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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
import torch.nn.functional as F
import numpy as np
from transformers import AutoConfig, AutoModelForCausalLM
from transformers import LlamaConfig, LlamaModel, LlamaForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
from dataclasses import dataclass
from ..ola_arch import OlaLlavaMetaModel, OlaLlavaMetaForCausalLM
from ola_vlm.model.aux_heads import GenHead, DepthHead, DAv2_Head
from ola_vlm.model.aux_heads.depth_anything_v2.dpt import DepthAnythingV2
from ola_vlm.model.aux_heads.oneformer_head import OneFormerHead, OneFormerSegHead
from transformers import OneFormerProcessor
from diffusers import (
DPMSolverMultistepScheduler,
StableUnCLIPImg2ImgPipeline,
)
import torch.distributed as dist
try:
import wandb
except:
pass
import os
import matplotlib
from ola_vlm.model.language_model.base_probe_vlm import BaseProbe_VLM
@dataclass
class ProbeDSGCausalLLMOutputWithPast(CausalLMOutputWithPast):
image_embs: Optional[Tuple[torch.FloatTensor]] = None
seg_embs: Optional[Tuple[torch.FloatTensor]] = None
depth_embs: Optional[Tuple[torch.FloatTensor]] = None
depth_preds: Optional[Tuple[torch.FloatTensor]] = None
class ProbeDSGLlavaLlamaConfig(LlamaConfig):
model_type = "probe_dsg_llava_llama"
class ProbeDSGLlavaLlamaModel(OlaLlavaMetaModel, LlamaModel):
config_class = ProbeDSGLlavaLlamaConfig
def __init__(self, config: LlamaConfig):
super(ProbeDSGLlavaLlamaModel, self).__init__(config)
class ProbeDSGLlavaLlamaForCausalLM(LlamaForCausalLM, OlaLlavaMetaForCausalLM, BaseProbe_VLM):
config_class = ProbeDSGLlavaLlamaConfig
def __init__(self, config):
super(LlamaForCausalLM, self).__init__(config)
config.rope_scaling = None
self.model = ProbeDSGLlavaLlamaModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
def _forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
pil_images = None,
) -> Union[Tuple, ProbeDSGCausalLLMOutputWithPast]:
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
with torch.no_grad():
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=True,
return_dict=return_dict,
)
hidden_states = outputs[0]
layer_states = outputs[-1][1:]
logits = self.lm_head(hidden_states)
logits = logits.float()
log_dict = {}
seg_embs, seg_loss, seg_log_dict = self.seg_emb_forward(pil_images, hidden_states, layer_states)
if self.mode == "seg":
if pil_images is not None:
if dist.get_rank() == 0:
log_dict = {
**log_dict,
**seg_log_dict
}
depth_preds, depth_embs, depth_loss, depth_log_dict = self.depth_emb_forward(pil_images, layer_states)
if self.mode == "depth" and hidden_states.shape[1] > 1:
if dist.get_rank() == 0:
log_dict = {
**log_dict,
**depth_log_dict
}
img_embs, gen_loss, log_dict = self.gen_emb_forward(pil_images, hidden_states, layer_states)
if self.mode == "gen" and hidden_states.shape[1] > 1:
if dist.get_rank() == 0:
log_dict = {
**log_dict,
**depth_log_dict
}
loss = seg_loss + depth_loss + gen_loss
try:
if dist.get_rank() == 0:
log_dict = {
**log_dict,
"depth_loss": depth_loss,
"gen_loss": gen_loss,
"seg_loss": seg_loss,
}
filtered_log_dict = {key: value for key, value in log_dict.items() if value > 0}
wandb.log(filtered_log_dict)
self.steps += 1
except:
pass
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return ProbeDSGCausalLLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_embs=img_embs,
seg_embs=seg_embs,
depth_embs=depth_embs,
depth_preds=depth_preds,
)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = 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,
image_sizes: Optional[List[List[int]]] = None,
return_dict: Optional[bool] = None,
pil_images: Optional[List[object]] = None,
**kwargs,
) -> Union[Tuple, CausalLMOutputWithPast]:
if inputs_embeds is None:
(
input_ids,
position_ids,
attention_mask,
past_key_values,
inputs_embeds,
labels,
_
) = self.prepare_inputs_labels_for_multimodal(
input_ids,
position_ids,
attention_mask,
past_key_values,
labels,
images,
image_sizes
)
return self._forward(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
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,
pil_images=pil_images
)
AutoConfig.register("probe_dsg_llava_llama", ProbeDSGLlavaLlamaConfig)
AutoModelForCausalLM.register(ProbeDSGLlavaLlamaConfig, ProbeDSGLlavaLlamaForCausalLM) |