rat-sql-gap/seq2struct/models/spider/spider_enc.py [866:985]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - bert_output = self.bert_model(tokens_tensor, attention_mask=att_masks_tensor)[0] enc_output = bert_output column_pointer_maps = [ { i: [i] for i in range(len(desc['columns'])) } for desc in descs ] table_pointer_maps = [ { i: [i] for i in range(len(desc['tables'])) } for desc in descs ] assert len(long_seq_set) == 0 # remove them for now result = [] for batch_idx, desc in enumerate(descs): c_boundary = list(range(len(desc["columns"]) + 1)) t_boundary = list(range(len(desc["tables"]) + 1)) if batch_idx in long_seq_set: q_enc, col_enc, tab_enc = self.encoder_long_seq(desc) else: bert_batch_idx = batch_id_map[batch_idx] q_enc = enc_output[bert_batch_idx][batch_id_to_retrieve_question[bert_batch_idx]] col_enc = enc_output[bert_batch_idx][batch_id_to_retrieve_column[bert_batch_idx]] tab_enc = enc_output[bert_batch_idx][batch_id_to_retrieve_table[bert_batch_idx]] if self.summarize_header == "avg": col_enc_2 = enc_output[bert_batch_idx][batch_id_to_retrieve_column_2[bert_batch_idx]] tab_enc_2 = enc_output[bert_batch_idx][batch_id_to_retrieve_table_2[bert_batch_idx]] col_enc = (col_enc + col_enc_2) / 2.0 # avg of first and last token tab_enc = (tab_enc + tab_enc_2) / 2.0 # avg of first and last token assert q_enc.size()[0] == len(desc["question"]) assert col_enc.size()[0] == c_boundary[-1] assert tab_enc.size()[0] == t_boundary[-1] q_enc_new_item, c_enc_new_item, t_enc_new_item, align_mat_item = \ self.encs_update.forward_unbatched( desc, q_enc.unsqueeze(1), col_enc.unsqueeze(1), c_boundary, tab_enc.unsqueeze(1), t_boundary) import pickle pickle.dump({"desc": desc, "q_enc": q_enc, "col_enc": col_enc, "c_boundary": c_boundary, "tab_enc": tab_enc, "t_boundary": t_boundary}, open("descs_{}.pkl".format(batch_idx), "wb")) memory = [] if 'question' in self.include_in_memory: memory.append(q_enc_new_item) if 'column' in self.include_in_memory: memory.append(c_enc_new_item) if 'table' in self.include_in_memory: memory.append(t_enc_new_item) memory = torch.cat(memory, dim=1) result.append(SpiderEncoderState( state=None, memory=memory, question_memory=q_enc_new_item, schema_memory=torch.cat((c_enc_new_item, t_enc_new_item), dim=1), # TODO: words should match memory words=desc['question'], pointer_memories={ 'column': c_enc_new_item, 'table': t_enc_new_item, }, pointer_maps={ 'column': column_pointer_maps[batch_idx], 'table': table_pointer_maps[batch_idx], }, m2c_align_mat=align_mat_item[0], m2t_align_mat=align_mat_item[1], )) return result @DeprecationWarning def encoder_long_seq(self, desc): """ Since bert cannot handle sequence longer than 512, each column/table is encoded individually The representation of a column/table is the vector of the first token [CLS] """ qs = self.pad_single_sentence_for_bert(desc['question'], cls=True) cols = [self.pad_single_sentence_for_bert(c, cls=True) for c in desc['columns']] tabs = [self.pad_single_sentence_for_bert(t, cls=True) for t in desc['tables']] enc_q = self._bert_encode(qs) enc_col = self._bert_encode(cols) enc_tab = self._bert_encode(tabs) return enc_q, enc_col, enc_tab @DeprecationWarning def _bert_encode(self, toks): if not isinstance(toks[0], list): # encode question words indexed_tokens = self.tokenizer.convert_tokens_to_ids(toks) tokens_tensor = torch.tensor([indexed_tokens]).to(self._device) outputs = self.bert_model(tokens_tensor) return outputs[0][0, 1:-1] # remove [CLS] and [SEP] else: max_len = max([len(it) for it in toks]) tok_ids = [] for item_toks in toks: item_toks = item_toks + [self.tokenizer.pad_token] * (max_len - len(item_toks)) indexed_tokens = self.tokenizer.convert_tokens_to_ids(item_toks) tok_ids.append(indexed_tokens) tokens_tensor = torch.tensor(tok_ids).to(self._device) outputs = self.bert_model(tokens_tensor) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rat-sql-gap/seq2struct/models/spider/spider_enc.py [1409:1527]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - bert_output = self.bert_model(tokens_tensor, attention_mask=att_masks_tensor)[0] enc_output = bert_output column_pointer_maps = [ { i: [i] for i in range(len(desc['columns'])) } for desc in descs ] table_pointer_maps = [ { i: [i] for i in range(len(desc['tables'])) } for desc in descs ] assert len(long_seq_set) == 0 # remove them for now result = [] for batch_idx, desc in enumerate(descs): c_boundary = list(range(len(desc["columns"]) + 1)) t_boundary = list(range(len(desc["tables"]) + 1)) if batch_idx in long_seq_set: q_enc, col_enc, tab_enc = self.encoder_long_seq(desc) else: bert_batch_idx = batch_id_map[batch_idx] q_enc = enc_output[bert_batch_idx][batch_id_to_retrieve_question[bert_batch_idx]] col_enc = enc_output[bert_batch_idx][batch_id_to_retrieve_column[bert_batch_idx]] tab_enc = enc_output[bert_batch_idx][batch_id_to_retrieve_table[bert_batch_idx]] if self.summarize_header == "avg": col_enc_2 = enc_output[bert_batch_idx][batch_id_to_retrieve_column_2[bert_batch_idx]] tab_enc_2 = enc_output[bert_batch_idx][batch_id_to_retrieve_table_2[bert_batch_idx]] col_enc = (col_enc + col_enc_2) / 2.0 # avg of first and last token tab_enc = (tab_enc + tab_enc_2) / 2.0 # avg of first and last token assert q_enc.size()[0] == len(desc["question"]) assert col_enc.size()[0] == c_boundary[-1] assert tab_enc.size()[0] == t_boundary[-1] q_enc_new_item, c_enc_new_item, t_enc_new_item, align_mat_item = \ self.encs_update.forward_unbatched( desc, q_enc.unsqueeze(1), col_enc.unsqueeze(1), c_boundary, tab_enc.unsqueeze(1), t_boundary) import pickle pickle.dump({"desc": desc, "q_enc": q_enc, "col_enc": col_enc, "c_boundary": c_boundary, "tab_enc": tab_enc, "t_boundary": t_boundary}, open("descs_{}.pkl".format(batch_idx), "wb")) memory = [] if 'question' in self.include_in_memory: memory.append(q_enc_new_item) if 'column' in self.include_in_memory: memory.append(c_enc_new_item) if 'table' in self.include_in_memory: memory.append(t_enc_new_item) memory = torch.cat(memory, dim=1) result.append(SpiderEncoderState( state=None, memory=memory, question_memory=q_enc_new_item, schema_memory=torch.cat((c_enc_new_item, t_enc_new_item), dim=1), # TODO: words should match memory words=desc['question'], pointer_memories={ 'column': c_enc_new_item, 'table': t_enc_new_item, }, pointer_maps={ 'column': column_pointer_maps[batch_idx], 'table': table_pointer_maps[batch_idx], }, m2c_align_mat=align_mat_item[0], m2t_align_mat=align_mat_item[1], )) return result @DeprecationWarning def encoder_long_seq(self, desc): """ Since bert cannot handle sequence longer than 512, each column/table is encoded individually The representation of a column/table is the vector of the first token [CLS] """ qs = self.pad_single_sentence_for_bert(desc['question'], cls=True) cols = [self.pad_single_sentence_for_bert(c, cls=True) for c in desc['columns']] tabs = [self.pad_single_sentence_for_bert(t, cls=True) for t in desc['tables']] enc_q = self._bert_encode(qs) enc_col = self._bert_encode(cols) enc_tab = self._bert_encode(tabs) return enc_q, enc_col, enc_tab @DeprecationWarning def _bert_encode(self, toks): if not isinstance(toks[0], list): # encode question words indexed_tokens = self.tokenizer.convert_tokens_to_ids(toks) tokens_tensor = torch.tensor([indexed_tokens]).to(self._device) outputs = self.bert_model(tokens_tensor) return outputs[0][0, 1:-1] # remove [CLS] and [SEP] else: max_len = max([len(it) for it in toks]) tok_ids = [] for item_toks in toks: item_toks = item_toks + [self.tokenizer.pad_token] * (max_len - len(item_toks)) indexed_tokens = self.tokenizer.convert_tokens_to_ids(item_toks) tok_ids.append(indexed_tokens) tokens_tensor = torch.tensor(tok_ids).to(self._device) outputs = self.bert_model(tokens_tensor) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -