Spaces:
Runtime error
Runtime error
File size: 5,346 Bytes
c7a96cd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 |
import re
import torch
from transformers import (
LogitsProcessorList,
TemperatureLogitsWarper,
TopKLogitsWarper,
TopPLogitsWarper,
TypicalLogitsWarper,
RepetitionPenaltyLogitsProcessor,
PreTrainedTokenizerBase,
)
from typing import List, Tuple, Optional
from text_generation_server.pb import generate_pb2
from text_generation_server.pb.generate_pb2 import FinishReason
from text_generation_server.utils.watermark import WatermarkLogitsProcessor
class Sampling:
def __init__(self, seed: int, device: str = "cpu"):
self.generator = torch.Generator(device)
self.generator.manual_seed(seed)
self.seed = seed
def __call__(self, logits):
probs = torch.nn.functional.softmax(logits, -1)
next_tokens = torch.multinomial(probs, num_samples=1, generator=self.generator)
return next_tokens
class Greedy:
def __call__(self, logits):
return logits.argmax()
class NextTokenChooser:
def __init__(
self,
watermark=False,
temperature=1.0,
repetition_penalty=1.0,
top_k=None,
top_p=None,
typical_p=None,
do_sample=False,
seed=0,
device="cpu",
):
warpers = LogitsProcessorList()
# the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
# all samplers can be found in `generation_utils_samplers.py`
sampling = do_sample
if watermark:
warpers.append(WatermarkLogitsProcessor(device=device))
if repetition_penalty is not None and repetition_penalty != 1.0:
warpers.append(RepetitionPenaltyLogitsProcessor(penalty=repetition_penalty))
if temperature is not None and temperature != 1.0:
temperature = float(temperature)
warpers.append(TemperatureLogitsWarper(temperature))
sampling = True
if top_k is not None and top_k != 0:
warpers.append(TopKLogitsWarper(top_k=top_k))
sampling = True
if top_p is not None and top_p < 1.0:
warpers.append(TopPLogitsWarper(top_p=top_p))
sampling = True
if typical_p is not None and typical_p < 1.0:
warpers.append(TypicalLogitsWarper(mass=typical_p))
sampling = True
self.warpers = warpers
self.choice = Sampling(seed, device) if sampling else Greedy()
def __call__(self, input_ids, scores):
# Warp logits
if scores.shape[0] > 1:
# only warp the last token logits
scores[-1:, :] = self.warpers(input_ids, scores[-1:, :])
else:
scores = self.warpers(input_ids, scores)
# Compute logprobs
logprobs = torch.log_softmax(scores, -1)
# Choose tokens
next_id = self.choice(scores[-1])
return next_id.view(1, 1), logprobs
@classmethod
def from_pb(
cls,
pb: generate_pb2.NextTokenChooserParameters,
device: torch.device,
) -> "NextTokenChooser":
return NextTokenChooser(
watermark=pb.watermark,
temperature=pb.temperature,
repetition_penalty=pb.repetition_penalty,
top_k=pb.top_k,
top_p=pb.top_p,
typical_p=pb.typical_p,
do_sample=pb.do_sample,
seed=pb.seed,
device=device,
)
class StopSequenceCriteria:
def __init__(self, stop_sequence: str):
stop_sequence = re.escape(stop_sequence)
self.regex = re.compile(f".*{stop_sequence}$")
def __call__(self, output: str) -> bool:
if self.regex.findall(output):
return True
return False
class StoppingCriteria:
def __init__(
self,
eos_token_id: int,
stop_sequence_criterias: List[StopSequenceCriteria],
max_new_tokens: int = 20,
ignore_eos_token: bool = False,
):
self.eos_token_id = eos_token_id
self.stop_sequence_criterias = stop_sequence_criterias
self.max_new_tokens = max_new_tokens
self.current_tokens = 0
self.current_output = ""
self.ignore_eos_token = ignore_eos_token
def __call__(self, last_token: int, last_output: str) -> Tuple[bool, Optional[str]]:
self.current_tokens += 1
if self.current_tokens >= self.max_new_tokens:
return True, FinishReason.FINISH_REASON_LENGTH
if not self.ignore_eos_token and last_token == self.eos_token_id:
return True, FinishReason.FINISH_REASON_EOS_TOKEN
self.current_output += last_output
for stop_sequence_criteria in self.stop_sequence_criterias:
if stop_sequence_criteria(self.current_output):
return True, FinishReason.FINISH_REASON_STOP_SEQUENCE
return False, None
@classmethod
def from_pb(
cls,
pb: generate_pb2.StoppingCriteriaParameters,
tokenizer: PreTrainedTokenizerBase,
) -> "StoppingCriteria":
stop_sequence_criterias = [
StopSequenceCriteria(sequence) for sequence in pb.stop_sequences
]
return StoppingCriteria(
tokenizer.eos_token_id,
stop_sequence_criterias,
pb.max_new_tokens,
pb.ignore_eos_token,
)
|