Full Code of Kwai-Kolors/MPS for AI

Repository: Kwai-Kolors/MPS
Branch: main
Commit: be9027f3e909
Files: 8
Total size: 31.7 KB

Directory structure:
gitextract_7pb8rbkq/

├── LICENSE
├── README.md
├── eval_overall_mps_on_hpdv2.py
├── eval_overall_mps_on_imagereward.py
├── requirements.txt
└── trainer/
    └── models/
        ├── base_model.py
        ├── clip_model.py
        └── cross_modeling.py

================================================
FILE CONTENTS
================================================

================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2021

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.


================================================
FILE: README.md
================================================
# Learning Multi-dimensional Human Preference for Text-to-Image Generation (CVPR 2024)
This repository contains the code and model for the paper [Learning Multi-dimensional Human Preference for Text-to-Image Generation](https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Learning_Multi-Dimensional_Human_Preference_for_Text-to-Image_Generation_CVPR_2024_paper.pdf). 

<img src="./framework.png" width="60%">

## Installation
Create a virual env and download torch:

```bash
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
```

Install the requirements:
```bash
pip install -r requirements.txt
pip install -e .
```

## Inference with MPS
We display here an example for running inference with MPS:
```python
# import
from transformers import AutoProcessor, AutoModel
from PIL import Image
import torch

# load model
device = "cuda"
processor_name_or_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
image_processor = CLIPImageProcessor.from_pretrained(processor_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(processor_name_or_path, trust_remote_code=True)

model_ckpt_path = "outputs/MPS_overall_checkpoint.pth"
model = torch.load(model_ckpt_path)
model.eval().to(device)

def infer_example(images, prompt, condition, clip_model, clip_processor, tokenizer, device):
    def _process_image(image):
        if isinstance(image, dict):
            image = image["bytes"]
        if isinstance(image, bytes):
            image = Image.open(BytesIO(image))
        if isinstance(image, str):
            image = Image.open( image )
        image = image.convert("RGB")
        pixel_values = clip_processor(image, return_tensors="pt")["pixel_values"]
        return pixel_values
    
    def _tokenize(caption):
        input_ids = tokenizer(
            caption,
            max_length=tokenizer.model_max_length,
            padding="max_length",
            truncation=True,
            return_tensors="pt"
        ).input_ids
        return input_ids
    
    image_inputs = torch.concatenate([_process_image(images[0]).to(device), _process_image(images[1]).to(device)])
    text_inputs = _tokenize(prompt).to(device)
    condition_inputs = _tokenize(condition).to(device)

    with torch.no_grad():
        text_features, image_0_features, image_1_features = clip_model(text_inputs, image_inputs, condition_inputs)
        image_0_features = image_0_features / image_0_features.norm(dim=-1, keepdim=True)
        image_1_features = image_1_features / image_1_features.norm(dim=-1, keepdim=True)
        text_features = text_features / text_features.norm(dim=-1, keepdim=True)
        image_0_scores = clip_model.logit_scale.exp() * torch.diag(torch.einsum('bd,cd->bc', text_features, image_0_features))
        image_1_scores = clip_model.logit_scale.exp() * torch.diag(torch.einsum('bd,cd->bc', text_features, image_1_features))
        scores = torch.stack([image_0_scores, image_1_scores], dim=-1)
        probs = torch.softmax(scores, dim=-1)[0]

    return probs.cpu().tolist()

img_0, img_1 = "image1.jpg", "image2.jpg"
# infer the best image for the caption
prompt = "the caption of image" 

# condition for overall
condition = "light, color, clarity, tone, style, ambiance, artistry, shape, face, hair, hands, limbs, structure, instance, texture, quantity, attributes, position, number, location, word, things." 

print(infer_example([img_0, img_1], prompt, condition, model, image_processor, tokenizer, device))
```

