modeling_multiheadcrf.py

import os
from typing import Optional, Union, List
from transformers import AutoModel, PreTrainedModel, AutoConfig, AutoModel, RobertaModel, BertModel
from transformers.modeling_outputs import  TokenClassifierOutput
from torch import nn
from torch.nn import CrossEntropyLoss
import torch
from itertools import islice
from.configuration_multiheadcrf import MultiHeadCRFConfig

NUM_PER_LAYER = 16

class RobertaMultiHeadCRFModel(PreTrainedModel):
    config_class = MultiHeadCRFConfig
    transformers_backbone_name = "roberta"
    transformers_backbone_class = RobertaModel
    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        
        self.number_of_layer_per_head = config.number_of_layer_per_head
        
        self.heads = config.classes #expected an array of classes we are predicting
        
        # this can be BERT ROBERTA and other BERT-variants
        # THIS IS BC HF needs to have "roberta" for roberta models and "bert" for BERT models as var so tha I can load
        # check https://github.com/huggingface/transformers/blob/b487096b02307cd6e0f132b676cdcc7255fe8e74/src/transformers/models/roberta/modeling_roberta.py#L1170C16-L1170C20
        setattr(self, self.transformers_backbone_name, self.transformers_backbone_class(config, add_pooling_layer=False))
        #self.roberta = self.transformer_backbone_class(config, add_pooling_layer=False)
                    #AutoModel(config, add_pooling_layer=False)
                    #AutoModel.from_pretrained(config._name_or_path, config=config, add_pooling_layer=False)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

        print(sorted(self.heads))
        for ent in  self.heads:
            for i in range(self.number_of_layer_per_head):
                setattr(self, f"{ent}_dense_{i}",            nn.Linear(config.hidden_size, config.hidden_size))
                setattr(self, f"{ent}_dense_activation_{i}", nn.GELU(approximate='none'))
            setattr(self, f"{ent}_classifier",       nn.Linear(config.hidden_size, config.num_labels))
            setattr(self, f"{ent}_crf",              CRF(num_tags=config.num_labels, batch_first=True))
            setattr(self, f"{ent}_reduction",        config.crf_reduction)
        self.reduction=config.crf_reduction

        if self.config.freeze == True:
            self.manage_freezing()
    
    def training_mode(self):

        # for some reason these layers are not being correctly init
        # probably related with the lifecycle of the hf .from_pretrained method
        for ent in  self.heads:
            for i in range(self.number_of_layer_per_head):
                getattr(self, f"{ent}_dense_{i}").reset_parameters()
            getattr(self, f"{ent}_classifier").reset_parameters()
            getattr(self, f"{ent}_crf").reset_parameters()
            getattr(self, f"{ent}_crf").mask_impossible_transitions()
    
    def manage_freezing(self):
        for _, param in getattr(self, self.transformers_backbone_name).embeddings.named_parameters():
            param.requires_grad = False
        
        num_encoders_to_freeze = self.config.num_frozen_encoder
        if num_encoders_to_freeze > 0:
            for _, param in islice(getattr(self, self.transformers_backbone_name).encoder.named_parameters(), num_encoders_to_freeze*NUM_PER_LAYER):
                param.requires_grad = False
    
    
    def forward(self,
                input_ids=None,
                attention_mask=None,
                token_type_ids=None,
                position_ids=None,
                head_mask=None,
                inputs_embeds=None,
                labels=None,
                output_attentions=None,
                output_hidden_states=None,
                return_dict=None
               ):
        # Default `model.config.use_return_dict´ is `True´
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        
        outputs = getattr(self, self.transformers_backbone_name)(input_ids,
                            attention_mask=attention_mask,
                            token_type_ids=token_type_ids,
                            position_ids=position_ids,
                            head_mask=head_mask,
                            inputs_embeds=inputs_embeds,
                            output_attentions=output_attentions,
                            output_hidden_states=output_hidden_states,
                            return_dict=return_dict)

