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# Copyright 2025 OpenAccess AI Collective and the LlamaFactory team.
#
# This code is inspired by the OpenAccess AI Collective's axolotl library.
# https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/src/axolotl/monkeypatch/utils.py
#
# 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 dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Literal, Optional
import numpy as np
import torch
import torch.nn.functional as F
from transformers import DataCollatorForSeq2Seq
from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER
from ..extras.packages import is_pillow_available
if is_pillow_available():
from PIL import Image
if TYPE_CHECKING:
from transformers import ProcessorMixin
from .template import Template
def prepare_4d_attention_mask(attention_mask_with_indices: "torch.Tensor", dtype: "torch.dtype") -> "torch.Tensor":
r"""Expand 2d attention mask to 4d attention mask.
Expand the attention mask with indices from (batch_size, seq_len) to (batch_size, 1, seq_len, seq_len),
handle packed sequences and transforms the mask to lower triangular form to prevent future peeking.
e.g.
```python
# input
[[1, 1, 2, 2, 2, 0]]
# output
[
[
[
[o, x, x, x, x, x],
[o, o, x, x, x, x],
[x, x, o, x, x, x],
[x, x, o, o, x, x],
[x, x, o, o, o, x],
[x, x, x, x, x, x],
]
]
]
```
where `o` equals to `0.0`, `x` equals to `min_dtype`.
"""
_, seq_len = attention_mask_with_indices.size()
min_dtype = torch.finfo(dtype).min
zero_tensor = torch.tensor(0, dtype=dtype)
# Create a non-padding mask.
non_padding_mask = (attention_mask_with_indices != 0).unsqueeze(1).unsqueeze(2)
# Create indices for comparison.
indices = attention_mask_with_indices.unsqueeze(1).unsqueeze(2) # [bsz, 1, 1, seq_len]
indices_t = attention_mask_with_indices.unsqueeze(1).unsqueeze(3) # [bsz, 1, seq_len, 1]
# Create a lower triangular mask.
tril_mask = torch.tril(torch.ones((seq_len, seq_len), dtype=torch.bool))
attention_mask_4d = (indices == indices_t) & non_padding_mask & tril_mask
# Invert the attention mask.
attention_mask_4d = torch.where(attention_mask_4d, zero_tensor, min_dtype)
return attention_mask_4d
@dataclass
class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
r"""Data collator that supports VLMs.
Features should contain input_ids, attention_mask, labels, and optionally contain images, videos and audios.
"""
template: Optional["Template"] = None
processor: Optional["ProcessorMixin"] = None
def __post_init__(self):
if self.template is None:
raise ValueError("Template is required for MultiModalDataCollator.")
def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
batch_images, batch_videos, batch_audios = [], [], []
batch_imglens, batch_vidlens, batch_audlens, batch_input_ids = [], [], [], []
for feature in features:
images = feature.pop("images", None) or []
videos = feature.pop("videos", None) or []
audios = feature.pop("audios", None) or []
batch_images.extend(images)
batch_videos.extend(videos)
batch_audios.extend(audios)
batch_imglens.append(len(images))
batch_vidlens.append(len(videos))
batch_audlens.append(len(audios))
batch_input_ids.append(feature["input_ids"])
fake_input_ids = []
if (
self.template.mm_plugin.image_token is not None and sum(batch_imglens) == 0 and sum(batch_vidlens) == 0
): # avoid process hanging in zero3/fsdp case
fake_messages = [{"role": "user", "content": IMAGE_PLACEHOLDER}]
fake_images = [Image.new("RGB", (64, 64), (255, 255, 255))]
fake_messages = self.template.mm_plugin.process_messages(
fake_messages, fake_images, [], [], self.processor
)
_fake_input_ids = self.tokenizer.encode(fake_messages[0]["content"], add_special_tokens=False)
_fake_input_ids, _ = self.template.mm_plugin.process_token_ids(
_fake_input_ids, None, fake_images, [], [], self.tokenizer, self.processor
)
fake_input_ids.extend(_fake_input_ids)
batch_images = fake_images
batch_imglens[0] = 1
if (
self.template.mm_plugin.audio_token is not None and sum(batch_audlens) == 0
): # avoid process hanging in zero3/fsdp case
fake_messages = [{"role": "user", "content": AUDIO_PLACEHOLDER}]
fake_audios = [np.zeros(1600)]
fake_messages = self.template.mm_plugin.process_messages(
fake_messages, [], [], fake_audios, self.processor
)
_fake_input_ids = self.tokenizer.encode(fake_messages[0]["content"], add_special_tokens=False)
_fake_input_ids, _ = self.template.mm_plugin.process_token_ids(
_fake_input_ids, None, [], [], fake_audios, self.tokenizer, self.processor
)
fake_input_ids.extend(_fake_input_ids)
batch_audios = fake_audios
batch_audlens[0] = 1
if len(fake_input_ids) != 0:
if self.tokenizer.padding_side == "right":
features[0]["input_ids"] = features[0]["input_ids"] + fake_input_ids
features[0]["attention_mask"] = features[0]["attention_mask"] + [0] * len(fake_input_ids)
features[0]["labels"] = features[0]["labels"] + [IGNORE_INDEX] * len(fake_input_ids)
else:
features[0]["input_ids"] = fake_input_ids + features[0]["input_ids"]
features[0]["attention_mask"] = [0] * len(fake_input_ids) + features[0]["attention_mask"]
features[0]["labels"] = [IGNORE_INDEX] * len(fake_input_ids) + features[0]["labels"]
batch_input_ids[0] = features[0]["input_ids"]
mm_inputs = self.template.mm_plugin.get_mm_inputs(
batch_images,
batch_videos,
batch_audios,
batch_imglens,
batch_vidlens,