## Download the MPS checkpoint
<table>
  <tr>
    <th rowspan="2" text-align="center">ID</th>
    <th colspan="4" text-align="center">Training Data</th>
    <th rowspan="2" text-align="center">MPS Model</th>
  </tr>
  <tr>
    <th text-align="center">Overall</th>
    <th text-align="center">Aesthetics</th>
    <th text-align="center">Alignment</th>
    <th text-align="center">Detail</th>
  </tr>
  <tr text-align="center">
    <td>&nbsp;1</td>
    <td>&nbsp;&nbsp;&nbsp;&nbsp;&#10003;</td>
    <td>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-</td>
    <td>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-</td>
    <td>&nbsp;&nbsp;&nbsp;&nbsp;-</td>
    <td>&nbsp;<a href="http://drive.google.com/file/d/17qrK_aJkVNM75ZEvMEePpLj6L867MLkN/view?usp=sharing">Model Link</a></td>
  </tr>
  <tr>
    <td>&nbsp;2</td>
    <td>&nbsp;&nbsp;&nbsp;&nbsp;&#10003;</td>
    <td>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&#10003;</td>
    <td>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&#10003;</td>
    <td>&nbsp;&nbsp;&nbsp;&nbsp;&#10003;</td>
    <td>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-</td>
  </tr>
</table>

Due to the internal model approval process within the company, we only release MPS trained on overall preference, while MPS trained on multi human preferences will be open-sourced once it passes the approval process; however, there is a risk of delays and the possibility of force majeure events.
(Move the checkpoint file to `outputs/MPS_overall_checkpoint.pth`)


## Evaluation
Test MPS on ImageReward benchmark:

Please download the file, `datasets/test.json` to `imagereward/test.json` from [ImageReward](https://github.com/kekewind/ImageReward) and the related images from [ImageRewardDB](https://huggingface.co/datasets/THUDM/ImageRewardDB) as well.
```bash
 python eval_overall_mhp_on_imagereward.py
```
Test MPS on hpd_v2 benchmark:

Please download the annotation file, `test.json` to `hpdv2/test.json` and the related images(test dataset) from [HPDv2](https://huggingface.co/datasets/ymhao/HPDv2/tree/main).
```bash
 python eval_overall_mhp_on_hpdv2.py
```

## Results on different datasets
| ID  | Preference Model     | ImageReward | HPD v2 | MHP (Overall) |
|:-:|:-:|:-:|:-:|:-:|
| 1   | CLIP score           | 54.3        | 71.2   | 63.7          |
| 2   | Aesthetic Score      | 57.4        | 72.6   | 62.9          |
| 3   | ImageReward          | 65.1        | 70.6   | 67.5          |
| 4   | HPS                  | 61.2        | 73.1   | 65.5          |
| 5   | PickScore            | 62.9        | 79.8   | 69.5          |
| 6   | HPS v2               | 65.7        | 83.3   | 65.5          |
| 7   | **MPS (Ours)**           | **67.5**        | **83.5**   | **74.2**          |


## Citation
If you find this work useful, please cite:
```bibtex
@inproceedings{MPS,
  title={Learning Multi-dimensional Human Preference for Text-to-Image Generation},
  author={Zhang, Sixian and Wang, Bohan and Wu, Junqiang and Li, Yan and Gao, Tingting and Zhang, Di and Wang, Zhongyuan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={8018--8027},
  year={2024}
}
```

## Acknowledgments
We thank the authors of [ImageReward](https://github.com/kekewind/ImageReward), [HPS](https://github.com/tgxs002/align_sd), [HPS v2](https://github.com/tgxs002/HPSv2), and [PickScore](https://github.com/yuvalkirstain/PickScore) for their codes and papers, which greatly contributed to our work.


================================================
FILE: eval_overall_mps_on_hpdv2.py
================================================
import numpy as np
import torch
from PIL import Image
from io import BytesIO
from tqdm.auto import tqdm
from fire import Fire
from transformers import CLIPFeatureExtractor, CLIPImageProcessor

from dataclasses import dataclass
from transformers import CLIPModel as HFCLIPModel

from torch import nn, einsum

from trainer.models.base_model import BaseModelConfig

from transformers import CLIPConfig
from transformers import AutoProcessor, AutoModel, AutoTokenizer
from typing import Any, Optional, Tuple, Union
import torch
import cv2
import os

from trainer.models.cross_modeling import Cross_model
import matplotlib.pyplot as plt
import torch.nn.functional as F

import gc
import json


@torch.no_grad()

