ros/LLaMA-Factory
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

add kto

Browse Source 
Former-commit-id: ec51986cf70b0bdd79b8141e45916670fb97a08e
This commit is contained in:
enji.zhou
2024-05-17 13:09:17 +08:00
parent 92b3697e2c
commit 66b5634ebf
12 changed files with 452 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files

104
src/llamafactory/data/preprocess.py
View File

@@ -70,7 +70,7 @@ def preprocess_supervised_dataset(
) -> Dict[str, List[List[int]]]:
# build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
# for multiturn examples, we only mask the prompt part in each prompt-response pair.
model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
model_inputs = {"input_ids": [], "attention_mask": [], "labels": [], "tag": []}
if processor is not None:
model_inputs["pixel_values"] = []
preprocess_visual_inputs = partial(_preprocess_visual_inputs, processor=processor)
@@ -111,11 +111,102 @@ def preprocess_supervised_dataset(
model_inputs["input_ids"].append(input_ids)
model_inputs["attention_mask"].append([1] * len(input_ids))
model_inputs["labels"].append(labels)
model_inputs["tag"].append(examples["tag"])
if processor is not None:
model_inputs["pixel_values"].append(preprocess_visual_inputs(examples["images"][i]))
return model_inputs
def preprocess_kto_dataset(
examples: Dict[str, List[Any]],
template: "Template",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
data_args: "DataArguments",
) -> Dict[str, List[List[int]]]:
# build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
# for multiturn examples, we only mask the prompt part in each prompt-response pair.
model_inputs = {"input_ids": [], "attention_mask": [], "labels": [],"kl_input_ids": [], "kl_attention_mask": [], "kl_labels": [], "tag": []}
"""Creates mismatched pairs of prompts and completions for the KL dataset by reversing the order of completions."""
examples['kl_response'] = examples['response'][::-1]
if processor is not None:
model_inputs["pixel_values"] = []
preprocess_visual_inputs = partial(_preprocess_visual_inputs, processor=processor)
for i in range(len(examples["prompt"])):
if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1:
continue
if processor is not None:
examples["prompt"][i][0]["content"] = "<image>" + examples["prompt"][i][0]["content"]
messages = examples["prompt"][i] + examples["response"][i]
kl_messages = examples["prompt"][i] + examples["kl_response"][i]
input_ids, labels = [], []
kl_input_ids, kl_labels = [], []
for turn_idx, (source_ids, target_ids) in enumerate(
template.encode_multiturn(
tokenizer,
messages,
examples["system"][i],
examples["tools"][i],
data_args.cutoff_len,
data_args.reserved_label_len,
)
):
if data_args.train_on_prompt:
source_mask = source_ids
elif turn_idx != 0 and template.efficient_eos:
source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1)
else:
source_mask = [IGNORE_INDEX] * len(source_ids)
input_ids += source_ids + target_ids
labels += source_mask + target_ids
if template.efficient_eos:
input_ids += [tokenizer.eos_token_id]
labels += [tokenizer.eos_token_id]
for turn_idx, (source_ids, target_ids) in enumerate(
template.encode_multiturn(
tokenizer,
kl_messages,
examples["system"][i],
examples["tools"][i],
data_args.cutoff_len,
data_args.reserved_label_len,
)
):
if data_args.train_on_prompt:
source_mask = source_ids
elif turn_idx != 0 and template.efficient_eos:
source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1)
else:
source_mask = [IGNORE_INDEX] * len(source_ids)
kl_input_ids += source_ids + target_ids
kl_labels += source_mask + target_ids
if template.efficient_eos:
kl_input_ids += [tokenizer.eos_token_id]
kl_labels += [tokenizer.eos_token_id]
model_inputs["input_ids"].append(input_ids)
model_inputs["attention_mask"].append([1] * len(input_ids))
model_inputs["labels"].append(labels)
model_inputs["kl_input_ids"].append(kl_input_ids)
model_inputs["kl_attention_mask"].append([1] * len(kl_input_ids))
model_inputs["kl_labels"].append(kl_labels)
model_inputs["tag"].append(examples["tag"][i])
if processor is not None:
model_inputs["pixel_values"].append(preprocess_visual_inputs(examples["images"][i]))
desirable = sum([1 for tag in model_inputs["tag"] if tag is True])
undesirable = sum([1 for tag in model_inputs["tag"] if tag is False])
logger.info("desirable data in KTO dataset: {},undesirable data in KTO dataset: {}".format(desirable, undesirable))
if desirable == 0 or undesirable == 0:
logger.warning("Your dataset only has one preference type.")
return model_inputs
def preprocess_packed_supervised_dataset(
examples: Dict[str, List[Any]],
@@ -289,7 +380,7 @@ def print_unsupervised_dataset_example(example: Dict[str, List[int]], tokenizer:
def get_preprocess_and_print_func(
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
stage: Literal["pt", "sft", "rm", "ppo"],
stage: Literal["pt", "sft", "rm", "ppo", "kto"],
template: "Template",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
@@ -328,6 +419,15 @@ def get_preprocess_and_print_func(
data_args=data_args,
)
print_function = partial(print_pairwise_dataset_example, tokenizer=tokenizer)
elif stage == "kto":
preprocess_func = partial(
preprocess_kto_dataset,
template=template,
tokenizer=tokenizer,
processor=processor,
data_args=data_args,
)
print_function = partial(print_supervised_dataset_example, tokenizer=tokenizer)
else:
preprocess_func = partial(
preprocess_unsupervised_dataset,