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# Copyright 2024 HuggingFace Inc., THUDM, and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library and the THUDM's ChatGLM implementation.
# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/summarization/run_summarization.py
# https://github.com/THUDM/ChatGLM-6B/blob/main/ptuning/main.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import torch
from transformers.utils import is_jieba_available, is_nltk_available
from ...extras.constants import IGNORE_INDEX
from ...extras.misc import numpify
from ...extras.packages import is_rouge_available
if TYPE_CHECKING:
from transformers import EvalPrediction, PreTrainedTokenizer
if is_jieba_available():
import jieba # type: ignore
if is_nltk_available():
from nltk.translate.bleu_score import SmoothingFunction, sentence_bleu
if is_rouge_available():
from rouge_chinese import Rouge
def eval_logit_processor(logits: "torch.Tensor", labels: "torch.Tensor") -> "torch.Tensor":
r"""
Computes the token with the largest likelihood to reduce memory footprint.
"""
if isinstance(logits, (list, tuple)):
if logits[0].dim() == 3: # (batch_size, seq_len, vocab_size)
logits = logits[0]
else: # moe models have aux loss
logits = logits[1]
if logits.dim() != 3:
raise ValueError("Cannot process the logits.")
return torch.argmax(logits, dim=-1)
@dataclass
class ComputeAccuracy:
r"""
Computes accuracy and supports `batch_eval_metrics`.
"""
def _dump(self) -> Optional[Dict[str, float]]:
result = None
if hasattr(self, "score_dict"):
result = {k: float(np.mean(v)) for k, v in self.score_dict.items()}
self.score_dict = {"accuracy": []}
return result
def __post_init__(self):
self._dump()
def __call__(self, eval_preds: "EvalPrediction", compute_result: bool = True) -> Optional[Dict[str, float]]:
preds, labels = numpify(eval_preds.predictions), numpify(eval_preds.label_ids)
for i in range(len(preds)):
pred, label = preds[i, :-1], labels[i, 1:]
label_mask = label != IGNORE_INDEX
self.score_dict["accuracy"].append(np.mean(pred[label_mask] == label[label_mask]))
if compute_result:
return self._dump()
@dataclass
class ComputeSimilarity:
r"""
Computes text similarity scores and supports `batch_eval_metrics`.
Wraps the tokenizer into metric functions, used in CustomSeq2SeqTrainer.
"""
tokenizer: "PreTrainedTokenizer"
def _dump(self) -> Optional[Dict[str, float]]:
result = None
if hasattr(self, "score_dict"):
result = {k: float(np.mean(v)) for k, v in self.score_dict.items()}
self.score_dict = {"rouge-1": [], "rouge-2": [], "rouge-l": [], "bleu-4": []}
return result
def __post_init__(self):
self._dump()
def __call__(self, eval_preds: "EvalPrediction", compute_result: bool = True) -> Optional[Dict[str, float]]:
preds, labels = numpify(eval_preds.predictions), numpify(eval_preds.label_ids)
preds = np.where(preds != IGNORE_INDEX, preds, self.tokenizer.pad_token_id)
labels = np.where(labels != IGNORE_INDEX, labels, self.tokenizer.pad_token_id)
decoded_preds = self.tokenizer.batch_decode(preds, skip_special_tokens=True)
decoded_labels = self.tokenizer.batch_decode(labels, skip_special_tokens=True)
for pred, label in zip(decoded_preds, decoded_labels):
hypothesis = list(jieba.cut(pred))
reference = list(jieba.cut(label))
if len(" ".join(hypothesis).split()) == 0 or len(" ".join(reference).split()) == 0:
result = {"rouge-1": {"f": 0.0}, "rouge-2": {"f": 0.0}, "rouge-l": {"f": 0.0}}
else:
rouge = Rouge()
scores = rouge.get_scores(" ".join(hypothesis), " ".join(reference))
result = scores[0]
for k, v in result.items():
self.score_dict[k].append(round(v["f"] * 100, 4))
bleu_score = sentence_bleu([list(label)], list(pred), smoothing_function=SmoothingFunction().method3)
self.score_dict["bleu-4"].append(round(bleu_score * 100, 4))
if compute_result:
return self._dump()
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