def infer_one_sample(image, prompt, clip_model, clip_processor, tokenizer, device, condition=None):
    def _process_image(image):
        if isinstance(image, dict):
            image = image["bytes"]
        if isinstance(image, bytes):
            image = Image.open(BytesIO(image))
        if isinstance(image, str):
            image = Image.open( image )
        image = image.convert("RGB")
        pixel_values = clip_processor(image, return_tensors="pt")["pixel_values"]
        return pixel_values
    
    def _tokenize(caption):
        input_ids = tokenizer(
            caption,
            max_length=tokenizer.model_max_length,
            padding="max_length",
            truncation=True,
            return_tensors="pt"
        ).input_ids
        return input_ids
    
    image_input = _process_image(image).to(device)
    text_input = _tokenize(prompt).to(device)
    if condition is None:
        condition = "light, color, clarity, tone, style, ambiance, artistry, shape, face, hair, hands, limbs, structure, instance, texture, quantity, attributes, position, number, location, word, things."
    condition_batch = _tokenize(condition).repeat(text_input.shape[0],1).to(device)

    with torch.no_grad():
        text_f, text_features = clip_model.model.get_text_features(text_input)

        image_f = clip_model.model.get_image_features(image_input.half())
        condition_f, _ = clip_model.model.get_text_features(condition_batch)

        sim_text_condition = einsum('b i d, b j d -> b j i', text_f, condition_f)
        sim_text_condition = torch.max(sim_text_condition, dim=1, keepdim=True)[0]
        sim_text_condition = sim_text_condition / sim_text_condition.max()
        mask = torch.where(sim_text_condition > 0.3, 0, float('-inf'))
        mask = mask.repeat(1,image_f.shape[1],1)
        image_features = clip_model.cross_model(image_f, text_f,mask.half())[:,0,:]

        image_features = image_features / image_features.norm(dim=-1, keepdim=True)
        text_features = text_features / text_features.norm(dim=-1, keepdim=True)
        image_score = clip_model.logit_scale.exp() * text_features @ image_features.T

    return image_score[0]

def infer_example(images, prompt, clip_model, clip_processor, tokenizer, device):
    scores = []
    for image in images:
        score = infer_one_sample(image, prompt, clip_model, clip_processor, tokenizer, device)
        scores.append(score)
    scores = torch.stack(scores, dim=-1)
    probs = torch.softmax(scores, dim=-1)[0]
    return probs.cpu().tolist()

def acc(score_sample, predict_sample):
    tol_cnt = 0.
    true_cnt = 0.
    for idx in range(len(score_sample)):
        item_base = score_sample[idx]["rank"]
        item = predict_sample[idx]["rewards"]
        for i in range(len(item_base)):
            for j in range(i+1, len(item_base)):
                if item_base[i] > item_base[j]:
                    if item[i] >= item[j]:
                        tol_cnt += 1
                    elif item[i] < item[j]:
                        tol_cnt += 1
                        true_cnt += 1
                elif item_base[i] < item_base[j]:
                    if item[i] > item[j]:
                        tol_cnt += 1
                        true_cnt += 1
                    elif item[i] <= item[j]:
                        tol_cnt += 1
    return true_cnt / tol_cnt

def inversion_score(predict_sample, score_sample):
    n = len(score_sample)
    cnt = 0
    for i in range(n-1):
        for j in range(i+1, n):
            if score_sample[i] > score_sample[j] and predict_sample[i] > predict_sample[j]:
                cnt += 1
            elif score_sample[i] < score_sample[j] and predict_sample[i] < predict_sample[j]:
                cnt += 1
    return 1 - cnt / (n * (n - 1) / 2)

def main():
    processor_name_or_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"

    device = "cuda"
    image_processor = CLIPImageProcessor.from_pretrained(processor_name_or_path)
    tokenizer = AutoTokenizer.from_pretrained(processor_name_or_path, trust_remote_code=True)

    model_ckpt_path = "outputs/MPS_overall_checkpoint.pth"
    model = torch.load(model_ckpt_path)
    model.eval().to(device)

    score_sample = []
    with open("hpdv2/test.json", "r") as f:
        score_sample = json.load(f)
    
    predict_sample = []
    score = 0.
    with torch.no_grad():
        for i in range(len(score_sample)):
            item = score_sample[i]
            rewards = infer_example(item["image_path"], item["prompt"], model, image_processor, tokenizer, device)
            score += inversion_score(rewards, item['rank'])
    test_acc = score / len(score_sample)
    print(f"HPDv2 Test Acc: {100 * test_acc:.2f}%")


if __name__ == '__main__':
    Fire(main)


