File size: 2,278 Bytes
6021a24 | 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 | import torch
from lm_eval.api.model import LM
from lm_eval.api.registry import register_model
import sys
sys.path.insert(0, "runtime")
from litgpt import Config
from litgpt.model import GPT
@register_model("obsidian_multiscreen")
class ObsidianMultiscreenLM(LM):
def __init__(
self,
checkpoint,
device="cuda",
dtype="bfloat16",
backend="triton",
**kwargs
):
self.device = device
if backend:
import os
os.environ["MULTISCREEN_BACKEND"] = backend
config = Config.from_file(
f"{checkpoint}/model_config.yaml"
)
self.model = GPT(config)
state = torch.load(
f"{checkpoint}/lit_model.pth",
map_location="cpu"
)
self.model.load_state_dict(
state["model"]
)
self.model.to(device)
if dtype == "bfloat16":
self.model.to(torch.bfloat16)
self.model.eval()
self.vocab_size = config.padded_vocab_size
self.max_length = config.block_size
@property
def eot_token_id(self):
return 0
@property
def max_length(self):
return self._max_length
@max_length.setter
def max_length(self, x):
self._max_length=x
def tok_encode(self, string):
return self.tokenizer.encode(string)
def loglikelihood(self, requests):
results=[]
for request in requests:
context, continuation = request.args
text=context+continuation
ids=torch.tensor(
[self.tokenizer.encode(text)],
device=self.device
)
with torch.no_grad():
logits=self.model(ids)
log_probs=torch.log_softmax(
logits,
dim=-1
)
cont_ids=self.tokenizer.encode(
continuation
)
score=0
for i,tok in enumerate(cont_ids):
score += log_probs[
0,
-(len(cont_ids)-i+1),
tok
]
results.append(
(float(score), True)
)
return results |