TorchScript flag in config; Tied weights when not running TorchScript; tuple concatenation clean-up.

pytorch_pretrained_bert/model_utils.py

@@ -46,6 +46,7 @@ class PretrainedConfig(object):
         self.num_labels = kwargs.pop('num_labels', 2)
         self.output_attentions = kwargs.pop('output_attentions', False)
         self.output_hidden_states = kwargs.pop('output_hidden_states', False)
+        self.torchscript = kwargs.pop('torchscript', False)
 
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):

pytorch_pretrained_bert/modeling.py

@@ -428,23 +428,23 @@ class BertEncoder(nn.Module):
         all_attentions = ()
         for i, layer_module in enumerate(self.layer):
             if self.output_hidden_states:
-                all_hidden_states += (hidden_states,)
+                all_hidden_states = all_hidden_states + (hidden_states,)
 
             layer_outputs = layer_module(hidden_states, attention_mask, head_mask[i])
             hidden_states = layer_outputs[0]
 
             if self.output_attentions:
-                all_attentions += (layer_outputs[1],)
+                all_attentions = all_attentions + (layer_outputs[1],)
 
         # Add last layer
         if self.output_hidden_states:
-            all_hidden_states += (hidden_states,)
+            all_hidden_states = all_hidden_states + (hidden_states,)
 
         outputs = (hidden_states,)
         if self.output_hidden_states:
-            outputs += (all_hidden_states,)
+            outputs = outputs + (all_hidden_states,)
         if self.output_attentions:
-            outputs += (all_attentions,)
+            outputs = outputs + (all_attentions,)
         return outputs  # outputs, (hidden states), (attentions)
 
 
@@ -484,13 +484,19 @@ class BertLMPredictionHead(nn.Module):
     def __init__(self, config, bert_model_embedding_weights):
         super(BertLMPredictionHead, self).__init__()
         self.transform = BertPredictionHeadTransform(config)
+        self.torchscript = config.torchscript
 
         # The output weights are the same as the input embeddings, but there is
         # an output-only bias for each token.
         self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
                                  bert_model_embedding_weights.size(0),
                                  bias=False)
-        self.decoder.weight = nn.Parameter(bert_model_embedding_weights.clone())
+
+        if self.torchscript:
+            self.decoder.weight = nn.Parameter(bert_model_embedding_weights.clone())
+        else:
+            self.decoder.weight = bert_model_embedding_weights
+
         self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0)))
 
     def forward(self, hidden_states):

pytorch_pretrained_bert/modeling_gpt2.py

@@ -322,6 +322,7 @@ class GPT2LMHead(nn.Module):
         self.n_embd = config.n_embd
         self.vocab_size = config.vocab_size
         self.predict_special_tokens = config.predict_special_tokens
+        self.torchscript = config.torchscript
         embed_shape = model_embeddings_weights.shape
         self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False)
         self.set_embeddings_weights(model_embeddings_weights)
@@ -329,7 +330,10 @@ class GPT2LMHead(nn.Module):
     def set_embeddings_weights(self, model_embeddings_weights, predict_special_tokens=True):
         self.predict_special_tokens = predict_special_tokens
         # Export to TorchScript can't handle parameter sharing so we are cloning them.
-        self.decoder.weight = nn.Parameter(model_embeddings_weights.clone())  # Tied weights
+        if self.torchscript:
+            self.decoder.weight = nn.Parameter(model_embeddings_weights.clone())
+        else:
+            self.decoder.weight = model_embeddings_weights  # Tied weights
 
     def forward(self, hidden_state):
         lm_logits = self.decoder(hidden_state)
@@ -563,11 +567,11 @@ class GPT2Model(GPT2PreTrainedModel):
         all_hidden_states = ()
         for i, (block, layer_past) in enumerate(zip(self.h, past)):
             if self.output_hidden_states:
-                all_hidden_states += (hidden_states.view(*output_shape),)
+                all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
 
             outputs = block(hidden_states, layer_past, head_mask[i])
             hidden_states, present = outputs[:2]
-            presents += (present,)
+            presents = presents + (present,)
 
             if self.output_attentions:
                 all_attentions.append(outputs[2])
@@ -577,16 +581,16 @@ class GPT2Model(GPT2PreTrainedModel):
         hidden_states = hidden_states.view(*output_shape)
         # Add last hidden state
         if self.output_hidden_states:
-            all_hidden_states += (hidden_states,)
+            all_hidden_states = all_hidden_states + (hidden_states,)
 
         outputs = (hidden_states, presents)
         if self.output_hidden_states:
-            outputs += (all_hidden_states,)
+            outputs = outputs + (all_hidden_states,)
         if self.output_attentions:
             # let the number of heads free (-1) so we can extract attention even after head pruning
             attention_output_shape = input_shape[:-1] + (-1,) + all_attentions[0].shape[-2:]
             all_attentions = tuple(t.view(*attention_output_shape) for t in all_attentions)
-            outputs += (all_attentions,)
+            outputs = outputs + (all_attentions,)
         return outputs  # last hidden state, presents, (all hidden_states), (attentions)
 
