modeling_octagon.py

from transformers import PretrainedConfig, PreTrainedModel
from torch import nn
import torch

class OctagonConfig(PretrainedConfig):
    model_type = "octagon"
    
    def __init__(
        self,
        vocab_size=30522,
        hidden_size=768,
        num_hidden_layers=8,  # Octagon has 8 sides!
        num_attention_heads=8,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        position_embedding_type="absolute",
        classifier_dropout=None,
        num_labels=2,
        **kwargs
    ):
        super().__init__(pad_token_id=pad_token_id, **kwargs)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.position_embedding_type = position_embedding_type
        self.classifier_dropout = classifier_dropout
        self.num_labels = num_labels

class OctagonEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
        
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        
        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))

    def forward(self, input_ids=None, token_type_ids=None, position_ids=None):
        seq_length = input_ids.size(1)
        
        if position_ids is None:
            position_ids = self.position_ids[:, :seq_length]
        
        if token_type_ids is None:
            token_type_ids = torch.zeros_like(input_ids)
            
        word_embeddings = self.word_embeddings(input_ids)
        position_embeddings = self.position_embeddings(position_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)
        
        embeddings = word_embeddings + position_embeddings + token_type_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings

class OctagonSelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        
        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)
        
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        
    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)
    
    def forward(self, hidden_states):
        query_layer = self.transpose_for_scores(self.query(hidden_states))
        key_layer = self.transpose_for_scores(self.key(hidden_states))
        value_layer = self.transpose_for_scores(self.value(hidden_states))
        
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
        attention_probs = self.dropout(attention_probs)
        
        context_layer = torch.matmul(attention_probs, value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)
        return context_layer

class OctagonSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    
    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states

class OctagonAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.self = OctagonSelfAttention(config)
        self.output = OctagonSelfOutput(config)
    
    def forward(self, hidden_states):
        self_outputs = self.self(hidden_states)
        attention_output = self.output(self_outputs, hidden_states)
        return attention_output

class OctagonIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        self.intermediate_act_fn = nn.GELU()
    
    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states

class OctagonOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    
    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states

class OctagonLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.attention = OctagonAttention(config)
        self.intermediate = OctagonIntermediate(config)
        self.output = OctagonOutput(config)
    
    def forward(self, hidden_states):
        attention_output = self.attention(hidden_states)
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output

class OctagonEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.layer = nn.ModuleList([OctagonLayer(config) for _ in range(config.num_hidden_layers)])
    
    def forward(self, hidden_states):
        for layer_module in self.layer:
            hidden_states = layer_module(hidden_states)
        return hidden_states

class OctagonModel(PreTrainedModel):
    config_class = OctagonConfig
    
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.embeddings = OctagonEmbeddings(config)
        self.encoder = OctagonEncoder(config)
        self.pooler = nn.Linear(config.hidden_size, config.hidden_size)
        self.tanh = nn.Tanh()
        
        self.post_init()
    
    def forward(self, input_ids=None, token_type_ids=None, position_ids=None):
        if input_ids is not None:
            input_shape = input_ids.size()
        else:
            raise ValueError("You have to specify input_ids")
        
        embedding_output = self.embeddings(
            input_ids=input_ids,
            token_type_ids=token_type_ids,
            position_ids=position_ids
        )
        
        encoder_outputs = self.encoder(embedding_output)
        pooled_output = self.pooler(encoder_outputs[:, 0])
        pooled_output = self.tanh(pooled_output)
        
        return encoder_outputs, pooled_output

class OctagonForSequenceClassification(PreTrainedModel):
    config_class = OctagonConfig
    
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.octagon = OctagonModel(config)
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        
        self.post_init()
    
    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, labels=None):
        _, pooled_output = self.octagon(
            input_ids=input_ids,
            token_type_ids=token_type_ids,
            position_ids=position_ids
        )
        
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        
        loss = None
        if labels is not None:
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
        
        return {"loss": loss, "logits": logits}