text: # coding=utf-8
  # Copyright (c) 2021  Josh Levy-Kramer <josh@levykramer.co.uk>. All rights reserved.
  # This file is based on code by the authors denoted below and has been modified from its original version.
  # Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """Utilities for generating text."""
  
  import copy
  import json
  import os
  import time
  from typing import List, Union
  
  import torch
  import torch.nn.functional as F
  
  from megatron import print_rank_0
  from megatron import mpu
  from megatron.utils import get_ltor_masks_and_position_ids, is_mp_rank_0
  
  
  def get_batch(neox_args, context_tokens: torch.Tensor):
      """
      Generate batch from context tokens. Attention mask and position ids are created. Returned tensors will be on CUDA.
  
      neox_args: NeoXArgs.
      context_tokens: torch tensor with dimensions [batch, context_size]
  
      returns: tuple of torch tensors (tokens, attention_mask, position_ids) on CUDA
      """
  
      # Move to GPU.
      tokens = context_tokens.contiguous().cuda()
      # Get the attention mask and postition ids.
      attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
          data=tokens,
          eod_token=neox_args.tokenizer.eod,
          eod_mask_loss=neox_args.eod_mask_loss,
      )
      return tokens, attention_mask, position_ids
  
  
  def pad_batch(context_tokens: List[List[int]], pad_id: int, pad_len: int):
      """
      pads context lengths in context_tokens with pad_id to equal neox_args.seq_length,
      and returns the padded batch and the new lengths.
  
      context_tokens: list of lists of tokens
      pad_id: int, integer to use as padding token
      pad_len: int, context length to be padded; all batch items will be padded to the same length
  
      returns: tuple of padded context tokens and a list of unpadded token count
      """
  
      context_lengths = []
      for tokens in context_tokens:
          context_length = len(tokens)
          if context_length < pad_len:
              tokens.extend([pad_id] * (pad_len - context_length))
          elif context_length > pad_len:
              raise ValueError("context_length is bigger than to be padded length")
          context_lengths.append(context_length)
      return context_tokens, context_lengths
  
  
  def filter_logits(logits, top_k=0, top_p=0.0, filter_value=-float("Inf")):
      """
      Filters the logits using top_k / top_p, filling any filtered vocab items with filter_value (defaults to -inf).
  
      This function has been mostly taken from huggingface conversational ai code at
      https://medium.com/huggingface/how-to-build-a-state-of-the-art-conversational-ai-with-transfer-learning-2d818ac26313
  
      logits: torch.Tensor -> logits of megatron model.
      top_k: integer -> integer between 0 and the models vocab size. Filters out any logits with a probability less than that of the top_kth token.
      top_p: float -> Top-p (nucleus) sampling chooses from the smallest possible set of tokens whose cumulative probability exceeds the probability top_p.
  
      returns: (filtered) logits"""
  
      if top_k > 0:
          # Remove all tokens with a probability less than the
          # last token of the top-k
          indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
          logits[indices_to_remove] = filter_value
  
      if top_p > 0.0:
          # convert to 1D
          sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
          cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
  
          # Remove tokens with cumulative probability above the threshold
          sorted_indices_to_remove = cumulative_probs > top_p
          # Shift the indices to the right to keep also the first token
          # above the threshold
          sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
          sorted_indices_to_remove[..., 0] = 0
          for i in range(sorted_indices.size(0)):
              indices_to_remove = sorted_indices[i][sorted_indices_to_remove[i]]
              logits[i][indices_to_remove] = filter_value
  
      return logits
  
  
  def switch(val1, val2, boolean):
      """
      replaces items in val1 with items in val2 where boolean = True
      """
      boolean = boolean.type_as(val1)
      return (1 - boolean) * val1 + boolean * val2
  
  
  def forward_model(model, model_inputs, is_pipe_parallel=False) -> torch.Tensor:
      """
      Runs model.forward(model_inputs)
  
      We need to create a wrapper for this function because deepspeed pipe parallel modules operate differently to normal models.
  