================================================
FILE: eval_overall_mps_on_imagereward.py
================================================
import numpy as np
# from transformers import AutoProcessor #, AutoModel
import torch
from PIL import Image
from io import BytesIO
from tqdm.auto import tqdm
from fire import Fire
from transformers import CLIPFeatureExtractor, CLIPImageProcessor

from dataclasses import dataclass
from transformers import CLIPModel as HFCLIPModel

from torch import nn, einsum

from trainer.models.base_model import BaseModelConfig

from transformers import CLIPConfig
from transformers import AutoProcessor, AutoModel, AutoTokenizer
from typing import Any, Optional, Tuple, Union
import torch
import cv2
import os

from trainer.models.cross_modeling import Cross_model
import matplotlib.pyplot as plt
import torch.nn.functional as F

import gc
import json


@torch.no_grad()

def infer_one_sample(image, prompt, clip_model, clip_processor, tokenizer, device, condition=None):
    def _process_image(image):
        if isinstance(image, dict):
            image = image["bytes"]
        if isinstance(image, bytes):
            image = Image.open(BytesIO(image))
        if isinstance(image, str):
            image = Image.open( image )
        image = image.convert("RGB")
        pixel_values = clip_processor(image, return_tensors="pt")["pixel_values"]
        return pixel_values
    
    def _tokenize(caption):
        input_ids = tokenizer(
            caption,
            max_length=tokenizer.model_max_length,
            padding="max_length",
            truncation=True,
            return_tensors="pt"
        ).input_ids
        return input_ids

    image_input = _process_image(image).to(device)
    text_input = _tokenize(prompt).to(device)
    if condition is None:
        condition = "light, color, clarity, tone, style, ambiance, artistry, shape, face, hair, hands, limbs, structure, instance, texture, quantity, attributes, position, number, location, word, things."
    condition_batch = _tokenize(condition).repeat(text_input.shape[0],1).to(device)

    with torch.no_grad():
        text_f, text_features = clip_model.model.get_text_features(text_input)

        image_f = clip_model.model.get_image_features(image_input.half())
        condition_f, _ = clip_model.model.get_text_features(condition_batch)

        sim_text_condition = einsum('b i d, b j d -> b j i', text_f, condition_f)
        sim_text_condition = torch.max(sim_text_condition, dim=1, keepdim=True)[0]
        sim_text_condition = sim_text_condition / sim_text_condition.max()
        mask = torch.where(sim_text_condition > 0.3, 0, float('-inf'))
        mask = mask.repeat(1,image_f.shape[1],1)
        image_features = clip_model.cross_model(image_f, text_f,mask.half())[:,0,:]

        image_features = image_features / image_features.norm(dim=-1, keepdim=True)
        text_features = text_features / text_features.norm(dim=-1, keepdim=True)
        image_score = clip_model.logit_scale.exp() * text_features @ image_features.T
    return image_score[0]

def infer_example(images, prompt, clip_model, clip_processor, tokenizer, device):
    scores = []
    for image in images:
        score = infer_one_sample(image, prompt, clip_model, clip_processor, tokenizer, device)
        scores.append(score)
    scores = torch.stack(scores, dim=-1)
    probs = torch.softmax(scores, dim=-1)[0]
    return probs.cpu().tolist()


def acc(score_sample, predict_sample):
    tol_cnt = 0.
    true_cnt = 0.
    for idx in range(len(score_sample)):
        item_base = score_sample[idx]["ranking"]
        item = predict_sample[idx]["rewards"]
        for i in range(len(item_base)):
            for j in range(i+1, len(item_base)):
                if item_base[i] > item_base[j]:
                    if item[i] >= item[j]:
                        tol_cnt += 1
                    elif item[i] < item[j]:
                        tol_cnt += 1
                        true_cnt += 1
                elif item_base[i] < item_base[j]:
                    if item[i] > item[j]:
                        tol_cnt += 1
                        true_cnt += 1
                    elif item[i] <= item[j]:
                        tol_cnt += 1
    return true_cnt / tol_cnt
                

def main():
    processor_name_or_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"

