(
+            config.activation, input_hidden=self.transformer_model.config.hidden_size,
+        )
+
+        if self.config.entity_type_loss or self.relation_disambiguation_loss:
+            self.re_entities_projector = self._get_projection_layer(
+                config.activation,
+                input_hidden=2 * self.transformer_model.config.hidden_size,
+            )
+            self.re_definition_projector = self._get_projection_layer(
+                config.activation,
+            )
+
+        self.re_classifier = self._get_projection_layer(
+            config.activation,
+            input_hidden=config.linears_hidden_size,
+            last_hidden=2,
+            layer_norm=False,
+        )
+
+        self.training = config.training
+
+        # criterion
+        self.criterion = torch.nn.CrossEntropyLoss()
+        self.criterion_type = torch.nn.BCEWithLogitsLoss()
+
+    def _get_projection_layer(
+        self,
+        activation: str,
+        last_hidden: Optional[int] = None,
+        hidden: Optional[int] = None,
+        input_hidden=None,
+        layer_norm: bool = True,
+    ) -> torch.nn.Sequential:
+        head_components = [
+            torch.nn.Dropout(0.1),
+            torch.nn.Linear(
+                (
+                    self.transformer_model.config.hidden_size * self.config.use_last_k_layers
+                    if input_hidden is None
+                    else input_hidden
+                ),
+                self.config.linears_hidden_size if hidden is None else hidden,
+            ),
+            activation2functions[activation],
+            torch.nn.Dropout(0.1),
+            torch.nn.Linear(
+                self.config.linears_hidden_size if hidden is None else hidden,
+                self.config.linears_hidden_size if last_hidden is None else last_hidden,
+            ),
+        ]
+
+        if layer_norm:
+            head_components.append(
+                torch.nn.LayerNorm(
+                    self.config.linears_hidden_size if last_hidden is None else last_hidden,
+                    self.transformer_model.config.layer_norm_eps,
+                )
+            )
+
+        return torch.nn.Sequential(*head_components)
+
+    def _mask_logits(self, logits: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+        mask = mask.unsqueeze(-1)
+        if next(self.parameters()).dtype == torch.float16:
+            logits = logits * (1 - mask) - 65500 * mask
+        else:
+            logits = logits * (1 - mask) - 1e30 * mask
+        return logits
+
+    def _get_model_features(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        token_type_ids: Optional[torch.Tensor],
+    ):
+        model_input = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "output_hidden_states": self.config.use_last_k_layers > 1,
+        }
+
+        if token_type_ids is not None:
+            model_input["token_type_ids"] = token_type_ids
+
+        model_output = self.transformer_model(**model_input)
+
+        if self.config.use_last_k_layers > 1:
+            model_features = torch.cat(
+                model_output[1][-self.config.use_last_k_layers :], dim=-1
+            )
+        else:
+            model_features = model_output[0]
+
+        return model_features
+
+    def compute_ned_end_logits(
+        self,
+        start_predictions,
+        start_labels,
+        model_features,
+        prediction_mask,
+        batch_size,
+        mask_preceding: bool = False,
+    ) -> Optional[torch.Tensor]:
+        # todo: maybe when constraining on the spans,
+        #  we should not use a prediction_mask for the end tokens.
+        #  at least we should not during training imo
+        start_positions = start_labels if self.training else start_predictions
+        start_positions_indices = (
+            torch.arange(start_positions.size(1), device=start_positions.device)
+            .unsqueeze(0)
+            .expand(batch_size, -1)[start_positions > 0]
+        ).to(start_positions.device)
+
+        if len(start_positions_indices) > 0:
+            expanded_features = model_features.repeat_interleave(
+                torch.sum(start_positions > 0, dim=-1), dim=0
+            )
+            expanded_prediction_mask = prediction_mask.repeat_interleave(
+                torch.sum(start_positions > 0, dim=-1), dim=0
+            )
+            if mask_preceding:
+                expanded_prediction_mask[
+                    torch.arange(
+                        expanded_prediction_mask.shape[1],
+                        device=expanded_prediction_mask.device,
+                    )
+                    < start_positions_indices.unsqueeze(1)
+                ] = 1
+            end_logits = self.ned_end_classifier(
+                hidden_states=expanded_features,
+                start_positions=start_positions_indices,
+                p_mask=expanded_prediction_mask,
+            )
+
+            return end_logits
+
+        return None
+
+    def compute_relation_logits(
+        self,
+        model_entity_features,
+        special_symbols_features,
+    ) -> torch.Tensor:
+        model_subject_object_features = self.re_projector(model_entity_features)
+        model_subject_features = model_subject_object_features[
+            :, :, : model_subject_object_features.shape[-1] // 2
