Extend config with task specific configs. (#3433)

* add new default configs

* change prefix default to None

src/transformers/configuration_utils.py

@@ -78,9 +78,6 @@ class PretrainedConfig(object):
         self.top_k = kwargs.pop("top_k", 50)
         self.top_p = kwargs.pop("top_p", 1.0)
         self.repetition_penalty = kwargs.pop("repetition_penalty", 1.0)
-        self.bos_token_id = kwargs.pop("bos_token_id", None)
-        self.pad_token_id = kwargs.pop("pad_token_id", None)
-        self.eos_token_id = kwargs.pop("eos_token_id", None)
         self.length_penalty = kwargs.pop("length_penalty", 1.0)
         self.no_repeat_ngram_size = kwargs.pop("no_repeat_ngram_size", 0)
         self.num_return_sequences = kwargs.pop("num_return_sequences", 1)
@@ -94,6 +91,16 @@ class PretrainedConfig(object):
         self.label2id = kwargs.pop("label2id", dict(zip(self.id2label.values(), self.id2label.keys())))
         self.label2id = dict((key, int(value)) for key, value in self.label2id.items())
 
+        # Tokenizer arguments TODO: eventually tokenizer and models should share the same config
+        self.prefix = kwargs.pop("prefix", None)
+        self.bos_token_id = kwargs.pop("bos_token_id", None)
+        self.pad_token_id = kwargs.pop("pad_token_id", None)
+        self.eos_token_id = kwargs.pop("eos_token_id", None)
+        self.decoder_start_token_id = kwargs.pop("decoder_start_token_id", None)
+
+        # task specific arguments
+        self.task_specific_params = kwargs.pop("task_specific_params", None)
+
         # Additional attributes without default values
         for key, value in kwargs.items():
             try:

src/transformers/modeling_tf_utils.py

@@ -610,7 +610,9 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
         num_return_sequences = (
             num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
         )
-        decoder_start_token_id = decoder_start_token_id if decoder_start_token_id is not None else bos_token_id
+        decoder_start_token_id = (
+            decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id
+        )
 
         if input_ids is not None:
             batch_size = shape_list(input_ids)[0]  # overriden by the input batch_size
@@ -635,9 +637,6 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
         assert (eos_token_id is None) or (
             isinstance(eos_token_id, int) and (eos_token_id >= 0)
         ), "`eos_token_id` should be a positive integer."
-        assert (
-            decoder_start_token_id is not None or self.config.is_encoder_decoder is False
-        ), "`decoder_start_token_id` has to be defined if model is encoder-decoder model"
         assert length_penalty > 0, "`length_penalty` should be strictely positive."
         assert (
             isinstance(num_return_sequences, int) and num_return_sequences > 0
@@ -708,8 +707,12 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
             )  # shape: (batch_size * num_return_sequences * num_beams, cur_len)
 
         if self.config.is_encoder_decoder:
+            if decoder_start_token_id is None:
+                decoder_start_token_id = bos_token_id
 
-            assert bos_token_id is not None, "Encoder Decoder Models need to have a bos_token_id"
+            assert (
+                decoder_start_token_id is not None
+            ), "decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation"
             assert hasattr(self, "get_encoder"), "{} should have a 'get_encoder' function defined".format(self)
             assert callable(self.get_encoder), "{} should be a method".format(self.get_encoder)
 

src/transformers/modeling_utils.py

@@ -809,7 +809,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
         num_return_sequences = (
             num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
         )
-        decoder_start_token_id = decoder_start_token_id if decoder_start_token_id is not None else bos_token_id
+        decoder_start_token_id = (
+            decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id
+        )
 
         if input_ids is not None:
             batch_size = input_ids.shape[0]  # overriden by the input batch_size
@@ -831,9 +833,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
         assert pad_token_id is None or (
             isinstance(pad_token_id, int) and (pad_token_id >= 0)
         ), "`pad_token_id` should be a positive integer."
-        assert (
-            decoder_start_token_id is not None or self.config.is_encoder_decoder is False
-        ), "`decoder_start_token_id` has to be defined if model is encoder-decoder model"
         assert (eos_token_id is None) or (
             isinstance(eos_token_id, int) and (eos_token_id >= 0)
         ), "`eos_token_id` should be a positive integer."
@@ -912,7 +911,12 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             )  # shape: (batch_size * num_return_sequences * num_beams, cur_len)
 
         if self.config.is_encoder_decoder:
-            assert bos_token_id is not None, "Encoder Decoder Models need to have a bos_token_id"
+            if decoder_start_token_id is None:
+                decoder_start_token_id = bos_token_id
+
+            assert (
+                decoder_start_token_id is not None
+            ), "decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation"
             assert hasattr(self, "get_encoder"), "{} should have a 'get_encoder' function defined".format(self)
             assert callable(self.get_encoder), "{} should be a method".format(self.get_encoder)