From f0122a799640b711c6cf74f87e4033f24ea5f43e Mon Sep 17 00:00:00 2001 From: smthem <2102561739@qq.com> Date: Wed, 30 Oct 2024 08:57:33 +0800 Subject: [PATCH] add new --- OmniParser/Florence-2-base/CODE_OF_CONDUCT.md | 9 + OmniParser/Florence-2-base/LICENSE | 21 + OmniParser/Florence-2-base/README.md | 259 + OmniParser/Florence-2-base/SECURITY.md | 41 + OmniParser/Florence-2-base/SUPPORT.md | 25 + OmniParser/Florence-2-base/config.json | 85 + .../configuration_florence2.py | 340 + .../Florence-2-base/modeling_florence2.py | 2847 + .../Florence-2-base/preprocessor_config.json | 39 + .../Florence-2-base/processing_florence2.py | 1088 + OmniParser/Florence-2-base/tokenizer.json | 1 + .../Florence-2-base/tokenizer_config.json | 4 + OmniParser/Florence-2-base/vocab.json | 50267 ++++++++ OmniParser/README.md | 56 + OmniParser/blip2-opt-2.7b/README.md | 174 + OmniParser/blip2-opt-2.7b/config.json | 255 + OmniParser/blip2-opt-2.7b/merges.txt | 50001 +++++++ .../model.safetensors.index.json | 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create mode 100644 utils.py diff --git a/OmniParser/Florence-2-base/CODE_OF_CONDUCT.md b/OmniParser/Florence-2-base/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..f9ba8cf --- /dev/null +++ b/OmniParser/Florence-2-base/CODE_OF_CONDUCT.md @@ -0,0 +1,9 @@ +# Microsoft Open Source Code of Conduct + +This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). + +Resources: + +- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/) +- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) +- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns diff --git a/OmniParser/Florence-2-base/LICENSE b/OmniParser/Florence-2-base/LICENSE new file mode 100644 index 0000000..9e841e7 --- /dev/null +++ b/OmniParser/Florence-2-base/LICENSE @@ -0,0 +1,21 @@ + MIT License + + Copyright (c) Microsoft Corporation. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE diff --git a/OmniParser/Florence-2-base/README.md b/OmniParser/Florence-2-base/README.md new file mode 100644 index 0000000..e87f055 --- /dev/null +++ b/OmniParser/Florence-2-base/README.md @@ -0,0 +1,259 @@ +--- +license: mit +license_link: https://huggingface.co/microsoft/Florence-2-base/resolve/main/LICENSE +pipeline_tag: image-text-to-text +tags: +- vision +--- + +# Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks + +## Model Summary + +This Hub repository contains a HuggingFace's `transformers` implementation of Florence-2 model from Microsoft. + +Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model. + +Resources and Technical Documentation: ++ [Florence-2 technical report](https://arxiv.org/abs/2311.06242). ++ [Jupyter Notebook for inference and visualization of Florence-2-large model](https://huggingface.co/microsoft/Florence-2-large/blob/main/sample_inference.ipynb) + +| Model | Model size | Model Description | +| ------- | ------------- | ------------- | +| Florence-2-base[[HF]](https://huggingface.co/microsoft/Florence-2-base) | 0.23B | Pretrained model with FLD-5B +| Florence-2-large[[HF]](https://huggingface.co/microsoft/Florence-2-large) | 0.77B | Pretrained model with FLD-5B +| Florence-2-base-ft[[HF]](https://huggingface.co/microsoft/Florence-2-base-ft) | 0.23B | Finetuned model on a colletion of downstream tasks +| Florence-2-large-ft[[HF]](https://huggingface.co/microsoft/Florence-2-large-ft) | 0.77B | Finetuned model on a colletion of downstream tasks + +## How to Get Started with the Model + +Use the code below to get started with the model. All models are trained with float16. + +```python +import requests + +from PIL import Image +from transformers import AutoProcessor, AutoModelForCausalLM + +device = "cuda:0" if torch.cuda.is_available() else "cpu" +torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 + +model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base", torch_dtype=torch_dtype, trust_remote_code=True).to(device) +processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True) + +prompt = "" + +url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true" +image = Image.open(requests.get(url, stream=True).raw) + +inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype) + +generated_ids = model.generate( + input_ids=inputs["input_ids"], + pixel_values=inputs["pixel_values"], + max_new_tokens=1024, + do_sample=False, + num_beams=3, +) +generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] + +parsed_answer = processor.post_process_generation(generated_text, task="", image_size=(image.width, image.height)) + +print(parsed_answer) + +``` + + +## Tasks + +This model is capable of performing different tasks through changing the prompts. + +First, let's define a function to run a prompt. + +
+ Click to expand + +```python +import requests + +from PIL import Image +from transformers import AutoProcessor, AutoModelForCausalLM + +device = "cuda:0" if torch.cuda.is_available() else "cpu" +torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 + +model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base", torch_dtype=torch_dtype, trust_remote_code=True).to(device) +processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True) + +url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true" +image = Image.open(requests.get(url, stream=True).raw) + +def run_example(task_prompt, text_input=None): + if text_input is None: + prompt = task_prompt + else: + prompt = task_prompt + text_input + inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype) + generated_ids = model.generate( + input_ids=inputs["input_ids"], + pixel_values=inputs["pixel_values"], + max_new_tokens=1024, + num_beams=3 + ) + generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] + + parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height)) + + print(parsed_answer) +``` +
+ +Here are the tasks `Florence-2` could perform: + +
+ Click to expand + + + +### Caption +```python +prompt = "" +run_example(prompt) +``` + +### Detailed Caption +```python +prompt = "" +run_example(prompt) +``` + +### More Detailed Caption +```python +prompt = "" +run_example(prompt) +``` + +### Caption to Phrase Grounding +caption to phrase grounding task requires additional text input, i.e. caption. + +Caption to phrase grounding results format: +{'\': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['', '', ...]}} +```python +task_prompt = "" +results = run_example(task_prompt, text_input="A green car parked in front of a yellow building.") +``` + +### Object Detection + +OD results format: +{'\': {'bboxes': [[x1, y1, x2, y2], ...], +'labels': ['label1', 'label2', ...]} } + +```python +prompt = "" +run_example(prompt) +``` + +### Dense Region Caption +Dense region caption results format: +{'\' : {'bboxes': [[x1, y1, x2, y2], ...], +'labels': ['label1', 'label2', ...]} } +```python +prompt = "" +run_example(prompt) +``` + +### Region proposal +Dense region caption results format: +{'\': {'bboxes': [[x1, y1, x2, y2], ...], +'labels': ['', '', ...]}} +```python +prompt = "" +run_example(prompt) +``` + +### OCR + +```python +prompt = "" +run_example(prompt) +``` + +### OCR with Region +OCR with region output format: +{'\': {'quad_boxes': [[x1, y1, x2, y2, x3, y3, x4, y4], ...], 'labels': ['text1', ...]}} +```python +prompt = "" +run_example(prompt) +``` + +for More detailed examples, please refer to [notebook](https://huggingface.co/microsoft/Florence-2-large/blob/main/sample_inference.ipynb) +
+ +# Benchmarks + +## Florence-2 Zero-shot performance + +The following table presents the zero-shot performance of generalist vision foundation models on image captioning and object detection evaluation tasks. These models have not been exposed to the training data of the evaluation tasks during their training phase. + +| Method | #params | COCO Cap. test CIDEr | NoCaps val CIDEr | TextCaps val CIDEr | COCO Det. val2017 mAP | +|--------|---------|----------------------|------------------|--------------------|-----------------------| +| Flamingo | 80B | 84.3 | - | - | - | +| Florence-2-base| 0.23B | 133.0 | 118.7 | 70.1 | 34.7 | +| Florence-2-large| 0.77B | 135.6 | 120.8 | 72.8 | 37.5 | + + +The following table continues the comparison with performance on other vision-language evaluation tasks. + +| Method | Flickr30k test R@1 | Refcoco val Accuracy | Refcoco test-A Accuracy | Refcoco test-B Accuracy | Refcoco+ val Accuracy | Refcoco+ test-A Accuracy | Refcoco+ test-B Accuracy | Refcocog val Accuracy | Refcocog test Accuracy | Refcoco RES val mIoU | +|--------|----------------------|----------------------|-------------------------|-------------------------|-----------------------|--------------------------|--------------------------|-----------------------|------------------------|----------------------| +| Kosmos-2 | 78.7 | 52.3 | 57.4 | 47.3 | 45.5 | 50.7 | 42.2 | 60.6 | 61.7 | - | +| Florence-2-base | 83.6 | 53.9 | 58.4 | 49.7 | 51.5 | 56.4 | 47.9 | 66.3 | 65.1 | 34.6 | +| Florence-2-large | 84.4 | 56.3 | 61.6 | 51.4 | 53.6 | 57.9 | 49.9 | 68.0 | 67.0 | 35.8 | + + + +## Florence-2 finetuned performance + +We finetune Florence-2 models with a collection of downstream tasks, resulting two generalist models *Florence-2-base-ft* and *Florence-2-large-ft* that can conduct a wide range of downstream tasks. + +The table below compares the performance of specialist and generalist models on various captioning and Visual Question Answering (VQA) tasks. Specialist models are fine-tuned specifically for each task, whereas generalist models are fine-tuned in a task-agnostic manner across all tasks. The symbol "▲" indicates the usage of external OCR as input. + +| Method | # Params | COCO Caption Karpathy test CIDEr | NoCaps val CIDEr | TextCaps val CIDEr | VQAv2 test-dev Acc | TextVQA test-dev Acc | VizWiz VQA test-dev Acc | +|----------------|----------|-----------------------------------|------------------|--------------------|--------------------|----------------------|-------------------------| +| **Specialist Models** | | | | | | | | +| CoCa | 2.1B | 143.6 | 122.4 | - | 82.3 | - | - | +| BLIP-2 | 7.8B | 144.5 | 121.6 | - | 82.2 | - | - | +| GIT2 | 5.1B | 145.0 | 126.9 | 148.6 | 81.7 | 67.3 | 71.0 | +| Flamingo | 80B | 138.1 | - | - | 82.0 | 54.1 | 65.7 | +| PaLI | 17B | 149.1 | 127.0 | 160.0▲ | 84.3 | 58.8 / 73.1▲ | 71.6 / 74.4▲ | +| PaLI-X | 55B | 149.2 | 126.3 | 147.0 / 163.7▲ | 86.0 | 71.4 / 80.8▲ | 70.9 / 74.6▲ | +| **Generalist Models** | | | | | | | | +| Unified-IO | 2.9B | - | 100.0 | - | 77.9 | - | 57.4 | +| Florence-2-base-ft | 0.23B | 140.0 | 116.7 | 143.9 | 79.7 | 63.6 | 63.6 | +| Florence-2-large-ft | 0.77B | 143.3 | 124.9 | 151.1 | 81.7 | 73.5 | 72.6 | + + +| Method | # Params | COCO Det. val2017 mAP | Flickr30k test R@1 | RefCOCO val Accuracy | RefCOCO test-A Accuracy | RefCOCO test-B Accuracy | RefCOCO+ val Accuracy | RefCOCO+ test-A Accuracy | RefCOCO+ test-B Accuracy | RefCOCOg val Accuracy | RefCOCOg test Accuracy | RefCOCO RES val mIoU | +|----------------------|----------|-----------------------|--------------------|----------------------|-------------------------|-------------------------|------------------------|---------------------------|---------------------------|------------------------|-----------------------|------------------------| +| **Specialist Models** | | | | | | | | | | | | | +| SeqTR | - | - | - | 83.7 | 86.5 | 81.2 | 71.5 | 76.3 | 64.9 | 74.9 | 74.2 | - | +| PolyFormer | - | - | - | 90.4 | 92.9 | 87.2 | 85.0 | 89.8 | 78.0 | 85.8 | 85.9 | 76.9 | +| UNINEXT | 0.74B | 60.6 | - | 92.6 | 94.3 | 91.5 | 85.2 | 89.6 | 79.8 | 88.7 | 89.4 | - | +| Ferret | 13B | - | - | 89.5 | 92.4 | 84.4 | 82.8 | 88.1 | 75.2 | 85.8 | 86.3 | - | +| **Generalist Models** | | | | | | | | | | | | | +| UniTAB | - | - | - | 88.6 | 91.1 | 83.8 | 81.0 | 85.4 | 71.6 | 84.6 | 84.7 | - | +| Florence-2-base-ft | 0.23B | 41.4 | 84.0 | 92.6 | 94.8 | 91.5 | 86.8 | 91.7 | 82.2 | 89.8 | 82.2 | 78.0 | +| Florence-2-large-ft| 0.77B | 43.4 | 85.2 | 93.4 | 95.3 | 92.0 | 88.3 | 92.9 | 83.6 | 91.2 | 91.7 | 80.5 | + + +## BibTex and citation info + +``` +@article{xiao2023florence, + title={Florence-2: Advancing a unified representation for a variety of vision tasks}, + author={Xiao, Bin and Wu, Haiping and Xu, Weijian and Dai, Xiyang and Hu, Houdong and Lu, Yumao and Zeng, Michael and Liu, Ce and Yuan, Lu}, + journal={arXiv preprint arXiv:2311.06242}, + year={2023} +} +``` \ No newline at end of file diff --git a/OmniParser/Florence-2-base/SECURITY.md b/OmniParser/Florence-2-base/SECURITY.md new file mode 100644 index 0000000..b3c89ef --- /dev/null +++ b/OmniParser/Florence-2-base/SECURITY.md @@ -0,0 +1,41 @@ + + +## Security + +Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet) and [Xamarin](https://github.com/xamarin). + +If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/security.md/definition), please report it to us as described below. + +## Reporting Security Issues + +**Please do not report security vulnerabilities through public GitHub issues.** + +Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/security.md/msrc/create-report). + +If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/security.md/msrc/pgp). + +You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc). + +Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue: + + * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.) + * Full paths of source file(s) related to the manifestation of the issue + * The location of the affected source code (tag/branch/commit or direct URL) + * Any special configuration required to reproduce the issue + * Step-by-step instructions to reproduce the issue + * Proof-of-concept or exploit code (if possible) + * Impact of the issue, including how an attacker might exploit the issue + +This information will help us triage your report more quickly. + +If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/security.md/msrc/bounty) page for more details about our active programs. + +## Preferred Languages + +We prefer all communications to be in English. + +## Policy + +Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/security.md/cvd). + + diff --git a/OmniParser/Florence-2-base/SUPPORT.md b/OmniParser/Florence-2-base/SUPPORT.md new file mode 100644 index 0000000..eaf439a --- /dev/null +++ b/OmniParser/Florence-2-base/SUPPORT.md @@ -0,0 +1,25 @@ +# TODO: The maintainer of this repo has not yet edited this file + +**REPO OWNER**: Do you want Customer Service & Support (CSS) support for this product/project? + +- **No CSS support:** Fill out this template with information about how to file issues and get help. +- **Yes CSS support:** Fill out an intake form at [aka.ms/onboardsupport](https://aka.ms/onboardsupport). CSS will work with/help you to determine next steps. +- **Not sure?** Fill out an intake as though the answer were "Yes". CSS will help you decide. + +*Then remove this first heading from this SUPPORT.MD file before publishing your repo.* + +# Support + +## How to file issues and get help + +This project uses GitHub Issues to track bugs and feature requests. Please search the existing +issues before filing new issues to avoid duplicates. For new issues, file your bug or +feature request as a new Issue. + +For help and questions about using this project, please **REPO MAINTAINER: INSERT INSTRUCTIONS HERE +FOR HOW TO ENGAGE REPO OWNERS OR COMMUNITY FOR HELP. COULD BE A STACK OVERFLOW TAG OR OTHER +CHANNEL. WHERE WILL YOU HELP PEOPLE?