        sequence_output = outputs[0]
        sequence_output = self.dropout(sequence_output) # B S E 

        logits = {k:0 for k in self.heads}
        for ent in self.heads:
            for i in range(self.number_of_layer_per_head):
                dense_output = getattr(self, f"{ent}_dense_{i}")(sequence_output)
                dense_output = getattr(self, f"{ent}_dense_activation_{i}")(dense_output)
            logits[ent] = getattr(self, f"{ent}_classifier")(dense_output)
        #logits = self.classifier(sequence_output)
        loss = None
        if labels is not None: 
            # During train/test as we don't pass labels during inference
            
            # loss
            outputs = {k:0 for k in self.heads}
            for ent in self.heads:
                
                outputs[ent] = getattr(self, f"{ent}_crf")(logits[ent],labels[ent], reduction=self.reduction) 

            # print(outputs)
            return sum(outputs.values()), logits
        else: #running prediction?
            # decoded tags
            # NOTE: This gather operation (multiGPU) not work here, bc it uses tensors that are on CPU...
            outputs = {k:0 for k in self.heads}
            
            for ent in self.heads:
                outputs[ent] = torch.Tensor(getattr(self, f"{ent}_crf").decode(logits[ent]))
            return [outputs[ent] for ent in sorted(self.heads)]


class BertMultiHeadCRFModel(RobertaMultiHeadCRFModel):
    config_class = MultiHeadCRFConfig
    transformers_backbone_name = "bert"
    transformers_backbone_class = BertModel
    _keys_to_ignore_on_load_unexpected = [r"pooler"]

# Taken from https://github.com/kmkurn/pytorch-crf/blob/master/torchcrf/__init__.py and fixed got uint8 warning
LARGE_NEGATIVE_NUMBER = -1e9
class CRF(nn.Module):
    """Conditional random field.
    This module implements a conditional random field [LMP01]_. The forward computation
    of this class computes the log likelihood of the given sequence of tags and
    emission score tensor. This class also has `~CRF.decode` method which finds
    the best tag sequence given an emission score tensor using `Viterbi algorithm`_.
    Args:
        num_tags: Number of tags.
        batch_first: Whether the first dimension corresponds to the size of a minibatch.
    Attributes:
        start_transitions (`~torch.nn.Parameter`): Start transition score tensor of size
            ``(num_tags,)``.
        end_transitions (`~torch.nn.Parameter`): End transition score tensor of size
            ``(num_tags,)``.
        transitions (`~torch.nn.Parameter`): Transition score tensor of size
            ``(num_tags, num_tags)``.
    .. [LMP01] Lafferty, J., McCallum, A., Pereira, F. (2001).
       "Conditional random fields: Probabilistic models for segmenting and
       labeling sequence data". *Proc. 18th International Conf. on Machine
       Learning*. Morgan Kaufmann. pp. 282–289.
    .. _Viterbi algorithm: https://en.wikipedia.org/wiki/Viterbi_algorithm
    """

    def __init__(self, num_tags: int, batch_first: bool = False) -> None:
        if num_tags <= 0:
            raise ValueError(f'invalid number of tags: {num_tags}')
        super().__init__()
        self.num_tags = num_tags
        self.batch_first = batch_first
        self.start_transitions = nn.Parameter(torch.empty(num_tags))
        self.end_transitions = nn.Parameter(torch.empty(num_tags))
        self.transitions = nn.Parameter(torch.empty(num_tags, num_tags))

        self.reset_parameters()
        self.mask_impossible_transitions()

    def reset_parameters(self) -> None:
        """Initialize the transition parameters.
        The parameters will be initialized randomly from a uniform distribution
        between -0.1 and 0.1.
        """
        nn.init.uniform_(self.start_transitions, -0.1, 0.1)
        nn.init.uniform_(self.end_transitions, -0.1, 0.1)
        nn.init.uniform_(self.transitions, -0.1, 0.1)
        