      model: a Megatron model.
      model_inputs: tuple containing model args
  
      returns: torch.Tensor containing the logits of the model
      """
      # because someone at deepspeed decided pipeline modules couldn't use kwargs,
      # we need to forward a pipe model differently to a normal model
      if not is_pipe_parallel:
          return model.module(model_inputs)
      else:
          # we need to format inputs this way because:
          # a) deepspeed pipeline only accepts iterables
          # b) deepspeed pipeline *requires* that you pass in labels for the loss, it's not easy to get around this
          # so we wrap the inputs in an iterable, and pad them (because internally, we get labels as inputs[:, 1:] and inputs as inputs[:, :-1])
          model_inputs = iter([{"text": F.pad(model_inputs[0], pad=(0, 1))}])
  
          # set num microbatches to 1 at inference time
          micro_batches_before = model.micro_batches
          model.micro_batches = 1
  
          # deepspeed sends metadata across pipeline stages only once in the first step, then assumes it will stay
          # constant. In inference, the metadata of the tensors being sent across pipe stages may change, so we need to set
          # these two flags in order for deepspeed to send the metadata every step, otherwise torch.distributed hangs
          # silently. Fun stuff.
          model.first_output_send = True
          model.pipe_recv_buf = None
  
          loss, logits = model.eval_batch(model_inputs, return_logits=True)
          model.micro_batches = micro_batches_before
          return logits
  
  
  def broadcast_terminate_signal(terminate_runs: int):
      """Send signal to all workers to terminate if we've finished the process"""
      terminate_runs_tensor = torch.cuda.LongTensor([terminate_runs])
      torch.distributed.broadcast(
          terminate_runs_tensor,
          mpu.get_model_parallel_src_rank(),
          group=mpu.get_model_parallel_group(),
      )
      return terminate_runs_tensor[0].item()
  
  
  def stop_tokens_in_completion(stop_tokens, context_tokens, batch_index, current_index):
      if stop_tokens is None:
          return False
      res = []
      for token_group in stop_tokens:
          context = context_tokens[batch_index, : current_index + 1]
          context = context[-len(token_group) :]
          if context.shape[0] == token_group.shape[0]:
              res.append(all(token_group == context))
          else:
              res.append(False)
      return any(res)
  
  
  def stream_tokens(
      neox_args,
      model,
      context_tokens: List[List[int]],
      eos_token_id: int = None,
      maximum_tokens: int = None,
      recompute: bool = False,
      temperature: float = 0.0,
      top_k: int = 0,
      top_p: float = 0.0,
      stop_tokens=None,
  ):
      """
      iterator producing text completions
  
      neox_args: NeoXArgs.
      model: a Megatron model.
      context_tokens: the prompt to complete; unpadded list of lists of tokens ids
      context_lengths: lengths of context tokens of dimension [batch]; the context length records for each bach item how many non-padded tokens are provided
      eos_token_id: end of text token at which completion is terminated, even if max_tokes count has not been reached
      attention_mask: attention mask for megatron model.
      position_ids: position ids for positional encoding.
      maximum_tokens: maximum number of tokens to be generated; careful! if a batch input is provided maximum_tokens specifies the maximum number of forwards.
                      longer batch items get less generated tokens.
      recompute: flag indicating whether a cache is used for already forwarded tokens (true) or whether all tokens are recomputed at every iteration (false)
      temperature (default 0.0): exponential scaling output distribution ("higher == more risk")
      top_k (default 0): integer -> integer between 0 and the models vocab size. Filters out any logits with a probability less than that of the top_kth token.
      top_p (default 0.0): float -> Top-p (nucleus) sampling chooses from the smallest possible set of tokens whose cumulative probability exceeds the probability top_p.
      note: greedy decoding is used if temperature is 0.0, top_k is 0 and top_p is 0.0
      yields: (
                  tokens (completions from model),
                  token_generation_start_index (token index per batch item for the first generated token),
                  token_generation_end_index (token index per batch item for the last generated token),
                  logits (logits which are so far computed, zeros otherwise),
                  is_done (flag for each bach item indicating whether an eod token was generated)
              )
  