**. + +## Microsoft Support Policy + +Support for this **PROJECT or PRODUCT** is limited to the resources listed above. diff --git a/OmniParser/Florence-2-base/config.json b/OmniParser/Florence-2-base/config.json new file mode 100644 index 0000000..74a4482 --- /dev/null +++ b/OmniParser/Florence-2-base/config.json @@ -0,0 +1,85 @@ +{ + "_name_or_path": "florence2", + "architectures": [ + "Florence2ForConditionalGeneration" + ], + "auto_map": { + "AutoConfig": "configuration_florence2.Florence2Config", + "AutoModelForCausalLM": "modeling_florence2.Florence2ForConditionalGeneration" + }, + "bos_token_id": 0, + "eos_token_id": 2, + "ignore_index": -100, + "model_type": "florence2", + "pad_token_id": 1, + "projection_dim": 768, + "text_config": { + "vocab_size": 51289, + "activation_dropout": 0.1, + "activation_function": "gelu", + "add_bias_logits": false, + "add_final_layer_norm": false, + "attention_dropout": 0.1, + "bos_token_id": 0, + "classif_dropout": 0.1, + "classifier_dropout": 0.0, + "d_model": 768, + "decoder_attention_heads": 12, + "decoder_ffn_dim": 3072, + "decoder_layerdrop": 0.0, + "decoder_layers": 6, + "decoder_start_token_id": 2, + "dropout": 0.1, + "early_stopping": true, + "encoder_attention_heads": 12, + "encoder_ffn_dim": 3072, + "encoder_layerdrop": 0.0, + "encoder_layers": 6, + "eos_token_id": 2, + "forced_eos_token_id": 2, + "forced_bos_token_id": 0, + "gradient_checkpointing": false, + "init_std": 0.02, + "is_encoder_decoder": true, + "label2id": { + "LABEL_0": 0, + "LABEL_1": 1, + "LABEL_2": 2 + }, + "max_position_embeddings": 1024, + "no_repeat_ngram_size": 3, + "normalize_before": false, + "num_hidden_layers": 6, + "pad_token_id": 1, + "scale_embedding": false, + "num_beams": 3 + }, + "vision_config": { + "model_type": "davit", + "drop_path_rate": 0.1, + "patch_size": [7, 3, 3, 3], + "patch_stride": [4, 2, 2, 2], + "patch_padding": [3, 1, 1, 1], + "patch_prenorm": [false, true, true, true], + "enable_checkpoint": false, + "dim_embed": [128, 256, 512, 1024], + "num_heads": [4, 8, 16, 32], + "num_groups": [4, 8, 16, 32], + "depths": [1, 1, 9, 1], + "window_size": 12, + "projection_dim": 768, + "visual_temporal_embedding": { + "type": "COSINE", + "max_temporal_embeddings": 100 + }, + "image_pos_embed": { + "type": "learned_abs_2d", + "max_pos_embeddings": 50 + }, + "image_feature_source": ["spatial_avg_pool", "temporal_avg_pool"] + }, + "vocab_size": 51289, + "torch_dtype": "float16", + "transformers_version": "4.41.0.dev0", + "is_encoder_decoder": true +} \ No newline at end of file diff --git a/OmniParser/Florence-2-base/configuration_florence2.py b/OmniParser/Florence-2-base/configuration_florence2.py new file mode 100644 index 0000000..622f749 --- /dev/null +++ b/OmniParser/Florence-2-base/configuration_florence2.py @@ -0,0 +1,340 @@ +# coding=utf-8 +# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import warnings +""" Florence-2 configuration""" + +from typing import Optional + +from transformers import AutoConfig +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + +logger = logging.get_logger(__name__) + +class Florence2VisionConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Florence2VisionModel`]. It is used to instantiate a Florence2VisionModel + according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the Florence2VisionModel architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + drop_path_rate (`float`, *optional*, defaults to 0.1): + The dropout rate of the drop path layer. + patch_size (`List[int]`, *optional*, defaults to [7, 3, 3, 3]): + The patch size of the image. + patch_stride (`List[int]`, *optional*, defaults to [4, 2, 2, 2]): + The patch stride of the image. + patch_padding (`List[int]`, *optional*, defaults to [3, 1, 1, 1]): + The patch padding of the image. + patch_prenorm (`List[bool]`, *optional*, defaults to [false, true, true, true]): + Whether to apply layer normalization before the patch embedding layer. + enable_checkpoint (`bool`, *optional*, defaults to False): + Whether to enable checkpointing. + dim_embed (`List[int]`, *optional*, defaults to [256, 512, 1024, 2048]): + The dimension of the embedding layer. + num_heads (`List[int]`, *optional*, defaults to [8, 16, 32, 64]): + The number of attention heads. + num_groups (`List[int]`, *optional*, defaults to [8, 16, 32, 64]): + The number of groups. + depths (`List[int]`, *optional*, defaults to [1, 1, 9, 1]): + The depth of the model. + window_size (`int`, *optional*, defaults to 12): + The window size of the model. + projection_dim (`int`, *optional*, defaults to 1024): + The dimension of the projection layer. + visual_temporal_embedding (`dict`, *optional*): + The configuration of the visual temporal embedding. + image_pos_embed (`dict`, *optional*): + The configuration of the image position embedding. + image_feature_source (`List[str]`, *optional*, defaults to ["spatial_avg_pool", "temporal_avg_pool"]): + The source of the image feature. + Example: + + ```python + >>> from transformers import Florence2VisionConfig, Florence2VisionModel + + >>> # Initializing a Florence2 Vision style configuration + >>> configuration = Florence2VisionConfig() + + >>> # Initializing a model (with random weights) + >>> model = Florence2VisionModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "florence2_vision" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + drop_path_rate=0.1, + patch_size=[7, 3, 3, 3], + patch_stride=[4, 2, 2, 2], + patch_padding=[3, 1, 1, 1], + patch_prenorm=[False, True, True, True], + enable_checkpoint=False, + dim_embed=[256, 512, 1024, 2048], + num_heads=[8, 16, 32, 64], + num_groups=[8, 16, 32, 64], + depths=[1, 1, 9, 1], + window_size=12, + projection_dim=1024, + visual_temporal_embedding=None, + image_pos_embed=None, + image_feature_source=["spatial_avg_pool", "temporal_avg_pool"], + **kwargs, + ): + self.drop_path_rate = drop_path_rate + self.patch_size = patch_size + self.patch_stride = patch_stride + self.patch_padding = patch_padding + self.patch_prenorm = patch_prenorm + self.enable_checkpoint = enable_checkpoint + self.dim_embed = dim_embed + self.num_heads = num_heads + self.num_groups = num_groups + self.depths = depths + self.window_size = window_size + self.projection_dim = projection_dim + self.visual_temporal_embedding = visual_temporal_embedding + self.image_pos_embed = image_pos_embed + self.image_feature_source = image_feature_source + + super().__init__(**kwargs) + + + +class Florence2LanguageConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Florence2LanguagePreTrainedModel`]. It is used to instantiate a BART + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the BART + [facebook/bart-large](https://huggingface.co/facebook/bart-large) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 51289): + Vocabulary size of the Florence2Language model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`Florence2LanguageModel`]. + d_model (`int`, *optional*, defaults to 1024): + Dimensionality of the layers and the pooler layer. + encoder_layers (`int`, *optional*, defaults to 12): + Number of encoder layers. + decoder_layers (`int`, *optional*, defaults to 12): + Number of decoder layers. + encoder_attention_heads (`int`, *optional*, defaults to 16): + Number of attention heads for each attention layer in the Transformer encoder. + decoder_attention_heads (`int`, *optional*, defaults to 16): + Number of attention heads for each attention layer in the Transformer decoder. + decoder_ffn_dim (`int`, *optional*, defaults to 4096): + Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. + encoder_ffn_dim (`int`, *optional*, defaults to 4096): + Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. + activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + activation_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for activations inside the fully connected layer. + classifier_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for classifier. + max_position_embeddings (`int`, *optional*, defaults to 1024): + The maximum sequence length that this model might ever be used with. Typically set this to something large + just in case (e.g., 512 or 1024 or 2048). + init_std (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + encoder_layerdrop (`float`, *optional*, defaults to 0.0): + The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) + for more details. + decoder_layerdrop (`float`, *optional*, defaults to 0.0): + The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) + for more details. + scale_embedding (`bool`, *optional*, defaults to `False`): + Scale embeddings by diving by sqrt(d_model). + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). + num_labels (`int`, *optional*, defaults to 3): + The number of labels to use in [`Florence2LanguageForSequenceClassification`]. + forced_eos_token_id (`int`, *optional*, defaults to 2): + The id of the token to force as the last generated token when `max_length` is reached. Usually set to + `eos_token_id`. + + Example: + + ```python + >>> from transformers import Florence2LanguageConfig, Florence2LanguageModel + + >>> # Initializing a Florence2 Language style configuration + >>> configuration = Florence2LanguageConfig() + + >>> # Initializing a model (with random weights) + >>> model = Florence2LangaugeModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "florence2_language" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} + + def __init__( + self, + vocab_size=51289, + max_position_embeddings=1024, + encoder_layers=12, + encoder_ffn_dim=4096, + encoder_attention_heads=16, + decoder_layers=12, + decoder_ffn_dim=4096, + decoder_attention_heads=16, + encoder_layerdrop=0.0, + decoder_layerdrop=0.0, + activation_function="gelu", + d_model=1024, + dropout=0.1, + attention_dropout=0.0, + activation_dropout=0.0, + init_std=0.02, + classifier_dropout=0.0, + scale_embedding=False, + use_cache=True, + num_labels=3, + pad_token_id=1, + bos_token_id=0, + eos_token_id=2, + is_encoder_decoder=True, + decoder_start_token_id=2, + forced_eos_token_id=2, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.d_model = d_model + self.encoder_ffn_dim = encoder_ffn_dim + self.encoder_layers = encoder_layers + self.encoder_attention_heads = encoder_attention_heads + self.decoder_ffn_dim = decoder_ffn_dim + self.decoder_layers = decoder_layers + self.decoder_attention_heads = decoder_attention_heads + self.dropout = dropout + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.activation_function = activation_function + self.init_std = init_std + self.encoder_layerdrop = encoder_layerdrop + self.decoder_layerdrop = decoder_layerdrop + self.classifier_dropout = classifier_dropout + self.use_cache = use_cache + self.num_hidden_layers = encoder_layers + self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True + + super().__init__( + num_labels=num_labels, + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + is_encoder_decoder=is_encoder_decoder, + decoder_start_token_id=decoder_start_token_id, + forced_eos_token_id=forced_eos_token_id, + **kwargs, + ) + + # ensure backward compatibility for BART CNN models + if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False): + self.forced_bos_token_id = self.bos_token_id + warnings.warn( + f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. " + "The config can simply be saved and uploaded again to be fixed." + ) + +class Florence2Config(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Florence2ForConditionalGeneration`]. It is used to instantiate an + Florence-2 model according to the specified arguments, defining the model architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + vision_config (`Florence2VisionConfig`, *optional*): + Custom vision config or dict + text_config (`Union[AutoConfig, dict]`, *optional*): + The config object of the text backbone. + ignore_index (`int`, *optional*, defaults to -100): + The ignore index for the loss function. + vocab_size (`int`, *optional*, defaults to 51289): + Vocabulary size of the Florence2model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`~Florence2ForConditionalGeneration`] + projection_dim (`int`, *optional*, defaults to 1024): + Dimension of the multimodal projection space. + + Example: + + ```python + >>> from transformers import Florence2ForConditionalGeneration, Florence2Config, CLIPVisionConfig, BartConfig + + >>> # Initializing a clip-like vision config + >>> vision_config = CLIPVisionConfig() + + >>> # Initializing a Bart config + >>> text_config = BartConfig() + + >>> # Initializing a Florence-2 configuration + >>> configuration = Florence2Config(vision_config, text_config) + + >>> # Initializing a model from the florence-2 configuration + >>> model = Florence2ForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "florence2" + is_composition = False + + def __init__( + self, + vision_config=None, + text_config=None, + ignore_index=-100, + vocab_size=51289, + projection_dim=1024, + **kwargs, + ): + self.ignore_index = ignore_index + self.vocab_size = vocab_size + self.projection_dim = projection_dim + if vision_config is not None: + vision_config = PretrainedConfig(**vision_config) + self.vision_config = vision_config + self.vocab_size = self.vocab_size + + self.text_config = text_config + if text_config is not None: + self.text_config = Florence2LanguageConfig(**text_config) + + + super().__init__(**kwargs) + diff --git a/OmniParser/Florence-2-base/modeling_florence2.py b/OmniParser/Florence-2-base/modeling_florence2.py new file mode 100644 index 0000000..e5ee651 --- /dev/null +++ b/OmniParser/Florence-2-base/modeling_florence2.py @@ -0,0 +1,2847 @@ +# coding=utf-8 +# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" PyTorch Florence-2 model.""" +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import math +import torch +import torch.utils.checkpoint +from torch import nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +from torch.nn import CrossEntropyLoss +from collections import OrderedDict +from einops import rearrange +from timm.models.layers import DropPath, trunc_normal_ + +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import ( + ModelOutput, + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_2_available, + logging, + replace_return_docstrings, + is_flash_attn_2_available, + is_flash_attn_greater_or_equal_2_10, +) +from .configuration_florence2 import Florence2Config +from .configuration_florence2 import Florence2LanguageConfig +from .configuration_florence2 import Florence2VisionConfig + + +from transformers.activations import ACT2FN +from transformers.modeling_attn_mask_utils import ( + _prepare_4d_attention_mask, + _prepare_4d_attention_mask_for_sdpa, + _prepare_4d_causal_attention_mask, + _prepare_4d_causal_attention_mask_for_sdpa, +) +from transformers.modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPastAndCrossAttentions, + Seq2SeqLMOutput, + Seq2SeqModelOutput, +) + + +if is_flash_attn_2_available(): + from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "Florence2Config" + +class LearnedAbsolutePositionEmbedding2D(nn.Module): + """ + This module learns positional embeddings up to a fixed maximum size. + """ + + def __init__(self, embedding_dim=256, num_pos=50): + super().__init__() + self.row_embeddings = nn.Embedding(num_pos, embedding_dim // 2) + self.column_embeddings = nn.Embedding(num_pos, embedding_dim - (embedding_dim // 2)) + + def forward(self, pixel_values): + """ + pixel_values: (batch_size, height, width, num_channels) + returns: (batch_size, height, width, embedding_dim * 2) + """ + if len(pixel_values.shape) != 4: + raise ValueError('pixel_values must be a 4D tensor') + height, width = pixel_values.shape[1:3] + width_values = torch.arange(width, device=pixel_values.device) + height_values = torch.arange(height, device=pixel_values.device) + x_emb = self.column_embeddings(width_values) + y_emb = self.row_embeddings(height_values) + # (height, width, embedding_dim * 2) + pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1) + # (embedding_dim * 2, height, width) + pos = pos.permute(2, 0, 1) + pos = pos.unsqueeze(0) + # (batch_size, embedding_dim * 2, height, width) + pos = pos.repeat(pixel_values.shape[0], 1, 1, 1) + # (batch_size, height, width, embedding_dim * 2) + pos = pos.permute(0, 2, 3, 1) + return pos + +class PositionalEmbeddingCosine1D(nn.Module): + """ + This class implements a very simple positional encoding. It follows closely + the encoder from the link below: + https://pytorch.org/tutorials/beginner/translation_transformer.html + + Args: + embed_dim: The dimension of the embeddings. + dropout_prob: The dropout probability. + max_seq_len: The maximum length to precompute the positional encodings. + """ + def __init__( + self, + embed_dim: int = 512, + max_seq_len: int = 1024) -> None: + super(PositionalEmbeddingCosine1D, self).__init__() + self.embed_dim = embed_dim + self.max_seq_len = max_seq_len + # Generate the sinusoidal arrays. + factor = math.log(10000) + denominator = torch.exp( + -factor * torch.arange(0, self.embed_dim, 2) / self.embed_dim) + # Matrix where rows correspond to a positional embedding as a function + # of the position index (i.e., the row index). + frequencies = \ + torch.arange(0, self.max_seq_len) \ + .reshape(self.max_seq_len, 1) * denominator + pos_idx_to_embed = torch.zeros((self.max_seq_len, self.embed_dim)) + # Populate uneven entries. + pos_idx_to_embed[:, 0::2] = torch.sin(frequencies) + pos_idx_to_embed[:, 1::2] = torch.cos(frequencies) + # Save the positional embeddings in a constant buffer. + self.register_buffer("pos_idx_to_embed", pos_idx_to_embed) + + def forward(self, seq_embeds: torch.Tensor) -> torch.Tensor: + """ + Args: + seq_embeds: The sequence embeddings in order. Allowed size: + 1. [T, D], where T is the length of the sequence, and D is the + frame embedding dimension. + 2. [B, T, D], where B is the batch size and T and D are the + same as above. + + Returns a tensor of with the same dimensions as the input: i.e., + [1, T, D] or [T, D]. + """ + shape_len = len(seq_embeds.shape) + assert 2 <= shape_len <= 3 + len_seq = seq_embeds.size(-2) + assert len_seq <= self.max_seq_len + pos_embeds = self.pos_idx_to_embed[0:seq_embeds.size(-2), :] + # Adapt pre-computed positional embeddings to the input. + if shape_len == 3: + pos_embeds = pos_embeds.view( + (1, pos_embeds.size(0), pos_embeds.size(1))) + return pos_embeds + + +class LearnedAbsolutePositionEmbedding1D(nn.Module): + """ + Learnable absolute positional embeddings for 1D sequences. + + Args: + embed_dim: The dimension of the embeddings. + max_seq_len: The maximum length to precompute the positional encodings. + """ + def __init__( + self, + embedding_dim: int = 512, + num_pos: int = 1024) -> None: + super(LearnedAbsolutePositionEmbedding1D, self).