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import re
import torch
import numpy as np
from queue import Queue
from typing import Tuple, List, Union, Iterable
from transformers.utils import logging, add_start_docstrings
from transformers.generation.logits_process import LogitsProcessor, LOGITS_PROCESSOR_INPUTS_DOCSTRING, LogitsProcessorList
def make_context(model, tokenizer,
messages: List[dict],
system: str = "You are a helpful assistant.",
max_new_tokens: int=0,
):
max_new_tokens = max_new_tokens or model.generation_config.max_new_tokens
max_input_length = model.config.model_max_length - max_new_tokens
im_start_id = [tokenizer.im_start_id]
im_end_id = [tokenizer.im_end_id]
nl_tokens = tokenizer.encode("\n")
def _tokenize_str(role, content):
return tokenizer.encode(role, allowed_special=set()) + nl_tokens + tokenizer.encode(content, allowed_special=set())
def _parse_messages(messages):
system, query, history = "", "", []
## system
if messages[0]["role"] == "system":
system = messages[0]["content"]
messages = messages[1:]
## query
assert messages[-1]["role"] == "user"
query = messages[-1]["content"]
messages = messages[:-1]
## history
assert len(messages) % 2 == 0
for i in range(0, len(messages), 2):
assert messages[i]["role"] == "user" and messages[i+1]["role"] == "assistant"
history.append([messages[i]["content"], messages[i+1]["content"]])
return system, query, history
_system, query, history = _parse_messages(messages)
## system
system_text = _system if _system != "" else system
system_tokens = []
if system_text:
system_tokens = im_start_id + _tokenize_str("system", system_text) + im_end_id + nl_tokens
## query
query_tokens = im_start_id + _tokenize_str("user", query) + im_end_id + nl_tokens
## final assistant
final_tokens = im_start_id + tokenizer.encode("assistant", allowed_special=set()) + nl_tokens
## max_history_tokens
max_history_length = max_input_length - len(system_tokens) - len(query_tokens) - len(final_tokens)
## history
context_tokens = []
for turn_query, turn_response in reversed(history):
## query tokens
history_query_tokens = im_start_id + _tokenize_str("user", turn_query) + im_end_id + nl_tokens
## answer tokens
histroy_response_tokens = im_start_id + _tokenize_str("assistant", turn_response) + im_end_id + nl_tokens
## this round tokens
next_context_tokens = history_query_tokens + histroy_response_tokens
## concat
current_context_size = len(next_context_tokens) + len(context_tokens)
if current_context_size < max_history_length:
context_tokens = next_context_tokens + context_tokens
else:
break
input_tokens = system_tokens + context_tokens + query_tokens + final_tokens
return torch.LongTensor([input_tokens]).to(model.device)
class TextIterStreamer:
def __init__(self, tokenizer, skip_prompt=False, skip_special_tokens=False):
self.tokenizer = tokenizer
self.skip_prompt = skip_prompt
self.skip_special_tokens = skip_special_tokens
self.tokens = []
self.text_queue = Queue()
self.next_tokens_are_prompt = True
def put(self, value):
if self.skip_prompt and self.next_tokens_are_prompt:
self.next_tokens_are_prompt = False
else:
if len(value.shape) > 1:
value = value[0]
self.tokens.extend(value.tolist())
tokens_str = self.tokenizer.decode(self.tokens, skip_special_tokens=self.skip_special_tokens, errors='ignore')
self.text_queue.put(tokens_str)
def end(self):
self.text_queue.put(None)
def __iter__(self):
return self
def __next__(self):
value = self.text_queue.get()
if value is None:
raise StopIteration()
else:
return value
class OutputRepetitionPenaltyLogitsProcessor(LogitsProcessor):
r"""
[`OutputLogitsProcessor`] that prevents the repetition of previous tokens through a penalty. This penalty is applied at
most once per token. Note that, for decoder-only models like most LLMs, the considered tokens include the prompt.
In the original [paper](https://arxiv.org/pdf/1909.05858.pdf), the authors suggest the use of a penalty of around
1.2 to achieve a good balance between truthful generation and lack of repetition. To penalize and reduce
repetition, use `penalty` values above 1.0, where a higher value penalizes more strongly. To reward and encourage
repetition, use `penalty` values between 0.0 and 1.0, where a lower value rewards more strongly.
Args:
penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. Above 1.0 penalizes previously generated
tokens. Between 0.0 and 1.0 rewards previously generated tokens.
"""
def __init__(self, input_length: int,
presence_penalties: float = 1.0,
frequency_penalties: float = 0,
repetition_penalties: float = 0):
if not (repetition_penalties > 0):
raise ValueError(f"`repetition_penalties` has to be a strictly positive float, but is {repetition_penalties}")
if not ( (frequency_penalties >= -2) and (frequency_penalties <= 2) ):
raise ValueError(f"`frequency_penalties` has to be [-2, 2], but is {frequency_penalties}")
if not ( (presence_penalties >= -2) and (presence_penalties <= 2) ):
raise ValueError(f"`presence_penalties` has to be [-2, 2], but is {presence_penalties}")
self.repetition_penalties = repetition_penalties
self.frequency_penalties = frequency_penalties
self.presence_penalties = presence_penalties
self.input_length = input_length
def _get_bin_counts_and_mask(
self,
tokens: torch.Tensor,
vocab_size: int,
num_seqs: int,
) -> Tuple[torch.Tensor, torch.Tensor]:
# Compute the bin counts for the tokens.
# vocab_size + 1 for padding.
bin_counts = torch.zeros((num_seqs, vocab_size + 1),
dtype=torch.long,
device=tokens.device)
bin_counts.scatter_add_(1, tokens, torch.ones_like(tokens))
bin_counts = bin_counts[:, :vocab_size]
mask = bin_counts > 0
return bin_counts, mask
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, logits: torch.FloatTensor) -> torch.FloatTensor:
prompt_tokens_tensor = input_ids[:, :self.input_length+1]
output_tokens_tensor = input_ids[:, self.input_length+1:]
num_seqs, vocab_size = logits.shape
_, prompt_mask = self._get_bin_counts_and_mask(
prompt_tokens_tensor, vocab_size, num_seqs)
output_bin_counts, output_mask = self._get_bin_counts_and_mask(
output_tokens_tensor, vocab_size, num_seqs)
repetition_penalties = torch.Tensor([self.repetition_penalties]).to(logits.device)
frequency_penalties = torch.Tensor([self.frequency_penalties]).to(logits.device)
presence_penalties = torch.Tensor([self.presence_penalties]).to(logits.device)
repetition_penalties = repetition_penalties[:, None].repeat(1, vocab_size)
repetition_penalties[~(prompt_mask | output_mask)] = 1.0
logits = torch.where(logits > 0, logits / repetition_penalties,
logits * repetition_penalties)
# We follow the definition in OpenAI API.
# Refer to https://platform.openai.com/docs/api-reference/parameter-details
logits -= frequency_penalties.unsqueeze_(dim=1) * output_bin_counts
logits -= presence_penalties.unsqueeze_(dim=1) * output_mask
return logits
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