              * each iteration adds a generated token to the context_tokens
              * output contains both context_tokens from input and generated tokens
              * if batch items have different lengths, the iterator will start at the first completion and return the unchanged input context token otherwise
      """
  
      model.eval()
  
      # pad batch in order to allow conversion to tensor
      context_tokens, context_lengths = pad_batch(
          copy.deepcopy(context_tokens),
          pad_id=neox_args.tokenizer.eod,
          pad_len=neox_args.seq_length,
      )
  
      # convert to tensor and broadcast
      context_tokens = torch.cuda.LongTensor(context_tokens)
      if stop_tokens:
          stop_tokens = torch.cuda.LongTensor(stop_tokens)
          if stop_tokens.ndim == 1:
              stop_tokens = stop_tokens.unsqueeze(0)
  
      # Make sure context tokens + start tokens are the same across all ranks
      token_generation_start_index = torch.cuda.LongTensor(context_lengths)
      torch.distributed.broadcast(
          context_tokens,
          mpu.get_model_parallel_src_rank(),
          group=mpu.get_model_parallel_group(),
      )
      torch.distributed.broadcast(
          token_generation_start_index,
          mpu.get_model_parallel_src_rank(),
          group=mpu.get_model_parallel_group(),
      )
  
      # get attention mask / position ids
      context_tokens, attention_mask, position_ids = get_batch(neox_args, context_tokens)
  
      # set variables
      eos_token_id = eos_token_id or neox_args.tokenizer.eod
      maximum_tokens = maximum_tokens or (
          neox_args.seq_length - token_generation_start_index.max().item() - 1
      )
      batch_size = context_tokens.size(0)
  
      # get the context_index at which generation is to start
      # we start generation at the position where the smallest context ends
      token_index_to_generate = token_generation_start_index.min().item()
      first_token_index_to_generate = token_index_to_generate
      last_token_index_to_generate = min(
          neox_args.seq_length
          - 1,  # never generate more than the model's sequence length
          token_index_to_generate + maximum_tokens - 1,
      )
  
      with torch.no_grad():
          # initialize generation variables
          state_is_done = torch.zeros([batch_size]).byte().cuda()
          token_generation_end_index = torch.ones([batch_size]).long().cuda() * (-1)
  
          while token_index_to_generate <= last_token_index_to_generate:
              if recompute:  # recompute all tokens
                  model_inputs = (
                      context_tokens,
                      position_ids,
                      attention_mask,
                  )
                  logits = forward_model(model, model_inputs, neox_args.is_pipe_parallel)
                  if logits is not None:  # if pipe parallel, not all ranks return logits
                      generated_token_logits = logits[
                          :, token_index_to_generate - 1, :
                      ]  # [bs, seq, vocab_size] -> [bs, vocab_size]
              else:  # use kv cache
                  if token_index_to_generate == first_token_index_to_generate:
                      tokens_to_use = context_tokens[:, :token_index_to_generate]
                      positions_to_use = position_ids[:, :token_index_to_generate]
                  else:
                      tokens_to_use = context_tokens[:, token_index_to_generate - 1].view(
                          batch_size, -1
                      )
                      positions_to_use = position_ids[
                          :, token_index_to_generate - 1
                      ].view(batch_size, -1)
  
                  model_inputs = (
                      tokens_to_use,  # input_ids
                      positions_to_use,  # position_ids
                      attention_mask,  # attention_mask
                  )
  
                  logits = forward_model(model, model_inputs, neox_args.is_pipe_parallel)
                  if logits is not None:  # if pipe parallel, not all ranks return logits
                      generated_token_logits = (
                          logits[:, -1].view(batch_size, -1).contiguous()
                      )  # [bs, seq, vocab_size] -> [bs, vocab_size]
  
