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

improve KTO impl., replace datasets

Browse Source 
Former-commit-id: e56a57ddcf061de6e4acc8679f7dbf0b68364986
This commit is contained in:
hiyouga
2024-05-18 03:44:56 +08:00
parent e4570e28a8
commit 2bff90719b
53 changed files with 448 additions and 330 deletions
Download Patch File Download Diff File Expand all files Collapse all files

179
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": [], "tag": []}
model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
if processor is not None:
model_inputs["pixel_values"] = []
preprocess_visual_inputs = partial(_preprocess_visual_inputs, processor=processor)
@@ -111,102 +111,11 @@ 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]],
@@ -352,6 +261,90 @@ def preprocess_pairwise_dataset(
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]]]:
# create unrelated input-output pairs for estimating the KL term by flipping the matched pairs
kl_response = examples["response"][::-1]
model_inputs = {
"input_ids": [],
"attention_mask": [],
"labels": [],
"kl_input_ids": [],
"kl_attention_mask": [],
"kl_labels": [],
"kto_tags": [],
}
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]) < 2:
continue
if processor is not None:
examples["prompt"][i][0]["content"] = "<image>" + examples["prompt"][i][0]["content"]
if examples["response"][i][0]["content"]: # desired example
kto_tag = True
messages = examples["prompt"][i] + [examples["response"][i][0]]
else: # undesired example
kto_tag = False
messages = examples["prompt"][i] + [examples["response"][i][1]]
if kl_response[i][0]["content"]:
kl_messages = examples["prompt"][i] + [kl_response[i][0]]
else:
kl_messages = examples["prompt"][i] + [kl_response[i][1]]
prompt_ids, response_ids = template.encode_oneturn(
tokenizer,
messages,
examples["system"][i],
examples["tools"][i],
data_args.cutoff_len,
data_args.reserved_label_len,
)
_, kl_response_ids = template.encode_oneturn(
tokenizer,
kl_messages,
examples["system"][i],
examples["tools"][i],
data_args.cutoff_len,
data_args.reserved_label_len,
)
if template.efficient_eos:
response_ids += [tokenizer.eos_token_id]
kl_response_ids += [tokenizer.eos_token_id]
input_ids = prompt_ids + response_ids
labels = [IGNORE_INDEX] * len(prompt_ids) + response_ids
kl_input_ids = prompt_ids + kl_response_ids
kl_labels = [IGNORE_INDEX] * len(prompt_ids) + kl_response_ids
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["kto_tags"].append(kto_tag)
if processor is not None:
model_inputs["pixel_values"].append(preprocess_visual_inputs(examples["images"][i]))
desirable_num = sum([1 for tag in model_inputs["kto_tags"] if tag])
undesirable_num = len(model_inputs["kto_tags"]) - desirable_num
if desirable_num == 0 or undesirable_num == 0:
logger.warning("Your dataset only has one preference type.")
return model_inputs
def print_supervised_dataset_example(example: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
print("input_ids:\n{}".format(example["input_ids"]))
print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
@@ -380,7 +373,7 @@ 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", "kto"],
stage: Literal["pt", "sft", "rm", "kto"],
template: "Template",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],