Resume hlip backup excluding two local secret files (part 2)

.gitattributes

@@ -67,3 +67,6 @@ hlip/hlip/data/ct_rate/metafiles/train_metadata.csv filter=lfs diff=lfs merge=lf
 hlip/hlip/data/ct_rate/metafiles/train_reports.csv filter=lfs diff=lfs merge=lfs -text
 hlip/hlip/data/rad_chestct/metafiles/imgtrain_Abnormality_and_Location_Labels.csv filter=lfs diff=lfs merge=lfs -text
 hlip/hlip/data/rad_chestct/metafiles/imgvalid_Abnormality_and_Location_Labels.csv filter=lfs diff=lfs merge=lfs -text
+hlip/selected_10k_reports.csv filter=lfs diff=lfs merge=lfs -text
+hlip/train_metadata.csv filter=lfs diff=lfs merge=lfs -text
+hlip/train_reports.csv filter=lfs diff=lfs merge=lfs -text

hlip/open_clip/docs/clipa.md

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+## CLIPA
+
+In this work, we present a surprising finding that there exists an _inverse_ scaling law for CLIP training, 
+whereby the larger the image/text encoders used, the shorter the sequence length of image/text tokens that can be applied in training. 
+Moreover, we showcase that the strategy for reducing image/text token length plays a crucial role in determining the quality of this scaling law.
+
+![](/docs/inverse_scaling_law.png)
+
+As a result of this finding, we are able to successfully train CLIP even by using academic resources. 
+For example, on an A100 eight-GPU server, our CLIP models achieve zero-shot top-1 ImageNet accuracies of **63.2%** in about **2 days**, 
+**67.8%** in about **3 days**, and **69.3%** in about **4 days**.
+
+Moreover, We find that CLIPA at scale leads to state-of-the-art performance. For example, our CLIPA-v2 H/14 achieves a zero-shot top-1 ImageNet accuracy of **81.8%**,
+with a budget less than **$15000**.
+
+![](/docs/clipa_acc_compute.png)
+
+For more details, please see our paper [An Inverse Scaling Law for CLIP Training](https://arxiv.org/abs/2305.07017) and 
+[CLIPA-v2: Scaling CLIP Training with 81.1% Zero-shot ImageNet Accuracy within a $10,000 Budget; An Extra $4,000 Unlocks 81.8% Accuracy](https://arxiv.org/abs/2306.15658).
+
+
+Eight token length reduction strategies are investigated in this work, detailed as follows.
+
+
+## Image token length reduction
+
+![](/docs/clipa_reduce_image_token.png)
+
+* `resize`: use `--force-image-size` to specify the image size you want to adopt. We find this strategy generally works the best as it retains full image information.
+
+* `random mask`: Randomly mask out image patches. use `--force-patch-dropout` to specify the mask ratio you want to adopt. 
+
+* `grid mask`: Preserve one patch in each 2 × 2 grid window. We do not provide implementation for grid masking, as it is only experimental and we generally find resizing works better.
+
+* `block mask`: Keep a single block and remove other patches. We do not provide implementation for block masking, as it is only experimental and we generally find resizing works better.
+
+
+## Text token length reduction
+
+* `syntax mask`: Assign different masking priorities to parts of speech. Specify `"text_mask": syntax` in `"tokenizer_kwargs"` in `"text_cfg"` of model config `json` file to use. 
+Specifically, we prioritize retaining nouns, followed by adjectives, and then other words. 
+We find this strategy generally works the best as it retains critical information for contrastive learning.
+
+* `truncate`: Truncation selects the first N text tokens and discards the rest. This is the default setting of `open_clip`. 
+
+* `random mask`: Randomly drops a portion of the text tokens. Specify `"text_mask": random` in `"tokenizer_kwargs"` in `"text_cfg"` of model config `json` file to use. 
+
+* `block mask`: Randomly preserves consecutive text sequences. Specify `"text_mask": block` in `"tokenizer_kwargs"` in `"text_cfg"` of model config `json` file to use. 
+
+
+## Installation
+
+The installation is really the same as `open_clip`, except for the usage of Natural Language Toolkit (NLTK) in `syntax mask` of text token length reduction.
+Please follow the [official doc](https://www.nltk.org/) to install NLTK.
+
+Note that the the usage of NLTK brings two constraints:
+* Because certain functions like `nltk.pos_tag` from NLTK only support English and Russian for now, the `syntax mask` only works for English. 
+we have not tested it on Russian or any other language. Theoretically, it should work the same, given a proper language processing toolkit for other languages.
+If you still want to apply `syntax mask` on other languages, try finding the right toolkit. Otherwise, use other text token length reduction strategies
+* some modules of NLTK like `punkt` or `averaged_perceptron_tagger` need to be downloaded first before using NLTK.
+We have included the downloading code in `tokenizer.py`, but this might cause trouble in certain cases.
+You may want to manually download those modules first, by `nltk.download('punkt')` and `nltk.download('averaged_perceptron_tagger')`,
+and then setup the environmental variable before running the script `export NLTK_DATA=cache`. 
+Note that this is a one-time effort. Remember to comment out those `nltk.download` lines in `tokenizer.py` afterwards.
+
+## Training
+We provide example scripts to reproduce our CLIPA results on an A100 eight-GPU machine under path `docs/script_examples/clipa`.
+
+For instance, to reproduce the CLIPA-L16(I37,T8) results, first run the pre-training script
+```
+bash docs/script_examples/clipa/vit_l16/i37_t8_pretrain.sh
+```
+and fine-tune the pre-trained checkpoint with
+```
+bash docs/script_examples/clipa/vit_l16/i37_t8_finetune.sh
+```
+- Remember to change the path to dataset to your own path.
+- This is a two-stage training pipeline. Remember to change the path to pre-trained checkpoint to your own when fine-tuning.
+- The training time is ~3 days for pre-training and ~1 day for fine-tuning on an A100 eight-GPU machine.
+
+## Model Weights
+Below are CLIPA trained weights on LAION-400M with an A100 eight-GPU machine. 
+All models are pre-trained for 6 epochs with reduced input token lengths and subsequently fine-tuned for 0.36 epoch with full input token lengths.
+
+
+|                     |                                      Pre-trained Weights                                       | zero-shot IN-1K |
+|---------------------|:----------------------------------------------------------------------------------------------:|:---------------:|
+| CLIPA-B/16(I50,T16) | [download](https://drive.google.com/file/d/1MDpz8gV2Vjaazk16rBhLxU8811U7_cGL/view?usp=sharing) |      59.7       |
+| CLIPA-L/16(I17,T16) | [download](https://drive.google.com/file/d/1Tr2GYiKAaMH6EGIn5l7eX_1K20eaA3WA/view?usp=sharing) |      60.3       |
+| CLIPA_L/16(I37,T8)  | [download](https://drive.google.com/file/d/1EM1ChRNARpLckkJjf6m7njCY3xyvpGBu/view?usp=sharing) |      57.9       |
+
+|                     |                                       Fine-tuned Weights                                       | zero-shot IN-1K |
+|---------------------|:----------------------------------------------------------------------------------------------:|:-----:|
+| CLIPA-B/16(I50,T16) | [download](https://drive.google.com/file/d/1fURK0K_a3-83jVEI4PVEbnEJb_V6UbGv/view?usp=sharing) | 63.2  |
+| CLIPA-L/16(I17,T16) | [download](https://drive.google.com/file/d/18qqZGOTGOgb3I3JWONuat6qObsgLq7sR/view?usp=sharing) | 67.8  |
+| CLIPA_L/16(I37,T8)  | [download](https://drive.google.com/file/d/1lV7pLORUK04T9QKKx9TpYtMws-AZrib0/view?usp=sharing) | 69.3  |
+
+
+## CLIPA-v2
+We also provide example scripts to reproduce our CLIPA-v2 H/14 results under path `docs/script_examples/clipav2`.
+Note that the original results are obtained with [our JAX implementation](https://github.com/UCSC-VLAA/CLIPA/tree/master/clipa_jax).
+These scripts are written after manually scanning the JAX config files.
+As it is infeasible for us to retrain those models again with pytorch, its correctness cannot be verified with 100% confidence. Use them at your own discretion.

hlip/open_clip/docs/datacomp_models.md

@@ -0,0 +1,69 @@
+## CommonPool and DataComp models
+
+As part of [DataComp](https://github.com/mlfoundations/datacomp), we trained models on CommonPool using various data filtering strategies.
+We release models for all four scales of the competition, small, medium, large and xlarge, corresponding to a pool size and number of samples seen of 12.8M, 128M, 1.28B and 12.8B, respectively.
+
+The models are specified below, see our paper [DataComp: In seearch of the next generation of multimodal datasets](https://arxiv.org/abs/2304.14108) for more details.
+
+
+## xlarge scale models
+
+* `datacomp_xl_s13b_b90k`: A ViT-L/14 trained on DataComp-1B for 12.8B steps and batch size 90k. Achieves 79.2% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K. 
+
+* `commonpool_xl_clip_s13b_b90k`: A ViT-L/14 trained on CommonPool-XL filtered using CLIP scores, for 12.8B steps and batch size 90k. Achieves 76.4% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-L-14-CommonPool.XL.clip-s13B-b90K.
+
+* `commonpool_xl_laion_s13b_b90k`: A ViT-L/14 trained on CommonPool-XL filtered using the LAION-2B filtering scheme, for 12.8B steps and batch size 90k. Achieves 75.5% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-L-14-CommonPool.XL.laion-s13B-b90K.
+
+* `commonpool_xl_s13b_b90k`: A ViT-L/14 trained on CommonPool-XL without any filtering, for 12.8B steps and batch size 90k. Achieves 72.3% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-L-14-CommonPool.XL-s13B-b90K.
+
+
+## large scale models
+
+* `datacomp_l_s1b_b8k`: A ViT-B/16 trained on a 140M subset of DataComp-1B, for 1.28B steps and batch size 8k. Achieves 63.1% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-DataComp.L-s1B-b8K.
+
+* `commonpool_l_clip_s1b_b8k`: A ViT-B/16 trained on CommonPool-L filtered using CLIP scores, for 1.28B steps and batch size 8k. Achieves 57.8% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L.clip-s1B-b8K.
+
+* `commonpool_l_laion_s1b_b8k`: A ViT-B/16 trained on CommonPool-L filtered using the LAION-2B filtering scheme, for 1.28B steps and batch size 8k. Achieves 55.3% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L.laion-s1B-b8K.
+
+* `commonpool_l_image_s1b_b8k`: A ViT-B/16 trained on CommonPool-L filtered using image-based filtering, for 1.28B steps and batch size 8k. Achieves 57.2% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L.image-s1B-b8K.
+
+* `commonpool_l_text_s1b_b8k`: A ViT-B/16 trained on CommonPool-L filtered using text-based filtering, for 1.28B steps and batch size 8k. Achieves 56.1% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L.text-s1B-b8K.
+
+* `commonpool_l_basic_s1b_b8k`: A ViT-B/16 trained on CommonPool-L filtered using basic filtering (English filtering + caption length and image size filtering), for 1.28B steps and batch size 8k. Achieves 51.6% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L.basic-s1B-b8K.
+
+* `commonpool_l_s1b_b8k`: A ViT-B/16 trained on CommonPool-L without any filtering, for 1.28B steps and batch size 8k. Achieves 45.9% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L-s1B-b8K.
+
+
+## medium scale models
+
+* `datacomp_m_s128m_b4k`: A ViT-B/32 trained on a 14M subset of DataComp-1B, for 128M steps and batch size 4k. Achieves 29.7% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-DataComp.M-s128M-b4K.
+
+* `commonpool_m_clip_s128m_b4k`: A ViT-B/32 trained on CommonPool-M filtered using CLIP scores, for 128M steps and batch size 4k. Achieves 27.3% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M.clip-s128M-b4K.
+
+* `commonpool_m_laion_s128m_b4k`: A ViT-B/32 trained on CommonPool-M filtered using the LAION-2B filtering scheme, for 128M steps and batch size 4k. Achieves 23.0% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M.laion-s128M-b4K.
+
+* `commonpool_m_image_s128m_b4k`: A ViT-B/32 trained on CommonPool-M filtered using image-based filtering, for 128M steps and batch size 4k. Achieves 26.8% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M.image-s128M-b4K.
+
+* `commonpool_m_text_s128m_b4k`:  A ViT-B/32 trained on CommonPool-M filtered using text-based filtering, for 128M steps and batch size 4k. Achieves 25.5% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M.text-s128M-b4K.
+
+* `commonpool_m_basic_s128m_b4k`:  A ViT-B/32 trained on CommonPool-M filtered using basic filtering (English filtering + caption length and image size filtering), for 128M steps and batch size 4k. Achieves 22.6% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M.basic-s128M-b4K.
+
+* `commonpool_m_s128m_b4k`: A ViT-B/32 trained on CommonPool-M without any filtering, for 128M steps and batch size 4k. Achieves 17.6% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M-s128M-b4K.
+
+
+## small scale models
+
+* `datacomp_s_s13m_b4k`: A ViT-B/32 trained on a 1.4M subset of DataComp-1B, for 12.8M steps and batch size 4k. Achieves 3.9% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-DataComp.S-s13M-b4K.
+
+* `commonpool_s_clip_s13m_b4k`: A ViT-B/32 trained on CommonPool-S filtered using CLIP scores, for 12.8M steps and batch size 4k. Achieves 5.1% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S.clip-s13M-b4K.
+
+* `commonpool_s_laion_s13m_b4k`: A ViT-B/32 trained on CommonPool-S filtered using the LAION-2B filtering scheme scores, for 12.8M steps and batch size 4k. Achieves 3.1% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S.laion-s13M-b4K.
+
+* `commonpool_s_image_s13m_b4k`: A ViT-B/32 trained on CommonPool-S filtered using image-based filtering, for 12.8M steps and batch size 4k. Achieves 4.3% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S.image-s13M-b4K.
+
+* `commonpool_s_text_s13m_b4k`: A ViT-B/32 trained on CommonPool-S filtered using text-based filtering, for 12.8M steps and batch size 4k. Achieves 4.6% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S.text-s13M-b4K.
+
+* `commonpool_s_basic_s13m_b4k`: A ViT-B/32 trained on CommonPool-S filtered using basic filtering (English filtering + caption length and image size filtering), for 12.8M steps and batch size 4k. Achieves 3.0% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S.basic-s13M-b4K.
+
+* `commonpool_s_s13m_b4k`: A ViT-B/32 trained on CommonPool-S without any filtering, for 12.8M steps and batch size 4k. Achieves 2.5% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S-s13M-b4K.
+

hlip/open_clip/docs/model_profile.csv

@@ -0,0 +1,86 @@
+model,image_size,image_width,text_width,embed_dim,mparams,image_mparams,text_mparams,gflops,image_gflops,text_gflops
+ViT-S-32-alt,224,384,256,256,43.22,22.59,20.63,3.56,2.29,1.27
+ViT-S-32,224,384,384,384,63.09,22.64,40.44,5.66,2.29,3.38
+ViT-M-32-alt,224,512,384,384,80.07,39.63,40.44,7.37,3.99,3.38
+ViT-M-32,224,512,512,512,103.12,39.69,63.43,9.95,3.99,5.96
+ViT-S-16-alt,224,384,256,256,42.4,21.76,20.63,10.47,9.2,1.27
+ViT-S-16,224,384,384,384,62.26,21.81,40.44,12.58,9.2,3.38
+ViT-B-32,224,768,512,512,151.28,87.85,63.43,14.78,8.82,5.96
+ViT-B-32-quickgelu,224,768,512,512,151.28,87.85,63.43,14.78,8.82,5.96
+convnext_tiny,224,768,512,1024,92.3,28.61,63.69,14.87,8.91,5.96
+ViT-B-32-256,256,768,512,512,151.29,87.86,63.43,17.46,11.5,5.96
+RN50,224,64,512,1024,102.01,38.32,63.69,18.18,12.22,5.96
+RN50-quickgelu,224,64,512,1024,102.01,38.32,63.69,18.18,12.22,5.96
+ViT-M-16-alt,224,512,384,384,78.98,38.53,40.44,19.36,15.98,3.38
+ViT-M-16,224,512,512,512,102.02,38.59,63.43,21.94,15.98,5.96
+vit_relpos_medium_patch16_cls_224,224,768,512,512,101.94,38.51,63.43,21.99,16.03,5.96
+mt5-base-ViT-B-32,224,768,512,512,365.71,87.85,277.86,22.12,8.82,13.3
+convnext_small,224,768,512,512,113.28,49.85,63.43,23.33,17.37,5.96
+ViT-B-32-plus-256,256,896,640,640,210.3,119.13,91.16,24.83,15.56,9.27
+RN101,224,64,512,512,119.69,56.26,63.43,25.5,19.54,5.96
+RN101-quickgelu,224,64,512,512,119.69,56.26,63.43,25.5,19.54,5.96
+vit_medium_patch16_gap_256,256,768,512,512,102.04,38.61,63.43,27.1,21.14,5.96
+coca_ViT-B-32,224,768,512,512,253.56,89.16,63.43,33.34,9.19,5.96
+convnext_base,224,768,512,512,151.52,88.09,63.43,36.67,30.71,5.96
+swin_base_patch4_window7_224,224,768,640,640,178.56,87.4,91.16,40.13,30.86,9.27
+ViT-B-16,224,768,512,512,149.62,86.19,63.43,41.09,35.13,5.96
+ViT-B-16-quickgelu,224,768,512,512,149.62,86.19,63.43,41.09,35.13,5.96
+EVA02-B-16,224,768,512,512,149.69,86.26,63.43,41.09,35.13,5.96
+ViT-B-16-SigLIP,224,768,768,768,203.16,92.88,110.27,46.44,35.42,11.02
+convnext_base_w,256,768,640,640,179.39,88.22,91.16,49.38,40.11,9.27
+RN50x4,288,80,640,640,178.3,87.14,91.16,51.82,42.56,9.27
+coca_roberta-ViT-B-32,224,768,768,512,420.37,87.85,124.45,53.12,8.82,13.12
+ViT-B-16-plus,224,896,640,640,208.35,117.19,91.16,56.75,47.49,9.27
+ViT-B-16-SigLIP-256,256,768,768,768,203.2,92.93,110.27,57.84,46.82,11.02
+ViT-B-16-SigLIP-i18n-256,256,768,768,768,370.63,92.93,277.7,57.84,46.82,11.02
+ViT-B-16-plus-240,240,896,640,640,208.38,117.21,91.16,64.03,54.76,9.27
+convnext_base_w_320,320,768,640,640,179.39,88.22,91.16,71.94,62.67,9.27
+convnext_large,224,768,768,768,321.06,197.41,123.65,82.02,68.72,13.3
+coca_base,288,768,768,512,440.34,86.4,134.66,99.09,46.47,13.3
+roberta-ViT-B-32,224,768,512,512,212.72,87.85,124.87,105.87,8.82,97.05
+xlm-roberta-base-ViT-B-32,224,768,512,512,366.12,87.85,278.27,105.87,8.82,97.05
+convnext_large_d,256,768,768,768,351.77,199.77,152.0,107.5,89.76,17.73
+ViT-B-16-SigLIP-384,384,768,768,768,203.45,93.18,110.27,123.15,112.13,11.02
+ViT-L-16,224,1024,768,768,427.74,304.09,123.65,136.41,123.11,13.3
+convnext_large_d_320,320,768,768,768,351.77,199.77,152.0,157.98,140.25,17.73
+RN50x16,384,96,768,768,290.98,167.33,123.65,162.69,149.39,13.3
+ViT-L-14-CLIPA,224,1024,768,768,414.21,303.96,110.25,167.5,162.03,5.47
+EVA02-L-14,224,768,768,768,427.76,304.11,123.65,175.3,162.0,13.3
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+ViT-L-14-quickgelu,224,1024,768,768,427.62,303.97,123.65,175.33,162.03,13.3
+convnext_xlarge,256,768,1024,1024,653.89,350.25,303.65,198.38,159.14,39.24
+ViT-L-16-SigLIP-256,256,768,1024,1024,652.15,315.96,336.19,201.62,162.56,39.06
+coca_ViT-L-14,224,1024,768,768,638.45,306.72,123.65,214.52,163.64,13.3
+ViT-B-16-SigLIP-512,512,768,768,768,203.79,93.52,110.27,227.26,216.24,11.02
+ViT-SO400M-14-SigLIP,224,768,1152,1152,877.36,427.68,449.68,233.54,220.35,13.19
+ViT-L-14-280,280,1024,768,768,427.76,304.11,123.65,271.79,258.49,13.3
+ViT-L-16-320,320,1024,768,768,427.95,304.3,123.65,271.93,258.63,13.3
+ViT-H-16,224,1280,1024,1024,986.26,632.23,354.03,301.72,254.63,47.09
+ViT-H-14-CLIPA,224,1280,1024,1024,968.24,632.07,336.16,354.02,334.59,19.43
+nllb-clip-base,224,768,512,512,501.89,87.85,414.04,369.6,8.82,360.78
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hlip/open_clip/docs/openclip_classification_results.csv

@@ -0,0 +1,122 @@
+name,pretrained,params (M),FLOPs (B),Average perf. on 35 datasets,ImageNet 1k,Caltech-101,CIFAR-10,CIFAR-100,CLEVR Counts,CLEVR Distance,Country211,Describable Textures,EuroSAT,FGVC Aircraft,Food-101,GTSRB,ImageNet Sketch,ImageNet v2,ImageNet-A,ImageNet-O,ImageNet-R,KITTI Vehicle Distance,MNIST,ObjectNet,Oxford Flowers-102,Oxford-IIIT Pet,Pascal VOC 2007,PatchCamelyon,Rendered SST2,RESISC45,Stanford Cars,STL-10,SUN397,SVHN,iWildCam,Camelyon17,FMoW,Dollar Street,GeoDE
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+coca_ViT-B-32,mscoco_finetuned_laion2b_s13b_b90k,253.56,33.34,0.1133,0.0079,0.0320,0.2564,0.0193,0.1245,0.2027,0.0044,0.0303,0.1157,0.0064,0.0159,0.0146,0.0028,0.0067,0.0121,0.0220,0.0199,0.3010,0.1506,0.0144,0.0054,0.0416,0.2023,0.5713,0.4992,0.0478,0.0056,0.2579,0.0204,0.1529,0.0004,0.5681,0.0000,0.1729,0.0589

hlip/open_clip/docs/openclip_results.csv

@@ -0,0 +1,122 @@
+name,pretrained,params (M),FLOPs (B),Average perf. on 38 datasets,ImageNet 1k,Caltech-101,CIFAR-10,CIFAR-100,CLEVR Counts,CLEVR Distance,Country211,Describable Textures,EuroSAT,FGVC Aircraft,Food-101,GTSRB,ImageNet Sketch,ImageNet v2,ImageNet-A,ImageNet-O,ImageNet-R,KITTI Vehicle Distance,MNIST,ObjectNet,Oxford Flowers-102,Oxford-IIIT Pet,Pascal VOC 2007,PatchCamelyon,Rendered SST2,RESISC45,Stanford Cars,STL-10,SUN397,SVHN,Flickr,MSCOCO,WinoGAViL,iWildCam,Camelyon17,FMoW,Dollar Street,GeoDE
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+EVA02-E-14-plus,laion2b_s9b_b144k,5044.89,2362.19,0.6930,0.8201,0.9535,0.9934,0.9316,0.2991,0.1998,0.3564,0.6777,0.7574,0.5360,0.9496,0.6740,0.7162,0.7564,0.8223,0.3540,0.9456,0.1842,0.7463,0.7937,0.8433,0.9567,0.8569,0.6442,0.6271,0.7490,0.9457,0.9926,0.7510,0.7560,0.8648,0.5991,0.4403,0.2591,0.6948,0.2668,0.6951,0.9244
+ViT-SO400M-14-SigLIP-384,webli,877.96,723.48,0.6921,0.8308,0.9599,0.9672,0.8357,0.4071,0.2246,0.3645,0.7303,0.6354,0.6069,0.9635,0.6429,0.7454,0.7717,0.8247,0.2775,0.9575,0.2082,0.8862,0.7695,0.9114,0.9680,0.7171,0.5268,0.7002,0.7211,0.9521,0.9930,0.7541,0.5151,0.8863,0.6331,0.5754,0.2294,0.6149,0.3309,0.7301,0.9328
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+ViT-bigG-14-CLIPA,datacomp1b,2517.22,1007.93,0.6822,0.8270,0.9513,0.9912,0.9135,0.1357,0.2113,0.3921,0.7207,0.6861,0.5576,0.9583,0.6460,0.7431,0.7699,0.8179,0.3075,0.9512,0.2743,0.8544,0.7694,0.8693,0.9576,0.8188,0.5345,0.6332,0.7137,0.9560,0.9965,0.7642,0.6811,0.8488,0.5888,0.4383,0.2269,0.5955,0.1959,0.6869,0.9382
+ViT-SO400M-14-SigLIP,webli,877.36,233.54,0.6808,0.8203,0.9600,0.9679,0.8417,0.4210,0.2213,0.3243,0.7106,0.6274,0.6029,0.9556,0.6382,0.7402,0.7607,0.7185,0.2960,0.9506,0.2489,0.8929,0.7060,0.8982,0.9522,0.7034,0.5057,0.6936,0.7257,0.9032,0.9939,0.7436,0.5670,0.8313,0.6071,0.5665,0.1915,0.6215,0.3163,0.7173,0.9278
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+ViT-L-16-SigLIP-384,webli,652.48,422.91,0.6683,0.8207,0.9611,0.9605,0.8188,0.3275,0.2077,0.2470,0.7080,0.5817,0.5312,0.9564,0.6385,0.7360,0.7593,0.7663,0.3130,0.9507,0.2222,0.8525,0.7284,0.8934,0.9681,0.7172,0.5466,0.5634,0.6789,0.9493,0.9924,0.7250,0.5672,0.8756,0.6290,0.5550,0.2236,0.6637,0.1489,0.6916,0.9207
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+ViT-bigG-14,laion2b_s39b_b160k,2539.57,1065.36,0.6667,0.8009,0.9484,0.9824,0.8752,0.2989,0.2002,0.3379,0.6867,0.6919,0.4953,0.9309,0.6244,0.6894,0.7359,0.6933,0.3785,0.9213,0.1308,0.7157,0.7284,0.8163,0.9529,0.8077,0.6364,0.6535,0.7235,0.9460,0.9850,0.7450,0.6961,0.8623,0.5938,0.4488,0.1760,0.5905,0.2352,0.6857,0.9127
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+ViT-L-14,datacomp_xl_s13b_b90k,427.62,175.33,0.6627,0.7921,0.9465,0.9824,0.8736,0.3555,0.2443,0.3157,0.6649,0.7124,0.4750,0.9452,0.5853,0.6795,0.7205,0.6959,0.3255,0.9083,0.2785,0.8661,0.7425,0.8262,0.9506,0.8247,0.5118,0.6101,0.6941,0.9305,0.9925,0.7427,0.6769,0.8119,0.5451,0.4666,0.1614,0.5089,0.2403,0.6624,0.9152
+EVA01-g-14-plus,merged2b_s11b_b114k,1366.62,581.15,0.6624,0.7933,0.9506,0.9910,0.9008,0.2302,0.2293,0.3087,0.6734,0.7280,0.3947,0.9366,0.6644,0.6814,0.7214,0.7416,0.3415,0.9246,0.1491,0.7176,0.7491,0.7959,0.9490,0.8285,0.6244,0.5854,0.7079,0.9073,0.9949,0.7426,0.5951,0.8535,0.5925,0.4684,0.1882,0.7100,0.2283,0.6589,0.9148
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hlip/open_clip/docs/openclip_retrieval_results.csv

