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Zero
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# Adopted from: https://github.com/haotian-liu/LLaVA. Below is the original copyright:
# 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 torch.nn import CrossEntropyLoss
from transformers import AutoConfig, AutoModelForCausalLM, \
Gemma2Config, Gemma2Model, Gemma2ForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
from .videollama2_arch import Videollama2MetaModel, Videollama2MetaForCausalLM
class Videollama2Gemma2Config(Gemma2Config):
model_type = "videollama2_gemma2"
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.model_type = "videollama2_gemma2"
class Videollama2Gemma2Model(Videollama2MetaModel, Gemma2Model):
config_class = Videollama2Gemma2Config
def __init__(self, config: Gemma2Config):
super(Videollama2Gemma2Model, self).__init__(config)
class Videollama2Gemma2ForCausalLM(Gemma2ForCausalLM, Videollama2MetaForCausalLM):
config_class = Videollama2Gemma2Config
def __init__(self, config, **kwargs):
super(Gemma2ForCausalLM, self).__init__(config)
self.model = Videollama2Gemma2Model(config)
# 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)
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.model
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,
cache_position: Optional[int] = None,
**kwargs
) -> Union[Tuple, CausalLMOutputWithPast]:
if inputs_embeds is None:
(
input_ids,
attention_mask,
past_key_values,
inputs_embeds,
labels
) = self.prepare_inputs_labels_for_multimodal(
input_ids,
attention_mask,
past_key_values,
labels,
images
)
outputs = super().forward(
input_ids=input_ids,
attention_mask=attention_mask,
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,
cache_position=cache_position,
)
outputs.labels = labels
return outputs
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
images: Optional[torch.Tensor] = None,
**kwargs,
) -> Union[GenerateOutput, torch.LongTensor]:
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:
(
input_ids,
attention_mask,
past_key_values,
inputs_embeds,
_
) = self.prepare_inputs_labels_for_multimodal(
input_ids=inputs,
attention_mask=attention_mask,
past_key_values=None,
labels=None,
images=images
)
else:
inputs_embeds = self.get_model().embed_tokens(inputs)
return super().generate(
position_ids=position_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
**kwargs
)
def _prepare_generated_length(self, model_input_name, inputs_tensor, **kwargs):
if model_input_name == "inputs_embeds":
self.inputs_embeds_length = inputs_tensor.size(1)
else:
self.inputs_embeds_length = 0
return super()._prepare_generated_length(
model_input_name=model_input_name,
inputs_tensor=inputs_tensor,
**kwargs)
def _get_cache(self, cache_implementation: str, max_batch_size: int, max_cache_len: int, **kwargs):
return super()._get_cache(
cache_implementation=cache_implementation,
max_batch_size=max_batch_size,
max_cache_len=max_cache_len + self.inputs_embeds_length,
**kwargs)
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
AutoConfig.register("videollama2_gemma2", Videollama2Gemma2Config)
AutoModelForCausalLM.register(Videollama2Gemma2Config, Videollama2Gemma2ForCausalLM)
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