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add rlhf-v dataset

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Former-commit-id: 3fd18fc34a0c994a738504746abfd5548e002437
This commit is contained in:
hiyouga
2024-09-01 22:57:41 +08:00
parent 7621526d22
commit 60cf12727b
12 changed files with 107 additions and 33 deletions
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16
src/llamafactory/data/aligner.py
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@@ -14,7 +14,7 @@
import os
from functools import partial
from typing import TYPE_CHECKING, Any, Dict, List, Union
from typing import TYPE_CHECKING, Any, Dict, List, Sequence, Union
from datasets import Features
@@ -33,19 +33,17 @@ if TYPE_CHECKING:
logger = get_logger(__name__)
def _convert_images(images: List[Any], dataset_attr: "DatasetAttr", data_args: "DataArguments") -> List[Any]:
def _convert_images(images: Sequence[Any], dataset_attr: "DatasetAttr", data_args: "DataArguments") -> List[Any]:
r"""
Optionally concatenates image path to dataset dir when loading from local disk.
"""
outputs = []
images = images[:]
if dataset_attr.load_from in ["script", "file"]:
for image in images:
if isinstance(image, str) and os.path.isfile(os.path.join(data_args.dataset_dir, image)):
outputs.append(os.path.join(data_args.dataset_dir, image))
else:
outputs.append(image)
for i in range(len(images)):
if isinstance(images[i], str) and os.path.isfile(os.path.join(data_args.dataset_dir, images[i])):
images[i] = os.path.join(data_args.dataset_dir, images[i])
return outputs
return images
def convert_alpaca(

14
src/llamafactory/data/collator.py
View File

@@ -142,15 +142,15 @@ class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
"attention_mask": feature["{}_attention_mask".format(key)],
"labels": feature["{}_labels".format(key)],
}
if "{}_token_type_ids".format(key) in feature:
target_feature["token_type_ids"] = feature["{}_token_type_ids".format(key)]
if "pixel_values" in feature: # image data are same for chosen and rejected
target_feature["pixel_values"] = feature["pixel_values"]
if "image_grid_thw" in feature:
target_feature["image_grid_thw"] = feature["image_grid_thw"]
if "{}_token_type_ids".format(key) in feature:
target_feature["token_type_ids"] = feature["{}_token_type_ids".format(key)]
concatenated_features.append(target_feature)
return super().__call__(concatenated_features)
@@ -177,16 +177,16 @@ class KTODataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
"attention_mask": feature["kl_attention_mask"],
"labels": feature["kl_labels"],
}
if "token_type_ids" in feature:
target_feature["token_type_ids"] = feature["token_type_ids"]
kl_feature["token_type_ids"] = feature["kl_token_type_ids"]
if "pixel_values" in feature:
target_feature["pixel_values"] = feature["pixel_values"]
if "image_grid_thw" in feature:
target_feature["image_grid_thw"] = feature["image_grid_thw"]
if "token_type_ids" in feature:
target_feature["token_type_ids"] = feature["token_type_ids"]
kl_feature["token_type_ids"] = feature["kl_token_type_ids"]
target_features.append(target_feature)
kl_features.append(kl_feature)
kto_tags.append(feature["kto_tags"])

18
src/llamafactory/data/mm_plugin.py
View File

@@ -19,6 +19,23 @@ if TYPE_CHECKING:
from transformers.image_processing_utils import BaseImageProcessor
def _regularize_images(images: Sequence["ImageObject"], processor: "ProcessorMixin") -> List["ImageObject"]:
r"""
Regularizes images to avoid error. Including resizing and mode convert.
"""
images = images[:]
image_resolution = getattr(processor, "image_resolution", 512)
for i in range(len(images)):
if max(images[i].width, images[i].height) > image_resolution:
factor = image_resolution / max(images[i].width, images[i].height)
images[i] = images[i].resize((int(images[i].width * factor), int(images[i].height * factor)))
if images[i].mode != "RGB":
images[i] = images[i].convert("RGB")
return images
def _get_mm_inputs(images: Sequence["ImageObject"], processor: "ProcessorMixin") -> Dict[str, "torch.Tensor"]:
r"""
Processes visual inputs.
@@ -34,6 +51,7 @@ def _get_mm_inputs(images: Sequence["ImageObject"], processor: "ProcessorMixin")
"""
image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
if len(images) != 0:
images = _regularize_images(images, processor)
image_inputs = image_processor(images=images, return_tensors="pt")
else: # add NoneType for fake images
image = Image.new("RGB", (64, 64), (255, 255, 255))