    def mask_impossible_transitions(self) -> None:
        """Set the value of impossible transitions to LARGE_NEGATIVE_NUMBER
        - start transition value of I-X 
        - transition score of O -> I
        """
        with torch.no_grad():
            self.start_transitions[2] = LARGE_NEGATIVE_NUMBER
            
            self.transitions[0][2] = LARGE_NEGATIVE_NUMBER
            
    def __repr__(self) -> str:
        return f'{self.__class__.__name__}(num_tags={self.num_tags})'

    def forward(
            self,
            emissions: torch.Tensor,
            tags: torch.LongTensor,
            mask: Optional[torch.ByteTensor] = None,
            reduction: str = 'sum',
    ) -> torch.Tensor:
        """Compute the conditional log likelihood of a sequence of tags given emission scores.
        Args:
            emissions (`~torch.Tensor`): Emission score tensor of size
                ``(seq_length, batch_size, num_tags)`` if ``batch_first`` is ``False``,
                ``(batch_size, seq_length, num_tags)`` otherwise.
            tags (`~torch.LongTensor`): Sequence of tags tensor of size
                ``(seq_length, batch_size)`` if ``batch_first`` is ``False``,
                ``(batch_size, seq_length)`` otherwise.
            mask (`~torch.ByteTensor`): Mask tensor of size ``(seq_length, batch_size)``
                if ``batch_first`` is ``False``, ``(batch_size, seq_length)`` otherwise.
            reduction: Specifies  the reduction to apply to the output:
                ``none|sum|mean|token_mean``. ``none``: no reduction will be applied.
                ``sum``: the output will be summed over batches. ``mean``: the output will be
                averaged over batches. ``token_mean``: the output will be averaged over tokens.
        Returns:
            `~torch.Tensor`: The log likelihood. This will have size ``(batch_size,)`` if
            reduction is ``none``, ``()`` otherwise.
        """
        #self.mask_impossible_transitions()
        self._validate(emissions, tags=tags, mask=mask)
        if reduction not in ('none', 'sum', 'mean', 'token_mean'):
            raise ValueError(f'invalid reduction: {reduction}')
        if mask is None:
            mask = torch.ones_like(tags, dtype=torch.uint8)

        if self.batch_first:
            emissions = emissions.transpose(0, 1)
            tags = tags.transpose(0, 1)
            mask = mask.transpose(0, 1)

        # shape: (batch_size,)
        numerator = self._compute_score(emissions, tags, mask)
        # shape: (batch_size,)
        denominator = self._compute_normalizer(emissions, mask)
        # shape: (batch_size,)
        llh = numerator - denominator
        nllh = -llh
        
        if reduction == 'none':
            return nllh
        if reduction == 'sum':
            return nllh.sum()
        if reduction == 'mean':
            return nllh.mean()
        assert reduction == 'token_mean'
        return nllh.sum() / mask.type_as(emissions).sum()

    def decode(self, emissions: torch.Tensor,
               mask: Optional[torch.ByteTensor] = None) -> List[List[int]]:
        """Find the most likely tag sequence using Viterbi algorithm.
        Args:
            emissions (`~torch.Tensor`): Emission score tensor of size
                ``(seq_length, batch_size, num_tags)`` if ``batch_first`` is ``False``,
                ``(batch_size, seq_length, num_tags)`` otherwise.
            mask (`~torch.ByteTensor`): Mask tensor of size ``(seq_length, batch_size)``
                if ``batch_first`` is ``False``, ``(batch_size, seq_length)`` otherwise.
        Returns:
            List of list containing the best tag sequence for each batch.
        """
        self._validate(emissions, mask=mask)
        if mask is None:
            mask = emissions.new_ones(emissions.shape[:2], dtype=torch.uint8)

        if self.batch_first:
            emissions = emissions.transpose(0, 1)
            mask = mask.transpose(0, 1)

