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feat: support mm_token_type_ids and 3D RoPE alignment for Qwen2/3-VL … #437

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feat: support mm_token_type_ids and 3D RoPE alignment for Qwen2/3-VL … #437

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20 changes: 20 additions & 0 deletions roll/datasets/collator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -138,6 +138,7 @@ def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, Any]:
# model_inputs for hf/deepspeed: input_id, attention_mask, pixel_values, image_grid_thw
padded_features = defaultdict(list)
un_padded_features = defaultdict(list)
mm_token_type_id_features = []
mm_feature_keys = set()
for feature in features:
# cannot process as batch directly though processor output as batch
Expand Down Expand Up @@ -165,6 +166,8 @@ def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, Any]:
model_inputs.pop(key)
for key in filter(lambda k: k in model_inputs, self.padded_keys):
padded_features[key].append(model_inputs.pop(key)[0])
if "mm_token_type_ids" in model_inputs:
mm_token_type_id_features.append(torch.as_tensor(model_inputs.pop("mm_token_type_ids")[0]))
# mm feature fileds can be different because of mixed data
mm_feature_keys = mm_feature_keys.union(model_inputs.keys())
# to tensors except padded_keys which would be converted after padding
Expand Down Expand Up @@ -208,6 +211,22 @@ def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, Any]:
return_tensors=self.return_tensors,
)
batch.update(un_padded_features)
if mm_token_type_id_features:
target_len = batch["input_ids"].shape[-1]
padded_mm_token_type_ids = []
for token_type_ids in mm_token_type_id_features:
pad_len = target_len - token_type_ids.shape[-1]
if pad_len < 0:
raise ValueError(
f"mm_token_type_ids length {token_type_ids.shape[-1]} exceeds padded input length {target_len}"
)
pad = torch.zeros(pad_len, dtype=token_type_ids.dtype, device=token_type_ids.device)
if self.tokenizer.padding_side == "left":
token_type_ids = torch.cat([pad, token_type_ids], dim=-1)
else:
token_type_ids = torch.cat([token_type_ids, pad], dim=-1)
padded_mm_token_type_ids.append(token_type_ids)
batch["mm_token_type_ids"] = torch.stack(padded_mm_token_type_ids, dim=0)

# other custom data fields: mainly for specific position_ids currently
# position_ids for qwen2-vl is optional and make sure it is a 3D tensor
Expand All @@ -226,6 +245,7 @@ def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, Any]:
kwargs[key] = fun_params[key].default
extra_data = self.extra_data_provider(**kwargs)
batch.update(extra_data)
batch.pop("mm_token_type_ids", None)

# each field should be a tensor or np.array(val=list_data, dtype=object)
# to be stored in DataProto
Expand Down
33 changes: 20 additions & 13 deletions roll/models/model_providers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -277,6 +277,8 @@ def load_model(
freeze_model(model, model_args)
else:
model = setup_lora_training(config, model, model_args, is_trainable)
if not model_args.disable_gradient_checkpointing and hasattr(model, "enable_input_require_grads"):
model.enable_input_require_grads()

if add_valuehead:
from trl import AutoModelForCausalLMWithValueHead
Expand Down Expand Up @@ -710,8 +712,6 @@ def get_extra_data_provider(model_name_or_path: str, processor=None):
if isinstance(model_type, str) and (("qwen2" in model_type) or (model_type in ("qwen3_vl", "qwen3_vl_moe"))):
import types

from transformers import BatchFeature # help define a object to accesss attr

def _call_get_rope_index(fn, input_ids: torch.LongTensor, **candidate_kwargs):
sig = inspect.signature(fn)
params = sig.parameters
Expand Down Expand Up @@ -745,17 +745,13 @@ def _call_get_rope_index(fn, input_ids: torch.LongTensor, **candidate_kwargs):
"<|vision_start|>"
)

dummy_self = BatchFeature(
{
"config": BatchFeature(
{
"vision_config": BatchFeature(vc),
"image_token_id": image_token_id,
"video_token_id": video_token_id,
"vision_start_token_id": vision_start_token_id,
}
)
}
dummy_self = types.SimpleNamespace(
config=types.SimpleNamespace(
vision_config=types.SimpleNamespace(**vc),
image_token_id=image_token_id,
video_token_id=video_token_id,
vision_start_token_id=vision_start_token_id,
)
)

is_tf_ge_4_52 = is_transformers_version_greater_than("4.52.0")
Expand All @@ -771,6 +767,9 @@ def _call_get_rope_index(fn, input_ids: torch.LongTensor, **candidate_kwargs):
elif model_type in ("qwen3_vl", "qwen3_vl_moe"):
from transformers.models.qwen3_vl.modeling_qwen3_vl import Qwen3VLModel

dummy_self.get_vision_position_ids = types.MethodType(
Qwen3VLModel.get_vision_position_ids, dummy_self
)
get_rope_index = types.MethodType(Qwen3VLModel.get_rope_index, dummy_self)
else:
if is_tf_ge_4_52:
Expand All @@ -787,8 +786,15 @@ def extra_data_provider(
image_grid_thw: Optional[torch.LongTensor] = None,
video_grid_thw: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
mm_token_type_ids: Optional[torch.Tensor] = None,
second_per_grid_ts: Optional[torch.Tensor] = None,
):
if model_type in ("qwen3_vl", "qwen3_vl_moe") and mm_token_type_ids is None:
mm_token_type_ids = torch.zeros_like(input_ids)
if image_token_id is not None:
mm_token_type_ids = torch.where(input_ids == image_token_id, 1, mm_token_type_ids)
if video_token_id is not None:
mm_token_type_ids = torch.where(input_ids == video_token_id, 2, mm_token_type_ids)
# Keep kwargs to be resilient to HF signature changes between versions/models.
out = _call_get_rope_index(
get_rope_index,
Expand All @@ -797,6 +803,7 @@ def extra_data_provider(
video_grid_thw=video_grid_thw,
second_per_grid_ts=second_per_grid_ts,
attention_mask=attention_mask,
mm_token_type_ids=mm_token_type_ids,
)
rope_index = out[0]
# PumpkinComment:
Expand Down