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Zero
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# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# 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.
#
# SPDX-License-Identifier: Apache-2.0
# This file is modified from https://github.com/haotian-liu/LLaVA/
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from transformers import AutoConfig, AutoModelForCausalLM, \
MistralConfig, MistralModel, MistralForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
class LlavaMistralConfig(MistralConfig):
model_type = "llava_mistral"
pretraining_tp = 1
class LlavaMistralModel(MistralModel, LlavaMetaModel):
config_class = LlavaMistralConfig
def __init__(self, config: MistralConfig):
super(LlavaMistralModel, self).__init__(config)
class LlavaMistralForCausalLM(MistralForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaMistralConfig
def __init__(self, config):
super(MistralForCausalLM, self).__init__(config)
self.model = LlavaMistralModel(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 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,
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
)
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
else:
new_attention_mask = attention_mask
new_position_ids = position_ids
new_inputs_embeds = inputs_embeds
new_labels = labels
sorted_seqlens_in_batch = attention_mask.sum(-1).int()
new_input_ids = input_ids
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,
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_mistral", LlavaMistralConfig)
AutoModelForCausalLM.register(LlavaMistralConfig, LlavaMistralForCausalLM)
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