docstring + check

transformers/modeling_gpt2.py

@@ -313,6 +313,9 @@ GPT2_INPUTS_DOCSTRING = r"""    Inputs:
             Mask to nullify selected heads of the self-attention modules.
             Mask values selected in ``[0, 1]``:
             ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
+        **inputs_embeds**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
+            Optionally, instead of passing ``input_ids`` you can choose to directly pass an embedded representation.
+            This is useful if you want to input a probability distribution of tokens rather than actual tokens.
 """
 
 @add_start_docstrings("The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.",
@@ -371,7 +374,9 @@ class GPT2Model(GPT2PreTrainedModel):
             self.h[layer].attn.prune_heads(heads)
 
     def forward(self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None):
-        if input_ids is not None:
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
             input_shape = input_ids.size()
             input_ids = input_ids.view(-1, input_shape[-1])
         elif inputs_embeds is not None:

transformers/modeling_tf_utils.py

@@ -35,7 +35,7 @@ class TFPreTrainedModel(tf.keras.Model):
     r""" Base class for all TF models.
 
         :class:`~transformers.TFPreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models
-        as well as a few methods commons to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
+        as well as a few methods common to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
 
         Class attributes (overridden by derived classes):
             - ``config_class``: a class derived from :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.

transformers/modeling_utils.py

@@ -53,7 +53,7 @@ class PreTrainedModel(nn.Module):
     r""" Base class for all models.
 
         :class:`~transformers.PreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models
-        as well as a few methods commons to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
+        as well as a few methods common to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
 
         Class attributes (overridden by derived classes):
             - ``config_class``: a class derived from :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.