4ad9737045
Co-authored-by: Binyao Jiang <byjiang1996@gmail.com> Co-authored-by: Lifu Huang <lifu.hlf@gmail.com>
844 lines
37 KiB
Python
844 lines
37 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 LLaVa model compatible with HuggingFace weights."""
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import math
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import re
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from functools import lru_cache
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from typing import Dict, Iterable, List, Optional, Tuple, Type, Union
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import numpy as np
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import torch
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from torch import nn
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from transformers import (
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CLIPVisionConfig,
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CLIPVisionModel,
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LlavaConfig,
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MistralConfig,
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Qwen2Config,
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SiglipVisionModel,
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)
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from transformers.models.auto.modeling_auto import AutoModel, AutoModelForCausalLM
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from transformers.models.llava.modeling_llava import LlavaMultiModalProjector
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# leave till last and symbol only in case circular import
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import sglang.srt.models as sgl_models
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.managers.mm_utils import general_mm_embed_routine
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from sglang.srt.managers.schedule_batch import (
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Modality,
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MultimodalDataItem,
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MultimodalInputs,
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)
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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.llama import LlamaForCausalLM
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from sglang.srt.models.mistral import MistralForCausalLM
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from sglang.srt.models.qwen2 import Qwen2ForCausalLM
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from sglang.srt.multimodal.mm_utils import (
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get_anyres_image_grid_shape,
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unpad_image,
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unpad_image_shape,
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)
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from sglang.srt.utils import add_prefix, flatten_nested_list, logger
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class LlavaBaseForCausalLM(nn.Module):
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def pad_input_ids(self, input_ids: List[int], image_inputs: MultimodalInputs):
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image_sizes = flatten_nested_list(
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[item.image_sizes for item in image_inputs.mm_items]
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)
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pad_values = [item.pad_value for item in image_inputs.mm_items]
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# hardcode for spatial_unpad + anyres
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if any(
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item.modality == Modality.MULTI_IMAGES or item.modality == Modality.VIDEO
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for item in image_inputs.mm_items
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):
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image_aspect_ratio = "pad"
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else:
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image_aspect_ratio = "anyres"
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offset_list = []
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image_inputs.image_pad_len = []
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for image_idx, image_s in enumerate(image_sizes):
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if len(image_sizes) > 16:
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# 2x2 pooling with stride 2
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new_image_feature_len = (
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math.ceil(self.image_size / self.patch_size / 2) ** 2
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)
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else:
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new_image_feature_len = self.image_feature_len # multi-image
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height = width = self.num_patches_per_side
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if "anyres" in image_aspect_ratio:
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num_patch_width, num_patch_height = get_anyres_image_grid_shape(
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image_s,
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self.image_grid_pinpoints,
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self.vision_tower.config.image_size,
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)
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h = num_patch_height * height
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w = num_patch_width * width
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new_h, new_w = unpad_image_shape(h, w, image_s)
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if "anyres_max" in self.config.image_aspect_ratio:
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matched_anyres_max_num_patches = re.match(
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r"anyres_max_(\d+)", self.config.image_aspect_ratio
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)
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if matched_anyres_max_num_patches:
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max_num_patches = int(matched_anyres_max_num_patches.group(1))
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# times = math.sqrt(h * w / (max_num_patches * unit**2))
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times = math.sqrt(
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new_h * new_w / (max_num_patches * self.image_feature_len)
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)
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if times > 1.1:
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new_h = int(new_h // times)
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new_w = int(new_w // times)
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new_image_feature_len += new_h * (new_w + 1)
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try:
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offset = input_ids.index(self.config.image_token_index)
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except ValueError:
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offset = 0
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# old_len + pad_len - 1, because we need to remove image_token_id
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input_ids = (
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input_ids[:offset]
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+ [pad_values[image_idx % len(pad_values)]] * new_image_feature_len
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+ input_ids[offset + 1 :]
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)
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offset_list.append(offset)
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image_inputs.image_pad_len.append(new_image_feature_len)
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image_inputs.image_offsets = offset_list
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return input_ids
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def encode_images(
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self, pixel_values: Union[torch.Tensor, List[torch.Tensor]]
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) -> torch.Tensor:
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"""
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encode images by vision tower and multimodal projector
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Args:
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pixel_values: torch.Tensor or List[torch.Tensor]: each tensor for an input image
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Returns:
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torch.Tensor: encoded image features from the input image; if multiple, flattened by seq_len axis
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"""
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image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
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# NOTE: This is not memory efficient. (output_hidden_states=True) will save all the hidden stated.
