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LLaMA-Factory-310P3
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fix up

This commit is contained in:
hiyouga 2024-07-15 01:04:56 +08:00
parent 15b399a82f
commit 29ebcd75d5
19 changed files with 47 additions and 42 deletions
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4
data/README_zh.md
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@ -11,9 +11,9 @@
"formatting": "数据集格式（可选，默认：alpaca，可以为 alpaca 或 sharegpt）",
"ranking": "是否为偏好数据集（可选，默认：False）",
"subset": "数据集子集的名称（可选，默认：None）",
"split": "所使用的数据集切分（可选，默认：train）",
"folder": "Hugging Face 仓库的文件夹名称（可选，默认：None）",
"num_samples": "该数据集中用于训练的样本数量。（可选，默认：None）",
"split": "数据集中的要使用的训练测试集切分（可选，默认：train）",
"num_samples": "该数据集所使用的样本数量。（可选，默认：None）",
"columns（可选）": {
"prompt": "数据集代表提示词的表头名称（默认：instruction）",
"query": "数据集代表请求的表头名称（默认：input）",

2
data/dataset_info.json
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@ -181,7 +181,7 @@
"response": "summary"
}
},
"adgen_val": {
"adgen_eval": {
"hf_hub_url": "HasturOfficial/adgen",
"ms_hub_url": "AI-ModelScope/adgen",
"split": "validation",

2
examples/train_lora/llama3_lora_predict.yaml
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@ -8,7 +8,7 @@ do_predict: true
finetuning_type: lora
### dataset
dataset: identity,alpaca_en_demo
eval_dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 50

5
scripts/cal_lr.py
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@ -61,11 +61,12 @@ def calculate_lr(
packing=packing,
output_dir="dummy_dir",
overwrite_cache=True,
do_train=True,
)
)
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
dataset_module = get_dataset(model_args, data_args, training_args, stage, **tokenizer_module)
trainset = get_dataset(model_args, data_args, training_args, stage, **tokenizer_module)["train_dataset"]
if stage == "pt":
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
elif stage == "sft":
@ -73,7 +74,7 @@ def calculate_lr(
else:
raise NotImplementedError("Stage does not supported: {}.".format(stage))
dataloader = DataLoader(dataset_module["eval_dataset"], batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True)
dataloader = DataLoader(trainset, batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True)
valid_tokens, total_tokens = 0, 0
for batch in tqdm(dataloader):
valid_tokens += torch.sum(batch["labels"] != IGNORE_INDEX).item()

5
scripts/cal_ppl.py
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@ -83,11 +83,12 @@ def cal_ppl(
train_on_prompt=train_on_prompt,
output_dir="dummy_dir",
overwrite_cache=True,
do_train=True,
)
)
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
dataset_module = get_dataset(model_args, data_args, training_args, stage, **tokenizer_module)
trainset = get_dataset(model_args, data_args, training_args, stage, **tokenizer_module)["train_dataset"]
model = load_model(tokenizer, model_args, finetuning_args, is_trainable=False)
if stage == "pt":
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
@ -100,7 +101,7 @@ def cal_ppl(
else:
raise NotImplementedError("Stage does not supported: {}.".format(stage))
dataloader = DataLoader(dataset_module["eval_dataset"], batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True)
dataloader = DataLoader(trainset, batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True)
criterion = torch.nn.CrossEntropyLoss(reduction="none")
total_ppl = 0
perplexities = []

7
scripts/length_cdf.py
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@ -44,13 +44,14 @@ def length_cdf(
cutoff_len=1_000_000,
output_dir="dummy_dir",
overwrite_cache=True,
do_train=True,
)
)
tokenizer_module = load_tokenizer(model_args)
dataset_module = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
total_num = len(dataset_module["eval_dataset"])
trainset = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)["train_dataset"]
total_num = len(trainset)
length_dict = defaultdict(int)
for sample in tqdm(dataset_module["eval_dataset"]["input_ids"]):
for sample in tqdm(trainset["input_ids"]):
length_dict[len(sample) // interval * interval] += 1
length_tuples = list(length_dict.items())