@@ -0,0 +1,122 @@
+name,pretrained,params (M),FLOPs (B),Average score,Flickr image retr. R@1,Flickr image retr. R@5,Flickr image retr. R@10,Flickr text retr. R@1,Flickr text retr. R@5,Flickr text retr. R@10,MSCOCO image retr. R@1,MSCOCO image retr. R@5,MSCOCO image retr. R@10,MSCOCO text retr. R@1,MSCOCO text retr. R@5,MSCOCO text retr. R@10,WinoGAViL avg jaccard score,WinoGAViL jaccard score 10,WinoGAViL jaccard score 10-12,WinoGAViL jaccard score 12,WinoGAViL jaccard score 5,WinoGAViL jaccard score 5-6,WinoGAViL jaccard score 6
+ViT-SO400M-14-SigLIP-384,webli,877.96,723.48,0.7721,0.8296,0.9610,0.9804,0.9430,0.9970,0.9980,0.5421,0.7678,0.8424,0.7242,0.8998,0.9448,0.6181,0.5807,0.5754,0.5701,0.6427,0.6316,0.6210
+ViT-H-14-378-quickgelu,dfn5b,986.71,1054.05,0.7719,0.8202,0.9598,0.9798,0.9400,0.9920,0.9960,0.5564,0.7920,0.8626,0.7188,0.9048,0.9496,0.6123,0.5668,0.5674,0.5679,0.6409,0.6265,0.6127
+ViT-L-16-SigLIP-384,webli,652.48,422.91,0.7642,0.8142,0.9536,0.9748,0.9370,0.9920,0.9990,0.5391,0.7657,0.8399,0.7190,0.9006,0.9410,0.6070,0.5562,0.5550,0.5539,0.6422,0.6234,0.6056
+ViT-H-14-quickgelu,dfn5b,986.11,381.68,0.7640,0.8012,0.9526,0.9738,0.9280,0.9940,0.9990,0.5391,0.7805,0.8553,0.7230,0.9024,0.9452,0.6022,0.5568,0.5561,0.5554,0.6315,0.6167,0.6028
+ViT-L-16-SigLIP-256,webli,652.15,201.62,0.7619,0.7904,0.9446,0.9684,0.9180,0.9900,0.9980,0.5228,0.7580,0.8334,0.7080,0.8870,0.9374,0.6142,0.5877,0.5748,0.5619,0.6450,0.6267,0.6092
+ViT-SO400M-14-SigLIP,webli,877.36,233.54,0.7567,0.7526,0.9226,0.9554,0.9100,0.9910,0.9980,0.5176,0.7527,0.8300,0.6966,0.8908,0.9348,0.6189,0.5736,0.5665,0.5594,0.6430,0.6354,0.6282
+ViT-B-16-SigLIP-512,webli,203.79,227.26,0.7554,0.7906,0.9458,0.9690,0.9250,0.9920,0.9960,0.5055,0.7421,0.8217,0.6872,0.8786,0.9266,0.6070,0.5761,0.5696,0.5633,0.6291,0.6187,0.6088
+ViT-B-16-SigLIP-384,webli,203.45,123.15,0.7542,0.7848,0.9416,0.9708,0.9270,0.9910,0.9980,0.4990,0.7366,0.8204,0.6774,0.8758,0.9258,0.6077,0.5730,0.5719,0.5709,0.6314,0.6190,0.6072
+ViT-B-16-SigLIP-256,webli,203.2,57.84,0.7462,0.7504,0.9242,0.9626,0.9040,0.9830,0.9930,0.4834,0.7254,0.8099,0.6614,0.8634,0.9182,0.6113,0.5754,0.5715,0.5677,0.6373,0.6238,0.6110
+ViT-B-16-SigLIP,webli,203.16,46.44,0.7442,0.7468,0.9230,0.9562,0.8910,0.9800,0.9930,0.4778,0.7244,0.8100,0.6574,0.8542,0.9126,0.6134,0.5726,0.5738,0.5750,0.6335,0.6259,0.6186
+coca_ViT-L-14,mscoco_finetuned_laion2b_s13b_b90k,638.45,214.52,0.7432,0.7846,0.9452,0.9712,0.8900,0.9870,0.9950,0.5374,0.7779,0.8567,0.6682,0.8762,0.9242,0.5762,0.5265,0.5146,0.5028,0.5978,0.5956,0.5934
+ViT-H-14-quickgelu,metaclip_fullcc,986.11,381.68,0.7412,0.7834,0.9464,0.9692,0.9180,0.9870,0.9970,0.4882,0.7323,0.8136,0.6622,0.8618,0.9188,0.5891,0.5316,0.5312,0.5308,0.6220,0.6073,0.5934
+ViT-L-14-quickgelu,dfn2b,427.62,175.33,0.7406,0.7546,0.9280,0.9608,0.8960,0.9850,0.9930,0.4856,0.7381,0.8237,0.6560,0.8584,0.9120,0.5955,0.5688,0.5519,0.5351,0.6219,0.6093,0.5973
+xlm-roberta-large-ViT-H-14,frozen_laion5b_s13b_b90k,1193.01,671.01,0.7363,0.7742,0.9392,0.9670,0.9180,0.9930,0.9980,0.4921,0.7305,0.8159,0.6596,0.8618,0.9184,0.5767,0.5277,0.5206,0.5135,0.6076,0.5943,0.5818
+ViT-B-16-SigLIP-i18n-256,webli,370.63,57.84,0.7347,0.7216,0.9036,0.9470,0.8960,0.9820,0.9910,0.4492,0.6948,0.7837,0.6448,0.8434,0.9060,0.6113,0.5887,0.5710,0.5534,0.6303,0.6240,0.6180
+ViT-L-14-quickgelu,metaclip_fullcc,427.62,175.33,0.7325,0.7642,0.9366,0.9646,0.9010,0.9850,0.9930,0.4709,0.7141,0.8026,0.6442,0.8504,0.9130,0.5817,0.5539,0.5357,0.5176,0.5990,0.5963,0.5937
+ViT-H-14-CLIPA-336,laion2b,968.64,800.88,0.7289,0.7814,0.9392,0.9684,0.9230,0.9920,0.9970,0.5013,0.7387,0.8224,0.6752,0.8802,0.9308,0.5452,0.4970,0.4878,0.4786,0.5764,0.5633,0.5508
+convnext_xxlarge,laion2b_s34b_b82k_augreg_rewind,1200.58,443.03,0.7238,0.7886,0.9438,0.9714,0.9110,0.9910,0.9970,0.5003,0.7407,0.8240,0.6740,0.8738,0.9264,0.5327,0.4826,0.4741,0.4656,0.5674,0.5512,0.5357
+EVA02-L-14-336,merged2b_s6b_b61k,428.08,395.16,0.7235,0.7802,0.9432,0.9684,0.8960,0.9890,0.9960,0.4794,0.7172,0.7999,0.6416,0.8522,0.9084,0.5532,0.5081,0.5053,0.5025,0.5719,0.5683,0.5649
+convnext_xxlarge,laion2b_s34b_b82k_augreg_soup,1200.58,443.03,0.7232,0.7902,0.9460,0.9706,0.9140,0.9920,0.9970,0.4997,0.7403,0.8227,0.6736,0.8722,0.9260,0.5316,0.4790,0.4702,0.4615,0.5651,0.5509,0.5374
+EVA01-g-14-plus,merged2b_s11b_b114k,1366.62,581.15,0.7231,0.7900,0.9448,0.9692,0.9170,0.9930,0.9970,0.5033,0.7398,0.8206,0.6816,0.8750,0.9250,0.5298,0.4787,0.4684,0.4582,0.5655,0.5491,0.5336
+ViT-bigG-14-CLIPA-336,datacomp1b,2517.76,2271.58,0.7220,0.7828,0.9408,0.9674,0.9220,0.9940,0.9980,0.5042,0.7432,0.8238,0.6776,0.8792,0.9306,0.5323,0.4624,0.4487,0.4350,0.5619,0.5587,0.5556
+ViT-bigG-14,laion2b_s39b_b160k,2539.57,1065.36,0.7213,0.7956,0.9498,0.9714,0.9290,0.9930,0.9980,0.5137,0.7490,0.8300,0.6738,0.8686,0.9266,0.5224,0.4714,0.4488,0.4263,0.5588,0.5456,0.5330
+EVA02-L-14,merged2b_s4b_b131k,427.76,175.3,0.7207,0.7732,0.9374,0.9678,0.8970,0.9850,0.9910,0.4747,0.7123,0.7971,0.6366,0.8434,0.9038,0.5518,0.5128,0.5081,0.5034,0.5767,0.5656,0.5551
+ViT-L-14,commonpool_xl_laion_s13b_b90k,427.62,175.33,0.7206,0.7490,0.9308,0.9630,0.8770,0.9900,0.9950,0.4677,0.7142,0.8042,0.6350,0.8458,0.9122,0.5635,0.5193,0.4966,0.4740,0.6023,0.5846,0.5677
+convnext_xxlarge,laion2b_s34b_b82k_augreg,1200.58,443.03,0.7201,0.7902,0.9414,0.9726,0.9160,0.9920,0.9970,0.4970,0.7400,0.8199,0.6698,0.8744,0.9236,0.5290,0.4730,0.4536,0.4343,0.5622,0.5527,0.5437
+ViT-H-14-CLIPA-336,datacomp1b,968.64,800.88,0.7192,0.7652,0.9350,0.9630,0.9060,0.9920,0.9950,0.4928,0.7309,0.8144,0.6716,0.8746,0.9276,0.5365,0.4801,0.4587,0.4375,0.5658,0.5610,0.5566
+EVA02-E-14-plus,laion2b_s9b_b144k,5044.89,2362.19,0.7188,0.7886,0.9434,0.9698,0.9410,0.9930,0.9980,0.5110,0.7492,0.8276,0.6872,0.8760,0.9274,0.5134,0.4576,0.4403,0.4232,0.5470,0.5364,0.5263
+ViT-H-14-CLIPA,datacomp1b,968.24,354.02,0.7178,0.7588,0.9330,0.9628,0.9100,0.9900,0.9960,0.4910,0.7291,0.8140,0.6698,0.8730,0.9272,0.5343,0.4793,0.4578,0.4365,0.5641,0.5584,0.5529
+ViT-bigG-14-CLIPA,datacomp1b,2517.22,1007.93,0.7175,0.7786,0.9374,0.9650,0.9190,0.9930,0.9980,0.4996,0.7414,0.8214,0.6780,0.8742,0.9312,0.5247,0.4461,0.4383,0.4306,0.5552,0.5519,0.5488
+ViT-B-16,dfn2b,149.62,41.09,0.7158,0.6912,0.8982,0.9406,0.8540,0.9780,0.9860,0.4339,0.6924,0.7882,0.6038,0.8310,0.8946,0.5852,0.5509,0.5417,0.5327,0.6058,0.5989,0.5923
+EVA02-E-14,laion2b_s4b_b115k,4704.59,2311.42,0.7152,0.7810,0.9438,0.9700,0.9220,0.9950,0.9970,0.5037,0.7429,0.8224,0.6746,0.8738,0.9256,0.5110,0.4316,0.4429,0.4542,0.5485,0.5325,0.5173
+convnext_large_d_320,laion2b_s29b_b131k_ft_soup,351.77,157.98,0.7148,0.7704,0.9394,0.9648,0.9230,0.9890,0.9980,0.4829,0.7251,0.8103,0.6502,0.8608,0.9180,0.5285,0.4574,0.4549,0.4525,0.5650,0.5517,0.5391
+ViT-L-14-quickgelu,metaclip_400m,427.62,175.33,0.7137,0.7342,0.9228,0.9574,0.8620,0.9800,0.9960,0.4381,0.6862,0.7780,0.6000,0.8290,0.8934,0.5701,0.5322,0.5175,0.5029,0.5941,0.5867,0.5796
+convnext_large_d_320,laion2b_s29b_b131k_ft,351.77,157.98,0.7122,0.7690,0.9364,0.9668,0.9140,0.9890,0.9980,0.4814,0.7232,0.8103,0.6384,0.8558,0.9168,0.5250,0.4727,0.4558,0.4389,0.5623,0.5468,0.5321
+convnext_large_d,laion2b_s26b_b102k_augreg,351.77,107.5,0.7102,0.7588,0.9310,0.9652,0.9180,0.9850,0.9940,0.4701,0.7139,0.8013,0.6400,0.8506,0.9140,0.5252,0.4835,0.4646,0.4457,0.5571,0.5443,0.5322
+ViT-L-14-CLIPA-336,datacomp1b,414.54,387.39,0.7092,0.7462,0.9240,0.9590,0.9040,0.9920,0.9980,0.4715,0.7159,0.8024,0.6564,0.8636,0.9180,0.5276,0.4601,0.4449,0.4297,0.5611,0.5537,0.5467
+ViT-L-14,datacomp_xl_s13b_b90k,427.62,175.33,0.7091,0.7338,0.9174,0.9554,0.8900,0.9860,0.9970,0.4573,0.7003,0.7916,0.6330,0.8414,0.9040,0.5471,0.4836,0.4666,0.4497,0.5912,0.5724,0.5546
+ViT-g-14,laion2b_s12b_b42k,1366.68,581.15,0.7085,0.7642,0.9364,0.9624,0.9090,0.9910,0.9980,0.4802,0.7238,0.8079,0.6492,0.8530,0.9152,0.5178,0.4498,0.4427,0.4357,0.5589,0.5414,0.5249
+coca_ViT-L-14,laion2b_s13b_b90k,638.45,214.52,0.7085,0.7428,0.9202,0.9542,0.8840,0.9930,0.9990,0.4565,0.7042,0.7921,0.6292,0.8370,0.9038,0.5403,0.4800,0.4739,0.4677,0.5704,0.5612,0.5525
+ViT-B-16-quickgelu,metaclip_fullcc,149.62,41.09,0.7077,0.7072,0.9082,0.9454,0.8550,0.9740,0.9890,0.4134,0.6718,0.7696,0.5936,0.8058,0.8784,0.5787,0.5391,0.5221,0.5052,0.6064,0.5965,0.5871
+ViT-H-14,laion2b_s32b_b79k,986.11,381.68,0.7057,0.7764,0.9418,0.9660,0.9070,0.9920,0.9970,0.4948,0.7338,0.8151,0.6592,0.8606,0.9188,0.4998,0.4289,0.4247,0.4206,0.5462,0.5234,0.5018
+convnext_base_w,laion_aesthetic_s13b_b82k,179.39,49.38,0.7047,0.7306,0.9160,0.9532,0.8880,0.9820,0.9930,0.4355,0.6858,0.7808,0.6120,0.8338,0.8978,0.5461,0.4937,0.4764,0.4592,0.5829,0.5681,0.5540
+ViT-L-14-CLIPA,datacomp1b,414.21,167.5,0.7044,0.7356,0.9206,0.9550,0.9020,0.9860,0.9930,0.4695,0.7084,0.7981,0.6512,0.8544,0.9150,0.5244,0.4495,0.4388,0.4282,0.5583,0.5514,0.5449
+ViT-g-14,laion2b_s34b_b88k,1366.68,581.15,0.7038,0.7772,0.9416,0.9690,0.9140,0.9930,0.9960,0.4878,0.7334,0.8151,0.6638,0.8598,0.9184,0.4973,0.4207,0.4104,0.4001,0.5458,0.5247,0.5047
+convnext_base_w_320,laion_aesthetic_s13b_b82k,179.39,71.94,0.7030,0.7402,0.9200,0.9524,0.9070,0.9830,0.9920,0.4387,0.6920,0.7824,0.6106,0.8312,0.8964,0.5385,0.4742,0.4610,0.4478,0.5695,0.5630,0.5567
+ViT-L-14,laion2b_s32b_b82k,427.62,175.33,0.7010,0.7552,0.9286,0.9594,0.8950,0.9870,0.9940,0.4651,0.7108,0.7980,0.6336,0.8398,0.9074,0.5142,0.4448,0.4385,0.4322,0.5507,0.5381,0.5261
+EVA01-g-14,laion400m_s11b_b41k,1136.44,547.36,0.7003,0.7264,0.9162,0.9510,0.8810,0.9830,0.9930,0.4406,0.6849,0.7732,0.6180,0.8328,0.8998,0.5363,0.4646,0.4640,0.4634,0.5665,0.5591,0.5521
+convnext_base_w,laion2b_s13b_b82k,179.39,49.38,0.6976,0.7214,0.9152,0.9538,0.8770,0.9800,0.9930,0.4285,0.6805,0.7770,0.6058,0.8324,0.8954,0.5311,0.4926,0.4706,0.4488,0.5648,0.5501,0.5362
+ViT-B-16-quickgelu,metaclip_400m,149.62,41.09,0.6962,0.6766,0.8960,0.9414,0.8570,0.9720,0.9870,0.3997,0.6536,0.7530,0.5648,0.7988,0.8714,0.5675,0.4984,0.5113,0.5242,0.5916,0.5853,0.5792
+ViT-L-14,commonpool_xl_clip_s13b_b90k,427.62,175.33,0.6956,0.6886,0.8996,0.9424,0.8650,0.9740,0.9920,0.4273,0.6761,0.7700,0.6040,0.8190,0.8858,0.5460,0.4833,0.4728,0.4622,0.5821,0.5691,0.5566
+convnext_base_w,laion2b_s13b_b82k_augreg,179.39,49.38,0.6922,0.7240,0.9144,0.9514,0.8730,0.9830,0.9920,0.4303,0.6786,0.7750,0.6140,0.8308,0.8964,0.5229,0.4504,0.4390,0.4276,0.5567,0.5494,0.5424
+EVA02-B-16,merged2b_s8b_b131k,149.69,41.09,0.6904,0.7146,0.9114,0.9468,0.8600,0.9660,0.9880,0.4215,0.6686,0.7629,0.5874,0.8056,0.8812,0.5325,0.4686,0.4722,0.4758,0.5522,0.5515,0.5509
+ViT-B-16,laion2b_s34b_b88k,149.62,41.09,0.6884,0.6984,0.9038,0.9456,0.8630,0.9790,0.9940,0.4231,0.6770,0.7706,0.5944,0.8178,0.8862,0.5220,0.4628,0.4601,0.4575,0.5549,0.5415,0.5288
+ViT-B-32-quickgelu,metaclip_fullcc,151.28,14.78,0.6879,0.6510,0.8766,0.9274,0.8080,0.9530,0.9730,0.3806,0.6411,0.7430,0.5518,0.7890,0.8650,0.5729,0.5351,0.5238,0.5126,0.5983,0.5883,0.5788
+convnext_base_w_320,laion_aesthetic_s13b_b82k_augreg,179.39,71.94,0.6870,0.7108,0.9032,0.9452,0.8910,0.9740,0.9900,0.4243,0.6722,0.7680,0.6040,0.8276,0.8912,0.5192,0.4599,0.4293,0.3989,0.5686,0.5475,0.5274
+ViT-B-16,datacomp_xl_s13b_b90k,149.62,41.09,0.6867,0.6756,0.8834,0.9300,0.8510,0.9720,0.9840,0.4016,0.6595,0.7562,0.5744,0.8086,0.8830,0.5408,0.4966,0.4832,0.4699,0.5730,0.5590,0.5457
+ViT-L-14,commonpool_xl_s13b_b90k,427.62,175.33,0.6817,0.6478,0.8656,0.9210,0.8200,0.9500,0.9740,0.3859,0.6310,0.7328,0.5446,0.7712,0.8484,0.5697,0.5115,0.5072,0.5030,0.5932,0.5894,0.5858
+ViT-B-32,laion2b_e16,151.28,14.78,0.6777,0.6638,0.8830,0.9322,0.8440,0.9650,0.9840,0.3913,0.6467,0.7481,0.5624,0.7956,0.8708,0.5343,0.4800,0.4602,0.4404,0.5669,0.5577,0.5490
+xlm-roberta-base-ViT-B-32,laion5b_s13b_b90k,366.12,105.87,0.6759,0.6448,0.8628,0.9168,0.8270,0.9640,0.9780,0.3778,0.6344,0.7348,0.5354,0.7798,0.8640,0.5519,0.4912,0.4827,0.4742,0.5834,0.5737,0.5645
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hlip/open_clip/docs/script_examples/clipa/vit_b16/i50_t16_finetune.sh

@@ -0,0 +1,27 @@
+torchrun --nproc_per_node 8 -m open_clip_train.main \
+    --save-frequency 1 \
+    --save-most-recent \
+    --zeroshot-frequency 1 \
+    --train-data '/path/to/laion-400m' \
+    --dataset-type webdataset \
+    --lr "2.56e-5" \
+    --beta1 0.9 \
+    --beta2 0.95 \
+    --warmup 3072 \
+    --wd 0.2 \
+    --batch-size 1024 \
+    --aug-cfg scale='(0.4, 1.0)' \
+    --epochs 1 \
+    --train-num-samples 131072000 \
+    --workers 6 \
+    --model ViT-B-16-CL16 \
+    --pretrained '/path/to/ckpt' \
+    --precision 'amp_bf16' \
+    --ddp-static-graph \
+    --local-loss \
+    --gather-with-grad \
+    --grad-checkpointing \
+    --log-every-n-steps 256 \
+    --seed 0 \
+    --logs ./logs/ \
+    --imagenet-val '/path/to/imagenet/val'

hlip/open_clip/docs/script_examples/clipa/vit_b16/i50_t16_pretrain.sh

@@ -0,0 +1,26 @@
+torchrun --nproc_per_node 8 -m open_clip_train.main \
+    --save-frequency 1 \
+    --save-most-recent \
+    --zeroshot-frequency 1 \
+    --train-data '/path/to/laion-400m' \
+    --dataset-type webdataset \
+    --lr "2.048e-3" \
+    --beta1 0.9 \
+    --beta2 0.95 \
+    --warmup 782 \
+    --wd 0.2 \
+    --batch-size 8192 \
+    --aug-cfg scale='(0.4, 1.0)' \
+    --epochs 6 \
+    --workers 6 \
+    --model ViT-B-16-CL16 \
+    --precision 'amp_bf16' \
+    --ddp-static-graph \
+    --local-loss \
+    --gather-with-grad \
+    --force-image-size 112 \
+    --grad-checkpointing \
+    --log-every-n-steps 32 \
+    --seed 0 \
+    --logs ./logs/ \
+    --imagenet-val '/path/to/imagenet/val'

hlip/open_clip/docs/script_examples/clipa/vit_l16/i17_t16_finetune.sh

@@ -0,0 +1,27 @@
+torchrun --nproc_per_node 8 -m open_clip_train.main \
+    --save-frequency 1 \
+    --save-most-recent \
+    --zeroshot-frequency 1 \
+    --train-data '/path/to/laion-400m' \
+    --dataset-type webdataset \
+    --lr "2.24e-5" \
+    --beta1 0.9 \
+    --beta2 0.95 \
+    --warmup 3571 \
+    --wd 0.2 \
+    --batch-size 896 \
+    --aug-cfg scale='(0.4, 1.0)' color_jitter='(0.32, 0.32, 0.32, 0.08)' color_jitter_prob=0.8 gray_scale_prob=0.2 \
+    --epochs 1 \
+    --train-num-samples 131072000 \
+    --workers 6 \
+    --model ViT-L-16-CL16-GAP \
+    --pretrained '/path/to/ckpt' \
+    --precision 'amp_bf16' \
+    --ddp-static-graph \
+    --local-loss \
+    --gather-with-grad \
+    --grad-checkpointing \
+    --log-every-n-steps 293 \
+    --seed 0 \
+    --logs ./logs/ \
+    --imagenet-val '/path/to/imagenet/val'

hlip/open_clip/docs/script_examples/clipa/vit_l16/i17_t16_pretrain.sh

@@ -0,0 +1,26 @@
+torchrun --nproc_per_node 8 -m open_clip_train.main \
+    --save-frequency 1 \
+    --save-most-recent \
+    --zeroshot-frequency 1 \
+    --train-data '/path/to/laion-400m' \
+    --dataset-type webdataset \
+    --lr "1.024e-3" \
+    --beta1 0.9 \
+    --beta2 0.95 \
+    --warmup 1563 \
+    --wd 0.2 \
+    --batch-size 4096 \
+    --aug-cfg scale='(0.4, 1.0)' color_jitter='(0.32, 0.32, 0.32, 0.08)' color_jitter_prob=0.8 gray_scale_prob=0.2 \
+    --epochs 6 \
+    --workers 6 \
+    --model ViT-L-16-CL16-GAP \
+    --precision 'amp_bf16' \
+    --ddp-static-graph \
+    --local-loss \
+    --gather-with-grad \
+    --force-image-size 64 \
+    --grad-checkpointing \
+    --log-every-n-steps 64 \
+    --seed 0 \
+    --logs ./logs/ \
+    --imagenet-val '/path/to/imagenet/val'

hlip/open_clip/docs/script_examples/clipa/vit_l16/i37_t8_finetune.sh

@@ -0,0 +1,27 @@
+torchrun --nproc_per_node 8 -m open_clip_train.main \
+    --save-frequency 1 \
+    --save-most-recent \
+    --zeroshot-frequency 1 \
+    --train-data '/path/to/laion-400m' \
+    --dataset-type webdataset \
+    --lr "2.24e-5" \
+    --beta1 0.9 \
+    --beta2 0.95 \
+    --warmup 3571 \
+    --wd 0.2 \
+    --batch-size 896 \
+    --aug-cfg scale='(0.4, 1.0)' color_jitter='(0.32, 0.32, 0.32, 0.08)' color_jitter_prob=0.8 gray_scale_prob=0.2 \
+    --epochs 1 \
+    --train-num-samples 131072000 \
+    --workers 6 \
+    --model ViT-L-16-CL32-GAP \
+    --pretrained '/path/to/ckpt' \
+    --precision 'amp_bf16' \
+    --ddp-static-graph \
+    --local-loss \
+    --gather-with-grad \
+    --grad-checkpointing \
+    --log-every-n-steps 293 \
+    --seed 0 \
+    --logs ./logs/ \
+    --imagenet-val '/path/to/imagenet/val'