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selected_image_feature = image_outputs.hidden_states[self.vision_feature_layer]
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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 strategy: {self.config.vision_feature_select_strategy}"
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)
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image_features = self.multi_modal_projector(selected_image_feature)
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return image_features
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@torch.no_grad()
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def forward(
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self,
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input_ids: torch.LongTensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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) -> torch.Tensor:
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image_inputs = forward_batch.mm_inputs
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if forward_batch.forward_mode.is_extend():
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# Clamp input ids. This is because the input_ids for the image tokens are
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# filled with the hash values of the image for the prefix matching in the radix attention.
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# There values are useless because their embeddings will be replaced by vision embeddings anyway.
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input_ids.clamp_(min=0, max=self.config.vocab_size - 1)
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# Embed text inputs
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input_embeds = self.language_model.model.embed_tokens(input_ids)
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# Got List[List[str]] extend it to List[str]
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# The length of the List should be equal to batch size
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modalities_list = []
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max_image_offset = []
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for im in image_inputs:
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if im:
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modalities_list.extend([item.modality for item in im.mm_items])
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if im and im.image_offsets:
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max_image_offset.append(
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np.max(np.array(im.image_offsets) + np.array(im.image_pad_len))
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)
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else:
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max_image_offset.append(-1)
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start_positions = positions[forward_batch.extend_start_loc].cpu().numpy()
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need_vision = start_positions <= np.array(max_image_offset)
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if need_vision.any():
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bs = forward_batch.batch_size
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pixel_values = flatten_nested_list(
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[
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[item.feature for item in image_inputs[i].mm_items]
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for i in range(bs)
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if need_vision[i]
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]
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)
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image_sizes = [
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flatten_nested_list(
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[item.image_sizes for item in image_inputs[i].mm_items]
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)
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for i in range(bs)
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if need_vision[i]
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]
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########## Encode Image ########
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if pixel_values[0].ndim == 4:
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# llava-hd: BS, num_patch, C=3, H=336, W=336, num_patch obtained from process_images
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np.concatenate(pixel_values, axis=0)
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# ndim=4
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concat_images = torch.tensor(
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np.concatenate(pixel_values, axis=0),
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device=self.vision_tower.device,
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)
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image_features = self.encode_images(concat_images)
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split_sizes = [image.shape[0] for image in pixel_values]
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image_features = torch.split(image_features, split_sizes, dim=0)
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# hd image_features: BS, num_patch, 576, 4096
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else:
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# normal pixel: BS, C=3, H=336, W=336
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pixel_values = torch.tensor(
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np.array(pixel_values), device=self.vision_tower.device
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)
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image_features = self.encode_images(pixel_values)
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# image_features: BS, 576, 4096
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if self.mm_patch_merge_type.startswith("spatial"):
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new_image_features = []
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height = width = self.num_patches_per_side
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for image_idx, image_feature in enumerate(image_features):
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if modalities_list[image_idx] == Modality.IMAGE:
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image_aspect_ratio = (
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self.config.image_aspect_ratio
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) # single image
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elif (
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modalities_list[image_idx] == Modality.MULTI_IMAGES
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or modalities_list[image_idx] == Modality.VIDEO
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):
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image_aspect_ratio = "pad" # multi image
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# image_aspect_ratio = (