__init__() + self.embeddings = nn.Embedding(num_pos, embedding_dim) + self.num_pos = num_pos + + def forward(self, seq_embeds: torch.Tensor) -> torch.Tensor: + """ + Args: + seq_embeds: The sequence embeddings in order. Allowed size: + 1. [T, D], where T is the length of the sequence, and D is the + frame embedding dimension. + 2. [B, T, D], where B is the batch size and T and D are the + same as above. + + Returns a tensor of with the same dimensions as the input: i.e., + [1, T, D] or [T, D]. + """ + shape_len = len(seq_embeds.shape) + assert 2 <= shape_len <= 3 + len_seq = seq_embeds.size(-2) + assert len_seq <= self.num_pos + # [T, D] + pos_embeds = self.embeddings(torch.arange(len_seq).to(seq_embeds.device)) + # Adapt pre-computed positional embeddings to the input. + if shape_len == 3: + pos_embeds = pos_embeds.view( + (1, pos_embeds.size(0), pos_embeds.size(1))) + return pos_embeds + + + +class MySequential(nn.Sequential): + def forward(self, *inputs): + for module in self._modules.values(): + if type(inputs) == tuple: + inputs = module(*inputs) + else: + inputs = module(inputs) + return inputs + + +class PreNorm(nn.Module): + def __init__(self, norm, fn, drop_path=None): + super().__init__() + self.norm = norm + self.fn = fn + self.drop_path = drop_path + + def forward(self, x, *args, **kwargs): + shortcut = x + if self.norm != None: + x, size = self.fn(self.norm(x), *args, **kwargs) + else: + x, size = self.fn(x, *args, **kwargs) + + if self.drop_path: + x = self.drop_path(x) + + x = shortcut + x + + return x, size + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.net = nn.Sequential(OrderedDict([ + ("fc1", nn.Linear(in_features, hidden_features)), + ("act", act_layer()), + ("fc2", nn.Linear(hidden_features, out_features)) + ])) + + def forward(self, x, size): + return self.net(x), size + + +class DepthWiseConv2d(nn.Module): + def __init__( + self, + dim_in, + kernel_size, + padding, + stride, + bias=True, + ): + super().__init__() + self.dw = nn.Conv2d( + dim_in, dim_in, + kernel_size=kernel_size, + padding=padding, + groups=dim_in, + stride=stride, + bias=bias + ) + + def forward(self, x, size): + B, N, C = x.shape + H, W = size + assert N == H * W + + x = self.dw(x.transpose(1, 2).view(B, C, H, W)) + size = (x.size(-2), x.size(-1)) + x = x.flatten(2).transpose(1, 2) + return x, size + + +class ConvEmbed(nn.Module): + """ Image to Patch Embedding + """ + + def __init__( + self, + patch_size=7, + in_chans=3, + embed_dim=64, + stride=4, + padding=2, + norm_layer=None, + pre_norm=True + ): + super().__init__() + self.patch_size = patch_size + + self.proj = nn.Conv2d( + in_chans, embed_dim, + kernel_size=patch_size, + stride=stride, + padding=padding + ) + + dim_norm = in_chans if pre_norm else embed_dim + self.norm = norm_layer(dim_norm) if norm_layer else None + + self.pre_norm = pre_norm + + def forward(self, x, size): + H, W = size + if len(x.size()) == 3: + if self.norm and self.pre_norm: + x = self.norm(x) + x = rearrange( + x, 'b (h w) c -> b c h w', + h=H, w=W + ) + + x = self.proj(x) + + _, _, H, W = x.shape + x = rearrange(x, 'b c h w -> b (h w) c') + if self.norm and not self.pre_norm: + x = self.norm(x) + + return x, (H, W) + + +class ChannelAttention(nn.Module): + + def __init__(self, dim, groups=8, qkv_bias=True): + super().__init__() + + self.groups = groups + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.proj = nn.Linear(dim, dim) + + def forward(self, x, size): + B, N, C = x.shape + + qkv = self.qkv(x).reshape(B, N, 3, self.groups, C // self.groups).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + q = q * (float(N) ** -0.5) + attention = q.transpose(-1, -2) @ k + attention = attention.softmax(dim=-1) + x = (attention @ v.transpose(-1, -2)).transpose(-1, -2) + x = x.transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + return x, size + + +class ChannelBlock(nn.Module): + + def __init__(self, dim, groups, mlp_ratio=4., qkv_bias=True, + drop_path_rate=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, + conv_at_attn=True, conv_at_ffn=True): + super().__init__() + + drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity() + + self.conv1 = PreNorm(None, DepthWiseConv2d(dim, 3, 1, 1)) if conv_at_attn else None + self.channel_attn = PreNorm( + norm_layer(dim), + ChannelAttention(dim, groups=groups, qkv_bias=qkv_bias), + drop_path + ) + self.conv2 = PreNorm(None, DepthWiseConv2d(dim, 3, 1, 1)) if conv_at_ffn else None + self.ffn = PreNorm( + norm_layer(dim), + Mlp(in_features=dim, hidden_features=int(dim*mlp_ratio), act_layer=act_layer), + drop_path + ) + + def forward(self, x, size): + if self.conv1: + x, size = self.conv1(x, size) + x, size = self.channel_attn(x, size) + + if self.conv2: + x, size = self.conv2(x, size) + x, size = self.ffn(x, size) + + return x, size + + +def window_partition(x, window_size: int): + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, batch_size: int, window_size: int, H: int, W: int): + B = batch_size + # this will cause onnx conversion failed for dynamic axis, because treated as constant + # int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + def __init__(self, dim, num_heads, window_size, qkv_bias=True): + + super().__init__() + self.dim = dim + self.window_size = window_size + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = float(head_dim) ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.proj = nn.Linear(dim, dim) + + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, size): + + H, W = size + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + x = x.view(B, H, W, C) + + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + x = window_partition(x, self.window_size) + x = x.view(-1, self.window_size * self.window_size, C) + + # W-MSA/SW-MSA + # attn_windows = self.attn(x_windows) + + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + attn = self.softmax(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + + # merge windows + x = x.view( + -1, self.window_size, self.window_size, C + ) + x = window_reverse(x, B, self.window_size, Hp, Wp) + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + return x, size + + +class SpatialBlock(nn.Module): + + def __init__(self, dim, num_heads, window_size, + mlp_ratio=4., qkv_bias=True, drop_path_rate=0., act_layer=nn.GELU, + norm_layer=nn.LayerNorm, conv_at_attn=True, conv_at_ffn=True): + super().__init__() + + drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity() + + self.conv1 = PreNorm(None, DepthWiseConv2d(dim, 3, 1, 1)) if conv_at_attn else None + self.window_attn = PreNorm( + norm_layer(dim), + WindowAttention(dim, num_heads, window_size, qkv_bias=qkv_bias), + drop_path + ) + self.conv2 = PreNorm(None, DepthWiseConv2d(dim, 3, 1, 1)) if conv_at_ffn else None + self.ffn = PreNorm( + norm_layer(dim), + Mlp(in_features=dim, hidden_features=int(dim*mlp_ratio), act_layer=act_layer), + drop_path + ) + + def forward(self, x, size): + if self.conv1: + x, size = self.conv1(x, size) + x, size = self.window_attn(x, size) + + if self.conv2: + x, size = self.conv2(x, size) + x, size = self.ffn(x, size) + return x, size + + +class DaViT(nn.Module): + """ DaViT: Dual-Attention Transformer + + Args: + in_chans (int): Number of input image channels. Default: 3. + num_classes (int): Number of classes for classification head. Default: 1000. + patch_size (tuple(int)): Patch size of convolution in different stages. Default: (7, 2, 2, 2). + patch_stride (tuple(int)): Patch stride of convolution in different stages. Default: (4, 2, 2, 2). + patch_padding (tuple(int)): Patch padding of convolution in different stages. Default: (3, 0, 0, 0). + patch_prenorm (tuple(bool)): If True, perform norm before convlution layer. Default: (True, False, False, False). + embed_dims (tuple(int)): Patch embedding dimension in different stages. Default: (64, 128, 192, 256). + num_heads (tuple(int)): Number of spatial attention heads in different stages. Default: (4, 8, 12, 16). + num_groups (tuple(int)): Number of channel groups in different stages. Default: (4, 8, 12, 16). + window_size (int): Window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True. + drop_path_rate (float): Stochastic depth rate. Default: 0.1. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + enable_checkpoint (bool): If True, enable checkpointing. Default: False. + conv_at_attn (bool): If True, performe depthwise convolution before attention layer. Default: True. + conv_at_ffn (bool): If True, performe depthwise convolution before ffn layer. Default: True. + """ + + def __init__( + self, + in_chans=3, + num_classes=1000, + depths=(1, 1, 3, 1), + patch_size=(7, 2, 2, 2), + patch_stride=(4, 2, 2, 2), + patch_padding=(3, 0, 0, 0), + patch_prenorm=(False, False, False, False), + embed_dims=(64, 128, 192, 256), + num_heads=(3, 6, 12, 24), + num_groups=(3, 6, 12, 24), + window_size=7, + mlp_ratio=4., + qkv_bias=True, + drop_path_rate=0.1, + norm_layer=nn.LayerNorm, + enable_checkpoint=False, + conv_at_attn=True, + conv_at_ffn=True, + ): + super().__init__() + + self.num_classes = num_classes + self.embed_dims = embed_dims + self.num_heads = num_heads + self.num_groups = num_groups + self.num_stages = len(self.embed_dims) + self.enable_checkpoint = enable_checkpoint + assert self.num_stages == len(self.num_heads) == len(self.num_groups) + + num_stages = len(embed_dims) + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)*2)] + + depth_offset = 0 + convs = [] + blocks = [] + for i in range(num_stages): + conv_embed = ConvEmbed( + patch_size=patch_size[i], + stride=patch_stride[i], + padding=patch_padding[i], + in_chans=in_chans if i == 0 else self.embed_dims[i - 1], + embed_dim=self.embed_dims[i], + norm_layer=norm_layer, + pre_norm=patch_prenorm[i] + ) + convs.append(conv_embed) + + block = MySequential( + *[ + MySequential(OrderedDict([ + ( + 'spatial_block', SpatialBlock( + embed_dims[i], + num_heads[i], + window_size, + drop_path_rate=dpr[depth_offset+j*2], + qkv_bias=qkv_bias, + mlp_ratio=mlp_ratio, + conv_at_attn=conv_at_attn, + conv_at_ffn=conv_at_ffn, + ) + ), + ( + 'channel_block', ChannelBlock( + embed_dims[i], + num_groups[i], + drop_path_rate=dpr[depth_offset+j*2+1], + qkv_bias=qkv_bias, + mlp_ratio=mlp_ratio, + conv_at_attn=conv_at_attn, + conv_at_ffn=conv_at_ffn, + ) + ) + ])) for j in range(depths[i]) + ] + ) + blocks.append(block) + depth_offset += depths[i]*2 + + self.convs = nn.ModuleList(convs) + self.blocks = nn.ModuleList(blocks) + + self.norms = norm_layer(self.embed_dims[-1]) + self.avgpool = nn.AdaptiveAvgPool1d(1) + self.head = nn.Linear(self.embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity() + + self.apply(self._init_weights) + + @property + def dim_out(self): + return self.embed_dims[-1] + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Conv2d): + nn.init.normal_(m.weight, std=0.02) + for name, _ in m.named_parameters(): + if name in ['bias']: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.weight, 1.0) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1.0) + nn.init.constant_(m.bias, 0) + + def forward_features_unpool(self, x): + """ + forward until avg pooling + Args: + x (_type_): input image tensor + """ + input_size = (x.size(2), x.size(3)) + for conv, block in zip(self.convs, self.blocks): + x, input_size = conv(x, input_size) + if self.enable_checkpoint: + x, input_size = checkpoint.checkpoint(block, x, input_size) + else: + x, input_size = block(x, input_size) + return x + + def forward_features(self, x): + x = self.forward_features_unpool(x) + + # (batch_size, num_tokens, token_dim) + x = self.avgpool(x.transpose(1, 2)) + # (batch_size, 1, num_tokens) + x = torch.flatten(x, 1) + x = self.norms(x) + + return x + + def forward(self, x): + x = self.forward_features(x) + x = self.head(x) + return x + + @classmethod + def from_config(cls, config): + return cls( + depths=config.depths, + embed_dims=config.dim_embed, + num_heads=config.num_heads, + num_groups=config.num_groups, + patch_size=config.patch_size, + patch_stride=config.patch_stride, + patch_padding=config.patch_padding, + patch_prenorm=config.patch_prenorm, + drop_path_rate=config.drop_path_rate, + window_size=config.window_size, + ) + + + + +if is_flash_attn_2_available(): + from flash_attn import flash_attn_func, flash_attn_varlen_func + from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa + +# Copied from transformers.models.llama.modeling_llama._get_unpad_data +def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + +def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): + """ + Shift input ids one token to the right. + """ + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() + shifted_input_ids[:, 0] = decoder_start_token_id + + if pad_token_id is None: + raise ValueError("self.model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) + + return shifted_input_ids + + +class Florence2LearnedPositionalEmbedding(nn.Embedding): + """ + This module learns positional embeddings up to a fixed maximum size. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int): + # Florence2 is set up so that if padding_idx is specified then offset the embedding ids by 2 + # and adjust num_embeddings appropriately. Other models don't have this hack + self.offset = 2 + super().__init__(num_embeddings + self.offset, embedding_dim) + + def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0): + """`input_ids' shape is expected to be [bsz x seqlen].""" + + bsz, seq_len = input_ids.shape[:2] + positions = torch.arange( + past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device + ).expand(bsz, -1) + + return super().forward(positions + self.offset) + + +class Florence2ScaledWordEmbedding(nn.Embedding): + """ + This module overrides nn.Embeddings' forward by multiplying with embeddings scale. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0): + super().