Deprecate old past arguments (#5671)

src/transformers/modeling_ctrl.py

@@ -17,6 +17,7 @@
 
 
 import logging
+import warnings
 
 import numpy as np
 import torch
@@ -246,20 +247,22 @@ CTRL_START_DOCSTRING = r"""
 CTRL_INPUTS_DOCSTRING = r"""
     Args:
         input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, input_ids_length)`):
-            :obj:`input_ids_length` = ``sequence_length`` if ``past`` is ``None`` else ``past[0].shape[-2]`` (``sequence_length`` of input past key value states).
+            :obj:`input_ids_length` = ``sequence_length`` if ``past_key_values`` is ``None`` else
+            ``past_key_values[0].shape[-2]`` (``sequence_length`` of input past key value states).
             Indices of input sequence tokens in the vocabulary.
 
-            If `past` is used, only input_ids that do not have their past calculated should be passed as input_ids.
+            If ``past_key_values`` is used, only input_ids that do not have their past calculated should be passed as
+            ``input_ids``.
 
             Indices can be obtained using :class:`transformers.CTRLTokenizer`.
             See :func:`transformers.PreTrainedTokenizer.encode` and
             :func:`transformers.PreTrainedTokenizer.__call__` for details.
 
             `What are input IDs? <../glossary.html#input-ids>`__
-        past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`):
+        past_key_values (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`):
             Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
-            (see `past` output below). Can be used to speed up sequential decoding.
-            The input_ids which have their past given to this model should not be passed as input ids as they have already been computed.
+            (see ``past_key_values`` output below). Can be used to speed up sequential decoding.
+            The ``input_ids`` which have their past given to this model should not be passed as input ids as they have already been computed.
         attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
             Mask to avoid performing attention on padding token indices.
             Mask values selected in ``[0, 1]``:
@@ -284,10 +287,10 @@ CTRL_INPUTS_DOCSTRING = r"""
         inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
             This is useful if you want more control over how to convert `input_ids` indices into associated vectors
             than the model's internal embedding lookup matrix.
-            If `past` is used, optionally only the last `inputs_embeds` have to be input (see `past`).
+            If ``past_key_values`` is used, optionally only the last `inputs_embeds` have to be input (see ``past_key_values``).
         use_cache (:obj:`bool`):
-            If `use_cache` is True, `past` key value states are returned and
-            can be used to speed up decoding (see `past`). Defaults to `True`.
+            If `use_cache` is True, ``past_key_values`` key value states are returned and
+            can be used to speed up decoding (see ``past_key_values``). Defaults to `True`.
         output_attentions (:obj:`bool`, `optional`, defaults to :obj:`None`):
             If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail.
         output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`None`):
@@ -343,7 +346,7 @@ class CTRLModel(CTRLPreTrainedModel):
     def forward(
         self,
         input_ids=None,
-        past=None,
+        past_key_values=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
@@ -353,7 +356,16 @@ class CTRLModel(CTRLPreTrainedModel):
         output_attentions=None,
         output_hidden_states=None,
         return_tuple=None,
+        **kwargs,
     ):
+        if "past" in kwargs:
+            warnings.warn(
+                "The `past` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
+                FutureWarning,
+            )
+            past_key_values = kwargs.pop("past")
+        assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
+
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         use_cache = use_cache if use_cache is not None else self.config.use_cache
         output_hidden_states = (
@@ -373,11 +385,11 @@ class CTRLModel(CTRLPreTrainedModel):
         else:
             raise ValueError("You have to specify either input_ids or inputs_embeds")
 
-        if past is None:
+        if past_key_values is None:
             past_length = 0
-            past = [None] * len(self.h)
+            past_key_values = [None] * len(self.h)
         else:
-            past_length = past[0][0].size(-2)
+            past_length = past_key_values[0][0].size(-2)
         if position_ids is None:
             device = input_ids.device if input_ids is not None else inputs_embeds.device
             position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
@@ -431,7 +443,7 @@ class CTRLModel(CTRLPreTrainedModel):
         presents = () if use_cache else None
         all_hidden_states = () if output_hidden_states else None
         all_attentions = [] if output_attentions else None
-        for i, (h, layer_past) in enumerate(zip(self.h, past)):
+        for i, (h, layer_past) in enumerate(zip(self.h, past_key_values)):
             if output_hidden_states:
                 all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
             outputs = h(
@@ -492,7 +504,7 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
         if past:
             input_ids = input_ids[:, -1].unsqueeze(-1)
 
-        return {"input_ids": input_ids, "past": past, "use_cache": kwargs["use_cache"]}
+        return {"input_ids": input_ids, "past_key_values": past, "use_cache": kwargs["use_cache"]}
 
     @add_start_docstrings_to_callable(CTRL_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
@@ -504,7 +516,7 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
     def forward(
         self,
         input_ids=None,
-        past=None,
+        past_key_values=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
@@ -515,6 +527,7 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
         output_attentions=None,
         output_hidden_states=None,
         return_tuple=None,
+        **kwargs,
     ):
         r"""
         labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
@@ -524,11 +537,18 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
             All labels set to ``-100`` are ignored (masked), the loss is only
             computed for labels in ``[0, ..., config.vocab_size]``
         """
+        if "past" in kwargs:
+            warnings.warn(
+                "The `past` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
+                FutureWarning,
+            )
+            past_key_values = kwargs.pop("past")
+        assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
         return_tuple = return_tuple if return_tuple is not None else self.config.use_return_tuple
 
         transformer_outputs = self.transformer(
             input_ids,
-            past=past,
+            past_key_values=past_key_values,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,