Delete cumo/model/language_model/llava_llama.py

cumo/model/language_model/llava_llama.py

@@ -1,159 +0,0 @@
-#    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, LlamaModel, LlamaForCausalLM
-
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.generation.utils import GenerateOutput
-
-from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
-
-
-class LlavaConfig(LlamaConfig):
-    model_type = "llava_llama"
-
-
-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):
-        super(LlamaForCausalLM, self).__init__(config)
-        self.model = LlavaLlamaModel(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] = 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,
-    ) -> 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
-        )
-
-    @torch.no_grad()
-    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,
-                past_key_values,
-                inputs_embeds,
-                labels,
-                *_
-            ) = 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_llama", LlavaConfig)
-AutoModelForCausalLM.register(LlavaConfig, LlavaLlamaForCausalLM)