From 6f4d0dbe31561e36ad7a21044e41853dc5aa1c60 Mon Sep 17 00:00:00 2001 From: xxl <505279206@qq.com> Date: Fri, 28 Feb 2025 11:09:24 +0800 Subject: [PATCH] first commit --- CODE_OF_CONDUCT.md | 9 + LICENSE | 21 + README.md | 645 +- SECURITY.md | 41 + SUPPORT.md | 25 + added_tokens.json | 12 + config.json | 221 + configuration_phi4mm.py | 235 + examples/what_is_shown_in_this_image.wav | Bin 0 -> 112844 bytes .../what_is_the_traffic_sign_in_the_image.wav | Bin 0 -> 741454 bytes figures/audio_understand.png | Bin 0 -> 42645 bytes figures/multi_image.png | Bin 0 -> 191950 bytes figures/speech_qa.png | Bin 0 -> 46754 bytes figures/speech_recog_by_lang.png | Bin 0 -> 90697 bytes figures/speech_recognition.png | Bin 0 -> 63547 bytes figures/speech_summarization.png | Bin 0 -> 40951 bytes figures/speech_translate.png | Bin 0 -> 47677 bytes figures/speech_translate_2.png | Bin 0 -> 46342 bytes figures/vision_radar.png | Bin 0 -> 173821 bytes generation_config.json | 11 + merges.txt | 199743 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| 1 + vision_siglip_navit.py | 1717 + vocab.json | 1 + 51 files changed, 212616 insertions(+), 2 deletions(-) create mode 100644 CODE_OF_CONDUCT.md create mode 100644 LICENSE create mode 100644 SECURITY.md create mode 100644 SUPPORT.md create mode 100644 added_tokens.json create mode 100644 config.json create mode 100644 configuration_phi4mm.py create mode 100644 examples/what_is_shown_in_this_image.wav create mode 100644 examples/what_is_the_traffic_sign_in_the_image.wav create mode 100644 figures/audio_understand.png create mode 100644 figures/multi_image.png create mode 100644 figures/speech_qa.png create mode 100644 figures/speech_recog_by_lang.png create mode 100644 figures/speech_recognition.png create mode 100644 figures/speech_summarization.png create mode 100644 figures/speech_translate.png create mode 100644 figures/speech_translate_2.png create mode 100644 figures/vision_radar.png create mode 100644 generation_config.json create mode 100644 merges.txt create mode 100644 model-00003-of-00003.safetensors create mode 100644 model.safetensors.index.json create mode 100644 modeling_phi4mm.py create mode 100644 phi_4_mm.tech_report.02252025.pdf create mode 100644 preprocessor_config.json create mode 100644 processing_phi4mm.py create mode 100644 processor_config.json create mode 100644 sample_finetune_speech.py create mode 100644 sample_finetune_vision.py create mode 100644 sample_inference_phi4mm.py create mode 100644 special_tokens_map.json create mode 100644 speech-lora/adapter_config.json create mode 100644 speech-lora/adapter_model.safetensors create mode 100644 speech-lora/added_tokens.json create mode 100644 speech-lora/special_tokens_map.json create mode 100644 speech-lora/tokenizer.json create mode 100644 speech-lora/tokenizer_config.json create mode 100644 speech-lora/vocab.json create mode 100644 speech_conformer_encoder.py create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 vision-lora/adapter_config.json create mode 100644 vision-lora/adapter_model.safetensors create mode 100644 vision-lora/added_tokens.json create mode 100644 vision-lora/special_tokens_map.json create mode 100644 vision-lora/tokenizer.json create mode 100644 vision-lora/tokenizer_config.json create mode 100644 vision-lora/vocab.json create mode 100644 vision_siglip_navit.py create mode 100644 vocab.json diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..f9ba8cf --- /dev/null +++ b/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/LICENSE b/LICENSE new file mode 100644 index 0000000..9e841e7 --- /dev/null +++ b/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/README.md b/README.md index 1f24d2c..57c60c4 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,644 @@ -# Phi-4-multimodal-instruct +--- +license: mit +license_link: https://huggingface.co/microsoft/Phi-4-multimodal-instruct/resolve/main/LICENSE +language: +- multilingual +- ar +- zh +- cs +- da +- nl +- en +- fi +- fr +- de +- he +- hu +- it +- ja +- ko +- no +- pl +- pt +- ru +- es +- sv +- th +- tr +- uk +tags: +- nlp +- code +- audio +- automatic-speech-recognition +- speech-summarization +- speech-translation +- visual-question-answering +- phi-4-multimodal +- phi +- phi-4-mini +widget: +- example_title: Librispeech sample 1 + src: https://cdn-media.huggingface.co/speech_samples/sample1.flac +- example_title: Librispeech sample 2 + src: https://cdn-media.huggingface.co/speech_samples/sample2.flac +- messages: + - role: user + content: Can you provide ways to eat combinations of bananas and dragonfruits? +library_name: transformers +--- -Phi-4-multimodal-instruct \ No newline at end of file +## Model Summary + +Phi-4-multimodal-instruct is a lightweight open multimodal foundation +model that leverages the language, vision, and speech research +and datasets used for Phi-3.5 and 4.0 models. The model processes text, +image, and audio inputs, generating text outputs, and comes with +128K token context length. The model underwent an enhancement process, +incorporating both supervised fine-tuning, direct preference +optimization and RLHF (Reinforcement Learning from Human Feedback) +to support precise instruction adherence and safety measures. +The languages that each modal supports are the following: +- Text: Arabic, Chinese, Czech, Danish, Dutch, English, Finnish, +French, German, Hebrew, Hungarian, Italian, Japanese, Korean, Norwegian, +Polish, Portuguese, Russian, Spanish, Swedish, Thai, Turkish, Ukrainian +- Vision: English +- Audio: English, Chinese, German, French, Italian, Japanese, Spanish, Portuguese + +📰 [Phi-4-multimodal Microsoft Blog](https://aka.ms/phi4-feb2025)
+📖 [Phi-4-multimodal Technical Report](https://aka.ms/phi-4-multimodal/techreport)
+🏡 [Phi Portal](https://aka.ms/phi-4-multimodal/azure)
+👩‍🍳 [Phi Cookbook](https://github.com/microsoft/PhiCookBook)
+🖥️ Try It on [Azure](https://aka.ms/phi-4-multimodal/azure), [Nvidia Playgroud](https://aka.ms/phi-4-multimodal/nvidia)
+📱Huggingface Spaces +[Thoughts Organizer](https://huggingface.co/spaces/microsoft/ThoughtsOrganizer), +[Stories Come Alive](https://huggingface.co/spaces/microsoft/StoriesComeAlive), +[Phine Speech Translator](https://huggingface.co/spaces/microsoft/PhineSpeechTranslator)
+ +**Phi-4**: [[multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) | [onnx](https://huggingface.co/microsoft/Phi-4-multimodal-instruct-onnx)]; [mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct); + +Watch as Phi-4 Multimodal analyzes spoken language to help plan a trip to Seattle, demonstrating its advanced audio processing and recommendation capabilities. + +
+ +
+ +See how Phi-4 Multimodal tackles complex mathematical problems through visual inputs, demonstrating its ability to process and solve equations presented in images. +
+ +
+ +Explore how Phi-4 Mini functions as an intelligent agent, showcasing its reasoning and task execution abilities in complex scenarios. +
+ +
+ + +## Intended Uses + +### Primary Use Cases + +The model is intended for broad multilingual and multimodal commercial and research use . The model provides uses for general purpose AI systems and applications which require + +1) Memory/compute constrained environments +2) Latency bound scenarios +3) Strong reasoning (especially math and logic) +4) Function and tool calling +5) General image understanding +6) Optical character recognition +7) Chart and table understanding +8) Multiple image comparison +9) Multi-image or video clip summarization +10) Speech recognition +11) Speech translation +12) Speech QA +13) Speech summarization +14) Audio understanding + +The model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features. + +### Use Case Considerations + +The model is not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models and multimodal models, as well as performance difference across languages, as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios. +Developers should be aware of and adhere to applicable laws or regulations (including but not limited to privacy, trade compliance laws, etc.) that are relevant to their use case. + +***Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.*** + +## Release Notes + +This release of Phi-4-multimodal-instruct is based on valuable user feedback from the Phi-3 series. Previously, users could use a speech recognition model to talk to the Mini and Vision models. To achieve this, users needed to use a pipeline of two models: one model to transcribe the audio to text, and another model for the language or vision tasks. This pipeline means that the core model was not provided the full breadth of input information – e.g. cannot directly observe multiple speakers, background noises, jointly align speech, vision, language information at the same time on the same representation space. +With Phi-4-multimodal-instruct, a single new open model has been trained across text, vision, and audio, meaning that all inputs and outputs are processed by the same neural network. The model employed new architecture, larger vocabulary for efficiency, multilingual, and multimodal support, and better post-training techniques were used for instruction following and function calling, as well as additional data leading to substantial gains on key multimodal capabilities. +It is anticipated that Phi-4-multimodal-instruct will greatly benefit app developers and various use cases. The enthusiastic support for the Phi-4 series is greatly appreciated. Feedback on Phi-4 is welcomed and crucial to the model's evolution and improvement. Thank you for being part of this journey! + +## Model Quality + +To understand the capabilities, Phi-4-multimodal-instruct was compared with a set of models over a variety of benchmarks using an internal benchmark platform (See Appendix A for benchmark methodology). Users can refer to the Phi-4-Mini-Instruct model card for details of language benchmarks. At the high-level overview of the model quality on representative speech and vision benchmarks: + +### Speech + +The Phi-4-multimodal-instruct was observed as +- Having strong automatic speech recognition (ASR) and speech translation (ST) performance, surpassing expert ASR model WhisperV3 and ST models SeamlessM4T-v2-Large. +- Ranking number 1 on the Huggingface OpenASR leaderboard with word error rate 6.14% in comparison with the current best model 6.5% as of Jan 17, 2025. +- Being the first open-sourced model that can perform speech summarization, and the performance is close to GPT4o. +- Having a gap with close models, e.g. Gemini-1.5-Flash and GPT-4o-realtime-preview, on speech QA task. Work is being undertaken to improve this capability in the next iterations. + +#### Speech Recognition (lower is better) + +The performance of Phi-4-multimodal-instruct on the aggregated benchmark datasets: +![alt text](./figures/speech_recognition.png) + +The performance of Phi-4-multimodal-instruct on different languages, averaging the WERs of CommonVoice and FLEURS: + +![alt text](./figures/speech_recog_by_lang.png) + +#### Speech Translation (higher is better) + +Translating from German, Spanish, French, Italian, Japanese, Portugues, Chinese to English: + +![alt text](./figures/speech_translate.png) + +Translating from English to German, Spanish, French, Italian, Japanese, Portugues, Chinese. Noted that WhiperV3 does not support this capability: + +![alt text](./figures/speech_translate_2.png) + + +#### Speech Summarization (higher is better) + +![alt text](./figures/speech_summarization.png) + +#### Speech QA + +MT bench scores are scaled by 10x to match the score range of MMMLU: + +![alt text](./figures/speech_qa.png) + +#### Audio Understanding + +AIR bench scores are scaled by 10x to match the score range of MMAU: + +![alt text](./figures/audio_understand.png) + +### Vision + +#### Vision-Speech tasks + +Phi-4-multimodal-instruct is capable of processing both image and audio together, the following table shows the model quality when the input query for vision content is synthetic speech on chart/table understanding and document reasoning tasks. Compared to other existing state-of-the-art omni models that can enable audio and visual signal as input, Phi-4-multimodal-instruct achieves much stronger performance on multiple benchmarks. + +| Benchmarks | Phi-4-multimodal-instruct | InternOmni-7B | Gemini-2.0-Flash-Lite-prv-02-05 | Gemini-2.0-Flash | Gemini-1.5-Pro | +|-----------------------|--------------------------|---------------|--------------------------------|-----------------|----------------| +| s_AI2D | **68.9** | 53.9 | 62.0 | **69.4** | 67.7 | +| s_ChartQA | **69.0** | 56.1 | 35.5 | 51.3 | 46.9 | +| s_DocVQA | **87.3** | 79.9 | 76.0 | 80.3 | 78.2 | +| s_InfoVQA | **63.7** | 60.3 | 59.4 | 63.6 | **66.1** | +| **Average** | **72.2** | **62.6** | **58.2** | **66.2** | **64.7** | + +### Vision tasks +To understand the vision capabilities, Phi-4-multimodal-instruct was compared with a set of models over a variety of zero-shot benchmarks using an internal benchmark platform. At the high-level overview of the model quality on representative benchmarks: + +| Dataset | Phi-4-multimodal-ins | Phi-3.5-vision-ins | Qwen 2.5-VL-3B-ins | Intern VL 2.5-4B | Qwen 2.5-VL-7B-ins | Intern VL 2.5-8B | Gemini 2.0-Flash Lite-preview-0205 | Gemini2.0-Flash | Claude-3.5-Sonnet-2024-10-22 | Gpt-4o-2024-11-20 | +|----------------------------------|---------------------|-------------------|-------------------|-----------------|-------------------|-----------------|--------------------------------|-----------------|----------------------------|------------------| +| **Popular aggregated benchmark** | | | | | | | | | | | +| MMMU | **55.1** | 43.0 | 47.0 | 48.3 | 51.8 | 50.6 | 54.1 | **64.7** | 55.8 | 61.7 | +| MMBench (dev-en) | **86.7** | 81.9 | 84.3 | 86.8 | 87.8 | 88.2 | 85.0 | **90.0** | 86.7 | 89.0 | +| MMMU-Pro (std/vision) | **38.5** | 21.8 | 29.9 | 32.4 | 36.9 | 34.4 | 45.1 | **54.4** | 54.3 | 53.0 | +| **Visual science reasoning** | | | | | | | | | | | +| ScienceQA Visual (img-test) | **97.5** | 91.3 | 79.4 | 96.2 | 87.7 | **97.3** | 85.0 | 88.3 | 81.2 | 88.2 | +| **Visual math reasoning** | | | | | | | | | | | +| MathVista (testmini) | **62.4** | 43.9 | 60.8 | 51.2 | **67.8** | 56.7 | 57.6 | 47.2 | 56.9 | 56.1 | +| InterGPS | **48.6** | 36.3 | 48.3 | 53.7 | 52.7 | 54.1 | 57.9 | **65.4** | 47.1 | 49.1 | +| **Chart & table reasoning** | | | | | | | | | | | +| AI2D | **82.3** | 78.1 | 78.4 | 80.0 | 82.6 | 83.0 | 77.6 | 82.1 | 70.6 | **83.8** | +| ChartQA | **81.4** | 81.8 | 80.0 | 79.1 | **85.0** | 81.0 | 73.0 | 79.0 | 78.4 | 75.1 | +| DocVQA | **93.2** | 69.3 | 93.9 | 91.6 | **95.7** | 93.0 | 91.2 | 92.1 | 95.2 | 90.9 | +| InfoVQA | **72.7** | 36.6 | 77.1 | 72.1 | **82.6** | 77.6 | 73.0 | 77.8 | 74.3 | 71.9 | +| **Document Intelligence** | | | | | | | | | | | +| TextVQA (val) | **75.6** | 72.0 | 76.8 | 70.9 | **77.7** | 74.8 | 72.9 | 74.4 | 58.6 | 73.1 | +| OCR Bench | **84.4** | 63.8 | 82.2 | 71.6 | **87.7** | 74.8 | 75.7 | 81.0 | 77.0 | 77.7 | +| **Object visual presence verification** | | | | | | | | | | | +| POPE | **85.6** | 86.1 | 87.9 | 89.4 | 87.5 | **89.1** | 87.5 | 88.0 | 82.6 | 86.5 | +| **Multi-image perception** | | | | | | | | | | | +| BLINK | **61.3** | 57.0 | 48.1 | 51.2 | 55.3 | 52.5 | 59.3 | **64.0** | 56.9 | 62.4 | +| Video MME 16 frames | **55.0** | 50.8 | 56.5 | 57.3 | 58.2 | 58.7 | 58.8 | 65.5 | 60.2 | **68.2** | +| **Average** | **72.0** | **60.9** | **68.7** | **68.8** | **73.1** | **71.1** | **70.2** | **74.3** | **69.1** | **72.4** | + +![alt text](./figures/vision_radar.png) + +#### Visual Perception + +Below are the comparison results on existing multi-image tasks. On average, Phi-4-multimodal-instruct outperforms competitor models of the same size and competitive with much bigger models on multi-frame capabilities. +BLINK is an aggregated benchmark with 14 visual tasks that humans can solve very quickly but are still hard for current multimodal LLMs. + +| Dataset | Phi-4-multimodal-instruct | Qwen2.5-VL-3B-Instruct | InternVL 2.5-4B | Qwen2.5-VL-7B-Instruct | InternVL 2.5-8B | Gemini-2.0-Flash-Lite-prv-02-05 | Gemini-2.0-Flash | Claude-3.5-Sonnet-2024-10-22 | Gpt-4o-2024-11-20 | +|----------------------------|--------------------------|----------------------|-----------------|----------------------|-----------------|--------------------------------|-----------------|----------------------------|------------------| +| Art Style | **86.3** | 58.1 | 59.8 | 65.0 | 65.0 | 76.9 | 76.9 | 68.4 | 73.5 | +| Counting | **60.0** | 67.5 | 60.0 | 66.7 | **71.7** | 45.8 | 69.2 | 60.8 | 65.0 | +| Forensic Detection | **90.2** | 34.8 | 22.0 | 43.9 | 37.9 | 31.8 | 74.2 | 63.6 | 71.2 | +| Functional Correspondence | **30.0** | 20.0 | 26.9 | 22.3 | 27.7 | 48.5 | **53.1** | 34.6 | 42.3 | +| IQ Test | **22.7** | 25.3 | 28.7 | 28.7 | 28.7 | 28.0 | **30.7** | 20.7 | 25.3 | +| Jigsaw | **68.7** | 52.0 | **71.3** | 69.3 | 53.3 | 62.7 | 69.3 | 61.3 | 68.7 | +| Multi-View Reasoning | **76.7** | 44.4 | 44.4 | 54.1 | 45.1 | 55.6 | 41.4 | 54.9 | 54.1 | +| Object Localization | **52.5** | 55.7 | 53.3 | 55.7 | 58.2 | 63.9 | **67.2** | 58.2 | 65.6 | +| Relative Depth | **69.4** | 68.5 | 68.5 | 80.6 | 76.6 | **81.5** | 72.6 | 66.1 | 73.4 | +| Relative Reflectance | **26.9** | **38.8** | **38.8** | 32.8 | **38.8** | 33.6 | 34.3 | 38.1 | 38.1 | +| Semantic Correspondence | **52.5** | 32.4 | 33.8 | 28.8 | 24.5 | **56.1** | 55.4 | 43.9 | 47.5 | +| Spatial Relation | **72.7** | 80.4 | 86.0 | **88.8** | 86.7 | 74.1 | 79.0 | 74.8 | 83.2 | +| Visual Correspondence | **67.4** | 28.5 | 39.5 | 50.0 | 44.2 | 84.9 | **91.3** | 72.7 | 82.6 | +| Visual Similarity | **86.7** | 67.4 | 88.1 | 87.4 | 85.2 | **87.4** | 80.7 | 79.3 | 83.0 | +| **Overall** | **61.6** | **48.1** | **51.2** | **55.3** | **52.5** | **59.3** | **64.0** | **56.9** | **62.4** | + +![alt text](./figures/multi_image.png) + + +## Usage + +### Requirements + +Phi-4 family has been integrated in the `4.48.2` version of `transformers`. The current `transformers` version can be verified with: `pip list | grep transformers`. + +Examples of required packages: +``` +flash_attn==2.7.4.post1 +torch==2.6.0 +transformers==4.48.2 +accelerate==1.3.0 +soundfile==0.13.1 +pillow==11.1.0 +scipy==1.15.2 +torchvision==0.21.0 +backoff==2.2.1 +peft==0.13.2 +``` + +Phi-4-multimodal-instruct is also available in [Azure AI Studio](https://aka.ms/phi-4-multimodal/azure) + +### Tokenizer + +Phi-4-multimodal-instruct supports a vocabulary size of up to `200064` tokens. The [tokenizer files](https://huggingface.co/microsoft/Phi-4-multimodal-instruct/blob/main/added_tokens.json) already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size. + +### Input Formats + +Given the nature of the training data, the Phi-4-multimodal-instruct model is best suited for prompts using the chat format as follows: + +#### Text chat format + +This format is used for general conversation and instructions: + +` +<|system|>You are a helpful assistant.<|end|><|user|>How to explain Internet for a medieval knight?<|end|><|assistant|> +` + +#### Tool-enabled function-calling format + +This format is used when the user wants the model to provide function calls based on +the given tools. The user should provide the available tools in the system prompt, +wrapped by <|tool|> and <|/tool|> tokens. The tools should be specified in JSON format, +using a JSON dump structure. Example: + +` +<|system|>You are a helpful assistant with some tools.<|tool|>[{"name": "get_weather_updates", "description": "Fetches weather updates for a given city using the RapidAPI Weather API.", "parameters": {"city": {"description": "The name of the city for which to retrieve weather information.", "type": "str", "default": "London"}}}]<|/tool|><|end|><|user|>What is the weather like in Paris today?<|end|><|assistant|> +` + +#### Vision-Language Format + +This format is used for conversation with image: + +` +<|user|><|image_1|>Describe the image in detail.<|end|><|assistant|> +` + +For multiple images, the user needs to insert multiple image placeholders in the prompt as below: + +` +<|user|><|image_1|><|image_2|><|image_3|>Summarize the content of the images.<|end|><|assistant|> +` + +#### Speech-Language Format + +This format is used for various speech and audio tasks: + +` +<|user|><|audio_1|>{task prompt}<|end|><|assistant|> +` + +The task prompt can vary for different task. +Automatic Speech Recognition: + +` +<|user|><|audio_1|>Transcribe the audio clip into text.<|end|><|assistant|> +` + +Automatic Speech Translation: + +` +<|user|><|audio_1|>Translate the audio to {lang}.<|end|><|assistant|> +` + +Automatic Speech Translation with chain-of-thoughts: + +` +<|user|><|audio_1|>Transcribe the audio to text, and then translate the audio to {lang}. Use as a separator between the original transcript and the translation.<|end|><|assistant|> +` + +Spoken-query Question Answering: + +` +<|user|><|audio_1|><|end|><|assistant|> +` + +#### Vision-Speech Format + +This format is used for conversation with image and audio. +The audio may contain query related to the image: + +` +<|user|><|image_1|><|audio_1|><|end|><|assistant|> +` + +For multiple images, the user needs to insert multiple image placeholders in the prompt as below: + +` +<|user|><|image_1|><|image_2|><|image_3|><|audio_1|><|end|><|assistant|> +` + +**Vision** +- Any common RGB/gray image format (e.g., (".jpg", ".jpeg", ".png", ".ppm", ".bmp", ".pgm", ".tif", ".tiff", ".webp")) can be supported. +- Resolution depends on the GPU memory size. Higher resolution and more images will produce more tokens, thus using more GPU memory. During training, 64 crops can be supported. +If it is a square image, the resolution would be around (8*448 by 8*448). For multiple-images, at most 64 frames can be supported, but with more frames as input, the resolution of each frame needs to be reduced to fit in the memory. + +**Audio** +- Any audio format that can be loaded by soundfile package should be supported. +- To keep the satisfactory performance, maximum audio length is suggested to be 40s. For summarization tasks, the maximum audio length is suggested to 30 mins. + + +### Loading the model locally + +After obtaining the Phi-4-multimodal-instruct model checkpoints, users can use this sample code for inference. + +```python +import requests +import torch +import os +import io +from PIL import Image +import soundfile as sf +from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig +from urllib.request import urlopen + + +# Define model path +model_path = "microsoft/Phi-4-multimodal-instruct" + +# Load model and processor +processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) +model = AutoModelForCausalLM.from_pretrained( + model_path, + device_map="cuda", + torch_dtype="auto", + trust_remote_code=True, + attn_implementation='flash_attention_2', +).cuda() + +# Load generation config +generation_config = GenerationConfig.from_pretrained(model_path) + +# Define prompt structure +user_prompt = '<|user|>' +assistant_prompt = '<|assistant|>' +prompt_suffix = '<|end|>' + +# Part 1: Image Processing +print("\n--- IMAGE PROCESSING ---") +image_url = 'https://www.ilankelman.org/stopsigns/australia.jpg' +prompt = f'{user_prompt}<|image_1|>What is shown in this image?{prompt_suffix}{assistant_prompt}' +print(f'>>> Prompt\n{prompt}') + +# Download and open image +image = Image.open(requests.get(image_url, stream=True).raw) +inputs = processor(text=prompt, images=image, return_tensors='pt').to('cuda:0') + +# Generate response +generate_ids = model.generate( + **inputs, + max_new_tokens=1000, + generation_config=generation_config, +) +generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:] +response = processor.batch_decode( + generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False +)[0] +print(f'>>> Response\n{response}') + +# Part 2: Audio Processing +print("\n--- AUDIO PROCESSING ---") +audio_url = "https://upload.wikimedia.org/wikipedia/commons/b/b0/Barbara_Sahakian_BBC_Radio4_The_Life_Scientific_29_May_2012_b01j5j24.flac" +speech_prompt = "Transcribe the audio to text, and then translate the audio to French. Use as a separator between the original transcript and the translation." +prompt = f'{user_prompt}<|audio_1|>{speech_prompt}{prompt_suffix}{assistant_prompt}' +print(f'>>> Prompt\n{prompt}') + +# Downlowd and open audio file +audio, samplerate = sf.read(io.BytesIO(urlopen(audio_url).read())) + +# Process with the model +inputs = processor(text=prompt, audios=[(audio, samplerate)], return_tensors='pt').to('cuda:0') + +generate_ids = model.generate( + **inputs, + max_new_tokens=1000, + generation_config=generation_config, +) +generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:] +response = processor.batch_decode( + generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False +)[0] +print(f'>>> Response\n{response}') +``` + +## Responsible AI Considerations + +Like other language models, the Phi family of models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include: ++ Quality of Service: The Phi models are trained primarily on English language content across text, speech, and visual inputs, with some additional multilingual coverage. Performance may vary significantly across different modalities and languages: + + Text: Languages other than English will experience reduced performance, with varying levels of degradation across different non-English languages. English language varieties with less representation in the training data may perform worse than standard American English. + + Speech: Speech recognition and processing shows similar language-based performance patterns, with optimal performance for standard American English accents and pronunciations. Other English accents, dialects, and non-English languages may experience lower recognition accuracy and response quality. Background noise, audio quality, and speaking speed can further impact performance. + + Vision: Visual processing capabilities may be influenced by cultural and geographical biases in the training data. The model may show reduced performance when analyzing images containing text in non-English languages or visual elements more commonly found in non-Western contexts. Image quality, lighting conditions, and composition can also affect processing accuracy. ++ Multilingual performance and safety gaps: We believe it is important to make language models more widely available across different languages, but the Phi 4 models still exhibit challenges common across multilingual releases. As with any deployment of LLMs, developers will be better positioned to test for performance or safety gaps for their linguistic and cultural context and customize the model with additional fine-tuning and appropriate safeguards. ++ Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups, cultural contexts, or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. ++ Inappropriate or Offensive Content: These models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the case. ++ Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. ++ Limited Scope for Code: The majority of Phi 4 training data is based in Python and uses common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, it is strongly recommended that users manually verify all API uses. ++ Long Conversation: Phi 4 models, like other models, can in some cases generate responses that are repetitive, unhelpful, or inconsistent in very long chat sessions in both English and non-English languages. Developers are encouraged to place appropriate mitigations, like limiting conversation turns to account for the possible conversational drift. ++ Inference of Sensitive Attributes: The Phi 4 models can sometimes attempt to infer sensitive attributes (such as personality characteristics, country of origin, gender, etc...) from the users’ voices when specifically asked to do so. Phi 4-multimodal-instruct is not designed or intended to be used as a biometric categorization system to categorize individuals based on their biometric data to deduce or infer their race, political opinions, trade union membership, religious or philosophical beliefs, sex life, or sexual orientation. This behavior can be easily and efficiently mitigated at the application level by a system message. + +Developers should apply responsible AI best practices, including mapping, measuring, and mitigating risks associated with their specific use case and cultural, linguistic context. Phi 4 family of models are general purpose models. As developers plan to deploy these models for specific use cases, they are encouraged to fine-tune the models for their use case and leverage the models as part of broader AI systems with language-specific safeguards in place. Important areas for consideration include: + ++ Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques. ++ High-Risk Scenarios: Developers should assess the suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. ++ Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). ++ Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. ++ Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations. + + +## Training + +### Model + ++ **Architecture:** Phi-4-multimodal-instruct has 5.6B parameters and is a multimodal transformer model. The model has the pretrained Phi-4-Mini-Instruct as the backbone language model, and the advanced encoders and adapters of vision and speech.
