better error messages

examples/run_generation.py

@@ -107,7 +107,8 @@ def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')
     return logits
 
 
-def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=0, top_p=0.0, repetition_penalty=1.0, is_xlnet=False, xlm_lang=None, device='cpu'):
+def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=0, top_p=0.0, repetition_penalty=1.0,
+                    is_xlnet=False, is_xlm_mlm=False, xlm_mask_token=None, xlm_lang=None, device='cpu'):
     context = torch.tensor(context, dtype=torch.long, device=device)
     context = context.unsqueeze(0).repeat(num_samples, 1)
     generated = context
@@ -125,10 +126,16 @@ def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=
                 target_mapping[0, 0, -1] = 1.0  # predict last token
                 inputs = {'input_ids': input_ids, 'perm_mask': perm_mask, 'target_mapping': target_mapping}
 
+            if is_xlm_mlm and xlm_mask_token:
+                # XLM MLM models are direct models (predict same token, not next token)
+                # => need one additional dummy token in the input (will be masked and guessed)
+                input_ids = torch.cat((generated, torch.full((1, 1), xlm_mask_token, dtype=torch.long, device=device)), dim=1)
+                inputs = {'input_ids': input_ids}
+
             if xlm_lang is not None:
                 inputs["langs"] = torch.tensor([xlm_lang] * inputs["input_ids"].shape[1], device=device).view(1, -1)
 
-            outputs = model(**inputs)  # Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet (cached hidden-states)
+            outputs = model(**inputs)  # Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet/CTRL (cached hidden-states)
             next_token_logits = outputs[0][0, -1, :] / (temperature if temperature > 0 else 1.)
 
             # reptition penalty from CTRL (https://arxiv.org/abs/1909.05858)
@@ -167,10 +174,7 @@ def main():
     parser.add_argument('--stop_token', type=str, default=None,
                         help="Token at which text generation is stopped")
     args = parser.parse_args()
-    if args.model_type in ["ctrl"]:
-        if args.temperature > 0.7 : 
-            print('CTRL typically works better with lower temperatures (and lower top_k).')
-        
+
     args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
     args.n_gpu = torch.cuda.device_count()
 
@@ -191,6 +195,10 @@ def main():
         args.length = MAX_LENGTH  # avoid infinite loop
 
     print(args)
+    if args.model_type in ["ctrl"]:
+        if args.temperature > 0.7 : 
+            logger.info('CTRL typically works better with lower temperatures (and lower top_k).')
+
     while True:
         xlm_lang = None
         # XLM Language usage detailed in the issues #1414
@@ -204,6 +212,13 @@ def main():
                     language = input("Using XLM. Select language in " + str(list(tokenizer.lang2id.keys())) + " >>> ")
             xlm_lang = tokenizer.lang2id[language]
 
+        # XLM masked-language modeling (MLM) models need masked token (see details in sample_sequence)
+        is_xlm_mlm = args.model_type in ["xlm"] and 'mlm' in args.model_name_or_path
+        if is_xlm_mlm:
+            xlm_mask_token = tokenizer.mask_token_id
+        else:
+            xlm_mask_token = None
+
         raw_text = args.prompt if args.prompt else input("Model prompt >>> ")
         if args.model_type in ["transfo-xl", "xlnet"]:
             # Models with memory likes to have a long prompt for short inputs.
@@ -218,6 +233,8 @@ def main():
             top_p=args.top_p,
             repetition_penalty=args.repetition_penalty,
             is_xlnet=bool(args.model_type == "xlnet"),
+            is_xlm_mlm=is_xlm_mlm,
+            xlm_mask_token=xlm_mask_token,
             xlm_lang=xlm_lang,
             device=args.device,
         )

