修复Embedding注释错误与core.utils.py代码错误

fastNLP/core/utils.py

@@ -779,7 +779,7 @@ def _check_forward_error(forward_func, batch_x, dataset, check_level):
             suggestions.append(f"You might need to set `{_miss_in_dataset}` as input. ")
         if _miss_out_dataset:
             _tmp = f"You need to provide `{_miss_out_dataset}` in DataSet and set it as input. "
-            if not dataset.collator.is_empty():
+            if not dataset.collater.is_empty():
                 _tmp += f'Or you need to add `{_miss_out_dataset}` in the output of your collate_fn. '
             suggestions.append(_tmp)
 

fastNLP/embeddings/gpt2_embedding.py

@@ -47,7 +47,7 @@ class GPT2Embedding(ContextualEmbedding):
         >>> # torch.Size([1, 5, 3096])
     """
 
-    def __init__(self, vocab: Vocabulary, model_dir_or_name: str = 'en-small', layers: str = '-1',
+    def __init__(self, vocab: Vocabulary, model_dir_or_name: str = 'en', layers: str = '-1',
                  pool_method: str = 'first', dropout=0, requires_grad: bool = True,
                  auto_truncate: bool = False, language_model: bool = False, **kwargs):
         """
@@ -152,7 +152,7 @@ class GPT2WordPieceEncoder(nn.Module):
 
     """
 
-    def __init__(self, model_dir_or_name: str = 'en-small', layers: str = '-1',
+    def __init__(self, model_dir_or_name: str = 'en', layers: str = '-1',
                  word_dropout=0, dropout=0, requires_grad: bool = True, language_model:bool=False):
         """
 

fastNLP/embeddings/roberta_embedding.py

@@ -47,7 +47,7 @@ class RobertaEmbedding(ContextualEmbedding):
         >>> # torch.Size([1, 5, 2304])
     """
 
-    def __init__(self, vocab: Vocabulary, model_dir_or_name: str = 'en-base-uncased', layers: str = '-1',
+    def __init__(self, vocab: Vocabulary, model_dir_or_name: str = 'en', layers: str = '-1',
                  pool_method: str = 'first', word_dropout=0, dropout=0, include_cls_sep: bool = False,
                  pooled_cls=True, requires_grad: bool = True, auto_truncate: bool = False, **kwargs):
         r"""
@@ -373,13 +373,13 @@ class RobertaWordPieceEncoder(nn.Module):
     r"""
     读取bert模型，读取之后调用index_dataset方法在dataset中生成word_pieces这一列。
 
-    BertWordPieceEncoder可以支持自动下载权重，当前支持的模型:
+    RobertaWordPieceEncoder可以支持自动下载权重，当前支持的模型:
         en: roberta-base
         en-large: roberta-large
 
     """
 
-    def __init__(self, model_dir_or_name: str = 'en-base-uncased', layers: str = '-1', pooled_cls: bool = False,
+    def __init__(self, model_dir_or_name: str = 'en', layers: str = '-1', pooled_cls: bool = False,
                  word_dropout=0, dropout=0, requires_grad: bool = True):
         r"""
 

test/core/test_trainer.py

@@ -1,9 +1,11 @@
 import time
 import unittest
+import os
 
 import numpy as np
 import torch.nn.functional as F
 from torch import nn
+import torch
 
 from fastNLP import DataSet
 from fastNLP import Instance
@@ -228,6 +230,33 @@ class TrainerTestGround(unittest.TestCase):
             trainer = Trainer(train_data=dataset, model=model, loss=CrossEntropyLoss(), print_every=2, dev_data=dataset,
                               metrics=AccuracyMetric(), use_tqdm=False)
 
+    @unittest.skipIf('TRAVIS' in os.environ, "Need to be tested in hosts with more than 1 gpus")
+    def test_trainer_data_parallel(self):
+        if torch.cuda.device_count()>1:
+            from fastNLP import AccuracyMetric
+            dataset = prepare_fake_dataset2('x1', 'x2')
+            dataset.set_input('x1', 'x2', 'y', flag=True)
+
+            class Model(nn.Module):
+                def __init__(self):
+                    super().__init__()
+                    self.fc = nn.Linear(5, 4)
+
+                def forward(self, x1, x2, y=None):
+                    x1 = self.fc(x1)
+                    x2 = self.fc(x2)
+                    x = x1 + x2
+                    if self.training:
+                        loss = F.cross_entropy(x, y)
+                        return {'loss': loss}
+                    else:
+                        return {'pred':x, 'target':y}
+
+            model = Model()
+            trainer = Trainer(train_data=dataset, model=model, print_every=2, use_tqdm=False,
+                              dev_data=dataset, metrics=AccuracyMetric(), device=[0, 1])
+            trainer.train(load_best_model=False)
+
     def test_udf_dataiter(self):
         import random
         import torch