Spaces:
Sleeping
Sleeping
File size: 6,056 Bytes
e81015c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
# Copyright 2025 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 collections import defaultdict
from typing import TYPE_CHECKING, Any, Optional
from ...extras import logging
from ...extras.constants import IGNORE_INDEX
from .processor_utils import DatasetProcessor, infer_seqlen
if TYPE_CHECKING:
from ..mm_plugin import AudioInput, ImageInput, VideoInput
logger = logging.get_logger(__name__)
class FeedbackDatasetProcessor(DatasetProcessor):
def _encode_data_example(
self,
prompt: list[dict[str, str]],
response: list[dict[str, str]],
kl_response: list[dict[str, str]],
system: Optional[str],
tools: Optional[str],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
) -> tuple[list[int], list[int], list[int], list[int], bool]:
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]]
messages = self.template.mm_plugin.process_messages(messages, images, videos, audios, self.processor)
kl_messages = self.template.mm_plugin.process_messages(kl_messages, images, videos, audios, self.processor)
prompt_ids, response_ids = self.template.encode_oneturn(self.tokenizer, messages, system, tools)
kl_prompt_ids, kl_response_ids = self.template.encode_oneturn(self.tokenizer, kl_messages, system, tools)
if self.template.efficient_eos:
response_ids += [self.tokenizer.eos_token_id]
kl_response_ids += [self.tokenizer.eos_token_id]
prompt_ids, _ = self.template.mm_plugin.process_token_ids(
prompt_ids, None, images, videos, audios, self.tokenizer, self.processor
)
kl_prompt_ids, _ = self.template.mm_plugin.process_token_ids(
kl_prompt_ids, None, images, videos, audios, self.tokenizer, self.processor
)
source_len, target_len = infer_seqlen(len(prompt_ids), len(response_ids), self.data_args.cutoff_len)
prompt_ids = prompt_ids[:source_len]
response_ids = response_ids[:target_len]
kl_source_len, kl_target_len = infer_seqlen(
len(kl_prompt_ids), len(kl_response_ids), self.data_args.cutoff_len
)
kl_prompt_ids = kl_prompt_ids[:kl_source_len]
kl_response_ids = kl_response_ids[:kl_target_len]
input_ids = prompt_ids + response_ids
labels = [IGNORE_INDEX] * source_len + response_ids
kl_input_ids = kl_prompt_ids + kl_response_ids
kl_labels = [IGNORE_INDEX] * kl_source_len + kl_response_ids
return input_ids, labels, kl_input_ids, kl_labels, kto_tag
def preprocess_dataset(self, examples: dict[str, list[Any]]) -> dict[str, list[Any]]:
# Creates mismatched pairs of prompts and completions for the KL dataset by adding a +1 offset to the order of completions.
kl_response = [examples["_response"][-1]] + examples["_response"][:-1]
model_inputs = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) < 2:
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
input_ids, labels, kl_input_ids, kl_labels, kto_tag = self._encode_data_example(
prompt=examples["_prompt"][i],
response=examples["_response"][i],
kl_response=kl_response[i],
system=examples["_system"][i],
tools=examples["_tools"][i],
images=examples["_images"][i] or [],
videos=examples["_videos"][i] or [],
audios=examples["_audios"][i] or [],
)
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)
model_inputs["images"].append(examples["_images"][i])
model_inputs["videos"].append(examples["_videos"][i])
model_inputs["audios"].append(examples["_audios"][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_rank0("Your dataset only has one preference type.")
return model_inputs
def print_data_example(self, example: dict[str, list[int]]) -> None:
valid_labels = list(filter(lambda x: x != IGNORE_INDEX, example["labels"]))
print("input_ids:\n{}".format(example["input_ids"]))
print("inputs:\n{}".format(self.tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
print("label_ids:\n{}".format(example["labels"]))
print(f"labels:\n{self.tokenizer.decode(valid_labels, skip_special_tokens=False)}")
|