hlip/open_clip/docs/script_examples/clipa/vit_l16/i37_t8_pretrain.sh

@@ -0,0 +1,26 @@
+torchrun --nproc_per_node 8 -m open_clip_train.main \
+    --save-frequency 1 \
+    --save-most-recent \
+    --zeroshot-frequency 1 \
+    --train-data '/path/to/laion-400m' \
+    --dataset-type webdataset \
+    --lr "1.024e-3" \
+    --beta1 0.9 \
+    --beta2 0.95 \
+    --warmup 1563 \
+    --wd 0.2 \
+    --batch-size 4096 \
+    --aug-cfg scale='(0.4, 1.0)' color_jitter='(0.32, 0.32, 0.32, 0.08)' color_jitter_prob=0.8 gray_scale_prob=0.2 \
+    --epochs 6 \
+    --workers 6 \
+    --model ViT-L-16-CL8-Syntax-GAP \
+    --precision 'amp_bf16' \
+    --ddp-static-graph \
+    --local-loss \
+    --gather-with-grad \
+    --force-image-size 96 \
+    --grad-checkpointing \
+    --log-every-n-steps 64 \
+    --seed 0 \
+    --logs ./logs/ \
+    --imagenet-val '/path/to/imagenet/val'

hlip/open_clip/docs/script_examples/clipav2/vit_h14/i257_t32_finetunex4.sh

@@ -0,0 +1,32 @@
+# have not been tested. use it at your own discretion
+# the original experiment was run on tpu v3-256.
+# this example script assumes 8 gpus, each with huge memory. Tune batchsize, warmup, and lr accordingly if you have different machine setups.
+torchrun --nproc_per_node 8 -m open_clip_train.main \
+    --save-frequency 1 \
+    --save-most-recent \
+    --zeroshot-frequency 1 \
+    --train-data '/path/to/laion2b_or_datacomp1b' \
+    --train-num-samples 131072000 \
+    --dataset-type webdataset \
+    --lr "5.12e-5" \
+    --beta1 0.9 \
+    --beta2 0.95 \
+    --warmup 800 \
+    --wd 0.2 \
+    --batch-size 4096 \
+    --aug-cfg scale='(0.4, 1.0)' color_jitter='(0.32, 0.32, 0.32, 0.08)' color_jitter_prob=0.8 gray_scale_prob=0.2 \
+    --epochs 4 \
+    --workers 6 \
+    --model ViT-H-14-CL32-GAP \
+    --pretrained '/path/to/pretrain84_ckpt' \
+    --precision 'amp_bf16' \
+    --ddp-static-graph \
+    --local-loss \
+    --gather-with-grad \
+    --force-image-size 224 \
+    --force-patch-dropout 0.3 \
+    --grad-checkpointing \
+    --log-every-n-steps 64 \
+    --seed 0 \
+    --logs ./logs/ \
+    --imagenet-val '/path/to/imagenet/val'

hlip/open_clip/docs/script_examples/clipav2/vit_h14/i50_t8_pretrain.sh

@@ -0,0 +1,30 @@
+# have not been tested. use it at your own discretion
+# the original experiment was run on tpu v3-256.
+# this example script assumes 8 gpus, each with huge memory. Tune batchsize, warmup, and lr accordingly if you have different machine setups.
+torchrun --nproc_per_node 8 -m open_clip_train.main \
+    --save-frequency 1 \
+    --save-most-recent \
+    --zeroshot-frequency 1 \
+    --train-data '/path/to/laion2b_or_datacomp1b' \
+    --train-num-samples 4e8 \
+    --dataset-type webdataset \
+    --lr "2.048e-3" \
+    --beta1 0.9 \
+    --beta2 0.95 \
+    --warmup 3200 \
+    --wd 0.2 \
+    --batch-size 8192 \
+    --aug-cfg scale='(0.4, 1.0)' color_jitter='(0.32, 0.32, 0.32, 0.08)' color_jitter_prob=0.8 gray_scale_prob=0.2 \
+    --epochs 32 \
+    --workers 6 \
+    --model ViT-H-14-CL8-Syntax-GAP \
+    --precision 'amp_bf16' \
+    --ddp-static-graph \
+    --local-loss \
+    --gather-with-grad \
+    --force-image-size 84 \
+    --grad-checkpointing \
+    --log-every-n-steps 32 \
+    --seed 0 \
+    --logs ./logs/ \
+    --imagenet-val '/path/to/imagenet/val'

hlip/open_clip/docs/script_examples/clipav2/vit_h14/i577_t32_finetunex1.sh

@@ -0,0 +1,32 @@
+# have not been tested. use it at your own discretion
+# the original experiment was run on tpu v3-256.
+# this example script assumes 8 gpus, each with huge memory. Tune batchsize, warmup, and lr accordingly if you have different machine setups.
+torchrun --nproc_per_node 8 -m open_clip_train.main \
+    --save-frequency 1 \
+    --save-most-recent \
+    --zeroshot-frequency 1 \
+    --train-data '/path/to/laion2b_or_datacomp1b' \
+    --train-num-samples 131072000 \
+    --dataset-type webdataset \
+    --lr "6.4e-6" \
+    --beta1 0.9 \
+    --beta2 0.95 \
+    --warmup 1600 \
+    --wd 0.2 \
+    --batch-size 2048 \
+    --aug-cfg scale='(0.4, 1.0)' color_jitter='(0.32, 0.32, 0.32, 0.08)' color_jitter_prob=0.8 gray_scale_prob=0.2 \
+    --epochs 1 \
+    --workers 6 \
+    --model ViT-H-14-CL32-GAP \
+    --pretrained '/path/to/finetune224_ckpt' \
+    --precision 'amp_bf16' \
+    --ddp-static-graph \
+    --local-loss \
+    --gather-with-grad \
+    --force-image-size 336 \
+    --force-patch-dropout 0.4 \
+    --grad-checkpointing \
+    --log-every-n-steps 64 \
+    --seed 0 \
+    --logs ./logs/ \
+    --imagenet-val '/path/to/imagenet/val'

hlip/open_clip/docs/script_examples/stability_example.sh

@@ -0,0 +1,60 @@
+#!/bin/bash
+#SBATCH --partition=g40423
+#SBATCH --job-name=testopenclip
+#SBATCH --nodes 30
+#SBATCH --ntasks-per-node=8
+#SBATCH --cpus-per-task=12
+#SBATCH --output=%x_%j.out
+#SBATCH --comment=laion
+#SBATCH --open-mode=append
+#SBATCH --exclusive
+
+module load openmpi
+module load cuda/11.7
+
+export MASTER_ADDR=`hostname`
+export MASTER_PORT=12802
+export NCCL_PROTO=simple
+export FI_EFA_FORK_SAFE=1
+export FI_LOG_LEVEL=1
+export FI_EFA_USE_DEVICE_RDMA=1
+export NCCL_DEBUG=info
+
+export PYTHONFAULTHANDLER=1
+
+export CUDA_LAUNCH_BLOCKING=0
+export OMPI_MCA_mtl_base_verbose=1
+export FI_EFA_ENABLE_SHM_TRANSFER=0
+export FI_PROVIDER=efa
+export FI_EFA_TX_MIN_CREDITS=64
+export NCCL_TREE_THRESHOLD=0
+
+cd /admin/home-mitchellw/open_clip/src
+export PYTHONPATH="$PYTHONPATH:/admin/home-mitchellw/open_clip/src"
+
+EXP_NAME="test-B-32-laion5b-lr1e-3-bs90k"
+
+srun --comment laion --cpu_bind=v --accel-bind=gn python -m open_clip_train.main \
+    --save-frequency 1 \
+    --train-data="pipe:aws s3 cp s3://s-datasets/laion5b/{laion2B-data/{000000..231349}.tar,laion2B-multi-data/{000000..226687}.tar,laion1B-nolang-data/{000000..127231}.tar} -" \
+    --train-num-samples 135646078 \
+    --dataset-type webdataset \
+    --dataset-resampled \
+    --warmup 2000 \
+    --batch-size=375 \
+    --epochs=97 \
+    --lr 1e-3 \
+    --workers=8 \
+    --report-to wandb \
+    --name ${EXP_NAME} \
+    --logs /scratch/logs/ \
+    --model ViT-B-32 \
+    --seed 0 \
+    --ddp-static-graph \
+    --local-loss \
+    --gather-with-grad \
+    --grad-checkpointing \
+    --precision amp_bfloat16 \
+    --wandb-project-name open_clip6 \
+    --resume "latest" \
+    --remote-sync s3://s-laion/mitchellw/logs

hlip/open_clip/pyproject.toml

@@ -0,0 +1,79 @@
+[build-system]
+requires = ["pdm-backend"]
+build-backend = "pdm.backend"
+
+[project]
+name = "open_clip_torch"
+# NOTE for full list of authors see https://github.com/mlfoundations/open_clip?tab=readme-ov-file#citing
+# below covers most active / recent maintainers
+authors = [
+    {name = "Ross Wightman", email = "ross@huggingface.co"},
+    {name = "Gabriel Ilharco"},
+    {name = "Mitchell Wortsman"},
+    {name = "Romain Beaumont"},
+]
+description = "Open reproduction of consastive language-image pretraining (CLIP) and related."
+readme = "README.md"
+requires-python = ">=3.9"
+keywords = ["pytorch", "clip", "image-text", "language-image", "multimodal"]
+license = {text = "MIT"}
+classifiers = [
+        'Development Status :: 4 - Beta',
+        'Intended Audience :: Education',
+        'Intended Audience :: Science/Research',
+        'License :: OSI Approved :: MIT License',
+        'Programming Language :: Python :: 3.9',
+        'Programming Language :: Python :: 3.10',
+        'Programming Language :: Python :: 3.11',
+        'Programming Language :: Python :: 3.12',
+        'Topic :: Scientific/Engineering',
+        'Topic :: Scientific/Engineering :: Artificial Intelligence',
+        'Topic :: Software Development',
+        'Topic :: Software Development :: Libraries',
+        'Topic :: Software Development :: Libraries :: Python Modules',
+]
+dependencies = [
+    'torch>=2.0',
+    'torchvision',
+    'regex',
+    'ftfy',
+    'tqdm',
+    'huggingface-hub',
+    'safetensors',
+    'timm>=1.0.17',
+]
+dynamic = ["version"]
+
+[project.optional-dependencies]
+training = [
+    'torch>=2.0',
+    'webdataset>=0.2.5,<=0.2.86',
+    'pandas',
+    'transformers[sentencepiece]',
+    'timm>=1.0.17',
+    'fsspec',
+]
+test = [
+    'pytest-split',
+    'pytest',
+    'open_clip_torch[training]'
+]
+
+[project.urls]
+homepage = "https://github.com/mlfoundations/open_clip"
+repository = "https://github.com/mlfoundations/open_clip"
+
+[tool.pdm.version]
+source = "file"
+path = "src/open_clip/version.py"
+
+[tool.pdm.build]
+excludes = ["./**/.git", "./**/logs/*"]
+package-dir = "src"
+includes = ["src/open_clip", "src/open_clip_train"]
+
+[tool.pytest.ini_options]
+testpaths = ['tests']
+markers = [
+    'regression_test'
+]

hlip/open_clip/pytest.ini

@@ -0,0 +1,3 @@
+[pytest]
+markers =
+    regression_test

hlip/open_clip/requirements-test.txt

@@ -0,0 +1,4 @@
+pytest-split==0.8.0
+pytest==7.2.0
+transformers[sentencepiece]
+timm>=1.0.17

hlip/open_clip/requirements-training.txt

@@ -0,0 +1,13 @@
+torch>=2.0
+torchvision
+webdataset>=0.2.5,<=0.2.86
+regex
+ftfy
+tqdm
+pandas
+braceexpand
+huggingface_hub
+safetensors
+transformers[sentencepiece]
+timm>=1.0.17
+fsspec

hlip/open_clip/requirements.txt

@@ -0,0 +1,8 @@
+torch>=2.0
+torchvision
+regex
+ftfy
+tqdm
+huggingface_hub
+safetensors
+timm

hlip/open_clip/scripts/clipav1_vit_l16_i37_t8.sh

@@ -0,0 +1,6 @@
+# eval on a single gpu
+CUDA_VISIBLE_DEVICES=2 TORCH_CUDNN_V8_API_ENABLED=1 TFDS_PREFETCH_SIZE=8192 python3 -m open_clip_train.main \
+    --model ViT-L-16-CL32-GAP \
+    --pretrained "/path/to/clipa_vit_l16_i37_t8.pt" \
+    --seed 0 \
+    --imagenet-val '/path/to/ImageNet/val'

hlip/open_clip/scripts/clipav2_vit_h14_i84_224_336_cl32_gap_datacomp1b.sh

@@ -0,0 +1,10 @@
+CUDA_VISIBLE_DEVICES=1 python3 -m open_clip_train.main \
+    --model ViT-H-14-CL32-GAP-BigVision \
+    --pretrained "/path/to/vit_h14_i84_224_336_cl32_gap_datacomp1b.pt" \
+    --force-image-size 336 \
+    --square-resize-only \
+    --interpolation 'bilinear' \
+    --image-mean 0.485 0.456 0.406 \
+    --image-std 0.229 0.224 0.225 \
+    --seed 0 \
+    --imagenet-val '/path/to/ImageNet/val'

hlip/open_clip/scripts/h14_224_32_finetune.sh

@@ -0,0 +1,31 @@
+# 64k batchsize for 2.048e-3 lr
+TORCH_CUDNN_V8_API_ENABLED=1 torchrun --nproc_per_node 8 -m open_clip_train.main \
+    --save-frequency 1 \
+    --save-most-recent \
+    --zeroshot-frequency 1 \
+    --train-data '/path/to/laion' \
+    --dataset-type webdataset \
+    --lr "2.048e-3" \
+    --beta1 0.9 \
+    --beta2 0.95 \
+    --warmup 782 \
+    --wd 0.2 \
+    --batch-size 4096 \
+    --aug-cfg scale='(0.4, 1.0)' color_jitter='(0.32, 0.32, 0.32, 0.08)' color_jitter_prob=0.8 gray_scale_prob=0.2 \
+    --epochs=7 \
+    --workers=6 \
+    --model ViT-H-14-CL32-GAP \
+    --precision 'amp_bf16' \
+    --local-loss \
+    --gather-with-grad \
+    --force-image-size 224 \
+    --grad-checkpointing \
+    --log-every-n-steps 32 \
+    --seed 0 \
+    --logs ./logs/ \
+    --imagenet-val '/path/to/ImageNet/val' \
+    --name 'name' \
+    --report-to "wandb" \
+    --wandb-project-name "project_name"
+
+

hlip/open_clip/scripts/h14_84_8_pretrain.sh

@@ -0,0 +1,31 @@
+# 64k batchsize for 2.048e-3 lr
+TORCH_CUDNN_V8_API_ENABLED=1 torchrun --nproc_per_node 8 -m open_clip_train.main \
+    --save-frequency 1 \
+    --save-most-recent \
+    --zeroshot-frequency 1 \
+    --train-data '/path/to/laion' \
+    --dataset-type webdataset \
+    --lr "2.048e-3" \
+    --beta1 0.9 \
+    --beta2 0.95 \
+    --warmup 782 \
+    --wd 0.2 \
+    --batch-size 4096 \
+    --aug-cfg scale='(0.4, 1.0)' color_jitter='(0.32, 0.32, 0.32, 0.08)' color_jitter_prob=0.8 gray_scale_prob=0.2 \
+    --epochs=7 \
+    --workers=6 \
+    --model ViT-H-14-CL8-SyntaxMask-GAP \
+    --precision 'amp_bf16' \
+    --local-loss \
+    --gather-with-grad \
+    --force-image-size 84 \
+    --grad-checkpointing \
+    --log-every-n-steps 32 \
+    --seed 0 \
+    --logs ./logs/ \
+    --imagenet-val '/path/to/ImageNet/val' \
+    --name 'name' \
+    --report-to "wandb" \
+    --wandb-project-name "project_name"
+
+

hlip/open_clip/src/open_clip/__init__.py

@@ -0,0 +1,18 @@
+from .version import __version__
+
+from .coca_model import CoCa
+from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
+from .factory import create_model, create_model_and_transforms, create_model_from_pretrained, get_tokenizer, create_loss
+from .factory import list_models, add_model_config, get_model_config, load_checkpoint
+from .loss import ClipLoss, DistillClipLoss, CoCaLoss
+from .model import CLIP, CustomTextCLIP, CLIPTextCfg, CLIPVisionCfg, \
+    convert_weights_to_lp, convert_weights_to_fp16, trace_model, get_cast_dtype, get_input_dtype, \
+    get_model_tokenize_cfg, get_model_preprocess_cfg, set_model_preprocess_cfg
+from .openai import load_openai_model, list_openai_models
+from .pretrained import list_pretrained, list_pretrained_models_by_tag, list_pretrained_tags_by_model, \
+    get_pretrained_url, download_pretrained_from_url, is_pretrained_cfg, get_pretrained_cfg, download_pretrained
+from .push_to_hf_hub import push_pretrained_to_hf_hub, push_to_hf_hub
+from .tokenizer import SimpleTokenizer, tokenize, decode
+from .transform import image_transform, AugmentationCfg
+from .zero_shot_classifier import build_zero_shot_classifier, build_zero_shot_classifier_legacy
+from .zero_shot_metadata import OPENAI_IMAGENET_TEMPLATES, SIMPLE_IMAGENET_TEMPLATES, IMAGENET_CLASSNAMES

hlip/open_clip/src/open_clip/bpe_simple_vocab_16e6.txt.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a
+size 1356917

