Deprecate old past arguments (#5671)

src/transformers/modeling_albert.py

@@ -690,7 +690,7 @@ class AlbertForPreTraining(AlbertPreTrainedModel):
         if "masked_lm_labels" in kwargs:
             warnings.warn(
                 "The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
-                DeprecationWarning,
+                FutureWarning,
             )
             labels = kwargs.pop("masked_lm_labels")
         assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."

src/transformers/modeling_bart.py

@@ -111,6 +111,15 @@ BART_INPUTS_DOCSTRING = r"""
             Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
             If you want to change padding behavior, you should read :func:`~transformers.modeling_bart._prepare_decoder_inputs` and modify.
             See diagram 1 in the paper for more info on the default strategy
+        decoder_past_key_value_states (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains pre-computed key and value hidden-states of the attention blocks.
+            Can be used to speed up decoding.
+            If ``decoder_past_key_value_states`` are used, the user can optionally input only the last
+            ``decoder_input_ids`` (those that don't have their past key value states given to this model) of shape
+            :obj:`(batch_size, 1)` instead of all ``decoder_input_ids`` of shape :obj:`(batch_size, sequence_length)`.
+        use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            If `use_cache` is True, ``decoder_past_key_values`` are returned and can be used to speed up decoding (see
+            ``decoder_past_key_values``).
         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`):
@@ -482,7 +491,7 @@ class BartDecoder(nn.Module):
         encoder_padding_mask,
         decoder_padding_mask,
         decoder_causal_mask,
-        decoder_cached_states=None,
+        decoder_past_key_values=None,
         use_cache=False,
         output_attentions=False,
         output_hidden_states=False,
@@ -499,7 +508,7 @@ class BartDecoder(nn.Module):
             encoder_hidden_states: output from the encoder, used for
                 encoder-side attention
             encoder_padding_mask: for ignoring pad tokens
-            decoder_cached_states (dict or None): dictionary used for storing state during generation
+            decoder_past_key_values (dict or None): dictionary used for storing state during generation
 
         Returns:
             BaseModelOutputWithPast or tuple:
@@ -508,6 +517,13 @@ class BartDecoder(nn.Module):
                 - hidden states
                 - attentions
         """
+        if "decoder_cached_states" in unused:
+            warnings.warn(
+                "The `decoder_cached_states` argument is deprecated and will be removed in a future version, use `decoder_past_key_values` instead.",
+                FutureWarning,
+            )
+            decoder_past_key_values = unused.pop("decoder_cached_states")
+
         # check attention mask and invert
         if encoder_padding_mask is not None:
             encoder_padding_mask = invert_mask(encoder_padding_mask)
@@ -541,7 +557,7 @@ class BartDecoder(nn.Module):
             if self.training and (dropout_probability < self.layerdrop):
                 continue
 
-            layer_state = decoder_cached_states[idx] if decoder_cached_states is not None else None
+            layer_state = decoder_past_key_values[idx] if decoder_past_key_values is not None else None
 
             x, layer_self_attn, layer_past = decoder_layer(
                 x,
@@ -854,11 +870,12 @@ class BartModel(PretrainedBartModel):
         decoder_input_ids=None,
         encoder_outputs: Optional[Tuple] = None,
         decoder_attention_mask=None,
-        decoder_cached_states=None,
+        decoder_past_key_values=None,
         use_cache=None,
         output_attentions=None,
         output_hidden_states=None,
         return_tuple=None,
+        **kwargs,
     ):
 
         if decoder_input_ids is None:
@@ -908,7 +925,7 @@ class BartModel(PretrainedBartModel):
             attention_mask,
             decoder_padding_mask,
             decoder_causal_mask=causal_mask,
-            decoder_cached_states=decoder_cached_states,
+            decoder_past_key_values=decoder_past_key_values,
             use_cache=use_cache,
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
@@ -977,7 +994,7 @@ class BartForConditionalGeneration(PretrainedBartModel):
         encoder_outputs=None,
         decoder_input_ids=None,
         decoder_attention_mask=None,
-        decoder_cached_states=None,
+        decoder_past_key_values=None,
         labels=None,
         use_cache=None,
         output_attentions=None,
@@ -1015,9 +1032,15 @@ class BartForConditionalGeneration(PretrainedBartModel):
         if "lm_labels" in unused:
             warnings.warn(
                 "The `lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
-                DeprecationWarning,
+                FutureWarning,
             )
             labels = unused.pop("lm_labels")
+        if "decoder_cached_states" in unused:
+            warnings.warn(
+                "The `decoder_cached_states` argument is deprecated and will be removed in a future version, use `decoder_past_key_values` instead.",
+                FutureWarning,
+            )
+            decoder_past_key_values = unused.pop("decoder_cached_states")
         return_tuple = return_tuple if return_tuple is not None else self.config.use_return_tuple
 
