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# This file may have been modified by Flash-VStream Authors (Flash-VStream Modifications”). All Flash-VStream Modifications are Copyright 2024 Flash-VStream Authors.
# ------------------------------------------------------------------------
# Based on 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.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Optional, Tuple, Union
from transformers import AutoConfig, AutoModelForCausalLM, \
LlamaConfig, LlamaModel, LlamaForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from flash_vstream.model.vstream_arch import VStreamMetaModel, VStreamMetaForCausalLM
class VStreamConfig(LlamaConfig):
model_type = "vstream"
class VStreamLlamaModel(VStreamMetaModel, LlamaModel):
config_class = VStreamConfig
def __init__(self, config: LlamaConfig):
super(VStreamLlamaModel, self).__init__(config)
class VStreamLlamaForCausalLM(VStreamMetaForCausalLM, LlamaForCausalLM):
config_class = VStreamConfig
def __init__(self, config):
super(VStreamLlamaForCausalLM, self).__init__(config)
self.model = VStreamLlamaModel(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] = True,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
images: Optional[torch.FloatTensor] = None,
features: Optional[torch.FloatTensor] = None,
return_dict: Optional[bool] = None,
cache_position=None,
) -> Union[Tuple, CausalLMOutputWithPast]:
if inputs_embeds is None:
if self.use_video_streaming_mode:
(
input_ids,
position_ids,
attention_mask,
past_key_values,
inputs_embeds,
labels
) = self.prepare_inputs_labels_for_multimodal_streaming(
input_ids,
position_ids,
attention_mask,
past_key_values,
labels,
)
else:
(
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,
features,
)
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)
features = kwargs.pop("features", 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
if features is not None:
_inputs['features'] = features
return _inputs
AutoConfig.register("vstream", VStreamConfig)
AutoModelForCausalLM.register(VStreamConfig, VStreamLlamaForCausalLM)
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