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llava_arch.py
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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 os
import torch
import torch.nn as nn
from .multimodal_encoder.builder import build_vision_tower
from .multimodal_projector.builder import build_vision_projector
from llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.mm_utils import get_anyres_image_grid_shape
class LlavaMetaModel:
def __init__(self, config):
super(LlavaMetaModel, self).__init__(config)
if hasattr(config, "mm_vision_tower"):
self.vision_tower = build_vision_tower(config, delay_load=True)
self.mm_projector = build_vision_projector(config, self.vision_tower.config)
if 'unpad' in getattr(config, 'mm_patch_merge_type', ''):
self.image_newline = nn.Parameter(
torch.empty(config.hidden_size, dtype=self.dtype)
)
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_vision_modules(self, model_args, fsdp=None, **kwargs):
vision_tower = model_args.vision_tower
mm_vision_select_layer = model_args.mm_vision_select_layer
mm_vision_select_feature = model_args.mm_vision_select_feature
pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
mm_patch_merge_type = model_args.mm_patch_merge_type
self.config.mm_vision_tower = vision_tower
if self.get_vision_tower() is None:
vision_tower = build_vision_tower(model_args, **kwargs)
if fsdp is not None and len(fsdp) > 0:
self.vision_tower = [vision_tower]
else:
self.vision_tower = vision_tower
else:
if fsdp is not None and len(fsdp) > 0:
vision_tower = self.vision_tower[0]
else:
vision_tower = self.vision_tower
vision_tower.load_model()
self.config.use_mm_proj = True
self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
self.config.mm_hidden_size = vision_tower.hidden_size
self.config.mm_vision_select_layer = mm_vision_select_layer
self.config.mm_vision_select_feature = mm_vision_select_feature
self.config.mm_patch_merge_type = mm_patch_merge_type
self.config.mm_vision_token_compression_type = model_args.mm_vision_token_compression_type
self.config.mm_vision_output_token_count = model_args.mm_vision_output_token_count
self.config.mm_vision_output_text_embedding_size = model_args.mm_vision_output_text_embedding_size
self.config.mm_vision_output_combined_token_count = model_args.mm_vision_output_combined_token_count
self.config.mm_vision_token_compression_kernel_size = model_args.mm_vision_token_compression_kernel_size
self.config.mm_vision_token_compression_stride = model_args.mm_vision_token_compression_stride
if getattr(self, 'mm_projector', None) is None:
self.mm_projector = build_vision_projector(self.config, self.vision_tower.config)
if 'unpad' in mm_patch_merge_type:
embed_std = 1 / torch.sqrt(torch.tensor(self.config.hidden_size, dtype=self.dtype))
self.image_newline = nn.Parameter(
torch.randn(self.config.hidden_size, dtype=self.dtype) * embed_std
)
else:
# In case it is frozen by LoRA
for p in self.mm_projector.parameters():
p.requires_grad = True
if pretrain_mm_mlp_adapter is not None:
if os.path.isdir(pretrain_mm_mlp_adapter):
pretrain_mm_mlp_adapter_path = os.path.join(pretrain_mm_mlp_adapter, "mm_projector.bin")
else:
pretrain_mm_mlp_adapter_path = pretrain_mm_mlp_adapter
mm_projector_weights = torch.load(pretrain_mm_mlp_adapter_path, 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.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
def unpad_image(tensor, original_size):
"""
Unpads a PyTorch tensor of a padded and resized image.
Args:
tensor (torch.Tensor): The image tensor, assumed to be in CxHxW format.
original_size (tuple): The original size of PIL image (width, height).
Returns:
torch.Tensor: The unpadded image tensor.
"""
original_width, original_height = original_size
current_height, current_width = tensor.shape[1:]
original_aspect_ratio = original_width / original_height
current_aspect_ratio = current_width / current_height
if original_aspect_ratio > current_aspect_ratio:
scale_factor = current_width / original_width
new_height = int(original_height * scale_factor)
padding = (current_height - new_height) // 2
unpadded_tensor = tensor[:, padding:current_height - padding, :]
else:
scale_factor = current_height / original_height
new_width = int(original_width * scale_factor)
padding = (current_width - new_width) // 2
unpadded_tensor = tensor[:, :, padding:current_width - padding]
return unpadded_tensor
class LlavaMetaForCausalLM(ABC):
@abstractmethod
def get_model(self):
pass
def get_vision_tower(self):
return self.get_model().get_vision_tower()
def encode_images(self, images, text=None):
features = self.get_model().get_vision_tower()(images, text)
image_features = self.get_model().mm_projector(features)
return image_features
def prepare_inputs_labels_for_multimodal(
self, input_ids, position_ids, attention_mask, past_key_values, labels,
images, image_sizes=None
):
vision_tower = self.get_vision_tower()
if vision_tower is None or images is None or input_ids.shape[1] == 1:
return input_ids, position_ids, attention_mask, past_key_values, None, labels
if type(images) is list or images.ndim == 5:
if self.get_model().config.mm_vision_token_compression_type == 'quecc':
raise NotImplementedError("The QueCC compression type is not supported for lists of images for now.")
