class_docstr: Fixed (2 lines):
      reshape(batch_size, tgt_len, hidden_size) -> reshape(batch_size, tgt_len, self.hidden_size)
      Add attention_mask = attention_mask.chunk(dist.get_world_size(self.mp_group), dim=0)[0]
code: def forward(
      self,
      hidden_states,
      key_value_states: Optional[Tensor] = None,
      attention_mask: Optional[Tensor] = None,
      layer_head_mask: Optional[Tensor] = None,
      past_key_value: Optional[Tuple[Tensor]] = None,
      output_attentions: bool = False,
  ) -> Tuple[Tensor, Optional[Tensor]]:
      batch_size, tgt_len, hidden_size = hidden_states.size()
  
      if attention_mask is not None:
          attention_mask = attention_mask.chunk(
              dist.get_world_size(self.mp_group), dim=0
          )[0]
  
      # if key_value_states are provided this layer is used as a cross-attention layer
      # for the decoder
      is_cross_attention = key_value_states is not None
      assert list(hidden_states.size()) == [
          batch_size,
          tgt_len,
          hidden_size,
      ], f"Size of hidden states should be {batch_size, tgt_len, hidden_size}, but is {hidden_states.size()}"
  
      # previous time steps are cached - no need to recompute key and value if they are static
      query_states = self.query_proj(hidden_states) / (
          self.head_dim**0.5
      )
  
      if is_cross_attention and past_key_value is not None:
          # reuse k,v, cross_attentions
          key_states = past_key_value[0]
          value_states = past_key_value[1]
      elif is_cross_attention:
          # cross_attentions
          key_states = self._shape(
              self.key_proj(key_value_states), -1, batch_size
          )
          value_states = self._shape(
              self.value_proj(key_value_states), -1, batch_size
          )
      else:
          # self_attention
          key_states = self._shape(
              self.key_proj(hidden_states), -1, batch_size
          )
          value_states = self._shape(
              self.value_proj(hidden_states), -1, batch_size
          )
  
      if is_cross_attention:
          # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
          # Further calls to cross_attention layer can then reuse all cross-attention
          # key/value_states (first "if" case)
          # if encoder bi-directional self-attention `past_key_value` is always `None`
          past_key_value = (key_states, value_states)
  
      # project states into the correct shape
      proj_shape = (batch_size * self.num_attn_heads, -1, self.head_dim)
      query_states = self._shape(
          query_states, tgt_len, batch_size
      ).view(*proj_shape)
      key_states = key_states.view(*proj_shape)
      value_states = value_states.view(*proj_shape)
  
      src_len = key_states.size(1)
      attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
      assert attn_weights.size() == (
          batch_size * self.num_attn_heads,
          tgt_len,
          src_len,
      ), f"`attn_weights` should be of size {batch_size * self.num_attn_heads, tgt_len, src_len}, but is of size {attn_weights.shape}"
  
      # This is part of a workaround to get around fork/join parallelism not supporting Optional types.
      if attention_mask is not None and attention_mask.dim() == 0:
          attention_mask = None
      assert attention_mask is None or attention_mask.size() == (
          self.num_attn_heads * batch_size,
          1,
          src_len,
      ), f"`attention_mask` should be `None` or of shape attention_mask.size() == {batch_size * self.num_attn_heads, 1, src_len}, but is {attention_mask.shape}"
  
      if attention_mask is not None:  # don't attend to padding symbols
          attn_weights = attn_weights + attention_mask
  
      if output_attentions:
          # this operation is a bit awkward, but it's required to
          # make sure that attn_weights keeps its gradient.
          # In order to do so, attn_weights have to be reshaped
          # twice and have to be reused in the following
          attn_weights_reshaped = attn_weights.view(
              batch_size, self.num_attn_heads, tgt_len, src_len
          )
          attn_weights = attn_weights_reshaped.view(
              batch_size * self.num_attn_heads, tgt_len, src_len
          )
      else:
          attn_weights_reshaped = None
  
      attn_weights = nn.functional.softmax(attn_weights, dim=-1)
  
      if layer_head_mask is not None:
          assert layer_head_mask.size() == (
              self.num_attn_heads,
          ), f"Head mask for a single layer should be of size {(self.num_attn_heads,)}, but is {layer_head_mask.size()}"
          attn_weights = layer_head_mask.view(
              1, -1, 1, 1
          ) * attn_weights.view(
              batch_size, self.num_attn_heads, tgt_len, src_len
          )
          attn_weights = attn_weights.view(
              batch_size * self.num_attn_heads, tgt_len, src_len
          )
  
          # apply head_mask also on attn_weights_reshaped which is used for n-gram attention inside the model
          attn_weights_reshaped = (
              layer_head_mask.view(1, -1, 1, 1) * attn_weights_reshaped
          )
  
      attn_probs = nn.functional.dropout(
          attn_weights,
          p=self.attention_dropout,
          training=self.training,
      )
  
      attn_output = torch.bmm(attn_probs, value_states)
      assert attn_output.size() == (
          batch_size * self.num_attn_heads,
          tgt_len,
          self.head_dim,
      ), "`attn_output` should be of shape {batch_size * self.num_attn_heads, tgt_len, self.head_dim}, but is of shape {attn_output.size()}"
  
      attn_output = (
          attn_output.view(
              batch_size, self.num_attn_heads, tgt_len, self.head_dim
          )
          .transpose(1, 2)
          .reshape(batch_size, tgt_len, self.hidden_size)
      )
  
      attn_output = self.out_proj(attn_output)
  
      attn_output = nn.functional.dropout(
          attn_output, p=self.dropout, training=self.training
      )
      return attn_output, attn_weights_reshaped, past_key_value
  
file_path: parallelformers/parallelformers/transformers/modeling_prophetnet.py
imports: from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from torch import distributed
  from torch import nn
  from transformers.models.prophetnet.modeling_prophetnet import ProphetNetAttention
label_desc: Use image node_id 1 for calling the ProphetNetAttention_ obj's underlying member method code with example usage: obj.forward(hidden_states, key_value_states, attention_mask, layer_head_mask, past_key_value, output_attentions) and returns: attn_output,  attn_weights_reshaped,  past_key_value