    device = "cuda"
    image_processor = CLIPImageProcessor.from_pretrained(processor_name_or_path)
    tokenizer = AutoTokenizer.from_pretrained(processor_name_or_path, trust_remote_code=True)

    model_ckpt_path = "outputs/MPS_overall_checkpoint.pth"
    model = torch.load(model_ckpt_path)
    model.eval().to(device)

    score_sample = []
    with open("imagereward/test.json", "r") as f: # change the path to the ImageReward test dataset
        score_sample = json.load(f)
    
    predict_sample = []
    with torch.no_grad():
        for item in score_sample:
            rewards = infer_example(item["generations"], item["prompt"], model, image_processor, tokenizer, device)
            predict_item = {
                "id": item["id"],
                "prompt": item["prompt"],
                "rewards": rewards
            }
            predict_sample.append(predict_item)
    test_acc = acc(score_sample, predict_sample)
    print(f"ImageReward Test Acc: {100 * test_acc:.2f}%")


if __name__ == '__main__':
    Fire(main)


================================================
FILE: requirements.txt
================================================
accelerate @ git+https://github.com/huggingface/accelerate.git@d1aa558119859c4b205a324afabaecabd9ef375e
datasets==2.10.1
deepspeed==0.8.3
fire==0.4.0
hydra-core==1.3.2
rich==13.3.2
submitit==1.4.5
transformers==4.27.3
wandb==0.12.21

================================================
FILE: trainer/models/base_model.py
================================================
from dataclasses import dataclass



@dataclass
class BaseModelConfig:
    pass


================================================
FILE: trainer/models/clip_model.py
================================================
from dataclasses import dataclass
from transformers import CLIPModel as HFCLIPModel
from transformers import AutoTokenizer

from torch import nn, einsum

from trainer.models.base_model import BaseModelConfig

from transformers import CLIPConfig
from typing import Any, Optional, Tuple, Union
import torch

from trainer.models.cross_modeling import Cross_model

import gc

class XCLIPModel(HFCLIPModel):
    def __init__(self, config: CLIPConfig):
        super().__init__(config)
    
    def get_text_features(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> torch.FloatTensor:

        # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
        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

        text_outputs = self.text_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        # pooled_output = text_outputs[1]
        # text_features = self.text_projection(pooled_output)
        last_hidden_state = text_outputs[0]
        text_features = self.text_projection(last_hidden_state)

        pooled_output = text_outputs[1]
        text_features_EOS = self.text_projection(pooled_output)


        # del last_hidden_state, text_outputs
        # gc.collect()

        return text_features, text_features_EOS

    def get_image_features(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> torch.FloatTensor:
        
        # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
        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

        vision_outputs = self.vision_model(
            pixel_values=pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        # pooled_output = vision_outputs[1]  # pooled_output
        # image_features = self.visual_projection(pooled_output)
        last_hidden_state = vision_outputs[0]
        image_features = self.visual_projection(last_hidden_state)

        return image_features



@dataclass
class ClipModelConfig(BaseModelConfig):
    _target_: str = "trainer.models.clip_model.CLIPModel"
    pretrained_model_name_or_path: str ="openai/clip-vit-base-patch32"


class CLIPModel(nn.Module):
    def __init__(self, ckpt):
        super().__init__()
        self.model = XCLIPModel.from_pretrained(ckpt)
        self.cross_model = Cross_model(dim=1024, layer_num=4, heads=16)
    
    def get_text_features(self, *args, **kwargs):
        return self.model.get_text_features(*args, **kwargs)

    def get_image_features(self, *args, **kwargs):
        return self.model.get_image_features(*args, **kwargs)

    def forward(self, text_inputs=None, image_inputs=None, condition_inputs=None):
        outputs = ()

        text_f, text_EOS = self.model.get_text_features(text_inputs) # B*77*1024
        outputs += text_EOS,

        image_f = self.model.get_image_features(image_inputs.half()) # 2B*257*1024
        condition_f, _ = self.model.get_text_features(condition_inputs) # B*5*1024

        sim_text_condition = einsum('b i d, b j d -> b j i', text_f, condition_f)
        sim_text_condition = torch.max(sim_text_condition, dim=1, keepdim=True)[0]
        sim_text_condition = sim_text_condition / sim_text_condition.max()
        mask = torch.where(sim_text_condition > 0.01, 0, float('-inf')) # B*1*77

        mask = mask.repeat(1,image_f.shape[1],1) # B*257*77
        bc = int(image_f.shape[0]/2)

        sim0 = self.cross_model(image_f[:bc,:,:], text_f,mask.half())
        sim1 = self.cross_model(image_f[bc:,:,:], text_f,mask.half())
        outputs += sim0[:,0,:],
        outputs += sim1[:,0,:],

        return outputs

    @property
    def logit_scale(self):
        return self.model.logit_scale

    def save(self, path):
        self.model.save_pretrained(path)