+        ]
+        model_object_features = model_subject_object_features[
+            :, :, model_subject_object_features.shape[-1] // 2 :
+        ]
+        special_symbols_start_representation = self.re_relation_projector(
+            special_symbols_features
+        )
+        re_logits = torch.einsum(
+            "bse,bde,bfe->bsdfe",
+            model_subject_features,
+            model_object_features,
+            special_symbols_start_representation,
+        )
+        re_logits = self.re_classifier(re_logits)
+
+        return re_logits
+
+    def compute_entity_logits(
+        self,
+        model_entity_features,
+        special_symbols_features,
+    ) -> torch.Tensor:
+        model_ed_features = self.re_entities_projector(model_entity_features)
+        special_symbols_ed_representation = self.re_definition_projector(
+            special_symbols_features
+        )
+
+        logits = torch.bmm(
+            model_ed_features,
+            torch.permute(special_symbols_ed_representation, (0, 2, 1)),
+        )
+        logits = self._mask_logits(
+            logits, (model_entity_features == -100).all(2).long()
+        )
+        return logits
+
+    def compute_loss(self, logits, labels, mask=None):
+        logits = logits.reshape(-1, logits.shape[-1])
+        labels = labels.reshape(-1).long()
+        if mask is not None:
+            return self.criterion(logits[mask], labels[mask])
+        return self.criterion(logits, labels)
+
+    def compute_ned_type_loss(
+        self,
+        disambiguation_labels,
+        re_ned_entities_logits,
+        ned_type_logits,
+        re_entities_logits,
+        entity_types,
+        mask,
+    ):
+        if self.config.entity_type_loss and self.relation_disambiguation_loss:
+            return self.criterion_type(
+                re_ned_entities_logits[disambiguation_labels != -100],
+                disambiguation_labels[disambiguation_labels != -100],
+            )
+        if self.config.entity_type_loss:
+            return self.criterion_type(
+                ned_type_logits[mask],
+                disambiguation_labels[:, :, :entity_types][mask],
+            )
+
+        if self.relation_disambiguation_loss:
+            return self.criterion_type(
+                re_entities_logits[disambiguation_labels != -100],
+                disambiguation_labels[disambiguation_labels != -100],
+            )
+        return 0
+
+    def compute_relation_loss(self, relation_labels, re_logits):
+        return self.compute_loss(
+            re_logits, relation_labels, relation_labels.view(-1) != -100
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        token_type_ids: torch.Tensor,
+        prediction_mask: Optional[torch.Tensor] = None,
+        special_symbols_mask: Optional[torch.Tensor] = None,
+        special_symbols_mask_entities: Optional[torch.Tensor] = None,
+        start_labels: Optional[torch.Tensor] = None,
+        end_labels: Optional[torch.Tensor] = None,
+        disambiguation_labels: Optional[torch.Tensor] = None,
+        relation_labels: Optional[torch.Tensor] = None,
+        relation_threshold: float = None,
+        is_validation: bool = False,
+        is_prediction: bool = False,
+        use_predefined_spans: bool = False,
+        *args,
+        **kwargs,
+    ) -> Dict[str, Any]:
+        
+        thresshold = self.config.threshold if relation_threshold is None else relation_threshold
+
+        batch_size = input_ids.shape[0]
+
+        model_features = self._get_model_features(
+            input_ids, attention_mask, token_type_ids
+        )
+
+        # named entity detection
+        if use_predefined_spans:
+            ned_start_logits, ned_start_probabilities, ned_start_predictions = (
+                None,
+                None,
+                torch.zeros_like(start_labels),
+            )
+            ned_end_logits, ned_end_probabilities, ned_end_predictions = (
+                None,
+                None,
+                torch.zeros_like(end_labels),
+            )
+
+            ned_start_predictions[start_labels > 0] = 1
+            ned_end_predictions[end_labels > 0] = 1
+            ned_end_predictions = ned_end_predictions[~(end_labels == -100).all(2)]
+            ned_start_labels = start_labels
+            ned_start_labels[start_labels > 0] = 1
+        else:
+            # start boundary prediction
+            ned_start_logits = self.ned_start_classifier(model_features)
+            if is_validation or is_prediction:
+                ned_start_logits = self._mask_logits(
+                    ned_start_logits, prediction_mask
+                )  # why?
+            ned_start_probabilities = torch.softmax(ned_start_logits, dim=-1)
+            ned_start_predictions = ned_start_probabilities.argmax(dim=-1)
+
+            # end boundary prediction
+            ned_start_labels = (
+                torch.zeros_like(start_labels) if start_labels is not None else None
+            )
+
+            # start_labels contain entity id at their position, we just need 1 for start of entity
+            if ned_start_labels is not None:
+                ned_start_labels[start_labels == -100] = -100
+                ned_start_labels[start_labels > 0] = 1
+
+            # compute end logits only if there are any start predictions.