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# "anyres" if len(image_sizes[image_idx]) == 1 else "pad"
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# )
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if (
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image_feature.shape[0] > 1
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and "anyres" in image_aspect_ratio
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and modalities_list[image_idx] == Modality.IMAGE
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):
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base_image_feature = image_feature[0]
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image_feature = image_feature[1:]
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assert height * width == base_image_feature.shape[0]
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if "anyres_max" in image_aspect_ratio:
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matched_anyres_max_num_patches = re.match(
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r"anyres_max_(\d+)", image_aspect_ratio
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)
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if matched_anyres_max_num_patches:
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max_num_patches = int(
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matched_anyres_max_num_patches.group(1)
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)
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if (
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image_aspect_ratio == "anyres"
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or "anyres_max" in image_aspect_ratio
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):
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vision_tower_image_size = self.image_size
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try:
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num_patch_width, num_patch_height = (
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get_anyres_image_grid_shape(
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image_sizes[image_idx][0],
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self.config.image_grid_pinpoints,
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vision_tower_image_size,
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)
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)
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except Exception as e:
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print(f"Error: {e}")
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num_patch_width, num_patch_height = 2, 2
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image_feature = image_feature.view(
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num_patch_height, num_patch_width, height, width, -1
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)
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else:
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image_feature = image_feature.view(
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2, 2, height, width, -1
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)
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# (
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# num_patch_width,
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# num_patch_height,
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# ) = get_anyres_image_grid_shape(
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# image_sizes[image_idx][0],
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# self.image_grid_pinpoints,
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# self.vision_tower.config.image_size,
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# )
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# image_feature = image_feature.view(
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# num_patch_height, num_patch_width, height, width, -1
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# )
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if "unpad" in self.mm_patch_merge_type:
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unit = image_feature.shape[2]
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image_feature = image_feature.permute(
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4, 0, 2, 1, 3
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).contiguous()
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image_feature = image_feature.flatten(1, 2).flatten(
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2, 3
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)
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image_feature = unpad_image(
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image_feature, image_sizes[image_idx][0]
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)
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if (
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"anyres_max" in image_aspect_ratio
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and matched_anyres_max_num_patches
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):
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c, h, w = image_feature.shape
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times = math.sqrt(
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h * w / (max_num_patches * unit**2)
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)
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if times > 1.1:
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image_feature = image_feature[None]
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image_feature = nn.functional.interpolate(
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image_feature,
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[int(h // times), int(w // times)],
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mode="bilinear",
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)[0]
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image_feature = torch.cat(
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(
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image_feature,
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self.language_model.model.image_newline[
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:, None, None
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].expand(*image_feature.shape[:-1], 1),
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),
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dim=-1,
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)
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image_feature = image_feature.flatten(1, 2).transpose(
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0, 1
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)
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else:
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image_feature = image_feature.permute(
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0, 2, 1, 3, 4
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).contiguous()
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image_feature = image_feature.flatten(0, 3)