         if labels is not None:
@@ -1029,7 +1052,7 @@ class BartForConditionalGeneration(PretrainedBartModel):
             decoder_input_ids=decoder_input_ids,
             encoder_outputs=encoder_outputs,
             decoder_attention_mask=decoder_attention_mask,
-            decoder_cached_states=decoder_cached_states,
+            decoder_past_key_values=decoder_past_key_values,
             use_cache=use_cache,
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
@@ -1061,11 +1084,11 @@ class BartForConditionalGeneration(PretrainedBartModel):
     def prepare_inputs_for_generation(self, decoder_input_ids, past, attention_mask, use_cache, **kwargs):
         assert past is not None, "past has to be defined for encoder_outputs"
 
-        encoder_outputs, decoder_cached_states = past
+        encoder_outputs, decoder_past_key_values = past
         return {
             "input_ids": None,  # encoder_outputs is defined. input_ids not needed
             "encoder_outputs": encoder_outputs,
-            "decoder_cached_states": decoder_cached_states,
+            "decoder_past_key_values": decoder_past_key_values,
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
@@ -1092,9 +1115,9 @@ class BartForConditionalGeneration(PretrainedBartModel):
 
     @staticmethod
     def _reorder_cache(past, beam_idx):
-        ((enc_out, enc_mask), decoder_cached_states) = past
+        ((enc_out, enc_mask), decoder_past_key_values) = past
         reordered_past = []
-        for layer_past in decoder_cached_states:
+        for layer_past in decoder_past_key_values:
             # get the correct batch idx from decoder layer's batch dim for cross and self-attn
             layer_past_new = {
                 attn_key: _reorder_buffer(attn_cache, beam_idx) for attn_key, attn_cache in layer_past.items()

src/transformers/modeling_bert.py

@@ -879,7 +879,7 @@ class BertForPreTraining(BertPreTrainedModel):
         if "masked_lm_labels" in kwargs:
             warnings.warn(
                 "The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
-                DeprecationWarning,
+                FutureWarning,
             )
             labels = kwargs.pop("masked_lm_labels")
         assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
@@ -1076,7 +1076,7 @@ class BertForMaskedLM(BertPreTrainedModel):
         if "masked_lm_labels" in kwargs:
             warnings.warn(
                 "The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
-                DeprecationWarning,
+                FutureWarning,
             )
             labels = kwargs.pop("masked_lm_labels")
         assert "lm_labels" not in kwargs, "Use `BertWithLMHead` for autoregressive language modeling task."

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,

src/transformers/modeling_distilbert.py

@@ -531,7 +531,7 @@ class DistilBertForMaskedLM(DistilBertPreTrainedModel):
         if "masked_lm_labels" in kwargs:
             warnings.warn(
                 "The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
-                DeprecationWarning,
+                FutureWarning,
             )
             labels = kwargs.pop("masked_lm_labels")
         assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."

src/transformers/modeling_electra.py

@@ -622,7 +622,7 @@ class ElectraForMaskedLM(ElectraPreTrainedModel):
         if "masked_lm_labels" in kwargs:
             warnings.warn(
                 "The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
-                DeprecationWarning,
+                FutureWarning,
             )
             labels = kwargs.pop("masked_lm_labels")
         assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."

src/transformers/modeling_gpt2.py

@@ -347,10 +347,12 @@ GPT2_START_DOCSTRING = r"""
 GPT2_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.GPT2Tokenizer`.
             See :func:`transformers.PreTrainedTokenizer.encode` and
@@ -358,10 +360,10 @@ GPT2_INPUTS_DOCSTRING = r"""
 
             `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]``:
@@ -386,9 +388,9 @@ GPT2_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`):
@@ -437,7 +439,7 @@ class GPT2Model(GPT2PreTrainedModel):
     def forward(
         self,
         input_ids=None,
-        past=None,
+        past_key_values=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
@@ -447,7 +449,16 @@ class GPT2Model(GPT2PreTrainedModel):
         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
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@@ -472,11 +483,11 @@ class GPT2Model(GPT2PreTrainedModel):
         if position_ids is not None:
             position_ids = position_ids.view(-1, input_shape[-1])
 
