File size: 7,045 Bytes
1d13cae |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 |
import os
from functools import cached_property
from typing import Any, Dict, List, Optional, Tuple, Union
from lm_eval.api.registry import register_model
from lm_eval.models.api_models import TemplateAPI
from lm_eval.utils import eval_logger
@register_model("local-completions")
class LocalCompletionsAPI(TemplateAPI):
def __init__(
self,
base_url=None,
tokenizer_backend="huggingface",
**kwargs,
):
super().__init__(
base_url=base_url, tokenizer_backend=tokenizer_backend, **kwargs
)
def _create_payload(
self,
messages: Union[List[List[int]], List[dict], List[str], str],
generate=False,
gen_kwargs: Optional[dict] = None,
seed: int = 1234,
**kwargs,
) -> dict:
if generate:
gen_kwargs.pop("do_sample", False)
max_tokens = gen_kwargs.pop("max_gen_toks", self._max_gen_toks)
temperature = gen_kwargs.pop("temperature", 0)
stop = gen_kwargs.pop("until", ["<|endoftext|>"])
return {
"prompt": messages,
"model": self.model,
"max_tokens": max_tokens,
"temperature": temperature,
"stop": stop,
"seed": seed,
**gen_kwargs,
}
else:
return {
"model": self.model,
"prompt": messages,
"temperature": 0,
"max_tokens": 1,
"logprobs": 1,
"seed": seed,
"echo": True,
}
@staticmethod
def parse_logprobs(
outputs: Union[Dict, List[Dict]],
tokens: List[List[int]] = None,
ctxlens: List[int] = None,
**kwargs,
) -> List[Tuple[float, bool]]:
res = []
if not isinstance(outputs, list):
outputs = [outputs]
for out in outputs:
for choice, ctxlen in zip(out["choices"], ctxlens):
assert ctxlen > 0, "Context length must be greater than 0"
logprobs = sum(choice["logprobs"]["token_logprobs"][ctxlen:-1])
tokens = choice["logprobs"]["token_logprobs"][ctxlen:-1]
top_logprobs = choice["logprobs"]["top_logprobs"][ctxlen:-1]
is_greedy = True
for tok, top in zip(tokens, top_logprobs):
if tok != max(top, key=top.get):
is_greedy = False
break
res.append((logprobs, is_greedy))
return res
@staticmethod
def parse_generations(outputs: Union[Dict, List[Dict]], **kwargs) -> List[str]:
res = []
if not isinstance(outputs, list):
outputs = [outputs]
for out in outputs:
for choices in out["choices"]:
res.append(choices["text"])
return res
@property
def api_key(self):
return os.environ.get("OPENAI_API_KEY", "")
@register_model("local-chat-completions")
class LocalChatCompletion(LocalCompletionsAPI):
def __init__(
self,
base_url=None,
tokenizer_backend=None,
tokenized_requests=False,
**kwargs,
):
eval_logger.warning(
"chat-completions endpoint requires the `--apply_chat_template` flag."
)
super().__init__(
base_url=base_url,
tokenizer_backend=tokenizer_backend,
tokenized_requests=tokenized_requests,
**kwargs,
)
if self._batch_size > 1:
eval_logger.warning(
"Chat completions does not support batching. Defaulting to batch size 1."
)
self._batch_size = 1
def _create_payload(
self,
messages: List[Dict],
generate=False,
gen_kwargs: dict = None,
seed=1234,
**kwargs,
) -> dict:
gen_kwargs.pop("do_sample", False)
max_tokens = gen_kwargs.pop("max_gen_toks", self._max_gen_toks)
temperature = gen_kwargs.pop("temperature", 0)
stop = gen_kwargs.pop("until", ["<|endoftext|>"])
if not isinstance(stop, (list, tuple)):
stop = [stop]
return {
"messages": messages,
"model": self.model,
"max_tokens": max_tokens,
"temperature": temperature,
"stop": stop[:4],
"seed": seed,
**gen_kwargs,
}
@staticmethod
def parse_generations(outputs: Union[Dict, List[Dict]], **kwargs) -> List[str]:
res = []
if not isinstance(outputs, list):
outputs = [outputs]
for out in outputs:
for choices in out["choices"]:
res.append(choices["message"]["content"])
return res
def tok_encode(
self,
string: Union[str, Any],
left_truncate_len=None,
add_special_tokens=None,
**kwargs,
) -> Union[List[str], List[int], Any]:
return string
def loglikelihood(self, requests, **kwargs):
raise NotImplementedError(
"Loglikelihood is not supported for chat completions. Consider using the completions API instead."
)
@register_model(
"openai-completions",
)
class OpenAICompletionsAPI(LocalCompletionsAPI):
def __init__(
self,
base_url="https://api.openai.com/v1/completions",
tokenizer_backend="tiktoken",
**kwargs,
):
super().__init__(
base_url=base_url, tokenizer_backend=tokenizer_backend, **kwargs
)
@cached_property
def api_key(self):
"""Override this property to return the API key for the API request."""
key = os.environ.get("OPENAI_API_KEY", None)
if key is None:
raise ValueError(
"API key not found. Please set the OPENAI_API_KEY environment variable."
)
return key
def loglikelihood(self, requests, **kwargs):
assert (
self.model != "gpt-3.5-turbo"
), "Loglikelihood is not supported for gpt-3.5-turbo"
return super().loglikelihood(requests, **kwargs)
@register_model("openai-chat-completions")
class OpenAIChatCompletion(LocalChatCompletion):
def __init__(
self,
base_url="https://api.openai.com/v1/chat/completions",
tokenizer_backend=None,
tokenized_requests=False,
**kwargs,
):
super().__init__(
base_url=base_url,
tokenizer_backend=tokenizer_backend,
tokenized_requests=tokenized_requests,
**kwargs,
)
@cached_property
def api_key(self):
"""Override this property to return the API key for the API request."""
key = os.environ.get("OPENAI_API_KEY", None)
if key is None:
raise ValueError(
"API key not found. Please set the OPENAI_API_KEY environment variable."
)
return key
|