if type(images) is list:
images = [x.unsqueeze(0) if x.ndim == 3 else x for x in images]
concat_images = torch.cat([image for image in images], dim=0)
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)
mm_patch_merge_type = getattr(self.config, 'mm_patch_merge_type', 'flat')
image_aspect_ratio = getattr(self.config, 'image_aspect_ratio', 'square')
if mm_patch_merge_type == 'flat':
image_features = [x.flatten(0, 1) for x in image_features]
elif mm_patch_merge_type.startswith('spatial'):
new_image_features = []
for image_idx, image_feature in enumerate(image_features):
if image_feature.shape[0] > 1:
base_image_feature = image_feature[0]
image_feature = image_feature[1:]
height = width = self.get_vision_tower().num_patches_per_side
assert height * width == base_image_feature.shape[0]
if image_aspect_ratio == 'anyres':
num_patch_width, num_patch_height = get_anyres_image_grid_shape(image_sizes[image_idx], self.config.image_grid_pinpoints, self.get_vision_tower().config.image_size)
image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
else:
raise NotImplementedError
if 'unpad' in mm_patch_merge_type:
image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
image_feature = image_feature.flatten(1, 2).flatten(2, 3)
image_feature = unpad_image(image_feature, image_sizes[image_idx])
image_feature = torch.cat((
image_feature,
self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)
), dim=-1)
image_feature = image_feature.flatten(1, 2).transpose(0, 1)
else:
image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous()
image_feature = image_feature.flatten(0, 3)
image_feature = torch.cat((base_image_feature, image_feature), dim=0)
else:
image_feature = image_feature[0]
if 'unpad' in mm_patch_merge_type:
image_feature = torch.cat((
image_feature,
self.model.image_newline[None].to(image_feature.device)
), dim=0)
new_image_features.append(image_feature)
image_features = new_image_features
else:
raise ValueError(f"Unexpected mm_patch_merge_type: {self.config.mm_patch_merge_type}")
else:
if getattr(self.get_model().config, 'mm_vision_token_compression_type', None) == 'quecc':
b = input_ids.size()[0]
split_idx = (input_ids == -200).nonzero(as_tuple=False) #returns batch_id, row_ids of -200, ex: [0:35, 1:35, 1:37, 2:35]
row_indices = split_idx[:, 0].tolist() #[0,1,1,2]
last_occurrence_indices = {row.item(): col.item() for row, col in split_idx} #{0:35,1:37, 2:35}
split_idx = [last_occurrence_indices[row.item()] for row, _ in split_idx] #[35,37,37,35]
main_text_ids = []
rows_with_ids = []
for k,idx in zip(row_indices, split_idx):
rows_with_ids.append(k)
main_text_ids.append(input_ids[k][idx+1:])
main_text = self.tokenizer.batch_decode(main_text_ids, skip_special_tokens=True)
query_text = []
idx = 0
for cur_row_idx, text in enumerate(main_text):
while idx < rows_with_ids[cur_row_idx]:
print("No input_ids split")
query_text.append("image")
idx += 1
start_index = text.find('\n') + 1
end_index = text.find('ASSISTANT')
query_text.append(text[start_index:end_index].strip())
idx += 1
while len(query_text) < b:
print("No input_ids split")
query_text.append("image")
image_features = self.encode_images(images, query_text)
else:
image_features = self.encode_images(images)
# TODO: image start / end is not implemented here to support pretraining.
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
raise NotImplementedError
# Let's just add dummy tensors if they do not exist,
# it is a headache to deal with None all the time.
# But it is not ideal, and if you have a better idea,
# please open an issue / submit a PR, thanks.
_labels = labels
_position_ids = position_ids
_attention_mask = attention_mask
if attention_mask is None:
attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
else:
attention_mask = attention_mask.bool()
if position_ids is None:
position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
if labels is None:
labels = torch.full_like(input_ids, IGNORE_INDEX)
# remove the padding using attention_mask -- FIXME
_input_ids = input_ids
input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
new_input_embeds = []
new_labels = []
cur_image_idx = 0
for batch_idx, cur_input_ids in enumerate(input_ids):
num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
if num_images == 0:
cur_image_features = image_features[cur_image_idx]
cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
new_input_embeds.append(cur_input_embeds)
new_labels.append(labels[batch_idx])
cur_image_idx += 1
continue
image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
cur_input_ids_noim = []
cur_labels = labels[batch_idx]
cur_labels_noim = []
for i in range(len(image_token_indices) - 1):
cur_input_ids_noim.append(cur_input_ids[image_token_indices[i]+1:image_token_indices[i+1]])
cur_labels_noim.append(cur_labels[image_token_indices[i]+1:image_token_indices[i+1]])
split_sizes = [x.shape[0] for x in cur_labels_noim]
cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim))
cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
cur_new_input_embeds = []
cur_new_labels = []
for i in range(num_images + 1):
cur_new_input_embeds.append(cur_input_embeds_no_im[i])
cur_new_labels.append(cur_labels_noim[i])
if i < num_images:
cur_image_features = image_features[cur_image_idx]
cur_image_idx += 1
cur_new_input_embeds.append(cur_image_features)
cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))
cur_new_input_embeds = [x.to(self.device) for x in cur_new_input_embeds]
cur_new_input_embeds = torch.cat(cur_new_input_embeds)
cur_new_labels = torch.cat(cur_new_labels)
new_input_embeds.append(cur_new_input_embeds)
new_labels.append(cur_new_labels)
# Truncate sequences to max length as image embeddings can make the sequence longer
tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None)
if tokenizer_model_max_length is not None:
new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds]
new_labels = [x[:tokenizer_model_max_length] for x in new_labels]
# Combine them
max_len = max(x.shape[0] for x in new_input_embeds)
batch_size = len(new_input_embeds)
new_input_embeds_padded = []
new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)
for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
cur_len = cur_new_embed.shape[0]
if getattr(self.config, 'tokenizer_padding_side', 'right') == "left":
new_input_embeds_padded.append(torch.cat((
torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device),
cur_new_embed
), dim=0))
if cur_len > 0:
new_labels_padded[i, -cur_len:] = cur_new_labels
attention_mask[i, -cur_len:] = True
position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
else:
new_input_embeds_padded.append(torch.cat((
cur_new_embed,
torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)
), dim=0))
if cur_len > 0:
new_labels_padded[i, :cur_len] = cur_new_labels
attention_mask[i, :cur_len] = True
position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
if _labels is None:
new_labels = None
else:
new_labels = new_labels_padded
if _attention_mask is None:
attention_mask = None
else:
attention_mask = attention_mask.to(dtype=_attention_mask.dtype)
if _position_ids is None:
position_ids = None
return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels
def initialize_vision_tokenizer(self, model_args, tokenizer):
if model_args.mm_use_im_patch_token:
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
self.resize_token_embeddings(len(tokenizer))
if model_args.mm_use_im_start_end:
num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
self.resize_token_embeddings(len(tokenizer))
if num_new_tokens > 0:
input_embeddings = self.get_input_embeddings().weight.data
output_embeddings = self.get_output_embeddings().weight.data
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
dim=0, keepdim=True)
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
dim=0, keepdim=True)
input_embeddings[-num_new_tokens:] = input_embeddings_avg
output_embeddings[-num_new_tokens:] = output_embeddings_avg
if model_args.tune_mm_mlp_adapter:
for p in self.get_input_embeddings().parameters():
p.requires_grad = True
for p in self.get_output_embeddings().parameters():
p.requires_grad = False
if model_args.pretrain_mm_mlp_adapter:
if os.path.isdir(model_args.pretrain_mm_mlp_adapter):
pretrain_mm_mlp_adapter_path = os.path.join(model_args.pretrain_mm_mlp_adapter, "mm_projector.bin")
else:
pretrain_mm_mlp_adapter_path = model_args.pretrain_mm_mlp_adapter
mm_projector_weights = torch.load(pretrain_mm_mlp_adapter_path, map_location='cpu')
embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
assert num_new_tokens == 2
if input_embeddings.shape == embed_tokens_weight.shape:
input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
elif embed_tokens_weight.shape[0] == num_new_tokens:
input_embeddings[-num_new_tokens:] = embed_tokens_weight
else:
raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
elif model_args.mm_use_im_patch_token:
if model_args.tune_mm_mlp_adapter:
for p in self.get_input_embeddings().parameters():
p.requires_grad = False
for p in self.get_output_embeddings().parameters():
p.requires_grad = False