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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. | |
# ------------------------------------------------------------------------ | |
# Modified from LLaVA (https://github.com/haotian-liu/LLaVA) | |
# Copyright 2024 Yanwei Li | |
# ------------------------------------------------------------------------ | |
from typing import List, Optional, Tuple, Union | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from transformers import AutoConfig, AutoModelForCausalLM, \ | |
LlamaConfig, LlamaModel, LlamaForCausalLM | |
from transformers.modeling_outputs import CausalLMOutputWithPast | |
from transformers.utils import logging | |
from transformers.generation.utils import GenerateOutput | |
from minigemini.model.mini_gemini_arch import MiniGeminiMetaModel, MiniGeminiMetaForCausalLM | |
from torch.nn import CrossEntropyLoss | |
logger = logging.get_logger(__name__) | |
class MiniGeminiConfig(LlamaConfig): | |
model_type = "mini_gemini" | |
class MiniGeminiLlamaModel(MiniGeminiMetaModel, LlamaModel): | |
config_class = MiniGeminiConfig | |
def __init__(self, config: LlamaConfig): | |
super(MiniGeminiLlamaModel, self).__init__(config) | |
class MiniGeminiLlamaForCausalLM(LlamaForCausalLM, MiniGeminiMetaForCausalLM): | |
config_class = MiniGeminiConfig | |
def __init__(self, config): | |
super(LlamaForCausalLM, self).__init__(config) | |
self.model = MiniGeminiLlamaModel(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 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, | |
images_aux: Optional[torch.FloatTensor] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple, CausalLMOutputWithPast]: | |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions | |
output_hidden_states = ( | |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states | |
) | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
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, | |
images_aux | |
) | |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) | |
outputs = self.model( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
position_ids=position_ids, | |
past_key_values=past_key_values, | |
inputs_embeds=inputs_embeds, | |
use_cache=use_cache, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
hidden_states = outputs[0] | |
if self.pretraining_tp > 1: | |
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.pretraining_tp, dim=0) | |
logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.pretraining_tp)] | |
logits = torch.cat(logits, dim=-1) | |
else: | |
logits = self.lm_head(hidden_states) | |
logits = logits.float() | |
loss = None | |
if labels is not None: | |
# Shift so that tokens < n predict n | |
shift_logits = logits[..., :-1, :].contiguous() | |
shift_labels = labels[..., 1:].contiguous() | |
# Flatten the tokens | |
loss_fct = CrossEntropyLoss() | |
shift_logits = shift_logits.view(-1, self.config.vocab_size) | |
shift_labels = shift_labels.view(-1) | |
# Enable model parallelism | |
shift_labels = shift_labels.to(shift_logits.device) | |
loss = loss_fct(shift_logits, shift_labels) | |
if not return_dict: | |
output = (logits,) + outputs[1:] | |
return (loss,) + output if loss is not None else output | |
return CausalLMOutputWithPast( | |
loss=loss, | |
logits=logits, | |
past_key_values=outputs.past_key_values, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |
def generate( | |
self, | |
inputs: Optional[torch.Tensor] = None, | |
images: Optional[torch.Tensor] = None, | |
images_aux: Optional[torch.FloatTensor] = 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, | |
images_aux | |
) | |
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) | |
images_aux = kwargs.pop("images_aux", 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 images_aux is not None: | |
_inputs['images_aux'] = images_aux | |
return _inputs | |
AutoConfig.register("mini_gemini", MiniGeminiConfig) | |
AutoModelForCausalLM.register(MiniGeminiConfig, MiniGeminiLlamaForCausalLM) |