++ **Inputs:** Text, image, and audio. It is best suited for prompts using the chat format.
++ **Context length:** 128K tokens
++ **GPUs:** 512 A100-80G
++ **Training time:** 28 days
++ **Training data:** 5T tokens, 2.3M speech hours, and 1.1T image-text tokens
++ **Outputs:** Generated text in response to the input
++ **Dates:** Trained between December 2024 and January 2025
++ **Status:** This is a static model trained on offline datasets with the cutoff date of June 2024 for publicly available data.
++ **Supported languages:** + + Text: Arabic, Chinese, Czech, Danish, Dutch, English, Finnish, French, German, Hebrew, Hungarian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, Turkish, Ukrainian
+ + Vision: English
+ + Audio: English, Chinese, German, French, Italian, Japanese, Spanish, Portuguese
++ **Release date:** February 2025
+ +### Training Datasets + +Phi-4-multimodal-instruct's training data includes a wide variety of sources, totaling 5 trillion text tokens, and is a combination of +1) publicly available documents filtered for quality, selected high-quality educational data, and code +2) newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (e.g., science, daily activities, theory of mind, etc.) +3) high quality human labeled data in chat format +4) selected high-quality image-text interleave data +5) synthetic and publicly available image, multi-image, and video data +6) anonymized in-house speech-text pair data with strong/weak transcriptions +7) selected high-quality publicly available and anonymized in-house speech data with task-specific supervisions +8) selected synthetic speech data +9) synthetic vision-speech data. + +Focus was placed on the quality of data that could potentially improve the reasoning ability for the model, and the publicly available documents were filtered to contain a preferred level of knowledge. As an example, the result of a game in premier league on a particular day might be good training data for large foundation models, but such information was removed for the Phi-4-multimodal-instruct to leave more model capacity for reasoning for the model's small size. The data collection process involved sourcing information from publicly available documents, with a focus on filtering out undesirable documents and images. To safeguard privacy, image and text data sources were filtered to remove or scrub potentially personal data from the training data. +The decontamination process involved normalizing and tokenizing the dataset, then generating and comparing n-grams between the target dataset and benchmark datasets. Samples with matching n-grams above a threshold were flagged as contaminated and removed from the dataset. A detailed contamination report was generated, summarizing the matched text, matching ratio, and filtered results for further analysis. + +### Fine-tuning + +A basic example of supervised fine-tuning (SFT) for [speech](https://huggingface.co/microsoft/Phi-4-multimodal-instruct/resolve/main/sample_finetune_speech.py) and [vision](https://huggingface.co/microsoft/Phi-4-multimodal-instruct/resolve/main/sample_finetune_vision.py) is provided respectively. + +## Safety + +The Phi-4 family of models has adopted a robust safety post-training approach. This approach leverages a variety of both open-source and in-house generated datasets. The overall technique employed for safety alignment is a combination of SFT (Supervised Fine-Tuning), DPO (Direct Preference Optimization), and RLHF (Reinforcement Learning from Human Feedback) approaches by utilizing human-labeled and synthetic English-language datasets, including publicly available datasets focusing on helpfulness and harmlessness, as well as various questions and answers targeted to multiple safety categories. For non-English languages, existing datasets were extended via machine translation. Speech Safety datasets were generated by running Text Safety datasets through Azure TTS (Text-To-Speech) Service, for both English and non-English languages. Vision (text & images) Safety datasets were created to cover harm categories identified both in public and internal multi-modal RAI datasets. + +### Safety Evaluation and Red-Teaming + +Various evaluation techniques including red teaming, adversarial conversation simulations, and multilingual safety evaluation benchmark datasets were leveraged to evaluate Phi-4 models' propensity to produce undesirable outputs across multiple languages and risk categories. Several approaches were used to compensate for the limitations of one approach alone. Findings across the various evaluation methods indicate that safety post-training that was done as detailed in the [Phi 3 Safety Post-Training paper](https://arxiv.org/abs/2407.13833) had a positive impact across multiple languages and risk categories as observed by refusal rates (refusal to output undesirable outputs) and robustness to jailbreak techniques. Details on prior red team evaluations across Phi models can be found in the [Phi 3 Safety Post-Training paper](https://arxiv.org/abs/2407.13833). For this release, the red teaming effort focused on the newest Audio input modality and on the following safety areas: harmful content, self-injury risks, and exploits. The model was found to be more susceptible to providing undesirable outputs when attacked with context manipulation or persuasive techniques. These findings applied to all languages, with the persuasive techniques mostly affecting French and Italian. This highlights the need for industry-wide investment in the development of high-quality safety evaluation datasets across multiple languages, including low resource languages, and risk areas that account for cultural nuances where those languages are spoken. + +### Vision Safety Evaluation + +To assess model safety in scenarios involving both text and images, Microsoft's Azure AI Evaluation SDK was utilized. This tool facilitates the simulation of single-turn conversations with the target model by providing prompt text and images designed to incite harmful responses. The target model's responses are subsequently evaluated by a capable model across multiple harm categories, including violence, sexual content, self-harm, hateful and unfair content, with each response scored based on the severity of the harm identified. The evaluation results were compared with those of Phi-3.5-Vision and open-source models of comparable size. In addition, we ran both an internal and the public RTVLM and VLGuard multi-modal (text & vision) RAI benchmarks, once again comparing scores with Phi-3.5-Vision and open-source models of comparable size. However, the model may be susceptible to language-specific attack prompts and cultural context. + +### Audio Safety Evaluation + +In addition to extensive red teaming, the Safety of the model was assessed through three distinct evaluations. First, as performed with Text and Vision inputs, Microsoft's Azure AI Evaluation SDK was leveraged to detect the presence of harmful content in the model's responses to Speech prompts. Second, [Microsoft's Speech Fairness evaluation](https://speech.microsoft.com/portal/responsibleai/assess) was run to verify that Speech-To-Text transcription worked well across a variety of demographics. Third, we proposed and evaluated a mitigation approach via a system message to help prevent the model from inferring sensitive attributes (such as gender, sexual orientation, profession, medical condition, etc...) from the voice of a user. + + +## Software +* [PyTorch](https://github.com/pytorch/pytorch) +* [Transformers](https://github.com/huggingface/transformers) +* [Flash-Attention](https://github.com/HazyResearch/flash-attention) +* [Accelerate](https://huggingface.co/docs/transformers/main/en/accelerate) +* [soundfile](https://github.com/bastibe/python-soundfile) +* [pillow](https://github.com/python-pillow/Pillow) + +## Hardware +Note that by default, the Phi-4-multimodal-instruct model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types: +* NVIDIA A100 +* NVIDIA A6000 +* NVIDIA H100 + +If you want to run the model on: +* NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from_pretrained() with attn_implementation="eager" + +## License +The model is licensed under the [MIT license](./LICENSE). + +## Trademarks +This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies. + +## Appendix A: Benchmark Methodology + +We include a brief word on methodology here - and in particular, how we think about optimizing prompts. +In an ideal world, we would never change any prompts in our benchmarks to ensure it is always an apples-to-apples comparison when comparing different models. Indeed, this is our default approach, and is the case in the vast majority of models we have run to date. +There are, however, some exceptions to this. In some cases, we see a model that performs worse than expected on a given eval due to a failure to respect the output format. For example: + ++ A model may refuse to answer questions (for no apparent reason), or in coding tasks models may prefix their response with “Sure, I can help with that. …” which may break the parser. In such cases, we have opted to try different system messages (e.g. “You must always respond to a question” or “Get to the point!”). ++ Some models, we observed that few shots actually hurt model performance. In this case we did allow running the benchmarks with 0-shots for all cases. ++ We have tools to convert between chat and completions APIs. When converting a chat prompt to a completion prompt, some models have different keywords e.g. Human vs User. In these cases, we do allow for model-specific mappings for chat to completion prompts. + +However, we do not: + ++ Pick different few-shot examples. Few shots will always be the same when comparing different models. ++ Change prompt format: e.g. if it is an A/B/C/D multiple choice, we do not tweak this to 1/2/3/4 multiple choice. + +### Vision Benchmark Settings + +The goal of the benchmark setup is to measure the performance of the LMM when a regular user utilizes these models for a task involving visual input. To this end, we selected 9 popular and publicly available single-frame datasets and 3 multi-frame benchmarks that cover a wide range of challenging topics and tasks (e.g., mathematics, OCR tasks, charts-and-plots understanding, etc.) as well as a set of high-quality models. +Our benchmarking setup utilizes zero-shot prompts and all the prompt content are the same for every model. We only formatted the prompt content to satisfy the model's prompt API. This ensures that our evaluation is fair across the set of models we tested. Many benchmarks necessitate models to choose their responses from a presented list of options. Therefore, we've included a directive in the prompt's conclusion, guiding all models to pick the option letter that corresponds to the answer they deem correct. +In terms of the visual input, we use the images from the benchmarks as they come from the original datasets. We converted these images to base-64 using a JPEG encoding for models that require this format (e.g., GPTV, Claude Sonnet 3.5, Gemini 1.5 Pro/Flash). For other models (e.g., Llava Interleave, and InternVL2 4B and 8B), we used their Huggingface interface and passed in PIL images or a JPEG image stored locally. We did not scale or pre-process images in any other way. +Lastly, we used the same code to extract answers and evaluate them using the same code for every considered model. This ensures that we are fair in assessing the quality of their answers. + +### Speech Benchmark Settings + +The objective of this benchmarking setup is to assess the performance of models in speech and audio understanding tasks as utilized by regular users. To accomplish this, we selected several state-of-the-art open-sourced and closed-sourced models and performed evaluations across a variety of public and in-house benchmarks. These benchmarks encompass diverse and challenging topics, including Automatic Speech Recognition (ASR), Automatic Speech Translation (AST), Spoken Query Question Answering (SQQA), Audio Understanding (AU), and Speech Summarization. +The results are derived from evaluations conducted on identical test data without any further clarifications. All results were obtained without sampling during inference. For an accurate comparison, we employed consistent prompts for models across different tasks, except for certain model APIs (e.g., GPT-4o), which may refuse to respond to specific prompts for some tasks. +In conclusion, we used uniform code to extract answers and evaluate them for all considered models. This approach ensured fairness by assessing the quality of their responses. + +### Benchmark datasets + +The model was evaluated across a breadth of public and internal benchmarks to understand it's capabilities under multiple tasks and conditions. While most evaluations use English, multilingual benchmark was incorporated to cover performance in select languages. More specifically, ++ Vision: + + Popular aggregated benchmark: + + MMMU and MMMU-Pro: massive multi-discipline tasks at college-level subject knowledge and deliberate reasoning. + + MMBench: large-scale benchmark to evaluate perception and reasoning capabilities. + + Visual reasoning: + + ScienceQA: multimodal visual question answering on science. + + MathVista: visual math reasoning. + + InterGPS: Visual 2D geometry reasoning. + + Chart reasoning: + + ChartQA: visual and logical reasoning on charts. + + AI2D: diagram understanding. + + Document Intelligence: + + TextVQA: read and reason about text in images to answer questions about them. + + InfoVQA: read and reason about high-resolution infographics images with arbitrary aspect ratios. + + DocVQA: read and reason about document images with dense texts and handwritten texts. + + OCRBench: test OCR and QA capability on diverse text related images. + + Vision speech multimodal understanding: + + s_AI2D: diagram understanding with speech as the question format. + + s_ChartQA: visual and logical reasoning on charts with speech as the question format. + + s_InfoVQA: read and reason about high-resolution infographics images with speech as the question format. + + s_DocVQA: read and reason about document images with dense texts and handwritten texts with speech as the question format. + + RAI & Security Benchmarks: + + VLGuardExt: VLGuard is a vision-language instruction following public dataset for model safety to address safety on deception + discrimination, privacy and risky behavior (advice, sexual, violence, political). This was extended to a few internal categories such as child safety and election critical information. + + RTVLM: Public benchmark for red-teaming vision-language model on model truthfulness, privacy, safety, and fairness. + + GPTV-RAI: In-house benchmark for GPT-4V released from Azure AI, measuring harmfulness (ex. sexual, violent, hate and self-harm), privacy, jailbreak, misinformation. + ++ Speech: + + CommonVoice v15 is an open-source, multilingual speech dataset developed by Mozilla. It includes over 33,000 hours of speech data in 133 languages, contributed and validated by volunteers worldwide.The evaluations were conducted in the eight supported languages. + + The OpenASR Leaderboard on Hugging Face is designed for benchmarking and evaluating the robustness of ASR models on English. The datasets in the leaderboard cover diverse speech domains including reading speech, conversations, meetings, and so on. + + CoVoST2 is a multilingual speech-to-text translation dataset derived from Mozilla's Common Voice project. It is one of the largest open datasets available for speech translation, providing support for both X-to-English (X→En) and English-to-X (En→X) translation tasks. The directions with supported languages were evaluated on the test sets. + + FLEURS is a multilingual speech dataset designed for evaluating speech recognition and speech-to-text translation models across a wide range of languages. The test sets for speech recognition and translation tasks were evaluated with the eight supported languages. + + MT Bench (Multi-turn Benchmark) is specifically designed to evaluate the conversational and instruction-following abilities of AI models in multi-turn question-answering (QA) scenarios. To support spoken questions, the text is synthesized into speech. + + MMMLU (Multilingual Massive Multitask Language Understanding) is an extensive benchmark designed to evaluate the general knowledge and reasoning capabilities of AI models across a wide array of subjects. To support spoken questions, the text is synthesized into its speech counterpart. The model was evaluated on the eight supported languages for this test set. + + AIR-Bench Chat (Audio Instruction and Response Benchmark) is a comprehensive evaluation framework designed to test the capabilities of large audio language models (LALMs). It includes both foundation and chat benchmarks. The chat benchmark is selected for its open-ended question answering for audio capability. + + MMAU (Massive Multi-Task Audio Understanding) is a comprehensive dataset designed to evaluate the capabilities of multi-modal models in audio-based understanding and reasoning tasks. The test sets are in the form of multiple-choices QA, covering the categories of music, sound, and speech. + + Golden3 is a real-world meeting dataset, containing 108 meeting recordings with corresponding transcripts, averaging 6 minutes each. It is recorded across 30 conference rooms, featuring 4-8 attendees. The dataset is primarily in English, covering a wide range of topics. GPT4 is employed to generate summarization instructions that ask to summarize partial or the entire conversation or control the output style/length/structure. + + AMI (Augmented Multi-Party Interaction) is a comprehensive collection of meeting recordings, encompassing approximately 100 hours of data. The test split contains 20 meeting recordings with an average duration of 32 minutes. The model was tested on the close-talking version of audio. GPT4 is employed to generate summarization instructions that ask to summarize partial or the entire conversation or control the output style/length/structure. + ++ Safety and RAI: + + Single-turn trustworthiness evaluation: + + DecodingTrust: DecodingTrust is a collection of trustworthiness benchmarks in eight different perspectives + + XSTest: XSTest is an exaggerated safety evaluation + + Toxigen: Toxigen is adversarial and hate speech detection + + Red Team: + + Responses to prompts provided by AI Red Team at Microsoft diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 0000000..b3c89ef --- /dev/null +++ b/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/SUPPORT.md b/SUPPORT.md new file mode 100644 index 0000000..eaf439a --- /dev/null +++ b/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". 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WHERE WILL YOU HELP PEOPLE?**. + +## Microsoft Support Policy + +Support for this **PROJECT or PRODUCT** is limited to the resources listed above. diff --git a/added_tokens.json b/added_tokens.json new file mode 100644 index 0000000..af52cde --- /dev/null +++ b/added_tokens.json @@ -0,0 +1,12 @@ +{ + "<|/tool_call|>": 200026, + "<|/tool|>": 200024, + "<|assistant|>": 200019, + "<|end|>": 200020, + "<|system|>": 200022, + "<|tag|>": 200028, + "<|tool_call|>": 200025, + "<|tool_response|>": 200027, + "<|tool|>": 200023, + "<|user|>": 200021 +} diff --git a/config.json b/config.json new file mode 100644 index 0000000..6f294fb --- /dev/null +++ b/config.json @@ -0,0 +1,221 @@ +{ + "_name_or_path": "Phi-4-multimodal-instruct", + "architectures": [ + "Phi4MMForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "audio_processor": { + "config": { + "activation": "swish", + "activation_checkpointing": { + "interval": 1, + "module": "transformer", + "offload": false + }, + "attention_dim": 1024, + "attention_heads": 16, + "batch_norm": false, + "bias_in_glu": true, + "causal": true, + "chunk_size": -1, + "cnn_layer_norm": true, + "conv_activation": "swish", + "conv_glu_type": "swish", + "depthwise_multiplier": 1, + "depthwise_seperable_out_channel": 1024, + "dropout_rate": 0.0, + "encoder_embedding_config": { + "input_size": 80 + }, + "ext_pw_kernel_size": 1, + "ext_pw_out_channel": 1024, + "input_layer": "nemo_conv", + "input_size": 80, + "kernel_size": 3, + "left_chunk": 18, + "linear_units": 1536, + "nemo_conv_settings": { + "conv_channels": 1024 + }, + "num_blocks": 24, + "relative_attention_bias_args": { + "t5_bias_max_distance": 500, + "type": "t5" + }, + "time_reduction": 8 + }, + "name": "cascades" + }, + "auto_map": { + "AutoConfig": "configuration_phi4mm.Phi4MMConfig", + "AutoModelForCausalLM": "modeling_phi4mm.Phi4MMForCausalLM", + "AutoTokenizer": "Xenova/gpt-4o" + }, + "bos_token_id": 199999, + "embd_layer": { + "audio_embd_layer": { + "compression_rate": 8, + "downsample_rate": 1, + "embedding_cls": "audio", + "enable_gradient_checkpointing": true, + "projection_cls": "mlp", + "use_conv_downsample": false, + "use_qformer": false + }, + "embedding_cls": "image_audio", + "image_embd_layer": { + "crop_size": 448, + "embedding_cls": "tune_image", + "enable_gradient_checkpointing": true, + "hd_transform_order": "sub_glb", + "image_token_compression_cls": "avg_pool_2d", + "projection_cls": "mlp", + "use_hd_transform": true, + "with_learnable_separator": true + } + }, + "embd_pdrop": 0.0, + "eos_token_id": 199999, + "full_attn_mod": 1, + "hidden_act": "silu", + "hidden_size": 3072, + "initializer_range": 0.02, + "intermediate_size": 8192, + "interpolate_factor": 1, + "lm_head_bias": false, + "vision_lora": { + "dp": 0.0, + "layer": "layers.*((self_attn\\.(qkv_proj|o_proj))|(mlp\\.(gate_up|down)_proj))", + "lora_alpha": 512, + "r": 256 + }, + "speech_lora": { + "dp": 0.01, + "layer": "((layers.*self_attn\\.(qkv|o)_proj)|(layers.*mlp\\.(gate_up|down)_proj))", + "lora_alpha": 640, + "r": 320 + }, + "max_position_embeddings": 131072, + "mlp_bias": false, + "model_type": "phi4mm", + "num_attention_heads": 24, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "original_max_position_embeddings": 4096, + "pad_token_id": 199999, + "partial_rotary_factor": 0.75, + "resid_pdrop": 0.0, + "rms_norm_eps": 1e-05, + "rope_scaling": { + "long_factor": [ + 1, + 1.118320672, + 1.250641126, + 1.398617824, + 1.564103225, + 1.74916897, + 1.956131817, + 2.187582649, + 2.446418898, + 2.735880826, + 3.059592084, + 3.421605075, + 3.826451687, + 4.279200023, + 4.785517845, + 5.351743533, + 5.984965424, + 6.693110555, + 7.485043894, + 8.370679318, + 9.36110372, + 10.4687158, + 11.70738129, + 13.09260651, + 14.64173252, + 16.37415215, + 18.31155283, + 20.47818807, + 22.90118105, + 25.61086418, + 28.64115884, + 32.03, + 32.1, + 32.13, + 32.23, + 32.6, + 32.61, + 32.64, + 32.66, + 32.7, + 32.71, + 32.93, + 32.97, + 33.28, + 33.49, + 33.5, + 44.16, + 47.77 + ], + "short_factor": [ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + "type": "longrope" + }, + "rope_theta": 10000.0, + "sliding_window": 262144, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "transformers_version": "4.46.1", + "use_cache": true, + "vocab_size": 200064, + "_attn_implementation": "flash_attention_2" +} diff --git a/configuration_phi4mm.py b/configuration_phi4mm.py new file mode 100644 index 0000000..0abf419 --- /dev/null +++ b/configuration_phi4mm.py @@ -0,0 +1,235 @@ +# 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. + +""" Phi-4-MM model configuration""" + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + + +class Phi4MMConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Phi4MMModel`]. It is used to instantiate a Phi-4-MM + 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: + vocab_size (`int`, *optional*, defaults to 200064): + Vocabulary size of the Phi-4-MM model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`Phi4MMModel`]. + hidden_size (`int`, *optional*, defaults to 3072): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 8192): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer decoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer decoder. + num_key_value_heads (`int`, *optional*): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to + `num_attention_heads`. + resid_pdrop (`float`, *optional*, defaults to 0.0): + Dropout probability for mlp outputs. + embd_pdrop (`int`, *optional*, defaults to 0.0): + The dropout ratio for the embeddings. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio after computing the attention scores. + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 4096): + The maximum sequence length that this model might ever be used with. + original_max_position_embeddings (`int`, *optional*, defaults to 4096): + The maximum sequence length that this model was trained with. This is used to determine the size of the + original RoPE embeddings when using long scaling. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + rms_norm_eps (`float`, *optional*, defaults to 1e-05): + The epsilon value used for the RMSNorm. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not. + tie_word_embeddings (`bool`, *optional*, defaults to `False`): + Whether to tie weight embeddings + rope_theta (`float`, *optional*, defaults to 10000.0): + The base period of the RoPE embeddings. + rope_scaling (`dict`, *optional*): + The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must + contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be `longrope` and + the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size + divided by the number of attention heads divided by 2. + partial_rotary_factor (`float`, *optional*, defaults to 0.5): + Percentage of the query and keys which will have rotary embedding. + bos_token_id (`int`, *optional*, defaults to 199999): + The id of the "beginning-of-sequence" token. + eos_token_id (`int`, *optional*, defaults to 199999): + The id of the "end-of-sequence" token. + pad_token_id (`int`, *optional*, defaults to 199999): + The id of the padding token. + sliding_window (`int`, *optional*): + Sliding window attention window size. If `None`, no sliding window is applied. + + Example: + + ```python + >>> from transformers import Phi4MMModel, Phi4MMConfig + + >>> # Initializing a Phi-4-MM style configuration + >>> configuration = Phi4MMConfig.from_pretrained("TBA") + + >>> # Initializing a model from the configuration + >>> model = Phi4MMModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "phi4mm" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=200064, + hidden_size=3072, + intermediate_size=8192, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=None, + resid_pdrop=0.0, + embd_pdrop=0.0, + attention_dropout=0.0, + hidden_act="silu", + max_position_embeddings=4096, + original_max_position_embeddings=4096, + initializer_range=0.02, + rms_norm_eps=1e-5, + use_cache=True, + tie_word_embeddings=False, + rope_theta=10000.0, + rope_scaling=None, + partial_rotary_factor=1, + bos_token_id=199999, + eos_token_id=199999, + pad_token_id=199999, + sliding_window=None, + embd_layer: str = "default", + img_processor=None, + audio_processor=None, + vision_lora=None, + speech_lora=None, + **kwargs, + ): + self.embd_layer = embd_layer + self.img_processor = img_processor + self.audio_processor = audio_processor + self.vision_lora = vision_lora + self.speech_lora = speech_lora + + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.resid_pdrop = resid_pdrop + self.embd_pdrop = embd_pdrop + self.attention_dropout = attention_dropout + self.hidden_act = hidden_act + self.max_position_embeddings = max_position_embeddings + self.original_max_position_embeddings = original_max_position_embeddings + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.use_cache = use_cache + self.rope_theta = rope_theta + self.rope_scaling = rope_scaling + self.partial_rotary_factor = partial_rotary_factor + self._rope_scaling_adjustment() + self._rope_scaling_validation() + self.sliding_window = sliding_window + + super().__init__( + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + pad_token_id=pad_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + + def _rope_scaling_adjustment(self): + """ + Adjust the `type` of the `rope_scaling` configuration for backward compatibility. + """ + if self.rope_scaling is None: + return + + rope_scaling_type = self.rope_scaling.get("type", None) + + # For backward compatibility if previous version used "su" or "yarn" + if rope_scaling_type is not None and rope_scaling_type in ["su", "yarn"]: + self.rope_scaling["type"] = "longrope" + + def _rope_scaling_validation(self): + """ + Validate the `rope_scaling` configuration. + """ + if self.rope_scaling is None: + return + + if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3: + raise ValueError( + "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, " + f"got {self.rope_scaling}" + ) + rope_scaling_type = self.rope_scaling.get("type", None) + rope_scaling_short_factor = self.rope_scaling.get("short_factor", None) + rope_scaling_long_factor = self.rope_scaling.get("long_factor", None) + if rope_scaling_type is None or rope_scaling_type not in ["longrope"]: + raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}") + if not ( + isinstance(rope_scaling_short_factor, list) + and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor) + ): + raise ValueError( + f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}" + ) + rotary_ndims = int(self.hidden_size // self.num_attention_heads * self.partial_rotary_factor) + if not len(rope_scaling_short_factor) == rotary_ndims // 2: + raise ValueError( + f"`rope_scaling`'s short_factor field must have length {rotary_ndims // 2}, got {len(rope_scaling_short_factor)}" + ) + if not ( + isinstance(rope_scaling_long_factor, list) + and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor) + ): + raise ValueError( + f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}" + ) + if not len(rope_scaling_long_factor) == rotary_ndims // 2: + raise ValueError( + f"`rope_scaling`'s long_factor field must have length {rotary_ndims // 2}, got {len(rope_scaling_long_factor)}" + ) diff --git a/examples/what_is_shown_in_this_image.wav b/examples/what_is_shown_in_this_image.wav new file mode 100644 index 0000000000000000000000000000000000000000..e66c76e29073e0d89a8cd11944b56b1387f4245c GIT binary patch literal 112844 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z*|aULsmb(>?