-        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)
@@ -522,7 +533,7 @@ class GPT2Model(GPT2PreTrainedModel):
         presents = () if use_cache else None
         all_attentions = () if output_attentions else None
         all_hidden_states = () if output_hidden_states else None
-        for i, (block, layer_past) in enumerate(zip(self.h, past)):
+        for i, (block, 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),)
 
@@ -581,7 +592,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
         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(GPT2_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
@@ -593,7 +604,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
     def forward(
         self,
         input_ids=None,
-        past=None,
+        past_key_values=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
@@ -604,6 +615,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
         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`):
@@ -613,11 +625,18 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
             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,
@@ -680,7 +699,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
     def forward(
         self,
         input_ids=None,
-        past=None,
+        past_key_values=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
@@ -693,7 +712,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
         output_attentions=None,
         output_hidden_states=None,
         return_tuple=None,
-        **kwargs
+        **kwargs,
     ):
         r"""
         mc_token_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, num_choices)`, `optional`, default to index of the last token of the input)
@@ -741,15 +760,21 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
         if "lm_labels" in kwargs:
             warnings.warn(
                 "The `lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
-                DeprecationWarning,
+                FutureWarning,
             )
             labels = kwargs.pop("lm_labels")
+        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,

src/transformers/modeling_longformer.py

@@ -1094,7 +1094,7 @@ class LongformerForMaskedLM(LongformerPreTrainedModel):
         if "masked_lm_labels" in kwargs:
             warnings.warn(
                 "The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
-                DeprecationWarning,
+                FutureWarning,
             )
             labels = kwargs.pop("masked_lm_labels")
         assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."

src/transformers/modeling_openai.py

@@ -665,7 +665,7 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
         if "lm_labels" in kwargs:
             warnings.warn(
                 "The `lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
-                DeprecationWarning,
+                FutureWarning,
             )
             labels = kwargs.pop("lm_labels")
         assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."

src/transformers/modeling_roberta.py

@@ -223,7 +223,7 @@ class RobertaForMaskedLM(BertPreTrainedModel):
         if "masked_lm_labels" in kwargs:
             warnings.warn(
                 "The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
-                DeprecationWarning,
+                FutureWarning,
             )
             labels = kwargs.pop("masked_lm_labels")
         assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."

src/transformers/modeling_t5.py

@@ -836,27 +836,27 @@ T5_INPUTS_DOCSTRING = r"""
             Used in the cross-attention of the decoder.
         decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`, defaults to :obj:`None`):
             Provide for sequence to sequence training. T5 uses the pad_token_id as the starting token for decoder_input_ids generation.
-            If `decoder_past_key_value_states` is used, optionally only the last `decoder_input_ids` have to be input (see `decoder_past_key_value_states`).
+            If `decoder_past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `decoder_past_key_values`).
             To know more on how to prepare :obj:`decoder_input_ids` for pre-training take a look at
             `T5 Training <./t5.html#training>`__. If decoder_input_ids and decoder_inputs_embeds are both None,
             decoder_input_ids takes the value of input_ids.
         decoder_attention_mask (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`, defaults to :obj:`None`):
             Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
-        decoder_past_key_value_states (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+        decoder_past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
             Contains pre-computed key and value hidden-states of the attention blocks.
             Can be used to speed up decoding.
-            If `decoder_past_key_value_states` are used, the user can optionally input only the last `decoder_input_ids`
+            If `decoder_past_key_values` are used, the user can optionally input only the last `decoder_input_ids`
             (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
             instead of all `decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
         use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
-            If `use_cache` is True, `decoder_past_key_value_states` are returned and can be used to speed up decoding (see `decoder_past_key_value_states`).
+            If `use_cache` is True, `decoder_past_key_values` are returned and can be used to speed up decoding (see `decoder_past_key_values`).
         inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
             Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
             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.
         decoder_inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, target_sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
             Optionally, instead of passing :obj:`decoder_input_ids` you can choose to directly pass an embedded representation.
-            If `decoder_past_key_value_states` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `decoder_past_key_value_states`).
+            If `decoder_past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `decoder_past_key_values`).
             This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors
             than the model's internal embedding lookup matrix. If decoder_input_ids and decoder_inputs_embeds are both None,
             decoder_inputs_embeds takes the value of inputs_embeds.
@@ -923,7 +923,7 @@ class T5Model(T5PreTrainedModel):
         encoder_outputs=None,
         decoder_input_ids=None,
         decoder_attention_mask=None,
-        decoder_past_key_value_states=None,
+        decoder_past_key_values=None,
         use_cache=None,
         inputs_embeds=None,
         decoder_inputs_embeds=None,
@@ -931,6 +931,7 @@ class T5Model(T5PreTrainedModel):
         output_attentions=None,
         output_hidden_states=None,
         return_tuple=None,
+        **kwargs,
     ):
         r"""
     Returns:
@@ -947,6 +948,14 @@ class T5Model(T5PreTrainedModel):
 
