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fix up

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Former-commit-id: 43a56cb331fae899ca35b0c312730d4ab79d0c42
This commit is contained in:
hiyouga
2024-07-15 01:04:56 +08:00
parent 68365045b4
commit e4d11a117b
18 changed files with 46 additions and 41 deletions
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8
src/llamafactory/eval/evaluator.py
View File

@@ -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
View File

@@ -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
View File

@@ -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
View File

@@ -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
View File

@@ -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
View File

@@ -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"),