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import os
from types import SimpleNamespace
from typing import Tuple, List, Optional, Union
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from transformers import Qwen2ForCausalLM, AutoModel, AutoModelForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.models.qwen2.modeling_qwen2 import Qwen2RMSNorm, Qwen2RotaryEmbedding, Qwen2DecoderLayer, Qwen2Model, Qwen2PreTrainedModel
from .configuration_xomni import XOmniConfig
from .modeling_siglip_tokenizer import create_anyres_preprocess, SiglipTokenizer
from .modeling_siglip_flux import FluxTransformer2DModelWithSigLIP, FluxPipelineWithSigLIP
from .modeling_vit import create_siglip_vit
class XOmniDecoderLayer(Qwen2DecoderLayer):
def __init__(self, config: XOmniConfig, layer_idx: int):
super().__init__(config, layer_idx)
self.layer_idx = layer_idx
self.is_lm_layer = config.num_mm_adap_layers <= layer_idx < config.num_hidden_layers - config.num_mm_head_layers
def forward(
self,
hidden_states: torch.Tensor,
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
hidden_states, multimodal_mask = torch.split(hidden_states, hidden_states.shape[-1] // 2, dim=-1)
if self.is_lm_layer:
output_hidden_states, *others = super().forward(hidden_states, **kwargs)
output_hidden_states = torch.cat([output_hidden_states, multimodal_mask], dim=-1)
return output_hidden_states, *others
# mm_hidden_states = torch.where(multimodal_mask.bool(), hidden_states, torch.zeros_like(hidden_states))
output_hidden_states, *others = super().forward(hidden_states, **kwargs)
output_hidden_states = torch.where(multimodal_mask.bool(), output_hidden_states, hidden_states)
output_hidden_states = torch.cat([output_hidden_states, multimodal_mask], dim=-1)
return output_hidden_states, *others
class XOmniModel(Qwen2Model, Qwen2PreTrainedModel):
model_type = "x-omni"
config_class = XOmniConfig
def __init__(self, config: XOmniConfig):
Qwen2PreTrainedModel.__init__(self, config)
self.padding_idx = -1
self.vocab_size = config.vocab_size
self.lm_embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.mm_embed_tokens = nn.Embedding(config.mm_vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList(
[XOmniDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self._attn_implementation = config._attn_implementation
self.lm_norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.mm_norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.rotary_emb = Qwen2RotaryEmbedding(config=config)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.lm_embed_tokens
def set_input_embeddings(self, value):
self.lm_embed_tokens = value
def embed_tokens(self, input_ids):
(B, L), C = input_ids.shape, self.config.hidden_size
multimodal_mask = input_ids >= self.config.vocab_size
lm_input_ids = input_ids[~multimodal_mask][None, :]
mm_input_ids = input_ids[multimodal_mask][None, :] - self.config.vocab_size
lm_embeds = self.lm_embed_tokens(lm_input_ids)
mm_embeds = self.mm_embed_tokens(mm_input_ids)
inputs_embeds = lm_embeds.new_empty((B, L, C))
multimodal_mask = multimodal_mask[:, :, None].expand_as(inputs_embeds)
inputs_embeds[~multimodal_mask] = lm_embeds.reshape(-1)
inputs_embeds[multimodal_mask] = mm_embeds.reshape(-1)
inputs_embeds = torch.cat([inputs_embeds, multimodal_mask.to(inputs_embeds.dtype)], dim=-1)
return inputs_embeds
def norm(self, hidden_states):
hidden_states, multimodal_mask = torch.split(hidden_states, hidden_states.shape[-1] // 2, dim=-1)
return torch.where(multimodal_mask.bool(), self.mm_norm(hidden_states), self.lm_norm(hidden_states))
class XOmniForCausalLM(Qwen2ForCausalLM):
model_type = "x-omni"
config_class = XOmniConfig
_keys_to_ignore_on_load_missing = r'image_tokenizer\.*'
def __init__(self, config):
super().__init__(config)
self.model = XOmniModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.mm_head = nn.Linear(config.hidden_size, config.mm_vocab_size, bias=False)
self.generation_mode = 'text'
# Initialize weights and apply final processing
self.post_init()
@property
def device(self):
return next(iter(self.parameters())).device
def init_vision(self, flux_pipe_path):
self.som_token = self.config.mm_special_tokens[0]
self.eom_token = self.config.mm_special_tokens[1]
self.img_token = self.config.mm_special_tokens[2]
self.vision_config = SimpleNamespace(**self.config.vision_config)
self.transform_config = SimpleNamespace(**self.vision_config.transform)
self.encoder_config = SimpleNamespace(**self.vision_config.encoder)
self.decoder_config = SimpleNamespace(**self.vision_config.decoder)
dtype_map = {'float32': torch.float32, 'float16': torch.float16, 'bfloat16': torch.bfloat16}
self.vision_dtype = dtype_map[self.vision_config.dtype]
self.image_transform = create_anyres_preprocess(**self.vision_config.transform)
self.encoder_config.siglip_path = os.path.join(self.name_or_path, self.encoder_config.siglip_path) if os.path.isdir(self.name_or_path) else hf_hub_download(repo_id=self.name_or_path, filename=self.encoder_config.siglip_path)
