docstr: :param x: activations or tokens
  :param hidden_state: hidden state from a model invocation earlier in the sequence
  :param act_dtype: the dtype of the activations (either fp16 or fp32)
  :return: A dictionary with either "acts" or "logits"
docstr_tok: [":","param","x",":","activations","or","tokens",":","param","hidden_state",":","hidden","state","from","a","model","invocation","earlier","in","the","sequence",":","param","act_dtype",":","the","dtype","of","the","activations","(","either","fp16","or","fp32",")",":","return",":","A","dictionary","with","either","``","acts","''","or","``","logits","''"]
code: def forward(
      self,
      x,
      hidden_state: Optional[PastKVHiddenState] = None,
      act_dtype=torch.float16,
  ) -> Dict[str, torch.Tensor]:
      """
      :param x: activations or tokens
      :param hidden_state: hidden state from a model invocation earlier in the sequence
      :param act_dtype: the dtype of the activations (either fp16 or fp32)
      :return: A dictionary with either "acts" or "logits"
      """
  
      if hidden_state is None:
          hidden_ctx_len = 0
          resblock_hidden_states = None
      else:
          hidden_ctx_len = hidden_state.hidden_ctx_len
          resblock_hidden_states = hidden_state.resblock_hidden_states
  
      assert x.numel() > 0, "Need at least one sequence element"
  
      if self.include_input_embeddings:
          assert x.dim() >= 1, f"need atleast n_ctx dims: {x.shape}"
          if self.flatten_multi_index_batch_dims:
              # batch so we have two dimensions
              n_ctx = x.shape[-1]
              x = x.reshape(-1, n_ctx)
          else:
              assert (
                  x.dim() == 2
              ), f"expecting (batch, n_ctx): {x.shape}"
  
          x = self.embed_tokens(
              x, ctx_len=hidden_ctx_len, act_dtype=act_dtype
          )
  
      assert (
          x.dim() == 3
      ), f"expect activations of size [n_batch, n_ctx, n_dim]: {x.shape}"
  
      # Transformer
      x, output_hidden_states = self.torso(
          x, hidden_states=resblock_hidden_states
      )
      assert x.dtype == act_dtype, x.dtype
  
      # we expect the output_hidden_state to be a list of length self.depth
      # with dtype act_dtype
      assert isinstance(
          output_hidden_states, list
      ), f"Expected list output for hidden state, got {type(output_hidden_states)}"
      assert (
          len(output_hidden_states) == self.depth
      ), "expected hidden state for each layer"
      assert all(
          state.dtype == act_dtype
          for state in output_hidden_states
          if state is not None
      ), f"got wrong dtype for hidden_state: {output_hidden_states[0].dtype}"
  
      if self.include_final_layer_norm:
          x = self.ln_f(x)
  
          # Make sure the output is the same at each shard, since the layer norm params can drift
          if self.mp_comm is not None:
              x = self.mp_comm.broadcast(
                  x, src=self.mp_comm.ranks[0], name="final"
              )
  
      outputs = dict(
          acts=x,
          hidden_state=PastKVHiddenState(
              hidden_ctx_len=x.size(1),
              resblock_hidden_states=output_hidden_states,
          ),
      )
      if self.include_output_unembeddings:
          if self.mp_comm is not None:
              x = torch.split(
                  x, x.size(-1) // self.mp_comm.size, dim=(-1)
              )[self.mp_comm.my_index]
  
          logits = F.linear(
              x.type(act_dtype),
              self.unembedding_weights.type(act_dtype),
          )
  
          # Mask out padding logits
          logits[:, :, self.n_vocab :] = -torch.finfo(logits.dtype).max
  
          if self.mp_comm is not None:
              logits = self.mp_comm.all_reduce(logits, "logits")
  
          outputs["logits"] = logits
  
      return outputs
  
imports: import logging
  from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from typing import List, Optional, Dict, TypeVar
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import LayerNorm
  from summarize_from_feedback.model_layout import ModelLayout
  from summarize_from_feedback.models.attention import Attention
  from summarize_from_feedback.models.ops import ACT_FNS, Conv1D
  from summarize_from_feedback.utils import exact_div
  from summarize_from_feedback.utils.dist_utils import create_model_parallel_comm, Comm
label_desc: Use image node_id 2 for calling the Transformer obj's underlying member method code with example usage: obj.forward(x, hidden_state, act_dtype) and returns: outputs