__init__(num_embeddings, embedding_dim, padding_idx) + self.embed_scale = embed_scale + + def forward(self, input_ids: torch.Tensor): + return super().forward(input_ids) * self.embed_scale + + +class Florence2Attention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + is_causal: bool = False, + config: Optional[Florence2LanguageConfig] = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + bsz, tgt_len, _ = hidden_states.size() + + # get query proj + query_states = self.q_proj(hidden_states) * self.scaling + # get key, value proj + # `past_key_value[0].shape[2] == key_value_states.shape[1]` + # is checking that the `sequence_length` of the `past_key_value` is the same as + # the provided `key_value_states` to support prefix tuning + if ( + is_cross_attention + and past_key_value is not None + and past_key_value[0].shape[2] == key_value_states.shape[1] + ): + # reuse k,v, cross_attentions + key_states = past_key_value[0] + value_states = past_key_value[1] + elif is_cross_attention: + # cross_attentions + key_states = self._shape(self.k_proj(key_value_states), -1, bsz) + value_states = self._shape(self.v_proj(key_value_states), -1, bsz) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + else: + # self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states, value_states) + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) + key_states = key_states.reshape(*proj_shape) + value_states = value_states.reshape(*proj_shape) + + src_len = key_states.size(1) + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if layer_head_mask is not None: + if layer_head_mask.size() != (self.num_heads,): + raise ValueError( + f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" + f" {layer_head_mask.size()}" + ) + attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + if output_attentions: + # this operation is a bit awkward, but it's required to + # make sure that attn_weights keeps its gradient. + # In order to do so, attn_weights have to be reshaped + # twice and have to be reused in the following + attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) + else: + attn_weights_reshaped = None + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.bmm(attn_probs, value_states) + + if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) + attn_output = attn_output.transpose(1, 2) + + # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be + # partitioned across GPUs when using tensor-parallelism. + attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights_reshaped, past_key_value + + +class Florence2FlashAttention2(Florence2Attention): + """ + Florence2 flash attention module. This module inherits from `Florence2Attention` as the weights of the module stays + untouched. The only required change would be on the forward pass where it needs to correctly call the public API of + flash attention and deal with padding tokens in case the input contains any of them. + """ + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def _reshape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim) + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + # Florence2FlashAttention2 attention does not support output_attentions + if output_attentions: + raise ValueError("Florence2FlashAttention2 attention does not support output_attentions") + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + bsz, q_len, _ = hidden_states.size() + + # get query proj + query_states = self._reshape(self.q_proj(hidden_states), -1, bsz) + # get key, value proj + # `past_key_value[0].shape[2] == key_value_states.shape[1]` + # is checking that the `sequence_length` of the `past_key_value` is the same as + # the provided `key_value_states` to support prefix tuning + if ( + is_cross_attention + and past_key_value is not None + and past_key_value[0].shape[2] == key_value_states.shape[1] + ): + # reuse k,v, cross_attentions + key_states = past_key_value[0].transpose(1, 2) + value_states = past_key_value[1].transpose(1, 2) + elif is_cross_attention: + # cross_attentions + key_states = self._reshape(self.k_proj(key_value_states), -1, bsz) + value_states = self._reshape(self.v_proj(key_value_states), -1, bsz) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self._reshape(self.k_proj(hidden_states), -1, bsz) + value_states = self._reshape(self.v_proj(hidden_states), -1, bsz) + key_states = torch.cat([past_key_value[0].transpose(1, 2), key_states], dim=1) + value_states = torch.cat([past_key_value[1].transpose(1, 2), value_states], dim=1) + else: + # self_attention + key_states = self._reshape(self.k_proj(hidden_states), -1, bsz) + value_states = self._reshape(self.v_proj(hidden_states), -1, bsz) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states.transpose(1, 2), value_states.transpose(1, 2)) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in the correct dtype just to be sure everything works as expected. + # This might slowdown training & inference so it is recommended to not cast the LayerNorms + # in fp32. (LlamaRMSNorm handles it correctly) + + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + attn_output = self._flash_attention_forward( + query_states, key_states, value_states, attention_mask, q_len, dropout=self.dropout + ) + + attn_output = attn_output.reshape(bsz, q_len, -1) + attn_output = self.out_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward + def _flash_attention_forward( + self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None + ): + """ + Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token + first unpad the input, then computes the attention scores and pad the final attention scores. + + Args: + query_states (`torch.Tensor`): + Input query states to be passed to Flash Attention API + key_states (`torch.Tensor`): + Input key states to be passed to Flash Attention API + value_states (`torch.Tensor`): + Input value states to be passed to Flash Attention API + attention_mask (`torch.Tensor`): + The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the + position of padding tokens and 1 for the position of non-padding tokens. + dropout (`float`): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + """ + if not self._flash_attn_uses_top_left_mask: + causal = self.is_causal + else: + # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. + causal = self.is_causal and query_length != 1 + + # Contains at least one padding token in the sequence + if attention_mask is not None: + batch_size = query_states.shape[0] + query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( + query_states, key_states, value_states, attention_mask, query_length + ) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + + attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) + else: + attn_output = flash_attn_func( + query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal + ) + + return attn_output + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input + def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape + + key_layer = index_first_axis( + key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k + ) + value_layer = index_first_axis( + value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k + ) + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +class Florence2SdpaAttention(Florence2Attention): + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + if output_attentions or layer_head_mask is not None: + # TODO: Improve this warning with e.g. `model.config._attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "Florence2Model is using Florence2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True` or `layer_head_mask` not None. Falling back to the manual attention" + ' implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + hidden_states, + key_value_states=key_value_states, + past_key_value=past_key_value, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + output_attentions=output_attentions, + ) + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + bsz, tgt_len, _ = hidden_states.size() + + # get query proj + query_states = self.q_proj(hidden_states) + # get key, value proj + # `past_key_value[0].shape[2] == key_value_states.shape[1]` + # is checking that the `sequence_length` of the `past_key_value` is the same as + # the provided `key_value_states` to support prefix tuning + if ( + is_cross_attention + and past_key_value is not None + and past_key_value[0].shape[2] == key_value_states.shape[1] + ): + # reuse k,v, cross_attentions + key_states = past_key_value[0] + value_states = past_key_value[1] + elif is_cross_attention: + # cross_attentions + key_states = self._shape(self.k_proj(key_value_states), -1, bsz) + value_states = self._shape(self.v_proj(key_value_states), -1, bsz) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + else: + # self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states, value_states) + + query_states = self._shape(query_states, tgt_len, bsz) + + # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment + # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. + # The tgt_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case tgt_len == 1. + is_causal = True if self.is_causal and attention_mask is None and tgt_len > 1 else False + + # NOTE: SDPA with memory-efficient backend is currently (torch==2.1.2) bugged when using non-contiguous inputs and a custom attn_mask, + # but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577 + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=attention_mask, + dropout_p=self.dropout if self.training else 0.0, + is_causal=is_causal, + ) + + if attn_output.size() != (bsz, self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2) + + # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be + # partitioned across GPUs when using tensor-parallelism. + attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, None, past_key_value + + +FLORENCE2_ATTENTION_CLASSES = { + "eager": Florence2Attention, + "sdpa": Florence2SdpaAttention, + "flash_attention_2": Florence2FlashAttention2, +} + + +class Florence2EncoderLayer(nn.Module): + def __init__(self, config: Florence2LanguageConfig): + super().__init__() + self.embed_dim = config.d_model + + self.self_attn = FLORENCE2_ATTENTION_CLASSES[config._attn_implementation]( + embed_dim=self.embed_dim, + num_heads=config.encoder_attention_heads, + dropout=config.attention_dropout, + config=config, + ) + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) + self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: torch.FloatTensor, + layer_head_mask: torch.FloatTensor, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size + `(encoder_attention_heads,)`. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + hidden_states, attn_weights, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + output_attentions=output_attentions, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + residual = hidden_states + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + if hidden_states.dtype == torch.float16 and ( + torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any() + ): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +class Florence2DecoderLayer(nn.Module): + def __init__(self, config: Florence2LanguageConfig): + super().__init__() + self.embed_dim = config.d_model + + self.self_attn = FLORENCE2_ATTENTION_CLASSES[config._attn_implementation]( + embed_dim=self.embed_dim, + num_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + is_causal=True, + config=config, + ) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.encoder_attn = FLORENCE2_ATTENTION_CLASSES[config._attn_implementation]( + self.embed_dim, + config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + config=config, + ) + self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) + self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + cross_attn_layer_head_mask: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = True, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(batch, seq_len, embed_dim)` + encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size + `(encoder_attention_heads,)`. + cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of + size `(decoder_attention_heads,)`. + past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + + # Self Attention + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + # add present self-attn cache to positions 1,2 of present_key_value tuple + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + past_key_value=self_attn_past_key_value, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + output_attentions=output_attentions, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Cross-Attention Block + cross_attn_present_key_value = None + cross_attn_weights = None + if encoder_hidden_states is not None: + residual = hidden_states + + # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple + cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None + hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( + hidden_states=hidden_states, + key_value_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + layer_head_mask=cross_attn_layer_head_mask, + past_key_value=cross_attn_past_key_value, + output_attentions=output_attentions, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + # add cross-attn to positions 3,4 of present_key_value tuple + present_key_value = present_key_value + cross_attn_present_key_value + + # Fully Connected + residual = hidden_states + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights, cross_attn_weights) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + + +class Florence2LanguagePreTrainedModel(PreTrainedModel): + config_class = Florence2LanguageConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _keys_to_ignore_on_load_unexpected = ["encoder.version", "decoder.version"] + _no_split_modules = [r"Florence2EncoderLayer", r"Florence2DecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_sdpa = True + + def _init_weights(self, module): + std = self.config.init_std + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + @property + def dummy_inputs(self): + pad_token = self.config.pad_token_id + input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device) + dummy_inputs = { + "attention_mask": input_ids.ne(pad_token), + "input_ids": input_ids, + } + return dummy_inputs + + +class Florence2Encoder(Florence2LanguagePreTrainedModel): + """ + Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a + [`Florence2EncoderLayer`]. + + Args: + config: Florence2LanguageConfig + embed_tokens (nn.Embedding): output embedding + """ + + def __init__(self, config: Florence2LanguageConfig, embed_tokens: Optional[nn.Embedding] = None): + super().__init__(config) + + self.dropout = config.dropout + self.layerdrop = config.encoder_layerdrop + + embed_dim = config.d_model + self.padding_idx = config.pad_token_id + self.max_source_positions = config.max_position_embeddings + embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 + + self.embed_tokens = Florence2ScaledWordEmbedding( + config.vocab_size, embed_dim, self.padding_idx, embed_scale=embed_scale + ) + + if embed_tokens is not None: + self.embed_tokens.weight = embed_tokens.weight + + self.embed_positions = Florence2LearnedPositionalEmbedding( + config.max_position_embeddings, + embed_dim, + ) + self.layers = nn.ModuleList([Florence2EncoderLayer(config) for _ in range(config.encoder_layers)]) + self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" + self._use_sdpa = config._attn_implementation == "sdpa" + self.layernorm_embedding = nn.LayerNorm(embed_dim) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutput]: + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input = input_ids + input_ids = input_ids.view(-1, input_ids.shape[-1]) + elif inputs_embeds is not None: + input = inputs_embeds[:, :, -1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + embed_pos = self.embed_positions(input) + embed_pos = embed_pos.to(inputs_embeds.device) + + hidden_states = inputs_embeds + embed_pos + hidden_states = self.layernorm_embedding(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + # expand attention_mask + if attention_mask is not None: + if self._use_flash_attention_2: + attention_mask = attention_mask if 0 in attention_mask else None + elif self._use_sdpa and head_mask is None and not output_attentions: + # output_attentions=True & head_mask can not be supported when using SDPA, fall back to + # the manual implementation that requires a 4D causal mask in all cases. + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + attention_mask = _prepare_4d_attention_mask_for_sdpa(attention_mask, inputs_embeds.dtype) + else: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype) + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + # check if head_mask has a correct number of layers specified if desired + if head_mask is not None: + if head_mask.size()[0] != (len(self.layers)): + raise ValueError( + f"The head_mask should be specified for {len(self.layers)} layers, but it is for" + f" {head_mask.size()[0]}." + ) + + for idx, encoder_layer in enumerate(self.layers): + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + to_drop = False + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: # skip the layer + to_drop = True + + if to_drop: + layer_outputs = (None, None) + else: + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + encoder_layer.__call__, + hidden_states, + attention_mask, + (head_mask[idx] if head_mask is not None else None), + output_attentions, + ) + else: + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + layer_head_mask=(head_mask[idx] if head_mask is not None else None), + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions + ) + + +class Florence2Decoder(Florence2LanguagePreTrainedModel): + """ + Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`Florence2DecoderLayer`] + + Args: + config: Florence2LanguageConfig + embed_tokens (nn.Embedding): output embedding + """ + + def __init__(self, config: Florence2LanguageConfig, embed_tokens: Optional[nn.Embedding] = None): + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.decoder_layerdrop + self.padding_idx = config.pad_token_id + self.max_target_positions = config.max_position_embeddings + embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 + + self.embed_tokens = Florence2ScaledWordEmbedding( + config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale + ) + + if embed_tokens is not None: + self.embed_tokens.weight = embed_tokens.weight + + self.embed_positions = Florence2LearnedPositionalEmbedding( + config.max_position_embeddings, + config.d_model, + ) + self.layers = nn.ModuleList([Florence2DecoderLayer(config) for _ in range(config.decoder_layers)]) + self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" + self._use_sdpa = config._attn_implementation == "sdpa" + + self.layernorm_embedding = nn.LayerNorm(config.d_model) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.Tensor] = None, + cross_attn_head_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): + Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values + selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing + cross-attention on hidden heads. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of + shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the + cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those + that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of + all `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + input = input_ids + input_shape = input.shape + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + input = inputs_embeds[:, :, -1] + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input) + + if self._use_flash_attention_2: + # 2d mask is passed through the layers + attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None + elif self._use_sdpa and not output_attentions and cross_attn_head_mask is None: + # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on + # the manual implementation that requires a 4D causal mask in all cases. + attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( + attention_mask, + input_shape, + inputs_embeds, + past_key_values_length, + ) + else: + # 4d mask is passed through the layers + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, input_shape, inputs_embeds, past_key_values_length + ) + + # expand encoder attention mask + if encoder_hidden_states is not None and encoder_attention_mask is not None: + if self._use_flash_attention_2: + encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None + elif self._use_sdpa and cross_attn_head_mask is None and not output_attentions: + # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on + # the manual implementation that requires a 4D causal mask in all cases. + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa( + encoder_attention_mask, + inputs_embeds.dtype, + tgt_len=input_shape[-1], + ) + else: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + encoder_attention_mask = _prepare_4d_attention_mask( + encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] + ) + + # embed positions + positions = self.embed_positions(input, past_key_values_length) + positions = positions.to(inputs_embeds.device) + + hidden_states = inputs_embeds + positions + hidden_states = self.layernorm_embedding(hidden_states) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None + next_decoder_cache = () if use_cache else None + + # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired + for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]): + if attn_mask is not None: + if attn_mask.size()[0] != (len(self.layers)): + raise ValueError( + f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" + f" {head_mask.size()[0]}." + ) + + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + if output_hidden_states: + all_hidden_states += (hidden_states,) + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: + continue + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + encoder_hidden_states, + encoder_attention_mask, + head_mask[idx] if head_mask is not None else None, + cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, + None, + output_attentions, + use_cache, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + layer_head_mask=(head_mask[idx] if head_mask is not None else None), + cross_attn_layer_head_mask=( + cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None + ), + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[3 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if encoder_hidden_states is not None: + all_cross_attentions += (layer_outputs[2],) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if not return_dict: + return tuple( + v + for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + cross_attentions=all_cross_attentions, + ) + + +class Florence2LanguageModel(Florence2LanguagePreTrainedModel): + _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"] + + def __init__(self, config: Florence2LanguageConfig): + super().__init__(config) + + padding_idx, vocab_size = config.pad_token_id, config.vocab_size + self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx) + + self.encoder = Florence2Encoder(config, self.shared) + self.decoder = Florence2Decoder(config, self.shared) + + # Initialize weights and apply final processing + self.post_init() + + def _tie_weights(self): + if self.config.tie_word_embeddings: + self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared) + self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared) + + def get_input_embeddings(self): + return self.shared + + def set_input_embeddings(self, value): + self.shared = value + self.encoder.embed_tokens = self.shared + self.decoder.embed_tokens = self.shared + + def get_encoder(self): + return self.encoder + + def get_decoder(self): + return self.decoder + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + decoder_input_ids: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.Tensor] = None, + decoder_head_mask: Optional[torch.Tensor] = None, + cross_attn_head_mask: Optional[torch.Tensor] = None, + encoder_outputs: Optional[List[torch.FloatTensor]] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + decoder_inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, Seq2SeqModelOutput]: + # different to other models, Florence2 automatically creates decoder_input_ids from + # input_ids if no decoder_input_ids are provided + if decoder_input_ids is None and decoder_inputs_embeds is None: + if input_ids is None: + raise ValueError( + "If no `decoder_input_ids` or `decoder_inputs_embeds` are " + "passed, `input_ids` cannot be `None`. Please pass either " + "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`." + ) + + decoder_input_ids = shift_tokens_right( + input_ids, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if encoder_outputs is None: + encoder_outputs = self.encoder( + input_ids=input_ids, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn) + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=attention_mask, + head_mask=decoder_head_mask, + cross_attn_head_mask=cross_attn_head_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if not return_dict: + return decoder_outputs + encoder_outputs + + return Seq2SeqModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + +class Florence2LanguageForConditionalGeneration(Florence2LanguagePreTrainedModel): + base_model_prefix = "model" + _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"] + _keys_to_ignore_on_load_missing = ["final_logits_bias"] + + def __init__(self, config: Florence2LanguageConfig): + super().__init__(config) + self.model = Florence2LanguageModel(config) + self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings))) + self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_encoder(self): + return self.model.get_encoder() + + def get_decoder(self): + return self.model.get_decoder() + + def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding: + new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of) + self._resize_final_logits_bias(new_embeddings.weight.shape[0]) + return new_embeddings + + def _resize_final_logits_bias(self, new_num_tokens: int) -> None: + old_num_tokens = self.final_logits_bias.shape[-1] + if new_num_tokens <= old_num_tokens: + new_bias = self.final_logits_bias[:, :new_num_tokens] + else: + extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device) + new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1) + self.register_buffer("final_logits_bias", new_bias) + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + decoder_input_ids: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.Tensor] = None, + decoder_head_mask: Optional[torch.Tensor] = None, + cross_attn_head_mask: Optional[torch.Tensor] = None, + encoder_outputs: Optional[List[torch.FloatTensor]] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + decoder_inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, Seq2SeqLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if labels is not None: + if use_cache: + logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") + use_cache = False + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + outputs = self.model( + input_ids, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + encoder_outputs=encoder_outputs, + decoder_attention_mask=decoder_attention_mask, + head_mask=head_mask, + decoder_head_mask=decoder_head_mask, + cross_attn_head_mask=cross_attn_head_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + lm_logits = self.lm_head(outputs[0]) + lm_logits = lm_logits + self.final_logits_bias.to(lm_logits.device) + + masked_lm_loss = None + if labels is not None: + labels = labels.to(lm_logits.device) + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (lm_logits,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + def prepare_inputs_for_generation( + self, + decoder_input_ids, + past_key_values=None, + attention_mask=None, + decoder_attention_mask=None, + head_mask=None, + decoder_head_mask=None, + cross_attn_head_mask=None, + use_cache=None, + encoder_outputs=None, + **kwargs, + ): + # cut decoder_input_ids if past_key_values is used + if past_key_values is not None: + past_length = past_key_values[0][0].shape[2] + + # Some generation methods already pass only the last input ID + if decoder_input_ids.shape[1] > past_length: + remove_prefix_length = past_length + else: + # Default to old behavior: keep only final ID + remove_prefix_length = decoder_input_ids.shape[1] - 1 + + decoder_input_ids = decoder_input_ids[:, remove_prefix_length:] + + return { + "input_ids": None, # encoder_outputs is defined. input_ids not needed + "encoder_outputs": encoder_outputs, + "past_key_values": past_key_values, + "decoder_input_ids": decoder_input_ids, + "attention_mask": attention_mask, + "decoder_attention_mask": decoder_attention_mask, + "head_mask": head_mask, + "decoder_head_mask": decoder_head_mask, + "cross_attn_head_mask": cross_attn_head_mask, + "use_cache": use_cache, # change this to avoid caching (presumably for debugging) + } + + def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): + return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id) + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + # cached cross_attention states don't have to be reordered -> they are always the same + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2]) + + layer_past[2:], + ) + return reordered_past + +@dataclass +class Florence2Seq2SeqLMOutput(ModelOutput): + """ + Base class for Florence-2 model's outputs that also contains : pre-computed hidden states that can speed up sequential + decoding. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss. + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + + If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, + hidden_size)` is output. + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. + decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the + self-attention heads. + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the + weighted average in the cross-attention heads. + encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder of the model. + encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs. + encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the + self-attention heads. + image_hidden_states (`tuple(torch.FloatTensor)`, *optional*): + Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, + num_image_tokens, hidden_size)`. + + image_hidden_states of the model produced by the vision encoder + """ + loss: Optional[torch.FloatTensor] = None + logits: torch.FloatTensor = None + last_hidden_state: torch.FloatTensor = None + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None + decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None + decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None + cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None + encoder_last_hidden_state: Optional[torch.FloatTensor] = None + encoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None + encoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None + image_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None + + +FLORENCE2_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`Florence2Config`] or [`Florence2VisionConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare Florence-2 Model outputting raw hidden-states without any specific head on top.", + FLORENCE2_START_DOCSTRING, +) +class Florence2PreTrainedModel(PreTrainedModel): + config_class = Florence2Config + base_model_prefix = "model" + supports_gradient_checkpointing = True + _skip_keys_device_placement = "past_key_values" + + @property + def _supports_flash_attn_2(self): + """ + Retrieve language_model's attribute to check whether the model supports + Flash Attention 2 or not. + """ + return self.language_model._supports_flash_attn_2 + + @property + def _supports_sdpa(self): + """ + Retrieve language_model's attribute to check whether the model supports + SDPA or not. + """ + return self.language_model._supports_sdpa + + +FLORENCE2_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)): + The tensors corresponding to the input images. Pixel values can be obtained using + [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details ([]`Florence2Processor`] uses + [`CLIPImageProcessor`] for processing images). + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + +@add_start_docstrings( + """The FLORENCE2 vision model without any head""", + FLORENCE2_START_DOCSTRING, +) +class Florence2VisionModel(Florence2PreTrainedModel): + def __init__(self, config: Florence2VisionConfig): + super().__init__(config) + assert config.model_type == 'davit', 'only DaViT is supported for now' + self.vision_tower = DaViT.from_config(config=config) + + self.post_init() + + def forward(self, pixel_values): + if len(pixel_values.shape) == 4: + x = self.vision_tower.forward_features_unpool(pixel_values) + else: + raise ValueError(f'invalid image shape {pixel_values.shape}') + return x + + +@add_start_docstrings( + """The FLORENCE2 vision model with projection layer""", + FLORENCE2_START_DOCSTRING, +) +class Florence2VisionModelWithProjection(Florence2PreTrainedModel): + def __init__(self, config: Florence2VisionConfig): + super().__init__(config) + assert config.model_type == 'davit', 'only DaViT is supported for now' + self.vision_tower = DaViT.from_config(config=config) + + self._build_image_projection_layers(config) + + self.post_init() + + def _build_image_projection_layers(self, config): + image_dim_out = config.dim_embed[-1] + dim_projection = config.projection_dim + self.image_projection = nn.Parameter( + torch.empty(image_dim_out, dim_projection) + ) + self.image_proj_norm = nn.LayerNorm(dim_projection) + image_pos_embed_config = config.image_pos_embed + if image_pos_embed_config['type'] == 'learned_abs_2d': + self.image_pos_embed = LearnedAbsolutePositionEmbedding2D( + embedding_dim=image_dim_out, + num_pos=image_pos_embed_config['max_pos_embeddings'] + ) + else: + raise NotImplementedError('Not implemented yet') + + self.image_feature_source = config.image_feature_source + + # temporal embedding + visual_temporal_embedding_config = config.visual_temporal_embedding + if visual_temporal_embedding_config['type'] == 'COSINE': + self.visual_temporal_embed = PositionalEmbeddingCosine1D( + embed_dim=image_dim_out, + max_seq_len=visual_temporal_embedding_config['max_temporal_embeddings'] + ) + else: + raise NotImplementedError('Not implemented yet') + + def forward(self, pixel_values): + if len(pixel_values.shape) == 4: + batch_size, C, H, W = pixel_values.shape + T = 1 + x = self.vision_tower.forward_features_unpool(pixel_values) + else: + raise ValueError(f'invalid image shape {pixel_values.shape}') + + if self.image_pos_embed is not None: + x = x.view(batch_size * T, -1, x.shape[-1]) + num_tokens = x.shape[-2] + h, w = int(num_tokens ** 0.5), int(num_tokens ** 0.5) + assert h * w == num_tokens, 'only support square feature maps for now' + x = x.view(batch_size * T, h, w, x.shape[-1]) + pos_embed = self.image_pos_embed(x) + x = x + pos_embed + x = x.view(batch_size, T * h*w, x.shape[-1]) + + if self.visual_temporal_embed is not None: + visual_temporal_embed = self.visual_temporal_embed(x.view(batch_size, T, -1, x.shape[-1])[:, :, 0]) + x = x.view(batch_size, T, -1, x.shape[-1]) + visual_temporal_embed.view(1, T, 1, x.shape[-1]) + + x_feat_dict = {} + + spatial_avg_pool_x = x.view(batch_size, T, -1, x.shape[-1]).mean(dim=2) + x_feat_dict['spatial_avg_pool'] = spatial_avg_pool_x + + temporal_avg_pool_x = x.view(batch_size, T, -1, x.shape[-1]).mean(dim=1) + x_feat_dict['temporal_avg_pool'] = temporal_avg_pool_x + + x = x.view(batch_size, T, -1, x.shape[-1])[:, -1] + x_feat_dict['last_frame'] = x + + new_x = [] + for _image_feature_source in self.image_feature_source: + if _image_feature_source not in x_feat_dict: + raise ValueError('invalid image feature source: {}'.format(_image_feature_source)) + new_x.append(x_feat_dict[_image_feature_source]) + + x = torch.cat(new_x, dim=1) + + x = x @ self.image_projection + x = self.image_proj_norm(x) + + + return x + + + +@add_start_docstrings( + """The FLORENCE2 model which consists of a vision backbone and a language model.""", + FLORENCE2_START_DOCSTRING, +) +class Florence2ForConditionalGeneration(Florence2PreTrainedModel): + def __init__(self, config: Florence2Config): + super().