 

pytorch_pretrained_bert/modeling_openai.py

@@ -348,14 +348,18 @@ class OpenAIGPTLMHead(nn.Module):
         self.n_embd = config.n_embd
         self.vocab_size = config.vocab_size
         self.predict_special_tokens = config.predict_special_tokens
+        self.torchscript = config.torchscript
         embed_shape = model_embeddings_weights.shape
         self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False)
         self.set_embeddings_weights(model_embeddings_weights)
 
     def set_embeddings_weights(self, model_embeddings_weights, predict_special_tokens=True):
         self.predict_special_tokens = predict_special_tokens
-        embed_shape = model_embeddings_weights.shape
+
-        self.decoder.weight = nn.Parameter(model_embeddings_weights.clone())  # Tied weights
+        if self.torchscript:
+            self.decoder.weight = nn.Parameter(model_embeddings_weights.clone())
+        else:
+            self.decoder.weight = model_embeddings_weights  # Tied weights
 
     def forward(self, hidden_state):
         lm_logits = self.decoder(hidden_state)
@@ -583,22 +587,22 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
         all_hidden_states = ()
         for i, block in enumerate(self.h):
             if self.output_hidden_states:
-                all_hidden_states += (hidden_states.view(*output_shape),)
+                all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
 
             outputs = block(hidden_states, head_mask[i])
             hidden_states = outputs[0]
             if self.output_attentions:
-                all_attentions += (outputs[1],)
+                all_attentions = all_attentions + (outputs[1],)
 
         # Add last layer
         if self.output_hidden_states:
-            all_hidden_states += (hidden_states.view(*output_shape),)
+            all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
 
         outputs = (hidden_states.view(*output_shape),)
         if self.output_hidden_states:
-            outputs += (all_hidden_states,)
+            outputs = outputs + (all_hidden_states,)
         if self.output_attentions:
-            outputs += (all_attentions,)
+            outputs = outputs + (all_attentions,)
         return outputs  # last hidden state, (all hidden states), (all attentions)
 
 

pytorch_pretrained_bert/modeling_xlnet.py

@@ -530,7 +530,7 @@ class XLNetRelativeAttention(nn.Module):
 
         outputs = (output_h, output_g)
         if self.output_attentions:
-            outputs += (attn_prob,)
+            outputs = outputs + (attn_prob,)
         return outputs
 
 class XLNetFeedForward(nn.Module):
@@ -878,7 +878,7 @@ class XLNetModel(XLNetPreTrainedModel):
         hidden_states = []
         for i, layer_module in enumerate(self.layer):
             # cache new mems
-            new_mems += (self.cache_mem(output_h, mems[i]),)
+            new_mems = new_mems + (self.cache_mem(output_h, mems[i]),)
             if self.output_hidden_states:
                 hidden_states.append((output_h, output_g) if output_g is not None else output_h)
 
@@ -902,10 +902,10 @@ class XLNetModel(XLNetPreTrainedModel):
                 hidden_states = tuple(h.permute(1, 0, 2).contiguous() for hs in hidden_states for h in hs)
             else:
                 hidden_states = tuple(hs.permute(1, 0, 2).contiguous() for hs in hidden_states)
-            outputs += (hidden_states,)
+            outputs = outputs + (hidden_states,)
         if self.output_attentions:
             attentions = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attentions)
-            outputs += (attentions,)
+            outputs = outputs + (attentions,)
 
         return outputs  # outputs, new_mems, (hidden_states), (attentions)
 
@@ -975,6 +975,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
         super(XLNetLMHeadModel, self).__init__(config)
         self.attn_type = config.attn_type
         self.same_length = config.same_length
+        self.torchscript = config.torchscript
 
         self.transformer = XLNetModel(config)
         self.lm_loss = nn.Linear(config.d_model, config.n_token, bias=True)
@@ -987,7 +988,10 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
     def tie_weights(self):
         """ Make sure we are sharing the embeddings
         """
-        self.lm_loss.weight = nn.Parameter(self.transformer.word_embedding.weight.clone())
+        if self.torchscript:
+            self.lm_loss.weight = nn.Parameter(self.transformer.word_embedding.weight.clone())
+        else:
+            self.lm_loss.weight = self.transformer.word_embedding.weight
 
     def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
                 mems=None, perm_mask=None, target_mapping=None, inp_q=None,

pytorch_pretrained_bert/tests/model_tests_commons.py

@@ -41,6 +41,7 @@ def _create_and_check_torchscript_output_hidden_state(tester, model_classes, con
 
 def _create_and_check_torchscript(tester, model_classes, config, inputs_dict):
     configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
+    configs_no_init.torchscript = True
     for model_class in model_classes:
         model = model_class(config=configs_no_init)
         model.eval()