code: def generate_token(
      self, batch: FlashCausalLMBatch
  ) -> Tuple[
      List[Generation], Optional[FlashCausalLMBatch], Tuple[int, int]
  ]:
      start = time.time_ns()
      prefill = batch.cu_seqlen_prefill is not None
      prefill_logprobs = batch.prefill_next_token_indices is not None
  
      if batch.needed_blocks_slots:
          # Allocate blocks to this batch
          (
              block_tables,
              block_tables_tensor,
              slots,
          ) = get_cache_manager().allocate(
              batch.needed_blocks_slots,
              batch.blocks,
              batch.max_blocks,
              batch.input_ids.device,
          )
          batch.needed_blocks_slots = None
          batch.block_tables = block_tables
          batch.block_tables_tensor = block_tables_tensor
          batch.slots = slots
  
      try:
          out = self.forward(batch)
      except Exception as e:
          del batch
          raise e
  
      if isinstance(out, tuple):
          out, speculative_logits = out
      else:
          speculative_logits = None
  
      if prefill:
          next_token_logits = (
              out[batch.prefill_next_token_indices]
              if prefill_logprobs
              else out
          )
          if speculative_logits is not None:
              speculative_logits = (
                  speculative_logits[batch.prefill_next_token_indices]
                  if prefill_logprobs
                  else speculative_logits
              )
      else:
          next_token_logits = out
  
      (
          next_input_ids,
          next_token_logprobs,
          logprobs,
          accepted_ids,
          speculative_ids,
      ) = batch.next_token_chooser(
          batch.all_input_ids_tensor[:, : batch.max_seqlen],
          next_token_logits,
          get_speculate(),
          batch.speculative_ids,
          speculative_logits,
      )
  
      batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
          batch.top_n_tokens, batch.top_n_tokens_tensor, logprobs
      )
  
      speculative_length = (
          0 if speculative_ids is None else speculative_ids.shape[1]
      )
      if prefill:
          if len(batch) > 1 and prefill_logprobs:
              # We create the prefill_tokens_indices tensor that will be used to gather prefill logprobs
              # When batch == 1, we will just use the batch.input_ids values directly
              prefill_tokens_indices = batch.input_ids.new_zeros(
                  len(out)
              )
  
          next_position_ids = batch.position_ids.new_empty(len(batch))
          batch.slot_indices = batch.slot_indices[
              batch.cu_seqlen_prefill[1:] - 1
          ]
          # We do not need cu_seqlen_prefill anymore
          batch.cu_seqlen_prefill = None
      else:
          prefill_logprobs = None
          next_position_ids = batch.position_ids
  
      # Cumulative length
      cumulative_length = 0
  
      # Results
      generations: List[Generation] = []
      stopped = True
  
      # Zipped iterator
      iterator = zip(
          batch.input_lengths, batch.all_input_ids, accepted_ids
      )
  
      # We do two for loops as the first one can run completely asynchronously from the GPU while for the second
      # one, we need to first do a GPU <-> CPU sync
      # It is faster if we delay this sync for the maximum amount of time
  
      # For each member of the batch
      index = 0
      for i, (input_length, all_input_ids, n_accepted_ids) in enumerate(
          iterator
      ):
          # Indexing metadata
          start_index = cumulative_length
          end_index = cumulative_length + input_length
  
          if prefill:
              # Indexing metadata
              out_start_index = batch.prefill_cu_outlens[i]
              out_end_index = batch.prefill_cu_outlens[i + 1]
              out_length = out_end_index - out_start_index
  
              # Initialize position_ids
              # In decode, we do not need this as we can just increment position ids
              next_position_ids[i] = batch.position_ids[end_index - 1]
  
              # Used to gather prefill logprobs
              # Copy batch.input_ids to prefill_token_indices
              if prefill_logprobs:
                  if len(batch) > 1:
                      prefill_tokens_indices[
                          out_start_index : out_end_index - 1
                      ] = batch.input_ids[
                          start_index + 1 : start_index + out_length
                      ]
                  else:
                      # Set prefill_tokens_indices to the correct slice
                      prefill_tokens_indices = batch.input_ids[
                          start_index + 1 : start_index + out_length
                      ]
  
          for j in range(n_accepted_ids):
              batch.all_input_ids_tensor[
                  i, input_length + j
              ] = next_input_ids[index]
              index += 1
  
          cumulative_length += input_length
  
      batch.input_ids = next_input_ids[accepted_ids.cumsum(dim=-1) - 1]
      batch.speculative_ids = speculative_ids
      batch.position_ids = next_position_ids + accepted_ids
      batch.input_lengths_tensor += accepted_ids
      batch.slot_indices += accepted_ids
  
      if prefill and prefill_logprobs:
          # Get prefill logprobs
          prefill_logprobs_tensor = torch.log_softmax(out, -1)
          prefill_logprobs = torch.gather(
              prefill_logprobs_tensor,
              1,
              prefill_tokens_indices.view(-1, 1),
          )
          # GPU <-> CPU sync
          prefill_logprobs = prefill_logprobs.view(-1).tolist()
  
      # GPU <-> CPU sync
      next_token_logprobs = next_token_logprobs.tolist()
      next_token_ids = next_input_ids.tolist()
      accepted_ids = accepted_ids.tolist()
      start_decode = time.time_ns()
  
      # Zipped iterator
      iterator = zip(
          batch.requests,
          batch.input_lengths,
          batch.prefix_offsets,
          batch.read_offsets,
          batch.stopping_criterias,
          batch.all_input_ids,
          batch.next_token_chooser.do_sample,
          batch.next_token_chooser.seeds,
          batch.top_n_tokens,
          accepted_ids,
          batch_top_token_ids,
          batch_top_token_logprobs,
      )
  
