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import abc
import hashlib
import json
import logging
import os
from typing import List, Optional, Tuple, Type, TypeVar
import transformers
from sqlitedict import SqliteDict
from tqdm import tqdm
from lm_eval import utils
eval_logger = logging.getLogger("lm-eval")
T = TypeVar("T", bound="LM")
class LM(abc.ABC):
def __init__(self) -> None:
"""Defines the interface that should be implemented by all LM subclasses.
LMs are assumed to take text (strings) as input and yield strings as output
(inputs/outputs should be tokenization-agnostic.)
"""
# set rank and world size to a single process, by default.
self._rank = 0
self._world_size = 1
self.cache_hook = CacheHook(None)
@abc.abstractmethod
def loglikelihood(self, requests) -> List[Tuple[float, bool]]:
"""Compute log-likelihood of generating a continuation from a context.
Downstream tasks should attempt to use loglikelihood instead of other
LM calls whenever possible.
:param requests: list[Instance]
A list of Instance objects, with property `args` which returns a tuple (context, continuation).
`context: str`
Context string. Implementations of LM must be able to handle an
empty context string.
`continuation: str`
The continuation over which log likelihood will be calculated. If
there is a word boundary, the space should be in the continuation.
For example, context="hello" continuation=" world" is correct.
:return: list[tuple[float, bool]]
A list of pairs (logprob, isgreedy)
`logprob: float`
The log probability of `continuation`.
`isgreedy`:
Whether `continuation` would be generated by greedy sampling from `context`.
"""
pass
@abc.abstractmethod
def loglikelihood_rolling(self, requests) -> List[Tuple[float]]:
"""Compute full log-likelihood of a string, with no truncation, for perplexity computation
- We will use the full max context length of the model.
- For inputs that exceed the max context length, we divide the tokenized string into chunks of up to
the max context length.
- IMPORTANT: Each document's loglikelihood/perplexity is computed *separately*, unlike other implementations
which may simply concatenate multiple documents together.
- IMPORTANT: We maximize the amount of context for each prediction. Specifically, for inputs that we break into
multiple chunks, the last input will still a full-sized context.
Example:
Input tokens: [ 0 1 2 3 4 5 6 7 8 9 ]
Prefix: BOS/EOS
Max context length: 4
Resulting input/prediction pairs:
INPUT: BOS 0 1 2
PRED: 0 1 2 3
INPUT: 3 4 5 6
PRED: 4 5 6 7
INPUT: 5 6 7 8
PRED: 8 9
Observe that:
1. Each token is predicted exactly once
2. For the last pair, we provide the full context, but only score the last two tokens
:param requests: list[Instance]
A list of Instance objects with property `args` which returns a tuple (context,).
string: str
String for which we are computing overall loglikelihood
:return: list[tuple[float]]
A list of tuples (logprob,)
logprob: float
The log probability of `context` conditioned on the BOS/EOS token.
Can also be overridden for custom cases by `prefix_token_id`.
"""
pass
# TODO: Add an optional max length
@abc.abstractmethod
def generate_until(self, requests) -> List[str]:
"""Generate greedily until a stopping sequence
:param requests: list[Instance]
A list of Instance objects with property `args` which returns a tuple (context, until).
context: str
Context string
until: [str]
The string sequences to generate until. These string sequences
may each span across multiple tokens, or may be part of one token.
:return: list[str]
A list of strings continuation
continuation: str
The generated continuation.
"""
pass
@classmethod
def create_from_arg_string(
cls: Type[T], arg_string: str, additional_config: Optional[dict] = None
) -> T:
"""
Creates an instance of the LM class using the given argument string and additional config.
Parameters:
- arg_string: A string containing arguments in the format key1=value1,key2=value2.
- additional_config: Optional dictionary containing additional configuration parameters.
Returns:
- Instance of the LM class.
"""
additional_config = {} if additional_config is None else additional_config
args = utils.simple_parse_args_string(arg_string)
args2 = {k: v for k, v in additional_config.items() if v is not None}
return cls(**args, **args2)
@classmethod
def create_from_arg_obj(
cls: Type[T], arg_dict: dict, additional_config: Optional[dict] = None
) -> T:
"""
Creates an instance of the LM class using the given arg_obj
Parameters:
- arg_obj: A dict containing arguments in the format key1=value1,key2=value2.
- additional_config: Optional dictionary containing additional configuration parameters.
Returns:
- Instance of the LM class.
