File size: 12,435 Bytes
f238c4c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 |
from typing import Any, List, Tuple
from tqdm import tqdm
from lm_eval import utils
from lm_eval.api.model import LM
from lm_eval.api.registry import register_model
from lm_eval.models.utils import retry_on_specific_exceptions
eval_logger = utils.eval_logger
def anthropic_completion(
client, #: anthropic.Anthropic,
model: str,
prompt: str,
max_tokens_to_sample: int,
temperature: float,
stop: List[str],
**kwargs: Any,
) -> str:
"""Wrapper function around the Anthropic completion API client with exponential back-off
in case of RateLimitError.
params:
client: anthropic.Anthropic
Anthropic API client
model: str
Anthropic model e.g. 'claude-instant-v1', 'claude-2'
prompt: str
Prompt to feed to the model
max_tokens_to_sample: int
Maximum number of tokens to sample from the model
temperature: float
Sampling temperature
stop: List[str]
List of stop sequences
kwargs: Any
Additional model_args to pass to the API client
"""
try:
import anthropic
except ModuleNotFoundError:
raise Exception(
"attempted to use 'anthropic' LM type, but package `anthropic` is not installed. \
please install anthropic via `pip install 'lm-eval[anthropic]'` or `pip install -e '.[anthropic]'`",
)
def _exception_callback(e: Exception, sleep_time: float) -> None:
eval_logger.warning(
f"RateLimitError occurred: {e.__cause__}\n Retrying in {sleep_time} seconds"
)
@retry_on_specific_exceptions(
on_exceptions=[anthropic.RateLimitError],
max_retries=None, # retry forever, consider changing
on_exception_callback=_exception_callback,
)
def completion():
response = client.completions.create(
prompt=f"{anthropic.HUMAN_PROMPT} {prompt}{anthropic.AI_PROMPT}",
model=model,
# NOTE: Claude really likes to do CoT, and overly aggressive stop sequences
# (e.g. gsm8k's ":") may truncate a lot of the input.
stop_sequences=[anthropic.HUMAN_PROMPT] + stop,
max_tokens_to_sample=max_tokens_to_sample,
temperature=temperature,
**kwargs,
)
return response.completion
return completion()
def anthropic_chat(
client, #: anthropic.Anthropic,
model: str,
prompt: str,
max_tokens: int,
temperature: float,
stop: List[str],
**kwargs: Any,
) -> str:
"""Wrapper function around the Anthropic completion API client with exponential back-off
in case of RateLimitError.
params:
client: anthropic.Anthropic
Anthropic API client
model: str
Anthropic model e.g. 'claude-3-opus-20240229', 'claude-3-sonnet-20240229'
prompt: str
Prompt to feed to the model
max_tokens: int
Maximum number of tokens to sample from the model
temperature: float
Sampling temperature
stop: List[str]
List of stop sequences
kwargs: Any
Additional model_args to pass to the API client
"""
try:
import anthropic
except ModuleNotFoundError:
raise Exception(
"attempted to use 'anthropic' LM type, but package `anthropic` is not installed. \
please install anthropic via `pip install 'lm-eval[anthropic]'` or `pip install -e '.[anthropic]'`",
)
def _exception_callback(e: Exception, sleep_time: float) -> None:
eval_logger.warning(
f"RateLimitError occurred: {e.__cause__}\n Retrying in {sleep_time} seconds"
)
@retry_on_specific_exceptions(
on_exceptions=[
anthropic.RateLimitError,
anthropic.APIConnectionError,
anthropic.APIStatusError,
],
max_retries=None, # retry forever, consider changing
on_exception_callback=_exception_callback,
)
def messages():
response = client.messages.create(
model=model,
max_tokens=max_tokens,
temperature=temperature,
messages=[{"role": "user", "content": f"{prompt}"}],
**kwargs,
)
return response.content[0].text
return messages()
@register_model("anthropic")
class AnthropicLM(LM):
REQ_CHUNK_SIZE = 20 # TODO: not used
def __init__(
self,
batch_size: int = 1,
model: str = "claude-2.0",
max_tokens_to_sample: int = 256,
temperature: float = 0, # defaults to 1
**kwargs, # top_p, top_k, etc.
) -> None:
"""Anthropic API wrapper.
:param model: str
Anthropic model e.g. 'claude-instant-v1', 'claude-2'
:param max_tokens_to_sample: int
Maximum number of tokens to sample from the model
:param temperature: float
Sampling temperature
:param kwargs: Any
Additional model_args to pass to the API client
"""
super().__init__()
try:
import anthropic
except ModuleNotFoundError:
raise Exception(
"attempted to use 'anthropic' LM type, but package `anthropic` is not installed. \
please install anthropic via `pip install 'lm-eval[anthropic]'` or `pip install -e '.[anthropic]'`",
)
self.model = model
# defaults to os.environ.get("ANTHROPIC_API_KEY")
self.client = anthropic.Anthropic()
self.temperature = temperature
self.max_tokens_to_sample = max_tokens_to_sample
self.tokenizer = self.client.get_tokenizer()
self.kwargs = kwargs
@property
def eot_token_id(self):
# Not sure but anthropic.HUMAN_PROMPT ?
raise NotImplementedError("No idea about anthropic tokenization.")
