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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 typing import List, Optional, Union
import torch
import torch.nn as nn
from transformers import PreTrainedModel
DEFAULT_IMAGE_TOKEN = '<image>'
DEFAULT_IMAGE_PATCH_TOKEN = '<im_patch>'
DEFAULT_IM_START_TOKEN = '<im_start>'
DEFAULT_IM_END_TOKEN = '<im_end>'
class LlavaLlamaForCausalLM(PreTrainedModel):
def __init__(self,
vision_encoder,
lang_encoder,
mm_hidden_size,
use_im_start_end=True,
use_mm_proj=True,
im_start_token: Optional[int] = None,
im_end_token: Optional[int] = None,
im_patch_token: Optional[int] = None,
mm_vision_select_layer: int = -1):
super().__init__(lang_encoder.config)
self.vision_tower = vision_encoder
self.lang_encoder = lang_encoder
self.use_im_start_end = use_im_start_end
self.im_start_token = im_start_token
self.im_end_token = im_end_token
self.im_patch_token = im_patch_token
self.mm_hidden_size = mm_hidden_size
self.mm_vision_select_layer = mm_vision_select_layer
self.lang_hidden_size = lang_encoder.config.hidden_size
if use_mm_proj and not hasattr(lang_encoder.model, 'mm_projector'):
mm_projector = nn.Linear(self.mm_hidden_size,
self.lang_hidden_size)
self.lang_encoder.model.add_module('mm_projector', mm_projector)
elif not use_mm_proj:
self.lang_encoder.model.add_module('mm_projector', nn.Identity())
self.post_init()
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
images: Optional[torch.FloatTensor] = None,
return_dict: Optional[bool] = None,
):
output_attentions = (
output_attentions if output_attentions is not None else
self.config.output_attentions)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else
self.config.output_hidden_states)
return_dict = (
return_dict
if return_dict is not None else self.config.use_return_dict)
# decoder outputs consists of
# (dec_features, layer_state, dec_hidden, dec_attn)
if inputs_embeds is None:
inputs_embeds = self.lang_encoder.model.embed_tokens(input_ids)
inputs_embeds = self.forward_vision_tower(input_ids, inputs_embeds,
images)
return self.lang_encoder(
input_ids=None,
attention_mask=attention_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
)
def prepare_inputs_for_generation(self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
**kwargs):
if past_key_values:
input_ids = input_ids[:, -1:]
# if `inputs_embeds` are passed, we only want to use
# them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {'inputs_embeds': inputs_embeds}
else:
model_inputs = {'input_ids': input_ids}
model_inputs.update({
'past_key_values': past_key_values,
'use_cache': kwargs.get('use_cache'),
'attention_mask': attention_mask,
'images': kwargs.get('images', None),
})
return model_inputs
def forward_vision_tower(
self,
input_ids: torch.LongTensor,
inputs_embeds: torch.FloatTensor,
images: Union[torch.FloatTensor, list, None] = None,
):
if self.use_im_start_end:
assert self.im_start_token is not None
assert self.im_end_token is not None
if images is not None:
assert self.im_patch_token is not None
if self.vision_tower is None or images is None or (
input_ids.shape[1] == 1 and not self.training):
return inputs_embeds
with torch.no_grad():
if isinstance(images, (list, tuple)):
# variable length images
image_features = []
for image in images:
feats = self.vision_tower(image.unsqueeze(0))
image_feature = feats[self.mm_vision_select_layer][:, 1:]
image_features.append(image_feature)
else:
feats = self.vision_tower(images)
image_features = feats[self.mm_vision_select_layer][:, 1:]
mm_projector = self.lang_encoder.model.mm_projector
if isinstance(images, (list, tuple)):
image_features = [
mm_projector(image_feature)[0]
for image_feature in image_features
]
else:
image_features = mm_projector(image_features)
dummy_image_features = torch.zeros(
256, 1024, device=inputs_embeds.device, dtype=inputs_embeds.dtype)
dummy_image_features = mm_projector(dummy_image_features)
new_input_embeds = []
cur_image_idx = 0
for cur_input_ids, cur_input_embeds in zip(input_ids, inputs_embeds):
if (cur_input_ids != self.im_patch_token).all():
# multimodal LLM, but the current sample is not multimodal
cur_input_embeds = cur_input_embeds + (
0. * dummy_image_features).sum()
new_input_embeds.append(cur_input_embeds)
cur_image_idx += 1
continue
if self.use_im_start_end:
cur_image_features = image_features[cur_image_idx]
num_patches = cur_image_features.shape[0]
if (cur_input_ids == self.im_start_token).sum() != (
cur_input_ids == self.im_end_token).sum():
raise ValueError('The number of image start tokens and '
'image end tokens should be the same.')
image_start_tokens = torch.where(
cur_input_ids == self.im_start_token)[0]
for image_start_token_pos in image_start_tokens:
cur_image_features = image_features[cur_image_idx].to(
device=cur_input_embeds.device)
num_patches = cur_image_features.shape[0]
if cur_input_ids[image_start_token_pos + num_patches +
1] != self.im_end_token:
raise ValueError('The image end token should follow '
'the image start token.')
cur_new_input_embeds = torch.cat(
(cur_input_embeds[:image_start_token_pos + 1],
cur_image_features,
cur_input_embeds[image_start_token_pos + num_patches +
1:]),
dim=0)
cur_image_idx += 1
new_input_embeds.append(cur_new_input_embeds)
else:
cur_image_features = image_features[cur_image_idx]
num_patches = cur_image_features.shape[0]
if (cur_input_ids == self.im_patch_token).sum() != num_patches:
print(f'Debug: num_patches: {num_patches}')
raise ValueError(
'The number of image patch tokens should '
'be the same as the number of image patches.')
masked_indices = torch.where(
cur_input_ids == self.im_patch_token)[0]
mask_index_start = masked_indices[0]
if (masked_indices != torch.arange(
mask_index_start,
mask_index_start + num_patches,
device=masked_indices.device,
dtype=masked_indices.dtype)).any():
raise ValueError(
'The image patch tokens should be consecutive.')
cur_new_input_embeds = torch.cat(
(cur_input_embeds[:mask_index_start], cur_image_features,
cur_input_embeds[mask_index_start + num_patches:]),
dim=0)
new_input_embeds.append(cur_new_input_embeds)
cur_image_idx += 1
inputs_embeds = torch.stack(new_input_embeds, dim=0)
return inputs_embeds
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (tuple(
past_state.index_select(0, beam_idx)
for past_state in layer_past), )
return reordered_past
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