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from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import transformers
from transformers import AutoConfig, AutoModelForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
from ..ola_arch import OlaMetaModel, OlaMetaForCausalLM
from transformers import Qwen2Config, Qwen2Model, Qwen2ForCausalLM
class OlaConfigQwen(Qwen2Config):
model_type = "ola_qwen"
class OlaQwenModel(OlaMetaModel, Qwen2Model):
config_class = OlaConfigQwen
def __init__(self, config: Qwen2Config):
super(OlaQwenModel, self).__init__(config)
class OlaQwenForCausalLM(Qwen2ForCausalLM, OlaMetaForCausalLM):
config_class = OlaConfigQwen
def __init__(self, config):
super(Qwen2ForCausalLM, self).__init__(config)
config.rope_scaling = None
self.model = OlaQwenModel(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,
speech: Optional[torch.FloatTensor] = None,
speech_lengths: Optional[torch.LongTensor] = None,
speech_chunks: Optional[torch.LongTensor] = None,
speech_wav: Optional[torch.FloatTensor] = None,
images: Optional[torch.FloatTensor] = None,
images_highres: Optional[List[torch.FloatTensor]] = None,
image_sizes: Optional[List[List[int]]] = None,
modalities: Optional[List[str]] = ["image"],
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = 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_speech_vision_text(
input_ids,
position_ids,
attention_mask,
past_key_values,
labels,
speech,
speech_lengths,
speech_chunks,
speech_wav,
images,
modalities,
image_sizes,
images_highres
)
if labels is None:
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,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict
)
else:
return self.forward_llm_efficient(
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
)
def forward_llm_efficient(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict):
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
# 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]
hidden_dim = hidden_states.size(-1)
shift_labels = labels[..., 1:].contiguous().reshape(-1)
shift_hidden_states = hidden_states[..., :-1, :].contiguous().reshape(-1, hidden_dim)
assert shift_labels.size(0) == shift_hidden_states.size(0)
mask = shift_labels > -1
assert mask.float().sum() > 0
shift_labels = shift_labels[mask]
shift_hidden_states = shift_hidden_states[mask, :]
logits = self.lm_head(shift_hidden_states)
logits = logits.float()
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(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,
)
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
speech: Optional[torch.Tensor] = None,
speech_lengths: Optional[torch.Tensor] = None,
speech_chunks: Optional[torch.Tensor] = None,
speech_wav: Optional[torch.FloatTensor] = None,
images: Optional[torch.Tensor] = None,
images_highres: Optional[List[torch.FloatTensor]] = None,
image_sizes: Optional[torch.Tensor] = None,
modalities: Optional[List[str]] = ["image"],
**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")
(
inputs,
position_ids,
attention_mask,
_,
inputs_embeds,
_
) = self.prepare_inputs_labels_for_speech_vision_text(
inputs,
position_ids,
attention_mask,
None,
None,
speech,
speech_lengths,
speech_chunks,
speech_wav,
images,
modalities,
image_sizes,
images_highres
)
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):
speech = kwargs.pop("speech", None)
speech_lengths = kwargs.pop("speech_lengths", None)
speech_chunks = kwargs.pop("speech_chunks", None)
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 speech is not None:
inputs['speech'] = speech
inputs['speech_lengths'] = speech_lengths
inputs['speech_chunks'] = speech_chunks
if images is not None:
inputs["images"] = images
if image_sizes is not None:
inputs["image_sizes"] = image_sizes
return inputs
AutoConfig.register("ola_qwen", OlaConfigQwen)
AutoModelForCausalLM.register(OlaConfigQwen, OlaQwenForCausalLM)
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