94 lines
3.4 KiB
Python
94 lines
3.4 KiB
Python
# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Inference-only Mistral model."""
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from typing import List, Union
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import torch
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from transformers.models.mistral3.modeling_mistral3 import Mistral3MultiModalProjector
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from sglang.srt.managers.schedule_batch import MultimodalDataItem
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from sglang.srt.models.llama import LlamaForCausalLM
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class MistralForCausalLM(LlamaForCausalLM):
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pass
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class Mistral3ForConditionalGeneration:
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MULTIMODAL_PROJECTOR_TYPE = Mistral3MultiModalProjector
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def __init__(self, **kwargs):
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# lazy load inner class
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# to bypass circular import
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from sglang.srt.models.llava import LlavaForConditionalGeneration
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# override config: mistral's projector adds patchmerger that doesn't require padding
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kwargs["config"].vision_config.pad_image_border = False
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self.inner = LlavaForConditionalGeneration(**kwargs)
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self.inner.multi_modal_projector = self.MULTIMODAL_PROJECTOR_TYPE(
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kwargs["config"]
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)
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self.inner.get_image_feature = self.get_image_feature
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def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
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"""Extract features from image inputs.
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Args:
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items: List of MultimodalDataItem objects containing image data
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Note that an item can be either "image" or "multi-images"
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Returns:
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torch.Tensor: features from image inputs, concatenated
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"""
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features = []
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for item in items:
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# in each item, we assume pixel_values is always batched
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pixel_values, image_sizes = item.pixel_values, item.image_sizes
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image_outputs = self.vision_tower(
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pixel_values, image_sizes, output_hidden_states=True
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)
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selected_image_feature = image_outputs.hidden_states[
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self.vision_feature_layer
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]
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if self.vision_feature_select_strategy in ["default", "patch"]:
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selected_image_feature = selected_image_feature[:, 1:]
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elif self.vision_feature_select_strategy == "full":
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selected_image_feature = selected_image_feature
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else:
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raise ValueError(
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f"Unexpected select feature: {self.vision_feature_select_strategy}"
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)
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features.append(
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self.multi_modal_projector(
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selected_image_feature.squeeze(0), image_sizes
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)
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)
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ret = torch.cat(features, dim=0)
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return ret
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def __getattr__(self, name):
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return getattr(self.inner, name)
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def __hasattr__(self, name):
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return hasattr(self.inner, name)
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def __call__(self, *args, **kwargs):
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return self.inner(*args, **kwargs)
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EntryClass = [MistralForCausalLM, Mistral3ForConditionalGeneration]
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