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[GPT-J] add deprecation warning (#21869)
* add deprecation warning * remove pos ids from args docstirng * fix failing test
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Arthur
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@ -391,11 +391,6 @@ GPTJ_INPUTS_DOCSTRING = r"""
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- 1 corresponds to a *sentence B* token.
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[What are token type IDs?](../glossary#token-type-ids)
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position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
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Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
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config.n_positions - 1]`.
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[What are position IDs?](../glossary#position-ids)
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head_mask (`torch.FloatTensor` of shape `(num_attention_heads,)` or `(n_layer, num_attention_heads)`, *optional*):
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Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
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@ -549,14 +544,24 @@ class GPTJModel(GPTJPreTrainedModel):
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past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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**deprecated_arguments,
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) -> Union[Tuple, BaseModelOutputWithPast]:
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if deprecated_arguments.pop("position_ids", False) is not False:
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# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
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warnings.warn(
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"`position_ids` have no functionality in GPT-J and will be removed in v5.0.0. You can safely ignore"
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" passing `position_ids`.",
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FutureWarning,
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)
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if len(deprecated_arguments) > 0:
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raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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@ -576,23 +581,11 @@ class GPTJModel(GPTJPreTrainedModel):
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else:
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raise ValueError("You have to specify either input_ids or inputs_embeds")
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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if token_type_ids is not None:
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token_type_ids = token_type_ids.view(-1, input_shape[-1])
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if position_ids is not None:
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position_ids = position_ids.view(-1, input_shape[-1])
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if past_key_values is None:
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past_length = 0
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past_key_values = tuple([None] * len(self.h))
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else:
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past_length = past_key_values[0][0].size(-2)
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if position_ids is None:
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position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
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position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
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# Attention mask.
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if attention_mask is not None:
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@ -773,7 +766,9 @@ class GPTJForCausalLM(GPTJPreTrainedModel):
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def set_output_embeddings(self, new_embeddings):
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self.lm_head = new_embeddings
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def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
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def prepare_inputs_for_generation(
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self, input_ids, past_key_values=None, inputs_embeds=None, use_cache=None, **kwargs
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):
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token_type_ids = kwargs.get("token_type_ids", None)
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# only last token for inputs_ids if past is defined in kwargs
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if past_key_values:
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@ -782,16 +777,6 @@ class GPTJForCausalLM(GPTJPreTrainedModel):
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token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
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attention_mask = kwargs.get("attention_mask", None)
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position_ids = kwargs.get("position_ids", None)
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if attention_mask is not None and position_ids is None:
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# create position_ids on the fly for batch generation
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position_ids = attention_mask.long().cumsum(-1) - 1
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position_ids.masked_fill_(attention_mask == 0, 1)
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if past_key_values:
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position_ids = position_ids[:, -1].unsqueeze(-1)
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else:
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position_ids = None
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# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
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if inputs_embeds is not None and past_key_values is None:
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@ -802,8 +787,7 @@ class GPTJForCausalLM(GPTJPreTrainedModel):
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model_inputs.update(
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{
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"past_key_values": past_key_values,
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"use_cache": kwargs.get("use_cache"),
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"position_ids": position_ids,
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"use_cache": use_cache,
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"attention_mask": attention_mask,
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"token_type_ids": token_type_ids,
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}
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@ -824,7 +808,6 @@ class GPTJForCausalLM(GPTJPreTrainedModel):
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past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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@ -832,6 +815,7 @@ class GPTJForCausalLM(GPTJPreTrainedModel):
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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**deprecated_arguments,
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) -> Union[Tuple, CausalLMOutputWithPast]:
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r"""
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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@ -839,6 +823,16 @@ class GPTJForCausalLM(GPTJPreTrainedModel):
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`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
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are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
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"""
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if deprecated_arguments.pop("position_ids", False) is not False:
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# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
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warnings.warn(
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"`position_ids` have no functionality in GPT-J and will be removed in v5.0.0. You can safely ignore"
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" passing `position_ids`.",
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FutureWarning,
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)
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if len(deprecated_arguments) > 0:
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raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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transformer_outputs = self.transformer(
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@ -846,7 +840,6 @@ class GPTJForCausalLM(GPTJPreTrainedModel):
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past_key_values=past_key_values,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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@ -948,7 +941,6 @@ class GPTJForSequenceClassification(GPTJPreTrainedModel):
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past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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@ -956,6 +948,7 @@ class GPTJForSequenceClassification(GPTJPreTrainedModel):
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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**deprecated_arguments,
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) -> Union[Tuple, SequenceClassifierOutputWithPast]:
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r"""
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labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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@ -963,6 +956,16 @@ class GPTJForSequenceClassification(GPTJPreTrainedModel):
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config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
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`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
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"""
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if deprecated_arguments.pop("position_ids", False) is not False:
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# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
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warnings.warn(
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"`position_ids` have no functionality in GPT-J and will be removed in v5.0.0. You can safely ignore"
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" passing `position_ids`.",
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FutureWarning,
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)
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if len(deprecated_arguments) > 0:
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raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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transformer_outputs = self.transformer(
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@ -970,7 +973,6 @@ class GPTJForSequenceClassification(GPTJPreTrainedModel):
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past_key_values=past_key_values,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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@ -1072,7 +1074,6 @@ class GPTJForQuestionAnswering(GPTJPreTrainedModel):
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input_ids: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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start_positions: Optional[torch.LongTensor] = None,
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@ -1080,6 +1081,7 @@ class GPTJForQuestionAnswering(GPTJPreTrainedModel):
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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**deprecated_arguments,
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) -> Union[Tuple, QuestionAnsweringModelOutput]:
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r"""
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start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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@ -1091,13 +1093,22 @@ class GPTJForQuestionAnswering(GPTJPreTrainedModel):
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Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
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are not taken into account for computing the loss.
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"""
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if deprecated_arguments.pop("position_ids", False) is not False:
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# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
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warnings.warn(
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"`position_ids` have no functionality in GPT-J and will be removed in v5.0.0. You can safely ignore"
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" passing `position_ids`.",
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FutureWarning,
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)
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if len(deprecated_arguments) > 0:
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raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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outputs = self.transformer(
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input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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output_attentions=output_attentions,
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