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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 warnings
import torch
import torch.nn as nn
from transformers import AutoConfig, AutoModelForCausalLM, AutoModel, PretrainedConfig
# StableLMEpochConfig, StableLMEpochModel, StableLMEpochForCausalLM
from transformers.modeling_utils import cached_file, CONFIG_NAME, extract_commit_hash, is_peft_available, find_adapter_config_file, json, os
from transformers.models.auto.auto_factory import _BaseAutoModelClass, _get_model_class
from transformers.dynamic_module_utils import resolve_trust_remote_code, get_class_from_dynamic_module
from transformers.modeling_outputs import CausalLMOutputWithPast
import pdb
import sys
from .llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
from .modeling_stablelm_epoch import StableLMEpochForCausalLM, StableLMEpochModel, StableLMEpochConfig
from .generation_utils import build_allava_input
################ stableLM ###############################
class LlavaStableLM_1_6bConfig(StableLMEpochConfig):
model_type = "llava_stablelm_1_6b"
# class LlavaStableLMModel(LlavaMetaModel, AutoModel):
class LlavaStableLMModel(LlavaMetaModel, StableLMEpochModel):
config_class = LlavaStableLM_1_6bConfig
def __init__(self, config: AutoConfig):
super(LlavaStableLMModel, self).__init__(config)
class LlavaStableLM_1_6bForCausalLM(StableLMEpochForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaStableLM_1_6bConfig
def __init__(self, config, init_vision_encoder_from_ckpt=True):
config._attn_implementation = "flash_attention_2"
super(StableLMEpochForCausalLM, self).__init__(config)
self.model = LlavaStableLMModel(config)
if hasattr(self.model, '_use_flash_attention_2'):
assert self.model._use_flash_attention_2, 'flash attn is not enabled. check it out!'
# self.pretraining_tp = config.pretraining_tp
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
if init_vision_encoder_from_ckpt:
vision_tower = self.get_vision_tower()
print(f'loading from CLIP first. This should only be used at inference!!!')
vision_tower.load_model()
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.model
def get_tokenizer(self):
return self.tokenizer
def get_processor(self):
return self.model.vision_tower.image_processor
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,
return_dict: Optional[bool] = None,
) -> 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(
) = self.prepare_inputs_labels_for_multimodal_new(
input_ids,
position_ids,
attention_mask,
past_key_values,
labels,
images
)
return super().forward(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict
)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=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, **kwargs
)
if images is not None:
_inputs['images'] = images
return _inputs
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
images: Optional[torch.Tensor] = None,
**kwargs,
) :
position_ids = kwargs.pop("position_ids", None)
attention_mask = kwargs.pop("attention_mask", None)
if "inputs_embeds" in kwargs:
raise NotImplementedError("`inputs_embeds` is not supported")
if images is not None:
(
inputs,
position_ids,
attention_mask,
_,
inputs_embeds,
_
) = self.prepare_inputs_labels_for_multimodal_new(
inputs,
position_ids,
attention_mask,
None,
None,
images
)
else:
inputs_embeds = self.get_model().embed_tokens(inputs)
# print(inputs_embeds.shape)
return super().generate(
position_ids=None,
attention_mask=None,
inputs_embeds=inputs_embeds,
**kwargs
)
def chat(
self,
texts: Optional[str | list[list[str, str]]],
images: Optional[str | list[str]] = None,
history: Optional[list[str]] = None,
stream = False,
return_history = False,
**kwargs
):
'''
texts: if `str`, then generate for a single round; if list[dict],
images: str (optional), local path to an image.
'''
use_cache = kwargs.pop('use_cache', True)
############################
# merge history
############################
input_ids, image_tensors, history = build_allava_input(
tokenizer = self.get_tokenizer(),
processor = self.get_processor(),
texts = texts,
images = images,
history=history,
return_history=return_history,
device = self.device
)
############################
# generate response
############################
# with torch.autocast(device_type='cuda'):
if 'cuda' in str(self.device):
device_type = 'cuda'
else:
device_type = 'cpu'
with torch.autocast(device_type=device_type, dtype=self.dtype):
output_ids = self.generate(
inputs=input_ids,
images=image_tensors,
use_cache=use_cache,
**kwargs)
answer = self.get_tokenizer().decode(output_ids[0, :], skip_special_tokens=True).strip()
if return_history:
history[-1][-1] = answer
return answer, history
return answer
AutoConfig.register("llava_stablelm_1_6b", LlavaStableLM_1_6bConfig)
# AutoConfig.register("stablelm_epoch", LlavaStableLMConfig)
AutoModelForCausalLM.register(LlavaStableLM_1_6bConfig, LlavaStableLM_1_6bForCausalLM)
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