Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,927 Bytes
e19aac6 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 |
# 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.
PAD_TOKEN_ID = 0
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from transformers import AutoConfig, AutoModelForCausalLM
from transformers.models.gemma import GemmaConfig, GemmaModel, GemmaForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from llava.constants import IGNORE_INDEX
from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
# import time
class LlavaGemmaConfig(GemmaConfig):
model_type = "llava_gemma"
class LlavaGemmaModel(GemmaModel, LlavaMetaModel):
config_class = LlavaGemmaConfig
def __init__(self, config: GemmaConfig):
super(LlavaGemmaModel, self).__init__(config)
class LlavaGemmaForCausalLM(GemmaForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaGemmaConfig
def __init__(self, config):
super(LlavaGemmaForCausalLM, self).__init__(config)
self.model = LlavaGemmaModel(config)
self.pretraining_tp = 1
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,
cache_position: Optional[torch.LongTensor] = 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
)
# TODO (kentang-mit@): fuse this function into the previous one.
# current design makes unit-test easier.
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
new_cache_position = None
else:
new_attention_mask = attention_mask
new_position_ids = position_ids
new_inputs_embeds = inputs_embeds
new_labels = labels
if attention_mask is not None:
sorted_seqlens_in_batch = attention_mask.sum(-1).int()
else:
sorted_seqlens_in_batch = None
new_input_ids = input_ids
# kentang-mit@: This only works for batch=1 currently
# model.generate of gemma does not correctly handle decoding stage currently
# need to manually adjust decoding stage input = 1 token
if past_key_values is not None:
if new_inputs_embeds is not None:
new_inputs_embeds = new_inputs_embeds[:, [-1]]
# kentang-mit@: seems to be a problem unique to gemma
if new_position_ids is not None:
new_position_ids = new_position_ids[:, [-1]]
new_cache_position = new_position_ids[0]
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,
cache_position=new_cache_position,
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_gemma", LlavaGemmaConfig)
AutoModelForCausalLM.register(LlavaGemmaConfig, LlavaGemmaForCausalLM)
|