================================================
FILE: trainer/models/cross_modeling.py
================================================
import torch
from torch import einsum, nn
import torch.nn.functional as F
from einops import rearrange, repeat

# helper functions

def exists(val):
    return val is not None

def default(val, d):
    return val if exists(val) else d

# normalization
# they use layernorm without bias, something that pytorch does not offer


class LayerNorm(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(dim))
        self.register_buffer("bias", torch.zeros(dim))

    def forward(self, x):
        return F.layer_norm(x, x.shape[-1:], self.weight, self.bias)

# residual


class Residual(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn

    def forward(self, x, *args, **kwargs):
        return self.fn(x, *args, **kwargs) + x


# rotary positional embedding
# https://arxiv.org/abs/2104.09864


class RotaryEmbedding(nn.Module):
    def __init__(self, dim):
        super().__init__()
        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer("inv_freq", inv_freq)

    def forward(self, max_seq_len, *, device):
        seq = torch.arange(max_seq_len, device=device, dtype=self.inv_freq.dtype)
        freqs = einsum("i , j -> i j", seq, self.inv_freq)
        return torch.cat((freqs, freqs), dim=-1)


def rotate_half(x):
    x = rearrange(x, "... (j d) -> ... j d", j=2)
    x1, x2 = x.unbind(dim=-2)
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(pos, t):
    return (t * pos.cos()) + (rotate_half(t) * pos.sin())


# classic Noam Shazeer paper, except here they use SwiGLU instead of the more popular GEGLU for gating the feedforward
# https://arxiv.org/abs/2002.05202


class SwiGLU(nn.Module):
    def forward(self, x):
        x, gate = x.chunk(2, dim=-1)
        return F.silu(gate) * x


# parallel attention and feedforward with residual
# discovered by Wang et al + EleutherAI from GPT-J fame

class ParallelTransformerBlock(nn.Module):
    def __init__(self, dim, dim_head=64, heads=8, ff_mult=4):
        super().__init__()
        self.norm = LayerNorm(dim)

        attn_inner_dim = dim_head * heads
        ff_inner_dim = dim * ff_mult
        self.fused_dims = (attn_inner_dim, dim_head, dim_head, (ff_inner_dim * 2))

        self.heads = heads
        self.scale = dim_head**-0.5
        self.rotary_emb = RotaryEmbedding(dim_head)

        self.fused_attn_ff_proj = nn.Linear(dim, sum(self.fused_dims), bias=False)
        self.attn_out = nn.Linear(attn_inner_dim, dim, bias=False)

        self.ff_out = nn.Sequential(
            SwiGLU(),
            nn.Linear(ff_inner_dim, dim, bias=False)
        )

        self.register_buffer("pos_emb", None, persistent=False)


    def get_rotary_embedding(self, n, device):
        if self.pos_emb is not None and self.pos_emb.shape[-2] >= n:
            return self.pos_emb[:n]

        pos_emb = self.rotary_emb(n, device=device)
        self.register_buffer("pos_emb", pos_emb, persistent=False)
        return pos_emb

    def forward(self, x, attn_mask=None):
        """
        einstein notation
        b - batch
        h - heads
        n, i, j - sequence length (base sequence length, source, target)
        d - feature dimension
        """

        n, device, h = x.shape[1], x.device, self.heads

        # pre layernorm

        x = self.norm(x)

        # attention queries, keys, values, and feedforward inner

        q, k, v, ff = self.fused_attn_ff_proj(x).split(self.fused_dims, dim=-1)

        # split heads
        # they use multi-query single-key-value attention, yet another Noam Shazeer paper
        # they found no performance loss past a certain scale, and more efficient decoding obviously
        # https://arxiv.org/abs/1911.02150

        q = rearrange(q, "b n (h d) -> b h n d", h=h)

