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Merge remote-tracking branch 'upstream/main'

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Former-commit-id: 37834a7e79473ccf50ad7f67745b97c274c326d9
This commit is contained in:
Jonery
2024-06-17 18:44:51 +08:00
parent 7408e778ca 485a80d294
commit 95ae30f678
181 changed files with 5362 additions and 1780 deletions
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14
src/llamafactory/train/sft/__init__.py
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@@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# 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 .workflow import run_sft

26
src/llamafactory/train/sft/metric.py
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@@ -1,21 +1,43 @@
# 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, Sequence, Tuple, Union
import numpy as np
from transformers.utils import is_jieba_available, is_nltk_available
from ...extras.constants import IGNORE_INDEX
from ...extras.packages import is_jieba_available, is_nltk_available, is_rouge_available
from ...extras.packages import is_rouge_available
if TYPE_CHECKING:
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers import 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

38
src/llamafactory/train/sft/trainer.py
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@@ -1,3 +1,20 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/trainer_seq2seq.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.
import json
import os
from types import MethodType
@@ -9,10 +26,11 @@ from transformers import Seq2SeqTrainer
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from ..utils import create_custom_optimzer, create_custom_scheduler
from ..trainer_utils import convert_pissa_adapter, create_custom_optimzer, create_custom_scheduler
if TYPE_CHECKING:
from torch.utils.data import Dataset
from transformers import ProcessorMixin
from transformers.trainer import PredictionOutput
@@ -33,6 +51,10 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
super().__init__(**kwargs)
self.finetuning_args = finetuning_args
self.processor = processor
if finetuning_args.pissa_convert:
self.save_model(os.path.join(self.args.output_dir, "pissa_init"))
if finetuning_args.use_badam:
from badam import clip_grad_norm_for_sparse_tensor
@@ -51,8 +73,11 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, "torch.Tensor"]] = None) -> None:
super()._save(output_dir, state_dict)
output_dir = output_dir if output_dir is not None else self.args.output_dir
if self.finetuning_args.pissa_convert:
convert_pissa_adapter(output_dir, state_dict, self.accelerator, self.model, self.args)
if self.processor is not None:
output_dir = output_dir if output_dir is not None else self.args.output_dir
getattr(self.processor, "image_processor").save_pretrained(output_dir)
def training_step(self, *args, **kwargs):
@@ -109,7 +134,7 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
padded_tensor[:, -src_tensor.shape[-1] :] = src_tensor # adopt left-padding
return padded_tensor.contiguous() # in contiguous memory
def save_predictions(self, predict_results: "PredictionOutput") -> None:
def save_predictions(self, dataset: "Dataset", predict_results: "PredictionOutput") -> None:
r"""
Saves model predictions to `output_dir`.
@@ -135,6 +160,9 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
(preds[i][pad_len[0] :], preds[i][: pad_len[0]]), axis=-1
) # move pad token to last
decoded_inputs = self.tokenizer.batch_decode(
dataset["input_ids"], skip_special_tokens=True, clean_up_tokenization_spaces=False
)
decoded_labels = self.tokenizer.batch_decode(
labels, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
@@ -142,6 +170,6 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
with open(output_prediction_file, "w", encoding="utf-8") as writer:
res: List[str] = []
for label, pred in zip(decoded_labels, decoded_preds):
res.append(json.dumps({"label": label, "predict": pred}, ensure_ascii=False))
for text, label, pred in zip(decoded_inputs, decoded_labels, decoded_preds):
res.append(json.dumps({"prompt": text, "label": label, "predict": pred}, ensure_ascii=False))
writer.write("\n".join(res))

21
src/llamafactory/train/sft/workflow.py
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@@ -1,4 +1,19 @@
# Inspired by: https://github.com/huggingface/transformers/blob/v4.34.1/examples/pytorch/summarization/run_summarization.py
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/summarization/run_summarization.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 typing import TYPE_CHECKING, List, Optional
@@ -9,7 +24,7 @@ from ...extras.constants import IGNORE_INDEX
from ...extras.misc import get_logits_processor
from ...extras.ploting import plot_loss
from ...model import load_model, load_tokenizer
from ..utils import create_modelcard_and_push
from ..trainer_utils import create_modelcard_and_push
from .metric import ComputeMetrics
from .trainer import CustomSeq2SeqTrainer
@@ -93,7 +108,7 @@ def run_sft(
predict_results.metrics.pop("predict_loss", None)
trainer.log_metrics("predict", predict_results.metrics)
trainer.save_metrics("predict", predict_results.metrics)
trainer.save_predictions(predict_results)
trainer.save_predictions(dataset, predict_results)
# Create model card
create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)