>RolAwQ;XhLt0)RAtC4e|$e1ZQ~U$?>Khk07*qoM6N<$f}4#VEC2ui literal 0 HcmV?d00001 diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..9876944 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,11 @@ +{ + "_from_model_config": true, + "bos_token_id": 199999, + "eos_token_id": [ + 200020, + 199999 + ], + "pad_token_id": 199999, + "transformers_version": "4.46.1", + "use_cache": true +} diff --git a/merges.txt b/merges.txt new file mode 100644 index 0000000..dcecc45 --- /dev/null +++ b/merges.txt @@ -0,0 +1,199743 @@ +#version: 0.2 +Ġ Ġ +ĠĠ ĠĠ +i n +e r +Ġ t +Ġ a +e n +o n +r e +Ġ s +a t +o r +e s +ĠĠĠĠ ĠĠĠĠ +a n +ĠĠ Ġ +Ġ d +h e +Ġ c +Ġ p +i s +a r +i t +Ċ Ċ +a l +à ¤ +l e +o u +Ġ m +Ġ f +Ġ w +Ġ b +a s +in g +Ġt he +i c +e t +Ġ o +i on +e d +e l +Ġ n +r o +en t +Ġ Ð +n d +s t +á ĥ +Ð ° +Ġ l +Ġ in +; Ċ +c t +ĠĠĠĠ ĠĠĠ +o m +i l +Ġ h +a m +Ġ = +i d +Ġt o +Ð ¾ +â Ģ +Ġ e +Ø § +i m +Ġ re +Ġ v +a d +Ġt h +Ġa nd +Ð µ +Ġo f +Ġ g +u r +Ð ¸ +c h +Ġ à +Ġd e +ĉ ĉ +Ġ S +Ġ u +Ñ Ĥ +u t +o l +Ð ½ +Ġ y +i g +s e +Ñ Ģ +o t +e m +a g +i v +Ġ ( +q u +ĠĠĠĠĠĠĠĠ ĠĠĠ +Ġ T +Ġ { +Ġ A +a y +Ġ I +à ¥ +a c +à ¦ +u l +) ;Ċ +Ġ C +. ĊĊ +o s +i f +Ġ ठ+o d +Ñ ģ +č Ċ +t r +Ġ k +u n +Ù Ħ +- - +es t +à © +a b +at e +i r +à ¸ +Ġ is +Ð » +o w +u s +à ´ +at ion +Ġ } +Ġ Ø +or t +Ġ M +Ġ " +/ / +u m +Ġf or +i e +à ª +Ġ P +t er +c e +ou r +Ġ on +a p +t h +Ġ{ Ċ +Ġc on +a v +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ +er s +* * +t e +, Ċ +Ġb e +Ù ħ +Ġ D +( ) +Ù Ī +Ġ B +en d +Ġs t +is t +a k +l y +p e +Ġ * +Ù Ĩ +an d +Ġ r +à ² +re s +u b +o c +Ø ± +Ġa l +à ° +es s +it h +âĢ Ļ +о Ð +Ġp ro +Ġ j +Ñ ĥ +Ù Ĭ +am e +v er +) Ċ +p ort +Ġa n +à ¡ +à ¤ +Ġ L +Ġc om +Ġn e +Ġ F +2 0 +as s +Ġ E +Ġs e +l ic +Ġ R +Ġ ' +Ð º +Ġ × +Ġ < +Ñ ĭ +u e +o p +ou t +Ġ en +h t +Ġw h +Ġa s +à ® +Ġ qu +ct ion +ã Ģ +ag e +Ġ N +o g +Ġ it +Ġy ou +i z +Ġw ith +Ġth at +. . +Ġ W +Ġ H +i p +Ð ´ +e x +in t +il l +an t +ur n +Ġth is +Ġ ا +ar t +Ġt r +Ġ G +Ð ¼ +Ġ he +; ĊĊ +Ġ or +0 0 +Ġ le +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ +i k +m ent +à § +o re +ro m +er e +Ġe x +in e +à µ +an g +Ø ª +-- -- +ठ¾ +Ð ² +in d +g et +> Ċ +a in +o k +i b +u d +p l +t urn +ĠĠĠĠ Ġ +à ³ +Ġ O +Ġ - +e ct +i a +it y +Ġu n +Ġ if +Ù ĩ +Ġa t +Û Į +c on +p er +Ñ ı +á » +at a +Ø ¯ +Ġ $ +Ġc h +Ġp r +or m +ac k +Ġa re +i le +p t +Ã Ń +Ġl a +. 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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 Phi-4-MM model.""" +import math +import warnings +from typing import List, Optional, Tuple, Union + +import numpy as np + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss + +from transformers.activations import ACT2FN +from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache +from transformers.generation import GenerationMixin +from transformers.modeling_attn_mask_utils import AttentionMaskConverter +from transformers.modeling_flash_attention_utils import _flash_attention_forward +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + SequenceClassifierOutputWithPast, + TokenClassifierOutput, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import ( + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) +from transformers import AutoConfig, AutoModelForCausalLM, PretrainedConfig + +from .configuration_phi4mm import Phi4MMConfig +from .processing_phi4mm import InputMode +from .vision_siglip_navit import get_siglip_vision_model +from .speech_conformer_encoder import ConformerEncoder + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "TBA" +_CONFIG_FOR_DOC = "Phi4MMConfig" + +# Special token ids +_IMAGE_SPECIAL_TOKEN_ID = 200010 # '<|endoftext10|>', or we can better name it (in `tokenizer_config.json`) +_AUDIO_SPECIAL_TOKEN_ID = 200011 # '<|endoftext11|>' +_COMPATIBLE_IMAGE_SPECIAL_TOKEN_ID_RANGE = [-9999, -1] # For backward compatibility +_COMPATIBLE_AUDIO_SPECIAL_TOKEN_ID_RANGE = [float('-inf'), -10000] # For backward compatibility + + +class Phi4MMImageEmbedding(nn.Module): + """Image embedding.""" + + def __init__(self, config: PretrainedConfig, **kwargs) -> None: + super().__init__() + + # n_embed or hidden_size + hidden_size = config.n_embd if hasattr(config, 'n_embd') else config.hidden_size + if hasattr(config, 'embd_pdrop') or hasattr(config, 'embed_pdrop'): + embd_drop = config.embd_pdrop if hasattr(config, 'embd_pdrop') else config.embed_pdrop + self.drop = nn.Dropout(embd_drop) + else: + self.drop = None + + logger.info(f"create image tower {config.img_processor}") + enable_gradient_checkpointing = kwargs.get('enable_gradient_checkpointing', False) + + # Load SigLIP model + self.img_processor = get_siglip_vision_model( + _flash_attn_2_enabled=config._attn_implementation == 'flash_attention_2' + ) + + pe_weight = self.img_processor.embeddings.position_embedding.weight + L, D = pe_weight.size() + H = int(math.sqrt(L)) + assert H**2 == L + if H % 2 != 0: #and kwargs.get('image_token_compression_cls', None) is None: + self.img_processor_padding = nn.ReflectionPad2d((0, 1, 0, 1)) + H += 1 + image_dim_out = D + # ((448/14)//2)**2 + self.num_img_tokens = (H//2)**2 + self.base_feat_height_target = H + + if enable_gradient_checkpointing: + self.img_processor.encoder.gradient_checkpointing = True + + self.image_dim_out = image_dim_out + self.img_sizes = None + self.image_attention_mask = None + + # global_gn and sub_gn for hd transform, serves as line separator + self.use_hd_transform = kwargs.get('use_hd_transform', False) + self.with_learnable_separator = kwargs.get('with_learnable_separator', False) + self.hd_transform_order = kwargs.get('hd_transform_order', 'glb_sub') + self.freeze_img_processor = kwargs.get('freeze_img_processor', False) + self.crop_size = kwargs.get('crop_size', 336) + logger.info(f'freeze_img_processor = {self.freeze_img_processor}') + + # image token compression + self.image_token_compression_cls = kwargs.get('image_token_compression_cls', None) + if self.image_token_compression_cls == 'avg_pool_2d': + self.image_token_compression = nn.AvgPool2d(kernel_size=2, stride=2) + self.base_feat_height_reduction = 1 + self.base_feat_height_target = self.base_feat_height_target // 2 + elif self.image_token_compression_cls is None: + self.image_token_compression = None + self.base_feat_height_reduction = 2 + else: + raise NotImplementedError(f'image_token_compression_cls = {self.image_token_compression_cls}, not implemented') + + # with_hd_transform and with_learnable_separator should have same value + assert self.use_hd_transform == self.with_learnable_separator, 'use_hd_transform and with_learnable_separator should have same value' + if self.with_learnable_separator: + assert self.use_hd_transform, 'learnable separator is only for hd transform' + # 1024 * 4, merge spatial to channel dimension + self.glb_GN = nn.Parameter(torch.zeros([1, 1, self.image_dim_out * self.base_feat_height_reduction**2])) + self.sub_GN = nn.Parameter(torch.zeros([1, 1, 1, self.image_dim_out * self.base_feat_height_reduction**2])) + logger.info(f'learnable separator enabled for hd transform, hd_transform_order = {self.hd_transform_order}') + + projection_cls = kwargs.get('projection_cls', 'linear') + if projection_cls == 'linear': + self.img_projection = nn.Linear(image_dim_out, hidden_size) + elif projection_cls == 'mlp' and self.use_hd_transform: + dim_projection = hidden_size + depth = 2 + layers = [nn.Linear(image_dim_out * self.base_feat_height_reduction**2, dim_projection)] + for _ in range(1, depth): + layers.extend([nn.GELU(), + nn.Linear(dim_projection, dim_projection)]) + self.img_projection = nn.Sequential(*layers) + elif projection_cls == 'mlp': + # follow llava-v1.5's implementation + # (do not use image_projection and image_proj_norm) + dim_projection = hidden_size + depth = 2 + layers = [nn.Linear(image_dim_out, dim_projection)] + for _ in range(1, depth): + layers.extend([nn.GELU(), + nn.Linear(dim_projection, dim_projection)]) + self.img_projection = nn.Sequential(*layers) + else: + raise NotImplementedError(f'projection_cls = {projection_cls}, not implemented') + + self.vocab_size = config.vocab_size + self.img_features = None + + if isinstance(config.img_processor, dict): + self.layer_idx = config.img_processor.get('layer_idx', -2) + self.type_feature = config.img_processor.get('type_feature', 'patch') + else: + self.layer_idx = -2 + self.type_feature = 'patch' + + def set_img_features(self, img_features: torch.FloatTensor) -> None: + self.img_features = img_features + + def set_img_sizes(self, img_sizes: torch.LongTensor) -> None: + self.img_sizes = img_sizes + + def set_img_attn_mask(self, image_attention_mask: torch.FloatTensor) -> None: + self.image_attention_mask = image_attention_mask + + def get_img_features(self, img_embeds: torch.FloatTensor, attention_mask=None) -> torch.FloatTensor: + LAYER_IDX = self.layer_idx + TYPE_FEATURE = self.type_feature + + if self.freeze_img_processor: + with torch.no_grad(): + if attention_mask is not None: + img_processor_output = self.img_processor(img_embeds, output_hidden_states=True, patch_attention_mask=attention_mask) + else: + img_processor_output = self.img_processor(img_embeds, output_hidden_states=True) + img_feature = img_processor_output.hidden_states[LAYER_IDX] + else: + if attention_mask is not None: + img_processor_output = self.img_processor(img_embeds, output_hidden_states=True, patch_attention_mask=attention_mask) + else: + img_processor_output = self.img_processor(img_embeds, output_hidden_states=True) + img_feature = img_processor_output.hidden_states[LAYER_IDX] + + if TYPE_FEATURE == "patch": + patch_feature = img_feature + if self.image_token_compression is not None: + # reshape to 2D tensor + width = int(math.sqrt(patch_feature.size(1))) + patch_feature = patch_feature.view(-1, width, width, patch_feature.size(-1)) + # convert to NCHW + patch_feature = patch_feature.permute(0, 3, 1, 2) + if getattr(self, 'img_processor_padding', None) is not None: + patch_feature = self.img_processor_padding(patch_feature) + patch_feature = self.image_token_compression(patch_feature) + # convert to NHWC + patch_feature = patch_feature.permute(0, 2, 3, 1) + patch_feature = patch_feature.view(-1, patch_feature.size(1) * patch_feature.size(2), patch_feature.size(-1)) + elif getattr(self, 'img_processor_padding', None) is not None: + width = int(math.sqrt(patch_feature.size(1))) + patch_feature = patch_feature.view(-1, width, width, patch_feature.size(-1)) + # convert to NCHW + patch_feature = patch_feature.permute(0, 3, 1, 2) + patch_feature = self.img_processor_padding(patch_feature) + # convert to NHWC + patch_feature = patch_feature.permute(0, 2, 3, 1) + patch_feature = patch_feature.view(-1, patch_feature.size(1) * patch_feature.size(2), patch_feature.size(-1)) + return patch_feature + + if TYPE_FEATURE == "cls_patch": + if self.image_token_compression is not None: + # reshape to 2D tensor + patch_feature = img_feature[:, 1:] + cls_feature = img_feature[:, 0] + width = math.sqrt(patch_feature.size(1)) + patch_feature = patch_feature.view(-1, width, width, patch_feature.size(-1)) + patch_feature = self.image_token_compression(patch_feature) + patch_feature = patch_feature.view(-1, patch_feature.size(-2) * patch_feature.size(-1)) + img_feature = torch.cat([cls_feature, patch_feature], dim=1) + return img_feature + + logger.info(f'processed img feature size = {img_feature.size()}') + raise NotImplementedError + + def spatiotemporal_pool(self, x, num_img_tokens, batch_size=1, T=1): + + 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]) + + new_x = [] + # [bsz, T * H' * W', C] -> [bsz, T, C] + spatial_avg_pool_x = x.view(batch_size, T, -1, x.shape[-1]).mean(dim=2) + new_x.append(spatial_avg_pool_x) + + # [bsz, T * H' * W', C] -> [bsz, H'*W', C] + temporal_avg_pool_x = x.view(batch_size, T, -1, x.shape[-1]).mean(dim=1) + new_x.append(temporal_avg_pool_x) + + x = torch.cat(new_x, dim=1).view(-1, self.image_dim_out) + num_img_tokens += T + return x, num_img_tokens + + def forward(self, input_ids: torch.LongTensor, input_embeds: torch.FloatTensor, image_sizes=None, **kwargs) -> torch.FloatTensor: + + if isinstance(input_ids, tuple): + # # pipeline parallel + input_ids, input_embeds = input_ids + + img_embeds = input_embeds + if image_sizes is None and 'image_sizes' in kwargs: + image_sizes = kwargs['image_sizes'] + img_sizes = image_sizes + + if self.img_features is not None: + img_embeds = self.img_features.clone() + self.img_features = None + + if self.img_sizes is not None: + img_sizes = self.img_sizes + + dtype = self.img_processor.embeddings.patch_embedding.weight.dtype + if img_embeds is not None: + # convert to bf16 + img_embeds = img_embeds.to(dtype) + + if self.image_attention_mask is not None: + image_attention_mask = self.image_attention_mask.clone() + self.image_attention_mask = None + elif 'image_attention_mask' in kwargs: + image_attention_mask = kwargs['image_attention_mask'] + else: + image_attention_mask = None + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + + with torch.no_grad(): + positions = torch.nonzero(input_ids == _IMAGE_SPECIAL_TOKEN_ID, as_tuple=False) + positions_tuple = torch.nonzero(input_ids == _IMAGE_SPECIAL_TOKEN_ID, as_tuple=True) + + # logger.info(f'position size: {positions.size()} ...') + fake_image_forward = False + select = False + hd_transform = False + + if isinstance(self.img_projection, nn.Sequential): + target_device = self.img_projection[0].bias.device + target_dtype = self.img_projection[0].bias.dtype + else: # It's a single nn.Linear layer + target_device = self.img_projection.bias.device + target_dtype = self.img_projection.bias.dtype + + num_img_tokens = self.num_img_tokens + if len(positions.tolist()) > 0: + if self.use_hd_transform and img_sizes is not None and len(img_sizes): + hd_transform = True + assert img_embeds.ndim == 5, f'(branch 1) img_embeds size: {img_embeds.size()}, expect 5D tensor for hd transform' + # img_embeds: (num_images, max_num_crops, 3, H, W) + # img_sizes: (num_images, 2).view(1, -1) + + bs = img_embeds.shape[0] + # Nx(HW)xC + if image_attention_mask is not None and len(image_attention_mask) > 0: + img_features = self.get_img_features(img_embeds.flatten(0, 1), attention_mask=image_attention_mask.type(torch.BoolTensor).flatten(0,1).to(target_device)) + else: + img_features = self.get_img_features(img_embeds.flatten(0, 1)) + + base_feat_height_target = self.base_feat_height_target + base_resolution = self.crop_size + base_feat_height_reduction = self.base_feat_height_reduction + + base_feat_height = base_feat_width = int(np.sqrt(img_features.shape[1])) + + assert base_feat_height == base_feat_height_target and base_feat_width == base_feat_height_target, f'base_feat_height: {base_feat_height}, base_feat_width: {base_feat_width}, expect {base_feat_height_target} features for hd transform' + + # bs x max_num_crops x (24x24) x C + img_features = img_features.view(bs, -1, base_feat_height * base_feat_width, self.image_dim_out) + C = self.image_dim_out + H = base_feat_height + + output_imgs = [] + output_len = [] + # training is tensor, inference is list + if isinstance(img_sizes, torch.Tensor): + img_sizes = img_sizes.view(-1, 2) + for _bs in range(bs): + h, w = img_sizes[_bs] + h = h // base_resolution + w = w // base_resolution + B_ = h * w + + # 1 x (24x24) x 1024 + global_img_feature = img_features[_bs, :1] + + # 1 x 12 x 12 x 4096 + glb_img = global_img_feature.reshape(1,H,H,C).reshape(1,H//base_feat_height_reduction,base_feat_height_reduction,H//base_feat_height_reduction,base_feat_height_reduction,C).contiguous().permute(0,1,3,2,4,5).reshape(1,H//base_feat_height_reduction,H//base_feat_height_reduction,base_feat_height_reduction*base_feat_height_reduction*C).contiguous() + temp_glb_GN = self.sub_GN.repeat(1, H//base_feat_height_reduction, 1, 1) + + # 1 x 156 x 4096 + glb_img = torch.cat([glb_img, temp_glb_GN], dim=2).reshape(1,-1,base_feat_height_reduction*base_feat_height_reduction*C) + + # (max_num_crops-1) x (12x12) x C + sub_img = img_features[_bs, 1:] + # 16x574x1024 + # get rid of padding sub_img + sub_img = sub_img[:B_] + + # (num_crops, 12, 2, 12, 2, 1024) -> (num_crops, 12, 12, 2, 2, 1024) -> (num_crops, 12*12, 4*1024) + sub_img = sub_img.reshape(B_,H,H,C).reshape(B_,H//base_feat_height_reduction,base_feat_height_reduction,H//base_feat_height_reduction,base_feat_height_reduction,C).contiguous().permute(0,1,3,2,4,5).reshape(B_,-1,base_feat_height_reduction*base_feat_height_reduction*C).contiguous() + sub_img = sub_img.reshape(1, h, w, base_feat_height // base_feat_height_reduction, base_feat_width // base_feat_height_reduction, -1).permute(0,1,3,2,4,5).reshape(1,h*base_feat_height//base_feat_height_reduction,w*base_feat_width//base_feat_height_reduction,base_feat_height_reduction*base_feat_height_reduction*C) + + if image_attention_mask is not None and len(image_attention_mask) > 0: + reshaped_image_attention_mask = image_attention_mask[_bs,1:B_+1,0::2,0::2].reshape(1, h, w, base_feat_height // base_feat_height_reduction, base_feat_width // base_feat_height_reduction).permute(0,1,3,2,4).reshape(1,h*base_feat_height//base_feat_height_reduction,w*base_feat_width//base_feat_height_reduction) + useful_height = int(reshaped_image_attention_mask[0,:,0].sum().item()) + useful_width = int(reshaped_image_attention_mask[0,0,:].sum().item()) + sub_img = sub_img[:,:useful_height, :useful_width] + temp_sub_GN = self.sub_GN.repeat(1, useful_height, 1, 1) + temp_len = int(image_attention_mask[_bs,:B_+1,0::2,0::2].sum().item()) + (useful_height+1) + base_feat_height//base_feat_height_reduction + else: + temp_sub_GN = self.sub_GN.repeat(1, h*base_feat_height//base_feat_height_reduction, 1, 1) + temp_len = int((h*w+1)*self.num_img_tokens+ 1 + (h+1)*base_feat_height//base_feat_height_reduction) + + sub_img = torch.cat([sub_img, temp_sub_GN], dim=2).reshape(1,-1,base_feat_height_reduction*base_feat_height_reduction*C) + # (1, num_img_tokens, 1024*4) + + # glb + sub + if self.hd_transform_order == 'glb_sub': + output_imgs.append(torch.cat([glb_img, self.glb_GN, sub_img], dim=1)) + elif self.hd_transform_order == 'sub_glb': + output_imgs.append(torch.cat([sub_img, self.glb_GN, glb_img], dim=1)) + else: + raise NotImplementedError(f'hd_transform_order = {self.hd_transform_order}, not implemented') + + #temp_len = int((h*w+1)*144 + 1 + (h+1)*12) + assert temp_len == output_imgs[-1].shape[1], f'temp_len: {temp_len}, output_imgs[-1].shape[1]: {output_imgs[-1].shape[1]}' + output_len.append(temp_len) + + num_img_tokens = output_len + img_set_tensor = [] + for _output_img in output_imgs: + img_feature_proj = self.img_projection(_output_img.to(target_device).to(target_dtype)) + img_set_tensor.append(img_feature_proj) + #logger.info(f'img_embeds size: {img_embeds.size()}, image sizes: {img_sizes} loading time {datetime.now() - start_time}') + #assert sum(num_img_tokens) == len(g_values), f'(branch 1) sum(num_img_tokens): {sum(num_img_tokens)}, g_values size: {len(g_values)}, g_values {g_values}' + + else: + raise NotImplementedError + select = True + else: + # # create a fake image tensor + # # TODO: need define image size for different vision model + if self.training: + img_embeds = torch.zeros(1, 3, self.crop_size, self.crop_size, dtype=target_dtype, device=input_ids.device) + + tt = ( + self.get_img_features(img_embeds) + .to(target_device) + .to(target_dtype) + .reshape(-1, 1024) + ) + if self.use_hd_transform: + img_set_tensor = self.img_projection(tt.reshape(-1, self.image_dim_out*self.base_feat_height_reduction**2) * self.glb_GN[0] * self.sub_GN[0, 0]) + else: + img_set_tensor = self.img_projection(tt) # adapted visual features. + fake_image_forward = True + + # we use the token embedding layer from the huggingface model, this is REQUIRED to make sure we are using the loaded weights. + hidden_states = kwargs['wte'](input_ids) + + if select: + if hd_transform: + # new implementation without in-place operation + # Ref: https://huggingface.co/microsoft/Phi-3.5-vision-instruct/blob/4a0d683eba9f1d0cbfb6151705d1ee73c25a80ca/modeling_phi3_v.py#L233 + # Ref: https://pytorch.org/docs/stable/generated/torch.Tensor.index_put.html + # Ref: https://pytorch.org/docs/stable/generated/torch.Tensor.index_put_.html#torch.Tensor.index_put_ + # img_set_tensor: a list of tensors, each tensor has shape (1, N_tokens, C) + assert all([_img_set_tensor.shape[0] == 1 for _img_set_tensor in img_set_tensor]), 'img_set_tensor should have shape (1, N_tokens, C)' + # Shape: (merged_N_tokens, C) + merged_img_set_tensor = torch.cat(img_set_tensor, dim=1).squeeze(0) + merged_img_set_tensor = merged_img_set_tensor.to(hidden_states.dtype).to(hidden_states.device) + # Temporarily disable autocast to avoid issue on bf16 tensors + # Ref: https://github.com/pytorch/pytorch/issues/132715 + with torch.autocast(device_type=hidden_states.device.type, enabled=False): + new_hidden_states = hidden_states.index_put( + indices=positions_tuple, + values=merged_img_set_tensor, + accumulate=False + ) + hidden_states = new_hidden_states + else: + raise NotImplementedError + + if fake_image_forward and self.training: + hidden_states = hidden_states + (0 * img_set_tensor[0].to(hidden_states.dtype).to(hidden_states.device)).sum() + + if self.drop is not None: + hidden_states = self.drop(hidden_states) + + return hidden_states + + +class Phi4MMAudioEmbedding(nn.Module): + """Audio embedding.""" + + def __init__(self, config: PretrainedConfig, **kwargs) -> None: + super().__init__() + self.config = config + # n_embed or hidden_size for text LM + hidden_size = config.n_embd if hasattr(config, 'n_embd') else config.hidden_size + + if hasattr(config, 'embd_pdrop') or hasattr(config, 'embed_pdrop'): + embd_drop = config.embd_pdrop if hasattr(config, 'embd_pdrop') else config.embed_pdrop + self.drop = nn.Dropout(embd_drop) + else: + self.drop = None + + audio_dim_out = None # Set this variable according to the actual audio processor + logger.info(f"create audio processor {config.audio_processor}") + self.layer_idx = -2 + + if isinstance(config.audio_processor, dict) and config.audio_processor.get('name', None) == "cascades": + encoder_config = config.audio_processor.get("config", None) + assert encoder_config is not None + self.encoder = ConformerEncoder(**encoder_config) + + # fake initialization, create encoder_embedding layer only so that + # in decoding, all parameters can be loaded in from_pretrained_function + # in training, we do post init after from_pretrained function to make sure the correct initialization + self.encoder.post_init({}) + + audio_dim_out = encoder_config["attention_dim"] + n_mels = encoder_config["input_size"] + else: + raise NotImplementedError + + assert audio_dim_out is not None, "Remember to set values for audio_dim_out" + self.audio_dim_out = audio_dim_out + self.audio_dim_in = n_mels + + self.freeze_audio_processor = kwargs.get('freeze_audio_processor', False) + logger.info(f'freeze_audio_processor = {self.freeze_audio_processor}') + + self.downsample_rate = kwargs.get('downsample_rate', 1) + + enable_gradient_checkpointing = kwargs.get('enable_gradient_checkpointing', False) + if enable_gradient_checkpointing: + self.encoder.gradient_checkpointing_enable() + logger.info(f'gradient checkpointing enabled for audio processor') + + projection_cls = kwargs.get('projection_cls', 'linear') + if projection_cls == 'linear': + self.audio_projection = nn.Linear(audio_dim_out, hidden_size) + elif projection_cls == 'mlp': + # follow llava-v1.5's implementation + # (do not use image_projection and image_proj_norm) + dim_projection = hidden_size + depth = 2 + self.linear_downsample_rate = self.downsample_rate + + layers_for_speech = [nn.Linear(audio_dim_out * self.linear_downsample_rate, dim_projection)] + for _ in range(1, depth): + layers_for_speech.extend([nn.GELU(), nn.Linear(dim_projection, dim_projection)]) + audio_projection_for_speech = nn.Sequential(*layers_for_speech) + + layers_for_vision = [nn.Linear(audio_dim_out * self.linear_downsample_rate, dim_projection)] + for _ in range(1, depth): + layers_for_vision.extend([nn.GELU(), nn.Linear(dim_projection, dim_projection)]) + audio_projection_for_vision = nn.Sequential(*layers_for_vision) + + self.audio_projection = nn.ModuleDict({ + 'speech': audio_projection_for_speech, + 'vision': audio_projection_for_vision + }) + else: + raise NotImplementedError(f'projection_cls = {projection_cls}, not implemented') + + self.vocab_size = config.vocab_size + self.input_embeds = None + self.audio_embed_sizes = None + + def post_init(self, audio_config): + # execute after the from_pretrained() initialization of the phi4mm model + if audio_config.get('name', None) == "cascades": + init_model_config = audio_config.get("init_model", {}) + self.encoder.post_init(init_model_config) + # remove the init model in config so it is not saved in the config. + # This might affect the model loading in resuming training and decoding. + if "init_model" in audio_config: + audio_config.pop("init_model") + + def set_audio_embeds(self, input_embeds: torch.FloatTensor) -> None: + self.input_embeds = input_embeds + + def set_audio_embed_sizes(self, audio_embed_sizes: torch.LongTensor) -> None: + self.audio_embed_sizes = audio_embed_sizes + + def get_audio_features(self, input_embeds: torch.FloatTensor, audio_attention_mask: torch.Tensor, audio_projection_mode: str='speech'): + + if self.freeze_audio_processor: + with torch.no_grad(): + audio_features, masks = self.encoder(input_embeds, audio_attention_mask) + else: + audio_features, masks = self.encoder(input_embeds, audio_attention_mask) + + if isinstance(self.audio_projection, nn.Sequential): + audio_set_tensor = self.audio_projection(audio_features) + elif isinstance(self.audio_projection, nn.ModuleDict): + audio_set_tensor = self.audio_projection[audio_projection_mode](audio_features) + else: + raise NotImplementedError + + return audio_set_tensor + + def forward(self, input_ids: torch.LongTensor, input_embeds: torch.FloatTensor, audio_embed_sizes=None, audio_attention_mask=None, audio_projection_mode='speech', **kwargs) -> torch.FloatTensor: + ''' + arguments: + input_ids: input text ids (B, U) + input_embeds: audio features (B, T, D) B: num audios in a sequence + ''' + if self.input_embeds is not None: + input_embeds = self.input_embeds.clone() + if self.audio_embed_sizes is not None: + audio_embed_sizes = self.audio_embed_sizes.clone() + + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + MAX_INPUT_ID = int(1e9) + + with torch.no_grad(): + positions = torch.nonzero(input_ids == _AUDIO_SPECIAL_TOKEN_ID, as_tuple=False) + positions_tuple = torch.nonzero(input_ids == _AUDIO_SPECIAL_TOKEN_ID, as_tuple=True) + + if isinstance(self.audio_projection, nn.Sequential): + target_device = self.audio_projection[0].bias.device + target_dtype = self.audio_projection[0].bias.dtype + elif isinstance(self.audio_projection, nn.ModuleDict): + target_device = self.audio_projection[audio_projection_mode][0].bias.device + target_dtype = self.audio_projection[audio_projection_mode][0].bias.dtype + else: # It's a single nn.Linear layer + target_device = self.audio_projection.bias.device + target_dtype = self.audio_projection.bias.dtype + + if input_embeds is not None: + input_embeds = input_embeds.to(target_device).to(target_dtype) + + if len(positions.tolist()) > 0: + audio_set_tensor = self.get_audio_features(input_embeds, audio_attention_mask, audio_projection_mode) + else: + # # create an audio tensor + # To do: not sure if this is required for text only input + if self.training: + audio_embeds = torch.zeros(1, 500, self.audio_dim_in).to(target_device).to(target_dtype) + audio_attention_mask = audio_embeds.new_ones(audio_embeds.size()[:2]).long() + audio_set_tensor = self.get_audio_features(audio_embeds, audio_attention_mask, audio_projection_mode) + + hidden_states = kwargs['wte'](input_ids) + + if len(positions.tolist()) > 0: + + assert audio_embed_sizes.sum().item() == len(positions), \ + f"please ensure the encoder outputs have the same length as defined in input_ids! \n audio_embed_sizes.sum().item(): {audio_embed_sizes.sum().item()} \n len(positions): {len(positions)} \n audio_embed_sizes: {audio_embed_sizes} \n positions: {positions} \n input_ids.shape \n {input_ids.shape}" + + # new implementation without in-place operation + # Ref: https://huggingface.co/microsoft/Phi-3.5-vision-instruct/blob/4a0d683eba9f1d0cbfb6151705d1ee73c25a80ca/modeling_phi3_v.py#L233 + # Ref: https://pytorch.org/docs/stable/generated/torch.Tensor.index_put.html + # Ref: https://pytorch.org/docs/stable/generated/torch.Tensor.index_put_.html#torch.Tensor.index_put_ + # audio_set_tensor: shape (N_audios, N_padded_tokens, C) + # Shape: (merged_N_tokens, C) + merged_audio_set_tensor = torch.cat([ + audio_set_tensor[i, :audio_embed_sizes[i], :] + for i in range(len(audio_embed_sizes)) + ], dim=0) + merged_audio_set_tensor = merged_audio_set_tensor.to(hidden_states.dtype).to(hidden_states.device) + # Temporarily disable autocast to avoid issue on bf16 tensors + # Ref: https://github.com/pytorch/pytorch/issues/132715 + with torch.autocast(device_type=hidden_states.device.type, enabled=False): + new_hidden_states = hidden_states.index_put( + indices=positions_tuple, + values=merged_audio_set_tensor, + accumulate=False + ) + hidden_states = new_hidden_states + else: + if self.training: + hidden_states = hidden_states + (0 * audio_set_tensor[:,0].to(hidden_states.dtype).to(hidden_states.device)).sum() + + if self.drop is not None: + hidden_states = self.drop(hidden_states) + + return hidden_states + + + +class Phi4MMImageAudioEmbedding(nn.Module): + """Image-audio embedding.""" + + def __init__(self, config: PretrainedConfig, **kwargs) -> None: + super().