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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_pairwise_example(
prompt: Sequence[Dict[str, str]],
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]]:
if processor is not None and not hasattr(processor, "image_seq_length"): # llava-like models
prompt[0]["content"] = template.image_token + prompt[0]["content"]
chosen_messages = prompt + [response[0]]
rejected_messages = prompt + [response[1]]
prompt_ids, chosen_ids = template.encode_oneturn(
tokenizer, chosen_messages, system, tools, data_args.cutoff_len, data_args.reserved_label_len
)
_, rejected_ids = template.encode_oneturn(
tokenizer, rejected_messages, system, tools, data_args.cutoff_len, data_args.reserved_label_len
)
if template.efficient_eos:
chosen_ids += [tokenizer.eos_token_id]
rejected_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
chosen_input_ids = prompt_ids + chosen_ids
chosen_labels = [IGNORE_INDEX] * len(prompt_ids) + chosen_ids
rejected_input_ids = prompt_ids + rejected_ids
rejected_labels = [IGNORE_INDEX] * len(prompt_ids) + rejected_ids
return chosen_input_ids, chosen_labels, rejected_input_ids, rejected_labels
def preprocess_pairwise_dataset(
examples: Dict[str, List[Any]],
template: "Template",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
data_args: "DataArguments",
) -> Dict[str, List[List[int]]]:
# build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
model_inputs = {
"chosen_input_ids": [],
"chosen_attention_mask": [],
"chosen_labels": [],
"rejected_input_ids": [],
"rejected_attention_mask": [],
"rejected_labels": [],
}
if processor is not None:
model_inputs["pixel_values"] = []
if hasattr(processor, "image_seq_length"): # paligemma models
model_inputs["chosen_token_type_ids"] = []
model_inputs["rejected_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
chosen_input_ids, chosen_labels, rejected_input_ids, rejected_labels = _encode_pairwise_example(
prompt=examples["prompt"][i],
response=examples["response"][i],
system=examples["system"][i],
tools=examples["tools"][i],
template=template,
tokenizer=tokenizer,
processor=processor,
data_args=data_args,
)
model_inputs["chosen_input_ids"].append(chosen_input_ids)
model_inputs["chosen_attention_mask"].append([1] * len(chosen_input_ids))
model_inputs["chosen_labels"].append(chosen_labels)
model_inputs["rejected_input_ids"].append(rejected_input_ids)
model_inputs["rejected_attention_mask"].append([1] * len(rejected_input_ids))
model_inputs["rejected_labels"].append(rejected_labels)
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["chosen_token_type_ids"].append(
get_paligemma_token_type_ids(len(chosen_input_ids), processor)
)
model_inputs["rejected_token_type_ids"].append(
get_paligemma_token_type_ids(len(rejected_input_ids), processor)
)
return model_inputs
def print_pairwise_dataset_example(example: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
valid_chosen_labels = list(filter(lambda x: x != IGNORE_INDEX, example["chosen_labels"]))
valid_rejected_labels = list(filter(lambda x: x != IGNORE_INDEX, example["rejected_labels"]))
print("chosen_input_ids:\n{}".format(example["chosen_input_ids"]))
print("chosen_inputs:\n{}".format(tokenizer.decode(example["chosen_input_ids"], skip_special_tokens=False)))
print("chosen_label_ids:\n{}".format(example["chosen_labels"]))
print("chosen_labels:\n{}".format(tokenizer.decode(valid_chosen_labels, skip_special_tokens=False)))
print("rejected_input_ids:\n{}".format(example["rejected_input_ids"]))
print("rejected_inputs:\n{}".format(tokenizer.decode(example["rejected_input_ids"], skip_special_tokens=False)))
print("rejected_label_ids:\n{}".format(example["rejected_labels"]))
print("rejected_labels:\n{}".format(tokenizer.decode(valid_rejected_labels, skip_special_tokens=False)))
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