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