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image_feature = torch.cat(
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(base_image_feature, image_feature), dim=0
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)
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image_feature = image_feature.unsqueeze(0)
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else:
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if modalities_list[image_idx] == Modality.VIDEO: # video
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# 2x2 pooling
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num_of_frames = image_feature.shape[0]
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image_feature = image_feature.view(
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num_of_frames, height, width, -1
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)
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image_feature = image_feature.permute(
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0, 3, 1, 2
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).contiguous() # N, C, H, W
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height, weight = image_feature.shape[2:]
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scaled_shape = [
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math.ceil(height / 2),
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math.ceil(weight / 2),
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]
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image_feature = nn.functional.interpolate(
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image_feature, size=scaled_shape, mode="bilinear"
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)
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image_feature = (
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image_feature.flatten(2)
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.transpose(1, 2)
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.contiguous()
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) # N, C, H*W
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if "unpad" in self.mm_patch_merge_type:
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image_feature = torch.cat(
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(
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image_feature,
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# Expand to (bs, 1, hidden_dim) and concat at the end of the image tokens
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self.language_model.model.image_newline[
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None, None
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].expand(
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image_feature.shape[0],
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1,
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image_feature.shape[-1],
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),
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),
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dim=1,
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)
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new_image_features.append(image_feature)
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image_features = new_image_features
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# Fill in the placeholder for the image
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extend_start_loc_cpu = forward_batch.extend_start_loc.cpu().numpy()
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extend_seq_lens = forward_batch.extend_seq_lens.cpu().numpy()
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prefix_lens_cpu = forward_batch.extend_prefix_lens_cpu
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pt = 0
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for i in range(bs):
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if not need_vision[i]:
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continue
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start_idx = extend_start_loc_cpu[i]
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seq_len = extend_seq_lens[i]
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prefix_len = prefix_lens_cpu[i]
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# Multiple images
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for image_idx, image_offset in enumerate(
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image_inputs[i].image_offsets
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):
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if (
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image_offset + image_inputs[i].image_pad_len[image_idx]
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<= prefix_len
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):
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continue
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if image_offset >= prefix_len + seq_len:
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break
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tmp_image_feature = image_features[pt][image_idx]
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pad_len = tmp_image_feature.shape[0]
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input_offset = image_offset - prefix_len
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left_idx = start_idx + input_offset
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right_idx = left_idx + pad_len
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assert right_idx > start_idx
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if input_offset < 0:
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left_idx = start_idx
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tmp_image_feature = tmp_image_feature[-input_offset:]
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if right_idx > start_idx + seq_len:
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tmp_image_feature = tmp_image_feature[
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: start_idx + seq_len - right_idx
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]
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right_idx = start_idx + seq_len
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try:
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input_embeds[left_idx:right_idx] = tmp_image_feature
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except RuntimeError as e:
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print(f"RuntimeError in image encoding: {e}")
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print(f"{input_embeds.shape=}, {tmp_image_feature.shape=}")
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print(
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f"{start_idx=}, {image_offset=}, {prefix_len=}, {pad_len=}"
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)
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pt += 1
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return self.language_model(
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input_ids, positions, forward_batch, input_embeds=input_embeds
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)
