File size: 4,583 Bytes
cc886b8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | from typing import TYPE_CHECKING, Any, Dict, List, Optional
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from .mm_utils import get_paligemma_token_type_ids, get_pixel_values
if TYPE_CHECKING:
from transformers import ProcessorMixin
from transformers.tokenization_utils import PreTrainedTokenizer
from ...hparams import DataArguments
from ..template import Template
logger = get_logger(__name__)
def preprocess_feedback_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"] = []
if hasattr(processor, "image_seq_length"): # paligemma models
model_inputs["token_type_ids"] = []
model_inputs["kl_token_type_ids"] = []
for i in range(len(examples["prompt"])):
if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) < 2:
logger.warning("Dropped invalid example: {}".format(examples["prompt"][i] + examples["response"][i]))
continue
if processor is not None and not hasattr(processor, "image_seq_length"): # llava-like models
examples["prompt"][i][0]["content"] = template.image_token + 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]
if processor is not None and hasattr(processor, "image_seq_length"): # paligemma models
image_token_id = tokenizer.convert_tokens_to_ids(template.image_token)
prompt_ids = [image_token_id] * getattr(processor, "image_seq_length") + prompt_ids
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(get_pixel_values(examples["images"][i], processor))
if hasattr(processor, "image_seq_length"): # paligemma models
model_inputs["token_type_ids"].append(get_paligemma_token_type_ids(len(input_ids), processor))
model_inputs["kl_token_type_ids"].append(get_paligemma_token_type_ids(len(kl_input_ids), processor))
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
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