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| # Copyright 2024 NVIDIA CORPORATION & AFFILIATES | |
| # | |
| # 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. | |
| # | |
| # SPDX-License-Identifier: Apache-2.0 | |
| # This file is modified from https://github.com/haotian-liu/LLaVA/ | |
| from typing import List, Optional, Tuple, Union | |
| import torch | |
| import torch.nn as nn | |
| from transformers import AutoConfig, AutoModelForCausalLM, \ | |
| MistralConfig, MistralModel, MistralForCausalLM | |
| from transformers.modeling_outputs import CausalLMOutputWithPast | |
| from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM | |
| class LlavaMistralConfig(MistralConfig): | |
| model_type = "llava_mistral" | |
| pretraining_tp = 1 | |
| class LlavaMistralModel(MistralModel, LlavaMetaModel): | |
| config_class = LlavaMistralConfig | |
| def __init__(self, config: MistralConfig): | |
| super(LlavaMistralModel, self).__init__(config) | |
| class LlavaMistralForCausalLM(MistralForCausalLM, LlavaMetaForCausalLM): | |
| config_class = LlavaMistralConfig | |
| def __init__(self, config): | |
| super(MistralForCausalLM, self).__init__(config) | |
| self.model = LlavaMistralModel(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 get_lm_head(self): | |
| return self.lm_head | |
| 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( | |
| input_ids, | |
| position_ids, | |
| attention_mask, | |
| past_key_values, | |
| labels, | |
| images | |
| ) | |
| if self.training: | |
| ( | |
| _, | |
| new_position_ids, | |
| new_attention_mask, | |
| _, | |
| new_inputs_embeds, | |
| new_labels, | |
| sorted_seqlens_in_batch | |
| ) = self.repack_multimodal_data( | |
| input_ids, | |
| position_ids, | |
| attention_mask, | |
| past_key_values, | |
| inputs_embeds, | |
| labels | |
| ) | |
| new_input_ids = None | |
| past_key_values = None | |
| else: | |
| new_attention_mask = attention_mask | |
| new_position_ids = position_ids | |
| new_inputs_embeds = inputs_embeds | |
| new_labels = labels | |
| sorted_seqlens_in_batch = attention_mask.sum(-1).int() | |
| new_input_ids = input_ids | |
| outputs = super().forward( | |
| input_ids=new_input_ids, | |
| attention_mask=new_attention_mask, | |
| position_ids=new_position_ids, | |
| past_key_values=past_key_values, | |
| inputs_embeds=new_inputs_embeds, | |
| labels=new_labels, | |
| use_cache=use_cache, | |
| output_attentions=output_attentions, | |
| output_hidden_states=output_hidden_states, | |
| return_dict=return_dict, | |
| seqlens_in_batch=sorted_seqlens_in_batch, | |
| ) | |
| return outputs | |
| 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("llava_mistral", LlavaMistralConfig) | |
| AutoModelForCausalLM.register(LlavaMistralConfig, LlavaMistralForCausalLM) | |