self.encoder_config.projector_path = os.path.join(self.name_or_path, self.encoder_config.projector_path) if os.path.isdir(self.name_or_path) else hf_hub_download(repo_id=self.name_or_path, filename=self.encoder_config.projector_path)
self.image_tokenizer = SiglipTokenizer(**vars(self.encoder_config))
self.image_tokenizer.to(self.device, self.vision_dtype)
self.decoder_pipe = FluxPipelineWithSigLIP.from_pretrained(
flux_pipe_path,
torch_dtype=self.vision_dtype,
)
self.decoder_pipe.transformer = FluxTransformer2DModelWithSigLIP.from_pretrained(
self.name_or_path,
siglip_channels=self.encoder_config.z_channels,
torch_dtype=self.vision_dtype,
subfolder=self.decoder_config.model_path,
)
self.decoder_pipe.set_progress_bar_config(disable=True)
self.decoder_pipe.to(self.device)
def set_generation_mode(self, mode):
assert mode in ('text', 'image'), f'Invalid generation mode: {mode}'
self.generation_mode = mode
def mmencode(self, tokenizer, texts=None, images=None, **kwargs):
texts = texts or []
images = images or []
doc = ''
while len(texts) > 0 or len(images) > 0:
if len(texts) > 0:
doc += texts.pop(0)
if len(images) > 0:
doc += self.tokenize_image(images.pop(0))
return tokenizer.encode(doc, **kwargs)
def mmdecode(self, tokenizer, token_ids, force_text=None, **kwargs):
force_text = force_text or []
if isinstance(token_ids, torch.Tensor):
if len(token_ids.shape) == 2:
assert token_ids.shape[0] == 1
token_ids = token_ids[0]
assert len(token_ids.shape) == 1
else:
if not isinstance(token_ids[0], int):
assert len(token_ids) == 1
token_ids = token_ids[0]
assert isinstance(token_ids[0], int)
doc = tokenizer.decode(token_ids, **kwargs)
doc = doc.replace(tokenizer.pad_token, '')
doc = doc.replace('<SEP>', '')
texts, images = [], []
text_image_chunks = doc.split(self.eom_token)
for chunk in text_image_chunks:
text, image_str = chunk.split(self.som_token) \
if self.som_token in chunk else (chunk, '')
texts.append(text)
if self.img_token in image_str:
image_meta, token_str = image_str.split(self.img_token)
H, W = tuple(map(int, image_meta.split(' ')))
token_ids = list(map(
lambda x: int(x.split('>')[0]),
token_str.split('<MM-Token-')[1:H*W+1],
))
if len(force_text) > 0:
image = self.detokenize_image([force_text.pop(0)], images, token_ids, (H, W))
else:
image = self.detokenize_image(texts, images, token_ids, (H, W))
images.append(image)
return texts, images
@torch.no_grad()
def tokenize_image(self, image):
assert hasattr(self, 'image_tokenizer'), 'Please call "init_vision" before that.'
image_str = self.som_token
image = self.image_transform(image)
assert image is not None, f'Unsupported image aspect ratio (max {self.transform_config.max_aspect_ratio}) or image resolution is too low (min {self.transform_config.min_short_size})'
image = image[None, ...].to(self.device, self.vision_dtype)
tokens = self.image_tokenizer.encode(image)
B, H, W = tokens.shape
tokens = tokens.view(B, -1).cpu().tolist()[0]
token_str = ''.join(map(lambda x: '<MM-Token-{token_id}>'.format(token_id=x), tokens))
image_str = f'{self.som_token}{H} {W}{self.img_token}{token_str}{self.eom_token}'
return image_str
@torch.no_grad()
def detokenize_image(self, texts, images, token_ids, shape):
assert hasattr(self, 'image_tokenizer'), 'Please call "init_vision" before that.'
assert len(texts) == 1 and len(images) == 0, 'Only support one image per sample.'
H, W = shape
tokens = torch.tensor(token_ids, device=self.device, dtype=torch.long)
latents = self.image_tokenizer.decode(tokens, (1, H, W, self.encoder_config.codebook_dim))
upscale_factor = self.decoder_config.upscale_factor
latents = latents.reshape(*latents.shape[:2], -1).transpose(1, 2).contiguous()
image = self.decoder_pipe(
latents,
[texts[0]],
negative_prompt=[''],
height=H * upscale_factor, width=W * upscale_factor,
num_inference_steps=self.decoder_config.num_inference_steps,
guidance_scale=1.0,
true_cfg_scale=self.decoder_config.cfg_scale,
true_cfg_scale_2=self.decoder_config.cfg_scale_2,
).images[0]
return image
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,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
num_logits_to_keep: int = 0,
) -> 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
# 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,
cache_position=cache_position,
)
hidden_states = outputs[0]
hidden_states = hidden_states[:, -num_logits_to_keep:, :]
logits = hidden_states.new_full(
(*hidden_states.shape[:-1], self.config.vocab_size + self.config.mm_vocab_size),
torch.finfo(hidden_states.dtype).min
)
if self.generation_mode == 'text':
logits[:, :, :self.config.vocab_size] = self.lm_head(hidden_states)
else:
logits[:, :, self.config.vocab_size:self.config.vocab_size + self.config.image_vocab_size] = self.mm_head(hidden_states)[:, :, :self.config.image_vocab_size]
logits = logits.float()
loss = None
if labels is not None:
# Upcast to float if we need to compute the loss to avoid potential precision issues
logits = logits.float()
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = nn.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,
)
AutoModel.register(XOmniConfig, XOmniModel)
AutoModelForCausalLM.register(XOmniConfig, XOmniForCausalLM)
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