Pass an embedding layer to the constructor of the BertModel class (#1135)

Reviewed By: zhangguanheng66

Differential Revision: D26369001

fbshipit-source-id: f5a67a2a812d568073505ec4d181f6e418eb4a3f

Author: datumbox

examples/BERT/model.py

@@ -43,7 +43,8 @@ def __init__(self, ntoken, ninp, dropout=0.5):
         self.norm = LayerNorm(ninp)
         self.dropout = Dropout(dropout)
 
-    def forward(self, src, token_type_input):
+    def forward(self, seq_inputs):
+        src, token_type_input = seq_inputs
         src = self.embed(src) + self.pos_embed(src) \
             + self.tok_type_embed(src, token_type_input)
         return self.dropout(self.norm(src))
@@ -99,16 +100,16 @@ def forward(self, src, src_mask=None, src_key_padding_mask=None):
 class BertModel(nn.Module):
     """Contain a transformer encoder."""
 
-    def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5):
+    def __init__(self, ntoken, ninp, nhead, nhid, nlayers, embed_layer, dropout=0.5):
         super(BertModel, self).__init__()
         self.model_type = 'Transformer'
-        self.bert_embed = BertEmbedding(ntoken, ninp)
+        self.bert_embed = embed_layer
         encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout)
         self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
         self.ninp = ninp
 
-    def forward(self, src, token_type_input):
-        src = self.bert_embed(src, token_type_input)
+    def forward(self, seq_inputs):
+        src = self.bert_embed(seq_inputs)
         output = self.transformer_encoder(src)
         return output
 
@@ -118,15 +119,16 @@ class MLMTask(nn.Module):
 
     def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5):
         super(MLMTask, self).__init__()
-        self.bert_model = BertModel(ntoken, ninp, nhead, nhid, nlayers, dropout=0.5)
+        embed_layer = BertEmbedding(ntoken, ninp)
+        self.bert_model = BertModel(ntoken, ninp, nhead, nhid, nlayers, embed_layer, dropout=0.5)
         self.mlm_span = Linear(ninp, ninp)
         self.activation = F.gelu
         self.norm_layer = LayerNorm(ninp, eps=1e-12)
         self.mlm_head = Linear(ninp, ntoken)
 
     def forward(self, src, token_type_input=None):
         src = src.transpose(0, 1)  # Wrap up by nn.DataParallel
-        output = self.bert_model(src, token_type_input)
+        output = self.bert_model((src, token_type_input))
         output = self.mlm_span(output)
         output = self.activation(output)
         output = self.norm_layer(output)
@@ -147,7 +149,7 @@ def __init__(self, bert_model):
 
     def forward(self, src, token_type_input):
         src = src.transpose(0, 1)  # Wrap up by nn.DataParallel
-        output = self.bert_model(src, token_type_input)
+        output = self.bert_model((src, token_type_input))
         # Send the first <'cls'> seq to a classifier
         output = self.activation(self.linear_layer(output[0]))
         output = self.ns_span(output)
@@ -164,7 +166,7 @@ def __init__(self, bert_model):
         self.qa_span = Linear(bert_model.ninp, 2)
 
     def forward(self, src, token_type_input):
-        output = self.bert_model(src, token_type_input)
+        output = self.bert_model((src, token_type_input))
         # transpose output (S, N, E) to (N, S, E)
         output = output.transpose(0, 1)
         output = self.activation(output)

examples/BERT/ns_task.py

@@ -5,7 +5,7 @@
 import torch.nn as nn
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.data import DataLoader
-from model import NextSentenceTask, BertModel
+from model import NextSentenceTask, BertModel, BertEmbedding
 from utils import run_demo, run_ddp, wrap_up
 
 
@@ -149,7 +149,8 @@ def run_main(args, rank=None):
     if args.checkpoint != 'None':
         model = torch.load(args.checkpoint)
     else:
-        pretrained_bert = BertModel(len(vocab), args.emsize, args.nhead, args.nhid, args.nlayers, args.dropout)
+        embed_layer = BertEmbedding(len(vocab), args.emsize)
+        pretrained_bert = BertModel(len(vocab), args.emsize, args.nhead, args.nhid, args.nlayers, embed_layer, args.dropout)
         pretrained_bert.load_state_dict(torch.load(args.bert_model))
         model = NextSentenceTask(pretrained_bert)
 

examples/BERT/qa_task.py

@@ -9,7 +9,7 @@
 from model import QuestionAnswerTask
 from metrics import compute_qa_exact, compute_qa_f1
 from utils import print_loss_log
-from model import BertModel
+from model import BertModel, BertEmbedding
 
 
 def process_raw_data(data):
@@ -174,7 +174,8 @@ def train():
     train_dataset = process_raw_data(train_dataset)
     dev_dataset = process_raw_data(dev_dataset)
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    pretrained_bert = BertModel(len(vocab), args.emsize, args.nhead, args.nhid, args.nlayers, args.dropout)
+    embed_layer = BertEmbedding(len(vocab), args.emsize)
+    pretrained_bert = BertModel(len(vocab), args.emsize, args.nhead, args.nhid, args.nlayers, embed_layer, args.dropout)
     pretrained_bert.load_state_dict(torch.load(args.bert_model))
     model = QuestionAnswerTask(pretrained_bert).to(device)
     criterion = nn.CrossEntropyLoss()