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elif forward_batch.forward_mode.is_decode():
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return self.language_model(input_ids, positions, forward_batch)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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# Load clip vision model by cfg['mm_vision_tower']:
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# huggingface_name or path_of_clip_relative_to_llava_model_dir
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# We put the initialization here instead of __init__ to allow it being reused by other subclasses.
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vision_path = self.config.mm_vision_tower
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if "clip" in vision_path:
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self.vision_tower = CLIPVisionModel.from_pretrained(
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vision_path, torch_dtype=torch.float16
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).cuda()
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elif "siglip" in vision_path:
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self.vision_tower = SiglipVisionModel.from_pretrained(
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vision_path, torch_dtype=torch.float16
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).cuda()
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# Siglip needs all feature tokens
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self.config.mm_vision_select_feature = "full"
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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 (
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self.vision_feature_select_strategy == "patch"
|
|
or self.vision_feature_select_strategy == "full"
|
|
):
|
|
pass
|
|
elif self.vision_feature_select_strategy == "cls_patch":
|
|
self.image_feature_len += 1
|
|
else:
|
|
raise ValueError(f"Unexpected select feature: {self.select_feature}")
|
|
|
|
# load mm_projector
|
|
projector_weights = {
|
|
"model.mm_projector.0": "multi_modal_projector.linear_1",
|
|
"model.mm_projector.2": "multi_modal_projector.linear_2",
|
|
"model.vision_tower.vision_tower": "vision_tower",
|
|
# Update the vision tower weights if we find them in the checkpoint (it may be finetuned).
|
|
"model.image_newline": "language_model.model.image_newline",
|
|
}
|
|
params_dict = dict(self.named_parameters())
|
|
for name, loaded_weight in weights:
|
|
if "projector" in name or "vision_tower" in name or "image_newline" in name:
|
|
for weight_name, param_name in projector_weights.items():
|
|
if weight_name in name:
|
|
name = name.replace(weight_name, param_name)
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
else:
|
|
self.language_model.load_weights([(name, loaded_weight)])
|
|
|
|
@property
|
|
def num_patches_per_side(self):
|
|
return self.image_size // self.patch_size
|
|
|
|
|
|
class LlavaLlamaForCausalLM(LlavaBaseForCausalLM):
|
|
def __init__(
|
|
self,
|
|
config: LlavaConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
self.config = config
|
|
self.vision_tower = None
|
|
self.config.vision_config.hidden_size = config.mm_hidden_size
|
|
self.config.text_config.hidden_size = config.hidden_size
|
|
|
|
self.multi_modal_projector = LlavaMultiModalProjector(config)
|
|
self.language_model = LlamaForCausalLM(
|
|
config,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("language_model", prefix),
|
|
)
|
|
if "unpad" in getattr(config, "mm_patch_merge_type", ""):
|
|
self.language_model.model.image_newline = nn.Parameter(
|
|
torch.empty(config.text_config.hidden_size, dtype=torch.float16)
|
|
)
|
|
|
|
|
|
class LlavaQwenForCausalLM(LlavaBaseForCausalLM):
|
|
def __init__(
|
|
self,
|
|
config: LlavaConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
self.config = config
|
|
self.vision_tower = None
|
|
|
|
if getattr(self.config, "vision_config", None) is None:
|
|
self.config.vision_config = CLIPVisionConfig(self.config.mm_vision_tower)
|
|
if getattr(self.config, "text_config", None) is None:
|
|
self.config.text_config = Qwen2Config(self.config._name_or_path)
|
|
|
|
self.config.vision_config.hidden_size = config.mm_hidden_size
|
|
self.config.text_config.hidden_size = config.hidden_size
|
|
|
|
if getattr(self.config, "projector_hidden_act", None) is None:
|
|
self.config.projector_hidden_act = "gelu"
|
|
if getattr(self.config, "image_token_index", None) is None:
|
|
self.config.image_token_index = 151646
|
|
|
|
self.multi_modal_projector = LlavaMultiModalProjector(config)
|
|
self.language_model = Qwen2ForCausalLM(
|
|
config,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("language_model", prefix),
|
|
)
|
|
if "unpad" in getattr(config, "mm_patch_merge_type", ""):
|
|
self.language_model.model.image_newline = nn.Parameter(
|
|
torch.empty(config.text_config.hidden_size, dtype=torch.float16)
|
|
)
|
|
|
|
|
|
class LlavaMistralForCausalLM(LlavaBaseForCausalLM):
|
|
def __init__(
|
|
self,
|
|
config: LlavaConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
self.config = config
|
|
self.vision_tower = None
|
|
|
|
if getattr(self.config, "vision_config", None) is None:
|
|
self.config.vision_config = CLIPVisionConfig(self.config.mm_vision_tower)
|
|
if getattr(self.config, "text_config", None) is None:
|
|
self.config.text_config = MistralConfig(self.config._name_or_path)
|
|
|
|
self.config.vision_config.hidden_size = config.mm_hidden_size
|
|
self.config.text_config.hidden_size = config.hidden_size
|
|
|
|
if getattr(self.config, "projector_hidden_act", None) is None:
|
|
self.config.projector_hidden_act = "gelu"
|
|
if getattr(self.config, "image_token_index", None) is None:
|
|
self.config.image_token_index = 32000
|
|
|
|
self.multi_modal_projector = LlavaMultiModalProjector(config)
|
|
self.language_model = MistralForCausalLM(
|
|
config,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("language_model", prefix),
|
|
)
|
|
if "unpad" in getattr(config, "mm_patch_merge_type", ""):
|
|
self.language_model.model.image_newline = nn.Parameter(
|
|
torch.empty(config.text_config.hidden_size, dtype=torch.float16)
|
|
)
|
|
|
|
|
|
class LlavaForConditionalGeneration(LlavaBaseForCausalLM):
|
|
"""
|
|
An adaptor class to enable support for multiple mmlm such as mistral-community/pixtral-12b
|
|
It follows the structure of (vision_tower, multi_modal_projector, language_model)
|
|
|
|
Once a model config is loaded, text_config and vision_config will be extracted, and
|
|
LlavaForConditionalGeneration will load the language_model and vision_tower models
|
|
according to config.
|
|
"""
|
|
|
|
MULTIMODAL_PROJECTOR_TYPE = LlavaMultiModalProjector
|
|
|
|
@property
|
|
def dtype(self):
|
|
return self.torch_dtype
|
|
|
|
def pad_input_ids(self, input_ids: List[int], image_inputs: MultimodalInputs):
|
|
if hasattr(self.vision_tower, "pad_input_ids"):
|
|
return self.vision_tower.pad_input_ids(input_ids, image_inputs)
|
|
else:
|
|
return super().pad_input_ids(input_ids, image_inputs)
|
|
|
|
def _get_sgl_model_cls(self, config, auto_model_type: Type[AutoModel] = AutoModel):
|
|
"""
|
|
Get the SGLang model implementation class according to config.
|
|
|
|
Args:
|
|
config: The config object of the model.
|
|
auto_model_type: The type of the auto model.
|
|
|
|
Returns:
|
|
The SGLang model implementation class.
|
|
"""
|
|
config_cls_name = config.__class__.__name__
|
|
arch_name_mapping = self._config_cls_name_to_arch_name_mapping(auto_model_type)
|
|
if arch := arch_name_mapping.get(config_cls_name):
|
|
if isinstance(arch, tuple):
|
|
arch = arch[0]
|
|
logger.warning(
|
|
f"Multiple {auto_model_type.__name__} models found for submodule config `{config_cls_name}`, defaulting to [0]: {arch.__name__}"
|
|
)
|
|
try:
|
|
return sgl_models.registry.ModelRegistry.resolve_model_cls(arch)[0]
|
|
except Exception as e:
|
|
raise ValueError(
|
|
f"{auto_model_type.__name__} found a corresponding model `{arch}` for config class `{config_cls_name}`, but failed to load it from SGLang ModelRegistry. \n{e}"
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
f"{auto_model_type.__name__} cannot find a corresponding model for config class `{config_cls_name}`"
|
|
)
|
|
|
|
@lru_cache
|
|
def _config_cls_name_to_arch_name_mapping(
|
|
self, auto_model_type: Type[AutoModel]
|
|
) -> Dict[str, str]:
|
|
mapping = {}
|
|
for config_cls in auto_model_type._model_mapping.keys():
|
|
archs = auto_model_type._model_mapping.get(config_cls, None)
|
|
if archs is not None:
|
|
if isinstance(archs, tuple):
|
|
mapping[config_cls.__name__] = tuple(
|
|
arch.__name__ for arch in archs
|
|
)
|
|
else:
|
|
mapping[config_cls.__name__] = archs.__name__
|
|
return mapping
|
|
|
|
def __init__(
|
|
self,
|
|
config: LlavaConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
assert hasattr(config, "text_config")
|
|
assert hasattr(config, "vision_config")
|
|
self.config = config
|
|
self.text_config = self.config.text_config
|
|
self.vision_config = self.config.vision_config
|
|
self.torch_dtype = getattr(self.config, "torch_dtype")
|
|
|
|
if not getattr(self.text_config, "torch_dtype"):
|
|
self.text_config.torch_dtype = self.torch_dtype
|
|
if not getattr(self.vision_config, "torch_dtype"):
|
|
self.vision_config.torch_dtype = self.torch_dtype
|
|
|
|
if not hasattr(self.config, "vocab_size"):
|
|
self.config.vocab_size = self.text_config.vocab_size
|
|
if not hasattr(self.config, "image_aspect_ratio"):
|
|
self.config.image_aspect_ratio = "anyres"
|
|
if not hasattr(self.config, "image_grid_pinpoints"):
|
|
# from transformers.models.llava_onevision.configuration_llava_onevision import LlavaOnevisionConfig
|
|
# self.config.image_grid_pinpoints = LlavaOnevisionConfig().image_grid_pinpoints
|
|
self.config.image_grid_pinpoints = [
|
|
[96, 96],
|
|
[224, 224],
|
|
[384, 384],
|
|
[512, 512],
|
|
[768, 768],
|
|
[1024, 1024],
|
|
]
|
|
if not hasattr(self.config, "mm_patch_merge_type"):
|
|
self.config.mm_patch_merge_type = "flat"
|
|
if not hasattr(self.config, "image_token_index"):
|
|
self.config.image_token_index = 10
|
|
if not hasattr(self.config, "projector_hidden_act"):
|
|
self.config.projector_hidden_act = "gelu"
|
|
|
|
self.vision_feature_layer = getattr(self.config, "vision_feature_layer", -1)
|
|
self.vision_feature_select_strategy = getattr(
|
|
self.config, "vision_feature_select_strategy", "full"
|
|
)
|
|
self.image_size = self.vision_config.image_size
|
|
self.patch_size = self.vision_config.patch_size
|
|
|
|
self.mm_patch_merge_type = self.config.mm_patch_merge_type
|
|
self.image_aspect_ratio = self.config.image_aspect_ratio
|
|
self.image_grid_pinpoints = self.config.image_grid_pinpoints
|
|
|
|
self.image_feature_len = int((self.image_size // self.patch_size) ** 2)
|
|
|
|
self.multi_modal_projector = self.MULTIMODAL_PROJECTOR_TYPE(config)
|
|
|
|
language_model_cls = self._get_sgl_model_cls(
|
|
self.text_config, AutoModelForCausalLM
|
|
)
|
|
vision_model_cls = self._get_sgl_model_cls(self.vision_config, AutoModel)
|
|
self.language_model = language_model_cls(
|
|
self.text_config,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("language_model", prefix),
|
|
)
|
|
self.vision_tower = vision_model_cls(
|
|
self.vision_config,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("vision_tower", prefix),
|
|
)
|
|
|
|
if "unpad" in getattr(self.config, "mm_patch_merge_type", ""):
|
|
self.language_model.model.image_newline = nn.Parameter(
|
|
torch.empty(self.text_config.hidden_size, dtype=self.torch_dtype)
|
|
)
|
|
|
|
def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
|
|
"""Extract features from image inputs.