batch_audlens,
batch_input_ids,
self.processor,
)
if "token_type_ids" in mm_inputs:
token_type_ids = mm_inputs.pop("token_type_ids")
for i, feature in enumerate(features):
feature["token_type_ids"] = token_type_ids[i]
features: dict[str, torch.Tensor] = super().__call__(features)
if self.model is not None and hasattr(self.model, "get_rope_index"): # for qwen2vl mrope
rope_index_kwargs = {
"input_ids": features["input_ids"],
"image_grid_thw": mm_inputs.get("image_grid_thw"),
"video_grid_thw": mm_inputs.get("video_grid_thw"),
"attention_mask": (features["attention_mask"] >= 1).float(),
}
if "second_per_grid_ts" in mm_inputs: # for qwen2vl
rope_index_kwargs["second_per_grid_ts"] = mm_inputs.get("second_per_grid_ts")
if "video_second_per_grid" in mm_inputs: # for qwen2omni
rope_index_kwargs["second_per_grids"] = mm_inputs.get("video_second_per_grid")
if getattr(self.model.config, "model_type", None) == "qwen2_5_omni_thinker": # for qwen2omni
rope_index_kwargs["use_audio_in_video"] = getattr(self.processor, "use_audio_in_video", False)
feature_attention_mask = mm_inputs.get("feature_attention_mask", None)
if feature_attention_mask is not None:
audio_feature_lengths = torch.sum(
feature_attention_mask, dim=1
) # FIXME need to get video image lengths
rope_index_kwargs["audio_seqlens"] = audio_feature_lengths # prepare for input
delta0 = (1 - rope_index_kwargs["attention_mask"]).sum(dim=-1).unsqueeze(1)
# avoid conflict
new_position_ids, rope_deltas = self.model.get_rope_index(**rope_index_kwargs)
features["position_ids"], features["rope_deltas"] = (
new_position_ids.clone(),
rope_deltas - delta0,
) # avoid inplace operation FIXME
else: # for qwen2vl
features["position_ids"], features["rope_deltas"] = self.model.get_rope_index(**rope_index_kwargs)
if "cross_attention_mask" in mm_inputs: # for mllama inputs when pad_to_multiple_of is enabled
cross_attention_mask = mm_inputs.pop("cross_attention_mask")
seq_len = features["input_ids"].size(1)
orig_len = cross_attention_mask.size(1)
mm_inputs["cross_attention_mask"] = F.pad(cross_attention_mask, (0, 0, 0, 0, 0, seq_len - orig_len))
features.update(mm_inputs)
if "image_bound" in features: # for minicpmv inputs
bsz, seq_length = features["input_ids"].shape
features["position_ids"] = torch.arange(seq_length).long().repeat(bsz, 1)
return {"data": features, "input_ids": features["input_ids"], "labels": features["labels"]}
return features
@dataclass
class SFTDataCollatorWith4DAttentionMask(MultiModalDataCollatorForSeq2Seq):
r"""Data collator for 4d attention mask."""
block_diag_attn: bool = False
attn_implementation: Literal["eager", "sdpa", "flash_attention_2"] = "eager"
compute_dtype: "torch.dtype" = torch.float32
def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
features = super().__call__(features)
if self.block_diag_attn and self.attn_implementation != "flash_attention_2":
features["attention_mask"] = prepare_4d_attention_mask(features["attention_mask"], self.compute_dtype)
for key, value in features.items(): # cast data dtype for paligemma
if torch.is_tensor(value) and torch.is_floating_point(value):
features[key] = value.to(self.compute_dtype)
return features
@dataclass
class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
r"""Data collator for pairwise data."""
def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
r"""Pad batched data to the longest sequence in the batch.
We generate 2 * n examples where the first n examples represent chosen examples and
the last n examples represent rejected examples.
"""
concatenated_features = []
for key in ("chosen", "rejected"):
for feature in features:
target_feature = {
"input_ids": feature[f"{key}_input_ids"],
"attention_mask": feature[f"{key}_attention_mask"],
"labels": feature[f"{key}_labels"],
"images": feature["images"],
"videos": feature["videos"],
"audios": feature["audios"],
}
concatenated_features.append(target_feature)
return super().__call__(concatenated_features)
@dataclass
class KTODataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
r"""Data collator for KTO data."""
def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
target_features = []
kl_features = []
kto_tags = []
for feature in features:
target_feature = {
"input_ids": feature["input_ids"],
"attention_mask": feature["attention_mask"],
"labels": feature["labels"],
"images": feature["images"],
"videos": feature["videos"],
"audios": feature["audios"],
}
kl_feature = {
"input_ids": feature["kl_input_ids"],
"attention_mask": feature["kl_attention_mask"],
"labels": feature["kl_labels"],
"images": feature["images"],
"videos": feature["videos"],
"audios": feature["audios"],
}
target_features.append(target_feature)
kl_features.append(kl_feature)
kto_tags.append(feature["kto_tags"])
batch = super().__call__(target_features)
kl_batch = super().__call__(kl_features)
batch["kl_input_ids"] = kl_batch["input_ids"]
batch["kl_attention_mask"] = kl_batch["attention_mask"]
batch["kl_labels"] = kl_batch["labels"]
if "cross_attention_mask" in kl_batch: # for mllama inputs
batch["kl_cross_attention_mask"] = kl_batch["cross_attention_mask"]
if "token_type_ids" in kl_batch:
batch["kl_token_type_ids"] = kl_batch["token_type_ids"]
batch["kto_tags"] = torch.tensor(kto_tags)
return batch
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