__init__() + + self.vocab_size = config.vocab_size + + self.image_input_id = kwargs.get('image_input_id', -1) + self.audio_input_id = kwargs.get('audio_input_id', -10000) + assert self.image_input_id != self.audio_input_id, 'image_input_id and audio_input_id should be different' + + self.image_embd_layer_kwargs = kwargs['image_embd_layer'] + self.image_embed = Phi4MMImageEmbedding(config, **self.image_embd_layer_kwargs) + self.audio_embd_layer_kwargs = kwargs['audio_embd_layer'] + self.audio_embed = Phi4MMAudioEmbedding(config, **self.audio_embd_layer_kwargs) + + self.input_image_embeds = None + self.image_sizes = None + self.image_attention_mask = None + self.input_audio_embeds = None + self.audio_embed_sizes = None + + def post_init(self, audio_config): + # post init for audio embedding + # ref: model.model.embed_tokens_extend.post_init(audio_config) in phyagi/getters/model.py + self.audio_embed.post_init(audio_config) + + def set_input_image_embeds(self, input_image_embeds: torch.FloatTensor) -> None: + self.input_image_embeds = input_image_embeds + + def set_image_sizes(self, image_sizes: torch.LongTensor) -> None: + self.image_sizes = image_sizes + + def set_img_attn_mask(self, image_attention_mask: torch.FloatTensor) -> None: + self.image_attention_mask = image_attention_mask + + def set_input_audio_embeds(self, input_audio_embeds: torch.FloatTensor) -> None: + self.input_audio_embeds = input_audio_embeds + + def set_audio_embed_sizes(self, audio_embed_sizes: torch.LongTensor) -> None: + self.audio_embed_sizes = audio_embed_sizes + + def forward( + self, + input_ids: torch.LongTensor, + input_embeds, + input_image_embeds: Optional[torch.FloatTensor]=None, + input_audio_embeds: Optional[torch.FloatTensor]=None, + image_sizes=None, + image_attention_mask=None, + audio_embed_sizes=None, + audio_attention_mask=None, + audio_projection_mode='speech', + wte=None, + ) -> torch.FloatTensor: + MAX_INPUT_ID = int(1e9) + assert -MAX_INPUT_ID < self.audio_input_id < self.image_input_id + + # override image and audio embeddings and sizes from object itself + # this is for inference + # ref: phyagi/eval/utils/text_generation_vision_audio_pipeline.py + if self.input_image_embeds is not None: + assert input_image_embeds is None + input_image_embeds = self.input_image_embeds.clone() + # NOTE weijian: set input_image_embeds to None after first call in for eval stage + # during evaluation, it will call model's forward() multiple times + # the first time input_ids contains the prompt (including <|image_{}|>) and input_embeds exists + # from the second time, the input_ids will only contain the generated text + # thus, the input_image_embeds is no longer needed + self.input_image_embeds = None + + if self.image_sizes is not None: + assert image_sizes is None + image_sizes = self.image_sizes + + if self.input_audio_embeds is not None: + assert input_audio_embeds is None + input_audio_embeds = self.input_audio_embeds.clone() + self.input_audio_embeds = None + + if self.audio_embed_sizes is not None: + assert audio_embed_sizes is None + audio_embed_sizes = self.audio_embed_sizes.clone() + + if self.image_attention_mask is not None: + assert image_attention_mask is None + image_attention_mask = self.image_attention_mask.clone() + self.image_attention_mask = None + + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + + # backward compatibility + with torch.no_grad(): + new_input_ids = input_ids.clone() + new_input_ids[(input_ids >= _COMPATIBLE_IMAGE_SPECIAL_TOKEN_ID_RANGE[0]) & + (input_ids <= _COMPATIBLE_IMAGE_SPECIAL_TOKEN_ID_RANGE[1])] = _IMAGE_SPECIAL_TOKEN_ID + new_input_ids[(input_ids >= _COMPATIBLE_AUDIO_SPECIAL_TOKEN_ID_RANGE[0]) & + (input_ids <= _COMPATIBLE_AUDIO_SPECIAL_TOKEN_ID_RANGE[1])] = _AUDIO_SPECIAL_TOKEN_ID + input_ids = new_input_ids + + with torch.no_grad(): + image_position_mask = input_ids == _IMAGE_SPECIAL_TOKEN_ID + non_image_position_mask = ~image_position_mask + + assert input_embeds is None + if self.training: + assert input_image_embeds is not None or input_audio_embeds is not None + + if input_image_embeds is not None: + image_hidden_states = self.image_embed( + input_ids=input_ids, + input_embeds=input_image_embeds, + image_sizes=image_sizes, + wte=wte, + image_attention_mask=image_attention_mask + ) + if input_audio_embeds is not None: + audio_hidden_states = self.audio_embed( + input_ids=input_ids, + input_embeds=input_audio_embeds, + audio_embed_sizes=audio_embed_sizes, + audio_attention_mask=audio_attention_mask, + wte=wte, + audio_projection_mode=audio_projection_mode, + ) + + # merge image and audio hidden states + # NOTE weijian: for non-image-audio tokens, here we use audio hidden states + # actually, in the debug code above, the non-image-audio tokens from image_hidden_states and audio_hidden_states should be the same + if input_image_embeds is not None and input_audio_embeds is not None: + dtype = image_hidden_states.dtype + hidden_states = image_hidden_states * image_position_mask.to(dtype).unsqueeze(-1) + audio_hidden_states * non_image_position_mask.to(dtype).unsqueeze(-1) + elif input_image_embeds is not None: + hidden_states = image_hidden_states + elif input_audio_embeds is not None: + hidden_states = audio_hidden_states + else: + assert wte is not None + hidden_states = wte(input_ids) + + return hidden_states + + +# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Phi3 +class Phi4MMRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + Phi4MMRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +# Copied from transformers.models.gemma.modeling_gemma.GemmaRotaryEmbedding with gemma->phi3, Gemma->Phi3 +class Phi4MMRotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim)) + self.register_buffer("inv_freq", tensor=inv_freq, persistent=False) + + @torch.no_grad() + def forward(self, x, position_ids, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + self.inv_freq.to(x.device) + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + # Force float32 since bfloat16 loses precision on long contexts + # See https://github.com/huggingface/transformers/pull/29285 + device_type = x.device.type + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() + sin = emb.sin() + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +class Phi4MMSuScaledRotaryEmbedding(Phi4MMRotaryEmbedding): + def __init__(self, dim, config, device=None): + warnings.warn( + "The class Phi4MMSuScaledRotaryEmbedding is deprecated and will be removed in version 5 of Transformers. Please" + " use Phi4MMLongRoPEScaledRotaryEmbedding instead.", + FutureWarning, + ) + super().__init__(dim, config.max_position_embeddings, config.rope_theta, device) + + self.short_factor = config.rope_scaling["short_factor"] + self.long_factor = config.rope_scaling["long_factor"] + self.original_max_position_embeddings = config.original_max_position_embeddings + + @torch.no_grad() + def forward(self, x, position_ids, seq_len=None): + seq_len = torch.max(position_ids) + 1 + if seq_len > self.original_max_position_embeddings: + ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device) + else: + ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device) + inv_freq_shape = torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim + self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape) + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + # Force float32 since bfloat16 loses precision on long contexts + # See https://github.com/huggingface/transformers/pull/29285 + device_type = x.device.type + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + scale = self.max_position_embeddings / self.original_max_position_embeddings + if scale <= 1.0: + scaling_factor = 1.0 + else: + scaling_factor = math.sqrt(1 + math.log(scale) / math.log(self.original_max_position_embeddings)) + cos = emb.cos() * scaling_factor + sin = emb.sin() * scaling_factor + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +class Phi4MMYarnScaledRotaryEmbedding(Phi4MMRotaryEmbedding): + def __init__(self, dim, config, device=None): + warnings.warn( + "The class Phi4MMYarnScaledRotaryEmbedding is deprecated and will be removed in version 5 of Transformers", + FutureWarning, + ) + super().__init__(dim, config.max_position_embeddings, config.rope_theta, device) + + self.short_factor = config.rope_scaling["short_factor"] + self.long_factor = config.rope_scaling["long_factor"] + self.original_max_position_embeddings = config.original_max_position_embeddings + + @torch.no_grad() + def forward(self, x, position_ids, seq_len=None): + seq_len = torch.max(position_ids) + 1 + if seq_len > self.original_max_position_embeddings: + ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device) + else: + ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device) + + inv_freq_shape = torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim + self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape) + + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + + # Force float32 since bfloat16 loses precision on long contexts + # See https://github.com/huggingface/transformers/pull/29285 + device_type = x.device.type + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + + scale = self.max_position_embeddings / self.original_max_position_embeddings + if scale <= 1.0: + scaling_factor = 1.0 + else: + scaling_factor = 0.1 * math.log(scale) + 1.0 + + cos = emb.cos() * scaling_factor + sin = emb.sin() * scaling_factor + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +class Phi4MMLongRoPEScaledRotaryEmbedding(Phi4MMRotaryEmbedding): + def __init__(self, dim, config, device=None): + super().__init__(dim, config.max_position_embeddings, config.rope_theta, device) + + self.short_factor = config.rope_scaling["short_factor"] + self.long_factor = config.rope_scaling["long_factor"] + self.original_max_position_embeddings = config.original_max_position_embeddings + + @torch.no_grad() + def forward(self, x, position_ids, seq_len=None): + seq_len = seq_len or torch.max(position_ids) + 1 + if seq_len > self.original_max_position_embeddings: + ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device) + else: + ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device) + + inv_freq_shape = torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim + self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape) + + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + + # Force float32 since bfloat16 loses precision on long contexts + # See https://github.com/huggingface/transformers/pull/29285 + device_type = x.device.type + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + + scale = self.max_position_embeddings / self.original_max_position_embeddings + if scale <= 1.0: + scaling_factor = 1.0 + else: + scaling_factor = math.sqrt(1 + math.log(scale) / math.log(self.original_max_position_embeddings)) + + cos = emb.cos() * scaling_factor + sin = emb.sin() * scaling_factor + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +# Copied from transformers.models.llama.modeling_llama.rotate_half +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`, *optional*): + Deprecated and unused. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + + rotary_dim = cos.shape[-1] + q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:] + k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:] + + q_embed = torch.cat([(q_rot * cos) + (rotate_half(q_rot) * sin), q_pass], dim=-1) + k_embed = torch.cat([(k_rot * cos) + (rotate_half(k_rot) * sin), k_pass], dim=-1) + return q_embed, k_embed + + +class Phi4MMMLP(nn.Module): + def __init__(self, config): + super().__init__() + + self.config = config + self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False) + self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False) + + self.activation_fn = ACT2FN[config.hidden_act] + + def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: + up_states = self.gate_up_proj(hidden_states) + + gate, up_states = up_states.chunk(2, dim=-1) + up_states = up_states * self.activation_fn(gate) + + return self.down_proj(up_states) + + +# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class Phi4MMAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: Phi4MMConfig, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " + "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.attention_dropout = config.attention_dropout + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.max_position_embeddings = config.max_position_embeddings + self.original_max_position_embeddings = config.original_max_position_embeddings + self.rope_theta = config.rope_theta + self.rope_scaling = config.rope_scaling + self.rotary_ndims = int(self.head_dim * config.partial_rotary_factor) + self.is_causal = True + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + + op_size = self.num_heads * self.head_dim + 2 * (self.num_key_value_heads * self.head_dim) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + self.qkv_proj = nn.Linear(self.hidden_size, op_size, bias=False) + self._init_rope() + + def _init_rope(self): + if self.rope_scaling is None: + self.rotary_emb = Phi4MMRotaryEmbedding( + self.rotary_ndims, + max_position_embeddings=self.max_position_embeddings, + base=self.rope_theta, + ) + else: + scaling_type = self.config.rope_scaling["type"] + if scaling_type == "longrope": + self.rotary_emb = Phi4MMLongRoPEScaledRotaryEmbedding(self.rotary_ndims, self.config) + else: + raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + logger.warning_once("You are not running the flash-attention implementation, expect numerical differences.") + + bsz, q_len, _ = hidden_states.size() + + qkv = self.qkv_proj(hidden_states) + query_pos = self.num_heads * self.head_dim + query_states = qkv[..., :query_pos] + key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim] + value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :] + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attention_mask is not None: + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] + attn_weights += causal_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) + + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class Phi4MMFlashAttention2(Phi4MMAttention): + """ + Phi-4-MM flash attention module. This module inherits from `Phi4MMAttention` 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 forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + # Phi4MMFlashAttention2 attention does not support output_attentions + + output_attentions = False + + bsz, q_len, _ = hidden_states.size() + + qkv = self.qkv_proj(hidden_states) + query_pos = self.num_heads * self.head_dim + query_states = qkv[..., :query_pos] + key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim] + value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :] + + # Flash attention requires the input to have the shape + # batch_size x seq_length x head_dim x hidden_dim + # therefore we just need to keep the original shape + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + + # Because the input can be padded, the absolute sequence length depends on the max position id. + rotary_seq_len = ( + max(kv_seq_len, position_ids[:, -1].max().item() + 1) if position_ids is not None else kv_seq_len + ) + + cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len, position_ids=position_ids) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_dropout = self.attention_dropout if self.training else 0.0 + + # 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. + + if query_states.