hlip/open_clip/src/open_clip/coca_model.py

@@ -0,0 +1,582 @@
+from typing import Dict, List, Optional, Union
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+import numpy as np
+from dataclasses import dataclass
+
+from .transformer import (
+    LayerNormFp32,
+    LayerNorm,
+    QuickGELU,
+    MultimodalTransformer,
+)
+from .model import CLIPTextCfg, CLIPVisionCfg, _build_vision_tower, _build_text_tower
+
+try:
+    from transformers import (
+        BeamSearchScorer,
+        LogitsProcessorList,
+        TopPLogitsWarper,
+        TopKLogitsWarper,
+        RepetitionPenaltyLogitsProcessor,
+        MinLengthLogitsProcessor,
+        MaxLengthCriteria,
+        StopStringCriteria,
+        EosTokenCriteria,
+        StoppingCriteriaList
+    )
+
+    GENERATION_TYPES = {
+        "top_k": TopKLogitsWarper,
+        "top_p": TopPLogitsWarper,
+        "beam_search": "beam_search"
+    }
+    _has_transformers = True
+except ImportError as e:
+    GENERATION_TYPES = {
+        "top_k": None,
+        "top_p": None,
+        "beam_search": "beam_search"
+    }
+    _has_transformers = False
+
+
+@dataclass
+class MultimodalCfg(CLIPTextCfg):
+    mlp_ratio: int = 4
+    dim_head: int = 64
+    heads: int = 8
+    n_queries: int = 256
+    attn_pooler_heads: int = 8
+
+
+def _build_text_decoder_tower(
+        embed_dim,
+        multimodal_cfg,
+        quick_gelu: bool = False,
+        cast_dtype: Optional[torch.dtype] = None,
+):
+    multimodal_cfg = MultimodalCfg(**multimodal_cfg) if isinstance(multimodal_cfg, dict) else multimodal_cfg
+    act_layer = QuickGELU if quick_gelu else nn.GELU
+    norm_layer = (
+        LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm
+    )
+
+    decoder = MultimodalTransformer(
+        context_length=multimodal_cfg.context_length,
+        width=multimodal_cfg.width,
+        heads=multimodal_cfg.heads,
+        layers=multimodal_cfg.layers,
+        ls_init_value=multimodal_cfg.ls_init_value,
+        output_dim=embed_dim,
+        act_layer=act_layer,
+        norm_layer=norm_layer,
+    )
+
+    return decoder
+
+
+def _token_to_tensor(token_id, device: str = "cpu") -> torch.Tensor:
+    if not isinstance(token_id, torch.Tensor):
+        if isinstance(token_id, int):
+            token_id = [token_id]
+        token_id = torch.tensor(token_id, device=device)
+    return token_id
+
+
+class CoCa(nn.Module):
+    def __init__(
+            self,
+            embed_dim,
+            multimodal_cfg: MultimodalCfg,
+            text_cfg: CLIPTextCfg,
+            vision_cfg: CLIPVisionCfg,
+            quick_gelu: bool = False,
+            init_logit_scale: float = np.log(1 / 0.07),
+            init_logit_bias: Optional[float] = None,
+            nonscalar_logit_scale: bool = False,
+            cast_dtype: Optional[torch.dtype] = None,
+            pad_id: int = 0,
+    ):
+        super().__init__()
+        multimodal_cfg = MultimodalCfg(**multimodal_cfg) if isinstance(multimodal_cfg, dict) else multimodal_cfg
+        text_cfg = CLIPTextCfg(**text_cfg) if isinstance(text_cfg, dict) else text_cfg
+        vision_cfg = CLIPVisionCfg(**vision_cfg) if isinstance(vision_cfg, dict) else vision_cfg
+
+        self.text = _build_text_tower(
+            embed_dim=embed_dim,
+            text_cfg=text_cfg,
+            quick_gelu=quick_gelu,
+            cast_dtype=cast_dtype,
+        )
+
+        vocab_size = (
+            text_cfg.vocab_size  # for hf models
+            if hasattr(text_cfg, "hf_model_name") and text_cfg.hf_model_name is not None
+            else text_cfg.vocab_size
+        )
+
+        self.visual = _build_vision_tower(
+            embed_dim=embed_dim,
+            vision_cfg=vision_cfg,
+            quick_gelu=quick_gelu,
+            cast_dtype=cast_dtype,
+        )
+
+        self.text_decoder = _build_text_decoder_tower(
+            vocab_size,
+            multimodal_cfg=multimodal_cfg,
+            quick_gelu=quick_gelu,
+            cast_dtype=cast_dtype,
+        )
+
+        lshape = [1] if nonscalar_logit_scale else []
+        self.logit_scale = nn.Parameter(torch.ones(lshape) * init_logit_scale)
+        if init_logit_bias is not None:
+            self.logit_bias = nn.Parameter(torch.ones(lshape) * init_logit_bias)
+        else:
+            self.logit_bias = None
+        self.pad_id = pad_id
+
+        self.context_length = multimodal_cfg.context_length
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable: bool = True):
+        self.visual.set_grad_checkpointing(enable)
+        self.text.set_grad_checkpointing(enable)
+        self.text_decoder.set_grad_checkpointing(enable)
+
+    def _encode_image(self, images, normalize: bool = True):
+        image_latent, tokens_embs = self.visual(images)
+        image_latent = F.normalize(image_latent, dim=-1) if normalize else image_latent
+        return image_latent, tokens_embs
+
+    def _encode_text(self, text, normalize: bool = True):
+        text_latent, token_emb = self.text(text)
+        text_latent = F.normalize(text_latent, dim=-1) if normalize else text_latent
+        return text_latent, token_emb
+
+    def encode_image(self, images, normalize: bool = True):
+        image_latent, _ = self._encode_image(images, normalize=normalize)
+        return image_latent
+
+    def encode_text(self, text, normalize: bool = True):
+        text_latent, _ = self._encode_text(text, normalize=normalize)
+        return text_latent
+
+    def forward_intermediates(
+            self,
+            image: Optional[torch.Tensor] = None,
+            text: Optional[torch.Tensor] = None,
+            image_indices: Optional[Union[int, List[int]]] = None,
+            text_indices: Optional[Union[int, List[int]]] = None,
+            stop_early: bool = False,
+            normalize: bool = True,
+            normalize_intermediates: bool = False,
+            intermediates_only: bool = False,
+            image_output_fmt: str = 'NCHW',
+            image_output_extra_tokens: bool = False,
+            text_output_fmt: str = 'NLC',
+            text_output_extra_tokens: bool = False,
+            output_logits: bool = False,
+            output_logit_scale_bias: bool = False,
+    ) -> Dict[str, Union[torch.Tensor, List[torch.Tensor]]]:
+        """ Forward features that returns intermediates.
+
+        Args:
+            image: Input image tensor
+            text: Input text tensor
+            image_indices: For image tower, Take last n blocks if int, all if None, select matching indices if sequence
+            text_indices: Take last n blocks if int, all if None, select matching indices if sequence
+            stop_early: Stop iterating over blocks when last desired intermediate hit
+            normalize: L2 Normalize final image and text features (if present)
+            normalize_intermediates: Apply final encoder norm layer to all intermediates (if possible)
+            intermediates_only: Only return intermediate features, do not return final features
+            image_output_fmt: Shape of intermediate image feature outputs
+            image_output_extra_tokens: Return both prefix and spatial intermediate tokens
+            text_output_fmt: Shape of intermediate text feature outputs
+            text_output_extra_tokens: Return both prefix and spatial intermediate tokens
+            output_logits: Include logits in output
+            output_logit_scale_bias: Include the logit scale bias in the output
+        Returns:
+
+        """
+        output = {}
+        if intermediates_only:
+            # intermediates only disables final feature normalization, and include logits
+            normalize = False
+            output_logits = False
+        if output_logits:
+            assert False, 'FIXME, needs implementing'
+
+        if image is not None:
+            image_output = self.visual.forward_intermediates(
+                image,
+                indices=image_indices,
+                stop_early=stop_early,
+                normalize_intermediates=normalize_intermediates,
+                intermediates_only=intermediates_only,
+                output_fmt=image_output_fmt,
+                output_extra_tokens=image_output_extra_tokens,
+            )
+            if normalize and "image_features" in image_output:
+                image_output["image_features"] = F.normalize(image_output["image_features"], dim=-1)
+            output.update(image_output)
+
+        if text is not None:
+            text_output = self.text.forward_intermediates(
+                text,
+                indices=text_indices,
+                stop_early=stop_early,
+                normalize_intermediates=normalize_intermediates,
+                intermediates_only=intermediates_only,
+                output_fmt=text_output_fmt,
+                output_extra_tokens=text_output_extra_tokens,
+            )
+            if normalize and "text_features" in text_output:
+                text_output["text_features"] = F.normalize(text_output["text_features"], dim=-1)
+            output.update(text_output)
+
+        # FIXME text decoder
+        logit_scale_exp = self.logit_scale.exp() if output_logits or output_logit_scale_bias else None
+        if output_logit_scale_bias:
+            output["logit_scale"] = logit_scale_exp
+            if self.logit_bias is not None:
+                output['logit_bias'] = self.logit_bias
+
+        return output
+
+    def forward(
+            self,
+            image,
+            text: Optional[torch.Tensor] = None,
+            image_latent: Optional[torch.Tensor] = None,
+            image_embs: Optional[torch.Tensor] = None,
+            output_labels: bool = True,
+    ):
+        if image_latent is None or image_embs is None:
+            image_latent, image_embs = self._encode_image(image)
+
+        if text is None:
+            return {"image_features": image_latent, "image_embs": image_embs}
+
+        text_latent, token_embs = self._encode_text(text)
+
+        # FIXME this isn't an ideal solution, would like to improve -RW
+        labels: Optional[torch.Tensor] = text[:, 1:] if output_labels else None
+        if output_labels:
+            # align text_embs and thus logits with labels for teacher-forcing caption loss
+            token_embs = token_embs[:, :-1]
+
+        logits = self.text_decoder(image_embs, token_embs)
+        out_dict = {
+            "image_features": image_latent,
+            "text_features": text_latent,
+            "logits": logits,
+            "logit_scale": self.logit_scale.exp()
+        }
+        if labels is not None:
+            out_dict["labels"] = labels
+        if self.logit_bias is not None:
+            out_dict["logit_bias"] = self.logit_bias
+        return out_dict
+
+    def generate(
+        self,
+        image,
+        text=None,
+        seq_len=30,
+        max_seq_len=77,
+        temperature=1.,
+        generation_type="beam_search",
+        top_p=0.1,  # keep tokens in the 1 - top_p quantile
+        top_k=1,  # keeps the top_k most probable tokens
+        pad_token_id=None,
+        eos_token_id=None,
+        sot_token_id=None,
+        num_beams=6,
+        num_beam_groups=3,
+        min_seq_len=5,
+        stopping_criteria=None,
+        repetition_penalty=1.0,
+        fixed_output_length=False # if True output.shape == (batch_size, seq_len)
+    ):
+        # taking many ideas and components from HuggingFace GenerationMixin
+        # https://huggingface.co/docs/transformers/main/en/main_classes/text_generation
+        assert _has_transformers, "Please install transformers for generate functionality. `pip install transformers`."
+        assert seq_len > min_seq_len, "seq_len must be larger than min_seq_len"
+        device = image.device
+
+        with torch.no_grad():
+            sot_token_id = _token_to_tensor(49406 if sot_token_id is None else sot_token_id, device=device)
+            eos_token_id = _token_to_tensor(49407 if eos_token_id is None else eos_token_id, device=device)
+            pad_token_id = self.pad_id if pad_token_id is None else pad_token_id
+            logit_processor = LogitsProcessorList(
+                [
+                    MinLengthLogitsProcessor(min_seq_len, eos_token_id),
+                    RepetitionPenaltyLogitsProcessor(repetition_penalty),
+                ]
+            )
+
+            if stopping_criteria is None:
+                stopping_criteria = [MaxLengthCriteria(max_length=seq_len)]
+            stopping_criteria = StoppingCriteriaList(stopping_criteria)
+
+            if generation_type == "beam_search":
+                output = self._generate_beamsearch(
+                    image_inputs=image,
+                    pad_token_id=pad_token_id,
+                    eos_token_id=eos_token_id,
+                    sot_token_id=sot_token_id,
+                    num_beams=num_beams,
+                    num_beam_groups=num_beam_groups,
+                    min_seq_len=min_seq_len,
+                    stopping_criteria=stopping_criteria,
+                    logit_processor=logit_processor,
+                )
+                if fixed_output_length and output.shape[1] < seq_len:
+                    pad_len = seq_len - output.shape[1]
+                    return torch.cat((
+                            output,
+                            torch.ones(output.shape[0], pad_len, device=device, dtype=output.dtype) * pad_token_id
+                        ),
+                        dim=1
+                    )
+                return output
+
+            elif generation_type == "top_p":
+                logit_warper = GENERATION_TYPES[generation_type](top_p)
+            elif generation_type == "top_k":
+                logit_warper = GENERATION_TYPES[generation_type](top_k)
+            else:
+                raise ValueError(
+                    f"generation_type has to be one of "
+                    f"{'| ' + ' | '.join(list(GENERATION_TYPES.keys())) + ' |'}."
+                )
+
+            image_latent, image_embs = self._encode_image(image)
+
+            if text is None:
+                text = torch.ones((image.shape[0], 1), device=device, dtype=torch.long) * sot_token_id
+
+            was_training = self.training
+            num_dims = len(text.shape)
+
+            if num_dims == 1:
+                text = text[None, :]
+
+            self.eval()
+            out = text
+
+            while True:
+                x = out[:, -max_seq_len:]
+                cur_len = x.shape[1]
+                logits = self(
+                    image,
+                    x,
+                    image_latent=image_latent,
+                    image_embs=image_embs,
+                    output_labels=False,
+                )["logits"][:, -1]
+                mask = (out[:, -1] == eos_token_id) | (out[:, -1] == pad_token_id)
+                sample = torch.ones((out.shape[0], 1), device=device, dtype=torch.long) * pad_token_id
+
+                if mask.all():
+                    if not fixed_output_length:
+                        break
+                else:
+                    logits = logits[~mask, :]
+                    filtered_logits = logit_processor(x[~mask, :], logits)
+                    filtered_logits = logit_warper(x[~mask, :], filtered_logits)
+                    probs = F.softmax(filtered_logits / temperature, dim=-1)
+
+                    if (cur_len + 1 == seq_len):
+                        sample[~mask, :] = torch.ones((sum(~mask), 1), device=device, dtype=torch.long) * eos_token_id
+                    else:
+                        sample[~mask, :] = torch.multinomial(probs, 1)
+
+                out = torch.cat((out, sample), dim=-1)
+
+                cur_len += 1
+
+                if all(stopping_criteria(out, None)):
+                    break
+
+            if num_dims == 1:
+                out = out.squeeze(0)
+
+            self.train(was_training)
+            return out
+
+    def _generate_beamsearch(
+            self,
+            image_inputs,
+            pad_token_id=None,
+            eos_token_id=None,
+            sot_token_id=None,
+            num_beams=6,
+            num_beam_groups=3,
+            min_seq_len=5,
+            stopping_criteria=None,
+            logit_processor=None,
+            logit_warper=None,
+    ):
+        device = image_inputs.device
+        batch_size = image_inputs.shape[0]
+        image_inputs = torch.repeat_interleave(image_inputs, num_beams, dim=0)
+        image_latent, image_embs = self._encode_image(image_inputs)
+
+        input_ids = torch.ones((batch_size * num_beams, 1), device=device, dtype=torch.long)
+        input_ids = input_ids * sot_token_id
+        beam_scorer = BeamSearchScorer(
+            batch_size=batch_size,
+            num_beams=num_beams,
+            device=device,
+            num_beam_groups=num_beam_groups,
+        )
+        # instantiate logits processors
+        logits_processor = (
+            LogitsProcessorList([MinLengthLogitsProcessor(min_seq_len, eos_token_id=eos_token_id)])
+            if logit_processor is None
+            else logit_processor
+        )
+
+        num_beams = beam_scorer.num_beams
+        num_beam_groups = beam_scorer.num_beam_groups
+        num_sub_beams = num_beams // num_beam_groups
+        batch_size = len(beam_scorer._beam_hyps) // num_beam_groups
+        batch_beam_size, cur_len = input_ids.shape
+        beam_indices = None
+
+        if num_beams * batch_size != batch_beam_size:
+            raise ValueError(
+                f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
+            )
+
+        beam_scores = torch.full((batch_size, num_beams), -1e9, dtype=torch.float, device=device)
+        # initialise score of first beam of each group with 0 and the rest with 1e-9. This ensures that the beams in
+        # the same group don't produce same tokens everytime.
+        beam_scores[:, ::num_sub_beams] = 0
+        beam_scores = beam_scores.view((batch_size * num_beams,))
+
+        while True:
+
+            # predicted tokens in cur_len step
+            current_tokens = torch.zeros(batch_size * num_beams, dtype=input_ids.dtype, device=device)
+
+            # indices which will form the beams in the next time step
+            reordering_indices = torch.zeros(batch_size * num_beams, dtype=torch.long, device=device)
+
+            # do one decoder step on all beams of all sentences in batch
+            model_inputs = prepare_inputs_for_generation(input_ids=input_ids, image_inputs=image_inputs)
+            outputs = self(
+                model_inputs['images'],
+                model_inputs['text'],
+                image_latent=image_latent,
+                image_embs=image_embs,
+                output_labels=False,
+            )
+
+            for beam_group_idx in range(num_beam_groups):
+                group_start_idx = beam_group_idx * num_sub_beams
+                group_end_idx = min(group_start_idx + num_sub_beams, num_beams)
+                group_size = group_end_idx - group_start_idx
+
+                # indices of beams of current group among all sentences in batch
+                batch_group_indices = []
+
+                for batch_idx in range(batch_size):
+                    batch_group_indices.extend(
+                        [batch_idx * num_beams + idx for idx in range(group_start_idx, group_end_idx)]
+                    )
+                group_input_ids = input_ids[batch_group_indices]
+
+                # select outputs of beams of currentg group only
+                next_token_logits = outputs['logits'][batch_group_indices, -1, :]
+                vocab_size = next_token_logits.shape[-1]
+
+                next_token_scores_processed = logits_processor(
+                    group_input_ids, next_token_logits, current_tokens=current_tokens, beam_group_idx=beam_group_idx
+                )
+                next_token_scores = next_token_scores_processed + beam_scores[batch_group_indices].unsqueeze(-1)
+                next_token_scores = next_token_scores.expand_as(next_token_scores_processed)
+
+                # reshape for beam search
+                next_token_scores = next_token_scores.view(batch_size, group_size * vocab_size)
+
+                next_token_scores, next_tokens = torch.topk(
+                    next_token_scores, 2 * group_size, dim=1, largest=True, sorted=True
+                )
+
+                next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor")
+                next_tokens = next_tokens % vocab_size
+
+                # stateless
+                process_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
+                beam_outputs = beam_scorer.process(
+                    group_input_ids,
+                    next_token_scores,
+                    next_tokens,
+                    next_indices,
+                    pad_token_id=pad_token_id,
+                    eos_token_id=eos_token_id,
+                    beam_indices=process_beam_indices,
+                    group_index=beam_group_idx,
+                )
+                beam_scores[batch_group_indices] = beam_outputs["next_beam_scores"]
+                beam_next_tokens = beam_outputs["next_beam_tokens"]
+                beam_idx = beam_outputs["next_beam_indices"]
+
+                input_ids[batch_group_indices] = group_input_ids[beam_idx]
+                group_input_ids = torch.cat([group_input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
+                current_tokens[batch_group_indices] = group_input_ids[:, -1]
+
+                # (beam_idx // group_size) -> batch_idx
+                # (beam_idx % group_size) -> offset of idx inside the group
+                reordering_indices[batch_group_indices] = (
+                    num_beams * torch.div(beam_idx, group_size, rounding_mode="floor") + group_start_idx + (beam_idx % group_size)
+                )
+
+            input_ids = torch.cat([input_ids, current_tokens.unsqueeze(-1)], dim=-1)
+
+            # increase cur_len
+            cur_len = cur_len + 1
+            if beam_scorer.is_done or all(stopping_criteria(input_ids, None)):
+                break
+
+        final_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
+        sequence_outputs = beam_scorer.finalize(
+            input_ids,
+            beam_scores,
+            next_tokens,
+            next_indices,
+            pad_token_id=pad_token_id,
+            eos_token_id=eos_token_id,
+            max_length=stopping_criteria.max_length,
+            beam_indices=final_beam_indices,
+        )
+        return sequence_outputs['sequences']
+
+
+def prepare_inputs_for_generation(input_ids, image_inputs, past=None, **kwargs):
+    if past:
+        input_ids = input_ids[:, -1].unsqueeze(-1)
+
+    attention_mask = kwargs.get("attention_mask", None)
+    position_ids = kwargs.get("position_ids", None)
+
+    if attention_mask is not None and position_ids is None:
+        # create position_ids on the fly for batch generation
+        position_ids = attention_mask.long().cumsum(-1) - 1
+        position_ids.masked_fill_(attention_mask == 0, 1)
+    else:
+        position_ids = None
+    return {
+        "text": input_ids,
+        "images": image_inputs,
+        "past_key_values": past,
+        "position_ids": position_ids,
+        "attention_mask": attention_mask,
+    }

hlip/open_clip/src/open_clip/constants.py

@@ -0,0 +1,11 @@
+OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
+OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+INCEPTION_MEAN = (0.5, 0.5, 0.5)
+INCEPTION_STD = (0.5, 0.5, 0.5)
+
+# Default name for a weights file hosted on the Huggingface Hub.
+HF_WEIGHTS_NAME = "open_clip_pytorch_model.bin"  # default pytorch pkl
+HF_SAFE_WEIGHTS_NAME = "open_clip_model.safetensors"  # safetensors version
+HF_CONFIG_NAME = 'open_clip_config.json'

hlip/open_clip/src/open_clip/convert.py

@@ -0,0 +1,206 @@
+""" Conversion functions for 3rd part state-dicts and non-torch native checkpoint formats.
+"""
+from typing import Union
+
+import torch
+import numpy as np
+
+from .model import CLIP, CustomTextCLIP
+from .transformer import TextTransformer, Transformer
+
+
+@torch.no_grad()
+def load_big_vision_weights(model: CustomTextCLIP, checkpoint_path: str):
+    """ Load weights from .npz checkpoints for official Google big_vision image-text models
+
+    Currently, the SigLIP source models are supported and a CustomTextCLIP destination model
+    w/ timm image encoder.
+    """
+    from timm.layers import resample_patch_embed, resample_abs_pos_embed
+
+    def _n2p(w, t=True, idx=None):
+        if idx is not None:
+            w = w[idx]
+        if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
+            w = w.flatten()
+        if t:
+            if w.ndim == 4:
+                w = w.transpose([3, 2, 0, 1])
+            elif w.ndim == 3:
+                w = w.transpose([2, 0, 1])
+            elif w.ndim == 2:
+                w = w.transpose([1, 0])
+        return torch.from_numpy(w)
+
+    w = np.load(checkpoint_path)
+    interpolation = 'bilinear'
+    antialias = False
+
+    def _convert_timm_img(module, prefix):
+        embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])
+        if embed_conv_w.shape[-2:] != module.patch_embed.proj.weight.shape[-2:]:
+            embed_conv_w = resample_patch_embed(
+                embed_conv_w,
+                module.patch_embed.proj.weight.shape[-2:],
+                interpolation=interpolation,
+                antialias=antialias,
+                verbose=True,
+            )
+        module.patch_embed.proj.weight.copy_(embed_conv_w)
+        module.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
+
+        if module.cls_token is not None:
+            module.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))
+
+        pos_embed_w = _n2p(w[f'{prefix}pos_embedding'], t=False)
+        if pos_embed_w.shape != module.pos_embed.shape:
+            assert False, f'{pos_embed_w.shape}, {module.pos_embed.shape}'
+            num_prefix_tokens = 0 if getattr(module, 'no_embed_class', False) else getattr(module, 'num_prefix_tokens', 1)
+            pos_embed_w = resample_abs_pos_embed(  # resize pos embedding when different size from pretrained weights
+                pos_embed_w,
+                new_size=module.patch_embed.grid_size,
+                num_prefix_tokens=num_prefix_tokens,
+                interpolation=interpolation,
+                antialias=antialias,
+                verbose=True,
+            )
+        module.pos_embed.copy_(pos_embed_w)
+
+        mha_sub, b_sub, ln1_sub = (0, 0, 1)
+        for i, block in enumerate(module.blocks.children()):
+            if f'{prefix}Transformer/encoderblock/LayerNorm_0/scale' in w:
+                block_prefix = f'{prefix}Transformer/encoderblock/'
+                idx = i
+            else:
+                block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
+                idx = None
+            mha_prefix = block_prefix + f'MultiHeadDotProductAttention_{mha_sub}/'
+            block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'], idx=idx))
+            block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'], idx=idx))
+            block.attn.qkv.weight.copy_(torch.cat([
+                _n2p(w[f'{mha_prefix}{n}/kernel'], t=False, idx=idx).flatten(1).T for n in ('query', 'key', 'value')]))
+            block.attn.qkv.bias.copy_(torch.cat([
+                _n2p(w[f'{mha_prefix}{n}/bias'], t=False, idx=idx).reshape(-1) for n in ('query', 'key', 'value')]))
+            block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel'], idx=idx).flatten(1))
+            block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'], idx=idx))
+            block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_{ln1_sub}/scale'], idx=idx))
+            block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_{ln1_sub}/bias'], idx=idx))
+            for r in range(2):
+                getattr(block.mlp, f'fc{r + 1}').weight.copy_(
+                    _n2p(w[f'{block_prefix}MlpBlock_{b_sub}/Dense_{r}/kernel'], idx=idx))
+                getattr(block.mlp, f'fc{r + 1}').bias.copy_(
+                    _n2p(w[f'{block_prefix}MlpBlock_{b_sub}/Dense_{r}/bias'], idx=idx))
+
+        module.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
+        module.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
+
+        if module.attn_pool is not None:
+            block_prefix = f'{prefix}MAPHead_0/'
+            mha_prefix = block_prefix + f'MultiHeadDotProductAttention_0/'
+            module.attn_pool.latent.copy_(_n2p(w[f'{block_prefix}probe'], t=False))
+            module.attn_pool.q.weight.copy_(_n2p(w[f'{mha_prefix}query/kernel'], t=False).flatten(1).T)
+            module.attn_pool.q.bias.copy_(_n2p(w[f'{mha_prefix}query/bias'], t=False).reshape(-1))
+            module.attn_pool.kv.weight.copy_(torch.cat([
+                _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('key', 'value')]))
+            module.attn_pool.kv.bias.copy_(torch.cat([
+                _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('key', 'value')]))
+            module.attn_pool.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
+            module.attn_pool.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
+            module.attn_pool.norm.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
+            module.attn_pool.norm.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
+            for r in range(2):
+                getattr(module.attn_pool.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_0/Dense_{r}/kernel']))
+                getattr(module.attn_pool.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_0/Dense_{r}/bias']))
+
+    def _convert_openclip_transformer(module: Transformer, prefix):
+        for i, block in enumerate(module.resblocks.children()):
+            if f'{prefix}encoderblock/LayerNorm_0/scale' in w:
+                block_prefix = f'{prefix}encoderblock/'
+                idx = i
+            else:
+                block_prefix = f'{prefix}encoderblock_{i}/'
+                idx = None
+            mha_prefix = block_prefix + f'MultiHeadDotProductAttention_0/'
+            block.ln_1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'], idx=idx))
+            block.ln_1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'], idx=idx))
+            block.attn.in_proj_weight.copy_(torch.cat([
+                _n2p(w[f'{mha_prefix}{n}/kernel'], t=False, idx=idx).flatten(1).T for n in ('query', 'key', 'value')]))
+            block.attn.in_proj_bias.copy_(torch.cat([
+                _n2p(w[f'{mha_prefix}{n}/bias'], t=False, idx=idx).reshape(-1) for n in ('query', 'key', 'value')]))
+            block.attn.out_proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel'], idx=idx).flatten(1))
+            block.attn.out_proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'], idx=idx))
+            block.ln_2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_1/scale'], idx=idx))
+            block.ln_2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_1/bias'], idx=idx))
+            block.mlp.c_fc.weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_0/Dense_0/kernel'], idx=idx))
+            block.mlp.c_fc.bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_0/Dense_0/bias'], idx=idx))
+            block.mlp.c_proj.weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_0/Dense_1/kernel'], idx=idx))
+            block.mlp.c_proj.bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_0/Dense_1/bias'], idx=idx))
+
+    def _convert_openclip_txt(module: TextTransformer, prefix):
+        module.token_embedding.weight.copy_(_n2p(w[f'{prefix}Embed_0/embedding'], t=False))
+        pos_embed_w = _n2p(w[f'{prefix}pos_embedding'], t=False).squeeze(0)
+        module.positional_embedding.copy_(pos_embed_w)
+        _convert_openclip_transformer(module.transformer, prefix=prefix + 'Encoder_0/')
+        module.ln_final.weight.copy_(_n2p(w[f'{prefix}Encoder_0/encoder_norm/scale']))
+        module.ln_final.bias.copy_(_n2p(w[f'{prefix}Encoder_0/encoder_norm/bias']))
+        if module.text_projection is not None:
+            module.text_projection.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
+            module.text_projection.bias.copy_(_n2p(w[f'{prefix}head/bias']))
+
+    root_prefix = 'params/' if 'params/b' in w else ''
+    _convert_timm_img(model.visual.trunk, f'{root_prefix}img/')
+    _convert_openclip_txt(model.text, f'{root_prefix}txt/')
+    model.logit_bias.copy_(_n2p(w[f'{root_prefix}b'])[0])
+    model.logit_scale.copy_(_n2p(w[f'{root_prefix}t'])[0])
+
+
+@torch.no_grad()
+def convert_mobile_clip_state_dict(model: CustomTextCLIP, state_dict, fastvit = True):
+
+    def _convert_timm_img(state_dict):
+        if fastvit:
+            from timm.models.fastvit import checkpoint_filter_fn
+        else:
+            from timm.models.vision_transformer_hybrid import checkpoint_filter_fn
+        timm_state_dict = checkpoint_filter_fn(state_dict, model.visual.trunk)
+        timm_state_dict = {'visual.trunk.' + k: v for k, v in timm_state_dict.items()}
+        return timm_state_dict
+
+    def _convert_openclip_txt(state_dict, prefix='text_encoder.'):
+        text_dict = {}
+        for k, v in state_dict.items():
+            if not k.startswith(prefix):
+                continue
+            k = k.replace(prefix, '')
+            k = k.replace('projection_layer', 'text_projection')
+            k = k.replace('embedding_layer', 'token_embedding')
+            if k.startswith('positional_embedding.pos_embed.pos_embed'):
+                k = k.replace('positional_embedding.pos_embed.pos_embed', 'positional_embedding')
+                v = v.squeeze()
+            k = k.replace('final_layer_norm', 'ln_final')
+            k = k.replace('pre_norm_mha.0', 'ln_1')
+            k = k.replace('pre_norm_mha.1', 'attn')
+            k = k.replace('pre_norm_ffn.0', 'ln_2')
+            k = k.replace('pre_norm_ffn.1', 'mlp.c_fc')
+            k = k.replace('pre_norm_ffn.4', 'mlp.c_proj')
+            k = k.replace('qkv_proj.weight', 'in_proj_weight')
+            k = k.replace('qkv_proj.bias', 'in_proj_bias')
+            k = k.replace('transformer.', 'transformer.resblocks.')
+            text_dict['text.' + k] = v
+        return text_dict
+
+    image_dict = _convert_timm_img(state_dict)
+    text_dict = _convert_openclip_txt(state_dict)
+    out_dict = {**image_dict, **text_dict}
+    out_dict['logit_scale'] = state_dict['logit_scale']
+    return out_dict
+
+
+def convert_state_dict(model: Union[CustomTextCLIP, CLIP], state_dict):
+    if 'image_encoder.model.patch_embed.0.rbr_conv.0.conv.weight' in state_dict:
+        # Apple MobileCLIP s1 & s2 state_dicts (s0 and b not currently supported)
+        state_dict = convert_mobile_clip_state_dict(model, state_dict)
+    if 'image_encoder.model.patch_emb.0.block.conv.weight' in state_dict:
+        # convert b model
+        state_dict = convert_mobile_clip_state_dict(model, state_dict, fastvit=False)
+    return state_dict

