531 lines
24 KiB
Python
531 lines
24 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 LLaVa model compatible with HuggingFace weights."""
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import math
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import re
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from typing import Iterable, List, Optional, Tuple
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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.llava.modeling_llava import LlavaMultiModalProjector
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from vllm.config import CacheConfig
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.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.model_executor.forward_batch_info import ForwardMode, InputMetadata
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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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class LlavaBaseForCausalLM(nn.Module):
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def pad_input_ids(
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self,
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input_ids: List[int],
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pad_value: List[int],
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pixel_values: List,
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image_sizes: List[List[int]],
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):
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# hardcode for spatial_unpad + anyres
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image_aspect_ratio = "anyres" if len(image_sizes) == 1 else "pad"
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offset_list = []
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for image_s in 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 # multiimage
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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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pad_ids = pad_value * (
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(new_image_feature_len + len(pad_value)) // len(pad_value)
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)
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# print("calculated new_image_feature_len: ", new_image_feature_len)
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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_ids[: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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return input_ids, offset_list
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def encode_images(self, pixel_values: torch.Tensor) -> torch.Tensor:
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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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input_metadata: InputMetadata,
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pixel_values: Optional[List[Optional[np.array]]] = None,
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image_sizes: Optional[List[List[int]]] = None,
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image_offsets: Optional[List[int]] = None,
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) -> torch.Tensor:
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if input_metadata.forward_mode.is_extend():
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bs = input_metadata.batch_size
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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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for modalities in input_metadata.modalities:
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if modalities is not None:
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modalities_list.extend(modalities)
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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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# Whether the requests need vision inputs
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max_image_offset = np.array(
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[max(image_offsets[i]) if image_offsets[i] else -1 for i in range(bs)]
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)
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start_positions = positions[input_metadata.extend_start_loc].cpu().numpy()
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need_vision = start_positions <= max_image_offset
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if need_vision.any():
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pixel_values = [pixel_values[i] for i in range(bs) if need_vision[i]]
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image_sizes = [image_sizes[i] for i in range(bs) if need_vision[i]]
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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] == "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] == "multi-images"
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or modalities_list[image_idx] == "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] == "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] == "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 = input_metadata.extend_start_loc.cpu().numpy()
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prefix_lens_cpu = input_metadata.extend_prefix_lens.cpu().numpy()
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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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prefix_len = prefix_lens_cpu[i]
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# Multiple images
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for j, image_offset in enumerate(image_offsets[i]):
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if image_offset < prefix_len:
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continue
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tmp_image_feature = image_features[pt][j]
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pad_len = tmp_image_feature.shape[0]
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left_idx = start_idx + (image_offset - prefix_len)
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right_idx = start_idx + (image_offset - prefix_len) + pad_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, input_metadata, input_embeds=input_embeds
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)
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elif input_metadata.forward_mode.is_decode():
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return self.language_model(input_ids, positions, input_metadata)
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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"
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or self.vision_feature_select_strategy == "full"
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):
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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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|
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# load mm_projector
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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.2": "multi_modal_projector.linear_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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"model.image_newline": "language_model.model.image_newline",
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}
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params_dict = dict(self.named_parameters())
|
|
for name, loaded_weight in weights:
|
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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:
|
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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)
|
|
else:
|
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self.language_model.load_weights([(name, loaded_weight)])
|
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|
|
@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,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
) -> None:
|
|
super().__init__()
|
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|
|
self.config = config
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|
self.vision_tower = None
|
|
self.config.vision_config.hidden_size = config.mm_hidden_size
|
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self.config.text_config.hidden_size = config.hidden_size
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|
|
self.multi_modal_projector = LlavaMultiModalProjector(config)
|
|
self.language_model = LlamaForCausalLM(config, quant_config=quant_config)
|
|
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,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
) -> 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)
|
|
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,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
) -> 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)
|
|
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)
|
|
)
|
|
|
|
|
|
EntryClass = [LlavaLlamaForCausalLM, LlavaQwenForCausalLM, LlavaMistralForCausalLM]
|