Llava-hd Support (#92)
Co-authored-by: Haotian Liu <liuhaotian.cn@gmail.com>
This commit is contained in:
shiyi.c_98
@@ -1,15 +1,18 @@
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"""Inference-only LLaVa model compatible with HuggingFace weights."""
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import json
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import os
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from typing import Any, Dict, List, Optional, Tuple
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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 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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from torch import nn
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from transformers import CLIPImageProcessor, CLIPVisionModel, LlavaConfig
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from transformers import CLIPVisionModel, LlamaConfig, LlavaConfig
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from transformers.models.llava.modeling_llava import LlavaMultiModalProjector
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from vllm.model_executor.layers.linear import LinearMethodBase
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from vllm.model_executor.weight_utils import (
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@@ -31,26 +34,64 @@ class LlavaLlamaForCausalLM(nn.Module):
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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, linear_method)
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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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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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def pad_input_ids(self, input_ids, pad_value):
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pad_ids = pad_value * (
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(self.image_feature_len + len(pad_value)) // len(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[: self.image_feature_len]
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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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@@ -75,23 +116,86 @@ class LlavaLlamaForCausalLM(nn.Module):
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device=self.vision_tower.device,
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)
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image_outputs = self.vision_tower(
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pixel_values, output_hidden_states=True
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)
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# NOTE: This is not memory efficient. (output_hidden_states=True) will save all the hidden stated.
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########## Encode Image ########
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selected_image_feature = image_outputs.hidden_states[
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self.vision_feature_layer
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]
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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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if pixel_values.ndim == 5:
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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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concat_images = torch.cat(
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[image for image in pixel_values], dim=0
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) # ndim=4
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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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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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# normal pixel: BS, C=3, H=336, W=336
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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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@@ -100,7 +204,7 @@ class LlavaLlamaForCausalLM(nn.Module):
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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
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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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@@ -146,6 +250,11 @@ class LlavaLlamaForCausalLM(nn.Module):
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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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@@ -159,13 +268,14 @@ class LlavaLlamaForCausalLM(nn.Module):
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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:
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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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@@ -180,6 +290,10 @@ class LlavaLlamaForCausalLM(nn.Module):
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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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