        return self._viterbi_decode(emissions, mask)

    def _validate(
            self,
            emissions: torch.Tensor,
            tags: Optional[torch.LongTensor] = None,
            mask: Optional[torch.ByteTensor] = None) -> None:
        if emissions.dim() != 3:
            raise ValueError(f'emissions must have dimension of 3, got {emissions.dim()}')
        if emissions.size(2) != self.num_tags:
            raise ValueError(
                f'expected last dimension of emissions is {self.num_tags}, '
                f'got {emissions.size(2)}')

        if tags is not None:
            if emissions.shape[:2] != tags.shape:
                raise ValueError(
                    'the first two dimensions of emissions and tags must match, '
                    f'got {tuple(emissions.shape[:2])} and {tuple(tags.shape)}')

        if mask is not None:
            if emissions.shape[:2] != mask.shape:
                raise ValueError(
                    'the first two dimensions of emissions and mask must match, '
                    f'got {tuple(emissions.shape[:2])} and {tuple(mask.shape)}')
            no_empty_seq = not self.batch_first and mask[0].all()
            no_empty_seq_bf = self.batch_first and mask[:, 0].all()
            if not no_empty_seq and not no_empty_seq_bf:
                raise ValueError('mask of the first timestep must all be on')

    def _compute_score(
            self, emissions: torch.Tensor, tags: torch.LongTensor,
            mask: torch.ByteTensor) -> torch.Tensor:
        # emissions: (seq_length, batch_size, num_tags)
        # tags: (seq_length, batch_size)
        # mask: (seq_length, batch_size)
        assert emissions.dim() == 3 and tags.dim() == 2
        assert emissions.shape[:2] == tags.shape
        assert emissions.size(2) == self.num_tags
        assert mask.shape == tags.shape
        assert mask[0].all()

        seq_length, batch_size = tags.shape
        mask = mask.type_as(emissions)

        # Start transition score and first emission
        # shape: (batch_size,)
        score = self.start_transitions[tags[0]]
        score += emissions[0, torch.arange(batch_size), tags[0]]

        for i in range(1, seq_length):
            # Transition score to next tag, only added if next timestep is valid (mask == 1)
            # shape: (batch_size,)
            score += self.transitions[tags[i - 1], tags[i]] * mask[i]

            # Emission score for next tag, only added if next timestep is valid (mask == 1)
            # shape: (batch_size,)
            score += emissions[i, torch.arange(batch_size), tags[i]] * mask[i]

        # End transition score
        # shape: (batch_size,)
        seq_ends = mask.long().sum(dim=0) - 1
        # shape: (batch_size,)
        last_tags = tags[seq_ends, torch.arange(batch_size)]
        # shape: (batch_size,)
        score += self.end_transitions[last_tags]

        return score

    def _compute_normalizer(
            self, emissions: torch.Tensor, mask: torch.ByteTensor) -> torch.Tensor:
        # emissions: (seq_length, batch_size, num_tags)
        # mask: (seq_length, batch_size)
        assert emissions.dim() == 3 and mask.dim() == 2
        assert emissions.shape[:2] == mask.shape
        assert emissions.size(2) == self.num_tags
        assert mask[0].all()

        seq_length = emissions.size(0)

        # Start transition score and first emission; score has size of
        # (batch_size, num_tags) where for each batch, the j-th column stores
        # the score that the first timestep has tag j
        # shape: (batch_size, num_tags)
        score = self.start_transitions + emissions[0]

        for i in range(1, seq_length):
            # Broadcast score for every possible next tag
            # shape: (batch_size, num_tags, 1)
            broadcast_score = score.unsqueeze(2)

            # Broadcast emission score for every possible current tag
            # shape: (batch_size, 1, num_tags)
            broadcast_emissions = emissions[i].unsqueeze(1)