        # rotary embeddings

        positions = self.get_rotary_embedding(n, device)
        q, k = map(lambda t: apply_rotary_pos_emb(positions, t), (q, k))

        # scale

        q = q * self.scale

        # similarity

        sim = einsum("b h i d, b j d -> b h i j", q, k)


        # extra attention mask - for masking out attention from text CLS token to padding

        if exists(attn_mask):
            attn_mask = rearrange(attn_mask, 'b i j -> b 1 i j')
            sim = sim.masked_fill(~attn_mask, -torch.finfo(sim.dtype).max)

        # attention

        sim = sim - sim.amax(dim=-1, keepdim=True).detach()
        attn = sim.softmax(dim=-1)

        # aggregate values

        out = einsum("b h i j, b j d -> b h i d", attn, v)

        # merge heads

        out = rearrange(out, "b h n d -> b n (h d)")
        return self.attn_out(out) + self.ff_out(ff)

# cross attention - using multi-query + one-headed key / values as in PaLM w/ optional parallel feedforward

class CrossAttention(nn.Module):
    def __init__(
        self,
        dim,
        *,
        context_dim=None,
        dim_head=64,
        heads=12,
        parallel_ff=False,
        ff_mult=4,
        norm_context=False
    ):
        super().__init__()
        self.heads = heads
        self.scale = dim_head ** -0.5
        inner_dim = heads * dim_head
        context_dim = default(context_dim, dim)

        self.norm = LayerNorm(dim)
        self.context_norm = LayerNorm(context_dim) if norm_context else nn.Identity()

        self.to_q = nn.Linear(dim, inner_dim, bias=False)
        self.to_kv = nn.Linear(context_dim, dim_head * 2, bias=False)
        self.to_out = nn.Linear(inner_dim, dim, bias=False)

        # whether to have parallel feedforward

        ff_inner_dim = ff_mult * dim

        self.ff = nn.Sequential(
            nn.Linear(dim, ff_inner_dim * 2, bias=False),
            SwiGLU(),
            nn.Linear(ff_inner_dim, dim, bias=False)
        ) if parallel_ff else None

    def forward(self, x, context, mask):
        """
        einstein notation
        b - batch
        h - heads
        n, i, j - sequence length (base sequence length, source, target)
        d - feature dimension
        """

        # pre-layernorm, for queries and context

        x = self.norm(x)
        context = self.context_norm(context)

        # get queries

        q = self.to_q(x)
        q = rearrange(q, 'b n (h d) -> b h n d', h = self.heads)

        # scale

        q = q * self.scale

        # get key / values

        k, v = self.to_kv(context).chunk(2, dim=-1)

        # query / key similarity

        sim = einsum('b h i d, b j d -> b h i j', q, k)

        # attention
        mask = mask.unsqueeze(1).repeat(1,self.heads,1,1)
        sim = sim + mask  # context mask
        sim = sim - sim.amax(dim=-1, keepdim=True)
        attn = sim.softmax(dim=-1)

        # aggregate

        out = einsum('b h i j, b j d -> b h i d', attn, v)

        # merge and combine heads

        out = rearrange(out, 'b h n d -> b n (h d)')
        out = self.to_out(out)

        # add parallel feedforward (for multimodal layers)

        if exists(self.ff):
            out = out + self.ff(x)

        return out


class Cross_model(nn.Module):
    def __init__(
        self,
        dim=512,
        layer_num=4,
        dim_head=64,
        heads=8,
        ff_mult=4
    ):
        super().__init__()

        self.layers = nn.ModuleList([])


        for ind in range(layer_num):
            self.layers.append(nn.ModuleList([
                Residual(CrossAttention(dim=dim, dim_head=dim_head, heads=heads, parallel_ff=True, ff_mult=ff_mult)),
                Residual(ParallelTransformerBlock(dim=dim, dim_head=dim_head, heads=heads, ff_mult=ff_mult))
            ]))

    def forward(
        self,
        query_tokens,
        context_tokens,
        mask
    ):

        for cross_attn, self_attn_ff in self.layers:
            query_tokens = cross_attn(query_tokens, context_tokens,mask)
            query_tokens = self_attn_ff(query_tokens)

        return query_tokens