[RAG] Add missing doc and attention_mask to rag (#7382)

* add docs

* add missing docs and attention_mask in fine-tune

examples/rag/finetune.py

@@ -265,6 +265,7 @@ class GenerativeQAModule(BaseTransformer):
         start_time = time.time()
         generated_ids = self.model.generate(
             batch["input_ids"],
+            attention_mask=batch["attention_mask"],
             do_deduplication=False,  # rag specific parameter
             use_cache=True,
             min_length=1,

src/transformers/modeling_rag.py

@@ -831,6 +831,14 @@ class RagSequenceForGeneration(RagPreTrainedModel):
             input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                 The sequence used as a prompt for the generation. If :obj:`input_ids` is not passed, then
                 :obj:`context_input_ids` has to be provided.
+            attention_mask (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+                Mask to avoid performing attention on padding token indices.
+                Mask values selected in ``[0, 1]``:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **maked**.
+
+                `What are attention masks? <../glossary.html#attention-mask>`__
             context_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size * config.n_docs, config.max_combined_length)`, `optional`, returned when `output_retrieved=True`):
                 Input IDs post-processed from the retrieved documents and the question encoder input_ids by the
                 retriever.
@@ -1207,6 +1215,14 @@ class RagTokenForGeneration(RagPreTrainedModel):
             input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                 The sequence used as a prompt for the generation. If :obj:`input_ids` is not passed, then
                 :obj:`context_input_ids` has to be provided.
+            attention_mask (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+                Mask to avoid performing attention on padding token indices.
+                Mask values selected in ``[0, 1]``:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **maked**.
+
+                `What are attention masks? <../glossary.html#attention-mask>`__
             context_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size * config.n_docs, config.max_combined_length)`, `optional`, returned when `output_retrieved=True`):
                 Input IDs post-processed from the retrieved documents and the question encoder :obj:`input_ids` by the
                 retriever.