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# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from abc import ABC, abstractmethod
import torch
import torch.nn as nn
from .multimodal_encoder.builder import build_vision_tower
from .multimodal_projector.builder import build_vision_projector
from .multimodal_adapter.builder import build_seg_projector
from vcoder_llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, SEG_TOKEN_INDEX
class VCoderLlavaMetaModel:
def __init__(self, config):
super(VCoderLlavaMetaModel, self).__init__(config)
self.config = config
if hasattr(config, "mm_vision_tower"):
self.vision_tower = build_vision_tower(config, delay_load=True)
self.mm_projector = build_vision_projector(config)
if hasattr(config, "seg_mm_projector_type"):
self.seg_mm_projector = build_seg_projector(config)
if hasattr(config, "use_mm2_proj"):
if config.use_mm2_proj:
self.mm2_projector = build_vision_projector(config)
if hasattr(config, "mm_vcoder_lm_emb"):
self.vcoder_lm_emb = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
def get_vision_tower(self):
vision_tower = getattr(self, 'vision_tower', None)
if type(vision_tower) is list:
vision_tower = vision_tower[0]
return vision_tower
def initialize_seg_modules(self, model_args, fsdp=None):
mm_seg_select_layer = model_args.mm_seg_select_layer
mm_seg_select_feature = model_args.mm_seg_select_feature
self.config.seg_mm_hidden_size = self.vision_tower.hidden_size
pretrain_mm2_mlp_adapter = model_args.pretrain_mm2_mlp_adapter
self.config.seg_use_mm_proj = True
self.config.seg_mm_projector_type = getattr(model_args, 'seg_mm_projector_type', 'linear')
self.config.mm_seg_select_layer = mm_seg_select_layer
self.config.mm_seg_select_feature = mm_seg_select_feature
self.seg_mm_projector = build_seg_projector(self.config)
self.vcoder_lm_emb = nn.Embedding(self.config.vocab_size, self.config.hidden_size, self.config.pad_token_id)
if getattr(model_args, "use_mm2_proj"):
self.config.use_mm2_proj = model_args.use_mm2_proj
self.mm2_projector = build_vision_projector(self.config)
if pretrain_mm2_mlp_adapter is not None:
mm2_projector_weights = torch.load(pretrain_mm2_mlp_adapter, map_location='cpu')
def get_w(weights, keyword):
return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
self.mm2_projector.load_state_dict(get_w(mm2_projector_weights, 'mm_projector'))
class VCoderLlavaMetaForCausalLM(ABC):
@abstractmethod
def get_model(self):
pass
def get_vision_tower(self):
return self.get_model().get_vision_tower()
def encode_seg_images(self, seg_images):
seg_features = self.get_model().get_vision_tower()(seg_images)
seg_features = self.get_model().seg_mm_projector(seg_features)
return seg_features
def encode_images(self, images):
image_features = self.get_model().get_vision_tower()(images)
image_features = self.get_model().mm_projector(image_features)
return image_features
def encode_images_w_seg(self, images):
image_features = self.get_model().get_vision_tower()(images)
image_features = self.get_model().mm2_projector(image_features)
return image_features
def prepare_inputs_labels_for_multimodal(
self, input_ids, attention_mask, past_key_values, labels, images, seg_images,
):
vision_tower = self.get_vision_tower()
if vision_tower is None or images is None or input_ids.shape[1] == 1:
if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1:
attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device)
return input_ids, attention_mask, past_key_values, None, labels
if type(images) is list or images.ndim == 5:
concat_images = torch.cat([image for image in images], dim=0)
if seg_images is not None and hasattr(self, 'mm2_projector'):
image_features = self.encode_images_w_seg(concat_images)
else:
image_features = self.encode_images(concat_images)
split_sizes = [image.shape[0] for image in images]
image_features = torch.split(image_features, split_sizes, dim=0)
image_features = [x.flatten(0, 1) for x in image_features]
else:
if seg_images is not None and hasattr(self, 'mm2_projector'):
image_features = self.encode_images_w_seg(images)
else:
image_features = self.encode_images(images)
if seg_images is not None:
if type(seg_images) is list or seg_images.ndim == 5:
concat_seg_images = torch.cat([image for image in seg_images], dim=0)
seg_features = self.encode_seg_images(concat_seg_images)
split_sizes = [image.shape[0] for image in seg_images]
seg_features = torch.split(seg_features, split_sizes, dim=0)
seg_features = [x.flatten(0, 1) for x in seg_features]
else:
seg_features = self.encode_seg_images(seg_images)
self.get_model().vcoder_lm_emb.weight.data = self.get_model().get_input_embeddings().weight.data.clone()
new_input_embeds = []
new_labels = [] if labels is not None else None
cur_image_idx = 0
cur_seg_idx = 0
for batch_idx, cur_input_ids in enumerate(input_ids):
if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0 or (cur_input_ids == SEG_TOKEN_INDEX).sum() == 0:
