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[misc] upgrade format to py39 (#7256)

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This commit is contained in:
hoshi-hiyouga
2025-03-12 00:08:41 +08:00
committed by GitHub
parent 5995800bce
commit 264538cb26
113 changed files with 984 additions and 1407 deletions
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25
scripts/stat_utils/cal_ppl.py
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@@ -13,8 +13,9 @@
# limitations under the License.
import json
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any, Dict, Literal, Optional, Sequence
from typing import Any, Literal, Optional
import fire
import torch
@@ -30,16 +31,12 @@ from llamafactory.model import load_model, load_tokenizer
@dataclass
class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
r"""
Data collator for pairwise data.
"""
r"""Data collator for pairwise data."""
train_on_prompt: bool = False
def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, torch.Tensor]:
r"""
Pads batched data to the longest sequence in the batch.
"""
def __call__(self, features: Sequence[dict[str, Any]]) -> dict[str, torch.Tensor]:
r"""Pad batched data to the longest sequence in the batch."""
chosen_features = []
for feature in features:
chosen_features.append(
@@ -68,8 +65,8 @@ def calculate_ppl(
max_samples: Optional[int] = None,
train_on_prompt: bool = False,
):
r"""
Calculates the ppl on the dataset of the pre-trained models.
r"""Calculate the ppl on the dataset of the pre-trained models.
Usage: export CUDA_VISIBLE_DEVICES=0
python cal_ppl.py --model_name_or_path path_to_model --dataset alpaca_en_demo --save_name ppl.json
"""
@@ -111,17 +108,17 @@ def calculate_ppl(
criterion = torch.nn.CrossEntropyLoss(reduction="none")
total_ppl = 0
perplexities = []
batch: Dict[str, "torch.Tensor"]
batch: dict[str, torch.Tensor]
with torch.no_grad():
for batch in tqdm(dataloader, desc="Computing perplexities"):
batch = batch.to(model.device)
outputs = model(**batch)
shift_logits: "torch.Tensor" = outputs["logits"][..., :-1, :]
shift_labels: "torch.Tensor" = batch["labels"][..., 1:]
shift_logits: torch.Tensor = outputs["logits"][..., :-1, :]
shift_labels: torch.Tensor = batch["labels"][..., 1:]
loss_mask = shift_labels != IGNORE_INDEX
flatten_logits = shift_logits.contiguous().view(shift_labels.size(0) * shift_labels.size(1), -1)
flatten_labels = shift_labels.contiguous().view(-1)
token_logps: "torch.Tensor" = criterion(flatten_logits, flatten_labels)
token_logps: torch.Tensor = criterion(flatten_logits, flatten_labels)
token_logps = token_logps.contiguous().view(shift_logits.size(0), -1)
sentence_logps = (token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)
total_ppl += sentence_logps.exp().sum().item()