            # Compute the score tensor of size (batch_size, num_tags, num_tags) where
            # for each sample, entry at row i and column j stores the sum of scores of all
            # possible tag sequences so far that end with transitioning from tag i to tag j
            # and emitting
            # shape: (batch_size, num_tags, num_tags)
            next_score = broadcast_score + self.transitions + broadcast_emissions

            # Sum over all possible current tags, but we're in score space, so a sum
            # becomes a log-sum-exp: for each sample, entry i stores the sum of scores of
            # all possible tag sequences so far, that end in tag i
            # shape: (batch_size, num_tags)
            next_score = torch.logsumexp(next_score, dim=1)

            # Set score to the next score if this timestep is valid (mask == 1)
            # shape: (batch_size, num_tags)
            score = torch.where(mask[i].unsqueeze(1).bool(), next_score, score)

        # End transition score
        # shape: (batch_size, num_tags)
        score += self.end_transitions

        # Sum (log-sum-exp) over all possible tags
        # shape: (batch_size,)
        return torch.logsumexp(score, dim=1)

    def _viterbi_decode(self, emissions: torch.FloatTensor,
                        mask: torch.ByteTensor) -> List[List[int]]:
        # emissions: (seq_length, batch_size, num_tags)
        # mask: (seq_length, batch_size)
        assert emissions.dim() == 3 and mask.dim() == 2
        assert emissions.shape[:2] == mask.shape
        assert emissions.size(2) == self.num_tags
        assert mask[0].all()

        seq_length, batch_size = mask.shape

        # Start transition and first emission
        # shape: (batch_size, num_tags)
        score = self.start_transitions + emissions[0]
        history = []

        # score is a tensor of size (batch_size, num_tags) where for every batch,
        # value at column j stores the score of the best tag sequence so far that ends
        # with tag j
        # history saves where the best tags candidate transitioned from; this is used
        # when we trace back the best tag sequence

        # Viterbi algorithm recursive case: we compute the score of the best tag sequence
        # for every possible next tag
        for i in range(1, seq_length):
            # Broadcast viterbi score for every possible next tag
            # shape: (batch_size, num_tags, 1)
            broadcast_score = score.unsqueeze(2)

            # Broadcast emission score for every possible current tag
            # shape: (batch_size, 1, num_tags)
            broadcast_emission = emissions[i].unsqueeze(1)

            # Compute the score tensor of size (batch_size, num_tags, num_tags) where
            # for each sample, entry at row i and column j stores the score of the best
            # tag sequence so far that ends with transitioning from tag i to tag j and emitting
            # shape: (batch_size, num_tags, num_tags)
            next_score = broadcast_score + self.transitions + broadcast_emission

            # Find the maximum score over all possible current tag
            # shape: (batch_size, num_tags)
            next_score, indices = next_score.max(dim=1)

            # Set score to the next score if this timestep is valid (mask == 1)
            # and save the index that produces the next score
            # shape: (batch_size, num_tags)
            score = torch.where(mask[i].unsqueeze(1).bool(), next_score, score)
            history.append(indices)

        # End transition score
        # shape: (batch_size, num_tags)
        score += self.end_transitions

        # Now, compute the best path for each sample

        # shape: (batch_size,)
        seq_ends = mask.long().sum(dim=0) - 1
        best_tags_list = []

        for idx in range(batch_size):
            # Find the tag which maximizes the score at the last timestep; this is our best tag
            # for the last timestep
            _, best_last_tag = score[idx].max(dim=0)
            best_tags = [best_last_tag.item()]

            # We trace back where the best last tag comes from, append that to our best tag
            # sequence, and trace it back again, and so on
            for hist in reversed(history[:seq_ends[idx]]):
                best_last_tag = hist[idx][best_tags[-1]]
                best_tags.append(best_last_tag.item())

            # Reverse the order because we start from the last timestep
            best_tags.reverse()
            best_tags_list.append(best_tags)

        return best_tags_list