add t2t model

fastNLP/models/seq2seq_models.py

@@ -0,0 +1,31 @@
+from fastNLP.modules.encoder.seq2seq_encoder import TransformerSeq2SeqEncoder
+from fastNLP.modules.decoder.seq2seq_decoder import TransformerSeq2SeqDecoder, TransformerPast
+from fastNLP.modules.decoder.seq2seq_generator import SequenceGenerator
+import torch.nn as nn
+import torch
+from typing import Union, Tuple
+import numpy as np
+
+
+class TransformerSeq2SeqModel(nn.Module):
+    def __init__(self, src_embed: Union[Tuple[int, int], nn.Module, torch.Tensor, np.ndarray],
+                 tgt_embed: Union[Tuple[int, int], nn.Module, torch.Tensor, np.ndarray],
+                 num_layers: int = 6,
+                 d_model: int = 512, n_head: int = 8, dim_ff: int = 2048, dropout: float = 0.1,
+                 output_embed: Union[Tuple[int, int], int, nn.Module, torch.Tensor, np.ndarray] = None,
+                 bind_input_output_embed=False,
+                 sos_id=None, eos_id=None):
+        super().__init__()
+        self.encoder = TransformerSeq2SeqEncoder(src_embed, num_layers, d_model, n_head, dim_ff, dropout)
+        self.decoder = TransformerSeq2SeqDecoder(tgt_embed, num_layers, d_model, n_head, dim_ff, dropout, output_embed,
+                                                 bind_input_output_embed)
+        self.sos_id = sos_id
+        self.eos_id = eos_id
+        self.num_layers = num_layers
+
+    def forward(self, words, target, seq_len):  # todo:这里的target有sos和eos吗，参考一下lstm怎么写的
+        encoder_output, encoder_mask = self.encoder(words, seq_len)
+        past = TransformerPast(encoder_output, encoder_mask, self.num_layers)
+        outputs = self.decoder(target, past, return_attention=False)
+
+        return outputs

fastNLP/modules/decoder/seq2seq_decoder.py

@@ -360,7 +360,6 @@ class TransformerSeq2SeqDecoder(Decoder):
     @torch.no_grad()
     def decode_one(self, tokens, past) -> Tuple[torch.Tensor, Past]:
         """
-        # todo: 对于transformer而言，因为position的原因，需要输入整个prefix序列，因此lstm的decode one和beam search需要改一下，以统一接口
         # todo: 是否不需要return past？ 因为past已经被改变了，不需要显式return？
         :param tokens: torch.LongTensor (batch_size,1)
         :param past: TransformerPast
@@ -378,20 +377,6 @@ class TransformerSeq2SeqDecoder(Decoder):
         return torch.tril(tensor).byte()
 
 
-class BiLSTMEncoder(nn.Module):
-    def __init__(self, embed, num_layers=3, hidden_size=400, dropout=0.3):
-        super().__init__()
-        self.embed = embed
-        self.lstm = LSTM(input_size=self.embed.embedding_dim, hidden_size=hidden_size // 2, bidirectional=True,
-                         batch_first=True, dropout=dropout, num_layers=num_layers)
-
-    def forward(self, words, seq_len):
-        words = self.embed(words)
-        words, hx = self.lstm(words, seq_len)
-
-        return words, hx
-
-
 class LSTMPast(Past):
     def __init__(self, encode_outputs=None, encode_mask=None, decode_states=None, hx=None):
         """

fastNLP/modules/encoder/seq2seq_encoder.py

@@ -12,7 +12,7 @@ class TransformerSeq2SeqEncoder(nn.Module):
                  d_model: int = 512, n_head: int = 8, dim_ff: int = 2048, dropout: float = 0.1):
         super(TransformerSeq2SeqEncoder, self).__init__()
         self.embed = embed
-        self.transformer = TransformerEncoder(nn.TransformerEncoderLayer(d_model, n_head), num_layers)
+        self.transformer = TransformerEncoder(nn.TransformerEncoderLayer(d_model, n_head,dim_ff,dropout), num_layers)
 
     def forward(self, words, seq_len):
         """