267 lines
12 KiB
Python
267 lines
12 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 video model compatible with HuggingFace weights."""
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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 CLIPVisionModel, LlavaConfig
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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.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.model_executor.forward_batch_info import ForwardMode, InputMetadata
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from sglang.srt.models.llama import LlamaForCausalLM
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class LlavaVidForCausalLM(nn.Module):
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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__()
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self.config = config
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self.vision_tower = None
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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)
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self.mm_spatial_pool_stride = getattr(self.config, "mm_spatial_pool_stride", 2)
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self.resampler = nn.AvgPool2d(
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kernel_size=self.mm_spatial_pool_stride, stride=self.mm_spatial_pool_stride
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)
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self.language_model = LlamaForCausalLM(config, quant_config=quant_config)
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self.num_frames = getattr(self.config, "num_frames", 16)
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if "unpad" in getattr(config, "mm_patch_merge_type", ""):
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self.language_model.model.image_newline = nn.Parameter(
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torch.empty(config.text_config.hidden_size, dtype=torch.float16)
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)
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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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new_image_feature_len = self.image_feature_len
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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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offset = input_ids.index(self.config.image_token_index)
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# old_len + pad_len - 1, because we need to remove image_token_id
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new_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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return new_input_ids, [offset]
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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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height = width = self.num_patches_per_side
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num_of_frames = selected_image_feature.shape[0]
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selected_image_feature = selected_image_feature.view(
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num_of_frames, height, width, -1
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)
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selected_image_feature = selected_image_feature.permute(0, 3, 1, 2).contiguous()
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selected_image_feature = (
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self.resampler(selected_image_feature)
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.flatten(2)
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.transpose(1, 2)
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.contiguous()
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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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# 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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########## 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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image_features = self.encode_images(
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concat_images
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) # , prompts)#, image_counts, long_video=long_video)
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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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new_image_features = []
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for image_idx, image_feature in enumerate(image_features):
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new_image_features.append(image_feature.flatten(0, 1))
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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 image_offset in 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]
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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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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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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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print(f"target_frames: {self.num_frames}")
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self.image_feature_len = self.num_frames * int(
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(self.image_size / self.patch_size / self.mm_spatial_pool_stride) ** 2
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)
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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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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_resampler.mm_projector.0": "multi_modal_projector.linear_1",
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"model.vision_resampler.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())
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for name, loaded_weight in weights:
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# FIXME: why projector weights read two times?
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if "projector" in name or "vision_tower" in name or "image_newline" 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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if name in params_dict:
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param = params_dict[name]
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else:
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print(f"Warning: {name} not found in the model")
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continue
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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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else:
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self.language_model.load_weights([(name, loaded_weight)])
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@property
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def num_patches_per_side(self):
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return self.image_size // self.patch_size
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EntryClass = LlavaVidForCausalLM
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