Oryx / oryx /model /oryx_arch.py
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from abc import ABC, abstractmethod
import torch
import torch.nn as nn
from .multimodal_encoder.builder import build_vision_tower
from .multimodal_resampler.builder import build_vision_resampler
from .multimodal_projector.builder import build_vision_projector
from oryx.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
import ast
import torch.distributed as dist
class OryxMetaModel:
def __init__(self, config):
super(OryxMetaModel, self).__init__(config)
if hasattr(config, "mm_vision_tower"):
self.vision_tower = build_vision_tower(config, delay_load=True)
self.vision_resampler = build_vision_resampler(config, vision_tower=self.vision_tower)
self.mm_projector = build_vision_projector(config, vision_cfg=self.vision_tower.config)
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):
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
self.config.mm_vision_tower = vision_tower
if self.get_vision_tower() is None:
vision_tower = build_vision_tower(model_args)
vision_resampler = build_vision_resampler(model_args, vision_tower=vision_tower)
## Get the mm_spatial_pool_mode and mm_spatial_pool_stride
for k, v in vision_resampler.config.items():
setattr(self.config, k, v)
if fsdp is not None and len(fsdp) > 0:
self.vision_tower = [vision_tower]
self.vision_resampler = [vision_resampler]
else:
self.vision_tower = vision_tower
self.vision_resampler = vision_resampler
else:
if fsdp is not None and len(fsdp) > 0:
vision_resampler = self.vision_resampler[0]
vision_tower = self.vision_tower[0]
else:
vision_resampler = self.vision_resampler
vision_tower = self.vision_tower
vision_tower.load_model()
# In case it is frozen by LoRA
for p in self.vision_resampler.parameters():
p.requires_grad = True
self.config.use_mm_proj = True
self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
self.config.mm_hidden_size = getattr(vision_resampler, '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
if getattr(self, 'mm_projector', None) is None:
self.mm_projector = build_vision_projector(self.config, vision_cfg=vision_tower.config)
else:
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}
self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
incompatible_keys = self.vision_resampler.load_state_dict(get_w(mm_projector_weights, 'vision_resampler'), strict=False)
print(incompatible_keys)
class OryxMetaForCausalLM(ABC):
@abstractmethod
def get_model(self):
pass
def get_vision_tower(self):
return self.get_model().get_vision_tower()
def prepare_inputs_labels_for_multimodal(
self, input_ids, position_ids, attention_mask, past_key_values, labels,
images, modalities, image_sizes=None, images_highres=None):
# print(modalities, len(images), len(images_highres), len(input_ids))
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 isinstance(modalities, str):
modalities = [modalities]
video_idx_in_batch = []
for modal in range(len(modalities)):
if 'video' in modalities[modal]:
video_idx_in_batch.append(modal)
# Fix training with deepspeed zero3
num_modality = len(modalities)
# try:
# world_size = dist.get_world_size()
# tensor_in = torch.zeros(1, dtype=torch.int64, device=images[0].device).fill_(num_modality)
# tensor_out = torch.zeros(world_size, dtype=torch.int64, device=images[0].device)
# dist.all_gather_into_tensor(tensor_out, tensor_in)
# max_num_modality = tensor_out.max().item()
# except:
max_num_modality = num_modality
aimg = images[-1]
lowres_img = []
for idx, img_feat in enumerate(images):
if idx in video_idx_in_batch:
img_feat = aimg.new(1, 3, 128, 128).fill_(0)
lowres_img.append(img_feat)
# Fix training with deepspeed zero3
if max_num_modality > num_modality:
for _ in range(max_num_modality - num_modality):
lowres_img.append(aimg.new(1, 3, 64, 64).fill_(0))
images_highres.append(aimg.new(1, 3, 64, 64).fill_(0))
modalities.append('image')
lowres_img_features, lowres_img_sizes = self.get_model().get_vision_tower()(lowres_img)
highres_img_features = []
highres_img_sizes = []
for idx, img_feat in enumerate(images_highres):
if img_feat.ndim == 5:
img_feat = img_feat.squeeze(1)
highres_img_feature, highres_img_size = self.get_model().get_vision_tower()(img_feat)
highres_img_features.append(highres_img_feature)
highres_img_sizes.append(highres_img_size)
image_features = []
for idx in range(len(modalities)):
img_feat_highres, img_size_highres = self.get_model().vision_resampler(highres_img_features[idx],
modalities[idx],
highres_img_sizes[idx])
img_feat_lowres, img_size_lowres = self.get_model().vision_resampler(lowres_img_features[idx],
modalities[idx],
lowres_img_sizes[idx])
img_feat = self.get_model().mm_projector(img_feat_lowres,
img_size_lowres,
img_feat_highres,
img_size_highres,
modalities[idx])
image_features.append(img_feat.flatten(0, 1))
if max_num_modality > num_modality:
image_features = image_features[:num_modality]
modalities = modalities[:num_modality]
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)
modality_max_length = getattr(self.config, 'modality_max_length', None)
if modality_max_length is None or modality_max_length == "None":
if tokenizer_model_max_length is not None:
# if new_input_embeds[0] > tokenizer_model_max_length:
# print(f"Embeds length ({new_input_embeds.shape[0]}) larger than max length")
new_input_embeds =[x[:tokenizer_model_max_length] for x, modality in zip(new_input_embeds, modalities)]
new_labels = [x[:tokenizer_model_max_length] for x, modality in zip(new_labels, modalities)]
else:
modality_max_length = ast.literal_eval(modality_max_length)
modality_max_length_dict = {"image": modality_max_length[0], "text": modality_max_length[1], "video": modality_max_length[2]}
new_input_embeds =[x[: modality_max_length_dict[modality]] for x, modality in zip(new_input_embeds, modalities)]
new_labels = [x[: modality_max_length_dict[modality]] for x, modality in zip(new_labels, modalities)]
# 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