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lazy image load

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hiyouga
2024-09-04 02:27:08 +08:00
parent 59d2b31e96
commit 47ea97fb1b
19 changed files with 353 additions and 366 deletions
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39
src/llamafactory/data/processors/feedback.py
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@@ -21,10 +21,10 @@ from .processor_utils import infer_seqlen
if TYPE_CHECKING:
from PIL.Image import Image
from transformers import PreTrainedTokenizer, ProcessorMixin
from ...hparams import DataArguments
from ..mm_plugin import ImageInput
from ..template import Template
@@ -37,12 +37,12 @@ def _encode_feedback_example(
kl_response: Sequence[Dict[str, str]],
system: Optional[str],
tools: Optional[str],
images: Sequence["Image"],
images: Sequence["ImageInput"],
template: "Template",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
cutoff_len: int,
) -> Tuple[List[int], List[int], List[int], List[int], bool, Dict[str, Any]]:
) -> Tuple[List[int], List[int], List[int], List[int], bool]:
if response[0]["content"]: # desired example
kto_tag = True
messages = prompt + [response[0]]
@@ -78,15 +78,7 @@ def _encode_feedback_example(
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
extra_inputs = template.mm_plugin.get_mm_inputs(
images=images,
feature_seqlens={
"token_type_ids": len(input_ids),
"kl_token_type_ids": len(kl_input_ids),
},
processor=processor,
)
return input_ids, labels, kl_input_ids, kl_labels, kto_tag, extra_inputs
return input_ids, labels, kl_input_ids, kl_labels, kto_tag
def preprocess_feedback_dataset(
@@ -97,20 +89,20 @@ def preprocess_feedback_dataset(
data_args: "DataArguments",
) -> Dict[str, List[Any]]:
# create unrelated input-output pairs for estimating the KL term by flipping the matched pairs
kl_response = examples["response"][::-1]
kl_response = 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("Dropped invalid example: {}".format(examples["prompt"][i] + examples["response"][i]))
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, extra_inputs = _encode_feedback_example(
prompt=examples["prompt"][i],
response=examples["response"][i],
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],
images=examples["images"][i],
system=examples["_system"][i],
tools=examples["_tools"][i],
images=examples["_images"][i] or [],
template=template,
tokenizer=tokenizer,
processor=processor,
@@ -123,8 +115,7 @@ def preprocess_feedback_dataset(
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)
for key, value in extra_inputs.items():
model_inputs[key].append(value)
model_inputs["images"].append(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