              if logits is not None:
                  # sample token id of the to be generated token
                  if temperature == 0.0 and top_k == 0 and top_p == 0.0:
                      generated_tokens = torch.argmax(
                          generated_token_logits, dim=-1
                      ).view(-1)
                  else:
                      generated_token_logits = generated_token_logits.float()
                      if temperature > 0.0:
                          generated_token_logits /= temperature
                      generated_token_logits = filter_logits(
                          generated_token_logits, top_k=top_k, top_p=top_p
                      )
                      next_token_log_probs = F.softmax(generated_token_logits, dim=-1)
                      generated_tokens = torch.multinomial(
                          next_token_log_probs, num_samples=1
                      ).view(-1)
  
              if neox_args.is_pipe_parallel:
                  # broadcast generated tokens to pipe parallel group
                  src_rank = model.grid.stage_to_global(model.num_stages - 1)
                  generated_tokens = (
                      generated_tokens
                      if logits is not None
                      else torch.zeros(batch_size, dtype=torch.long).cuda()
                  )
                  torch.distributed.broadcast(
                      tensor=generated_tokens,
                      src=src_rank,
                      group=mpu.get_pipe_parallel_group(),
                  )
  
              # determine if state has started for each batch item
              state_started = (
                  token_generation_start_index <= token_index_to_generate
              )  # check which batch items have been started
  
              # switch out padding tokens for generated tokens
              context_tokens[:, token_index_to_generate] = switch(
                  context_tokens[:, token_index_to_generate].view(-1),
                  generated_tokens,
                  state_started,
              )
  
              # determine if state has finished for each batch item
              state_done = (
                  generated_tokens == eos_token_id
              ).byte() & state_started.byte()  # check which batch items produce an eos_token in the current iteration
              state_just_finished = (state_done & ~state_is_done).bool()
              state_is_done = state_is_done | state_done
              stop_tokens_produced = torch.zeros_like(state_is_done)
              for batch_idx, ctx in enumerate(context_tokens):
                  stop_tokens_produced[batch_idx] = stop_tokens_in_completion(
                      stop_tokens, context_tokens, batch_idx, token_index_to_generate
                  )
              state_is_done = state_is_done | stop_tokens_produced
  
              token_generation_end_index[
                  (state_started.byte() & ~state_is_done).bool()
              ] = token_index_to_generate
  
              token_index_to_generate += 1
  
              yield context_tokens, token_generation_start_index, token_generation_end_index, state_is_done.bool()
              if torch.all(state_is_done):
                  break
  
  
  def generate_samples_from_prompt(
      neox_args,
      model,
      text: Union[List[str], str],
      eos_token_id: int = None,
      maximum_tokens: int = 64,
      recompute: bool = False,
      temperature: float = 0.0,
      top_k: int = 0,
      top_p: float = 0.0,
      stop_tokens=None,
  ):
      """
      Generates samples from raw text and returns them in a dictionary.
  
      neox_args: NeoXArgs.
      model: a Megatron model
      text: either a single prompt (str) or a list of prompts (List[str]).
  
      eos_token_id: end of text token at which completion is terminated, even if max_tokes count has not been reached
      maximum_tokens: maximum number of tokens to be generated
  
      recompute: flag indicating whether a cache is used for already forwarded tokens (true) or whether all tokens are recomputed at every iteration (false)
  
      temperature (default 0.0): exponential scaling output distribution ("higher == more risk")
      top_k (default 0): integer -> integer between 0 and the models vocab size. Filters out any logits with a probability less than that of the top_kth token.
      top_p (default 0.0): float -> Top-p (nucleus) sampling chooses from the smallest possible set of tokens whose cumulative probability exceeds the probability top_p.
      note: greedy decoding is used if temperature is 0.0, top_k is 0 and top_p is 0.0
  
      returns: List[dict] -> a list of dicts containing the following fields:
          - 'context' (the input)
          - 'text' (the completion)
          - 'length' (the length of the completion in number of tokens)
          - 'finished':
          - 'message': a messaged associated with the generation procedure, can be a warning or error
          - 'duration_seconds': duration of the generation in seconds
  
