model: Minicpmo (#3023)
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@@ -11,7 +11,7 @@ from sglang.srt.configs.deepseekvl2 import (
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)
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from sglang.srt.layers.linear import ReplicatedLinear
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.managers.schedule_batch import ImageInputs
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from sglang.srt.managers.schedule_batch import MultimodalInputs
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.models.deepseek_v2 import DeepseekV2ForCausalLM
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@@ -222,7 +222,7 @@ class DeepseekVL2ForCausalLM(nn.Module):
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):
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extend_start_loc_cpu = forward_batch.extend_start_loc.cpu().numpy()
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extend_seq_lens_cpu = forward_batch.extend_seq_lens.cpu().numpy()
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for idx, image in enumerate(forward_batch.image_inputs):
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for idx, image in enumerate(forward_batch.mm_inputs):
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if image is None:
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continue
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start_idx = extend_start_loc_cpu[idx]
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@@ -262,10 +262,10 @@ class DeepseekVL2ForCausalLM(nn.Module):
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weights_loader = getattr(param, "weight_loader", default_weight_loader)
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weights_loader(param, loaded_weight)
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def pad_input_ids(self, input_ids: List[int], image_inputs: ImageInputs):
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def pad_input_ids(self, input_ids: List[int], image_inputs: MultimodalInputs):
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return input_ids
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def get_image_feature(self, image_input: ImageInputs):
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def get_image_feature(self, image_input: MultimodalInputs):
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pixel_values = image_input.pixel_values.type(
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next(self.vision.parameters()).dtype
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).to(device=next(self.vision.parameters()).device)
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