Create modeling_octagon.py

modeling_octagon.py

@@ -0,0 +1,241 @@
+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}