      """
      eos_token_id = eos_token_id or neox_args.tokenizer.eod
  
      # type check
      assert any(
          [isinstance(text, str), isinstance(text, list)]
      ), "Text should be in string or list form"
      if isinstance(text, str):
          text = [text]
  
      input_count = len(text)
      input_pos = 0
  
      # generate completions
      generated_texts = []
      while True:
          model.module.clear_cache() # clear kv cache between batches
  
          start_time = time.time()
          # Tokenize text, and check whether we should terminate process
          terminate_runs = 0
          if input_pos == input_count:
              terminate_runs = 1
          else:
              raw_text = text[input_pos]
              input_pos += 1
  
              if raw_text == "":
                  context_tokens = [eos_token_id]
              else:
                  context_tokens = neox_args.tokenizer.tokenize(raw_text)
              context_length = len(context_tokens)
  
              if context_length >= (neox_args.seq_length // 2):
                  print_rank_0(
                      "\nWarning! Context length",
                      context_length,
                      "\nPlease give smaller context (e.g. half of the "
                      "max sequence length)!",
                  )
          if not is_mp_rank_0():
              context_tokens = neox_args.tokenizer.tokenize("EMPTY TEXT")
              context_length = len(context_tokens)
              terminate_runs = 0
  
          terminate_runs = broadcast_terminate_signal(terminate_runs)
          if terminate_runs == 1:
              return generated_texts
  
          for (
              batch_context_tokens,
              batch_token_generation_start_index,
              batch_token_generation_end_index,
              is_done,
          ) in stream_tokens(
              neox_args=neox_args,
              model=model,
              context_tokens=[context_tokens],
              eos_token_id=eos_token_id,
              maximum_tokens=maximum_tokens,
              recompute=recompute,
              temperature=temperature,
              top_k=top_k,
              top_p=top_p,
              stop_tokens=stop_tokens,
          ):
              pass  # finish generation and use all results below
  
          batch_context_tokens = batch_context_tokens.cpu().numpy().tolist()
          batch_token_generation_start_index = (
              batch_token_generation_start_index.cpu().numpy().tolist()
          )
          batch_token_generation_end_index = (
              batch_token_generation_end_index.cpu().numpy().tolist()
          )
          batch_is_done = is_done.cpu().numpy().tolist()
  
          for tokens, start_index, end_index, is_done in zip(
              batch_context_tokens,
              batch_token_generation_start_index,
              batch_token_generation_end_index,
              batch_is_done,
          ):
  
              if end_index >= start_index:
                  generated_tokens = tokens[start_index : end_index + 1]
                  try:
                      generated_text = neox_args.tokenizer.detokenize(generated_tokens)
                      message = None
                  except KeyError:
                      generated_text = None
                      message = "WARNING: generated token which doesn't exist."
              else:
                  generated_text = None
                  generated_tokens = []
                  # this will happen if the first generated token is a stop token or eos token
                  message = "WARNING: text generation did not start; try different batching or adjust parameters"
              if is_mp_rank_0():
                  data = {
                      "context": raw_text,
                      "text": generated_text,
                      "length": len(generated_tokens),
                      "finished": is_done,
                      "message": message,
                      "duration_seconds": float(time.time() - start_time),
                  }
                  generated_texts.append(data)
  
      return generated_texts
  
  
  def generate_samples_input_from_file(
      neox_args,
      model,
      input_file,
      output_file=None,
      eos_token_id: int = None,
      maximum_tokens: int = 64,
      recompute: bool = False,
      temperature: float = 0.0,
      top_k: int = 0,
      top_p: float = 0.0,
  ):
      """
      Generates samples from an input file and writes them to an output file.
  