# FIXME: this is a hacky fix, for deepspeed zero3 to work
cur_image_features = image_features[cur_image_idx]
if seg_images is not None:
cur_seg_features = seg_features[cur_seg_idx]
half_len = cur_input_ids.shape[0] // 2
if seg_images is not None:
cur_input_embeds_1 = self.get_model().vcoder_lm_emb(cur_input_ids[:half_len])
cur_input_embeds_2 = self.get_model().vcoder_lm_emb(cur_input_ids[half_len:])
cur_input_embeds = torch.cat([cur_input_embeds_1, cur_seg_features[0:0], cur_image_features[0:0], cur_input_embeds_2], dim=0)
else:
cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids[:half_len])
cur_input_embeds_2 = self.get_model().embed_tokens(cur_input_ids[half_len:])
cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0], cur_input_embeds_2], dim=0)
new_input_embeds.append(cur_input_embeds)
if labels is not None:
new_labels.append(labels[batch_idx])
cur_image_idx += 1
cur_seg_idx += 1
continue
image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
cur_new_input_embeds = []
if labels is not None:
cur_labels = labels[batch_idx]
cur_new_labels = []
assert cur_labels.shape == cur_input_ids.shape
while image_token_indices.numel() > 0:
cur_image_features = image_features[cur_image_idx]
image_token_start = image_token_indices[0]
if seg_images is None:
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start]))
else:
cur_new_input_embeds.append(self.get_model().vcoder_lm_emb(cur_input_ids[:image_token_start]))
cur_new_input_embeds.append(cur_image_features)
if labels is not None:
cur_new_labels.append(cur_labels[:image_token_start])
cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
cur_labels = cur_labels[image_token_start+1:]
cur_image_idx += 1
cur_input_ids = cur_input_ids[image_token_start+1:]
image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
if seg_images is not None:
seg_token_indices = torch.where(cur_input_ids == SEG_TOKEN_INDEX)[0]
while seg_token_indices.numel() > 0:
cur_seg_features = seg_features[cur_seg_idx]
seg_token_start = seg_token_indices[0]
cur_new_input_embeds.append(self.get_model().vcoder_lm_emb(cur_input_ids[:seg_token_start]))
cur_new_input_embeds.append(cur_seg_features)
if labels is not None:
cur_new_labels.append(cur_labels[:seg_token_start])
cur_new_labels.append(torch.full((cur_seg_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
cur_labels = cur_labels[seg_token_start+1:]
cur_seg_idx += 1
cur_input_ids = cur_input_ids[seg_token_start+1:]
seg_token_indices = torch.where(cur_input_ids == SEG_TOKEN_INDEX)[0]
if cur_input_ids.numel() > 0:
if seg_images is None:
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids))
else:
cur_new_input_embeds.append(self.get_model().vcoder_lm_emb(cur_input_ids))
if labels is not None:
cur_new_labels.append(cur_labels)
cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds]
cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0)
new_input_embeds.append(cur_new_input_embeds)
if labels is not None:
cur_new_labels = torch.cat(cur_new_labels, dim=0)
new_labels.append(cur_new_labels)
if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds):
max_len = max(x.shape[0] for x in new_input_embeds)
new_input_embeds_align = []
for cur_new_embed in new_input_embeds:
cur_new_embed = torch.cat((cur_new_embed, torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0)
new_input_embeds_align.append(cur_new_embed)
new_input_embeds = torch.stack(new_input_embeds_align, dim=0)
if labels is not None:
new_labels_align = []
_new_labels = new_labels
for cur_new_label in new_labels:
cur_new_label = torch.cat((cur_new_label, torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX, dtype=cur_new_label.dtype, device=cur_new_label.device)), dim=0)
new_labels_align.append(cur_new_label)
new_labels = torch.stack(new_labels_align, dim=0)
if attention_mask is not None:
new_attention_mask = []
for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels):
new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True, dtype=attention_mask.dtype, device=attention_mask.device)
new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), False, dtype=attention_mask.dtype, device=attention_mask.device)
cur_new_attention_mask = torch.cat((new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0)
new_attention_mask.append(cur_new_attention_mask)
attention_mask = torch.stack(new_attention_mask, dim=0)
assert attention_mask.shape == new_labels.shape
else:
new_input_embeds = torch.stack(new_input_embeds, dim=0)
if labels is not None:
new_labels = torch.stack(new_labels, dim=0)
if attention_mask is not None:
new_attn_mask_pad_left = torch.full((attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True, dtype=attention_mask.dtype, device=attention_mask.device)
attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1)
assert attention_mask.shape == new_input_embeds.shape[:2]
return None, attention_mask, past_key_values, new_input_embeds, new_labels
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