8
src/llamafactory/eval/evaluator.py
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@ -37,7 +37,6 @@
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import inspect
import json
import os
from typing import Any, Dict, List, Optional
@ -88,18 +87,13 @@ class Evaluator:
pbar = tqdm(categorys.keys(), desc="Processing subjects", position=0)
results = {}
for subject in pbar:
if "trust_remote_code" in inspect.signature(load_dataset).parameters: # for datasets==2.16.0
kwargs = {"trust_remote_code": True}
else:
kwargs = {}
dataset = load_dataset(
path=os.path.join(self.eval_args.task_dir, self.eval_args.task),
name=subject,
cache_dir=self.model_args.cache_dir,
download_mode=self.eval_args.download_mode,
token=self.model_args.hf_hub_token,
**kwargs,
trust_remote_code=True,
)
pbar.set_postfix_str(categorys[subject]["name"])
inputs, outputs, labels = [], [], []

12
src/llamafactory/hparams/parser.py
View File

@ -104,7 +104,7 @@ def _verify_model_args(
raise ValueError("Quantized model only accepts a single adapter. Merge them first.")
if data_args.template == "yi" and model_args.use_fast_tokenizer:
logger.warning("We should use slow tokenizer for the Yi models.")
logger.warning("We should use slow tokenizer for the Yi models. Change `use_fast_tokenizer` to False.")
model_args.use_fast_tokenizer = False
@ -203,6 +203,14 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
if training_args.do_train and training_args.predict_with_generate:
raise ValueError("`predict_with_generate` cannot be set as True while training.")
if training_args.do_train and data_args.dataset is None:
raise ValueError("Please specify dataset for training.")
if (training_args.do_eval or training_args.do_predict) and (
data_args.eval_dataset is None and data_args.val_size < 1e-6
):
raise ValueError("Please specify dataset for evaluation.")
if training_args.do_train and model_args.quantization_device_map == "auto":
raise ValueError("Cannot use device map for quantized models in training.")
@ -242,7 +250,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
raise ValueError("Unsloth is incompatible with DeepSpeed ZeRO-3.")
if data_args.neat_packing and not data_args.packing:
logger.warning("`neat_packing` requires `packing` is True. Change it to True.")
logger.warning("`neat_packing` requires `packing` is True. Change `packing` to True.")
data_args.packing = True
_verify_model_args(model_args, data_args, finetuning_args)

4
src/llamafactory/model/model_utils/longlora.py
View File

@ -71,8 +71,6 @@ def llama_attention_forward(
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
past_key_value = getattr(self, "past_key_value", past_key_value)
if past_key_value is not None:
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
@ -156,8 +154,6 @@ def llama_flash_attention_2_forward(
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
past_key_value = getattr(self, "past_key_value", past_key_value)
if past_key_value is not None:
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

4
src/llamafactory/train/dpo/workflow.py
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@ -17,7 +17,7 @@
from typing import TYPE_CHECKING, List, Optional
from ...data import PairwiseDataCollatorWithPadding, get_dataset, split_dataset
from ...data import PairwiseDataCollatorWithPadding, get_dataset
from ...extras.constants import IGNORE_INDEX
from ...extras.ploting import plot_loss
from ...hparams import ModelArguments
@ -70,8 +70,8 @@ def run_dpo(
finetuning_args=finetuning_args,
data_collator=data_collator,
callbacks=callbacks,
**tokenizer_module,
**dataset_module,
**tokenizer_module,
)
# Training

4
src/llamafactory/train/kto/workflow.py
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@ -17,7 +17,7 @@
from typing import TYPE_CHECKING, List, Optional
from ...data import KTODataCollatorWithPadding, get_dataset, split_dataset
from ...data import KTODataCollatorWithPadding, get_dataset
from ...extras.constants import IGNORE_INDEX
from ...extras.ploting import plot_loss
from ...hparams import ModelArguments
@ -67,8 +67,8 @@ def run_kto(
finetuning_args=finetuning_args,
data_collator=data_collator,
callbacks=callbacks,
**tokenizer_module,
**dataset_module,
**tokenizer_module,
)
# Training

10
src/llamafactory/train/ppo/trainer.py
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@ -77,9 +77,13 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
ref_model: Optional["AutoModelForCausalLMWithValueHead"],
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
dataset: "Dataset",
data_collator: "DataCollatorWithPadding",
train_dataset: Optional["Dataset"] = None,
eval_dataset: Optional["Dataset"] = None,
) -> None:
if eval_dataset is not None:
raise NotImplementedError("PPOTrainer does not support eval dataset yet.")
backward_batch_size = training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps
ppo_config = PPOConfig(
model_name=model_args.model_name_or_path,
@ -115,7 +119,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
num_training_steps = training_args.max_steps
else:
total_train_batch_size = backward_batch_size * finetuning_args.ppo_buffer_size * training_args.world_size
num_training_steps = training_args.num_train_epochs * math.ceil(len(dataset) / total_train_batch_size)
num_training_steps = training_args.num_train_epochs * math.ceil(len(train_dataset) / total_train_batch_size)
optimizer = self.create_optimizer(model, training_args, finetuning_args)
scheduler = self.create_scheduler(training_args, num_training_steps, optimizer)
@ -126,7 +130,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
model=model,
ref_model=ref_model,
tokenizer=tokenizer,
dataset=dataset,
dataset=train_dataset,
data_collator=data_collator,
lr_scheduler=scheduler,
)

