Spaces:
Runtime error
Runtime error
File size: 5,237 Bytes
a65550c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 |
# Copyright 2024 Hao Zhang
#
# 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, Dict
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
import transformers
from transformers import AutoConfig, AutoModelForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
# from ...constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.model.llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
from transformers import Qwen2MoeConfig, Qwen2MoeModel, Qwen2MoeForCausalLM
# from .qwen.modeling_qwen import QWenLMHeadModel, QWenModel
# from .qwen.configuration_qwen import QWenConfig
class LlavaQwenMoeConfig(Qwen2MoeConfig):
model_type = "llava_qwen_moe"
class LlavaQwenMoeModel(LlavaMetaModel, Qwen2MoeModel):
config_class = LlavaQwenMoeConfig
def __init__(self, config: Qwen2MoeConfig):
super(LlavaQwenMoeModel, self).__init__(config)
class LlavaQwenMoeForCausalLM(Qwen2MoeForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaQwenMoeConfig
def __init__(self, config):
# super(Qwen2MoeForCausalLM, self).__init__(config)
Qwen2MoeForCausalLM.__init__(self, config)
config.model_type = "llava_qwen_moe"
config.rope_scaling = None
self.model = LlavaQwenMoeModel(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,
)
@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, _, 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_qwen_moe", LlavaQwenMoeConfig)
AutoModelForCausalLM.register(LlavaQwenMoeConfig, LlavaQwenMoeForCausalLM)
|