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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 Optional, Tuple
import torch
from transformers import AutoConfig, AutoModelForCausalLM, \
MptConfig, MptForCausalLM, MptModel
from llava.model.llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
class LlavaMptConfig(MptConfig):
model_type = "llava_mpt"
class LlavaMptModel(LlavaMetaModel, MptModel):
config_class = LlavaMptConfig
def __init__(self, config: MptConfig):
config.hidden_size = config.d_model
super(LlavaMptModel, self).__init__(config)
def embed_tokens(self, x):
return self.wte(x)
class LlavaMptForCausalLM(MptForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaMptConfig
supports_gradient_checkpointing = True
def __init__(self, config):
super(MptForCausalLM, self).__init__(config)
self.transformer = LlavaMptModel(config)
self.lm_head = torch.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.transformer
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, LlavaMptModel):
module.gradient_checkpointing = value
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
images=None):
input_ids, attention_mask, past_key_values, inputs_embeds, labels = self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images)
return super().forward(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
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 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
)
_inputs['images'] = images
return _inputs
AutoConfig.register("llava_mpt", LlavaMptConfig)
AutoModelForCausalLM.register(LlavaMptConfig, LlavaMptForCausalLM)
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