@property
def max_length(self) -> int:
return 2048
@property
def max_gen_toks(self) -> int:
return self.max_tokens_to_sample
@property
def batch_size(self):
# Isn't used because we override _loglikelihood_tokens
raise NotImplementedError("No support for logits.")
@property
def device(self):
# Isn't used because we override _loglikelihood_tokens
raise NotImplementedError("No support for logits.")
def tok_encode(self, string: str) -> List[int]:
return self.tokenizer.encode(string).ids
def tok_decode(self, tokens: List[int]) -> str:
return self.tokenizer.decode(tokens)
def _loglikelihood_tokens(self, requests, disable_tqdm: bool = False):
raise NotImplementedError("No support for logits.")
def generate_until(self, requests, disable_tqdm: bool = False) -> List[str]:
try:
import anthropic
except ModuleNotFoundError:
raise Exception(
"attempted to use 'anthropic' LM type, but package `anthropic` is not installed. \
please install anthropic via `pip install 'lm-eval[anthropic]'` or `pip install -e '.[anthropic]'`",
)
if not requests:
return []
_requests: List[Tuple[str, dict]] = [req.args for req in requests]
res = []
for request in tqdm(_requests, disable=disable_tqdm):
try:
inp = request[0]
request_args = request[1]
# generation_kwargs
until = request_args.get("until")
max_gen_toks = request_args.get("max_gen_toks", self.max_length)
temperature = request_args.get("temperature", self.temperature)
response = anthropic_completion(
client=self.client,
model=self.model,
prompt=inp,
max_tokens_to_sample=max_gen_toks,
temperature=temperature, # TODO: implement non-greedy sampling for Anthropic
stop=until, # type: ignore
**self.kwargs,
)
res.append(response)
self.cache_hook.add_partial("generate_until", request, response)
except anthropic.APIConnectionError as e: # type: ignore # noqa: F821
eval_logger.critical(f"Server unreachable: {e.__cause__}")
break
except anthropic.APIStatusError as e: # type: ignore # noqa: F821
eval_logger.critical(f"API error {e.status_code}: {e.message}")
break
return res
def _model_call(self, inps):
# Isn't used because we override _loglikelihood_tokens
raise NotImplementedError()
def _model_generate(self, context, max_length, eos_token_id):
# Isn't used because we override generate_until
raise NotImplementedError()
def loglikelihood(self, requests, disable_tqdm: bool = False):
raise NotImplementedError("No support for logits.")
def loglikelihood_rolling(self, requests, disable_tqdm: bool = False):
raise NotImplementedError("No support for logits.")
@register_model("anthropic-chat", "anthropic-chat-completions")
class AnthropicChatLM(AnthropicLM):
REQ_CHUNK_SIZE = 20 # TODO: not used
def __init__(
self,
model: str,
batch_size: int = 1,
max_tokens: int = 256,
temperature: float = 0, # defaults to 1
**kwargs, # top_p, top_k, etc.
) -> None:
"""Anthropic API wrapper.
:param model: str
Anthropic model e.g. 'claude-3-opus-20240229', 'claude-3-sonnet-20240229'
:param max_tokens: int
Maximum number of tokens to sample from the model
:param temperature: float
Sampling temperature
:param kwargs: Any
Additional model_args to pass to the API client
"""
super().__init__()
try:
import anthropic
except ModuleNotFoundError:
raise Exception(
"attempted to use 'anthropic' LM type, but package `anthropic` is not installed. \
please install anthropic via `pip install 'lm-eval[anthropic]'` or `pip install -e '.[anthropic]'`",
)
self.model = model
# defaults to os.environ.get("ANTHROPIC_API_KEY")
self.client = anthropic.Anthropic()
self.temperature = temperature
self.max_token = max_tokens
self.tokenizer = self.client.get_tokenizer()
self.kwargs = kwargs
@property
def max_gen_toks(self) -> int:
return self.max_tokens
def generate_until(self, requests) -> List[str]:
try:
import anthropic
except ModuleNotFoundError:
raise Exception(
"attempted to use 'anthropic' LM type, but package `anthropic` is not installed. \
please install anthropic via `pip install 'lm-eval[anthropic]'` or `pip install -e '.[anthropic]'`",
)
if not requests:
return []
_requests: List[Tuple[str, dict]] = [req.args for req in requests]
res = []
for request in tqdm(_requests):
try:
inp = request[0]
request_args = request[1]
# generation_kwargs
until = request_args.get("until")
max_tokens = request_args.get("max_gen_toks", self.max_length)
temperature = request_args.get("temperature", self.temperature)
response = anthropic_chat(
client=self.client,
model=self.model,
prompt=inp,
max_tokens=max_tokens,
temperature=temperature, # TODO: implement non-greedy sampling for Anthropic
stop=until, # type: ignore
**self.kwargs,
)
res.append(response)
self.cache_hook.add_partial("generate_until", request, response)
except anthropic.APIConnectionError as e: # type: ignore # noqa: F821
eval_logger.critical(f"Server unreachable: {e.__cause__}")
break
except anthropic.APIStatusError as e: # type: ignore # noqa: F821
eval_logger.critical(f"API error {e.status_code}: {e.message}")
break
return res
|