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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) | |