VLMEvalKit-sudoku/llava/model/language_model/llava_llama.py

#    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 transformers import AutoConfig, AutoModelForCausalLM, LlamaConfig

from torch.nn import CrossEntropyLoss


# , LlamaModel, LlamaForCausalLM, GenerationConfig
# from .modeling_llama import LlamaModel, LlamaForCausalLM
from transformers import LlamaModel, LlamaForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput

from llava.model.llava_arch import LlavaMetaModel, LlavaMetaForCausalLM


class LlavaConfig(LlamaConfig):
    model_type = "llava_llama"
    temperature: float = 0.0  # reset to 0.0, previously 0.9 for Vicuna
    max_new_tokens: int = 1024
    do_sample: bool = False
    top_p: Optional[float] = None
    # rope_scaling: Optional[dict] = {}


class LlavaLlamaModel(LlavaMetaModel, LlamaModel):
    config_class = LlavaConfig

    def __init__(self, config: LlamaConfig):
        super(LlavaLlamaModel, self).__init__(config)


class LlavaLlamaForCausalLM(LlamaForCausalLM, LlavaMetaForCausalLM):
    config_class = LlavaConfig

    def __init__(self, config):
        LlamaForCausalLM.__init__(self, config)

        # configure default generation settings
        config.model_type = "llava_llama"
        # config.rope_scaling = None

        self.model = LlavaLlamaModel(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,
        modalities: Optional[List[str]] = ["image"],
        dpo_forward: Optional[bool] = None,
        cache_position=None,
        patch_images: Optional[torch.FloatTensor] = None,
        ind_tokens: Optional[List[int]] = 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, modalities, image_sizes,patch_images=patch_images,ind_tokens=ind_tokens)

        if dpo_forward:
            outputs = self.model(
                input_ids=input_ids,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_values=past_key_values,
                inputs_embeds=inputs_embeds,
                use_cache=use_cache,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )

            hidden_states = outputs[0]
            logits = self.lm_head(hidden_states)
            return logits, labels

        else:
            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,
            )

    @torch.no_grad()
    def generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        images: Optional[torch.Tensor] = None,
        image_sizes: Optional[torch.Tensor] = None,
        modalities: Optional[List[str]] = ["image"],
        patch_images: Optional[torch.FloatTensor] = None,
        ind_tokens: Optional[List[int]] = None,
        **kwargs,
    ) -> Union[GenerateOutput, torch.LongTensor]:
        modalities = kwargs.pop("modalities", None) if "modalities" in kwargs and modalities is None else modalities
        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, modalities, image_sizes=image_sizes,
                patch_images=patch_images,
                ind_tokens=ind_tokens)
        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)
        patch_images = kwargs.pop("patch_images", None)
        ind_tokens = kwargs.pop("ind_tokens", 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
        if patch_images is not None:
            inputs['patch_images'] = patch_images
        if ind_tokens is not None:
            inputs['ind_tokens'] = ind_tokens
        return inputs


AutoConfig.register("llava_llama", LlavaConfig)
AutoModelForCausalLM.register(LlavaConfig, LlavaLlamaForCausalLM)