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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, Tuple, Union
import torch
import torch.nn as nn
from transformers import AutoConfig, AutoModelForCausalLM
from modeling_qwen import *
from configuration_qwen import Qwen2Config
from transformers.modeling_outputs import CausalLMOutputWithPast
from llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
class GroundQwenConfig(Qwen2Config):
model_type = "ground_qwen"
class LlavaQwenModel(LlavaMetaModel, Qwen2Model):
config_class = GroundQwenConfig
def __init__(self, config: Qwen2Config):
super(LlavaQwenModel, self).__init__(config)
class GroundQwenForCausalLM(Qwen2ForCausalLM, LlavaMetaForCausalLM):
config_class = GroundQwenConfig
supports_gradient_checkpointing = True
def __init__(self, config):
super(Qwen2ForCausalLM, self).__init__(config)
self.model = LlavaQwenModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.vocab_size = config.vocab_size
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.model
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, LlavaQwenModel):
module.gradient_checkpointing = value
def forward_grounding(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
qs_ids: Optional[torch.LongTensor] = None,
qs_mask: Optional[torch.Tensor] = None,
time_labels: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = 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,
projector: Optional[torch.LongTensor] = None,
select_layer: Optional[int] = None,
return_simi: Optional[bool] = False,
return_dict: Optional[bool] = None,
) -> torch.FloatTensor:
if inputs_embeds is None:
(
input_ids,
position_ids,
attention_mask,
past_key_values,
inputs_embeds,
clip_embeds,
qs_embeds,
qs_mask,
labels
) = self.prepare_inputs_labels_for_multimodal(
input_ids,
position_ids,
attention_mask,
qs_ids,
qs_mask,
past_key_values,
labels,
images,
projector
)
if isinstance(labels, tuple):
labels, indicators = labels
else:
indicators = None
loss, similarity, global_memory, clip_memory = super().forward_grounding_hm(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
clip_embeds=torch.stack(clip_embeds, dim=0),
qs_embeds=qs_embeds,
qs_mask=qs_mask,
labels=labels,
time_labels=time_labels,
indicators=indicators,
return_simi=return_simi,
select_layer=100,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict
)
return similarity, global_memory, clip_memory
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
qs_ids: Optional[torch.LongTensor] = None,
qs_mask: Optional[torch.Tensor] = None,
time_labels: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = 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,
projector: Optional[torch.LongTensor] = None,
select_layer: Optional[int] = None,
return_dict: Optional[bool] = None,
) -> torch.FloatTensor:
if inputs_embeds is None:
(
input_ids,
position_ids,
attention_mask,
past_key_values,
inputs_embeds,
clip_embeds,
qs_embeds,
qs_mask,
labels
) = self.prepare_inputs_labels_for_multimodal(
input_ids,
position_ids,
attention_mask,
qs_ids,
qs_mask,
past_key_values,
labels,
images,
projector
)
if isinstance(labels, tuple):
labels, indicators = labels
else:
indicators = None
loss, similarity = super().forward_grounding_hm(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
clip_embeds=torch.stack(clip_embeds, dim=0),
qs_embeds=qs_embeds,
qs_mask=qs_mask,
labels=labels,
time_labels=time_labels,
indicators=indicators,
select_layer=100,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict
)
return CausalLMOutputWithPast(loss=loss, past_key_values=past_key_values)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, indicators=None, **kwargs):
images = kwargs.pop("images", None)
_inputs = super().prepare_inputs_for_generation(
input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, indicators=indicators, **kwargs
)
if images is not None:
_inputs['images'] = images
return _inputs
AutoConfig.register("ground_qwen", GroundQwenConfig)
AutoModelForCausalLM.register(GroundQwenConfig, GroundQwenForCausalLM)
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