+            # For each start prediction, n end predictions are made
+            ned_end_logits = self.compute_ned_end_logits(
+                ned_start_predictions,
+                ned_start_labels,
+                model_features,
+                prediction_mask,
+                batch_size,
+                True,
+            )
+
+            if ned_end_logits is not None:
+                # For each start prediction, n end predictions are made based on
+                # binary classification ie. argmax at each position.
+                ned_end_probabilities = torch.softmax(ned_end_logits, dim=-1)
+                ned_end_predictions = ned_end_probabilities.argmax(dim=-1)
+            else:
+                ned_end_logits, ned_end_probabilities = None, None
+                ned_end_predictions = torch.zeros_like(ned_start_predictions)
+
+            if is_prediction or is_validation:
+                end_preds_count = ned_end_predictions.sum(1)
+                # If there are no end predictions for a start prediction, remove the start prediction
+                if (end_preds_count == 0).any() and (ned_start_predictions > 0).any():
+                    ned_start_predictions[ned_start_predictions == 1] = (
+                        end_preds_count != 0
+                    ).long()
+                    ned_end_predictions = ned_end_predictions[end_preds_count != 0]
+
+        if end_labels is not None:
+            end_labels = end_labels[~(end_labels == -100).all(2)]
+
+        start_position, end_position = (
+            (start_labels, end_labels)
+            if (not is_prediction and not is_validation)
+            else (ned_start_predictions, ned_end_predictions)
+        )
+
+        start_counts = (start_position > 0).sum(1)
+        if (start_counts > 0).any():
+            ned_end_predictions = ned_end_predictions.split(start_counts.tolist())
+        # limit to 30 predictions per document using start_counts, by setting all po after sum is 30 to 0
+        # if is_validation or is_prediction:
+        #     ned_start_predictions[ned_start_predictions == 1] = start_counts
+        # We can only predict relations if we have start and end predictions
+        if (end_position > 0).sum() > 0:
+            ends_count = (end_position > 0).sum(1)
+            model_subject_features = torch.cat(
+                [
+                    torch.repeat_interleave(
+                        model_features[start_position > 0], ends_count, dim=0
+                    ),  # start position features
+                    torch.repeat_interleave(model_features, start_counts, dim=0)[
+                        end_position > 0
+                    ],  # end position features
+                ],
+                dim=-1,
+            )
+            ents_count = torch.nn.utils.rnn.pad_sequence(
+                torch.split(ends_count, start_counts.tolist()),
+                batch_first=True,
+                padding_value=0,
+            ).sum(1)
+            model_subject_features = torch.nn.utils.rnn.pad_sequence(
+                torch.split(model_subject_features, ents_count.tolist()),
+                batch_first=True,
+                padding_value=-100,
+            )
+
+            # if is_validation or is_prediction:
+            #     model_subject_features = model_subject_features[:, :30, :]
+
+            # entity disambiguation. Here relation_disambiguation_loss would only be useful to
+            # reduce the number of candidate relations for the next step, but currently unused.
+            if self.config.entity_type_loss or self.relation_disambiguation_loss:
+                (re_ned_entities_logits) = self.compute_entity_logits(
+                    model_subject_features,
+                    model_features[
+                        special_symbols_mask | special_symbols_mask_entities
+                    ].view(batch_size, -1, model_features.shape[-1]),
+                )
+                entity_types = torch.sum(special_symbols_mask_entities, dim=1)[0].item()
+                ned_type_logits = re_ned_entities_logits[:, :, :entity_types]
+                re_entities_logits = re_ned_entities_logits[:, :, entity_types:]
+
+                if self.config.entity_type_loss:
+                    ned_type_probabilities = torch.sigmoid(ned_type_logits)
+                    ned_type_predictions = ned_type_probabilities.argmax(dim=-1)
+
+                    if self.config.add_entity_embedding:
+                        special_symbols_representation = model_features[
+                            special_symbols_mask_entities
+                        ].view(batch_size, entity_types, -1)
+
+                        entities_representation = torch.einsum(
+                            "bsp,bpe->bse",
+                            ned_type_probabilities,
+                            special_symbols_representation,
+                        )
+                        model_subject_features = torch.cat(
+                            [model_subject_features, entities_representation], dim=-1
+                        )
+                re_entities_probabilities = torch.sigmoid(re_entities_logits)