hlip/open_clip/src/open_clip/factory.py

@@ -0,0 +1,1108 @@
+import json
+import logging
+import os
+import re
+import warnings
+from copy import deepcopy
+from dataclasses import asdict
+from pathlib import Path
+from typing import Any, Dict, Optional, Tuple, Union
+
+import torch
+
+from .convert import convert_state_dict
+from .model import CLIP, CustomTextCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\
+    resize_pos_embed, get_cast_dtype, resize_text_pos_embed, set_model_preprocess_cfg
+from .coca_model import CoCa
+from .loss import ClipLoss, DistillClipLoss, CoCaLoss, SigLipLoss
+from .pretrained import is_pretrained_cfg, get_pretrained_cfg, download_pretrained,\
+    list_pretrained_tags_by_model, download_pretrained_from_hf
+from .transform import image_transform_v2, AugmentationCfg, PreprocessCfg, merge_preprocess_dict, merge_preprocess_kwargs
+from .tokenizer import HFTokenizer, SimpleTokenizer, SigLipTokenizer, DEFAULT_CONTEXT_LENGTH
+
+HF_HUB_PREFIX = 'hf-hub:'
+_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"]
+_MODEL_CONFIGS = {}  # directory (model_name: config) of model architecture configs
+
+
+def _natural_key(string_):
+    return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
+
+
+def _rescan_model_configs():
+    global _MODEL_CONFIGS
+
+    config_ext = ('.json',)
+    config_files = []
+    for config_path in _MODEL_CONFIG_PATHS:
+        if config_path.is_file() and config_path.suffix in config_ext:
+            config_files.append(config_path)
+        elif config_path.is_dir():
+            for ext in config_ext:
+                config_files.extend(config_path.glob(f'*{ext}'))
+
+    for cf in config_files:
+        with open(cf, 'r') as f:
+            model_cfg = json.load(f)
+            if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')):
+                _MODEL_CONFIGS[cf.stem] = model_cfg
+
+    _MODEL_CONFIGS = {k: v for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))}
+
+
+_rescan_model_configs()  # initial populate of model config registry
+
+
+def list_models():
+    """ enumerate available model architectures based on config files """
+    return list(_MODEL_CONFIGS.keys())
+
+
+def add_model_config(path):
+    """ add model config path or file and update registry """
+    if not isinstance(path, Path):
+        path = Path(path)
+    _MODEL_CONFIG_PATHS.append(path)
+    _rescan_model_configs()
+
+
+# Define Schema Prefixes as constants
+HF_HUB_PREFIX = 'hf-hub:'
+LOCAL_DIR_PREFIX = 'local-dir:'
+
+def parse_model_name(model_name: str) -> Tuple[Optional[str], str]:
+    """
+    Parses a model name string to identify a schema and the remaining identifier.
+
+    Args:
+        model_name: The model name string (e.g., 'ViT-B-32',
+                    'hf-hub:org/repo', 'local-dir:/path/to/dir',
+                    'local-dir:./relative/path').
+
+    Returns:
+        A tuple (schema, identifier):
+          - schema (Optional[str]): 'hf-hub', 'local-dir', or None if no schema detected.
+          - identifier (str): The part after the schema prefix, or the original
+                              string if no schema was present. For 'local-dir',
+                              this is the raw path string provided.
+    Raises:
+        ValueError: If a schema prefix is present but the identifier part is empty.
+    """
+    # Check for local directory schema first
+    if model_name.startswith(LOCAL_DIR_PREFIX):
+        # Extract the identifier (path) after the prefix
+        identifier = model_name[len(LOCAL_DIR_PREFIX):]
+        # Validate that the identifier (path) is not empty
+        if not identifier:
+            raise ValueError("Empty path specified after 'local-dir:' schema.")
+        # Return the schema and the raw path identifier
+        # Note: We don't resolve or fully validate the path here,
+        #       that's left to the calling function (e.g., using os.path.isdir)
+        return 'local-dir', identifier
+
+    # Check for Hugging Face Hub schema
+    elif model_name.startswith(HF_HUB_PREFIX):
+        # Extract the identifier (HF Hub ID) after the prefix
+        identifier = model_name[len(HF_HUB_PREFIX):]
+        # Validate that the identifier is not empty
+        if not identifier:
+            raise ValueError("Empty identifier specified after 'hf-hub:' schema.")
+        # Return the schema and the HF Hub ID
+        return 'hf-hub', identifier
+
+    # If neither schema prefix is found
+    else:
+        # No schema detected, return None for schema and the original string as identifier
+        return None, model_name
+
+
+def _get_hf_config(
+        model_id: str,
+        cache_dir: Optional[str] = None,
+):
+    """ Fetch model config from HuggingFace Hub.
+    """
+    config_path = download_pretrained_from_hf(
+        model_id,
+        filename='open_clip_config.json',
+        cache_dir=cache_dir,
+    )
+    with open(config_path, 'r', encoding='utf-8') as f:
+        config = json.load(f)
+    return config
+
+
+def get_model_config(model_name):
+    """ Fetch model config from schema specified location or local library configs.
+    """
+    loc, model_id = parse_model_name(model_name)
+    if loc == 'local-dir':
+        local_path = Path(model_id) / 'open_clip_config.json'
+        with open(local_path, 'r', encoding='utf-8') as f:
+            config = json.load(f)
+        return config.get('model_cfg', config)
+    elif loc == 'hf-hub':
+        config = _get_hf_config(model_id)
+        return config.get('model_cfg', config)
+    elif model_name in _MODEL_CONFIGS:
+        return deepcopy(_MODEL_CONFIGS[model_name])
+    else:
+        return None
+
+
+def load_state_dict(
+        checkpoint_path: str,
+        device='cpu',
+        weights_only=True,
+):
+    # Check if safetensors or not and load weights accordingly
+    if str(checkpoint_path).endswith(".safetensors"):
+        from safetensors.torch import load_file
+        checkpoint = load_file(checkpoint_path, device=device)
+    else:
+        try:
+            checkpoint = torch.load(checkpoint_path, map_location=device, weights_only=weights_only)
+        except TypeError:
+            checkpoint = torch.load(checkpoint_path, map_location=device)
+
+    if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
+        state_dict = checkpoint['state_dict']
+    elif isinstance(checkpoint, torch.jit.ScriptModule):
+        state_dict = checkpoint.state_dict()
+        for key in ["input_resolution", "context_length", "vocab_size"]:
+            state_dict.pop(key, None)
+    else:
+        state_dict = checkpoint
+    if next(iter(state_dict.items()))[0].startswith('module'):
+        state_dict = {k[7:]: v for k, v in state_dict.items()}
+    return state_dict
+
+
+def load_checkpoint(
+        model: Union[CLIP, CustomTextCLIP],
+        checkpoint_path: str,
+        strict: bool = True,
+        weights_only: bool = True,
+        device='cpu',
+):
+    if Path(checkpoint_path).suffix in ('.npz', '.npy'):
+        # Separate path loading numpy big_vision (SigLIP) weights
+        from open_clip.convert import load_big_vision_weights
+        load_big_vision_weights(model, checkpoint_path)
+        return {}
+
+    state_dict = load_state_dict(checkpoint_path, device=device, weights_only=weights_only)
+
+    # Detect & convert 3rd party state_dicts -> open_clip
+    state_dict = convert_state_dict(model, state_dict)
+
+    # Detect old format and make compatible with new format
+    if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'):
+        state_dict = convert_to_custom_text_state_dict(state_dict)
+
+    # correct if logit_scale differs in being scaler vs 1d param
+    if 'logit_scale' in state_dict and model.logit_scale.ndim != state_dict['logit_scale'].ndim:
+        state_dict['logit_scale'] = state_dict['logit_scale'].reshape(model.logit_scale.shape)
+
+    # correct if logit_bias differs in being scaler vs 1d param
+    if 'logit_bias' in state_dict and model.logit_bias.ndim != state_dict['logit_bias'].ndim:
+        state_dict['logit_bias'] = state_dict['logit_bias'].reshape(model.logit_bias.shape)
+
+    # If loading a non-SigLIP model for SigLIP training. See https://github.com/mlfoundations/open_clip/issues/712
+    if 'logit_bias' not in state_dict and model.logit_bias is not None:
+        state_dict["logit_bias"] = torch.zeros_like(state_dict["logit_scale"])
+
+    # Certain text transformers no longer expect position_ids after transformers==4.31
+    position_id_key = 'text.transformer.embeddings.position_ids'
+    if position_id_key in state_dict and not hasattr(model, position_id_key):
+        del state_dict[position_id_key]
+
+    resize_pos_embed(state_dict, model)
+    resize_text_pos_embed(state_dict, model)
+
+    # Finally, load the massaged state_dict into model
+    incompatible_keys = model.load_state_dict(state_dict, strict=strict)
+    return incompatible_keys
+
+
+def _find_checkpoint_in_dir(dir_path: Path) -> Optional[str]:
+    checkpoints = list(dir_path.glob('*.safetensors')) + list(dir_path.glob('*.bin')) + list(dir_path.glob('*.pth'))
+    if not checkpoints:
+        return None
+    checkpoints.sort()
+    checkpoints.sort(key=lambda x: x.suffix == '.safetensors', reverse=True)
+    preferred_order = [
+        "open_clip_model.safetensors", "open_clip_pytorch_model.safetensors",
+        "open_clip_pytorch_model.bin",  "open_clip_pytorch_model.pth",
+        "model.safetensors", "pytorch_model.bin", "pytorch_model.pth", "model.pth"
+    ]
+    preferred_checkpoints = [c for c in checkpoints if c.name in preferred_order]
+    if preferred_checkpoints:
+        preferred_checkpoints.sort(key=lambda x: preferred_order.index(x.name))
+        chosen = preferred_checkpoints[0]
+        logging.info(f"Found preferred checkpoint file: {chosen.name} in {dir_path}")
+        return str(chosen)
+    chosen = checkpoints[0]
+    logging.warning(
+        f"Multiple checkpoints found in {dir_path}: {[c.name for c in checkpoints]}. Using '{chosen.name}'.")
+    return str(chosen)
+
+
+def create_model(
+        model_name: str, # Can contain schemas 'hf-hub:' or 'local-dir:'
+        pretrained: Optional[str] = None, # Used ONLY if model_name has NO schema
+        load_weights: bool = True,
+        precision: str = 'fp32',
+        device: Union[str, torch.device] = 'cpu',
+        jit: bool = False,
+        force_quick_gelu: bool = False,
+        force_custom_text: bool = False,
+        force_patch_dropout: Optional[float] = None,
+        force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
+        force_preprocess_cfg: Optional[Dict[str, Any]] = None,
+        force_context_length: Optional[int] = None,
+        pretrained_image: bool = False, # Load default base image weights (at creation, if no CLIP weights)
+        pretrained_text: bool = True,  # Load default base text weights (at creation, if no CLIP weights) - NEW
+        pretrained_image_path: Optional[str] = None, # Load specific image weights from file (after creation)
+        pretrained_text_path: Optional[str] = None, # Load specific text weights from file (after creation)
+        cache_dir: Optional[str] = None,
+        output_dict: Optional[bool] = None,
+        require_pretrained: bool = False,
+        weights_only: bool = True,
+        **model_kwargs,
+) -> torch.nn.Module:
+    """
+    创建并配置一个对比式视觉-语言模型。
+
+    `model_name` 用于指定结构 / 配置的来源：
+    - 'ViT-B-32'：内置模型名称。`pretrained` 指定 CLIP 权重来源（tag 或文件路径）。
+    - 'hf-hub:org/repo'：从 HF Hub 加载配置 / 权重，此时会忽略 `pretrained`。
+    - 'local-dir:/path/to/folder'：从本地目录加载配置 / 权重，此时会忽略 `pretrained`。
+
+    基础 tower 权重的加载由 `pretrained_image` 和 `pretrained_text` 这两个标志控制，
+    仅在没有加载完整 CLIP checkpoint（通过 `pretrained` 或带 schema 的来源）时才会生效。
+
+    可以在创建 *之后* 通过 `pretrained_image_path` 和 `pretrained_text_path`
+    为各自的 tower 加载特定权重。
+
+    Args:
+        model_name: 模型标识符，可能包含 schema（'hf-hub:'、'local-dir:'）。
+        pretrained: 当 model_name 不带 schema 时，CLIP 权重的来源（tag 或文件路径）。
+        load_weights: 若为 True，则加载解析出的预训练权重；否则仅随机初始化或应用 tower 级别的覆盖。
+        precision: 模型精度（'fp32'、'fp16'、'bf16' 等）。
+        device: 设备（'cpu'、'cuda' 等）。
+        jit: 若为 True，则对模型进行 JIT 编译。
+        force_quick_gelu: 在模型配置中强制使用 QuickGELU 激活函数。
+        force_custom_text: 强制使用自定义文本编码器结构。
+        force_patch_dropout: 覆盖模型配置中的 patch dropout 值。
+        force_image_size: 覆盖模型配置中的图像尺寸。
+        force_preprocess_cfg: 用于覆盖最终预处理配置中部分参数的 dict。
+        force_context_length: 覆盖模型配置中的上下文长度。
+        pretrained_image: 若未加载 CLIP 权重，在创建时为 image tower 加载默认基础权重。
+        pretrained_text: 若未加载 CLIP 权重，在创建时为 text tower 加载默认基础权重（默认：True）。
+        pretrained_image_path: 在创建后，将指定路径的权重加载到 image tower 中。
+        pretrained_text_path: 在创建后，将指定路径的权重加载到 text tower 中。
+        cache_dir: 下载使用的缓存目录。
+        output_dict: 若为 True 且模型支持，则返回 dict 形式的输出。
+        require_pretrained: 在需要 `pretrained` CLIP 权重却未加载时抛出错误。
+        weights_only: 为 torch.load 使用 weights_only=True（更安全）。
+        **model_kwargs: 传入模型构造函数的额外关键字参数（覆盖优先级最高）。
+
+    Returns:
+        创建好的模型实例。
+    """
+    schema, identifier = parse_model_name(model_name)
+    if 'pretrained_hf' in model_kwargs:
+        # for backwards compat, override pretrained_text
+        pretrained_text = model_kwargs.pop('pretrained_hf')
+    if isinstance(device, str):
+        device = torch.device(device)
+
+    model_cfg = None
+    preprocess_cfg = asdict(PreprocessCfg())  # Populate with defaults
+    checkpoint_path = None # Final path for full CLIP weights
+    pretrained_cfg_for_tag = None # Store tag config if pretrained is a tag and schema is None
+
+    logging.info(f"Parsing model identifier. Schema: {schema}, Identifier: {identifier}")
+    if schema and pretrained:
+        logging.warning(f"Ignoring `pretrained='{pretrained}'` because `model_name` has '{schema}' schema.")
+        pretrained = None  # Nullify pretrained as it's ignored
+
+    # Handle schemas first - these ignore the `pretrained` argument
+    if schema == 'local-dir':
+        # Handle local directory schema
+        local_path = Path(identifier)
+        if not local_path.is_dir():
+            raise FileNotFoundError(f"Directory specified via 'local-dir:' schema not found: {local_path}")
+
+        local_config_path = local_path / 'open_clip_config.json'
+        logging.info(f"Attempting to load config from local dir: {local_config_path}")
+        if local_config_path.is_file():
+            try:
+                # Try loading and parsing the JSON config
+                with open(local_config_path, 'r', encoding='utf-8') as f:
+                    local_json_config = json.load(f)
+                # Check if the required 'model_cfg' key is present
+                if 'model_cfg' in local_json_config:
+                    # Load model config and merge preprocess config
+                    model_cfg = local_json_config['model_cfg']
+                    preprocess_cfg = merge_preprocess_dict(preprocess_cfg, local_json_config.get('preprocess_cfg', {}))
+                    logging.info(f"Loaded model config and preprocess from: {local_config_path}")
+                    # Look for weights checkpoint in the same directory
+                    checkpoint_path = _find_checkpoint_in_dir(local_path)
+                    if checkpoint_path:
+                        logging.info(f"Found CLIP weights in local folder: {checkpoint_path}")
+                    else:
+                        logging.warning(f"Local config loaded, but no CLIP weights found in {local_path}")
+                else:
+                    # Config file exists but lacks the necessary key
+                    raise ValueError(f"Local config {local_config_path} missing 'model_cfg'.")
+            except Exception as e:
+                # Handle JSON parsing errors or other exceptions during config load
+                raise ValueError(f"Could not load valid config from specified 'local-dir:{identifier}': {e}") from e
+        else:
+            # Directory exists but the config file is missing
+            raise FileNotFoundError(f"'local-dir:' specified, but config file missing: {local_config_path}")
+
+    elif schema == 'hf-hub':
+        # Handle Hugging Face Hub schema
+        model_id = identifier
+        logging.info(f"Attempting to load config from HF Hub: {model_id}")
+        try:
+            # Fetch configuration from Hugging Face Hub
+            hf_config = _get_hf_config(model_id, cache_dir=cache_dir)
+            if 'model_cfg' not in hf_config:
+                raise RuntimeError(f"'model_cfg' not found in config from {model_id}")
+            # Load model config and merge preprocess config
+            model_cfg = hf_config['model_cfg']
+            preprocess_cfg = merge_preprocess_dict(preprocess_cfg, hf_config.get('preprocess_cfg', {}))
+            logging.info(f"Loaded model config from HF Hub: {model_id}")
+            # Attempt find default weights file from the Hub repo
+            try:
+                checkpoint_path = download_pretrained_from_hf(model_id, cache_dir=cache_dir)
+                logging.info(f"Found default weights file on HF Hub: {checkpoint_path}")
+            except Exception as e_weights:
+                # Log warning if weights download fails, but proceed (might only need config)
+                logging.warning(f"Could not find/download default weights on HF Hub for {model_id}: {e_weights}")
+        except Exception as e_config:
+            # Handle errors during config fetching from HF Hub
+            raise RuntimeError(f"Failed initial config/weights load from HF Hub {model_id}: {e_config}") from e_config
+
+    # No Schema Prefix - Use built-in name + pretrained arg (tag or file)
+    elif schema is None:
+        # Handle model names without schema prefix
+        # Use identifier (original model_name) and clean it for lookup
+        model_name_cleaned = identifier.replace('/', '-')
+
+        # Get base config from built-in name using the cleaned identifier
+        model_cfg = get_model_config(model_name_cleaned)
+        if model_cfg is None:
+            # Raise error if no matching built-in config found
+            raise RuntimeError(
+                f"Model config for '{model_name_cleaned}' not found in built-ins. Available: {list_models()}")
+        logging.info(f"Loaded built-in {model_name_cleaned} model config.")
+
+        # Determine checkpoint path and update preprocess_cfg based on `pretrained` arg (tag or file)
+        if pretrained:
+            # Check if `pretrained` is a known tag
+            pretrained_cfg_for_tag = get_pretrained_cfg(model_name_cleaned, pretrained)
+            if pretrained_cfg_for_tag:
+                try:
+                    # Download weights associated with the tag
+                    checkpoint_path = download_pretrained(pretrained_cfg_for_tag, cache_dir=cache_dir)
+                    preprocess_cfg = merge_preprocess_dict(preprocess_cfg, pretrained_cfg_for_tag)
+                    # QuickGELU compatibility check will happen in after force overrides
+                except Exception as e:
+                    logging.error(f"Failed to download weights for tag '{pretrained}': {e}")
+                    raise RuntimeError(f"Failed to download weights for tag '{pretrained}': {e}")
+            elif os.path.isfile(pretrained):
+                # Handle pretrained file path
+                logging.info(f"`pretrained` specifies file path: {pretrained}")
+                checkpoint_path = pretrained
+            else:
+                logging.error(
+                    f"Pretrained tag or path ({pretrained}) for '{model_name_cleaned}' not found. "
+                    f"Available tags: {list_pretrained_tags_by_model(model_name_cleaned)}"
+                )
+                raise RuntimeError(f"Pretrained value '{pretrained}' is not a known tag or valid file path")
+
+    # Apply model config overrides
+    if model_cfg is None:
+        raise RuntimeError("Model configuration could not be determined after Stage 1.")
+    
+    # print("model_cfg:", model_cfg)  
+    # >>> model_cfg: {'embed_dim': 512, 'vision_cfg': {'timm_model_name': 'vit_base_singlescan_h2_token2744', 'timm_model_pretrained': True, 'timm_drop_path': 0.2, 'timm_pool': 'token', 'timm_proj': 'linear'}, 'text_cfg': {'hf_model_name': 'microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext', 'hf_tokenizer_name': 'microsoft/BiomedVLP-CXR-BERT-specialized', 'hf_pooler_type': 'cls_last_hidden_state_pooler', 'hf_proj_type': 'linear', 'context_length': 512}}
+    # import sys
+    # sys.exit()
+    
+    text_cfg = model_cfg['text_cfg']
+    vision_cfg = model_cfg['vision_cfg']
+    if force_quick_gelu:
+        model_cfg["quick_gelu"] = True
+    if force_patch_dropout is not None:
+        vision_cfg["patch_dropout"] = force_patch_dropout
+    if force_image_size is not None:
+        vision_cfg["image_size"] = force_image_size
+    if force_context_length is not None:
+        text_cfg["context_length"] = force_context_length
+
+    # Check compatibility (e.g., QuickGELU warning for tags)
+    if schema is None and pretrained_cfg_for_tag:
+        # Only perform check if config came from built-in and weights from a tag
+        model_quick_gelu = model_cfg.get('quick_gelu', False) # Check the potentially overridden value
+        tag_quick_gelu = pretrained_cfg_for_tag.get('quick_gelu', False)
+        if tag_quick_gelu != model_quick_gelu:
+            # Warn if the final model config's GELU setting mismatches the tag's training setting
+             warnings.warn(
+                 f"QuickGELU mismatch between final model config (quick_gelu={model_quick_gelu}) "
+                 f"and pretrained tag '{pretrained}' (quick_gelu={tag_quick_gelu}).",
+                 UserWarning
+             )
+
+    # Decide whether to use the checkpoint path based on load_weights
+    if checkpoint_path is not None:
+        if not load_weights:
+            logging.info(
+                f"Potential checkpoint path '{checkpoint_path}' found, but skipping assignment due to load_weights=False.")
+            checkpoint_path = None
+    else:
+        logging.info("No potential checkpoint path found from config source or pretrained arg.")
+
+    # Set default base weight loading flags for image and text towers
+    # Only load base pretrained weights if other weights will not be loaded into respective towers
+    enable_default_image_weights = pretrained_image and pretrained_image_path is None and checkpoint_path is None
+    enable_default_text_weights = pretrained_text and pretrained_text_path is None and checkpoint_path is None
+    is_timm_model = 'timm_model_name' in model_cfg.get("vision_cfg", {})
+    is_hf_text_model = 'hf_model_name' in model_cfg.get('text_cfg', {})
+    if is_timm_model:
+        vision_cfg['timm_model_pretrained'] = enable_default_image_weights
+    else:
+        enable_default_image_weights = False  # for accurate logging
+    if is_hf_text_model:
+        text_cfg['hf_model_pretrained'] = enable_default_text_weights
+    else:
+        enable_default_text_weights = False  # for accurate logging
+
+    # Determine model class (CLIP, CustomTextCLIP, CoCa)
+    custom_text = model_cfg.pop('custom_text', False) or force_custom_text or is_hf_text_model
+    if custom_text:
+        # Use CustomTextCLIP (or CoCa if multimodal_cfg is present)
+        if "multimodal_cfg" in model_cfg:
+            model_class = CoCa
+        else:
+            model_class = CustomTextCLIP
+    else:
+        # Default to standard CLIP
+        model_class = CLIP
+
+    # Apply final **kwargs overrides (highest priority) to a copy of model_cfg
+    final_model_cfg = deepcopy(model_cfg)
+    final_model_cfg.update(model_kwargs)
+
+    # Get casting dtype based on precision argument
+    cast_dtype = get_cast_dtype(precision)
+
+    # Instantiate the model
+    logging.info(f"Instantiating model architecture: {model_class.__name__}")
+    model = model_class(**final_model_cfg, cast_dtype=cast_dtype)
+    _set_model_device_and_precision(model, device, precision, is_timm_model)
+
+    # Load Full Pretrained CLIP Weights (if path exists)
+    pretrained_loaded = False
+    if checkpoint_path:
+        logging.info(f'Loading full pretrained weights from: {checkpoint_path}')
+        # Use the load_checkpoint helper which handles state dict loading, conversions, etc.
+        # Use strict=True by default for full model loading to catch mismatches.
+        load_checkpoint(
+            model,
+            checkpoint_path,
+            strict=True,
+            weights_only=weights_only,
+            device='cpu' # Load to CPU first
+        )
+        pretrained_loaded = True
+
+    # Load tower-specific weights (image and text), after the full CLIP checkpoint, potentially overwriting parts.
+    pretrained_image_loaded = False # Track if specific image weights loaded
+    if pretrained_image_path:
+        if os.path.isfile(pretrained_image_path):
+            logging.info(f"Attempting to load image tower weights from: {pretrained_image_path}")
+            try:
+                # Load the state dict from the file
+                image_state_dict = load_state_dict(
+                    pretrained_image_path,
+                    device='cpu',
+                    weights_only=weights_only
+                )
+                # Check if model has the 'visual' attribute
+                if hasattr(model, 'visual'):
+                    # Load into the visual tower, use strict=False for flexibility
+                    incompatible_keys = model.visual.load_state_dict(image_state_dict, strict=False)
+                    logging.info(
+                        f"Loaded image tower weights from {pretrained_image_path}. Incompatible keys: {incompatible_keys}")
+                    pretrained_image_loaded = True # Mark specific image weights as loaded
+                else:
+                    # Model structure doesn't match expectation
+                    logging.warning(
+                        f"Model does not have a 'visual' attribute, cannot load image tower weights from {pretrained_image_path}")
+            except Exception as e:
+                # Handle errors during image tower weight loading
+                logging.error(f"Error loading image tower weights from {pretrained_image_path}: {e}")
+        else:
+            # Path provided is not a valid file
+            logging.warning(f"Invalid file path specified for pretrained_image_path: {pretrained_image_path}")
+
+    pretrained_text_loaded = False # Track if specific text weights loaded
+    if pretrained_text_path:
+        if os.path.isfile(pretrained_text_path):
+            logging.info(f"Attempting to load text tower weights from: {pretrained_text_path}")
+            try:
+                # Load the state dict from the file
+                text_state_dict = load_state_dict(
+                    pretrained_text_path,
+                    device='cpu',
+                    weights_only=weights_only
+                )
+                # Safely get the text attribute (usually 'text', but could be different)
+                text_module = getattr(model, 'text', model)
+                if text_module is not None:
+                    # Load into the text tower, use strict=False for flexibility
+                    incompatible_keys = text_module.load_state_dict(text_state_dict, strict=False)
+                    logging.info(f"Loaded text tower weights from {pretrained_text_path}. Incompatible keys: {incompatible_keys}")
+                    pretrained_text_loaded = True # Mark specific text weights as loaded
+                else:
+                    # Model structure doesn't match expectation
+                    logging.warning(f"Model does not have a standard 'text' attribute, cannot load text tower weights from {pretrained_text_path}")
+            except Exception as e:
+                # Handle errors during text tower weight loading
+                logging.error(f"Error loading text tower weights from {pretrained_text_path}: {e}")
+        else:
+            # Path provided is not a valid file
+            logging.warning(f"Invalid file path specified for pretrained_text_path: {pretrained_text_path}")
+
+    partially_loaded = enable_default_text_weights or enable_default_image_weights \
+        or pretrained_image_loaded or pretrained_text_loaded
+    if require_pretrained and not pretrained_loaded:
+         # If CLIP weights were required but failed to load, raise an error.
+         # Loading tower-specific weights does not satisfy `require_pretrained`.
+         raise RuntimeError(
+             f"Required pretrained weights (`model_name='{model_name}', pretrained='{pretrained}'`) could not be loaded. "
+         )
+    elif not pretrained_loaded and partially_loaded:
+         # Some tower weights loaded
+         logging.warning(f"Model {model_name} initialized partially.")
+    elif not pretrained_loaded and not partially_loaded:
+         # Absolutely no weights were loaded from any source
+         logging.warning(f"No pretrained weights loaded for model '{model_name}'. Model initialized randomly.")
+
+    if output_dict and hasattr(model, "output_dict"):
+        # Enable dictionary output if model supports it
+        model.output_dict = True
+
+    # If force_image_size was specified and we have a timm model, call set_input_size after loading weights
+    if force_image_size is not None and is_timm_model and hasattr(model.visual, 'set_input_size'):
+        logging.info(f"Calling set_input_size({force_image_size}) on timm vision model.")
+        model.visual.set_input_size(force_image_size)
+
+    if jit:
+        logging.info("Attempting JIT scripting...")
+        try:
+            model = torch.jit.script(model)
+            logging.info("JIT scripting successful.")
+        except Exception as e:
+            logging.warning(f"JIT scripting failed: {e}. Returning non-JIT model.")
+
+    # Prepare and set final preprocessing configuration on the model
+    final_preprocess_cfg = deepcopy(preprocess_cfg) # Start with config determined earlier
+    # Ensure image_size in preprocess config matches the actual model's visual component size, if possible
+    visual_module = getattr(model, 'visual', None)
+    if visual_module is not None and hasattr(visual_module, 'image_size'):
+        # Update preprocess size from the instantiated visual module
+         final_preprocess_cfg['size'] = visual_module.image_size
+    # Apply force_preprocess_cfg overrides (highest priority for preprocessing)
+    final_preprocess_cfg = merge_preprocess_dict(final_preprocess_cfg, force_preprocess_cfg or {})
+
+    # Attach the final config to the model
+    set_model_preprocess_cfg(model, final_preprocess_cfg)
+    logging.info(f"Final image preprocessing configuration set: {final_preprocess_cfg}")
+
+    # Log completion and return the configured model
+    logging.info(f"Model {model_name} creation process complete.")
+    return model
+
+
+def get_tokenizer(
+        model_name: str = '',
+        context_length: Optional[int] = None,
+        cache_dir: Optional[str] = None,
+        **kwargs, # Additional tokenizer kwargs passed to constructor
+):
+    """
+    Gets the appropriate tokenizer based on the model identifier schema or name.
+
+    `model_name` can specify source via schema:
+      - 'ViT-B-32': Looks up built-in config to determine tokenizer type.
+      - 'hf-hub:org/repo': Loads config from HF Hub to determine tokenizer type.
+      - 'local-dir:/path/to/folder': Loads config from local dir to determine tokenizer type.
+    """
+    schema, identifier = parse_model_name(model_name)
+
+    config = {} # Stores the loaded model_cfg relevant section (usually text_cfg)
+    local_dir_path = None # Store path if schema is local-dir to resolve relative paths
+    hf_fallback_id = None
+
+    # Determine Configuration Source based on Schema
+    logging.info(f"Parsing tokenizer identifier. Schema: {schema}, Identifier: {identifier}")
+
+    if schema == 'local-dir':
+        # Handle local directory schema
+        local_dir_path = Path(identifier) # Store the path for later use
+        if not local_dir_path.is_dir():
+            raise FileNotFoundError(f"Directory specified via 'local-dir:' schema not found at {local_dir_path}")
+        local_config_path = local_dir_path / 'open_clip_config.json'
+        logging.info(f"Attempting to load config from local-dir: {local_config_path}")
+        if local_config_path.is_file():
+            try:
+                # Load and parse the JSON config
+                with open(local_config_path, 'r', encoding='utf-8') as f:
+                    local_json_config = json.load(f)
+                if 'model_cfg' in local_json_config:
+                    config = local_json_config['model_cfg']
+                else:
+                    raise ValueError(f"Local config {local_config_path} missing 'model_cfg'.")
+            except Exception as e:
+                raise ValueError(f"Could not load valid config for 'local-dir:{identifier}' ({e}).") from e
+        else:
+             raise FileNotFoundError(f"'local-dir:' specified, but config file missing: {local_config_path}")
+
+    elif schema == 'hf-hub':
+        # Handle Hugging Face Hub schema
+        model_id = identifier
+        logging.info(f"Attempting to load config from hf-hub:{model_id}")
+        config_err = ''
+        try:
+            # Fetch config from HF Hub
+            hf_config = _get_hf_config(model_id, cache_dir=cache_dir)
+            config = hf_config.get('model_cfg', None)
+            if not config:
+                config_err = 'model_cfg key not found'
+        except Exception as e:
+            config_err = str(e)
+        if not config:
+            hf_fallback_id = model_id
+            config = {}
+            logging.warning(
+                f"Could not load config from hf-hub:{model_id} ({config_err})."
+                f"Falling back to using model_id for tokenizer.")
+
+    elif schema is None and identifier:
+        # Try built-in config lookup using the identifier (original model_name)
+        logging.info(f"Attempting to load config from built-in: {identifier}")
+        config = get_model_config(identifier)
+
+    # Check if config determination failed completely (should only be possible if initial schema parsing failed badly)
+    if config is None:
+        logging.warning(f"Model configuration not found, returning default SimpleTokenizer.")
+        return SimpleTokenizer(context_length=context_length or DEFAULT_CONTEXT_LENGTH, **kwargs)
+
+    # Safely access text_cfg even if config is {} (from non-builtin name case)
+    text_config = config.get('text_cfg', {})
+
+    # Resolve context length: argument > config > default
+    if context_length is None:
+        # Use context_length from text_cfg if available, otherwise default
+        context_length = text_config.get('context_length', DEFAULT_CONTEXT_LENGTH)
+
+    # Merge tokenizer kwargs: function kwargs override config kwargs
+    tokenizer_kwargs = text_config.get('tokenizer_kwargs', {}) # Start with config kwargs
+    tokenizer_kwargs.update(kwargs) # Apply caller kwargs, overriding config ones
+
+    # Get the specified HF tokenizer name from config, if any
+    hf_tokenizer_name = text_config.get('hf_tokenizer_name', '')
+    if not hf_tokenizer_name and hf_fallback_id:
+        hf_tokenizer_name = hf_fallback_id
+
+    if hf_tokenizer_name:
+        # If 'hf_tokenizer_name' key exists in text_cfg (even if empty string): Use HFTokenizer.
+        if schema == 'local-dir':
+            # If config came from local-dir, ALWAYS use the local dir path for HFTokenizer.
+            # This assumes the tokenizer files are inside that directory.
+            tokenizer_source = local_dir_path
+        else:
+            tokenizer_source = hf_tokenizer_name
+        tokenizer_mode = text_config.get('tokenizer_mode', None)
+
+        logging.info(f"Using HFTokenizer with source: '{tokenizer_source}', mode: '{tokenizer_mode}'")
+        tokenizer = HFTokenizer(
+            tokenizer_source,
+            context_length=context_length,
+            cache_dir=cache_dir,
+            tokenizer_mode=tokenizer_mode,
+            **tokenizer_kwargs,
+        )
+
+    elif schema is None and 'siglip' in identifier.lower():
+        # Check for SigLIP naming convention ONLY if no schema was present AND no hf_tokenizer_name found
+        # Avoids misinterpreting 'local-dir:/path/with/siglip/in/name'
+        tn_variant = 'gemma' if 'siglip2' in identifier.lower() else 'mc4' if 'i18n' in identifier.lower() else 'c4-en'
+        logging.info(f"Using SigLipTokenizer variant: {tn_variant}")
+        tokenizer = SigLipTokenizer(
+            tn_variant,
+            context_length=context_length,
+        )
+    else:
+        # Default to SimpleTokenizer if no HF specified and not SigLIP name match
+        logging.info("Using default SimpleTokenizer.")
+        tokenizer = SimpleTokenizer(
+            context_length=context_length,
+            **tokenizer_kwargs,
+        )
+
+    return tokenizer
+
+
+def _set_model_device_and_precision(
+        model: torch.nn.Module,
+        device: torch.device,
+        precision: str,
+        is_timm_model: bool = False
+):
+    if precision in ("fp16", "bf16"):
+        dtype = torch.float16 if 'fp16' in precision else torch.bfloat16
+        # manual mixed precision that matches original OpenAI behaviour
+        if is_timm_model:
+            from .transformer import LayerNormFp32
+            # FIXME this is a bit janky, create timm based model in low-precision and
+            # then cast only LayerNormFp32 instances back to float32 so they don't break.
+            # Why? The convert_weights_to_lp fn only works with native models.
+            model.to(device=device, dtype=dtype)
+
+            def _convert_ln(m):
+                if isinstance(m, LayerNormFp32):
+                    m.weight.data = m.weight.data.to(torch.float32)
+                    m.bias.data = m.bias.data.to(torch.float32)
+
+            model.apply(_convert_ln)
+        else:
+            model.to(device=device)
+            convert_weights_to_lp(model, dtype=dtype)
+    elif precision in ("pure_fp16", "pure_bf16"):
+        dtype = torch.float16 if 'fp16' in precision else torch.bfloat16
+        model.to(device=device, dtype=dtype)
+    else:
+        model.to(device=device)
+
+
+def create_loss(args):
+    if args.distill:
+        return DistillClipLoss(
+            local_loss=args.local_loss,
+            gather_with_grad=args.gather_with_grad,
+            cache_labels=True,
+            rank=args.rank,
+            world_size=args.world_size,
+            use_horovod=args.horovod,
+        )
+    elif "coca" in args.model.lower():
+        return CoCaLoss(
+            caption_loss_weight=args.coca_caption_loss_weight,
+            clip_loss_weight=args.coca_contrastive_loss_weight,
+            local_loss=args.local_loss,
+            gather_with_grad=args.gather_with_grad,
+            cache_labels=True,
+            rank=args.rank,
+            world_size=args.world_size,
+            use_horovod=args.horovod,
+        )
+    elif args.siglip:
+        assert not args.horovod, "Horovod not currently supported for SigLip"
+        return SigLipLoss(
+            rank=args.rank,
+            world_size=args.world_size,
+            dist_impl=args.loss_dist_impl,  # siglip has multiple distributed implementations to choose from
+        )
+
+    return ClipLoss(
+        local_loss=args.local_loss,
+        gather_with_grad=args.gather_with_grad,
+        cache_labels=True,
+        rank=args.rank,
+        world_size=args.world_size,
+        use_horovod=args.horovod,
+    )
+
+
+def create_model_and_transforms(
+        model_name: str,
+        pretrained: Optional[str] = None,
+        load_weights: bool = True,
+        precision: str = 'fp32',
+        device: Union[str, torch.device] = 'cpu',
+        jit: bool = False,
+        force_quick_gelu: bool = False,
+        force_custom_text: bool = False,
+        force_patch_dropout: Optional[float] = None,
+        force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
+        force_context_length: Optional[int] = None,
+        image_mean: Optional[Tuple[float, ...]] = None,
+        image_std: Optional[Tuple[float, ...]] = None,
+        image_interpolation: Optional[str] = None,
+        image_resize_mode: Optional[str] = None,  # only effective for inference
+        aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None,
+        pretrained_image: bool = False,
+        pretrained_text: bool = True,
+        pretrained_image_path: Optional[str] = None,
+        pretrained_text_path: Optional[str] = None,
+        cache_dir: Optional[str] = None,
+        output_dict: Optional[bool] = None,
+        weights_only: bool = True,
+        **model_kwargs,
+):
+    """
+    创建一个对比式视觉-语言模型，并同时创建用于训练和验证的预处理 transforms。
+
+    这个函数将模型创建与合适的图像预处理流水线组合在一起，
+    方便在既需要模型又需要 transforms 的训练工作流中使用。
+
+    `model_name` 用来指定结构 / 配置的来源：
+    - 'ViT-B-32'：内置模型名称。`pretrained` 指定 CLIP 权重来源（tag 或文件路径）。
+    - 'hf-hub:org/repo'：从 Hugging Face Hub 加载配置 / 权重。此时会忽略 `pretrained`。
+    - 'local-dir:/path/to/folder'：从本地文件夹加载配置 / 权重。此时会忽略 `pretrained`。
+
+    预处理 transforms 会根据模型的需求自动配置，
+    并为训练（带数据增强）和验证（不带数据增强）分别构建独立流水线。
+
+    Args:
+        model_name: 模型标识符，可能带有 schema（'hf-hub:', 'local-dir:'）。
+        pretrained: 当 `model_name` 没有 schema 时，CLIP 权重的来源（tag 或文件路径）。
+        load_weights: 若为 True，则加载解析得到的预训练权重；否则仅随机初始化或应用 tower 覆盖。
+        precision: 模型精度（'fp32'、'fp16'、'bf16' 等）。
+        device: 设备（'cpu'、'cuda' 等）。
+        jit: 若为 True，则对模型进行 JIT 编译。
+        force_quick_gelu: 在模型配置中强制使用 QuickGELU 激活函数。
+        force_custom_text: 强制使用自定义的文本编码器结构。
+        force_patch_dropout: 覆盖模型配置中的 patch dropout 值。
+        force_image_size: 覆盖模型配置中的图像尺寸。
+        force_context_length: 覆盖模型配置中的上下文长度。
+        image_mean: 覆盖默认图像归一化的均值（按通道）。
+        image_std: 覆盖默认图像归一化的标准差（按通道）。
+        image_interpolation: 覆盖图像缩放时默认使用的插值方式。
+        image_resize_mode: 覆盖推理预处理中的缩放模式（'squash'、'longest'、'shortest'）。
+        aug_cfg: 训练 transforms 的数据增强配置。可以是 dict 或 AugmentationCfg 对象。
+                控制随机裁剪、颜色抖动等。如果为 None，则使用模型默认设置。
+        pretrained_image: 当没有加载 CLIP 权重时，在创建时为 image tower 加载默认的（timm）基础权重。
+        pretrained_text: 当没有加载 CLIP 权重时，在创建时为 text tower 加载默认的（hf）基础权重。
+        pretrained_image_path: 在创建后，将指定路径的权重加载到 image tower 中。
+        pretrained_text_path: 在创建后，将指定路径的权重加载到 text tower 中。
+        cache_dir: 下载文件的缓存目录。
+        output_dict: 若为 True 且模型支持，则使用 dict 形式的输出。
+        weights_only: 为 torch.load 使用 weights_only=True（更安全）。
+        **model_kwargs: 传给模型构造函数的额外关键字参数（优先级最高的覆盖项）。
+
+    Returns:
+        Tuple[torch.nn.Module, Callable, Callable]: 包含以下内容的三元组：
+            - model: 创建好的模型实例
+            - preprocess_train: 训练用的图像预处理 transform（包含数据增强）
+            - preprocess_val: 验证 / 推理用的图像预处理 transform（不包含数据增强）
+
+    Example:
+        >>> # 使用内置模型的基础示例
+        >>> model, train_transform, val_transform = create_model_and_transforms('ViT-B-32', pretrained='openai')
+        >>>
+        >>> # 使用自定义数据增强
+        >>> aug_cfg = {'scale': (0.9, 1.0), 'ratio': (1.0, 1.0)}
+        >>> model, train_transform, val_transform = create_model_and_transforms(
+        ...     'ViT-L-14',
+        ...     pretrained='datacomp_xl_s13b_b90k',
+        ...     aug_cfg=aug_cfg
+        ... )
+        >>>
+        >>> # 从 Hugging Face Hub 加载
+        >>> model, train_transform, val_transform = create_model_and_transforms('hf-hub:org/model-repo')
+
+    Note:
+        训练用的 transform 会根据 `aug_cfg` 启用数据增强，而验证用的 transform
+        只执行必要的预处理（缩放、中心裁剪、归一化），不会进行任何随机增强。
+    """
+    force_preprocess_cfg = merge_preprocess_kwargs(
+        {},
+        mean=image_mean,
+        std=image_std,
+        interpolation=image_interpolation,
+        resize_mode=image_resize_mode,
+    )
+
+    model = create_model(
+        model_name,
+        pretrained,
+        load_weights=load_weights,
+        precision=precision,
+        device=device,
+        jit=jit,
+        force_quick_gelu=force_quick_gelu,
+        force_custom_text=force_custom_text,
+        force_patch_dropout=force_patch_dropout,
+        force_image_size=force_image_size,
+        force_preprocess_cfg=force_preprocess_cfg,
+        force_context_length=force_context_length,
+        pretrained_image=pretrained_image,
+        pretrained_text=pretrained_text,
+        pretrained_image_path=pretrained_image_path,
+        pretrained_text_path=pretrained_text_path,
+        cache_dir=cache_dir,
+        output_dict=output_dict,
+        weights_only=weights_only,
+        **model_kwargs,
+    )
+
+    # ** 不是幂运算，而是 “字典拆包（关键词参数展开）” 的意思
+
+    # # 假设 model.visual.preprocess_cfg 是这样的字典：
+    # model.visual.preprocess_cfg = {
+    #     "image_size": 224,
+    #     "interpolation": "bicubic",
+    #     "mean": [0.5, 0.5, 0.5],
+    # }
+
+    # # 那这一行就等价于：
+    # pp_cfg = PreprocessCfg(
+    #     image_size=224,
+    #     interpolation="bicubic",
+    #     mean=[0.5, 0.5, 0.5],
+    # )
+
+    pp_cfg = PreprocessCfg(**model.visual.preprocess_cfg)
+
+    preprocess_train = image_transform_v2(
+        pp_cfg,
+        is_train=True,
+        aug_cfg=aug_cfg,
+    )
+    preprocess_val = image_transform_v2(
+        pp_cfg,
+        is_train=False,
+    )
+
+    return model, preprocess_train, preprocess_val
+
+
+def create_model_from_pretrained(
+        model_name: str,
+        pretrained: Optional[str] = None,
+        precision: str = 'fp32',
+        device: Union[str, torch.device] = 'cpu',
+        jit: bool = False,
+        force_quick_gelu: bool = False,
+        force_custom_text: bool = False,
+        force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
+        force_context_length: Optional[int] = None,
+        image_mean: Optional[Tuple[float, ...]] = None,
+        image_std: Optional[Tuple[float, ...]] = None,
+        image_interpolation: Optional[str] = None,
+        image_resize_mode: Optional[str] = None,  # only effective for inference
+        return_transform: bool = True,
+        cache_dir: Optional[str] = None,
+        weights_only: bool = True,
+        **model_kwargs,
+):
+    """
+    Creates a contrastive vision-language model from pretrained weights with optional preprocessing transform.
+
+    This function is a convenience wrapper around `create_model` that enforces loading of pretrained weights
+    (require_pretrained=True) and optionally returns the appropriate preprocessing transform for inference.
+    It's designed for use cases where a pretrained model is required, such as feature extraction,
+    zero-shot classification, or fine-tuning.
+
+    `model_name` specifies architecture/config source:
+      - 'ViT-B-32': Built-in model name. `pretrained` specifies CLIP weights source (tag or file path).
+      - 'hf-hub:org/repo': Loads config/weights from HF Hub. `pretrained` is IGNORED.
+      - 'local-dir:/path/to/folder': Loads config/weights from local dir. `pretrained` is IGNORED.
+
+    Unlike `create_model`, this function will raise an error if pretrained weights cannot be loaded.
+
+    Args:
+        model_name: Model identifier, potentially with schema ('hf-hub:', 'local-dir:').
+        pretrained: Source for CLIP weights (tag or file path) ONLY if model_name has no schema.
+                   If None and schema requires it, will raise an error.
+        precision: Model precision ('fp32', 'fp16', 'bf16', ...).
+        device: Device ('cpu', 'cuda', ...).
+        jit: If True, JIT compile the model.
+        force_quick_gelu: Force use of QuickGELU activation in model config.
+        force_custom_text: Force use of custom text encoder architecture.
+        force_image_size: Override image size in model config. Useful for using models at different resolutions.
+        force_context_length: Override context length in model config.
+        image_mean: Override default image normalization mean values (per channel).
+        image_std: Override default image normalization std values (per channel).
+        image_interpolation: Override default interpolation method for image resizing ('bicubic', 'bilinear', 'nearest').
+        image_resize_mode: Override resize mode for inference preprocessing ('squash', 'longest', 'shortest').
+            Only affects the returned preprocessing transform, not training.
+        return_transform: If True, returns (model, preprocess). If False, returns only model.
+        cache_dir: Cache directory for downloads.
+        weights_only: Use weights_only=True for torch.load (safer).
+        **model_kwargs: Additional keyword arguments for model constructor (highest override priority).
+
+    Returns:
+        Union[torch.nn.Module, Tuple[torch.nn.Module, Callable]]:
+            - If return_transform=False: Just the model instance
+            - If return_transform=True: Tuple of (model, preprocess) where preprocess is the
+              inference preprocessing transform
+
+    Raises:
+        RuntimeError: If pretrained weights are required but cannot be loaded.
+
+    Example:
+        >>> # Load model with preprocessing
+        >>> model, preprocess = create_model_from_pretrained('ViT-B-32', pretrained='openai')
+        >>>
+        >>> # Load model without preprocessing (e.g., when using custom preprocessing)
+        >>> model = create_model_from_pretrained('ViT-B-32', pretrained='openai', return_transform=False)
+        >>>
+        >>> # Load from Hugging Face Hub
+        >>> model, preprocess = create_model_from_pretrained('hf-hub:laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K')
+        >>>
+        >>> # Load with custom image size
+        >>> model, preprocess = create_model_from_pretrained(
+        ...     'ViT-L-14',
+        ...     pretrained='openai',
+        ...     force_image_size=336
+        ... )
+
+    Note:
+        This function always requires pretrained weights to be available and loaded successfully.
+        For cases where you want to create a model without pretrained weights or with only
+        partial weight loading, use `create_model` or `create_model_and_transforms` instead.
+    """
+    force_preprocess_cfg = merge_preprocess_kwargs(
+        {},
+        mean=image_mean,
+        std=image_std,
+        interpolation=image_interpolation,
+        resize_mode=image_resize_mode,
+    )
+
+    model = create_model(
+        model_name,
+        pretrained,
+        precision=precision,
+        device=device,
+        jit=jit,
+        force_quick_gelu=force_quick_gelu,
+        force_custom_text=force_custom_text,
+        force_image_size=force_image_size,
+        force_preprocess_cfg=force_preprocess_cfg,
+        force_context_length=force_context_length,
+        cache_dir=cache_dir,
+        require_pretrained=True,
+        weights_only=weights_only,
+        **model_kwargs,
+    )
+
+    if not return_transform:
+        return model
+
+    preprocess = image_transform_v2(
+        PreprocessCfg(**model.visual.preprocess_cfg),
+        is_train=False,
+    )
+
+    return model, preprocess

