33b242df30
Co-authored-by: ZX <zx@lbx.dev> Co-authored-by: ZhouXingg <165115237+ZhouXingg@users.noreply.github.com>
336 lines
15 KiB
Python
336 lines
15 KiB
Python
"""Inference-only LLaVa model compatible with HuggingFace weights."""
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from typing import List, Optional
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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.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from sglang.srt.weight_utils import (
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default_weight_loader,
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hf_model_weights_iterator,
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)
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from sglang.srt.managers.router.infer_batch import ForwardMode
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from sglang.srt.managers.router.model_runner import InputMetadata
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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.models.llama2 import LlamaForCausalLM
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class LlavaLlamaForCausalLM(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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) -> 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.language_model = LlamaForCausalLM(config, quant_config=quant_config)
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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(self, input_ids, pad_value, pt_shape=None, image_size=None):
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new_image_feature_len = self.image_feature_len
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# now only support spatial_unpad + anyres
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if self.mm_patch_merge_type.startswith("spatial"):
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height = width = self.num_patches_per_side
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if pt_shape[0] > 1:
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if self.image_aspect_ratio == "anyres":
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num_patch_width, num_patch_height = get_anyres_image_grid_shape(
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image_size,
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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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if "unpad" in self.mm_patch_merge_type:
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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_size)
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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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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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image_features = self.multi_modal_projector(selected_image_feature)
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return image_features
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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 == ForwardMode.EXTEND:
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bs = input_metadata.batch_size
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# Embed text input
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input_embeds = self.language_model.model.embed_tokens(input_ids)
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# Embed vision input
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need_vision = (
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(positions[input_metadata.extend_start_loc] < self.image_feature_len)
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.cpu()
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.numpy()
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)
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# FIXME: We need to substract the length of the system prompt
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has_pixel = np.array([pixel_values[i] is not None for i in range(bs)])
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need_vision = need_vision & has_pixel
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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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for image_idx, image_feature in enumerate(image_features):
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if image_feature.shape[0] > 1:
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base_image_feature = image_feature[0]
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image_feature = image_feature[1:]
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height = width = self.num_patches_per_side
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assert height * width == base_image_feature.shape[0]
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if self.image_aspect_ratio == "anyres":
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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],
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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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else:
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raise NotImplementedError()
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if "unpad" in self.mm_patch_merge_type:
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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]
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)
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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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else:
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image_feature = image_feature[0]
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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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self.language_model.model.image_newline[None],
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),
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dim=0,
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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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extend_start_loc_cpu = input_metadata.extend_start_loc.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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pad_len, pad_dim = image_features[pt].shape # 576, 4096
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dim = input_embeds.shape[1]
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assert (
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pad_dim == dim
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), "invalid pad_dim={}, input_embed_dim={}!".format(pad_dim, dim)
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# Fill in the placeholder for the image
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try:
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input_embeds[
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start_idx
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+ image_offsets[i] : start_idx
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+ image_offsets[i]
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+ pad_len
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] = image_features[pt]
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except RuntimeError as e:
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print(f"RuntimeError in llava image encoding: {e}")
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print(input_embeds.shape)
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print(start_idx, image_offsets[i])
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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 == ForwardMode.DECODE:
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return self.language_model(input_ids, positions, input_metadata)
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def load_weights(
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self,
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model_name_or_path: str,
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cache_dir: Optional[str] = None,
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load_format: str = "auto",
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revision: Optional[str] = None,
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):
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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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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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self.image_feature_len = int((self.image_size / self.patch_size) ** 2)
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if self.vision_feature_select_strategy == "patch":
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pass
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elif self.vision_feature_select_strategy == "cls_patch":
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self.image_feature_len += 1
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else:
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raise ValueError(f"Unexpected select feature: {self.select_feature}")
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# load mm_projector
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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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}
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in hf_model_weights_iterator(
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model_name_or_path, cache_dir, load_format, revision
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):
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# FIXME: why projector weights read two times?
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if "projector" in name or "vision_tower" in name:
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for weight_name, param_name in projector_weights.items():
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if weight_name in name:
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name = name.replace(weight_name, param_name)
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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# load language model
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self.language_model.load_weights(
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model_name_or_path, cache_dir, load_format, revision
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)
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monkey_path_clip_vision_embed_forward()
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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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first_call = True
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def clip_vision_embed_forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
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batch_size = pixel_values.shape[0]
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# Move this conv layer to CPU to avoid a bug in torch >= 2.1 on A10G.
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global first_call
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if first_call:
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self.patch_embedding.cpu().float()
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first_call = False
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pixel_values = pixel_values.to(dtype=torch.float32, device="cpu")
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patch_embeds = self.patch_embedding(pixel_values).cuda().half()
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patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
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class_embeds = self.class_embedding.expand(batch_size, 1, -1)
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embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
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embeddings = embeddings + self.position_embedding(self.position_ids)
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return embeddings
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def monkey_path_clip_vision_embed_forward():
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import transformers
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setattr(
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transformers.models.clip.modeling_clip.CLIPVisionEmbeddings,
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"forward",
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clip_vision_embed_forward,
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)
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EntryClass = LlavaLlamaForCausalLM
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