+                re_entities_predictions = re_entities_probabilities.round()
+            else:
+                (
+                    ned_type_logits,
+                    ned_type_probabilities,
+                    re_entities_logits,
+                    re_entities_probabilities,
+                ) = (None, None, None, None)
+                ned_type_predictions, re_entities_predictions = (
+                    torch.zeros([batch_size, 1], dtype=torch.long).to(input_ids.device),
+                    torch.zeros([batch_size, 1], dtype=torch.long).to(input_ids.device),
+                )
+
+            # Compute relation logits
+            re_logits = self.compute_relation_logits(
+                model_subject_features,
+                model_features[special_symbols_mask].view(
+                    batch_size, -1, model_features.shape[-1]
+                ),
+            )
+
+            re_probabilities = torch.softmax(re_logits, dim=-1)
+            # we set a thresshold instead of argmax in cause it needs to be tweaked
+            re_predictions = re_probabilities[:, :, :, :, 1] > relation_threshold
+            re_probabilities = re_probabilities[:, :, :, :, 1]
+
+        else:
+            (
+                ned_type_logits,
+                ned_type_probabilities,
+                re_entities_logits,
+                re_entities_probabilities,
+            ) = (None, None, None, None)
+            ned_type_predictions, re_entities_predictions = (
+                torch.zeros([batch_size, 1], dtype=torch.long).to(input_ids.device),
+                torch.zeros([batch_size, 1], dtype=torch.long).to(input_ids.device),
+            )
+            re_logits, re_probabilities, re_predictions = (
+                torch.zeros(
+                    [batch_size, 1, 1, special_symbols_mask.sum(1)[0]], dtype=torch.long
+                ).to(input_ids.device),
+                torch.zeros(
+                    [batch_size, 1, 1, special_symbols_mask.sum(1)[0]], dtype=torch.long
+                ).to(input_ids.device),
+                torch.zeros(
+                    [batch_size, 1, 1, special_symbols_mask.sum(1)[0]], dtype=torch.long
+                ).to(input_ids.device),
+            )
+
+        # output build
+        output_dict = dict(
+            batch_size=batch_size,
+            ned_start_logits=ned_start_logits,
+            ned_start_probabilities=ned_start_probabilities,
+            ned_start_predictions=ned_start_predictions,
+            ned_end_logits=ned_end_logits,
+            ned_end_probabilities=ned_end_probabilities,
+            ned_end_predictions=ned_end_predictions,
+            ned_type_logits=ned_type_logits,
+            ned_type_probabilities=ned_type_probabilities,
+            ned_type_predictions=ned_type_predictions,
+            re_entities_logits=re_entities_logits,
+            re_entities_probabilities=re_entities_probabilities,
+            re_entities_predictions=re_entities_predictions,
+            re_logits=re_logits,
+            re_probabilities=re_probabilities,
+            re_predictions=re_predictions,
+        )
+
+        if (
+            start_labels is not None
+            and end_labels is not None
+            and relation_labels is not None
+            and is_prediction is False
+        ):
+            ned_start_loss = self.compute_loss(ned_start_logits, ned_start_labels)
+            end_labels[end_labels > 0] = 1
+            ned_end_loss = self.compute_loss(ned_end_logits, end_labels)
+            if self.config.entity_type_loss or self.relation_disambiguation_loss:
+                ned_type_loss = self.compute_ned_type_loss(
+                    disambiguation_labels,
+                    re_ned_entities_logits,
+                    ned_type_logits,
+                    re_entities_logits,
+                    entity_types,
+                    (model_subject_features != -100).all(2),
+                )
+            relation_loss = self.compute_relation_loss(relation_labels, re_logits)
+            # compute loss. We can skip the relation loss if we are in the first epochs (optional)
+            if self.config.entity_type_loss or self.relation_disambiguation_loss:
+                output_dict["loss"] = (
+                    ned_start_loss + ned_end_loss + relation_loss + ned_type_loss
+                ) / 4
+                output_dict["ned_type_loss"] = ned_type_loss
+            else:
+                # output_dict["loss"] = ((1 / 4) * (ned_start_loss + ned_end_loss)) + (
+                #     (1 / 2) * relation_loss
+                # )
+                output_dict["loss"] = ((1 / 20) * (ned_start_loss + ned_end_loss)) + (
+                    (9 / 10) * relation_loss
+                )
+
+            output_dict["ned_start_loss"] = ned_start_loss
+            output_dict["ned_end_loss"] = ned_end_loss
+            output_dict["re_loss"] = relation_loss
+
+        return output_dict

pytorch_model.bin

@@ -0,0 +1,3 @@
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+oid sha256:dceb2b0ba198d2bb49e448cd48906dae7a34b00d6fea56c914820bdc895b03bd
+size 1763625485