hlip/open_clip/src/open_clip/hf_configs.py

@@ -0,0 +1,67 @@
+# HF architecture dict:
+arch_dict = {
+    # https://huggingface.co/docs/transformers/model_doc/roberta#roberta
+    "roberta": {
+        "config_names": {
+            "context_length": "max_position_embeddings",
+            "vocab_size": "vocab_size",
+            "width": "hidden_size",
+            "heads": "num_attention_heads",
+            "layers": "num_hidden_layers",
+            "layer_attr": "layer",
+            "token_embeddings_attr": "embeddings"
+        },
+        "pooler": "mean_pooler",
+    },
+    # https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaConfig
+    "xlm-roberta": {
+        "config_names": {
+            "context_length": "max_position_embeddings",
+            "vocab_size": "vocab_size",
+            "width": "hidden_size",
+            "heads": "num_attention_heads",
+            "layers": "num_hidden_layers",
+            "layer_attr": "layer",
+            "token_embeddings_attr": "embeddings"
+        },
+        "pooler": "mean_pooler",
+    },
+    # https://huggingface.co/docs/transformers/model_doc/mt5#mt5
+    "mt5": {
+        "config_names": {
+            # unlimited seqlen
+            # https://github.com/google-research/text-to-text-transfer-transformer/issues/273
+            # https://github.com/huggingface/transformers/blob/v4.24.0/src/transformers/models/t5/modeling_t5.py#L374
+            "context_length": "",
+            "vocab_size": "vocab_size",
+            "width": "d_model",
+            "heads": "num_heads",
+            "layers": "num_layers",
+            "layer_attr": "block",
+            "token_embeddings_attr": "embed_tokens"
+        },
+        "pooler": "mean_pooler",
+    },
+    # https://huggingface.co/docs/transformers/model_doc/bert
+    "bert": {
+        "config_names": {
+            "context_length": "max_position_embeddings",
+            "vocab_size": "vocab_size",
+            "width": "hidden_size",
+            "heads": "num_attention_heads",
+            "layers": "num_hidden_layers",
+        },
+        "pooler": "cls_pooler",
+    },
+    # https://huggingface.co/docs/transformers/model_doc/m2m_100
+    "m2m_100": {
+        "config_names": {
+            "context_length": "max_position_embeddings",
+            "vocab_size": "vocab_size",
+            "width": "d_model",
+            "heads": "encoder_attention_heads",
+            "layers": "encoder_layers",
+        },
+        "pooler": "cls_pooler",
+    },
+}

