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# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from .processor_utils import get_paligemma_token_type_ids, get_pixel_values
if TYPE_CHECKING:
from transformers import PreTrainedTokenizer, ProcessorMixin
from ...hparams import DataArguments
from ..template import Template
logger = get_logger(__name__)
def _encode_feedback_example(
prompt: Sequence[Dict[str, str]],
response: Sequence[Dict[str, str]],
kl_response: Sequence[Dict[str, str]],
system: Optional[str],
tools: Optional[str],
template: "Template",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
data_args: "DataArguments",
) -> Tuple[List[int], List[int], List[int], List[int], bool]:
if processor is not None and not hasattr(processor, "image_seq_length"): # llava-like models
prompt[0]["content"] = template.image_token + prompt[0]["content"]
if response[0]["content"]: # desired example
kto_tag = True
messages = prompt + [response[0]]
else: # undesired example
kto_tag = False
messages = prompt + [response[1]]
if kl_response[0]["content"]:
kl_messages = prompt + [kl_response[0]]
else:
kl_messages = prompt + [kl_response[1]]
prompt_ids, response_ids = template.encode_oneturn(
tokenizer, messages, system, tools, data_args.cutoff_len, data_args.reserved_label_len
)
_, kl_response_ids = template.encode_oneturn(
tokenizer, kl_messages, system, tools, 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
return input_ids, labels, kl_input_ids, kl_labels, kto_tag
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
input_ids, labels, kl_input_ids, kl_labels, kto_tag = _encode_feedback_example(
prompt=examples["prompt"][i],
response=examples["response"][i],
kl_response=kl_response[i],
system=examples["system"][i],
tools=examples["tools"][i],
template=template,
tokenizer=tokenizer,
processor=processor,
data_args=data_args,
)
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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