Fix decode_input_ids to bare T5Model and improve doc (#18791)

* use tokenizer to output tensor

* add preprocessing for decoder_input_ids for bare T5Model

* add preprocessing to tf and flax

* linting

* linting

* Update src/transformers/models/t5/modeling_flax_t5.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/transformers/models/t5/modeling_tf_t5.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/transformers/models/t5/modeling_t5.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

docs/source/en/model_doc/t5.mdx

@@ -187,12 +187,15 @@ ignored. The code example below illustrates all of this.
 
 >>> # encode the targets
 >>> target_encoding = tokenizer(
-...     [output_sequence_1, output_sequence_2], padding="longest", max_length=max_target_length, truncation=True
+...     [output_sequence_1, output_sequence_2],
+...     padding="longest",
+...     max_length=max_target_length,
+...     truncation=True,
+...     return_tensors="pt",
 ... )
 >>> labels = target_encoding.input_ids
 
 >>> # replace padding token id's of the labels by -100 so it's ignored by the loss
->>> labels = torch.tensor(labels)
 >>> labels[labels == tokenizer.pad_token_id] = -100
 
 >>> # forward pass

src/transformers/models/t5/modeling_flax_t5.py

@@ -1388,6 +1388,10 @@ FLAX_T5_MODEL_DOCSTRING = """
     ... ).input_ids
     >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="np").input_ids
 
+    >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for T5Model.
+    >>> # This is not needed for torch's T5ForConditionalGeneration as it does this internally using labels arg.
+    >>> decoder_input_ids = model._shift_right(decoder_input_ids)
+
     >>> # forward pass
     >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
     >>> last_hidden_states = outputs.last_hidden_state

src/transformers/models/t5/modeling_t5.py

@@ -1383,6 +1383,10 @@ class T5Model(T5PreTrainedModel):
         ... ).input_ids  # Batch size 1
         >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
 
+        >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for T5Model.
+        >>> # This is not needed for torch's T5ForConditionalGeneration as it does this internally using labels arg.
+        >>> decoder_input_ids = model._shift_right(decoder_input_ids)
+
         >>> # forward pass
         >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
         >>> last_hidden_states = outputs.last_hidden_state

src/transformers/models/t5/modeling_tf_t5.py

@@ -1180,6 +1180,10 @@ class TFT5Model(TFT5PreTrainedModel):
         ... ).input_ids  # Batch size 1
         >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="tf").input_ids  # Batch size 1
 
+        >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for T5Model.
+        >>> # This is not needed for torch's T5ForConditionalGeneration as it does this internally using labels arg.
+        >>> decoder_input_ids = model._shift_right(decoder_input_ids)
+
         >>> # forward pass
         >>> outputs = model(input_ids, decoder_input_ids=decoder_input_ids)
         >>> last_hidden_states = outputs.last_hidden_state