Method: Transformers::Mpnet::MPNetModel#forward

Defined in:

lib/transformers/models/mpnet/modeling_mpnet.rb

#forward(input_ids: nil, attention_mask: nil, position_ids: nil, head_mask: nil, inputs_embeds: nil, output_attentions: nil, output_hidden_states: nil, return_dict: nil, **kwargs) ⇒ Object

# File 'lib/transformers/models/mpnet/modeling_mpnet.rb', line 410

def forward(
  input_ids: nil,
  attention_mask: nil,
  position_ids: nil,
  head_mask: nil,
  inputs_embeds: nil,
  output_attentions: nil,
  output_hidden_states: nil,
  return_dict: nil,
  **kwargs
)
  output_attentions = !output_attentions.nil? ? output_attentions : @config.output_attentions
  output_hidden_states = !output_hidden_states.nil? ? output_hidden_states : @config.output_hidden_states
  return_dict = !return_dict.nil? ? return_dict : @config.use_return_dict

  if !input_ids.nil? && !inputs_embeds.nil?
    raise ArgumentError, "You cannot specify both input_ids and inputs_embeds at the same time"
  elsif !input_ids.nil?
    warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
    input_shape = input_ids.size
  elsif !inputs_embeds.nil?
    input_shape = inputs_embeds.size[...-1]
  else
    raise ArgumentError, "You have to specify either input_ids or inputs_embeds"
  end

  device = !input_ids.nil? ? input_ids.device : inputs_embeds.device

  if attention_mask.nil?
    attention_mask = Torch.ones(input_shape, device: device)
  end
  extended_attention_mask = get_extended_attention_mask(attention_mask, input_shape)

  head_mask = get_head_mask(head_mask, @config.num_hidden_layers)
  embedding_output = @embeddings.(input_ids: input_ids, position_ids: position_ids, inputs_embeds: inputs_embeds)
  encoder_outputs = @encoder.(embedding_output, attention_mask: extended_attention_mask, head_mask: head_mask, output_attentions: output_attentions, output_hidden_states: output_hidden_states, return_dict: return_dict)
  sequence_output = encoder_outputs[0]
  pooled_output = !@pooler.nil? ? @pooler.(sequence_output) : nil

  if !return_dict
    return [sequence_output, pooled_output] + encoder_outputs[1..]
  end

  BaseModelOutputWithPooling.new(last_hidden_state: sequence_output, pooler_output: pooled_output, hidden_states: encoder_outputs.hidden_states, attentions: encoder_outputs.attentions)
end