from typing import Any, Callable, Optional, Tuple, Union
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
try:
from transformers.generation_logits_process import (
LogitsProcessorList,
TemperatureLogitsWarper,
TopKLogitsWarper,
TopPLogitsWarper,
)
except ImportError:
from transformers.generation import LogitsProcessorList, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper
def prepare_logits_processor(top_k: Optional[int] = None,
top_p: Optional[float] = None,
temperature: Optional[float] = None) -> LogitsProcessorList:
processor_list = LogitsProcessorList()
if temperature is not None and temperature != 1.0:
processor_list.append(TemperatureLogitsWarper(temperature))
if top_k is not None and top_k != 0:
processor_list.append(TopKLogitsWarper(top_k))
if top_p is not None and top_p < 1.0:
processor_list.append(TopPLogitsWarper(top_p))
return processor_list
def _is_sequence_finished(unfinished_sequences: torch.Tensor) -> bool:
if dist.is_initialized() and dist.get_world_size() > 1:
# consider DP
unfinished_sequences = unfinished_sequences.clone()
dist.all_reduce(unfinished_sequences)
return unfinished_sequences.max() == 0
def sample(model: nn.Module,
input_ids: torch.Tensor,
max_length: int,
early_stopping: bool = False,
eos_token_id: Optional[int] = None,
pad_token_id: Optional[int] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
temperature: Optional[float] = None,
prepare_inputs_fn: Optional[Callable[[torch.Tensor, Any], dict]] = None,
update_model_kwargs_fn: Optional[Callable[[dict, Any], dict]] = None,
**model_kwargs) -> torch.Tensor:
if input_ids.size(1) >= max_length:
return input_ids
logits_processor = prepare_logits_processor(top_k, top_p, temperature)
unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
for _ in range(input_ids.size(1), max_length):
model_inputs = prepare_inputs_fn(input_ids, **model_kwargs) \
if prepare_inputs_fn is not None else {'input_ids': input_ids}
outputs = model(**model_inputs)
next_token_logits = outputs['logits'][:, -1, :]
# pre-process distribution
next_token_logits = logits_processor(input_ids, next_token_logits)
# sample
probs = torch.softmax(next_token_logits, dim=-1, dtype=torch.float)
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
# finished sentences should have their next token be a padding token
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
# update generated ids, model inputs for next step
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
if update_model_kwargs_fn is not None:
model_kwargs = update_model_kwargs_fn(outputs, model_kwargs)
# if eos_token was found in one sentence, set sentence to finished
if eos_token_id is not None:
unfinished_sequences = unfinished_sequences.mul((next_tokens != eos_token_id).long())
# stop when each sentence is finished if early_stopping=True
if early_stopping and _is_sequence_finished(unfinished_sequences):
break
return input_ids
def generate(model: nn.Module,
input_ids: torch.Tensor,
max_length: int,
num_beams: int = 1,
do_sample: bool = True,
early_stopping: bool = False,
eos_token_id: Optional[int] = None,
pad_token_id: Optional[int] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
temperature: Optional[float] = None,
prepare_inputs_fn: Optional[Callable[[torch.Tensor, Any], dict]] = None,
update_model_kwargs_fn: Optional[Callable[[dict, Any], dict]] = None,
**model_kwargs) -> torch.Tensor:
"""Generate token sequence. The returned sequence is input_ids + generated_tokens.
Args:
model (nn.Module): model
input_ids (torch.Tensor): input sequence
max_length (int): max length of the returned sequence
num_beams (int, optional): number of beams. Defaults to 1.
do_sample (bool, optional): whether to do sample. Defaults to True.
early_stopping (bool, optional): if True, the sequence length may be smaller than max_length due to finding eos. Defaults to False.
eos_token_id (Optional[int], optional): end of sequence token id. Defaults to None.
pad_token_id (Optional[int], optional): pad token id. Defaults to None.
top_k (Optional[int], optional): the number of highest probability vocabulary tokens to keep for top-k-filtering. Defaults to None.
top_p (Optional[float], optional): If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation. Defaults to None.
temperature (Optional[float], optional): The value used to module the next token probabilities. Defaults to None.
prepare_inputs_fn (Optional[Callable[[torch.Tensor, Any], dict]], optional): Function to preprocess model inputs. Arguments of this function should be input_ids and model_kwargs. Defaults to None.
update_model_kwargs_fn (Optional[Callable[[dict, Any], dict]], optional): Function to update model_kwargs based on outputs. Arguments of this function should be outputs and model_kwargs. Defaults to None.
"""
is_greedy_gen_mode = ((num_beams == 1) and do_sample is False)
is_sample_gen_mode = ((num_beams == 1) and do_sample is True)
is_beam_gen_mode = ((num_beams > 1) and do_sample is False)
if is_greedy_gen_mode:
# run greedy search
raise NotImplementedError
elif is_sample_gen_mode:
# run sample
return sample(model,
input_ids,
max_length,
early_stopping=early_stopping,
eos_token_id=eos_token_id,
pad_token_id=pad_token_id,
top_k=top_k,
top_p=top_p,
temperature=temperature,
prepare_inputs_fn=prepare_inputs_fn,
update_model_kwargs_fn=update_model_kwargs_fn,
**model_kwargs)
elif is_beam_gen_mode:
raise NotImplementedError
else:
raise ValueError("Unsupported generation mode")
@torch.no_grad()
def generate_with_actor(actor_model: nn.Module,
input_ids: torch.Tensor,
return_action_mask: bool = True,
**kwargs
) -> Union[Tuple[torch.LongTensor, torch.LongTensor],
Tuple[torch.LongTensor, torch.LongTensor, torch.BoolTensor]]:
"""Generate token sequence with actor model. Refer to `generate` for more details.
"""
# generate sequences
sequences = generate(actor_model, input_ids, **kwargs)
# calculate auxiliary tensors
attention_mask = None
pad_token_id = kwargs.get('pad_token_id', None)
if pad_token_id is not None:
attention_mask = sequences.not_equal(pad_token_id).to(dtype=torch.long, device=sequences.device)
if not return_action_mask:
return sequences, attention_mask, None
input_len = input_ids.size(1)
eos_token_id = kwargs.get('eos_token_id', None)
if eos_token_id is None:
action_mask = torch.ones_like(sequences, dtype=torch.bool)
else:
# left padding may be applied, only mask action
action_mask = (sequences[:, input_len:] == eos_token_id).cumsum(dim=-1) == 0
action_mask = F.pad(action_mask, (1 + input_len, -1), value=True) # include eos token and input
action_mask[:, :input_len] = False
action_mask = action_mask[:, 1:]
return sequences, attention_mask, action_mask[:, -(sequences.size(1) - input_len):]