hlip/open_clip/src/open_clip/hf_model.py

@@ -0,0 +1,193 @@
+""" huggingface model adapter
+
+Wraps HuggingFace transformers (https://github.com/huggingface/transformers) models for use as a text tower in CLIP model.
+"""
+import re
+
+import torch
+import torch.nn as nn
+from torch import TensorType
+
+try:
+    import transformers
+    from transformers import AutoModel, AutoTokenizer, AutoConfig, PretrainedConfig
+    from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, \
+        BaseModelOutputWithPoolingAndCrossAttentions
+except ImportError as e:
+    transformers = None
+
+
+    class BaseModelOutput:
+        pass
+
+
+    class PretrainedConfig:
+        pass
+
+from .hf_configs import arch_dict
+
+
+# utils
+def _camel2snake(s):
+    return re.sub(r'(?<!^)(?=[A-Z])', '_', s).lower()
+
+
+# TODO: ?last - for gpt-like models
+_POOLERS = {}
+
+
+def register_pooler(cls):
+    """Decorator registering pooler class"""
+    _POOLERS[_camel2snake(cls.__name__)] = cls
+    return cls
+
+
+@register_pooler
+class MeanPooler(nn.Module):
+    """Mean pooling"""
+
+    def forward(self, x: BaseModelOutput, attention_mask: TensorType):
+        masked_output = x.last_hidden_state * attention_mask.unsqueeze(-1)
+        return masked_output.sum(dim=1) / attention_mask.sum(-1, keepdim=True)
+
+
+@register_pooler
+class MaxPooler(nn.Module):
+    """Max pooling"""
+
+    def forward(self, x: BaseModelOutput, attention_mask: TensorType):
+        masked_output = x.last_hidden_state.masked_fill(attention_mask.unsqueeze(-1), -torch.inf)
+        return masked_output.max(1).values
+
+
+@register_pooler
+class ClsPooler(nn.Module):
+    """CLS token pooling"""
+
+    def __init__(self, use_pooler_output=True):
+        super().__init__()
+        self.cls_token_position = 0
+        self.use_pooler_output = use_pooler_output
+
+    def forward(self, x: BaseModelOutput, attention_mask: TensorType):
+        if (self.use_pooler_output and
+            isinstance(x, (BaseModelOutputWithPooling, BaseModelOutputWithPoolingAndCrossAttentions)) and
+            (x.pooler_output is not None)
+        ):
+            return x.pooler_output
+
+        return x.last_hidden_state[:, self.cls_token_position, :]
+
+
+@register_pooler
+class ClsLastHiddenStatePooler(nn.Module):
+    """CLS token pooling
+    NOTE: this is equivalent to ClsPooler above with use_pooler_output=False
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.cls_token_position = 0
+
+    def forward(self, x: BaseModelOutput, attention_mask: TensorType):
+        return x.last_hidden_state[:, self.cls_token_position, :]
+
+
+class HFTextEncoder(nn.Module):
+    """HuggingFace model adapter"""
+    output_tokens: torch.jit.Final[bool]
+
+    def __init__(
+            self,
+            model_name_or_path: str,
+            output_dim: int,
+            config: PretrainedConfig = None,
+            pooler_type: str = None,
+            proj_type: str = None,
+            pretrained: bool = True,
+            output_tokens: bool = False,
+    ):
+        super().__init__()
+        self.output_tokens = output_tokens
+        self.output_dim = output_dim
+
+        # TODO: find better way to get this information
+        uses_transformer_pooler = (pooler_type == "cls_pooler")
+
+        if transformers is None:
+            raise RuntimeError("Please `pip install transformers` to use pre-trained HuggingFace models")
+        if config is None:
+            self.config = AutoConfig.from_pretrained(model_name_or_path)
+            create_func, model_args = (AutoModel.from_pretrained, model_name_or_path) if pretrained else (
+                AutoModel.from_config, self.config)
+            # TODO: do all model configs have this attribute? PretrainedConfig does so yes??
+            if hasattr(self.config, "is_encoder_decoder") and self.config.is_encoder_decoder:
+                self.transformer = create_func(model_args)
+                self.transformer = self.transformer.encoder
+            else:
+                self.transformer = create_func(model_args, add_pooling_layer=uses_transformer_pooler)
+        else:
+            self.config = config
+            self.transformer = AutoModel.from_config(config)
+        if pooler_type is None:  # get default arch pooler
+            pooler_type = (arch_dict[self.config.model_type]["pooler"])
+
+        # FIXME downstream users of OpenCLIP models use these attr, need to verify valid across all models
+        self.vocab_size = getattr(self.config, 'vocab_size', 0)
+        self.context_length = getattr(self.config, 'max_position_embeddings', 0)
+
+        self.pooler = _POOLERS[pooler_type]()
+
+        d_model = getattr(self.config, arch_dict[self.config.model_type]["config_names"]["width"])
+        if (d_model == output_dim) and (proj_type is None):  # do we always need a proj?
+            self.proj = nn.Identity()
+        elif proj_type == 'linear':
+            self.proj = nn.Linear(d_model, output_dim, bias=False)
+        elif proj_type == 'mlp':
+            hidden_size = (d_model + output_dim) // 2
+            self.proj = nn.Sequential(
+                nn.Linear(d_model, hidden_size, bias=False),
+                nn.GELU(),
+                nn.Linear(hidden_size, output_dim, bias=False),
+            )
+
+    def forward(self, x: TensorType):
+        attn_mask = (x != self.config.pad_token_id).long()
+        out = self.transformer(input_ids=x, attention_mask=attn_mask)
+        pooled_out = self.pooler(out, attn_mask)
+        projected = self.proj(pooled_out)
+
+        seq_len = out.last_hidden_state.shape[1]
+        tokens = (
+            out.last_hidden_state[:, torch.arange(seq_len) != self.pooler.cls_token_position, :] 
+            if type(self.pooler) == ClsPooler 
+            else out.last_hidden_state
+        )
+        
+        if self.output_tokens:
+            return projected, tokens
+        return projected
+
+    def lock(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):
+        if not unlocked_layers:  # full freezing
+            for n, p in self.transformer.named_parameters():
+                p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False
+            return
+
+        encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer
+        layer_list = getattr(encoder, arch_dict[self.config.model_type]["config_names"]["layer_attr"])
+        print(f"Unlocking {unlocked_layers}/{len(layer_list) + 1} layers of hf model")
+        embeddings = getattr(
+            self.transformer, arch_dict[self.config.model_type]["config_names"]["token_embeddings_attr"])
+        modules = [embeddings, *layer_list][:-unlocked_layers]
+        # freeze layers
+        for module in modules:
+            for n, p in module.named_parameters():
+                p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        self.transformer.gradient_checkpointing_enable()
+
+    def init_parameters(self):
+        pass

hlip/open_clip/src/open_clip/loss.py

@@ -0,0 +1,464 @@
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+try:
+    import torch.distributed.nn
+    from torch import distributed as dist
+
+    has_distributed = True
+except ImportError:
+    has_distributed = False
+
+try:
+    import horovod.torch as hvd
+except ImportError:
+    hvd = None
+
+
+def gather_features(
+        image_features,
+        text_features,
+        local_loss=False,
+        gather_with_grad=False,
+        rank=0,
+        world_size=1,
+        use_horovod=False
+):
+    assert has_distributed, 'torch.distributed did not import correctly, please use a PyTorch version with support.'
+    if use_horovod:
+        assert hvd is not None, 'Please install horovod'
+        if gather_with_grad:
+            all_image_features = hvd.allgather(image_features)
+            all_text_features = hvd.allgather(text_features)
+        else:
+            with torch.no_grad():
+                all_image_features = hvd.allgather(image_features)
+                all_text_features = hvd.allgather(text_features)
+            if not local_loss:
+                # ensure grads for local rank when all_* features don't have a gradient
+                gathered_image_features = list(all_image_features.chunk(world_size, dim=0))
+                gathered_text_features = list(all_text_features.chunk(world_size, dim=0))
+                gathered_image_features[rank] = image_features
+                gathered_text_features[rank] = text_features
+                all_image_features = torch.cat(gathered_image_features, dim=0)
+                all_text_features = torch.cat(gathered_text_features, dim=0)
+    else:
+        # We gather tensors from all gpus
+        if gather_with_grad:
+            all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features), dim=0)
+            all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0)
+        else:
+            gathered_image_features = [torch.zeros_like(image_features) for _ in range(world_size)]
+            gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)]
+            dist.all_gather(gathered_image_features, image_features)
+            dist.all_gather(gathered_text_features, text_features)
+            if not local_loss:
+                # ensure grads for local rank when all_* features don't have a gradient
+                gathered_image_features[rank] = image_features
+                gathered_text_features[rank] = text_features
+            all_image_features = torch.cat(gathered_image_features, dim=0)
+            all_text_features = torch.cat(gathered_text_features, dim=0)
+
+    return all_image_features, all_text_features
+
+
+class ClipLoss(nn.Module):
+
+    def __init__(
+            self,
+            local_loss=False,
+            gather_with_grad=False,
+            cache_labels=False,
+            rank=0,
+            world_size=1,
+            use_horovod=False,
+    ):
+        super().__init__()
+        self.local_loss = local_loss
+        self.gather_with_grad = gather_with_grad
+        self.cache_labels = cache_labels
+        self.rank = rank
+        self.world_size = world_size
+        self.use_horovod = use_horovod
+
+        # cache state
+        self.prev_num_logits = 0
+        self.labels = {}
+
+    def get_ground_truth(self, device, num_logits) -> torch.Tensor:
+        # calculated ground-truth and cache if enabled
+        if self.prev_num_logits != num_logits or device not in self.labels:
+            labels = torch.arange(num_logits, device=device, dtype=torch.long)
+            if self.world_size > 1 and self.local_loss:
+                labels = labels + num_logits * self.rank
+            if self.cache_labels:
+                self.labels[device] = labels
+                self.prev_num_logits = num_logits
+        else:
+            labels = self.labels[device]
+        return labels
+
+    def get_logits(self, image_features, text_features, logit_scale, logit_bias=None):
+        if self.world_size > 1:
+            all_image_features, all_text_features = gather_features(
+                image_features,
+                text_features,
+                local_loss=self.local_loss,
+                gather_with_grad=self.gather_with_grad,
+                rank=self.rank,
+                world_size=self.world_size,
+                use_horovod=self.use_horovod,
+            )
+
+            if self.local_loss:
+                logits_per_image = logit_scale * image_features @ all_text_features.T
+                logits_per_text = logit_scale * text_features @ all_image_features.T
+            else:
+                logits_per_image = logit_scale * all_image_features @ all_text_features.T
+                logits_per_text = logits_per_image.T
+        else:
+            logits_per_image = logit_scale * image_features @ text_features.T
+            logits_per_text = logit_scale * text_features @ image_features.T
+
+        if logit_bias is not None:
+            logits_per_image += logit_bias
+            logits_per_text += logit_bias
+
+        return logits_per_image, logits_per_text
+
+    def forward(
+            self,
+            image_features,
+            text_features,
+            logit_scale,
+            logit_bias=None,
+            output_dict=False,
+    ):
+        device = image_features.device
+        logits_per_image, logits_per_text = self.get_logits(
+            image_features,
+            text_features,
+            logit_scale,
+            logit_bias=logit_bias,
+        )
+
+        labels = self.get_ground_truth(device, logits_per_image.shape[0])
+
+        total_loss = (
+            F.cross_entropy(logits_per_image, labels) +
+            F.cross_entropy(logits_per_text, labels)
+        ) / 2
+
+        return {"contrastive_loss": total_loss} if output_dict else total_loss
+
+
+class CoCaLoss(ClipLoss):
+    def __init__(
+            self,
+            caption_loss_weight,
+            clip_loss_weight,
+            pad_id=0,  # pad_token for open_clip custom tokenizer
+            local_loss=False,
+            gather_with_grad=False,
+            cache_labels=False,
+            rank=0,
+            world_size=1,
+            use_horovod=False,
+    ):
+        super().__init__(
+            local_loss=local_loss,
+            gather_with_grad=gather_with_grad,
+            cache_labels=cache_labels,
+            rank=rank,
+            world_size=world_size,
+            use_horovod=use_horovod
+        )
+
+        self.clip_loss_weight = clip_loss_weight
+        self.caption_loss_weight = caption_loss_weight
+        self.caption_loss = nn.CrossEntropyLoss(ignore_index=pad_id)
+
+    def forward(self, image_features, text_features, logits, labels, logit_scale, output_dict=False):
+        if self.clip_loss_weight:
+            clip_loss = super().forward(image_features, text_features, logit_scale)
+            clip_loss = self.clip_loss_weight * clip_loss
+        else:
+            clip_loss = torch.tensor(0, device=logits.device)
+
+        caption_loss = self.caption_loss(
+            logits.permute(0, 2, 1),
+            labels,
+        )
+        caption_loss = caption_loss * self.caption_loss_weight
+
+        if output_dict:
+            return {"contrastive_loss": clip_loss, "caption_loss": caption_loss}
+
+        return clip_loss, caption_loss
+
+
+class DistillClipLoss(ClipLoss):
+
+    def dist_loss(self, teacher_logits, student_logits):
+        return -(teacher_logits.softmax(dim=1) * student_logits.log_softmax(dim=1)).sum(dim=1).mean(dim=0)
+
+    def forward(
+            self,
+            image_features,
+            text_features,
+            logit_scale,
+            dist_image_features,
+            dist_text_features,
+            dist_logit_scale,
+            output_dict=False,
+    ):
+        logits_per_image, logits_per_text = \
+            self.get_logits(image_features, text_features, logit_scale)
+
+        dist_logits_per_image, dist_logits_per_text = \
+            self.get_logits(dist_image_features, dist_text_features, dist_logit_scale)
+
+        labels = self.get_ground_truth(image_features.device, logits_per_image.shape[0])
+
+        contrastive_loss = (
+            F.cross_entropy(logits_per_image, labels) +
+            F.cross_entropy(logits_per_text, labels)
+        ) / 2
+
+        distill_loss = (
+            self.dist_loss(dist_logits_per_image, logits_per_image) +
+            self.dist_loss(dist_logits_per_text, logits_per_text)
+        ) / 2
+
+        if output_dict:
+            return {"contrastive_loss": contrastive_loss, "distill_loss": distill_loss}
+
+        return contrastive_loss, distill_loss
+
+
+def neighbour_exchange(from_rank, to_rank, tensor, group=None):
+    tensor_recv = torch.zeros_like(tensor)
+    send_op = torch.distributed.P2POp(
+        torch.distributed.isend,
+        tensor,
+        to_rank,
+        group=group,
+    )
+    recv_op = torch.distributed.P2POp(
+        torch.distributed.irecv,
+        tensor_recv,
+        from_rank,
+        group=group,
+    )
+    reqs = torch.distributed.batch_isend_irecv([send_op, recv_op])
+    for req in reqs:
+        req.wait()
+    return tensor_recv
+
+
+def neighbour_exchange_bidir(left_rank, right_rank, tensor_to_left, tensor_to_right, group=None):
+    tensor_from_left = torch.zeros_like(tensor_to_right)
+    tensor_from_right = torch.zeros_like(tensor_to_left)
+    send_op_left = torch.distributed.P2POp(
+        torch.distributed.isend,
+        tensor_to_left,
+        left_rank,
+        group=group,
+    )
+    send_op_right = torch.distributed.P2POp(
+        torch.distributed.isend,
+        tensor_to_right,
+        right_rank,
+        group=group,
+    )
+    recv_op_left = torch.distributed.P2POp(
+        torch.distributed.irecv,
+        tensor_from_left,
+        left_rank,
+        group=group,
+    )
+    recv_op_right = torch.distributed.P2POp(
+        torch.distributed.irecv,
+        tensor_from_right,
+        right_rank,
+        group=group,
+    )
+    reqs = torch.distributed.batch_isend_irecv([send_op_right, send_op_left, recv_op_right, recv_op_left])
+    for req in reqs:
+        req.wait()
+    return tensor_from_right, tensor_from_left
+
+
+class NeighbourExchange(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, from_rank, to_rank, group, tensor):
+        ctx.group = group
+        ctx.from_rank = from_rank
+        ctx.to_rank = to_rank
+        return neighbour_exchange(from_rank, to_rank, tensor, group=group)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return (None, None, None) + (NeighbourExchange.apply(ctx.to_rank, ctx.from_rank, ctx.group, grad_output),)
+
+
+def neighbour_exchange_with_grad(from_rank, to_rank, tensor, group=None):
+    return NeighbourExchange.apply(from_rank, to_rank, group, tensor)
+
+
+class NeighbourExchangeBidir(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, left_rank, right_rank, group, tensor_to_left, tensor_to_right):
+        ctx.group = group
+        ctx.left_rank = left_rank
+        ctx.right_rank = right_rank
+        return neighbour_exchange_bidir(left_rank, right_rank, tensor_to_left, tensor_to_right, group=group)
+
+    @staticmethod
+    def backward(ctx, *grad_outputs):
+        return (None, None, None) + \
+            NeighbourExchangeBidir.apply(ctx.right_rank, ctx.left_rank, ctx.group, *grad_outputs)
+
+
+def neighbour_exchange_bidir_with_grad(left_rank, right_rank, tensor_to_left, tensor_to_right, group=None):
+    return NeighbourExchangeBidir.apply(left_rank, right_rank, group, tensor_to_left, tensor_to_right)
+
+
+class SigLipLoss(nn.Module):
+    """ Sigmoid Loss for Language Image Pre-Training (SigLIP) - https://arxiv.org/abs/2303.15343
+
+    @article{zhai2023sigmoid,
+      title={Sigmoid loss for language image pre-training},
+      author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
+      journal={arXiv preprint arXiv:2303.15343},
+      year={2023}
+    }
+    """
+    def __init__(
+            self,
+            cache_labels: bool = False,
+            rank: int = 0,
+            world_size: int = 1,
+            dist_impl: Optional[str] = None,
+    ):
+        super().__init__()
+        self.cache_labels = cache_labels
+        self.rank = rank
+        self.world_size = world_size
+        self.dist_impl = dist_impl or 'bidir'  # default to bidir exchange for now, this will likely change
+        assert self.dist_impl in ('bidir', 'shift', 'reduce', 'gather')
+
+        # cache state FIXME cache not currently used, worthwhile?
+        self.prev_num_logits = 0
+        self.labels = {}
+
+    def get_ground_truth(self, device, dtype, num_logits, negative_only=False) -> torch.Tensor:
+        labels = -torch.ones((num_logits, num_logits), device=device, dtype=dtype)
+        if not negative_only:
+            labels = 2 * torch.eye(num_logits, device=device, dtype=dtype) + labels
+        return labels
+
+    def get_logits(self, image_features, text_features, logit_scale, logit_bias=None):
+        logits = logit_scale * image_features @ text_features.T
+        if logit_bias is not None:
+            logits += logit_bias
+        return logits
+
+    def _loss(self, image_features, text_features, logit_scale, logit_bias=None, negative_only=False):
+        logits = self.get_logits(image_features, text_features, logit_scale, logit_bias)
+        labels = self.get_ground_truth(
+            image_features.device,
+            image_features.dtype,
+            image_features.shape[0],
+            negative_only=negative_only,
+        )
+        loss = -F.logsigmoid(labels * logits).sum() / image_features.shape[0]
+        return loss
+
+    def forward(self, image_features, text_features, logit_scale, logit_bias, output_dict=False):
+        loss = self._loss(image_features, text_features, logit_scale, logit_bias)
+
+        if self.world_size > 1:
+            if self.dist_impl == 'bidir':
+                right_rank = (self.rank + 1) % self.world_size
+                left_rank = (self.rank - 1 + self.world_size) % self.world_size
+                text_features_to_right = text_features_to_left = text_features
+                num_bidir, remainder = divmod(self.world_size - 1, 2)
+                for i in range(num_bidir):
+                    text_features_recv = neighbour_exchange_bidir_with_grad(
+                        left_rank,
+                        right_rank,
+                        text_features_to_left,
+                        text_features_to_right,
+                    )
+                    for f in text_features_recv:
+                        loss += self._loss(
+                            image_features,
+                            f,
+                            logit_scale,
+                            logit_bias,
+                            negative_only=True,
+                        )
+                    text_features_to_left, text_features_to_right = text_features_recv
+
+                if remainder:
+                    text_features_recv = neighbour_exchange_with_grad(
+                        left_rank,
+                        right_rank,
+                        text_features_to_right
+                    )
+                    loss += self._loss(
+                        image_features,
+                        text_features_recv,
+                        logit_scale,
+                        logit_bias,
+                        negative_only=True,
+                    )
+            elif self.dist_impl == "shift":
+                right_rank = (self.rank + 1) % self.world_size
+                left_rank = (self.rank - 1 + self.world_size) % self.world_size
+                text_features_to_right = text_features
+                for i in range(self.world_size - 1):
+                    text_features_from_left = neighbour_exchange_with_grad(
+                        left_rank,
+                        right_rank,
+                        text_features_to_right,
+                    )
+                    loss += self._loss(
+                        image_features,
+                        text_features_from_left,
+                        logit_scale,
+                        logit_bias,
+                        negative_only=True,
+                    )
+                    text_features_to_right = text_features_from_left
+            elif self.dist_impl == "reduce":
+                for i in range(self.world_size):
+                    text_from_other = torch.distributed.nn.all_reduce(
+                        text_features * (self.rank == i),
+                        torch.distributed.ReduceOp.SUM,
+                    )
+                    loss += float(i != self.rank) * self._loss(
+                        image_features,
+                        text_from_other,
+                        logit_scale,
+                        logit_bias,
+                        negative_only=True,
+                    )
+            elif self.dist_impl == "gather":
+                all_text = torch.distributed.nn.all_gather(text_features)
+                for i in range(self.world_size):
+                    loss += float(i != self.rank) * self._loss(
+                        image_features,
+                        all_text[i],
+                        logit_scale,
+                        logit_bias,
+                        negative_only=True,
+                    )
+            else:
+                assert False
+
+        return {"contrastive_loss": loss} if output_dict else loss