transformers/configuration_utils.py

@@ -130,20 +130,19 @@ class PretrainedConfig(object):
         # redirect to the cache, if necessary
         try:
             resolved_config_file = cached_path(config_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
-        except EnvironmentError as e:
+        except EnvironmentError:
             if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
-                logger.error(
-                    "Couldn't reach server at '{}' to download pretrained model configuration file.".format(
-                        config_file))
+                msg = "Couldn't reach server at '{}' to download pretrained model configuration file.".format(
+                        config_file)
             else:
-                logger.error(
-                    "Model name '{}' was not found in model name list ({}). "
-                    "We assumed '{}' was a path or url but couldn't find any file "
-                    "associated to this path or url.".format(
+                msg = "Model name '{}' was not found in model name list ({}). " \
+                      "We assumed '{}' was a path or url to a configuration file named {} or " \
+                      "a directory containing such a file but couldn't find any such file at this path or url.".format(
                         pretrained_model_name_or_path,
                         ', '.join(cls.pretrained_config_archive_map.keys()),
-                        config_file))
-            raise e
+                        config_file, CONFIG_NAME)
+            raise EnvironmentError(msg)
+
         if resolved_config_file == config_file:
             logger.info("loading configuration file {}".format(config_file))
         else:

transformers/modeling_utils.py

@@ -316,20 +316,20 @@ class PreTrainedModel(nn.Module):
             # redirect to the cache, if necessary
             try:
                 resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
-            except EnvironmentError as e:
+            except EnvironmentError:
                 if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
-                    logger.error(
-                        "Couldn't reach server at '{}' to download pretrained weights.".format(
-                            archive_file))
+                    msg = "Couldn't reach server at '{}' to download pretrained weights.".format(
+                            archive_file)
                 else:
-                    logger.error(
-                        "Model name '{}' was not found in model name list ({}). "
-                        "We assumed '{}' was a path or url but couldn't find any file "
-                        "associated to this path or url.".format(
+                    msg = "Model name '{}' was not found in model name list ({}). " \
+                        "We assumed '{}' was a path or url to model weight files named one of {} but " \
+                        "couldn't find any such file at this path or url.".format(
                             pretrained_model_name_or_path,
                             ', '.join(cls.pretrained_model_archive_map.keys()),
-                            archive_file))
-                raise e
+                            archive_file,
+                            [WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME])
+                raise EnvironmentError(msg)
+
             if resolved_archive_file == archive_file:
                 logger.info("loading weights file {}".format(archive_file))
             else:

transformers/tokenization_utils.py

@@ -337,13 +337,13 @@ class PreTrainedTokenizer(object):
                 vocab_files[file_id] = full_file_name
 
             if all(full_file_name is None for full_file_name in vocab_files.values()):
-                logger.error(
-                    "Model name '{}' was not found in model name list ({}). "
-                    "We assumed '{}' was a path or url but couldn't find tokenizer files"
-                    "at this path or url.".format(
+                raise EnvironmentError(
+                    "Model name '{}' was not found in tokenizers model name list ({}). "
+                    "We assumed '{}' was a path or url to a directory containing vocabulary files "
+                    "named {} but couldn't find such vocabulary files at this path or url.".format(
                         pretrained_model_name_or_path, ', '.join(s3_models),
-                        pretrained_model_name_or_path, ))
-                return None
+                        pretrained_model_name_or_path, 
+                        list(cls.vocab_files_names.values())))
 
         # Get files from url, cache, or disk depending on the case
         try:
@@ -353,17 +353,18 @@ class PreTrainedTokenizer(object):
                     resolved_vocab_files[file_id] = None
                 else:
                     resolved_vocab_files[file_id] = cached_path(file_path, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
-        except EnvironmentError as e:
+        except EnvironmentError:
             if pretrained_model_name_or_path in s3_models:
-                logger.error("Couldn't reach server to download vocabulary.")
+                msg = "Couldn't reach server at '{}' to download vocabulary files."
             else:
-                logger.error(
-                    "Model name '{}' was not found in model name list ({}). "
-                    "We assumed '{}' was a path or url but couldn't find files {} "
-                    "at this path or url.".format(
+                msg = "Model name '{}' was not found in tokenizers model name list ({}). " \
+                    "We assumed '{}' was a path or url to a directory containing vocabulary files " \
+                    "named {}, but couldn't find such vocabulary files at this path or url.".format(
                         pretrained_model_name_or_path, ', '.join(s3_models),
-                        pretrained_model_name_or_path, str(vocab_files.keys())))
-            raise e
+                        pretrained_model_name_or_path,
+                        list(cls.vocab_files_names.values()))
+
+            raise EnvironmentError(msg)
 
         for file_id, file_path in vocab_files.items():
             if file_path == resolved_vocab_files[file_id]: