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code refactor

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Former-commit-id: 40627c601efc9f144a227dded8c6b40babff4e8b
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Ting 2024-11-19 20:33:18 +08:00
parent 32656bc50d
commit e27a0c3d53
4 changed files with 29 additions and 22 deletions
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9
src/llamafactory/extras/misc.py
View File

@ -20,6 +20,7 @@ import os
from typing import TYPE_CHECKING, Tuple, Union
import torch
import torch.distributed as dist
import transformers.dynamic_module_utils
from transformers import InfNanRemoveLogitsProcessor, LogitsProcessorList
from transformers.dynamic_module_utils import get_relative_imports
@ -263,3 +264,11 @@ def use_modelscope() -> bool:
def use_openmind() -> bool:
return os.environ.get("USE_OPENMIND_HUB", "0").lower() in ["true", "1"]
def cal_effective_tokens(effective_token_num, epoch, train_runtime) -> int:
r"""
calculate effective tokens.
"""
result = effective_token_num * epoch / train_runtime
return result / dist.get_world_size() if dist.is_initialized() else result

4
src/llamafactory/hparams/finetuning_args.py
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@ -346,6 +346,10 @@ class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreA
default=False,
metadata={"help": "Whether or not to save the training loss curves."},
)
include_effective_tokens_per_second: bool = field(
default=False,
metadata={"help": "Whether or not to compute effective tokens per second."},
)
def __post_init__(self):
def split_arg(arg):

20
src/llamafactory/train/dpo/workflow.py
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@ -17,10 +17,9 @@
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
from ...extras.misc import cal_effective_tokens
from ...extras.ploting import plot_loss
from ...hparams import ModelArguments
from ...model import load_model, load_tokenizer
@ -67,9 +66,10 @@ def run_dpo(
training_args.remove_unused_columns = False # important for multimodal and pairwise dataset
effective_token_num = 0.0
for data in dataset_module["train_dataset"]:
effective_token_num += len(data["chosen_input_ids"])
effective_token_num += len(data["rejected_input_ids"])
if finetuning_args.include_effective_tokens_per_second:
for data in dataset_module["train_dataset"]:
effective_token_num += len(data["chosen_input_ids"])
effective_token_num += len(data["rejected_input_ids"])
# Initialize our Trainer
trainer = CustomDPOTrainer(
@ -86,12 +86,10 @@ 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"] = (
effective_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()
if finetuning_args.include_effective_tokens_per_second:
train_result.metrics["effective_tokens_per_sec"] = cal_effective_tokens(
effective_token_num, train_result.metrics["epoch"], train_result.metrics["train_runtime"]
)
trainer.save_model()

18
src/llamafactory/train/sft/workflow.py
View File

@ -17,11 +17,9 @@
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
from ...extras.misc import get_logits_processor
from ...extras.misc import cal_effective_tokens, get_logits_processor
from ...extras.ploting import plot_loss
from ...model import load_model, load_tokenizer
from ..trainer_utils import create_modelcard_and_push
@ -68,8 +66,9 @@ def run_sft(
training_args.remove_unused_columns = False # important for multimodal dataset
effective_token_num = 0.0
for data in dataset_module["train_dataset"]:
effective_token_num += len(data["input_ids"])
if finetuning_args.include_effective_tokens_per_second:
for data in dataset_module["train_dataset"]:
effective_token_num += len(data["input_ids"])
# Metric utils
metric_module = {}
@ -100,12 +99,9 @@ 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"] = (
effective_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()
if finetuning_args.include_effective_tokens_per_second:
train_result.metrics["effective_tokens_per_sec"] = cal_effective_tokens(
effective_token_num, train_result.metrics["epoch"], train_result.metrics["train_runtime"]
)
trainer.save_model()