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Running
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Zero
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from typing import List, Optional, Tuple, Union, Dict
import torch
import os
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 oryx.model.oryx_arch import OryxMetaModel, OryxMetaForCausalLM
from transformers import Qwen2Config, Qwen2Model, Qwen2ForCausalLM
class OryxQwenConfig(Qwen2Config):
model_type = "oryx_qwen"
class OryxQwenModel(OryxMetaModel, Qwen2Model):
config_class = OryxQwenConfig
def __init__(self, config: Qwen2Config):
super(OryxQwenModel, self).__init__(config)
class OryxQwenForCausalLM(Qwen2ForCausalLM, OryxMetaForCausalLM):
config_class = OryxQwenConfig
def __init__(self, config):
# super(Qwen2ForCausalLM, self).__init__(config)
Qwen2ForCausalLM.__init__(self, config)
config.model_type = "oryx_qwen"
config.rope_scaling = None
self.model = OryxQwenModel(config)
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_highres: Optional[List[torch.FloatTensor]] = None,
image_sizes: Optional[List[List[int]]] = None,
return_dict: Optional[bool] = None,
modalities: Optional[List[str]] = ["image"],
) -> 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,
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,
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")
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, modalities, image_sizes=image_sizes, images_highres=images_highres)
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
AutoConfig.register("oryx_qwen", OryxQwenConfig)
AutoModelForCausalLM.register(OryxQwenConfig, OryxQwenForCausalLM)
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