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Use relative import paths #9

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Use relative import paths #9

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4 changes: 2 additions & 2 deletions inferencer.py
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Expand Up @@ -7,8 +7,8 @@
from PIL import Image
import torch

from data.data_utils import pil_img2rgb
from modeling.bagel.qwen2_navit import NaiveCache
from .data.data_utils import pil_img2rgb
from .modeling.bagel.qwen2_navit import NaiveCache



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2 changes: 1 addition & 1 deletion modeling/bagel/bagel.py
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Expand Up @@ -13,7 +13,7 @@
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_utils import PreTrainedModel

from data.data_utils import (
from ...data.data_utils import (
create_sparse_mask,
get_flattened_position_ids_extrapolate,
get_flattened_position_ids_interpolate,
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4 changes: 2 additions & 2 deletions modeling/bagel/qwen2_navit.py
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Expand Up @@ -22,7 +22,7 @@
from transformers.utils import ModelOutput

from flash_attn import flash_attn_varlen_func
from modeling.qwen2.modeling_qwen2 import (
from ..qwen2.modeling_qwen2 import (
Qwen2Attention,
Qwen2MLP,
Qwen2PreTrainedModel,
Expand All @@ -31,7 +31,7 @@
apply_rotary_pos_emb,
)

from modeling.qwen2.configuration_qwen2 import Qwen2Config as _Qwen2Config
from ..qwen2.configuration_qwen2 import Qwen2Config as _Qwen2Config


torch._dynamo.config.cache_size_limit = 512
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4 changes: 2 additions & 2 deletions modeling/bagel/siglip_navit.py
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Expand Up @@ -13,8 +13,8 @@
from torch import nn

from transformers.activations import ACT2FN
from modeling.siglip.configuration_siglip import SiglipVisionConfig as _SiglipVisionConfig
from modeling.siglip.modeling_siglip import SiglipAttention, SiglipPreTrainedModel
from ..siglip.configuration_siglip import SiglipVisionConfig as _SiglipVisionConfig
from ..siglip.modeling_siglip import SiglipAttention, SiglipPreTrainedModel
from flash_attn import flash_attn_varlen_func


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14 changes: 7 additions & 7 deletions nodes.py
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Expand Up @@ -22,15 +22,15 @@
from accelerate import infer_auto_device_map, load_checkpoint_and_dispatch, init_empty_weights
from safetensors.torch import load_file

from data.transforms import ImageTransform
from data.data_utils import pil_img2rgb, add_special_tokens
from modeling.bagel import (
from .data.transforms import ImageTransform
from .data.data_utils import pil_img2rgb, add_special_tokens
from .modeling.bagel import (
BagelConfig, Bagel, Qwen2Config, Qwen2ForCausalLM, SiglipVisionConfig, SiglipVisionModel
)
from modeling.qwen2 import Qwen2Tokenizer
from modeling.bagel.qwen2_navit import NaiveCache
from modeling.autoencoder import load_ae
from inferencer import InterleaveInferencer
from .modeling.qwen2 import Qwen2Tokenizer
from .modeling.bagel.qwen2_navit import NaiveCache
from .modeling.autoencoder import load_ae
from .inferencer import InterleaveInferencer


class LoadBAGELModel:
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