__init__(config) + assert config.vision_config.model_type == 'davit', 'only DaViT is supported for now' + self.vision_tower = DaViT.from_config(config=config.vision_config) + # remove unused layers + del self.vision_tower.head + del self.vision_tower.norms + + self.vocab_size = config.vocab_size + self._attn_implementation = config._attn_implementation + self._build_image_projection_layers(config) + + language_model = Florence2LanguageForConditionalGeneration(config=config.text_config) + + if language_model._tied_weights_keys is not None: + self._tied_weights_keys = [f"language_model.{k}" for k in language_model._tied_weights_keys] + self.language_model = language_model + + self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1 + self.post_init() + + def _build_image_projection_layers(self, config): + image_dim_out = config.vision_config.dim_embed[-1] + dim_projection = config.vision_config.projection_dim + self.image_projection = nn.Parameter( + torch.empty(image_dim_out, dim_projection) + ) + self.image_proj_norm = nn.LayerNorm(dim_projection) + image_pos_embed_config = config.vision_config.image_pos_embed + if image_pos_embed_config['type'] == 'learned_abs_2d': + self.image_pos_embed = LearnedAbsolutePositionEmbedding2D( + embedding_dim=image_dim_out, + num_pos=image_pos_embed_config['max_pos_embeddings'] + ) + else: + raise NotImplementedError('Not implemented yet') + + self.image_feature_source = config.vision_config.image_feature_source + + # temporal embedding + visual_temporal_embedding_config = config.vision_config.visual_temporal_embedding + if visual_temporal_embedding_config['type'] == 'COSINE': + self.visual_temporal_embed = PositionalEmbeddingCosine1D( + embed_dim=image_dim_out, + max_seq_len=visual_temporal_embedding_config['max_temporal_embeddings'] + ) + else: + raise NotImplementedError('Not implemented yet') + + def get_encoder(self): + return self.language_model.get_encoder() + + def get_decoder(self): + return self.language_model.get_decoder() + + def get_input_embeddings(self): + return self.language_model.get_input_embeddings() + + def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding: + model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of) + # update vocab size + self.config.text_config.vocab_size = model_embeds.num_embeddings + self.config.vocab_size = model_embeds.num_embeddings + self.vocab_size = model_embeds.num_embeddings + return model_embeds + + def _encode_image(self, pixel_values): + if len(pixel_values.shape) == 4: + batch_size, C, H, W = pixel_values.shape + T = 1 + x = self.vision_tower.forward_features_unpool(pixel_values) + else: + raise ValueError(f'invalid image shape {pixel_values.shape}') + + if self.image_pos_embed is not None: + x = x.view(batch_size * T, -1, x.shape[-1]) + num_tokens = x.shape[-2] + h, w = int(num_tokens ** 0.5), int(num_tokens ** 0.5) + assert h * w == num_tokens, 'only support square feature maps for now' + x = x.view(batch_size * T, h, w, x.shape[-1]) + pos_embed = self.image_pos_embed(x) + x = x + pos_embed + x = x.view(batch_size, T * h*w, x.shape[-1]) + + if self.visual_temporal_embed is not None: + visual_temporal_embed = self.visual_temporal_embed(x.view(batch_size, T, -1, x.shape[-1])[:, :, 0]) + x = x.view(batch_size, T, -1, x.shape[-1]) + visual_temporal_embed.view(1, T, 1, x.shape[-1]) + + x_feat_dict = {} + + spatial_avg_pool_x = x.view(batch_size, T, -1, x.shape[-1]).mean(dim=2) + x_feat_dict['spatial_avg_pool'] = spatial_avg_pool_x + + temporal_avg_pool_x = x.view(batch_size, T, -1, x.shape[-1]).mean(dim=1) + x_feat_dict['temporal_avg_pool'] = temporal_avg_pool_x + + x = x.view(batch_size, T, -1, x.shape[-1])[:, -1] + x_feat_dict['last_frame'] = x + + new_x = [] + for _image_feature_source in self.image_feature_source: + if _image_feature_source not in x_feat_dict: + raise ValueError('invalid image feature source: {}'.format(_image_feature_source)) + new_x.append(x_feat_dict[_image_feature_source]) + + x = torch.cat(new_x, dim=1) + + x = x @ self.image_projection + x = self.image_proj_norm(x) + + return x + + def _merge_input_ids_with_image_features( + self, image_features, inputs_embeds + ): + batch_size, image_token_length = image_features.size()[:-1] + device = image_features.device + image_attention_mask = torch.ones(batch_size, image_token_length, device=device) + + # task_prefix_embeds: [batch_size, padded_context_length, hidden_size] + # task_prefix_attention_mask: [batch_size, context_length] + if inputs_embeds is None: + return image_features, image_attention_mask + + task_prefix_embeds = inputs_embeds + task_prefix_attention_mask = torch.ones(batch_size, task_prefix_embeds.size(1), device=device) + + if len(task_prefix_attention_mask.shape) == 3: + task_prefix_attention_mask = task_prefix_attention_mask[:, 0] + + # concat [image embeds, task prefix embeds] + inputs_embeds = torch.cat([image_features, task_prefix_embeds], dim=1) + attention_mask = torch.cat([image_attention_mask, task_prefix_attention_mask], dim=1) + + return inputs_embeds, attention_mask + + + @add_start_docstrings_to_model_forward(FLORENCE2_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=Florence2Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + pixel_values: torch.FloatTensor = None, + attention_mask: Optional[torch.Tensor] = None, + decoder_input_ids: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.Tensor] = None, + decoder_head_mask: Optional[torch.Tensor] = None, + cross_attn_head_mask: Optional[torch.Tensor] = None, + encoder_outputs: Optional[List[torch.FloatTensor]] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + decoder_inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, Florence2Seq2SeqLMOutput]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import AutoProcessor, Florence2ForConditionalGeneration + + >>> model = Florence2ForConditionalGeneration.from_pretrained("microsoft/Florence-2-large") + >>> processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large") + + >>> prompt = "" + >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> inputs = processor(text=prompt, images=image, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(**inputs, max_length=100) + >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "A green car parked in front of a yellow building." + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + image_features = None + if inputs_embeds is None: + # 1. Extra the input embeddings + if input_ids is not None: + inputs_embeds = self.get_input_embeddings()(input_ids) + # 2. Merge text and images + if pixel_values is not None: + # (batch_size, num_image_tokens, hidden_size) + image_features = self._encode_image(pixel_values) + inputs_embeds, attention_mask = self._merge_input_ids_with_image_features(image_features, inputs_embeds) + + if inputs_embeds is not None: + attention_mask = attention_mask.to(inputs_embeds.dtype) + outputs = self.language_model( + attention_mask=attention_mask, + labels=labels, + inputs_embeds=inputs_embeds, + decoder_input_ids=decoder_input_ids, + encoder_outputs=encoder_outputs, + decoder_attention_mask=decoder_attention_mask, + head_mask=head_mask, + decoder_head_mask=decoder_head_mask, + cross_attn_head_mask=cross_attn_head_mask, + past_key_values=past_key_values, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + logits = outputs.logits + logits = logits.float() + loss = outputs.loss + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return Florence2Seq2SeqLMOutput( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + image_hidden_states=image_features + ) + + def generate( + self, + input_ids, + inputs_embeds=None, + pixel_values=None, + **kwargs + ): + + if inputs_embeds is None: + # 1. Extra the input embeddings + if input_ids is not None: + inputs_embeds = self.get_input_embeddings()(input_ids) + # 2. Merge text and images + if pixel_values is not None: + image_features = self._encode_image(pixel_values) + inputs_embeds, attention_mask = self._merge_input_ids_with_image_features(image_features, inputs_embeds) + + return self.language_model.generate( + input_ids=None, + inputs_embeds=inputs_embeds, + **kwargs + ) + + def prepare_inputs_for_generation( + self, + decoder_input_ids, + past_key_values=None, + attention_mask=None, + pixel_values=None, + decoder_attention_mask=None, + head_mask=None, + decoder_head_mask=None, + cross_attn_head_mask=None, + use_cache=None, + encoder_outputs=None, + **kwargs, + ): + # cut decoder_input_ids if past_key_values is used + if past_key_values is not None: + past_length = past_key_values[0][0].shape[2] + + # Some generation methods already pass only the last input ID + if decoder_input_ids.shape[1] > past_length: + remove_prefix_length = past_length + else: + # Default to old behavior: keep only final ID + remove_prefix_length = decoder_input_ids.shape[1] - 1 + + decoder_input_ids = decoder_input_ids[:, remove_prefix_length:] + + return { + "input_ids": None, # encoder_outputs is defined. input_ids not needed + "encoder_outputs": encoder_outputs, + "past_key_values": past_key_values, + "decoder_input_ids": decoder_input_ids, + "attention_mask": attention_mask, + "pixel_values": pixel_values, + "decoder_attention_mask": decoder_attention_mask, + "head_mask": head_mask, + "decoder_head_mask": decoder_head_mask, + "cross_attn_head_mask": cross_attn_head_mask, + "use_cache": use_cache, # change this to avoid caching (presumably for debugging) + } + + def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): + return self.language_model.shift_tokens_right(labels) + + def _reorder_cache(self, *args, **kwargs): + return self.language_model._reorder_cache(*args, **kwargs) \ No newline at end of file diff --git a/OmniParser/Florence-2-base/preprocessor_config.json b/OmniParser/Florence-2-base/preprocessor_config.json new file mode 100644 index 0000000..85cd7be --- /dev/null +++ b/OmniParser/Florence-2-base/preprocessor_config.json @@ -0,0 +1,39 @@ +{ + "auto_map": { + "AutoProcessor": "processing_florence2.Florence2Processor" + }, + "_valid_processor_keys": [ + "images", + "do_resize", + "size", + "resample", + "do_rescale", + "rescale_factor", + "do_normalize", + "image_mean", + "image_std", + "return_tensors", + "data_format", + "input_data_format", + "do_convert_rgb" + ], + "do_convert_rgb": null, + "do_normalize": true, + "do_rescale": true, + "do_resize": true, + "do_center_crop": false, + "image_processor_type": "CLIPImageProcessor", + "image_seq_length": 577, + "image_mean": [0.485, 0.456, 0.406], + "image_std": [0.229, 0.224, 0.225], + "processor_class": "Florence2Processor", + "resample": 3, + "size": { + "height": 768, + "width":768 + }, + "crop_size": { + "height": 768, + "width": 768 + } +} \ No newline at end of file diff --git a/OmniParser/Florence-2-base/processing_florence2.py b/OmniParser/Florence-2-base/processing_florence2.py new file mode 100644 index 0000000..538110e --- /dev/null +++ b/OmniParser/Florence-2-base/processing_florence2.py @@ -0,0 +1,1088 @@ +# coding=utf-8 +# Copyright 2024 Microsoft and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for Florence-2. +""" + +import re +import logging +from typing import List, Optional, Union +import numpy as np + +import torch + +from transformers.feature_extraction_utils import BatchFeature +from transformers.image_utils import ImageInput, is_valid_image +from transformers.processing_utils import ProcessorMixin +from transformers.tokenization_utils_base import ( + PaddingStrategy, + PreTokenizedInput, + TextInput, + TruncationStrategy, +) +from transformers.utils import TensorType + + +logger = logging.getLogger(__name__) + +# Copied from transformers.models.idefics2.processing_idefics2.is_url +def is_url(val) -> bool: + return isinstance(val, str) and val.startswith("http") + +# Copied from transformers.models.idefics2.processing_idefics2.is_image_or_image_url +def is_image_or_image_url(elem): + return is_url(elem) or is_valid_image(elem) + + +def _is_str_or_image(elem): + return isinstance(elem, (str)) or is_image_or_image_url(elem) + + +class Florence2Processor(ProcessorMixin): + r""" + Constructs a Florence2 processor which wraps a Florence2 image processor and a Florence2 tokenizer into a single processor. + + [`Florence2Processor`] offers all the functionalities of [`CLIPImageProcessor`] and [`BartTokenizerFast`]. See the + [`~Florence2Processor.__call__`] and [`~Florence2Processor.decode`] for more information. + + Args: + image_processor ([`CLIPImageProcessor`], *optional*): + The image processor is a required input. + tokenizer ([`BartTokenizerFast`], *optional*): + The tokenizer is a required input. + """ + + attributes = ["image_processor", "tokenizer"] + image_processor_class = "CLIPImageProcessor" + tokenizer_class = ("BartTokenizer", "BartTokenizerFast") + + def __init__( + self, + image_processor=None, + tokenizer=None, + ): + if image_processor is None: + raise ValueError("You need to specify an `image_processor`.") + if tokenizer is None: + raise ValueError("You need to specify a `tokenizer`.") + if not hasattr(image_processor, "image_seq_length"): + raise ValueError("Image processor is missing an `image_seq_length` attribute.") + + self.image_seq_length = image_processor.image_seq_length + + tokens_to_add = { + 'additional_special_tokens': \ + tokenizer.additional_special_tokens + \ + ['', '', '', ''] + \ + [f'' for x in range(1000)] + \ + ['', '', '', '','', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''] + } + tokenizer.add_special_tokens(tokens_to_add) + + self.tasks_answer_post_processing_type = { + '': 'pure_text', + '': 'ocr', + '': 'pure_text', + '': 'pure_text', + '': 'pure_text', + '': 'description_with_bboxes', + '': 'description_with_bboxes', + '': "phrase_grounding", + '': 'polygons', + '': 'polygons', + '': 'description_with_bboxes_or_polygons', + '': 'pure_text', + '': 'pure_text', + '': 'pure_text', + '': 'bboxes' + } + + self.task_prompts_without_inputs = { + '': 'What is the text in the image?', + '': 'What is the text in the image, with regions?', + '': 'What does the image describe?', + '': 'Describe in detail what is shown in the image.', + '': 'Describe with a paragraph what is shown in the image.', + '': 'Locate the objects with category name in the image.', + '': 'Locate the objects in the image, with their descriptions.', + '': 'Locate the region proposals in the image.' + } + + self.task_prompts_with_input = { + '': "Locate the phrases in the caption: {input}", + '': 'Locate {input} in the image with mask', + '': 'What is the polygon mask of region {input}', + '': 'Locate {input} in the image.', + '': 'What is the region {input}?', + '': 'What does the region {input} describe?', + '': 'What text is in the region {input}?', + } + + self.post_processor = Florence2PostProcesser(tokenizer=tokenizer) + + + super().__init__(image_processor, tokenizer) + + def _construct_prompts(self, text): + # replace the task tokens with the task prompts if task token is in the text + prompts = [] + for _text in text: + # 1. fixed task prompts without additional inputs + for task_token, task_prompt in self.task_prompts_without_inputs.items(): + if task_token in _text: + assert _text == task_token, f"Task token {task_token} should be the only token in the text." + _text = task_prompt + break + # 2. task prompts with additional inputs + for task_token, task_prompt in self.task_prompts_with_input.items(): + if task_token in _text: + _text = task_prompt.format(input=_text.replace(task_token, '')) + break + prompts.append(_text) + return prompts + + def __call__( + self, + text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, + images: ImageInput = None, + tokenize_newline_separately: bool = True, + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Union[bool, str, TruncationStrategy] = None, + max_length=None, + return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH, + do_resize: bool = None, + do_normalize: bool = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + data_format: Optional["ChannelDimension"] = "channels_first", # noqa: F821 + input_data_format: Optional[ + Union[str, "ChannelDimension"] # noqa: F821 + ] = None, + resample: "PILImageResampling" = None, # noqa: F821 + do_convert_rgb: bool = None, + do_thumbnail: bool = None, + do_align_long_axis: bool = None, + do_rescale: bool = None, + ) -> BatchFeature: + """ + Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` + and `kwargs` arguments to BartTokenizerFast's [`~BartTokenizerFast.