119 lines
4.7 KiB
Python
119 lines
4.7 KiB
Python
"""
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 Yi-VL model."""
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from typing import Iterable, Optional, Tuple
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import torch
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import torch.nn as nn
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from transformers import CLIPVisionModel, LlavaConfig
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from vllm.config import CacheConfig
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from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from sglang.srt.models.llava import LlavaLlamaForCausalLM
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class YiVLForCausalLM(LlavaLlamaForCausalLM):
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def __init__(
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self,
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config: LlavaConfig,
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quant_config: Optional[QuantizationConfig] = None,
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cache_config: Optional[CacheConfig] = None,
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) -> None:
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super().__init__(config, quant_config, cache_config)
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self.multi_modal_projector = YiVLMultiModalProjector(self.config)
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self.vision_tower_subfolder = self.config.mm_vision_tower.replace(
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"./", ""
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) # Everything after "./"
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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# We have to use the subfolder of the main model directory (e.g. 01-ai/Yi-VL-6B)
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self.vision_tower = CLIPVisionModel.from_pretrained(
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self.config._name_or_path,
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torch_dtype=torch.float16,
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subfolder=self.vision_tower_subfolder,
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).to("cuda")
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self.vision_tower.eval()
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self.vision_feature_layer = self.config.mm_vision_select_layer
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self.vision_feature_select_strategy = self.config.mm_vision_select_feature
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self.image_size = self.vision_tower.config.image_size
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self.patch_size = self.vision_tower.config.patch_size
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self.mm_patch_merge_type = getattr(self.config, "mm_patch_merge_type", "flat")
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self.image_aspect_ratio = getattr(self.config, "image_aspect_ratio", "square")
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self.image_grid_pinpoints = getattr(self.config, "image_grid_pinpoints", None)
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self.image_feature_len = int((self.image_size / self.patch_size) ** 2)
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if self.vision_feature_select_strategy == "patch":
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pass
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elif self.vision_feature_select_strategy == "cls_patch":
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self.image_feature_len += 1
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else:
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raise ValueError(f"Unexpected select feature: {self.select_feature}")
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# load mm_projector
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# TODO: support TP?
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projector_weights = {
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"model.mm_projector.0": "multi_modal_projector.linear_1",
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"model.mm_projector.1": "multi_modal_projector.ln_1",
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"model.mm_projector.3": "multi_modal_projector.linear_2",
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"model.mm_projector.4": "multi_modal_projector.ln_2",
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"model.vision_tower.vision_tower": "vision_tower", # Update the vision tower weights if we find them in the checkpoint (it may be finetuned).
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}
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params_dict = dict(self.named_parameters())
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weights = list(weights)
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for name, loaded_weight in weights:
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if "projector" in name or "vision_tower" in name:
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for weight_name, param_name in projector_weights.items():
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if weight_name in name:
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name = name.replace(weight_name, param_name)
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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# load language model
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self.language_model.load_weights(weights)
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class YiVLMultiModalProjector(nn.Module):
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def __init__(self, config: LlavaConfig):
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super().__init__()
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self.linear_1 = nn.Linear(
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config.vision_config.hidden_size, config.text_config.hidden_size
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)
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self.ln_1 = nn.LayerNorm(config.text_config.hidden_size)
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self.act = nn.GELU()
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self.linear_2 = nn.Linear(
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config.text_config.hidden_size, config.text_config.hidden_size
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)
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self.ln_2 = nn.LayerNorm(config.text_config.hidden_size)
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def forward(self, image_features):
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hidden_states = self.linear_1(image_features)
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hidden_states = self.ln_1(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states = self.linear_2(hidden_states)
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hidden_states = self.ln_2(hidden_states)
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return hidden_states
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EntryClass = YiVLForCausalLM
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