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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 torch | |
| import torch.nn as nn | |
| from torch.nn import CrossEntropyLoss | |
| from transformers import AutoConfig, AutoModelForCausalLM, MixtralConfig, MixtralModel, MixtralForCausalLM, GenerationConfig | |
| from transformers.modeling_outputs import CausalLMOutputWithPast | |
| from transformers.generation.utils import GenerateOutput | |
| from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM | |
| class LlavaMixtralConfig(MixtralConfig): | |
| model_type = "llava_mixtral" | |
| class LlavaMixtralModel(LlavaMetaModel, MixtralModel): | |
| config_class = LlavaMixtralConfig | |
| def __init__(self, config: MixtralConfig): | |
| super(LlavaMixtralModel, self).__init__(config) | |
| class LlavaMixtralForCausalLM(MixtralForCausalLM, LlavaMetaForCausalLM): | |
| config_class = LlavaMixtralConfig | |
| def __init__(self, config): | |
| super(MixtralForCausalLM, self).__init__(config) | |
| config.model_type = "llava_mixtral" | |
| config.rope_scaling = None | |
| self.model = LlavaMixtralModel(config) | |
| 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, | |
| image_sizes: Optional[List[List[int]]] = None, | |
| return_dict: Optional[bool] = None, | |
| cache_position=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, image_sizes) | |
| 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 generate( | |
| self, | |
| inputs: Optional[torch.Tensor] = None, | |
| images: Optional[torch.Tensor] = None, | |
| image_sizes: 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: | |
| (inputs, position_ids, attention_mask, _, inputs_embeds, _) = self.prepare_inputs_labels_for_multimodal(inputs, position_ids, attention_mask, None, None, images, image_sizes=image_sizes) | |
| 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_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs): | |
| images = kwargs.pop("images", None) | |
| image_sizes = kwargs.pop("image_sizes", 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 image_sizes is not None: | |
| inputs["image_sizes"] = image_sizes | |
| return inputs | |
| AutoConfig.register("llava_mixtral", LlavaMixtralConfig) | |
| AutoModelForCausalLM.register(LlavaMixtralConfig, LlavaMixtralForCausalLM) | |