# 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
from .multimodal_encoder.clip_encoder import CLIPVisionTower
from .multimodal_projector.builder import build_vision_projector
from .language_model.configuration_llava_phi import (
LlavaPhiConfig,
LlavaPhiVisionConfig,
ProjectorConfig,
)
# from llava_phi.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
IGNORE_INDEX = -100
IMAGE_TOKEN_INDEX = -200
DEFAULT_IMAGE_TOKEN = "<image>"
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
DEFAULT_IM_START_TOKEN = "<im_start>"
DEFAULT_IM_END_TOKEN = "<im_end>"
class LlavaMetaModel:
def __init__(self, config):
super(LlavaMetaModel, self).__init__(config)
self.vision_tower = CLIPVisionTower(
LlavaPhiVisionConfig(**config.vision_config["vision_tower"])
)
self.mm_projector = build_vision_projector(
ProjectorConfig(**config.vision_config["mm_projector"])
)
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
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):
image_features = self.get_model().get_vision_tower()(images)
image_features = self.get_model().mm_projector(image_features)
return image_features
def prepare_inputs_labels_for_multimodal(
self, input_ids, attention_mask, past_key_values, labels, 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)
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:
image_features = self.encode_images(images)
new_input_embeds = []
new_labels = [] if labels is not None else None
cur_image_idx = 0
for batch_idx, cur_input_ids in enumerate(input_ids):
if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0:
# multimodal LLM, but the current sample is not multimodal
# FIXME: this is a hacky fix, for deepspeed zero3 to work
half_len = cur_input_ids.shape[0] // 2
cur_image_features = image_features[cur_image_idx]
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
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 getattr(self.config, "tune_mm_mlp_adapter", False) and getattr(
self.config, "mm_use_im_start_end", False
):
cur_new_input_embeds.append(
self.get_model()
.embed_tokens(cur_input_ids[: image_token_start - 1])
.detach()
)
cur_new_input_embeds.append(
self.get_model().embed_tokens(
cur_input_ids[image_token_start - 1 : image_token_start]
)
)
cur_new_input_embeds.append(cur_image_features)
cur_new_input_embeds.append(
self.get_model().embed_tokens(
cur_input_ids[image_token_start + 1 : image_token_start + 2]
)
)
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_new_labels.append(
cur_labels[image_token_start : image_token_start + 1]
)
cur_labels = cur_labels[image_token_start + 2 :]
else:
cur_new_input_embeds.append(
self.get_model().embed_tokens(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
if getattr(self.config, "tune_mm_mlp_adapter", False) and getattr(
self.config, "mm_use_im_start_end", False
):
cur_input_ids = cur_input_ids[image_token_start + 2 :]
else:
cur_input_ids = cur_input_ids[image_token_start + 1 :]
image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
if cur_input_ids.numel() > 0:
if getattr(self.config, "tune_mm_mlp_adapter", False) and getattr(
self.config, "mm_use_im_start_end", False
):
cur_new_input_embeds.append(
self.get_model().embed_tokens(cur_input_ids).detach()
)
else:
cur_new_input_embeds.append(
self.get_model().embed_tokens(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
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
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