__call__`] if `text` is not `None` to encode + the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to + CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring + of the above two methods for more information. + + Args: + text (`str`, `List[str]`, `List[List[str]]`): + The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings + (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set + `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). + images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): + The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch + tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a + number of channels, H and W are image height and width. + tokenize_newline_separately (`bool`, defaults to `True`): + Adds a separately tokenized '\n' at the end of the prompt. + padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): + Select a strategy to pad the returned sequences (according to the model's padding side and padding + index) among: + - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single + sequence if provided). + - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum + acceptable input length for the model if that argument is not provided. + - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different + lengths). + max_length (`int`, *optional*): + Maximum length of the returned list and optionally padding length (see above). + truncation (`bool`, *optional*): + Activates truncation to cut input sequences longer than `max_length` to `max_length`. + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors of a particular framework. Acceptable values are: + + - `'tf'`: Return TensorFlow `tf.constant` objects. + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return NumPy `np.ndarray` objects. + - `'jax'`: Return JAX `jnp.ndarray` objects. + + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. If `suffix` + is provided, the `input_ids` will also contain the suffix input ids. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + - **labels** -- Labels compatible with training if `suffix` is not None + """ + + return_token_type_ids = False + + if images is None: + raise ValueError("`images` are expected as arguments to a `Florence2Processor` instance.") + if text is None: + logger.warning_once( + "You are using Florence-2 without a text prompt." + ) + text = "" + + if isinstance(text, List) and isinstance(images, List): + if len(images) < len(text): + raise ValueError( + f"Received {len(images)} images for {len(text)} prompts. Each prompt should be associated with an image." + ) + if _is_str_or_image(text): + text = [text] + elif isinstance(text, list) and _is_str_or_image(text[0]): + pass + + pixel_values = self.image_processor( + images, + do_resize=do_resize, + do_normalize=do_normalize, + return_tensors=return_tensors, + image_mean=image_mean, + image_std=image_std, + input_data_format=input_data_format, + data_format=data_format, + resample=resample, + do_convert_rgb=do_convert_rgb, + )["pixel_values"] + + if max_length is not None: + max_length -= self.image_seq_length # max_length has to account for the image tokens + + text = self._construct_prompts(text) + + inputs = self.tokenizer( + text, + return_tensors=return_tensors, + padding=padding, + max_length=max_length, + truncation=truncation, + return_token_type_ids=return_token_type_ids, + ) + + return_data = {**inputs, "pixel_values": pixel_values} + + if return_token_type_ids: + labels = inputs["input_ids"].masked_fill(inputs["token_type_ids"] == 0, -100) + return_data.update({"labels": labels}) + return BatchFeature(data=return_data) + + # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Florence2 + def batch_decode(self, *args, **kwargs): + """ + This method forwards all its arguments to BartTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please + refer to the docstring of this method for more information. + """ + return self.tokenizer.batch_decode(*args, **kwargs) + + # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Florence2 + def decode(self, *args, **kwargs): + """ + This method forwards all its arguments to BartTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to + the docstring of this method for more information. + """ + return self.tokenizer.decode(*args, **kwargs) + + @property + # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names with CLIP->Florence2 + def model_input_names(self): + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) + + def post_process_generation(self, text, task, image_size): + """ + Post-process the output of the model to each of the task outputs. + + Args: + text (`str`): The text to post-process. + task (`str`): The task to post-process the text for. + image_size (`Tuple[int, int]`): The size of the image. height x width. + """ + + task_answer_post_processing_type = self.tasks_answer_post_processing_type.get(task, 'pure_text') + task_answer = self.post_processor( + text=text, + image_size=image_size, + parse_tasks=task_answer_post_processing_type, + )[task_answer_post_processing_type] + + if task_answer_post_processing_type == 'pure_text': + final_answer = task_answer + # remove the special tokens + final_answer = final_answer.replace('', '').replace('', '') + elif task_answer_post_processing_type in ['od', 'description_with_bboxes', 'bboxes']: + od_instances = task_answer + bboxes_od = [_od_instance['bbox'] for _od_instance in od_instances] + labels_od = [str(_od_instance['cat_name']) for _od_instance in od_instances] + final_answer = {'bboxes': bboxes_od, 'labels': labels_od} + elif task_answer_post_processing_type in ['ocr']: + bboxes = [_od_instance['quad_box'] for _od_instance in task_answer] + labels = [str(_od_instance['text']) for _od_instance in task_answer] + final_answer = {'quad_boxes': bboxes, 'labels': labels} + elif task_answer_post_processing_type in ['phrase_grounding']: + bboxes = [] + labels = [] + for _grounded_phrase in task_answer: + for _bbox in _grounded_phrase['bbox']: + bboxes.append(_bbox) + labels.append(_grounded_phrase['cat_name']) + final_answer = {'bboxes': bboxes, 'labels': labels} + elif task_answer_post_processing_type in ['description_with_polygons', 'polygons']: + labels = [] + polygons = [] + for result in task_answer: + label = result['cat_name'] + _polygons = result['polygons'] + labels.append(label) + polygons.append(_polygons) + final_answer = {'polygons': polygons, 'labels': labels} + elif task_answer_post_processing_type in ['description_with_bboxes_or_polygons']: + bboxes = [] + bboxes_labels = [] + polygons = [] + polygons_labels = [] + for result in task_answer: + label = result['cat_name'] + if 'polygons' in result: + _polygons = result['polygons'] + polygons.append(_polygons) + polygons_labels.append(label) + else: + _bbox = result['bbox'] + bboxes.append(_bbox) + bboxes_labels.append(label) + final_answer = {'bboxes': bboxes, 'bboxes_labels': bboxes_labels, 'polygons': polygons, 'polygons_labels': polygons_labels} + else: + raise ValueError('Unknown task answer post processing type: {}'.format(task_answer_post_processing_type)) + + final_answer = { + task: final_answer} + return final_answer + +class BoxQuantizer(object): + def __init__(self, mode, bins): + self.mode = mode + self.bins = bins + + def quantize(self, boxes: torch.Tensor, size): + bins_w, bins_h = self.bins # Quantization bins. + size_w, size_h = size # Original image size. + size_per_bin_w = size_w / bins_w + size_per_bin_h = size_h / bins_h + xmin, ymin, xmax, ymax = boxes.split(1, dim=-1) # Shape: 4 * [N, 1]. + + if self.mode == 'floor': + quantized_xmin = ( + xmin / size_per_bin_w).floor().clamp(0, bins_w - 1) + quantized_ymin = ( + ymin / size_per_bin_h).floor().clamp(0, bins_h - 1) + quantized_xmax = ( + xmax / size_per_bin_w).floor().clamp(0, bins_w - 1) + quantized_ymax = ( + ymax / size_per_bin_h).floor().clamp(0, bins_h - 1) + + elif self.mode == 'round': + raise NotImplementedError() + + else: + raise ValueError('Incorrect quantization type.') + + quantized_boxes = torch.cat( + (quantized_xmin, quantized_ymin, quantized_xmax, quantized_ymax), dim=-1 + ).int() + + return quantized_boxes + + def dequantize(self, boxes: torch.Tensor, size): + bins_w, bins_h = self.bins # Quantization bins. + size_w, size_h = size # Original image size. + size_per_bin_w = size_w / bins_w + size_per_bin_h = size_h / bins_h + xmin, ymin, xmax, ymax = boxes.split(1, dim=-1) # Shape: 4 * [N, 1]. + + if self.mode == 'floor': + # Add 0.5 to use the center position of the bin as the coordinate. + dequantized_xmin = (xmin + 0.5) * size_per_bin_w + dequantized_ymin = (ymin + 0.5) * size_per_bin_h + dequantized_xmax = (xmax + 0.5) * size_per_bin_w + dequantized_ymax = (ymax + 0.5) * size_per_bin_h + + elif self.mode == 'round': + raise NotImplementedError() + + else: + raise ValueError('Incorrect quantization type.') + + dequantized_boxes = torch.cat( + (dequantized_xmin, dequantized_ymin, + dequantized_xmax, dequantized_ymax), dim=-1 + ) + + return dequantized_boxes + + +class CoordinatesQuantizer(object): + """ + Quantize coornidates (Nx2) + """ + + def __init__(self, mode, bins): + self.mode = mode + self.bins = bins + + def quantize(self, coordinates: torch.Tensor, size): + bins_w, bins_h = self.bins # Quantization bins. + size_w, size_h = size # Original image size. + size_per_bin_w = size_w / bins_w + size_per_bin_h = size_h / bins_h + assert coordinates.shape[-1] == 2, 'coordinates should be shape (N, 2)' + x, y = coordinates.split(1, dim=-1) # Shape: 4 * [N, 1]. + + if self.mode == 'floor': + quantized_x = (x / size_per_bin_w).floor().clamp(0, bins_w - 1) + quantized_y = (y / size_per_bin_h).floor().clamp(0, bins_h - 1) + + elif self.mode == 'round': + raise NotImplementedError() + + else: + raise ValueError('Incorrect quantization type.') + + quantized_coordinates = torch.cat( + (quantized_x, quantized_y), dim=-1 + ).int() + + return quantized_coordinates + + def dequantize(self, coordinates: torch.Tensor, size): + bins_w, bins_h = self.bins # Quantization bins. + size_w, size_h = size # Original image size. + size_per_bin_w = size_w / bins_w + size_per_bin_h = size_h / bins_h + assert coordinates.shape[-1] == 2, 'coordinates should be shape (N, 2)' + x, y = coordinates.split(1, dim=-1) # Shape: 4 * [N, 1]. + + if self.mode == 'floor': + # Add 0.5 to use the center position of the bin as the coordinate. + dequantized_x = (x + 0.5) * size_per_bin_w + dequantized_y = (y + 0.5) * size_per_bin_h + + elif self.mode == 'round': + raise NotImplementedError() + + else: + raise ValueError('Incorrect quantization type.') + + dequantized_coordinates = torch.cat( + (dequantized_x, dequantized_y), dim=-1 + ) + + return dequantized_coordinates + + +class Florence2PostProcesser(object): + """ + Florence-2 post process for converting text prediction to various tasks results. + + Args: + config: A dict of configs. + tokenizer: A tokenizer for decoding text to spans. + sample config: + UNIFIED_POST_PROCESS: + # commom configs + NUM_BBOX_HEIGHT_BINS: 1000 + NUM_BBOX_WIDTH_BINS: 1000 + COORDINATES_HEIGHT_BINS: 1000 + COORDINATES_WIDTH_BINS: 1000 + # task specific configs, override the common configs + PRASE_TASKS: + - TASK_NAME: 'video_dense_caption' + PATTERN: 'r([a-zA-Z0-9 ]+)' + SCORE_MODE: 'avg_cat_name_scores' + NUM_BINS: 100 + - TASK_NAME: 'od' + PATTERN: 'r([a-zA-Z0-9 ]+)' + SCORE_MODE: 'avg_cat_name_scores' + + Returns: + parsed_dict (dict): A dict of parsed results. + """ + def __init__( + self, + tokenizer=None + ): + parse_tasks = [] + parse_task_configs = {} + config = self._create_default_config() + for task in config['PARSE_TASKS']: + parse_tasks.append(task['TASK_NAME']) + parse_task_configs[task['TASK_NAME']] = task + + self.config = config + self.parse_tasks = parse_tasks + self.parse_tasks_configs = parse_task_configs + + self.tokenizer = tokenizer + if self.tokenizer is not None: + self.all_special_tokens = set(self.tokenizer.all_special_tokens) + + self.init_quantizers() + self.black_list_of_phrase_grounding = self._create_black_list_of_phrase_grounding() + + def _create_black_list_of_phrase_grounding(self): + black_list = {} + + if 'phrase_grounding' in self.parse_tasks and self.parse_tasks_configs['phrase_grounding']['FILTER_BY_BLACK_LIST']: + black_list = set( + ['it', 'I', 'me', 'mine', + 'you', 'your', 'yours', + 'he', 'him', 'his', + 'she', 'her', 'hers', + 'they', 'them', 'their', 'theirs', + 'one', 'oneself', + 'we', 'us', 'our', 'ours', + 'you', 'your', 'yours', + 'they', 'them', 'their', 'theirs', + 'mine', 'yours', 'his', 'hers', 'its', + 'ours', 'yours', 'theirs', + 'myself', 'yourself', 'himself', 'herself', 'itself', + 'ourselves', 'yourselves', 'themselves', + 'this', 'that', + 'these', 'those', + 'who', 'whom', 'whose', 'which', 'what', + 'who', 'whom', 'whose', 'which', 'that', + 'all', 'another', 'any', 'anybody', 'anyone', 'anything', + 'each', 'everybody', 'everyone', 'everything', + 'few', 'many', 'nobody', 'none', 'one', 'several', + 'some', 'somebody', 'someone', 'something', + 'each other', 'one another', + 'myself', 'yourself', 'himself', 'herself', 'itself', + 'ourselves', 'yourselves', 'themselves', + 'the image', 'image', 'images', 'the', 'a', 'an', 'a group', + 'other objects', 'lots', 'a set', + ] + ) + + return black_list + + def _create_default_config(self): + config = { + 'NUM_BBOX_HEIGHT_BINS': 1000, + 'NUM_BBOX_WIDTH_BINS': 1000, + 'BOX_QUANTIZATION_MODE': 'floor', + 'COORDINATES_HEIGHT_BINS': 1000, + 'COORDINATES_WIDTH_BINS': 1000, + 'COORDINATES_QUANTIZATION_MODE': 'floor', + 'PARSE_TASKS': [ + { + 'TASK_NAME': 'od', + 'PATTERN': r'([a-zA-Z0-9 ]+)' + }, + { + 'TASK_NAME': 'ocr', + 'PATTERN': r'(.