"""
additional_config = {} if additional_config is None else additional_config
additional_config = {
k: v for k, v in additional_config.items() if v is not None
}
return cls(**arg_dict, **additional_config)
@property
def rank(self):
# used in the case of parallelism. Hardcoded to
# ensure no errors arise using API models which do
# not support multi-device parallelism nor expect it.
return self._rank
@property
def world_size(self):
# used in the case of parallelism. Hardcoded to
# ensure no errors arise using API models which do
# not support multi-device parallelism nor expect it.
return self._world_size
def set_cache_hook(self, cache_hook) -> None:
self.cache_hook = cache_hook
### SQLite-based caching of LM responses
def hash_args(attr, args):
dat = json.dumps([attr] + list(args))
return hashlib.sha256(dat.encode("utf-8")).hexdigest()
class CacheHook:
def __init__(self, cachinglm) -> None:
if cachinglm is None:
self.dbdict = None
return
self.dbdict = cachinglm.dbdict
def add_partial(self, attr, req, res) -> None:
if self.dbdict is None:
return
hsh = hash_args(attr, req)
self.dbdict[hsh] = res
class CachingLM:
def __init__(self, lm, cache_db) -> None:
"""LM wrapper that returns cached results if they exist, and uses the underlying LM if not.
:param lm: LM
Underlying LM
:param cache_db: str
Path to cache db
"""
self.lm = lm
self.cache_db = cache_db
if os.path.dirname(cache_db):
os.makedirs(os.path.dirname(cache_db), exist_ok=True)
self.dbdict = SqliteDict(cache_db, autocommit=True)
# add hook to lm
lm.set_cache_hook(self.get_cache_hook())
def __getattr__(self, attr):
lm_attr = getattr(self.lm, attr)
if not callable(lm_attr):
return lm_attr
def fn(requests):
res = []
remaining_reqs = []
warned = False
# figure out which ones are cached and which ones are new
eval_logger.info(
f"Loading '{attr}' responses from cache '{self.cache_db}' where possible..."
)
for req in tqdm(requests, desc="Checking cached requests"):
hsh = hash_args(attr, req.args)
if attr == "generate_until" and req.args[1].get("do_sample", False):
# when we are doing non-greedy generation, don't use the cache
# (else every "randomly sampled" generation would be identical for repeats > 1).
if not warned:
eval_logger.warning(
f"Arguments to lm.generate_until() '{req.args[1]}' include non-deterministic sampling. Caching will not be performed for such requests."
)
warned = True
res.append(None)
remaining_reqs.append(req)
elif hsh in self.dbdict:
ob = self.dbdict[hsh]
assert ob is not None
res.append(ob)
else:
res.append(None)
remaining_reqs.append(req)
eval_logger.info(
f"Cached requests: {len(requests) - len(remaining_reqs)}, Requests remaining: {len(remaining_reqs)}"
)
# actually run the LM on the requests that do not have cached results
rem_res = getattr(self.lm, attr)(remaining_reqs)
# stick the new ones back into the list and also cache any of the new ones
resptr = 0
for req, r in zip(remaining_reqs, rem_res):
while res[resptr] is not None:
resptr += 1
res[resptr] = r
# caching
hsh = hash_args(attr, req.args)
self.dbdict[hsh] = r
self.dbdict.commit()
return res
return fn
def get_cache_hook(self):
return CacheHook(self)
class TemplateLM(LM):
"""
A class acting as intermediary between the LM base class
and boilerplate often included in other LM subclasses.
"""
@property
@abc.abstractmethod
def eot_token_id(self):
pass
@property
def prefix_token_id(self):
# it is used as prefix for loglikelihood
return self.eot_token_id
@abc.abstractmethod
def tok_encode(self, string: str, **kwargs):
pass
@abc.abstractmethod
def _loglikelihood_tokens(self, requests, **kwargs):
pass
def _encode_pair(self, context, continuation):
n_spaces = len(context) - len(context.rstrip())
if n_spaces > 0:
continuation = context[-n_spaces:] + continuation
context = context[:-n_spaces]
model_class = getattr(self, "AUTO_MODEL_CLASS", None)
if model_class == transformers.AutoModelForSeq2SeqLM:
context_enc = self.tok_encode(context)
continuation_enc = self.tok_encode(continuation, add_special_tokens=False)
else:
whole_enc = self.tok_encode(context + continuation)
context_enc = self.tok_encode(context)
context_enc_len = len(context_enc)
continuation_enc = whole_enc[context_enc_len:]
return context_enc, continuation_enc
def loglikelihood(
self, requests, disable_tqdm: bool = False
) -> List[Tuple[float, bool]]:
new_reqs = []
for context, continuation in [req.args for req in requests]:
if context == "":
# BOS or EOS as context
context_enc, continuation_enc = (
[self.prefix_token_id],
self.tok_encode(continuation),
)
else:
context_enc, continuation_enc = self._encode_pair(context, continuation)
new_reqs.append(((context, continuation), context_enc, continuation_enc))
return self._loglikelihood_tokens(new_reqs, disable_tqdm=disable_tqdm)
@abc.abstractmethod
def loglikelihood_rolling(
self, requests, disable_tqdm: bool = False
) -> List[Tuple[float, bool]]:
pass
@abc.abstractmethod
def generate_until(self, requests, disable_tqdm: bool = False) -> List[str]:
pass
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