解决在DataParallel模型场景下无法进行参数匹配的问题

fastNLP/core/tester.py

@@ -120,7 +120,10 @@ class Tester(object):
                 raise TypeError(f"`{_model_name}.predict` must be callable to be used "
                                 f"for evaluation, not `{type(self._predict_func)}`.")
         else:
-            self._predict_func = self._model.forward
+            if isinstance(model, nn.DataParallel):
+                self._predict_func = self._model.module.forward
+            else:
+                self._predict_func = self._model.forward
     
     def test(self):
         """开始进行验证，并返回验证结果。

fastNLP/core/trainer.py

@@ -578,7 +578,10 @@ class Trainer(object):
         self.step = 0
         self.epoch = 0
         start = time.time()
-        
+        if isinstance(self.model, nn.DataParallel):
+            self._forward_func = self.model.module.forward
+        else:
+            self._forward_func = self.model.forward
         with inner_tqdm(total=self.n_steps, postfix='loss:{0:<6.5f}', leave=False, dynamic_ncols=True) as pbar:
             self.pbar = pbar
             avg_loss = 0
@@ -682,11 +685,11 @@ class Trainer(object):
             self.optimizer.step()
     
     def _data_forward(self, network, x):
-        x = _build_args(network.forward, **x)
+        x = _build_args(self._forward_func, **x)
         y = network(**x)
         if not isinstance(y, dict):
             raise TypeError(
-                f"The return value of {_get_func_signature(network.forward)} should be dict, got {type(y)}.")
+                f"The return value of {_get_func_signature(self._forward_func)} should be dict, got {type(y)}.")
         return y
     
     def _grad_backward(self, loss):
@@ -845,8 +848,11 @@ def _check_code(dataset, model, losser, metrics, batch_size=DEFAULT_CHECK_BATCH_
             print(info_str)
             _check_forward_error(forward_func=model.forward, dataset=dataset,
                                  batch_x=batch_x, check_level=check_level)
-        
-        refined_batch_x = _build_args(model.forward, **batch_x)
+        if isinstance(model, nn.DataParallel):
+            forward_func = model.module.forward
+        else:
+            forward_func = model.forward
+        refined_batch_x = _build_args(forward_func, **batch_x)
         pred_dict = model(**refined_batch_x)
         func_signature = _get_func_signature(model.forward)
         if not isinstance(pred_dict, dict):

fastNLP/modules/encoder/_elmo.py

@@ -709,7 +709,7 @@ class _ElmoModel(nn.Module):
                 config, word_emb_layer, char_emb_layer)
         self.token_embedder.load_state_dict(token_embedder_states, strict=False)
         if config['token_embedder']['word_dim'] > 0 and vocab._no_create_word_length > 0:  # 需要映射，使得来自于dev, test的idx指向unk
-            words_to_words = nn.Parameter(torch.arange(len(vocab)).long(), requires_grad=False)
+            words_to_words = nn.Parameter(torch.arange(len(vocab)+2).long(), requires_grad=False)
             for word, idx in vocab:
                 if vocab._is_word_no_create_entry(word):
                     words_to_words[idx] = vocab.unknown_idx