      Reads prompts from neox_args.sample_input_file and writes completions to neox_args.sample_output_file
  
      neox_args: NeoXArgs.
      model: a Megatron model
  
      input_file: path to input file. Each line in the input file will be treated as separate prompt. The line break at the end of the line is not included in the prompt.
      output_file: file where generation results are to be stored in jsonl format. defaults to input_file+'.output.jsonl' if not defined
  
      eos_token_id: end of text token at which completion is terminated, even if max_tokes count has not been reached
      maximum_tokens: maximum number of tokens to be generated
  
      recompute: flag indicating whether a cache is used for already forwarded tokens (true) or whether all tokens are recomputed at every iteration (false)
  
      temperature (default 0.0): exponential scaling output distribution ("higher == more risk")
      top_k (default 0): integer -> integer between 0 and the models vocab size. Filters out any logits with a probability less than that of the top_kth token.
      top_p (default 0.0): float -> Top-p (nucleus) sampling chooses from the smallest possible set of tokens whose cumulative probability exceeds the probability top_p.
  
      note: greedy decoding is used if temperature is 0.0, top_k is 0 and top_p is 0.0
  
  
      returns: List[dict] -> a list of dicts containing the following fields:
          - 'context' (the input)
          - 'text' (the completion)
          - 'length' (the length of the completion in number of tokens)
          - 'finished':
          - 'message': a messaged associated with the generation procedure, can be a warning or error
          - 'duration_seconds': duration of the generation in seconds
      """
      # Read the sample file
      print_rank_0(
          "generate_samples_input_from_file() loading input from {}".format(input_file)
      )
      with open(input_file, "r") as f:
          prompts = f.readlines()
      prompts = [p.strip() for p in prompts]
      prompts = [p for p in prompts if len(p) > 0]
      print_rank_0(
          "generate_samples_input_from_file() prompts loaded: {}".format(len(prompts))
      )
  
      if is_mp_rank_0():
          if output_file is None:
              output_file = str(input_file) + ".output.jsonl"
              print_rank_0(
                  "generate_samples_input_from_file() setting default output file to {}".format(
                      output_file
                  )
              )
  
      print_rank_0("generate_samples_input_from_file() generating...")
      generated_texts = generate_samples_from_prompt(
          neox_args=neox_args,
          model=model,
          text=prompts,
          eos_token_id=eos_token_id,
          maximum_tokens=maximum_tokens,
          recompute=recompute,
          temperature=temperature,
          top_k=top_k,
          top_p=top_p,
      )
  
      if is_mp_rank_0():
          with open(output_file, "w") as f_out:
              for item in generated_texts:
                  f_out.write(json.dumps(item) + "\n")
      print_rank_0("generate_samples_input_from_file() done")
      return generated_texts
  
  
  def generate_samples_unconditional(
      neox_args,
      model,
      number_of_samples: int = 10,
      output_file=None,
      eos_token_id: int = None,
      maximum_tokens: int = 64,
      recompute: bool = False,
      temperature: float = 0.0,
      top_k: int = 0,
      top_p: float = 0.0,
  ):
      """
      Generates samples unconditionially (no prompt) and yields them in a dictionary.
  
      neox_args: NeoXArgs.
      model: a Megatron model
  
      number_of_samples (default 10): number of unconditional samples to be generated
  
      output_file: file where generation results are to be stored in jsonl format. no file will be stored if ommitted
  
      eos_token_id: end of text token at which completion is terminated, even if max_tokes count has not been reached
      maximum_tokens: maximum number of tokens to be generated
  
      recompute: flag indicating whether a cache is used for already forwarded tokens (true) or whether all tokens are recomputed at every iteration (false)
  
      temperature (default 0.0): exponential scaling output distribution ("higher == more risk")
      top_k (default 0): integer -> integer between 0 and the models vocab size. Filters out any logits with a probability less than that of the top_kth token.
      top_p (default 0.0): float -> Top-p (nucleus) sampling chooses from the smallest possible set of tokens whose cumulative probability exceeds the probability top_p.
  
