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# Copyright 2024 Baichuan Zhou , Junlong Jia, 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
from transformers import PhiConfig, PhiModel, PhiForCausalLM
from transformers.generation.utils import GenerateOutput
from transformers.modeling_outputs import CausalLMOutputWithPast
from tinychart.model.llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
from tinychart.model.model_factory import *
class TinyChartPhiConfig(PhiConfig):
model_type = "tiny_chart_phi"
class TinyChartPhiModel(LlavaMetaModel, PhiModel):
config_class = TinyChartPhiConfig
def __init__(self, config: PhiConfig):
super(TinyChartPhiModel, self).__init__(config)
self.gradient_checkpointing = False
@register_model('tinychart-3b')
class TinyChartPhiForCausalLM(PhiForCausalLM, LlavaMetaForCausalLM):
config_class = TinyChartPhiConfig
def __init__(self, config):
super(PhiForCausalLM, self).__init__(config)
self.model = TinyChartPhiModel(config)
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,
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,
)
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,
**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,
)
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
@register_tokenizer('phi')
def get_tokenizer():
from transformers import AutoTokenizer
def post_init(tokenizer):
return tokenizer
return AutoTokenizer, post_init
AutoConfig.register("tiny_chart_phi", TinyChartPhiConfig)
AutoModelForCausalLM.register(TinyChartPhiConfig, TinyChartPhiForCausalLM)
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