         >>> last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
         """
+        if "decoder_past_key_value_states" in kwargs:
+            warnings.warn(
+                "The `decoder_past_key_value_states` argument is deprecated and will be removed in a future version, use `decoder_past_key_values` instead.",
+                FutureWarning,
+            )
+            decoder_past_key_values = kwargs.pop("decoder_past_key_value_states")
+        assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
+
         use_cache = use_cache if use_cache is not None else self.config.use_cache
         return_tuple = return_tuple if return_tuple is not None else self.config.use_return_tuple
 
@@ -978,7 +987,7 @@ class T5Model(T5PreTrainedModel):
 
         # If decoding with past key value states, only the last tokens
         # should be given as an input
-        if decoder_past_key_value_states is not None:
+        if decoder_past_key_values is not None:
             if decoder_input_ids is not None:
                 decoder_input_ids = decoder_input_ids[:, -1:]
             if decoder_inputs_embeds is not None:
@@ -989,7 +998,7 @@ class T5Model(T5PreTrainedModel):
             input_ids=decoder_input_ids,
             attention_mask=decoder_attention_mask,
             inputs_embeds=decoder_inputs_embeds,
-            past_key_value_states=decoder_past_key_value_states,
+            past_key_value_states=decoder_past_key_values,
             encoder_hidden_states=hidden_states,
             encoder_attention_mask=attention_mask,
             head_mask=head_mask,
@@ -1062,7 +1071,7 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
         encoder_outputs=None,
         decoder_input_ids=None,
         decoder_attention_mask=None,
-        decoder_past_key_value_states=None,
+        decoder_past_key_values=None,
         use_cache=None,
         labels=None,
         inputs_embeds=None,
@@ -1071,7 +1080,7 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
         output_attentions=None,
         output_hidden_states=None,
         return_tuple=None,
-        **kwargs
+        **kwargs,
     ):
         r"""
         labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
@@ -1103,9 +1112,15 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
         if "lm_labels" in kwargs:
             warnings.warn(
                 "The `lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
-                DeprecationWarning,
+                FutureWarning,
             )
             labels = kwargs.pop("lm_labels")
+        if "decoder_past_key_value_states" in kwargs:
+            warnings.warn(
+                "The `decoder_past_key_value_states` argument is deprecated and will be removed in a future version, use `decoder_past_key_values` instead.",
+                FutureWarning,
+            )
+            decoder_past_key_values = kwargs.pop("decoder_past_key_value_states")
         assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
 
         use_cache = use_cache if use_cache is not None else self.config.use_cache
@@ -1138,7 +1153,7 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
 
         # If decoding with past key value states, only the last tokens
         # should be given as an input
-        if decoder_past_key_value_states is not None:
+        if decoder_past_key_values is not None:
             assert labels is None, "Decoder should not use cached key value states when training."
             if decoder_input_ids is not None:
                 decoder_input_ids = decoder_input_ids[:, -1:]
@@ -1150,7 +1165,7 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
             input_ids=decoder_input_ids,
             attention_mask=decoder_attention_mask,
             inputs_embeds=decoder_inputs_embeds,
-            past_key_value_states=decoder_past_key_value_states,
+            past_key_value_states=decoder_past_key_values,
             encoder_hidden_states=hidden_states,
             encoder_attention_mask=attention_mask,
             head_mask=head_mask,
@@ -1193,11 +1208,11 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
     def prepare_inputs_for_generation(self, input_ids, past, attention_mask, use_cache, **kwargs):
         assert past is not None, "past has to be defined for encoder_outputs"
 
-        encoder_outputs, decoder_past_key_value_states = past
+        encoder_outputs, decoder_past_key_values = past
 
         return {
             "decoder_input_ids": input_ids,
-            "decoder_past_key_value_states": decoder_past_key_value_states,
+            "decoder_past_key_values": decoder_past_key_values,
             "encoder_outputs": encoder_outputs,
             "attention_mask": attention_mask,
             "use_cache": use_cache,