      note: greedy decoding is used if temperature is 0.0, top_k is 0 and top_p is 0.0
  
      yields: dict containing the following fields:
          - 'context' (the input)
          - 'text' (the completion)
          - 'length' (the length of the completion in number of tokens)
          - 'finished':
          - 'message': a messaged associated with the generation procedure, can be a warning or error
          - 'duration_seconds': duration of the generation in seconds
      """
  
      print_rank_0("generate_samples_unconditional() generating...")
      assert number_of_samples > 0, "number_of_samples must be > 0"
      generated_texts = generate_samples_from_prompt(
          neox_args=neox_args,
          model=model,
          text=["" for _ in range(number_of_samples)],
          eos_token_id=eos_token_id,
          maximum_tokens=maximum_tokens,
          recompute=recompute,
          temperature=temperature,
          top_k=top_k,
          top_p=top_p,
      )
  
      if is_mp_rank_0():
          if output_file is not None:
              with open(output_file, "w") as f_out:
                  for item in generated_texts:
                      f_out.write(json.dumps(item) + "\n")
      print_rank_0("generate_samples_unconditional() done")
      return generated_texts
  
  
  def generate_samples_interactive(
      neox_args,
      model,
      maximum_tokens: int = 64,
      eos_token_id: int = None,
      recompute: bool = False,
      temperature: float = 0.0,
      top_k: int = 0,
      top_p: float = 0.0,
  ):
      """
      Generates samples unconditionially (no prompt) and yields them in a dictionary.
  
      neox_args: NeoXArgs.
      model: a Megatron model
  
      maximum_tokens: maximum number of tokens to be generated
      eos_token_id: end of text token at which completion is terminated, even if max_tokes count has not been reached
  
      recompute: flag indicating whether a cache is used for already forwarded tokens (true) or whether all tokens are recomputed at every iteration (false)
  
      temperature (default 0.0): exponential scaling output distribution ("higher == more risk")
      top_k (default 0): integer -> integer between 0 and the models vocab size. Filters out any logits with a probability less than that of the top_kth token.
      top_p (default 0.0): float -> Top-p (nucleus) sampling chooses from the smallest possible set of tokens whose cumulative probability exceeds the probability top_p.
  
      note: greedy decoding is used if temperature is 0.0, top_k is 0 and top_p is 0.0
  
      yields: dict containing the following fields:
          - 'context' (the input)
          - 'text' (the completion)
          - 'length' (the length of the completion in number of tokens)
          - 'finished':
          - 'message': a messaged associated with the generation procedure, can be a warning or error
          - 'duration_seconds': duration of the generation in seconds
      """
  
      while True:
          model.module.clear_cache() # clear kv cache between batches
          torch.distributed.barrier(group=mpu.get_model_parallel_group())
          terminate_runs = 0
  
          if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0:
              os.system("clear")
              raw_text = input("Context prompt >>> ")
              context_tokens = neox_args.tokenizer.tokenize(raw_text)
              if len(context_tokens) == 0:
                  context_tokens = [neox_args.tokenizer.eod]
              context_length = len(context_tokens)
              if context_length >= (neox_args.seq_length - 1):
                  print_rank_0(
                      "\nContext length"
                      + str(context_length)
                      + "\nReached max sequence length!"
                  )
                  terminate_runs = 1
          else:
              context_tokens = neox_args.tokenizer.tokenize("EMPTY TEXT")
              context_length = len(context_tokens)
  
          terminate_runs = broadcast_terminate_signal(terminate_runs)
          if terminate_runs == 1:
              return
          for (
              batch_context_tokens,
              batch_token_generation_start_index,
              batch_token_generation_end_index,
              is_done,
          ) in stream_tokens(
              neox_args=neox_args,
              model=model,
              context_tokens=[context_tokens],
              eos_token_id=eos_token_id,
              maximum_tokens=maximum_tokens,
              recompute=recompute,
              temperature=temperature,
              top_k=top_k,
              top_p=top_p,
          ):
              if mpu.get_model_parallel_rank() == 0:
                  generated_tokens = (
                      batch_context_tokens[0]
                      .cpu()
                      .numpy()
                      .tolist()[
                          batch_token_generation_start_index[0]
                          .item() : batch_token_generation_end_index[0]
                          .item()
                      ]
                  )
                  generated_text = neox_args.tokenizer.detokenize(generated_tokens)
  
          print_rank_0("Generated Text: " + generated_text)
          if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0:
              _ = input("\n<press enter to continue>")
  