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Fix VLM preprocessing and add mRoPE position handling in target head #527

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Fix VLM preprocessing and add mRoPE position handling in target head #527

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d70b121
fix
liusy58 Apr 8, 2026
01d0b92
fix lint
liusy58 Apr 9, 2026
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12 changes: 11 additions & 1 deletion specforge/data/preprocessing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -216,7 +216,11 @@ def preprocess_vlm_conversations(
# Note: currently, we assume that each example has only one image
for i, image in enumerate(examples["image"]):
source = examples["conversations"][i]
messages = [{"role": "system", "content": system_prompt}]
if source[0]["role"] == "system":
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@gemini-code-assist gemini-code-assist bot Apr 8, 2026

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critical

There's a potential IndexError here. If source (which is examples["conversations"][i]) is an empty list, accessing source[0] will raise an exception. The check for an empty source on line 224 happens after this access.

To prevent this, you should add a check to ensure source is not empty before accessing its first element.

Suggested change
if source[0]["role"] == "system":
if source and source[0]["role"] == "system":

messages = [{"role": "system", "content": source[0]["content"]}]
source = source[1:]
else:
messages = [{"role": "system", "content": system_prompt}]
if not source:
# if the source is None, skip it
continue
Expand Down Expand Up @@ -533,6 +537,12 @@ def process_data(data, max_len, transform=None):
new_data["target"] = target
new_data["hidden_state"] = hidden_state
new_data["input_ids"] = input_ids

if "pixel_values" in data:
new_data["pixel_values"] = data["pixel_values"]
if "image_grid_thw" in data:
new_data["image_grid_thw"] = data["image_grid_thw"]

if transform:
new_data = transform(new_data)
return new_data
Expand Down
166 changes: 166 additions & 0 deletions specforge/modeling/target/target_head.py
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Original file line number Diff line number Diff line change
Expand Up @@ -95,3 +95,169 @@ def preprocess(self, input_ids, target, loss_mask):
input_ids = padding(input_ids, left=False)
loss_mask = loss_mask[..., None]
return input_ids, target, loss_mask

def get_rope_index(
self,
input_ids: torch.LongTensor,
image_grid_thw: Optional[torch.LongTensor] = None,
video_grid_thw: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
) -> tuple[torch.Tensor, torch.Tensor]:

if hasattr(self.config, "vision_config"):
spatial_merge_size = self.config.vision_config.spatial_merge_size
else:
spatial_merge_size = getattr(self.config, "spatial_merge_size", 2)

image_token_id = self.config.image_token_id
video_token_id = self.config.video_token_id
vision_start_token_id = self.config.vision_start_token_id

if video_grid_thw is not None:
video_grid_thw = torch.repeat_interleave(
video_grid_thw, video_grid_thw[:, 0], dim=0
)
video_grid_thw[:, 0] = 1

if input_ids is not None and (
image_grid_thw is not None or video_grid_thw is not None
):
total_input_ids = input_ids
if attention_mask is None:
attention_mask = torch.ones_like(total_input_ids)

position_ids = torch.ones(
3,
input_ids.shape[0],
input_ids.shape[1],
dtype=input_ids.dtype,
device=input_ids.device,
)

image_index, video_index = 0, 0
mrope_position_deltas = []

for i, curr_input_ids in enumerate(total_input_ids):
curr_mask = attention_mask[i] == 1
masked_ids = curr_input_ids[curr_mask]

vision_start_indices = torch.argwhere(
masked_ids == vision_start_token_id
).squeeze(1)
vision_tokens = masked_ids[vision_start_indices + 1]
image_nums = (vision_tokens == image_token_id).sum()
video_nums = (vision_tokens == video_token_id).sum()

input_tokens = masked_ids.tolist()
llm_pos_ids_list = []
st = 0
remain_images, remain_videos = image_nums, video_nums

for _ in range(image_nums + video_nums):
ed_image = (
input_tokens.index(image_token_id, st)
if image_token_id in input_tokens[st:] and remain_images > 0
else len(input_tokens) + 1
)
ed_video = (
input_tokens.index(video_token_id, st)
if video_token_id in input_tokens[st:] and remain_videos > 0
else len(input_tokens) + 1
)

if ed_image < ed_video:
t, h, w = image_grid_thw[image_index]
image_index += 1
remain_images -= 1
ed = ed_image
else:
t, h, w = video_grid_thw[video_index]
video_index += 1
remain_videos -= 1
ed = ed_video

llm_grid_t, llm_grid_h, llm_grid_w = (
t.item(),
h.item() // spatial_merge_size,
w.item() // spatial_merge_size,
)
text_len = ed - st

st_idx = (
llm_pos_ids_list[-1].max() + 1
if len(llm_pos_ids_list) > 0
else 0
)
llm_pos_ids_list.append(
torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
)

t_index = (
torch.arange(llm_grid_t)
.view(-1, 1)
.expand(-1, llm_grid_h * llm_grid_w)
.flatten()
)
h_index = (
torch.arange(llm_grid_h)
.view(1, -1, 1)
.expand(llm_grid_t, -1, llm_grid_w)
.flatten()
)
w_index = (
torch.arange(llm_grid_w)
.view(1, 1, -1)
.expand(llm_grid_t, llm_grid_h, -1)
.flatten()
)

llm_pos_ids_list.append(
torch.stack([t_index, h_index, w_index]) + text_len + st_idx
)
st = ed + llm_grid_t * llm_grid_h * llm_grid_w

if st < len(input_tokens):
st_idx = (
llm_pos_ids_list[-1].max() + 1
if len(llm_pos_ids_list) > 0
else 0
)
text_len = len(input_tokens) - st
llm_pos_ids_list.append(
torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
)

llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
position_ids[..., i, curr_mask] = llm_positions.to(position_ids.device)
mrope_position_deltas.append(
llm_positions.max() + 1 - len(curr_input_ids)
)

mrope_deltas = torch.tensor(
mrope_position_deltas, device=input_ids.device
).unsqueeze(1)
return position_ids, mrope_deltas
else:
if attention_mask is not None:
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
position_ids = (
position_ids.unsqueeze(0)
.expand(3, -1, -1)
.to(attention_mask.device)
)
max_pos = position_ids.max(0)[0].max(-1, keepdim=True)[0]
mrope_deltas = max_pos + 1 - attention_mask.shape[-1]
else:
position_ids = (
torch.arange(input_ids.shape[1], device=input_ids.device)
.view(1, 1, -1)
.expand(3, input_ids.shape[0], -1)
)
mrope_deltas = torch.zeros(
[input_ids.shape[0], 1],
device=input_ids.device,
dtype=input_ids.dtype,
)

return position_ids, mrope_deltas
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