Update inference.py

inference.py

@@ -1,10 +1,8 @@
-from torch import nn
-import torch
+from transformers import AutoTokenizer
+from MetaQA_Model import MetaQA_Model
 import numpy as np
 
-from transformers import BertPreTrainedModel, AutoTokenizer
-from transformers.modeling_outputs import TokenClassifierOutput, BaseModelOutputWithPoolingAndCrossAttentions
-from transformers.models.bert.modeling_bert import BertPooler, BertEncoder
+import torch
 
 class PredictionRequest():
     input_question: str
@@ -107,297 +105,3 @@ class MetaQA():
         agent_name = self.metaqa_model.config.agents[idx]
         agent_score = input_predictions[idx][1]
         return (pred, agent_name, metaqa_score, agent_score)
-
-
-class MetaQA_Model(BertPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.bert = MetaQABertModel(config)
-        self.num_agents = config.num_agents
-        
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.list_MoSeN = nn.ModuleList([nn.Linear(config.hidden_size, 1) for i in range(self.num_agents)])
-        self.input_size_ans_sel = 1 + config.hidden_size
-        interm_size = int(config.hidden_size/2) 
-        self.ans_sel = nn.Sequential(nn.Linear(self.input_size_ans_sel, interm_size),
-                                         nn.ReLU(),
-                                         nn.Dropout(config.hidden_dropout_prob),
-                                         nn.Linear(interm_size, 2))
-        
-        self.init_weights()
-
-    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,
-        ans_sc=None,
-        agent_sc=None,
-    ):
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.bert(
-            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,
-            ans_sc=ans_sc,
-            agent_sc=agent_sc,
-        )
-        # domain classification
-        pooled_output = outputs[1]
-
-        pooled_output = self.dropout(pooled_output)
-        list_domains_logits = []
-        for MoSeN in self.list_MoSeN:
-            domain_logits = MoSeN(pooled_output)
-            list_domains_logits.append(domain_logits)
-        domain_logits = torch.stack(list_domains_logits)
-        # shape = (num_agents, batch_size, 1)
-        # we have to transpose the shape to (batch_size, num_agents, 1)
-        domain_logits = domain_logits.transpose(0,1)
-        
-        # ans classifier
-        sequence_output = outputs[0] # (batch_size, seq_len, hidden_size)
-        # select the [RANK] token embeddings
-        idx_rank = (input_ids == 1).nonzero() # (batch_size x num_agents, 2)
-        idx_rank = idx_rank[:,1].view(-1, self.num_agents)
-        list_emb = []
-        for i in range(idx_rank.shape[0]):
-            rank_emb = sequence_output[i][idx_rank[i], :]
-            # rank shape = (1, hidden_size)
-            list_emb.append(rank_emb)
-        
-        rank_emb = torch.stack(list_emb)
-        
-        rank_emb = self.dropout(rank_emb)
-        rank_emb = torch.cat((rank_emb, domain_logits), dim=2)
-        # rank emb shape = (batch_size, num_agents, hidden_size+1)
-        logits = self.ans_sel(rank_emb) # (batch_size, num_agents, 2) 
-        
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return output
-
-        return TokenClassifierOutput(
-            loss=None,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-class MetaQABertModel(BertPreTrainedModel):
-    def __init__(self, config, add_pooling_layer=True):
-        super().__init__(config)
-        self.config = config
-
-        self.embeddings = MetaQABertEmbeddings(config)
-        self.encoder = BertEncoder(config)
-        self.pooler = BertPooler(config) if add_pooling_layer else None
-
-        self.init_weights()
-
-    def get_input_embeddings(self):
-        return self.embeddings.word_embeddings
-
-    def set_input_embeddings(self, value):
-        self.embeddings.word_embeddings = value
-
-    def _prune_heads(self, heads_to_prune):
-        for layer, heads in heads_to_prune.items():
-            self.encoder.layer[layer].attention.prune_heads(heads)
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        ans_sc=None,
-        agent_sc=None,
-    ):
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if self.config.is_decoder:
-            use_cache = use_cache if use_cache is not None else self.config.use_cache
-        else:
-            use_cache = False
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-            batch_size, seq_length = input_shape
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-            batch_size, seq_length = input_shape
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        # past_key_values_length
-        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
-
-        if attention_mask is None:
-            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
-
-        if token_type_ids is None:
-            if hasattr(self.embeddings, "token_type_ids"):
-                buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
-                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
-                token_type_ids = buffered_token_type_ids_expanded
-            else:
-                token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
-
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
-
-        # If a 2D or 3D attention mask is provided for the cross-attention
-        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if self.config.is_decoder and encoder_hidden_states is not None:
-            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-            if encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
-            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-        else:
-            encoder_extended_attention_mask = None
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-
-        embedding_output = self.embeddings(
-            input_ids=input_ids,
-            position_ids=position_ids,
-            token_type_ids=token_type_ids,
-            inputs_embeds=inputs_embeds,
-            past_key_values_length=past_key_values_length,
-            ans_sc=ans_sc,
-            agent_sc=agent_sc,
-        )
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=head_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_extended_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_output = encoder_outputs[0]
-        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
-
-        if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPoolingAndCrossAttentions(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-            past_key_values=encoder_outputs.past_key_values,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-            cross_attentions=encoder_outputs.cross_attentions,
-        )
-
-class MetaQABertEmbeddings(nn.Module):
-    """Construct the embeddings from word, position and token_type embeddings."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
-        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.ans_sc_proj = nn.Linear(1, config.hidden_size)
-        self.agent_sc_proj = nn.Linear(1, config.hidden_size)
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
-        self.register_buffer(
-            "token_type_ids",
-            torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device),
-            persistent=False,
-        )
-            
-
-    def forward(
-        self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0,
-    ans_sc=None, agent_sc=None):
-        if input_ids is not None:
-            input_shape = input_ids.size()
-        else:
-            input_shape = inputs_embeds.size()[:-1]
-
-        seq_length = input_shape[1]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
-
-        # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
-        # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
-        # issue #5664
-        if token_type_ids is None:
-            if hasattr(self, "token_type_ids"):
-                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
-                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
-                token_type_ids = buffered_token_type_ids_expanded
-            else:
-                token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-        token_type_embeddings = self.token_type_embeddings(token_type_ids)
-
-        embeddings = inputs_embeds + token_type_embeddings
-        if self.position_embedding_type == "absolute":
-            position_embeddings = self.position_embeddings(position_ids)
-            embeddings += position_embeddings
-
-        if ans_sc is not None:
-            ans_sc_emb = self.ans_sc_proj(ans_sc.unsqueeze(2))
-            embeddings += ans_sc_emb
-        if agent_sc is not None:
-            agent_sc_emb = self.agent_sc_proj(agent_sc.unsqueeze(2))
-            embeddings += agent_sc_emb
-        
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings