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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.
# ------------------------------------------------------------------------
# Modified from LLaVA (https://github.com/haotian-liu/LLaVA)
# Copyright 2024 Yanwei Li
# ------------------------------------------------------------------------
from abc import ABC, abstractmethod
import torch
import torch.nn as nn
import torch.nn.functional as F
import json
import os
import transformers
import safetensors
from transformers.deepspeed import is_deepspeed_zero3_enabled
import deepspeed
from .multimodal_encoder.builder import build_vision_tower, build_vision_tower_aux
from .multimodal_projector.builder import build_vision_projector
from mgm.constants import (IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN,
DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN)
IS_NEW_TRANSFORMERS = transformers.__version__ >= "4.34.0"
class MGMMetaModel:
def __init__(self, config):
super(MGMMetaModel, 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)
if hasattr(config, "mm_vision_tower_aux"):
self.vision_tower_aux = build_vision_tower_aux(config, delay_load=True)
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 get_vision_tower_aux(self):
vision_tower_aux = getattr(self, 'vision_tower_aux', None)
if type(vision_tower_aux) is list:
vision_tower_aux = vision_tower_aux[0]
return vision_tower_aux
def initialize_vision_modules(self, model_args, fsdp=None):
vision_tower = model_args.vision_tower
vision_tower_aux = model_args.vision_tower_aux
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
self.config.mm_vision_tower = vision_tower
self.config.mm_vision_tower_aux = vision_tower_aux
if self.get_vision_tower() is None:
vision_tower = build_vision_tower(model_args)
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()
if vision_tower_aux is not None:
if self.get_vision_tower_aux() is None:
vision_tower_aux = build_vision_tower_aux(model_args)
if fsdp is not None and len(fsdp) > 0:
self.vision_tower_aux = [vision_tower_aux]
else:
self.vision_tower_aux = vision_tower_aux
else:
if fsdp is not None and len(fsdp) > 0:
vision_tower_aux = self.vision_tower_aux[0]
else:
vision_tower_aux = self.vision_tower_aux
vision_tower_aux.load_model()
self.config.mm_hidden_size_aux = vision_tower_aux.hidden_size
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_hidden_size = 3072
self.config.mm_hidden_size_uni = vision_tower.hidden_size
self.config.mm_vision_select_layer = mm_vision_select_layer
self.config.mm_vision_select_feature = mm_vision_select_feature
if getattr(self, 'mm_projector', None) is None:
self.mm_projector = build_vision_projector(self.config)
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:
mm_projector_weights = torch.load(pretrain_mm_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}
if 'model' in mm_projector_weights.keys():
mm_projector_weights = mm_projector_weights['model']
if is_deepspeed_zero3_enabled():
if len(mm_projector_weights) > 0:
with deepspeed.zero.GatheredParameters(mm_projector_weights, modifier_rank=0):
if torch.distributed.get_rank() == 0:
self.mm_projector.load_state_dict(mm_projector_weights)
else:
status = self.mm_projector.load_state_dict(mm_projector_weights, strict=False)
print('missing_keys:', status.missing_keys)
else:
if is_deepspeed_zero3_enabled():
named_parameters = get_w(mm_projector_weights, 'mm_projector')
if len(named_parameters) > 0:
with deepspeed.zero.GatheredParameters(named_parameters, modifier_rank=0):
if torch.distributed.get_rank() == 0:
self.mm_projector.load_state_dict(named_parameters)
else:
status = self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'), strict=False)
print('missing_keys:', status.missing_keys)
self.mm_projector = self.mm_projector.to(device='cuda')
def initialize_uni_modules(self, model_args, for_eval=False):
pretrain_mm_mlp_adapter = getattr(model_args, "pretrain_mm_mlp_adapter", None)
self.config.image_size_aux = getattr(model_args, 'image_size_aux', 320)
self.config.optimize_vision_tower = getattr(model_args, 'optimize_vision_tower', False)
self.config.optimize_vision_tower_aux = getattr(model_args, 'optimize_vision_tower_aux', False)
self.vlm_uni_query_projector = nn.Sequential(nn.LayerNorm(self.config.mm_hidden_size_uni),
nn.Linear(self.config.mm_hidden_size_uni, self.config.mm_hidden_size_uni))
self.vlm_uni_aux_projector = nn.Sequential(nn.LayerNorm(self.config.mm_hidden_size_aux),
nn.Linear(self.config.mm_hidden_size_aux, self.config.mm_hidden_size_uni))
self.vlm_uni_val_projector = nn.Sequential(nn.LayerNorm(self.config.mm_hidden_size_aux),
nn.Linear(self.config.mm_hidden_size_aux, self.config.mm_hidden_size_uni))
if pretrain_mm_mlp_adapter is not None:
projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
else:
trainable_module = ['vlm_uni', 'vision_fpn', 'vision_stages']
if hasattr(model_args, 'model_name_or_path'):
model_save_path = model_args.model_name_or_path
else:
model_save_path = model_args.model_path
model_idx_path = getattr(model_args, 'model_path', model_save_path)
if IS_NEW_TRANSFORMERS:
try:
weight_file = json.load(open(os.path.join(model_idx_path, 'model.safetensors.index.json'), 'r'))['weight_map']
except:
weight_file = json.load(open(os.path.join(model_idx_path, 'pytorch_model.bin.index.json'), 'r'))['weight_map']
else:
weight_file = json.load(open(os.path.join(model_idx_path, 'pytorch_model.bin.index.json'), 'r'))['weight_map']
model_path = set([weight_file[_key] for _key in weight_file if any([_module in _key for _module in trainable_module])])
projector_weights = {}
for _model in model_path:
if not IS_NEW_TRANSFORMERS:
projector_weights.update(torch.load(os.path.join(model_idx_path, _model), map_location='cpu'))
else:
with safetensors.safe_open(os.path.join(model_idx_path, _model), framework="pt", device='cpu') as f:
for _key in f.keys():
projector_weights.update({_key: f.get_tensor(_key)})
if len(projector_weights) == 0:
return
def get_w(weights, keyword, main_module, sub_module):
if getattr(main_module, sub_module, None) is None:
return
pretrain_weight = {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword + '.' in k}
if len(pretrain_weight) == 0:
return
if is_deepspeed_zero3_enabled():
named_parameters = [v for k, v in getattr(main_module, sub_module).named_parameters()]
if len(named_parameters) > 0:
# because zero3 puts placeholders in model params, this context
# manager gathers (unpartitions) the params of the current layer, then loads from
# the state dict and then re-partitions them again
with deepspeed.zero.GatheredParameters(named_parameters, modifier_rank=0):
if torch.distributed.get_rank() == 0:
getattr(main_module, sub_module).load_state_dict(pretrain_weight)
with deepspeed.zero.GatheredParameters(self.mm_projector[0].weight, modifier_rank=None):
weight_type = self.mm_projector[0].weight.dtype
device_type = self.mm_projector[0].weight.device
else:
weight_type = self.mm_projector[0].weight.dtype
device_type = self.mm_projector[0].weight.device
getattr(main_module, sub_module).load_state_dict(pretrain_weight)
if weight_type == torch.uint8 or weight_type == torch.int8 or weight_type == torch.int16:
weight_type = torch.float16
getattr(main_module, sub_module).to(device=device_type, dtype=weight_type)
print(f"Loading {sub_module} weights...")
# load pretrained weights
get_w(projector_weights, 'vision_tower.vision_tower', self.vision_tower, 'vision_tower')
# load pretrained weights
if self.config.optimize_vision_tower_aux:
# not optimize vision stem, just used to check
get_w(projector_weights, 'vision_tower_aux.vision_stem', self.vision_tower_aux, 'vision_stem')
get_w(projector_weights, 'vision_tower_aux.vision_stages', self.vision_tower_aux, 'vision_stages')
get_w(projector_weights, 'vlm_uni_query_projector', self, 'vlm_uni_query_projector')
get_w(projector_weights, 'vlm_uni_aux_projector', self, 'vlm_uni_aux_projector')
get_w(projector_weights, 'vlm_uni_val_projector', self, 'vlm_uni_val_projector')
class MGMMetaForCausalLM(ABC):
@abstractmethod
def get_model(self):
pass
def get_vision_tower(self):
return self.get_model().get_vision_tower()
def get_vision_tower_aux(self):
return self.get_model().get_vision_tower_aux()
def encode_images(self, images, images_aux=None, is_video=False):
image_grid = getattr(self.config, 'image_grid', 1)
image_global = getattr(self.config, 'image_global', False)
if image_grid > 1:
batch_size = images.shape[0]
if image_global:
global_images = images[:, -1:].flatten(0,1).contiguous()
grid_images = images[:, :-1].flatten(0,1).contiguous()
images = torch.cat([grid_images, global_images], dim=0)
else:
images = images.flatten(0,1).contiguous()
image_features, image_forward_outs = self.get_model().get_vision_tower()(images)
if image_global: # false
image_feat_global = image_features[-len(global_images):]
image_features = image_features[:len(grid_images)]
if images_aux is not None:
image_aux_features_raw = self.get_model().get_vision_tower_aux()(images_aux).to(
dtype=image_features.dtype, device=image_features.device)
if image_global:
image_aux_features_global = F.interpolate(image_aux_features_raw.float(),
scale_factor=1/image_grid,
mode='bilinear',
align_corners=False).to(dtype=image_aux_features_raw.dtype)
image_feat_global, image_aux_feat_global = self.unified_resampler(image_feat_global, image_aux_features_global)
if image_grid > 1:
image_aux_features_raw = image_aux_features_raw.reshape(*image_aux_features_raw.shape[:2],
image_grid,
image_aux_features_raw.shape[-2]//image_grid,
image_grid,
image_aux_features_raw.shape[-1]//image_grid)
image_aux_features_raw = image_aux_features_raw.permute(0, 2, 4, 1, 3, 5).flatten(1,2).flatten(0,1).contiguous()
image_features, image_aux_features = self.unified_resampler(image_features, image_aux_features_raw)
if image_grid > 1:
image_features = image_features.reshape(batch_size, image_grid**2, *image_features.shape[1:])
image_features = image_features.flatten(1,2).contiguous()
image_aux_features = image_aux_features.reshape(batch_size, image_grid**2, *image_aux_features.shape[1:])
image_aux_features = image_aux_features.flatten(1,2).contiguous()
# add global features, [global, local]
if image_global:
image_features = torch.cat([image_feat_global, image_features], dim=1)
image_aux_features = torch.cat([image_aux_feat_global, image_aux_features], dim=1)
# token generation
image_features = image_features + image_aux_features
# dense connector
image_features_1 = []
image_features_2 = []
for i in range(0, 12):
image_features_1.append(image_forward_outs.hidden_states[i][:, 1:].to(image_features.dtype))
image_features_1 = torch.stack(image_features_1, dim=0)
image_features_1 = torch.sum(image_features_1, dim=0) / 12
for i in range(12, 24):
image_features_2.append(image_forward_outs.hidden_states[i][:, 1:].to(image_features.dtype))
image_features_2 = torch.stack(image_features_2, dim=0)
image_features_2 = torch.sum(image_features_2, dim=0) / 12
image_features = torch.cat([image_features, image_features_1, image_features_2], dim=-1)
## dense connector end
# process image features after token generation
image_features = self.get_model().mm_projector(image_features)
return image_features
def unified_resampler(self, images, images_aux):
# patchwise with square images
patch_num = int(images.shape[1]**0.5)
patch_size = images_aux.shape[-1]//patch_num
# within patch attention
images_aux = images_aux.permute(0,2,3,1)
images_aux = images_aux.reshape(len(images_aux), patch_num, patch_size, patch_num, patch_size, images_aux.shape[-1])
images_aux = images_aux.permute(0,1,3,2,4,5)
images_aux = images_aux.reshape(len(images_aux), patch_num**2, patch_size**2, images_aux.shape[-1]).contiguous()
# token attention
embed_query = self.get_model().vlm_uni_query_projector(images)
embed_aux = self.get_model().vlm_uni_aux_projector(images_aux)
embed_value = self.get_model().vlm_uni_val_projector(images_aux)
embed_att = embed_query[:,:,None] @ (embed_aux.transpose(-1,-2) / (embed_aux.shape[-1]**0.5))
embed_att = embed_att.nan_to_num()
embed_feat = (embed_att.softmax(-1) @ embed_value).mean(2)
return images, embed_feat
def prepare_inputs_labels_for_multimodal(
self, input_ids, position_ids, attention_mask, past_key_values, labels, images=None, images_aux=None,
):
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:
target_shape = past_key_values[-1][-1].shape[-2] + 1
attention_mask = torch.cat((attention_mask, torch.ones(
(attention_mask.shape[0], target_shape - attention_mask.shape[1]),
dtype=attention_mask.dtype,
device=attention_mask.device
)), dim=1)
position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
return input_ids, position_ids, attention_mask, past_key_values, None, labels
if isinstance(images, list):
images = torch.stack(images, dim=0)
if isinstance(images_aux, list):
images_aux = torch.stack(images_aux, dim=0)
image_features = self.encode_images(images, images_aux)
# 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 -- TODO: double check
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 = []
max_pos_id = 0
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])
max_pos_id += cur_input_embeds_no_im[i].shape[0]
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))
max_pos_id += cur_image_features.shape[0]
cur_new_input_embeds = [x.to(device=cur_input_embeds.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:
mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, 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