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# Copyright (c) 2023-2024 DeepSeek.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
# the Software, and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
from attrdict import AttrDict
from einops import rearrange
import torch
from transformers.configuration_utils import PretrainedConfig
from transformers import (
AutoConfig,
AutoModelForCausalLM,
PreTrainedModel,
LlamaConfig,
LlamaForCausalLM,
)
from transformers.models.llama.modeling_llama import LlamaRMSNorm
from janus.janusflow.models.clip_encoder import CLIPVisionTower
from janus.janusflow.models.uvit import ShallowUViTEncoder, ShallowUViTDecoder
import torch.nn as nn
def model_name_to_cls(cls_name):
if "CLIPVisionTower" in cls_name:
cls = CLIPVisionTower
elif "ShallowUViTEncoder" in cls_name:
cls = ShallowUViTEncoder
elif "ShallowUViTDecoder" in cls_name:
cls = ShallowUViTDecoder
else:
raise ValueError(f"class_name {cls_name} is invalid.")
return cls
class VisionUnderstandEncoderConfig(PretrainedConfig):
model_type = "vision_und_enc"
cls: str = ""
params: AttrDict = {}
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.cls = kwargs.get("cls", "")
if not isinstance(self.cls, str):
self.cls = self.cls.__name__
self.params = AttrDict(kwargs.get("params", {}))
class VisionGenerationEncoderConfig(PretrainedConfig):
model_type = "vision_gen_enc"
cls: str = ""
params: AttrDict = {}
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.cls = kwargs.get("cls", "")
if not isinstance(self.cls, str):
self.cls = self.cls.__name__
self.params = AttrDict(kwargs.get("params", {}))
class VisionGenerationDecoderConfig(PretrainedConfig):
model_type = "vision_gen_dec"
cls: str = ""
params: AttrDict = {}
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.cls = kwargs.get("cls", "")
if not isinstance(self.cls, str):
self.cls = self.cls.__name__
self.params = AttrDict(kwargs.get("params", {}))
class MultiModalityConfig(PretrainedConfig):
model_type = "multi_modality"
vision_und_enc_config: VisionUnderstandEncoderConfig
language_config: LlamaConfig
def __init__(self, **kwargs):
super().__init__(**kwargs)
vision_und_enc_config = kwargs.get("vision_und_enc_config", {})
self.vision_und_enc_config = VisionUnderstandEncoderConfig(
**vision_und_enc_config
)
vision_gen_enc_config = kwargs.get("vision_gen_enc_config", {})
self.vision_gen_enc_config = VisionGenerationEncoderConfig(
**vision_gen_enc_config
)
vision_gen_dec_config = kwargs.get("vision_gen_dec_config", {})
self.vision_gen_dec_config = VisionGenerationDecoderConfig(
**vision_gen_dec_config
)
language_config = kwargs.get("language_config", {})
if isinstance(language_config, LlamaConfig):
self.language_config = language_config
else:
self.language_config = LlamaConfig(**language_config)
class MultiModalityPreTrainedModel(PreTrainedModel):
config_class = MultiModalityConfig
base_model_prefix = "multi_modality"
_no_split_modules = []
_skip_keys_device_placement = "past_key_values"
class MultiModalityCausalLM(MultiModalityPreTrainedModel):
def __init__(self, config: MultiModalityConfig):
super().__init__(config)
# vision understanding encoder
vision_und_enc_config = config.vision_und_enc_config
vision_und_enc_cls = model_name_to_cls(vision_und_enc_config.cls)
self.vision_und_enc_model = vision_und_enc_cls(**vision_und_enc_config.params)
# vision understanding aligner
self.vision_und_enc_aligner = nn.Linear(1024, 2048, bias=True)
# begin of understanding embedding
self.beg_of_und_embed = nn.Parameter(torch.zeros(1, 2048))
# vision generation encoder
vision_gen_enc_config = config.vision_gen_enc_config
vision_gen_enc_cls = model_name_to_cls(vision_gen_enc_config.cls)
self.vision_gen_enc_model = vision_gen_enc_cls(**vision_gen_enc_config.params)
# vision generation encoder aligner
self.vision_gen_enc_aligner = nn.Linear(768, 2048, bias=True)
# vision generation decoder
vision_gen_dec_config = config.vision_gen_dec_config
vision_gen_dec_cls = model_name_to_cls(vision_gen_dec_config.cls)
self.vision_gen_dec_model = vision_gen_dec_cls(**vision_gen_dec_config.params)
# language model
language_config = config.language_config
self.language_model = LlamaForCausalLM(language_config)
# vision generation decoder aligner
self.vision_gen_dec_aligner_norm = LlamaRMSNorm(
2048, eps=language_config.rms_norm_eps
)
self.vision_gen_dec_aligner = nn.Linear(2048, 768, bias=True)
def prepare_inputs_embeds(
self,
input_ids: torch.LongTensor,
pixel_values: torch.FloatTensor,
images_seq_mask: torch.LongTensor,
images_emb_mask: torch.LongTensor,
**kwargs,
):
"""
Args:
input_ids (torch.LongTensor): [b, T]
pixel_values (torch.FloatTensor): [b, n_images, 3, h, w]
images_seq_mask (torch.BoolTensor): [b, T]
images_emb_mask (torch.BoolTensor): [b, n_images, n_image_tokens]
assert torch.sum(images_seq_mask) == torch.sum(images_emb_mask)
Returns:
input_embeds (torch.Tensor): [b, T, D]
"""
bs, n = pixel_values.shape[0:2]
images = rearrange(pixel_values, "b n c h w -> (b n) c h w")
# [b x n, T2, D]
images_embeds = self.vision_und_enc_model(images)
images_embeds = self.vision_und_enc_aligner(images_embeds)
# print(images_embeds.shape, self.beg_of_und_embed.shape, images_seq_mask.shape, input_ids.shape)
beg_of_und_embed = self.beg_of_und_embed[0].detach().clone()
images_embeds = torch.cat(
[
beg_of_und_embed.view(1, 1, -1).repeat(images_embeds.shape[0], 1, 1),
images_embeds,
],
dim=1,
)
# [b x n, T2, D] -> [b, n x T2, D]
images_embeds = rearrange(images_embeds, "(b n) t d -> b (n t) d", b=bs, n=n)
# [b, n, T2] -> [b, n x T2]
images_emb_mask = rearrange(images_emb_mask, "b n t -> b (n t)")
# [b, T, D]
input_ids[input_ids < 0] = 0 # ignore the image embeddings
inputs_embeds = self.language_model.get_input_embeddings()(input_ids)
# replace with the image embeddings
inputs_embeds[images_seq_mask] = images_embeds[images_emb_mask]
return inputs_embeds
AutoConfig.register("vision_und_enc", VisionUnderstandEncoderConfig)
AutoConfig.register("vision_gen_enc", VisionGenerationEncoderConfig)
AutoConfig.register("vision_gen_dec", VisionGenerationDecoderConfig)
AutoConfig.register("multi_modality", MultiModalityConfig)
AutoModelForCausalLM.register(MultiModalityConfig, MultiModalityCausalLM)
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