+?)', + 'AREA_THRESHOLD': 0.00 + }, + { + 'TASK_NAME': 'phrase_grounding', + 'FILTER_BY_BLACK_LIST': True + }, + { + 'TASK_NAME': 'pure_text', + }, + { + 'TASK_NAME': 'description_with_bboxes', + }, + { + 'TASK_NAME': 'description_with_polygons', + }, + { + 'TASK_NAME': 'polygons', + }, + { + 'TASK_NAME': 'bboxes', + }, + { + 'TASK_NAME': 'description_with_bboxes_or_polygons', + } + ] + } + + return config + + def init_quantizers(self): + # we have box_quantizer (od, grounding) and coordinates_quantizer (ocr, referring_segmentation) + num_bbox_height_bins = self.config.get('NUM_BBOX_HEIGHT_BINS', 1000) + num_bbox_width_bins = self.config.get('NUM_BBOX_WIDTH_BINS', 1000) + box_quantization_mode = self.config.get('BOX_QUANTIZATION_MODE', 'floor') + self.box_quantizer = BoxQuantizer( + box_quantization_mode, + (num_bbox_width_bins, num_bbox_height_bins), + ) + + num_bbox_height_bins = self.config['COORDINATES_HEIGHT_BINS'] if 'COORDINATES_HEIGHT_BINS' in self.config else self.config.get('NUM_BBOX_HEIGHT_BINS', 1000) + num_bbox_width_bins = self.config['COORDINATES_WIDTH_BINS'] if 'COORDINATES_WIDTH_BINS' in self.config else self.config.get('NUM_BBOX_WIDTH_BINS', 1000) + box_quantization_mode = self.config.get('COORDINATES_QUANTIZATION_MODE') if 'COORDINATES_QUANTIZATION_MODE' in self.config else self.config.get('BOX_QUANTIZATION_MODE', 'floor') + self.coordinates_quantizer = CoordinatesQuantizer( + box_quantization_mode, + (num_bbox_width_bins, num_bbox_height_bins), + ) + + def decode_with_spans(self, tokenizer, token_ids): + filtered_tokens = tokenizer.convert_ids_to_tokens( + token_ids, skip_special_tokens=False) + assert len(filtered_tokens) == len(token_ids) + + # To avoid mixing byte-level and unicode for byte-level BPT + # we need to build string separately for added tokens and byte-level tokens + # cf. https://github.com/huggingface/transformers/issues/1133 + sub_texts = [] + for token in filtered_tokens: + if token in self.all_special_tokens: + sub_texts.append(token) + else: + if isinstance(tokenizer, (BartTokenizer, BartTokenizerFast)): + sub_text = tokenizer.convert_tokens_to_string([token]) + elif isinstance(tokenizer, (T5Tokenizer, T5TokenizerFast)): + # Ref: https://github.com/google/sentencepiece#whitespace-is-treated-as-a-basic-symbol + # Note: Do not strip sub_text as it may have functional whitespace + sub_text = token.replace('▁', ' ') + else: + raise ValueError(f'type {type(tokenizer)} not supported') + sub_texts.append(sub_text) + + text = '' + spans = [] + for sub_text in sub_texts: + span = (len(text), len(text) + len(sub_text)) # [start index, end index). + text += sub_text + spans.append(span) + + # Text format: + # 1. T5Tokenizer/T5TokenizerFast: + # " transplanting dog cat" + # Equivalent to t5_tokenizer.decode(input_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False, spaces_between_special_tokens=False) + # 2. BartTokenizer (need to double check): + # "transplanting dogcat" + # Equivalent to bart_tokenizer.decode(input_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False, spaces_between_special_tokens=False) + return text, spans + + def parse_od_from_text_and_spans( + self, + text, + pattern, + image_size, + phrase_centric=False + ): + parsed = list(re.finditer(pattern, text)) + + instances = [] + for i in range(len(parsed)): + # Prepare instance. + instance = {} + + if phrase_centric: + bbox_bins = [int(parsed[i].group(j)) for j in range(2, 6)] + else: + bbox_bins = [int(parsed[i].group(j)) for j in range(1, 5)] + instance['bbox'] = self.box_quantizer.dequantize( + boxes=torch.tensor(bbox_bins), + size=image_size + ).tolist() + + if phrase_centric: + instance['cat_name'] = parsed[i].group(1).lower().strip() + else: + instance['cat_name'] = parsed[i].group(5).lower().strip() + instances.append(instance) + + return instances + + def parse_ocr_from_text_and_spans(self, + text, + pattern, + image_size, + area_threshold=-1.0, + ): + bboxes = [] + labels = [] + text = text.replace('', '') + # ocr with regions + parsed = re.findall(pattern, text) + instances = [] + image_width, image_height = image_size + + for ocr_line in parsed: + ocr_content = ocr_line[0] + quad_box = ocr_line[1:] + quad_box = [int(i) for i in quad_box] + quad_box = self.coordinates_quantizer.dequantize( + torch.tensor(np.array(quad_box).reshape(-1, 2)), + size=image_size + ).reshape(-1).tolist() + + if area_threshold > 0: + x_coords = [i for i in quad_box[0::2]] + y_coords = [i for i in quad_box[1::2]] + + # apply the Shoelace formula + area = 0.5 * abs(sum(x_coords[i] * y_coords[i + 1] - x_coords[i + 1] * y_coords[i] for i in range(4 - 1))) + + if area < (image_width * image_height) * area_threshold: + continue + + bboxes.append(quad_box) + labels.append(ocr_content) + instances.append({ + 'quad_box': quad_box, + 'text': ocr_content, + }) + return instances + + def parse_phrase_grounding_from_text_and_spans(self, text, pattern, image_size): + # ignore and + cur_span = 0 + if text.startswith(''): + cur_span += 3 + + text = text.replace('', '') + text = text.replace('', '') + text = text.replace('', '') + + pattern = r"([^<]+(?:){4,})" + phrases = re.findall(pattern, text) + + # pattern should be text pattern and od pattern + pattern = r'^\s*(.*?)(?=||||||' + + instances = [] + for pharse_text in phrases: + phrase_text_strip = pharse_text.replace('', '', 1) + phrase_text_strip = pharse_text.replace('', '', 1) + + if phrase_text_strip == '': + cur_span += len(pharse_text) + continue + + # Prepare instance. + instance = {} + + # parse phrase, get string + phrase = re.search(pattern, phrase_text_strip) + if phrase is None: + cur_span += len(pharse_text) + continue + + # parse bboxes by box_pattern + bboxes_parsed = list(re.finditer(box_pattern, pharse_text)) + if len(bboxes_parsed) == 0: + cur_span += len(pharse_text) + continue + + phrase = phrase.group() + # remove leading and trailing spaces + phrase = phrase.strip() + + if phrase in self.black_list_of_phrase_grounding: + cur_span += len(pharse_text) + continue + + # a list of list + bbox_bins = [[int(_bboxes_parsed.group(j)) for j in range(1, 5)] for _bboxes_parsed in bboxes_parsed] + instance['bbox'] = self.box_quantizer.dequantize( + boxes=torch.tensor(bbox_bins), + size=image_size + ).tolist() + + # exclude non-ascii characters + phrase = phrase.encode('ascii',errors='ignore').decode('ascii') + instance['cat_name'] = phrase + + instances.append(instance) + + return instances + + def parse_description_with_bboxes_from_text_and_spans(self, text, pattern, image_size, allow_empty_phrase=False): + # temporary parse solution, split by '.' + # ignore and + + text = text.replace('', '') + text = text.replace('', '') + text = text.replace('', '') + + if allow_empty_phrase: + pattern = rf"(?:(?:){{4,}})" + else: + pattern = r"([^<]+(?:){4,})" + phrases = re.findall(pattern, text) + + # pattern should be text pattern and od pattern + pattern = r'^\s*(.*?)(?=||||||' + + instances = [] + for pharse_text in phrases: + phrase_text_strip = pharse_text.replace('', '', 1) + phrase_text_strip = pharse_text.replace('', '', 1) + + if phrase_text_strip == '' and not allow_empty_phrase: + continue + + # parse phrase, get string + phrase = re.search(pattern, phrase_text_strip) + if phrase is None: + continue + + phrase = phrase.group() + # remove leading and trailing spaces + phrase = phrase.strip() + + # parse bboxes by box_pattern + bboxes_parsed = list(re.finditer(box_pattern, pharse_text)) + if len(bboxes_parsed) == 0: + continue + + # a list of list + bbox_bins = [[int(_bboxes_parsed.group(j)) for j in range(1, 5)] for _bboxes_parsed in bboxes_parsed] + + bboxes = self.box_quantizer.dequantize( + boxes=torch.tensor(bbox_bins), + size=image_size + ).tolist() + + phrase = phrase.encode('ascii',errors='ignore').decode('ascii') + for _bboxes in bboxes: + # Prepare instance. + instance = {} + instance['bbox'] = _bboxes + # exclude non-ascii characters + instance['cat_name'] = phrase + instances.append(instance) + + return instances + + def parse_description_with_polygons_from_text_and_spans(self, text, pattern, image_size, + allow_empty_phrase=False, + polygon_sep_token='', + polygon_start_token='', + polygon_end_token='', + with_box_at_start=False, + ): + + # ref_seg format: '<><><><><><>' + # ignore and + + text = text.replace('', '') + text = text.replace('', '') + text = text.replace('', '') + + if allow_empty_phrase: + pattern = rf"(?:(?:|{re.escape(polygon_sep_token)}|{re.escape(polygon_start_token)}|{re.escape(polygon_end_token)}){{4,}})" + else: + # [^<]+: This part matches one or more characters that are not the < symbol. + # The ^ inside the square brackets [] is a negation, meaning it matches anything except <. + # + pattern = rf"([^<]+(?:|{re.escape(polygon_sep_token)}|{re.escape(polygon_start_token)}|{re.escape(polygon_end_token)}){{4,}})" + phrases = re.findall(pattern, text) + + phrase_string_pattern = r'^\s*(.*?)(?=||||||)' + box_pattern = rf'((?:)+)(?:{re.escape(polygon_sep_token)}|$)' + + # one polygons instance is separated by polygon_start_token and polygon_end_token + polygons_instance_pattern = rf'{re.escape(polygon_start_token)}(.*?){re.escape(polygon_end_token)}' + + instances = [] + for phrase_text in phrases: + + # exclude loc_\d+> + # need to get span if want to include category score + phrase_text_strip = re.sub(r'^loc_\d+>', '', phrase_text, count=1) + + # phrase = phrase.replace('', '') + # phrase = phrase.replace('poly>', '') + + if phrase_text_strip == '' and not allow_empty_phrase: + continue + + + # parse phrase, get string + phrase = re.search(phrase_string_pattern, phrase_text_strip) + if phrase is None: + continue + phrase = phrase.group() + # remove leading and trailing spaces + phrase = phrase.strip() + + # parse bboxes by box_pattern + + # split by polygon_start_token and polygon_end_token first using polygons_instance_pattern + if polygon_start_token in phrase_text and polygon_end_token in phrase_text: + polygons_instances_parsed = list(re.finditer(polygons_instance_pattern, phrase_text)) + else: + polygons_instances_parsed = [phrase_text] + + for _polygons_instances_parsed in polygons_instances_parsed: + # Prepare instance. + instance = {} + + # polygons_parsed= list(re.finditer(box_pattern, phrase_text)) + if isinstance(_polygons_instances_parsed, str): + polygons_parsed= list(re.finditer(box_pattern, _polygons_instances_parsed)) + else: + polygons_parsed= list(re.finditer(box_pattern, _polygons_instances_parsed.group(1))) + if len(polygons_parsed) == 0: + continue + + # a list of list (polygon) + bbox = [] + polygons = [] + for _polygon_parsed in polygons_parsed: + # group 1: whole ... + _polygon = _polygon_parsed.group(1) + # parse into list of int + _polygon = [int(_loc_parsed.group(1)) for _loc_parsed in re.finditer(r'', _polygon)] + if with_box_at_start and len(bbox) == 0: + if len(_polygon) > 4: + # no valid bbox prediction + bbox = _polygon[:4] + _polygon = _polygon[4:] + else: + bbox = [0, 0, 0, 0] + # abandon last element if is not paired + if len(_polygon) % 2 == 1: + _polygon = _polygon[:-1] + + # reshape into (n, 2) + _polygon = self.coordinates_quantizer.dequantize( + torch.tensor(np.array(_polygon).reshape(-1, 2)), + size=image_size + ).reshape(-1).tolist() + # reshape back + polygons.append(_polygon) + + instance['cat_name'] = phrase + instance['polygons'] = polygons + if len(bbox) != 0: + instance['bbox'] = self.box_quantizer.dequantize( + boxes=torch.tensor([bbox]), + size=image_size + ).tolist()[0] + + instances.append(instance) + + return instances + + def __call__( + self, + text=None, + image_size=None, + parse_tasks=None, + ): + """ + Args: + text: model outputs + image_size: (width, height) + parse_tasks: a list of tasks to parse, if None, parse all tasks. + + """ + if parse_tasks is not None: + if isinstance(parse_tasks, str): + parse_tasks = [parse_tasks] + for _parse_task in parse_tasks: + assert _parse_task in self.parse_tasks, f'parse task {_parse_task} not supported' + + # sequence or text should be provided + assert text is not None, 'text should be provided' + + parsed_dict = { + 'text': text + } + + for task in self.parse_tasks: + if parse_tasks is not None and task not in parse_tasks: + continue + + pattern = self.parse_tasks_configs[task].get('PATTERN', None) + + if task == 'ocr': + instances = self.parse_ocr_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + area_threshold=self.parse_tasks_configs[task].get('AREA_THRESHOLD', 0.0), + ) + parsed_dict['ocr'] = instances + elif task == 'phrase_grounding': + instances = self.parse_phrase_grounding_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + ) + parsed_dict['phrase_grounding'] = instances + elif task == 'pure_text': + parsed_dict['pure_text'] = text + elif task == 'description_with_bboxes': + instances = self.parse_description_with_bboxes_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + ) + parsed_dict['description_with_bboxes'] = instances + elif task == 'description_with_polygons': + instances = self.parse_description_with_polygons_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + ) + parsed_dict['description_with_polygons'] = instances + elif task == 'polygons': + instances = self.parse_description_with_polygons_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + allow_empty_phrase=True, + ) + parsed_dict['polygons'] = instances + elif task == 'bboxes': + instances = self.parse_description_with_bboxes_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + allow_empty_phrase=True, + ) + parsed_dict['bboxes'] = instances + elif task == 'description_with_bboxes_or_polygons': + if '' in text: + # only support either polygons or bboxes, not both at the same time + instances = self.parse_description_with_polygons_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + ) + else: + instances = self.parse_description_with_bboxes_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + ) + parsed_dict['description_with_bboxes_or_polygons'] = instances + else: + raise ValueError("task {} is not supported".format(task)) + + return parsed_dict diff --git a/OmniParser/Florence-2-base/tokenizer.json b/OmniParser/Florence-2-base/tokenizer.json new file mode 100644 index 0000000..ad0bcbe --- /dev/null +++ b/OmniParser/Florence-2-base/tokenizer.json @@ -0,0 +1 @@ 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zWtO>67*UlYOa-#u<+{G<3Cp2Ko0_!O4Q+0dOd2z9kRVUI}5aJsJpx&IxO z#)(7K$xVOI+ljZx+@qEoMh%R)8^XRGW-W(&OPF_Inqomvj$rP{yY)-9o+PR9&~eHv z;pS?cUD48QITN-P773RLvy04_&|^K-^_X-dAH$32a9NaXv)bADB=ne=vDSqSS53B` z;yb}p;TYVyGMbIDoF~b%Dc*I+Tb&4g@XhshHYL|5LeQr{3rO&^k!}Xtr?j;$G#BDY f^dz*)%-5UYHZT{=VSWK#|6aZVY$)3t?&=1 + video_list = [] + if isinstance(img_tensor, list): + if isinstance(img_tensor[0], torch.Tensor): + video_list = img_tensor + elif isinstance(img_tensor, torch.Tensor): + b, _, _, _ = img_tensor.size() + if b == 1: + img = [b] + while img is not []: + video_list += img + else: + video_list = torch.chunk(img_tensor, chunks=b) + return video_list + +def tensor2imglist(image,np_out=True):# pil first + B, _, _, _ = image.size() + if B == 1: + if np_out: + list_out = [tensor2pil(image)] + else: + list_out = [tensor2cv(image.squeeze())] + else: + image_list = torch.chunk(image, chunks=B) + if np_out: + list_out = [tensor2pil(i) for i in image_list] + else: + list_out = [tensor2cv(i.squeeze()) for i in image_list] + return list_out,B + + +def tensor2pil(tensor): + image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy() + image = Image.fromarray(image_np, mode='RGB') + return image + +def pil2narry(img): + narry = torch.from_numpy(np.array(img).astype(np.float32) / 255.0).unsqueeze(0) + return narry + +def tensor_upscale2pil(img_tensor, width, height): + samples = img_tensor.movedim(-1, 1) + img = common_upscale(samples, width, height, "nearest-exact", "center") + samples = img.movedim(1, -1) + img_pil = tensor2pil(samples) + return img_pil + +def tensor_upscale(img_tensor, width, height): #torch tensor + samples = img_tensor.movedim(-1, 1) + img = common_upscale(samples, width, height, "nearest-exact", "center") + samples = img.movedim(1, -1) + return samples + +def convert_from_cv2_to_image(img: np.ndarray) -> Image: + # return Image.fromarray(img) + return Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) + + +def convert_from_image_to_cv2(img: Image) -> np.ndarray: + # return np.asarray(img) + return cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) + +def tensor2cv(tensor_image): + if tensor_image.is_cuda: + tensor_image = tensor_image.detach().cpu() + tensor_image=tensor_image.numpy() + #反归一化 + maxValue=tensor_image.max() + tensor_image=tensor_image*255/maxValue + img_cv2=np.uint8(tensor_image)#32 to uint8 + img_cv2=cv2.cvtColor(img_cv2,cv2.COLOR_RGB2BGR) + return img_cv2 + +def cvargb2tensor(img): + assert type(img) == np.ndarray, 'the img type is {}, but ndarry expected'.format(type(img)) + img = torch.from_numpy(img.transpose((2, 0, 1))) + return img.float().div(255).unsqueeze(0) # 255也可以改为256 + +def cv2tensor(img): + assert type(img) == np.ndarray, 'the img type is {}, but ndarry expected'.format(type(img)) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = torch.from_numpy(img.transpose((2, 0, 1))) + return img.float().div(255).unsqueeze(0) # 255也可以改为256 + +def images_generator(img_list: list,): + #get img size + sizes = {} + for image_ in img_list: + if isinstance(image_,Image.Image): + count = sizes.get(image_.size, 0) + sizes[image_.size] = count + 1 + elif isinstance(image_,np.ndarray): + count = sizes.get(image_.shape[:2][::-1], 0) + sizes[image_.shape[:2][::-1]] = count + 1 + else: + raise "unsupport image list,must be pil or cv2!!!" + size = max(sizes.items(), key=lambda x: x[1])[0] + yield size[0], size[1] + + # any to tensor + def load_image(img_in): + if isinstance(img_in, Image.Image): + img_in=img_in.convert("RGB") + i = np.array(img_in, dtype=np.float32) + i = torch.from_numpy(i).div_(255) + if i.shape[0] != size[1] or i.shape[1] != size[0]: + i = torch.from_numpy(i).movedim(-1, 0).unsqueeze(0) + i = common_upscale(i, size[0], size[1], "lanczos", "center") + i = i.squeeze(0).movedim(0, -1).numpy() + return i + elif isinstance(img_in,np.ndarray): + i=cv2.cvtColor(img_in,cv2.COLOR_BGR2RGB).astype(np.float32) + i = torch.from_numpy(i).div_(255) + print(i.shape) + return i + else: + raise "unsupport image list,must be pil,cv2 or tensor!!!" + + total_images = len(img_list) + processed_images = 0 + pbar = ProgressBar(total_images) + images = map(load_image, img_list) + try: + prev_image = next(images) + while True: + next_image = next(images) + yield prev_image + processed_images += 1 + pbar.update_absolute(processed_images, total_images) + prev_image = next_image + except StopIteration: + pass + if prev_image is not None: + yield prev_image + +def load_list_images(img_list: list,): + gen = images_generator(img_list) + (width, height) = next(gen) + images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (height, width, 3))))) + if len(images) == 0: + raise FileNotFoundError(f"No images could be loaded .") + return images +

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