hlip/open_clip/src/open_clip/model.py

@@ -0,0 +1,892 @@
+""" CLIP Model
+
+Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
+"""
+import copy
+import logging
+import math
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.utils.checkpoint import checkpoint
+from functools import partial
+
+from .hf_model import HFTextEncoder
+from .modified_resnet import ModifiedResNet
+from .timm_model import TimmModel
+from .transformer import (
+    LayerNormFp32,
+    LayerNorm,
+    QuickGELU,
+    Attention,
+    VisionTransformer,
+    TextTransformer,
+    text_global_pool,
+    lock_text_tower,
+)
+from .utils import to_2tuple
+
+
+@dataclass
+class CLIPVisionCfg:
+    layers: Union[Tuple[int, int, int, int], int] = 12
+    width: int = 768
+    head_width: int = 64
+    mlp_ratio: float = 4.0
+    patch_size: int = 16
+    image_size: Union[Tuple[int, int], int] = 224
+
+    ls_init_value: Optional[float] = None  # layer scale initial value
+    patch_dropout: float = 0.  # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results
+    attentional_pool: bool = False  # whether to use attentional pooler in the last embedding layer (overrides pool_type)
+    attn_pooler_queries: int = 256  # n_queries for attentional pooler
+    attn_pooler_heads: int = 8  # n heads for attentional_pooling
+    no_ln_pre: bool = False  # disable pre transformer LayerNorm
+    pos_embed_type: str = 'learnable'
+    final_ln_after_pool: bool = False  # apply final LayerNorm after pooling
+    pool_type: str = 'tok'
+    output_tokens: bool = False
+    act_kwargs: Optional[dict] = None
+    norm_kwargs: Optional[dict] = None
+
+    # Custom attention block settings
+    block_type: Optional[str] = None  # attention block type ('default', 'custom'), auto-selects 'custom' if any below features enabled
+    qk_norm: bool = False  # apply layer norm to q and k in attention
+    scaled_cosine_attn: bool = False  # use scaled cosine attention
+    scale_heads: bool = False  # learnable head-specific scale applied to attention logits
+    scale_attn_inner: bool = False  # apply layer norm on attention context, before output projection
+    scale_attn: bool = False  # apply layer norm after full attention block
+    scale_fc: bool = False  # apply layer norm in MLP block
+
+    timm_model_name: Optional[str] = None  # a valid model name overrides layers, width, patch_size
+    timm_model_pretrained: bool = False  # use (imagenet) pretrained weights for named model
+    timm_pool: str = 'avg'  # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')
+    timm_proj: str = 'linear'  # linear projection for timm model output ('linear', 'mlp', '')
+    timm_proj_bias: bool = False  # enable bias final projection
+    timm_drop: float = 0.  # head dropout
+    timm_drop_path: Optional[float] = None  # backbone stochastic depth
+
+
+@dataclass
+class CLIPTextCfg:
+    context_length: int = 77
+    vocab_size: int = 49408
+    hf_tokenizer_name: Optional[str] = None
+    tokenizer_mode: Optional[str] = None
+    tokenizer_kwargs: Optional[dict] = None
+
+    width: int = 512
+    heads: int = 8
+    layers: int = 12
+    mlp_ratio: float = 4.0
+    ls_init_value: Optional[float] = None  # layer scale initial value
+    embed_cls: bool = False
+    pad_id: int = 0
+    eos_id: int = 2  # only used for when pool_type == 'eos', must match tokenizer eos
+    no_causal_mask: bool = False  # disable causal masking
+    final_ln_after_pool: bool = False  # apply final LayerNorm after pooling
+    pool_type: str = 'argmax'
+    proj_bias: bool = False
+    proj_type: str = 'linear'  # control final text projection, 'none' forces no projection
+    output_tokens: bool = False
+    act_kwargs: dict = None
+    norm_kwargs: dict = None
+
+    # Custom attention block settings
+    block_type: Optional[str] = None  # attention block type ('default', 'custom'), auto-selects 'custom' if any custom features enabled
+    qk_norm: bool = False  # apply layer norm to q and k in attention
+    scaled_cosine_attn: bool = False  # use scaled cosine attention
+    scale_heads: bool = False  # learnable head-specific scale applied to attention logits
+    scale_attn_inner: bool = False  # apply layer norm on attention context, before output projection
+    scale_attn: bool = False  # apply layer norm after full attention block
+    scale_fc: bool = False  # apply layer norm in MLP block
+
+    # HuggingFace specific text tower config
+    hf_model_name: Optional[str] = None
+    hf_model_pretrained: bool = True
+    hf_proj_type: str = 'mlp'
+    hf_pooler_type: str = 'mean_pooler'  # attentional pooling for HF models
+
+
+def get_cast_dtype(precision: str):
+    cast_dtype = None
+    if precision == 'bf16':
+        cast_dtype = torch.bfloat16
+    elif precision == 'fp16':
+        cast_dtype = torch.float16
+    return cast_dtype
+
+
+def get_input_dtype(precision: str):
+    input_dtype = None
+    if precision in ('bf16', 'pure_bf16'):
+        input_dtype = torch.bfloat16
+    elif precision in ('fp16', 'pure_fp16'):
+        input_dtype = torch.float16
+    return input_dtype
+
+
+def _build_vision_tower(
+        embed_dim: int,
+        vision_cfg: CLIPVisionCfg,
+        quick_gelu: bool = False,
+        cast_dtype: Optional[torch.dtype] = None
+):
+    if isinstance(vision_cfg, dict):
+        vision_cfg = CLIPVisionCfg(**vision_cfg)
+
+    # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more
+    # memory efficient in recent PyTorch releases (>= 1.10).
+    # NOTE: timm models always use native GELU regardless of quick_gelu flag.
+    act_layer = QuickGELU if quick_gelu else nn.GELU
+
+    if vision_cfg.timm_model_name:
+        visual = TimmModel(
+            vision_cfg.timm_model_name,
+            pretrained=vision_cfg.timm_model_pretrained,
+            pool=vision_cfg.timm_pool,
+            proj=vision_cfg.timm_proj,
+            proj_bias=vision_cfg.timm_proj_bias,
+            drop=vision_cfg.timm_drop,
+            drop_path=vision_cfg.timm_drop_path,
+            patch_drop=vision_cfg.patch_dropout if vision_cfg.patch_dropout > 0 else None,
+            embed_dim=embed_dim,
+            image_size=vision_cfg.image_size,
+        )
+    elif isinstance(vision_cfg.layers, (tuple, list)):
+        vision_heads = vision_cfg.width * 32 // vision_cfg.head_width
+        visual = ModifiedResNet(
+            layers=vision_cfg.layers,
+            output_dim=embed_dim,
+            heads=vision_heads,
+            image_size=vision_cfg.image_size,
+            width=vision_cfg.width,
+        )
+    else:
+        vision_heads = vision_cfg.width // vision_cfg.head_width
+        norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm
+        if vision_cfg.norm_kwargs:
+            norm_layer = partial(norm_layer, **vision_cfg.norm_kwargs)
+        if vision_cfg.act_kwargs is not None:
+            act_layer = partial(act_layer, **vision_cfg.act_kwargs)
+
+        visual = VisionTransformer(
+            image_size=vision_cfg.image_size,
+            patch_size=vision_cfg.patch_size,
+            width=vision_cfg.width,
+            layers=vision_cfg.layers,
+            heads=vision_heads,
+            mlp_ratio=vision_cfg.mlp_ratio,
+            ls_init_value=vision_cfg.ls_init_value,
+            patch_dropout=vision_cfg.patch_dropout,
+            attentional_pool=vision_cfg.attentional_pool,
+            attn_pooler_queries=vision_cfg.attn_pooler_queries,
+            attn_pooler_heads=vision_cfg.attn_pooler_heads,
+            pos_embed_type=vision_cfg.pos_embed_type,
+            no_ln_pre=vision_cfg.no_ln_pre,
+            final_ln_after_pool=vision_cfg.final_ln_after_pool,
+            pool_type=vision_cfg.pool_type,
+            output_tokens=vision_cfg.output_tokens,
+            output_dim=embed_dim,
+            act_layer=act_layer,
+            norm_layer=norm_layer,
+            block_type=vision_cfg.block_type,
+            qk_norm=vision_cfg.qk_norm,
+            scaled_cosine_attn=vision_cfg.scaled_cosine_attn,
+            scale_heads=vision_cfg.scale_heads,
+            scale_attn_inner=vision_cfg.scale_attn_inner,
+            scale_attn=vision_cfg.scale_attn,
+            scale_fc=vision_cfg.scale_fc,
+        )
+
+    return visual
+
+
+def _build_text_tower(
+        embed_dim: int,
+        text_cfg: CLIPTextCfg,
+        quick_gelu: bool = False,
+        cast_dtype: Optional[torch.dtype] = None,
+):
+    if isinstance(text_cfg, dict):
+        text_cfg = CLIPTextCfg(**text_cfg)
+
+    if text_cfg.hf_model_name:
+        text = HFTextEncoder(
+            text_cfg.hf_model_name,
+            output_dim=embed_dim,
+            proj_type=text_cfg.hf_proj_type,
+            pooler_type=text_cfg.hf_pooler_type,
+            pretrained=text_cfg.hf_model_pretrained,
+            output_tokens=text_cfg.output_tokens,
+        )
+    else:
+        act_layer = QuickGELU if quick_gelu else nn.GELU
+        norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm
+        if text_cfg.norm_kwargs:
+            norm_layer = partial(norm_layer, **text_cfg.norm_kwargs)
+        if text_cfg.act_kwargs is not None:
+            act_layer = partial(act_layer, **text_cfg.act_kwargs)
+
+        text = TextTransformer(
+            context_length=text_cfg.context_length,
+            vocab_size=text_cfg.vocab_size,
+            width=text_cfg.width,
+            heads=text_cfg.heads,
+            layers=text_cfg.layers,
+            mlp_ratio=text_cfg.mlp_ratio,
+            ls_init_value=text_cfg.ls_init_value,
+            output_dim=embed_dim,
+            embed_cls=text_cfg.embed_cls,
+            no_causal_mask=text_cfg.no_causal_mask,
+            pad_id=text_cfg.pad_id,
+            eos_id=text_cfg.eos_id,
+            pool_type=text_cfg.pool_type,
+            proj_type=text_cfg.proj_type,
+            proj_bias=text_cfg.proj_bias,
+            output_tokens=text_cfg.output_tokens,
+            act_layer=act_layer,
+            norm_layer=norm_layer,
+            block_type=text_cfg.block_type,
+            qk_norm=text_cfg.qk_norm,
+            scaled_cosine_attn=text_cfg.scaled_cosine_attn,
+            scale_heads=text_cfg.scale_heads,
+            scale_attn_inner=text_cfg.scale_attn_inner,
+            scale_attn=text_cfg.scale_attn,
+            scale_fc=text_cfg.scale_fc,
+        )
+    return text
+
+
+class CLIP(nn.Module):
+    output_dict: torch.jit.Final[bool]
+
+    def __init__(
+            self,
+            embed_dim: int,
+            vision_cfg: CLIPVisionCfg,
+            text_cfg: CLIPTextCfg,
+            quick_gelu: bool = False,
+            init_logit_scale: float = np.log(1 / 0.07),
+            init_logit_bias: Optional[float] = None,
+            nonscalar_logit_scale: bool = False,
+            cast_dtype: Optional[torch.dtype] = None,
+            output_dict: bool = False,
+    ):
+        super().__init__()
+        self.output_dict = output_dict
+
+        self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype)
+
+        text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype)
+        self.transformer = text.transformer
+        self.context_length = text.context_length
+        self.vocab_size = text.vocab_size
+        self.token_embedding = text.token_embedding
+        self.positional_embedding = text.positional_embedding
+        self.ln_final = text.ln_final
+        self.text_projection = text.text_projection
+        self.text_pool_type = text.pool_type
+        self.text_eos_id = text.eos_id
+        self.register_buffer('attn_mask', text.attn_mask, persistent=False)
+
+        lshape = [1] if nonscalar_logit_scale else []
+        self.logit_scale = nn.Parameter(torch.ones(lshape) * init_logit_scale)
+        if init_logit_bias is not None:
+            self.logit_bias = nn.Parameter(torch.ones(lshape) * init_logit_bias)
+        else:
+            self.logit_bias = None
+
+    def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):
+        # lock image tower as per LiT - https://arxiv.org/abs/2111.07991
+        self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats)
+
+    def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):
+        assert freeze_layer_norm, 'Unfreezing LayerNorm is not supported. LayerNorm treated like other weights.'
+        lock_text_tower(self, unlocked_layers)
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        self.visual.set_grad_checkpointing(enable)
+        self.transformer.grad_checkpointing = enable
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        # for timm optimizers, 1d params like logit_scale, logit_bias, ln/bn scale, biases are excluded by default
+        no_wd = {'positional_embedding'}
+        if hasattr(self.visual, 'no_weight_decay'):
+            for n in self.visual.no_weight_decay():
+                no_wd.add('visual.' + n)
+        return no_wd
+
+    def encode_image(self, image, normalize: bool = False):
+        features = self.visual(image)
+        return F.normalize(features, dim=-1) if normalize else features
+
+    def encode_text(self, text, normalize: bool = False):
+        cast_dtype = self.transformer.get_cast_dtype()
+
+        x = self.token_embedding(text).to(cast_dtype)  # [batch_size, n_ctx, d_model]
+
+        x = x + self.positional_embedding.to(cast_dtype)
+        x = self.transformer(x, attn_mask=self.attn_mask)
+        x = self.ln_final(x)  # [batch_size, n_ctx, transformer.width]
+        x = text_global_pool(x, text, self.text_pool_type, eos_token_id=getattr(self, "text_eos_id", None))
+        if self.text_projection is not None:
+            if isinstance(self.text_projection, nn.Linear):
+                x = self.text_projection(x)
+            else:
+                x = x @ self.text_projection
+
+        return F.normalize(x, dim=-1) if normalize else x
+
+    def get_logits(self, image, text):
+        image_features = self.encode_image(image, normalize=True)
+        text_features = self.encode_text(text, normalize=True)
+        image_logits = self.logit_scale.exp() * image_features @ text_features.T
+        if self.logit_bias is not None:
+            image_logits += self.logit_bias
+        text_logits = image_logits.T
+        return image_logits, text_logits
+
+    def forward_intermediates(
+            self,
+            image: Optional[torch.Tensor] = None,
+            text: Optional[torch.Tensor] = None,
+            image_indices: Optional[Union[int, List[int]]] = None,
+            text_indices: Optional[Union[int, List[int]]] = None,
+            stop_early: bool = False,
+            normalize: bool = True,
+            normalize_intermediates: bool = False,
+            intermediates_only: bool = False,
+            image_output_fmt: str = 'NCHW',
+            image_output_extra_tokens: bool = False,
+            text_output_fmt: str = 'NLC',
+            text_output_extra_tokens: bool = False,
+            output_logits: bool = False,
+            output_logit_scale_bias: bool = False,
+    ) -> Dict[str, Union[torch.Tensor, List[torch.Tensor]]]:
+        """ Forward features that returns intermediates.
+
+        Args:
+            image: Input image tensor
+            text: Input text tensor
+            image_indices: For image tower, Take last n blocks if int, all if None, select matching indices if sequence
+            text_indices: Take last n blocks if int, all if None, select matching indices if sequence
+            stop_early: Stop iterating over blocks when last desired intermediate hit
+            normalize_intermediates: Apply final norm layer to all intermediates
+            normalize: L2 Normalize final features
+            intermediates_only: Only return intermediate features, do not return final features
+            image_output_fmt: Shape of intermediate image feature outputs
+            image_output_extra_tokens: Return both prefix and spatial intermediate tokens
+            text_output_fmt: Shape of intermediate text feature outputs (ignored for this model)
+            text_output_extra_tokens: Return both prefix and spatial intermediate tokens (ignored for this model)
+            output_logits: Include logits in output
+            output_logit_scale_bias: Include the logit scale bias in the output
+        Returns:
+
+        """
+        output = {}
+        if intermediates_only:
+            # intermediates only disables final feature normalization, and include logits
+            normalize = False
+            output_logits = False
+        if output_logits:
+            assert image is not None and text is not None, 'Both image and text inputs are required to compute logits'
+
+        if image is not None:
+            image_output = self.visual.forward_intermediates(
+                image,
+                indices=image_indices,
+                stop_early=stop_early,
+                normalize_intermediates=normalize_intermediates,
+                intermediates_only=intermediates_only,
+                output_fmt=image_output_fmt,
+                output_extra_tokens=image_output_extra_tokens,
+            )
+            if normalize and "image_features" in image_output:
+                image_output["image_features"] = F.normalize(image_output["image_features"], dim=-1)
+            output.update(image_output)
+
+        if text is not None:
+            cast_dtype = self.transformer.get_cast_dtype()
+            x = self.token_embedding(text).to(cast_dtype)  # [batch_size, n_ctx, d_model]
+            x = x + self.positional_embedding.to(cast_dtype)
+            x, intermediates = self.transformer.forward_intermediates(
+                x,
+                attn_mask=self.attn_mask,
+                indices=text_indices
+            )
+            if normalize_intermediates:
+                intermediates = [self.ln_final(xi) for xi in intermediates]
+
+            # NOTE this model doesn't support cls embed in text transformer, no need for extra intermediate tokens
+            output["text_intermediates"] = intermediates
+
+            if not intermediates_only:
+                x = self.ln_final(x)  # [batch_size, n_ctx, transformer.width]
+                x = text_global_pool(x, text, self.text_pool_type, eos_token_id=getattr(self, "text_eos_id", None))
+                if self.text_projection is not None:
+                    if isinstance(self.text_projection, nn.Linear):
+                        x = self.text_projection(x)
+                    else:
+                        x = x @ self.text_projection
+                if normalize:
+                    x = F.normalize(x, dim=-1)
+                output["text_features"] = x
+
+        logit_scale_exp = self.logit_scale.exp() if output_logits or output_logit_scale_bias else None
+
+        if output_logits:
+            image_logits = logit_scale_exp * output["image_features"] @ output["text_features"].T
+            if self.logit_bias is not None:
+                image_logits += self.logit_bias
+            text_logits = image_logits.T
+            output["image_logits"] = image_logits
+            output["text_logits"] = text_logits
+
+        if output_logit_scale_bias:
+            output["logit_scale"] = logit_scale_exp
+            if self.logit_bias is not None:
+                output['logit_bias'] = self.logit_bias
+
+        return output
+
+    def forward(
+            self,
+            image: Optional[torch.Tensor] = None,
+            text: Optional[torch.Tensor] = None,
+    ):
+        image_features = self.encode_image(image, normalize=True) if image is not None else None
+        text_features = self.encode_text(text, normalize=True) if text is not None else None
+
+        if self.output_dict:
+            out_dict = {
+                "image_features": image_features,
+                "text_features": text_features,
+                "logit_scale": self.logit_scale.exp()
+            }
+            if self.logit_bias is not None:
+                out_dict['logit_bias'] = self.logit_bias
+            return out_dict
+
+        if self.logit_bias is not None:
+            return image_features, text_features, self.logit_scale.exp(), self.logit_bias
+        return image_features, text_features, self.logit_scale.exp()
+
+
+class CustomTextCLIP(nn.Module):
+    output_dict: torch.jit.Final[bool]
+
+    def __init__(
+            self,
+            embed_dim: int,
+            vision_cfg: CLIPVisionCfg,
+            text_cfg: CLIPTextCfg,
+            quick_gelu: bool = False,
+            init_logit_scale: float = np.log(1 / 0.07),
+            init_logit_bias: Optional[float] = None,
+            nonscalar_logit_scale: bool = False,
+            cast_dtype: Optional[torch.dtype] = None,
+            output_dict: bool = False,
+    ):
+        super().__init__()
+        self.output_dict = output_dict
+        self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype)
+        self.text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype)
+        self.context_length = self.text.context_length
+        self.vocab_size = self.text.vocab_size
+
+        lshape = [1] if nonscalar_logit_scale else []
+        self.logit_scale = nn.Parameter(torch.ones(lshape) * init_logit_scale)
+        if init_logit_bias is not None:
+            self.logit_bias = nn.Parameter(torch.ones(lshape) * init_logit_bias)
+        else:
+            self.logit_bias = None
+
+    def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):
+        # lock image tower as per LiT - https://arxiv.org/abs/2111.07991
+        self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats)
+
+    def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):
+        self.text.lock(unlocked_layers, freeze_layer_norm)
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        self.visual.set_grad_checkpointing(enable)
+        self.text.set_grad_checkpointing(enable)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        # for timm optimizers, 1d params like logit_scale, logit_bias, ln/bn scale, biases are excluded by default
+        no_wd = set()
+        if hasattr(self.visual, 'no_weight_decay'):
+            for n in self.visual.no_weight_decay():
+                no_wd.add('visual.' + n)
+        if hasattr(self.text, 'no_weight_decay'):
+            for n in self.text.no_weight_decay():
+                no_wd.add('text.' + n)
+        return no_wd
+
+    def encode_image(self, image, normalize: bool = False):
+        features = self.visual(image)
+        return F.normalize(features, dim=-1) if normalize else features
+
+    def encode_text(self, text, normalize: bool = False):
+        features = self.text(text)
+        return F.normalize(features, dim=-1) if normalize else features
+
+    def get_logits(self, image, text):
+        image_features = self.encode_image(image, normalize=True)
+        text_features = self.encode_text(text, normalize=True)
+        image_logits = self.logit_scale.exp() * image_features @ text_features.T
+        if self.logit_bias is not None:
+            image_logits += self.logit_bias
+        text_logits = image_logits.T
+        return image_logits, text_logits
+
+    def forward_intermediates(
+            self,
+            image: Optional[torch.Tensor] = None,
+            text: Optional[torch.Tensor] = None,
+            image_indices: Optional[Union[int, List[int]]] = None,
+            text_indices: Optional[Union[int, List[int]]] = None,
+            stop_early: bool = False,
+            normalize: bool = True,
+            normalize_intermediates: bool = False,
+            intermediates_only: bool = False,
+            image_output_fmt: str = 'NCHW',
+            image_output_extra_tokens: bool = False,
+            text_output_fmt: str = 'NLC',
+            text_output_extra_tokens: bool = False,
+            output_logits: bool = False,
+            output_logit_scale_bias: bool = False,
+    ) -> Dict[str, Union[torch.Tensor, List[torch.Tensor]]]:
+        """ Forward features that returns intermediates.
+
+        Args:
+            image: Input image tensor
+            text: Input text tensor
+            image_indices: For image tower, Take last n blocks if int, all if None, select matching indices if sequence
+            text_indices: Take last n blocks if int, all if None, select matching indices if sequence
+            stop_early: Stop iterating over blocks when last desired intermediate hit
+            normalize: L2 Normalize final image and text features (if present)
+            normalize_intermediates: Apply final encoder norm layer to all intermediates (if possible)
+            intermediates_only: Only return intermediate features, do not return final features
+            image_output_fmt: Shape of intermediate image feature outputs
+            image_output_extra_tokens: Return both prefix and spatial intermediate tokens
+            text_output_fmt: Shape of intermediate text feature outputs
+            text_output_extra_tokens: Return both prefix and spatial intermediate tokens
+            output_logits: Include logits in output
+            output_logit_scale_bias: Include the logit scale bias in the output
+        Returns:
+
+        """
+        output = {}
+        if intermediates_only:
+            # intermediates only disables final feature normalization, and include logits
+            normalize = False
+            output_logits = False
+        if output_logits:
+            assert image is not None and text is not None, 'Both image and text inputs are required to compute logits'
+
+        if image is not None:
+            image_output = self.visual.forward_intermediates(
+                image,
+                indices=image_indices,
+                stop_early=stop_early,
+                normalize_intermediates=normalize_intermediates,
+                intermediates_only=intermediates_only,
+                output_fmt=image_output_fmt,
+                output_extra_tokens=image_output_extra_tokens,
+            )
+            if normalize and "image_features" in image_output:
+                image_output["image_features"] = F.normalize(image_output["image_features"], dim=-1)
+            output.update(image_output)
+
+        if text is not None:
+            text_output = self.text.forward_intermediates(
+                text,
+                indices=text_indices,
+                stop_early=stop_early,
+                normalize_intermediates=normalize_intermediates,
+                intermediates_only=intermediates_only,
+                output_fmt=text_output_fmt,
+                output_extra_tokens=text_output_extra_tokens,
+            )
+            if normalize and "text_features" in text_output:
+                text_output["text_features"] = F.normalize(text_output["text_features"], dim=-1)
+            output.update(text_output)
+
+        logit_scale_exp = self.logit_scale.exp() if output_logits or output_logit_scale_bias else None
+
+        if output_logits:
+            image_logits = logit_scale_exp * output["image_features"] @ output["text_features"].T
+            if self.logit_bias is not None:
+                image_logits += self.logit_bias
+            text_logits = image_logits.T
+            output["image_logits"] = image_logits
+            output["text_logits"] = text_logits
+
+        if output_logit_scale_bias:
+            output["logit_scale"] = logit_scale_exp
+            if self.logit_bias is not None:
+                output['logit_bias'] = self.logit_bias
+
+        return output
+
+    def forward(
+            self,
+            image: Optional[torch.Tensor] = None,
+            text: Optional[torch.Tensor] = None,
+    ):
+        image_features = self.encode_image(image, normalize=True) if image is not None else None
+        text_features = self.encode_text(text, normalize=True) if text is not None else None
+
+        if self.output_dict:
+            out_dict = {
+                "image_features": image_features,
+                "text_features": text_features,
+                "logit_scale": self.logit_scale.exp()
+            }
+            if self.logit_bias is not None:
+                out_dict['logit_bias'] = self.logit_bias
+            return out_dict
+
+        if self.logit_bias is not None:
+            return image_features, text_features, self.logit_scale.exp(), self.logit_bias
+        return image_features, text_features, self.logit_scale.exp()
+
+
+def convert_weights_to_lp(model: nn.Module, dtype=torch.float16):
+    """Convert applicable model parameters to low-precision (bf16 or fp16)"""
+
+    def _convert_weights(l):
+        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
+            l.weight.data = l.weight.data.to(dtype)
+            if l.bias is not None:
+                l.bias.data = l.bias.data.to(dtype)
+
+        if isinstance(l, (nn.MultiheadAttention, Attention)):
+            for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
+                tensor = getattr(l, attr, None)
+                if tensor is not None:
+                    tensor.data = tensor.data.to(dtype)
+
+        if isinstance(l, (CLIP, TextTransformer)):
+            # convert text nn.Parameter projections
+            attr = getattr(l, "text_projection", None)
+            if attr is not None:
+                attr.data = attr.data.to(dtype)
+
+        if isinstance(l, VisionTransformer):
+            # convert vision nn.Parameter projections
+            attr = getattr(l, "proj", None)
+            if attr is not None:
+                attr.data = attr.data.to(dtype)
+
+    model.apply(_convert_weights)
+
+
+convert_weights_to_fp16 = convert_weights_to_lp  # backwards compat
+
+
+# used to maintain checkpoint compatibility
+def convert_to_custom_text_state_dict(state_dict: dict):
+    if 'text_projection' in state_dict:
+        # old format state_dict, move text tower -> .text
+        new_state_dict = {}
+        for k, v in state_dict.items():
+            if any(k.startswith(p) for p in (
+                'text_projection',
+                'positional_embedding',
+                'token_embedding',
+                'transformer',
+                'ln_final',
+            )):
+                k = 'text.' + k
+            new_state_dict[k] = v
+        return new_state_dict
+    return state_dict
+
+
+def build_model_from_openai_state_dict(
+        state_dict: dict,
+        quick_gelu=True,
+        cast_dtype=torch.float16,
+):
+    vit = "visual.proj" in state_dict
+
+    if vit:
+        vision_width = state_dict["visual.conv1.weight"].shape[0]
+        vision_layers = len(
+            [k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
+        vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
+        grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
+        image_size = vision_patch_size * grid_size
+    else:
+        counts: list = [
+            len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
+        vision_layers = tuple(counts)
+        vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
+        output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
+        vision_patch_size = None
+        assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
+        image_size = output_width * 32
+
+    embed_dim = state_dict["text_projection"].shape[1]
+    context_length = state_dict["positional_embedding"].shape[0]
+    vocab_size = state_dict["token_embedding.weight"].shape[0]
+    transformer_width = state_dict["ln_final.weight"].shape[0]
+    transformer_heads = transformer_width // 64
+    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
+
+    vision_cfg = CLIPVisionCfg(
+        layers=vision_layers,
+        width=vision_width,
+        patch_size=vision_patch_size,
+        image_size=image_size,
+    )
+    text_cfg = CLIPTextCfg(
+        context_length=context_length,
+        vocab_size=vocab_size,
+        width=transformer_width,
+        heads=transformer_heads,
+        layers=transformer_layers,
+    )
+    model = CLIP(
+        embed_dim,
+        vision_cfg=vision_cfg,
+        text_cfg=text_cfg,
+        quick_gelu=quick_gelu,  # OpenAI models were trained with QuickGELU
+        cast_dtype=cast_dtype,
+    )
+
+    for key in ["input_resolution", "context_length", "vocab_size"]:
+        state_dict.pop(key, None)
+    convert_weights_to_fp16(model)  # OpenAI state dicts are partially converted to float16
+    model.load_state_dict(state_dict)
+    return model.eval()
+
+
+def trace_model(model, batch_size=256, device=torch.device('cpu')):
+    model.eval()
+    image_size = model.visual.image_size
+    example_images = torch.ones((batch_size, 3, image_size, image_size), device=device)
+    example_text = torch.zeros((batch_size, model.context_length), dtype=torch.int, device=device)
+    model = torch.jit.trace_module(
+        model,
+        inputs=dict(
+            forward=(example_images, example_text),
+            encode_text=(example_text,),
+            encode_image=(example_images,)
+        ))
+    model.visual.image_size = image_size
+    return model
+
+
+def resize_pos_embed(state_dict, model, interpolation: str = 'bicubic', antialias: bool = True):
+    # Rescale the grid of position embeddings when loading from state_dict
+    old_pos_embed = state_dict.get('visual.positional_embedding', None)
+    if old_pos_embed is None or not hasattr(model.visual, 'grid_size'):
+        return
+    grid_size = to_2tuple(model.visual.grid_size)
+    extra_tokens = 1  # FIXME detect different token configs (ie no class token, or more)
+    new_seq_len = grid_size[0] * grid_size[1] + extra_tokens
+    if new_seq_len == old_pos_embed.shape[0]:
+        return
+
+    if extra_tokens:
+        pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:]
+    else:
+        pos_emb_tok, pos_emb_img = None, old_pos_embed
+    old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img))))
+
+    logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size)
+    pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2)
+    pos_emb_img = F.interpolate(
+        pos_emb_img,
+        size=grid_size,
+        mode=interpolation,
+        antialias=antialias,
+        align_corners=False,
+    )
+    pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0]
+    if pos_emb_tok is not None:
+        new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0)
+    else:
+        new_pos_embed = pos_emb_img
+    state_dict['visual.positional_embedding'] = new_pos_embed
+
+
+def resize_text_pos_embed(state_dict, model, interpolation: str = 'linear', antialias: bool = False):
+    pos_embed_key = 'positional_embedding' if 'positional_embedding' in state_dict else 'text.positional_embedding'
+    old_pos_embed = state_dict.get(pos_embed_key, None)
+    if old_pos_embed is None:
+        return
+    # FIXME add support for text cls_token
+    model_pos_embed = getattr(model, 'positional_embedding', None)
+    if model_pos_embed is None:
+        model_pos_embed = getattr(model.text, 'positional_embedding', None)
+
+    old_num_pos = old_pos_embed.shape[0]
+    old_width = old_pos_embed.shape[1]
+    num_pos = model_pos_embed.shape[0]
+    width = model_pos_embed.shape[1]
+    assert old_width == width, 'text pos_embed width changed!'
+    if old_num_pos == num_pos:
+        return
+
+    logging.info('Resizing text position embedding num_pos from %s to %s', old_num_pos, num_pos)
+    old_pos_embed = old_pos_embed.reshape(1, old_num_pos, old_width).permute(0, 2, 1)
+    old_pos_embed = F.interpolate(
+        old_pos_embed,
+        size=num_pos,
+        mode=interpolation,
+        antialias=antialias,
+        align_corners=False,
+    )
+    old_pos_embed = old_pos_embed.permute(0, 2, 1)[0]
+    new_pos_embed = old_pos_embed
+
+    state_dict[pos_embed_key] = new_pos_embed
+
+
+def get_model_preprocess_cfg(model):
+    module = getattr(model, 'visual', model)
+    preprocess_cfg = getattr(module, 'preprocess_cfg', {})
+    if not preprocess_cfg:
+        # use separate legacy attributes if preprocess_cfg dict not found
+        size = getattr(module, 'image_size')
+        if size is not None:
+            preprocess_cfg['size'] = size
+        mean = getattr(module, 'image_mean', None)
+        if mean is not None:
+            preprocess_cfg['mean'] = mean
+        std = getattr(module, 'image_std', None)
+        if std is not None:
+            preprocess_cfg['std'] = std
+    return preprocess_cfg
+
+
+def set_model_preprocess_cfg(model, preprocess_cfg: Dict[str, Any]):
+    module = getattr(model, 'visual', model)
+    module.image_mean = preprocess_cfg['mean']  # legacy attribute, keeping for bwd compat
+    module.image_std = preprocess_cfg['std']  # legacy attribute, keeping for bwd compat
+    module.preprocess_cfg = copy.deepcopy(preprocess_cfg)  # new attr, package all pp cfg as dict
+
+
+def get_model_tokenize_cfg(model):
+    module = getattr(model, 'text', model)
+    cfg = {}
+    context_length = getattr(module, 'context_length', None)
+    if context_length is not None:
+        cfg['context_length'] = context_length
+    vocab_size = getattr(module, 'vocab_size', None)
+    if vocab_size is not None:
+        cfg['vocab_size'] = vocab_size
+    return cfg