|
|
|
|
Args:
|
|
items: List of MultimodalDataItem objects containing image data
|
|
Note that an item can be either "image" or "multi-images"
|
|
|
|
Returns:
|
|
torch.Tensor: features from image inputs, concatenated
|
|
"""
|
|
features = []
|
|
for item in items:
|
|
# in each item, we assume pixel_values is always batched
|
|
pixel_values, image_sizes = item.feature, item.image_sizes
|
|
image_outputs = self.vision_tower(
|
|
pixel_values, image_sizes, output_hidden_states=True
|
|
)
|
|
selected_image_feature = image_outputs.hidden_states[
|
|
self.vision_feature_layer
|
|
]
|
|
|
|
if self.vision_feature_select_strategy in ["default", "patch"]:
|
|
selected_image_feature = selected_image_feature[:, 1:]
|
|
elif self.vision_feature_select_strategy == "full":
|
|
selected_image_feature = selected_image_feature
|
|
else:
|
|
raise ValueError(
|
|
f"Unexpected select feature: {self.vision_feature_select_strategy}"
|
|
)
|
|
features.append(
|
|
self.multi_modal_projector(selected_image_feature.squeeze(0))
|
|
)
|
|
ret = torch.cat(features, dim=0)
|
|
return ret
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
get_embedding: bool = False,
|
|
):
|
|
hidden_states = general_mm_embed_routine(
|
|
input_ids=input_ids,
|
|
forward_batch=forward_batch,
|
|
get_embedding=get_embedding,
|
|
language_model=self.language_model,
|
|
data_embedding_funcs={
|
|
Modality.IMAGE: self.get_image_feature,
|
|
},
|
|
placeholder_tokens=None, # using mm_item.pad_value
|
|
positions=positions,
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
"""Load weights for LlavaForConditionalGeneration.
|
|
|
|
Unlike the base class implementation, this one doesn't need to handle
|
|
weight name remapping as the weights are already properly structured with
|
|
'language_model' and 'vision_tower' prefixes in the safetensors files.
|
|
"""
|
|
if (
|
|
self.vision_feature_select_strategy == "patch"
|
|
or self.vision_feature_select_strategy == "full"
|
|
):
|
|
pass
|
|
elif self.vision_feature_select_strategy == "cls_patch":
|
|
self.image_feature_len += 1
|
|
else:
|
|
raise ValueError(
|
|
f"Unexpected select feature: {self.vision_feature_select_strategy}"
|
|
)
|
|
|
|
# Create dictionaries for direct parameter loading
|
|
params_dict = dict(self.named_parameters())
|
|
|
|
# Load weights directly without remapping
|
|
for name, loaded_weight in weights:
|
|
for part in ("language_model", "vision_tower"):
|
|
if name.startswith(part):
|
|
name = name[len(part + ".") :]
|
|
getattr(self, part).load_weights([(name, loaded_weight)])
|
|
break
|
|
else:
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
|
|
|
|
EntryClass = [
|
|
LlavaLlamaForCausalLM,
|
|
LlavaQwenForCausalLM,
|
|
LlavaMistralForCausalLM,
|
|
LlavaForConditionalGeneration,
|
|
]
|