2
src/llamafactory/train/ppo/workflow.py
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@ -63,8 +63,8 @@ def run_ppo(
model=model,
reward_model=reward_model,
ref_model=ref_model,
dataset=dataset_module["train_dataset"],
data_collator=data_collator,
**dataset_module,
**tokenizer_module,
)

4
src/llamafactory/train/pt/workflow.py
View File

@ -20,7 +20,7 @@ from typing import TYPE_CHECKING, List, Optional
from transformers import DataCollatorForLanguageModeling
from ...data import get_dataset, split_dataset
from ...data import get_dataset
from ...extras.ploting import plot_loss
from ...model import load_model, load_tokenizer
from ..trainer_utils import create_modelcard_and_push
@ -53,8 +53,8 @@ def run_pt(
finetuning_args=finetuning_args,
data_collator=data_collator,
callbacks=callbacks,
**tokenizer_module,
**dataset_module,
**tokenizer_module,
)
# Training

4
src/llamafactory/train/rm/workflow.py
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@ -17,7 +17,7 @@
from typing import TYPE_CHECKING, List, Optional
from ...data import PairwiseDataCollatorWithPadding, get_dataset, split_dataset
from ...data import PairwiseDataCollatorWithPadding, get_dataset
from ...extras.ploting import plot_loss
from ...model import load_model, load_tokenizer
from ..callbacks import fix_valuehead_checkpoint
@ -56,8 +56,8 @@ def run_rm(
data_collator=data_collator,
callbacks=callbacks,
compute_metrics=compute_accuracy,
**tokenizer_module,
**dataset_module,
**tokenizer_module,
)
# Training

4
src/llamafactory/train/sft/workflow.py
View File

@ -17,7 +17,7 @@
from typing import TYPE_CHECKING, List, Optional
from ...data import SFTDataCollatorWith4DAttentionMask, get_dataset, split_dataset
from ...data import SFTDataCollatorWith4DAttentionMask, get_dataset
from ...extras.constants import IGNORE_INDEX
from ...extras.misc import get_logits_processor
from ...extras.ploting import plot_loss
@ -75,8 +75,8 @@ def run_sft(
callbacks=callbacks,
compute_metrics=ComputeMetrics(tokenizer) if training_args.predict_with_generate else compute_accuracy,
preprocess_logits_for_metrics=None if training_args.predict_with_generate else eval_logit_processor,
**tokenizer_module,
**dataset_module,
**tokenizer_module,
)
# Keyword arguments for `model.generate`

2
src/llamafactory/train/trainer_utils.py
View File

@ -79,7 +79,7 @@ def create_modelcard_and_push(
"tags": ["llama-factory", finetuning_args.finetuning_type],
}
if data_args.dataset is not None:
kwargs["dataset"] = [dataset.strip() for dataset in data_args.dataset.split(",")]
kwargs["dataset"] = data_args.dataset
if model_args.use_unsloth:
kwargs["tags"] = kwargs["tags"] + ["unsloth"]

4
src/llamafactory/webui/common.py
View File

@ -174,8 +174,8 @@ def load_dataset_info(dataset_dir: str) -> Dict[str, Dict[str, Any]]:
r"""
Loads dataset_info.json.
"""
if dataset_dir == "ONLINE":
logger.info("dataset_dir is ONLINE, using online dataset.")
if dataset_dir == "ONLINE" or dataset_dir.startswith("REMOTE:"):
logger.info("dataset_dir is {}, using online dataset.".format(dataset_dir))
return {}
try:

2
src/llamafactory/webui/runner.py
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@ -259,7 +259,7 @@ class Runner:
use_unsloth=(get("top.booster") == "unsloth"),
visual_inputs=get("top.visual_inputs"),
dataset_dir=get("eval.dataset_dir"),
dataset=",".join(get("eval.dataset")),
eval_dataset=",".join(get("eval.dataset")),
cutoff_len=get("eval.cutoff_len"),
max_samples=int(get("eval.max_samples")),
per_device_eval_batch_size=get("eval.batch_size"),