Skip to content

Fixed the loglikelihood method in inference endpoints models #119

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Mar 21, 2024
Merged

Fixed the loglikelihood method in inference endpoints models #119

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
2cfd761
fixed the loglikelihood models, was not returning the boolean value
clefourrier Mar 20, 2024
7331483
Merge branch 'main' into clem_fix_iemodel
clefourrier Mar 21, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
19 changes: 14 additions & 5 deletions src/lighteval/models/endpoint_model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@
import asyncio
from typing import Coroutine, List, Optional, Union

import torch
from huggingface_hub import (
AsyncInferenceClient,
InferenceClient,
Expand Down Expand Up @@ -314,17 +315,22 @@ def loglikelihood(
):
dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=lambda batch: batch)

for batch in tqdm(dataloader, desc="Loglikleihoods", position=1, leave=False, disable=self.disable_tqdm):
for batch in tqdm(dataloader, desc="Loglikelihoods", position=1, leave=False, disable=self.disable_tqdm):
if self.use_async:
responses = asyncio.run(self.__async_process_batch_logprob(batch))
else:
responses = self.__process_batch_logprob(batch)
for ix, response in enumerate(responses):
len_choice = len(batch[ix].tokenized_continuation)
for cur_request, response in zip(batch, responses):
cont_toks = torch.tensor(cur_request.tokenized_continuation)
len_choice = len(cont_toks)

logits = [t.logprob for t in response.details.prefill[-len_choice:] if t.logprob is not None]

greedy_tokens = torch.tensor(logits).argmax(dim=-1)
max_equal = (greedy_tokens == cont_toks).all().squeeze(0)
results.append(
LoglikelihoodReturn(
result=sum(logits),
result=(sum(logits), bool(max_equal)),
input_tokens=[t.id for t in response.details.prefill[:-len_choice]],
generated_tokens=[t.id for t in response.details.prefill[-len_choice:]],
truncated_tokens_count=-1,
Expand Down Expand Up @@ -355,13 +361,16 @@ def loglikelihood_rolling(
):
dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=lambda batch: batch)

for batch in tqdm(dataloader, desc="Loglikleihoods", position=1, leave=False, disable=self.disable_tqdm):
for batch in tqdm(
dataloader, desc="Loglikelihoods, rolling", position=1, leave=False, disable=self.disable_tqdm
):
if self.use_async:
responses = asyncio.run(self.__async_process_batch_logprob(batch, rolling=True))
else:
responses = self.__process_batch_logprob(batch, rolling=True)
for response in responses:
logits = [t.logprob for t in response.details.tokens[:-1]]

results.append(
LoglikelihoodReturn(
result=sum(logits),
Expand Down