Add support to provide initial tokens to decoder of encoder-decoder type models (#7577)

* Add support to provide initial tokens for decoding

* Add docstring

* improve code quality

* code reformat

* code reformat

* minor change

* remove appending decoder start token
Co-authored-by: Ayush Jain <a.jain@sprinklr.com>

src/transformers/generation_utils.py

@@ -111,6 +111,7 @@ class GenerationMixin:
     def generate(
         self,
         input_ids: Optional[torch.LongTensor] = None,
+        decoder_input_ids: Optional[torch.LongTensor] = None,
         max_length: Optional[int] = None,
         min_length: Optional[int] = None,
         do_sample: Optional[bool] = None,
@@ -151,6 +152,9 @@ class GenerationMixin:
             input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                 The sequence used as a prompt for the generation. If :obj:`None` the method initializes
                 it as an empty :obj:`torch.LongTensor` of shape :obj:`(1,)`.
+            decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+                initial input_ids for the decoder of encoder-decoder type models. If :obj:`None` then only
+                decoder_start_token_id is passed as the first token to the decoder.
             max_length (:obj:`int`, `optional`, defaults to 20):
                 The maximum length of the sequence to be generated.
             min_length (:obj:`int`, `optional`, defaults to 10):
@@ -417,14 +421,19 @@ class GenerationMixin:
             )  # shape: (batch_size * num_return_sequences * num_beams, cur_len)
 
         if self.config.is_encoder_decoder:
-            # create empty decoder input_ids
-            input_ids = torch.full(
-                (effective_batch_size * num_beams, 1),
-                decoder_start_token_id,
-                dtype=torch.long,
-                device=next(self.parameters()).device,
-            )
-            cur_len = 1
+            device = next(self.parameters()).device
+            if decoder_input_ids is not None:
+                # give initial decoder input ids
+                input_ids = decoder_input_ids.repeat(effective_batch_size * num_beams, 1).to(device)
+            else:
+                # create empty decoder input_ids
+                input_ids = torch.full(
+                    (effective_batch_size * num_beams, 1),
+                    decoder_start_token_id,
+                    dtype=torch.long,
+                    device=device,
+                )
+            cur_len = input_ids.shape[-1]
 
             assert (
                 batch_size == encoder_outputs.last_hidden_state.shape[0]