      # For each member of the batch
      index = 0
      for i, (
          request,
          input_length,
          prefix_offset,
          read_offset,
          stopping_criteria,
          all_input_ids,
          do_sample,
          seed,
          top_n_tokens,
          n_accepted_ids,
          top_token_ids,
          top_token_logprobs,
      ) in enumerate(iterator):
          # Append next token to all tokens
          next_token_texts = []
          left = 0
  
          current_stopped = False
          for j in range(index, index + n_accepted_ids):
              # Generated token
              next_token_id = next_token_ids[j]
              all_input_ids.append(next_token_id)
              (
                  next_token_text,
                  prefix_offset,
                  read_offset,
              ) = self.decode_token(
                  all_input_ids,
                  prefix_offset,
                  read_offset,
              )
              next_token_texts.append(next_token_text)
  
              stop, reason = stopping_criteria(
                  next_token_id,
                  next_token_text,
              )
  
              if stop:
                  left = index + n_accepted_ids - j - 1
                  current_stopped = True
                  break
              else:
                  current_stopped = False
          stopped = stopped and current_stopped
  
          _next_token_ids = next_token_ids[
              index : index + n_accepted_ids - left
          ]
          _next_token_logprobs = next_token_logprobs[
              index : index + n_accepted_ids - left
          ]
          index += n_accepted_ids
  
          # Shard generations
          # All generations will be appended in the rust sharded client
          if i % self.world_size == self.rank:
              if stop:
                  # Decode generated tokens
                  output_text, _, _ = self.decode_token(
                      all_input_ids,
                      prefix_offset=len(all_input_ids)
                      - stopping_criteria.current_tokens
                      - 1,
                      read_offset=len(all_input_ids)
                      - stopping_criteria.current_tokens,
                      skip_special_tokens=True,
                  )
                  generated_text = GeneratedText(
                      output_text,
                      stopping_criteria.current_tokens,
                      reason,
                      seed if do_sample else None,
                  )
              else:
                  generated_text = None
  
              # Prefill
              if prefill and request.prefill_logprobs:
                  out_start_index = batch.prefill_cu_outlens[i]
                  out_end_index = batch.prefill_cu_outlens[i + 1]
  
                  # Remove generated token to only have prefill and add nan for first prompt token
                  request_prefill_logprobs = [
                      float("nan")
                  ] + prefill_logprobs[
                      out_start_index : out_end_index - 1
                  ]
                  prefill_token_ids = all_input_ids[:-1]
                  prefill_texts = self.tokenizer.batch_decode(
                      prefill_token_ids,
                      clean_up_tokenization_spaces=False,
                      skip_special_tokens=False,
                  )
  
                  prefill_tokens = Tokens(
                      prefill_token_ids,
                      request_prefill_logprobs,
                      prefill_texts,
                      is_special=[],
                  )
              else:
                  prefill_tokens = None
  
              if top_n_tokens > 0:
                  toptoken_texts = self.tokenizer.batch_decode(
                      top_token_ids,
                      clean_up_tokenization_spaces=False,
                      skip_special_tokens=False,
                  )
                  special_toptokens = [
                      token_id in self.all_special_ids
                      for token_id in top_token_ids
                  ]
                  top_tokens = Tokens(
                      top_token_ids,
                      top_token_logprobs,
                      toptoken_texts,
                      special_toptokens,
                  )
              else:
                  top_tokens = None
  
              generation = Generation(
                  request.id,
                  prefill_tokens,
                  Tokens(
                      _next_token_ids,
                      _next_token_logprobs,
                      next_token_texts,
                      [
                          nid in self.all_special_ids
                          for nid in _next_token_ids
                      ],
                  ),
                  generated_text,
                  top_tokens,
              )
  
              generations.append(generation)
  
          # Update values
          batch.input_lengths[i] = input_length + n_accepted_ids
          if batch.input_lengths[i] > batch.max_seqlen:
              batch.max_seqlen = batch.input_lengths[i]
          batch.prefix_offsets[i] = prefix_offset
          batch.read_offsets[i] = read_offset
          batch.all_input_ids[i] = all_input_ids
  
      if stopped:
          del batch
          # No need to return a batch if we know that all requests stopped
          forward_ns = start_decode - start
          decode_ns = time.time_ns() - start_decode
          return generations, None, (forward_ns, decode_ns)
  
      batch.prefill_cu_outlens = None
      batch.prefill_head_indices = None
      batch.prefill_next_token_indices = None
  
      forward_ns = start_decode - start
      decode_ns = time.time_ns() - start_decode
      return generations, batch, (forward_ns, decode_ns)
  
imports: import math
  import time
  import itertools
  import torch
  import torch.distributed
  import numpy
  from dataclasses import dataclass
  from opentelemetry import trace
  from transformers import PreTrainedTokenizerBase
  from typing import Optional, Tuple, List, Type, Dict
  from text_generation_server.models import Model
  from text_generation_server.utils.tokens import batch_top_tokens
  from text_generation_server.utils.speculate import get_speculate
  from text_generation_server.models.types import Batch, Tokens, Generation, GeneratedText
  from text_generation_server.models.cache_manager import get_cache_manager, set_cache_manager, BLOCK_SIZE
  from text_generation_server.pb import generate_pb2
  from text_generation_server.utils import StoppingCriteria, HeterogeneousNextTokenChooser
  from text_generation_server.utils.dist import MEMORY_FRACTION
label_desc: Use image node_id 5 for calling the FlashCausalLM obj's underlying member method code with example usage: obj.generate_token(batch) and returns: generations,  batch,  forward_ns,  decode_ns, generations,  None,  forward_ns,  decode_ns