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support efficient tokens calculation on sft/dpo

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Former-commit-id: b9f00286d8a017ed9fd2876986da3b4d7034ef07
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Ting 2024-11-19 17:15:47 +08:00
parent d6b9a2024b
commit 7ad5b5c088
2 changed files with 20 additions and 0 deletions
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10
src/llamafactory/train/dpo/workflow.py
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@ -16,6 +16,7 @@
# limitations under the License.
from typing import TYPE_CHECKING, List, Optional
import torch.distributed as dist
from ...data import PairwiseDataCollatorWithPadding, get_dataset, get_template_and_fix_tokenizer
from ...extras.constants import IGNORE_INDEX
@ -64,6 +65,11 @@ def run_dpo(
# Update arguments
training_args.remove_unused_columns = False # important for multimodal and pairwise dataset
effi_token_num = 0.0
for data in dataset_module["train_dataset"]:
effi_token_num += len(data["chosen_input_ids"])
effi_token_num += len(data["rejected_input_ids"])
# Initialize our Trainer
trainer = CustomDPOTrainer(
model=model,
@ -79,6 +85,10 @@ def run_dpo(
# Training
if training_args.do_train:
train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
train_result.metrics['effective_tokens_per_sec'] = effi_token_num * train_result.metrics['epoch'] / train_result.metrics['train_runtime']
if dist.is_initialized():
train_result.metrics['effective_tokens_per_sec'] = train_result.metrics['effective_tokens_per_sec'] / dist.get_world_size()
trainer.save_model()
trainer.log_metrics("train", train_result.metrics)
trainer.save_metrics("train", train_result.metrics)

10
src/llamafactory/train/sft/workflow.py
View File

@ -16,6 +16,7 @@
# limitations under the License.
from typing import TYPE_CHECKING, List, Optional
import torch.distributed as dist
from ...data import SFTDataCollatorWith4DAttentionMask, get_dataset, get_template_and_fix_tokenizer
from ...extras.constants import IGNORE_INDEX
@ -65,6 +66,10 @@ def run_sft(
training_args.generation_num_beams = data_args.eval_num_beams or training_args.generation_num_beams
training_args.remove_unused_columns = False # important for multimodal dataset
effi_token_num = 0.0
for data in dataset_module["train_dataset"]:
effi_token_num += len(data["input_ids"])
# Metric utils
metric_module = {}
if training_args.predict_with_generate:
@ -94,6 +99,10 @@ def run_sft(
# Training
if training_args.do_train:
train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
train_result.metrics['effective_tokens_per_sec'] = effi_token_num * train_result.metrics['epoch'] / train_result.metrics['train_runtime']
if dist.is_initialized():
train_result.metrics['effective_tokens_per_sec'] = train_result.metrics['effective_tokens_per_sec'] / dist.get_world_size()
trainer.save_model()
trainer.log_metrics("train", train_result.metrics)
trainer.save_metrics("train", train_result.metrics)
@ -123,3 +132,4 @@ def run_sft(
# Create model card
create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)