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.qkv_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) + + # Reashape to the expected shape for Flash Attention + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + attn_output = _flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + position_ids=position_ids, + dropout=attn_dropout, + sliding_window=getattr(self.config, "sliding_window", None), + use_top_left_mask=self._flash_attn_uses_top_left_mask, + is_causal=self.is_causal, + ) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() + attn_output = self.o_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.LlamaSdpaAttention with Llama->Phi +# TODO @Arthur no longer copied from LLama after static cache +class Phi4MMSdpaAttention(Phi4MMAttention): + """ + Phi4MM attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `Phi4MMAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from Phi4MMAttention.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if output_attentions: + # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "Phi4MMModel is using Phi4MMSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. 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=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + bsz, q_len, _ = hidden_states.size() + + qkv = self.qkv_proj(hidden_states) + query_pos = self.num_heads * self.head_dim + query_states = qkv[..., :query_pos] + key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim] + value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :] + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + causal_mask = attention_mask + if attention_mask is not None: + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] + + # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, + # Reference: https://github.com/pytorch/pytorch/issues/112577. + if query_states.device.type == "cuda" and attention_mask is not None: + query_states = query_states.contiguous() + key_states = key_states.contiguous() + value_states = value_states.contiguous() + + # 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 q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1. + is_causal = True if causal_mask is None and q_len > 1 else False + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=causal_mask, + dropout_p=self.attention_dropout if self.training else 0.0, + is_causal=is_causal, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.view(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + return attn_output, None, past_key_value + + +PHI4MM_ATTENTION_CLASSES = { + "eager": Phi4MMAttention, + "flash_attention_2": Phi4MMFlashAttention2, + "sdpa": Phi4MMSdpaAttention, +} + + +class Phi4MMDecoderLayer(nn.Module): + def __init__(self, config: Phi4MMConfig, layer_idx: int): + super().__init__() + + self.config = config + self.self_attn = PHI4MM_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx) + + self.mlp = Phi4MMMLP(config) + self.input_layernorm = Phi4MMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.resid_attn_dropout = nn.Dropout(config.resid_pdrop) + self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop) + self.post_attention_layernorm = Phi4MMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, + **kwargs, + ) -> 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`, *optional*): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + position_ids (`torch.LongTensor` of shape `({0})`, *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) + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + 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`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence + kwargs (`dict`, *optional*): + Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code + into the model + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + attn_outputs, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + ) + + hidden_states = residual + self.resid_attn_dropout(attn_outputs) + + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + self.resid_mlp_dropout(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +PHI4MM_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 ([`Phi4MMConfig`]): + 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 Phi-4-MM model outputting raw hidden-states without any specific head on top.", + PHI4MM_START_DOCSTRING, +) +class Phi4MMPreTrainedModel(PreTrainedModel): + config_class = Phi4MMConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["Phi4MMDecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_cache_class = True + + _version = "0.0.5" + + def _init_weights(self, module): + std = self.config.initializer_range + 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_() + + +PHI4MM_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) + 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 `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 (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance, see our + [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache); + - 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)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `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. + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, + this tensor is not affected by padding. It is used to update the cache in the correct position and to infer + the complete sequence length. +""" + + +@add_start_docstrings( + "The bare Phi-4-MM model outputting raw hidden-states without any specific head on top.", + PHI4MM_START_DOCSTRING, +) +class Phi4MMModel(Phi4MMPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi4MMDecoderLayer`] + + Args: + config: Phi4MMConfig + """ + + def __init__(self, config: Phi4MMConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.embed_dropout = nn.Dropout(config.embd_pdrop) + + self.embed_tokens_extend = None + if isinstance(config.embd_layer, dict): + embedding_config = { + 'embedding_cls': config.embd_layer['embedding_cls'], + **config.embd_layer + } + self.embed_tokens_extend = Phi4MMImageAudioEmbedding(config, **embedding_config) + + self.layers = nn.ModuleList( + [Phi4MMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self._attn_implementation = config._attn_implementation + self.norm = Phi4MMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + 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 + + @add_start_docstrings_to_model_forward(PHI4MM_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + input_image_embeds: Optional[torch.FloatTensor] = None, + image_sizes: Optional[torch.LongTensor] = None, + image_attention_mask=None, + input_audio_embeds: Optional[torch.FloatTensor] = None, + audio_embed_sizes=None, + audio_attention_mask=None, + audio_projection_mode=None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + **kwargs, + ) -> Union[Tuple, BaseModelOutputWithPast]: + 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 (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + + 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 + + # kept for BC (non `Cache` `past_key_values` inputs) + return_legacy_cache = False + if use_cache and not isinstance(past_key_values, Cache): + return_legacy_cache = True + if past_key_values is None: + past_key_values = DynamicCache() + else: + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and " + "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class " + "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)" + ) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens_extend( + input_ids=input_ids, + input_embeds=inputs_embeds, + input_image_embeds=input_image_embeds, + input_audio_embeds=input_audio_embeds, + image_sizes=image_sizes, + image_attention_mask=image_attention_mask, + audio_embed_sizes=audio_embed_sizes, + audio_attention_mask=audio_attention_mask, + audio_projection_mode=audio_projection_mode, + wte=self.embed_tokens, + ) + + if cache_position is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + cache_position = torch.arange( + past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device + ) + if position_ids is None: + position_ids = cache_position.unsqueeze(0) + + causal_mask = self._update_causal_mask( + attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions + ) + + hidden_states = inputs_embeds + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + causal_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + cache_position, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # 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 return_legacy_cache: + next_cache = next_cache.to_legacy_cache() + + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + def _update_causal_mask( + self, + attention_mask: torch.Tensor, + input_tensor: torch.Tensor, + cache_position: torch.Tensor, + past_key_values: Cache, + output_attentions: bool, + ): + if self.config._attn_implementation == "flash_attention_2": + if attention_mask is not None and 0.0 in attention_mask: + return attention_mask + return None + + # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in + # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail + # to infer the attention mask. + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + using_static_cache = isinstance(past_key_values, StaticCache) + using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache) + + # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward + if ( + self.config._attn_implementation == "sdpa" + and not (using_static_cache or using_sliding_window_cache) + and not output_attentions + ): + if AttentionMaskConverter._ignore_causal_mask_sdpa( + attention_mask, + inputs_embeds=input_tensor, + past_key_values_length=past_seen_tokens, + sliding_window=self.config.sliding_window, + is_training=self.training, + ): + return None + + dtype, device = input_tensor.dtype, input_tensor.device + min_dtype = torch.finfo(dtype).min + sequence_length = input_tensor.shape[1] + # SlidingWindowCache or StaticCache + if using_sliding_window_cache or using_static_cache: + target_length = past_key_values.get_max_cache_shape() + # DynamicCache or no cache + else: + target_length = ( + attention_mask.shape[-1] + if isinstance(attention_mask, torch.Tensor) + else past_seen_tokens + sequence_length + 1 + ) + + # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). + causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=target_length, + dtype=dtype, + device=device, + cache_position=cache_position, + batch_size=input_tensor.shape[0], + config=self.config, + past_key_values=past_key_values, + ) + + if ( + self.config._attn_implementation == "sdpa" + and attention_mask is not None + and attention_mask.device.type == "cuda" + and not output_attentions + ): + # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when + # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. + # Details: https://github.com/pytorch/pytorch/issues/110213 + causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) + + return causal_mask + + @staticmethod + # Copied from transformers.models.mistral.modeling_mistral.MistralModel._prepare_4d_causal_attention_mask_with_cache_position with Mistral->Phi3 + def _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask: torch.Tensor, + sequence_length: int, + target_length: int, + dtype: torch.dtype, + device: torch.device, + cache_position: torch.Tensor, + batch_size: int, + config: Phi4MMConfig, + past_key_values: Cache, + ): + """ + Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape + `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. + + Args: + attention_mask (`torch.Tensor`): + A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. + sequence_length (`int`): + The sequence length being processed. + target_length (`int`): + The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. + dtype (`torch.dtype`): + The dtype to use for the 4D attention mask. + device (`torch.device`): + The device to plcae the 4D attention mask on. + cache_position (`torch.Tensor`): + Indices depicting the position of the input sequence tokens in the sequence. + batch_size (`torch.Tensor`): + Batch size. + config (`Phi4MMConfig`): + The model's configuration class + past_key_values (`Cache`): + The cache class that is being used currently to generate + """ + if attention_mask is not None and attention_mask.dim() == 4: + # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. + causal_mask = attention_mask + else: + min_dtype = torch.finfo(dtype).min + causal_mask = torch.full( + (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device + ) + diagonal_attend_mask = torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) + if config.sliding_window is not None: + # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also + # the check is needed to verify is current checkpoint was trained with sliding window or not + if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length: + sliding_attend_mask = torch.arange(target_length, device=device) <= ( + cache_position.reshape(-1, 1) - config.sliding_window + ) + diagonal_attend_mask.bitwise_or_(sliding_attend_mask) + causal_mask *= diagonal_attend_mask + causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) + if attention_mask is not None: + causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit + if attention_mask.shape[-1] > target_length: + attention_mask = attention_mask[:, :target_length] + mask_length = attention_mask.shape[-1] + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + return causal_mask + + +class Phi4MMForCausalLM(Phi4MMPreTrainedModel, GenerationMixin): + _tied_weights_keys = ["lm_head.weight"] + + # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi + def __init__(self, config): + super().__init__(config) + self.model = Phi4MMModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + # LoRA related settings + assert getattr(config, "vision_lora", None) is not None + from peft import LoraConfig, get_peft_model + vision_lora_config = LoraConfig( + r=config.vision_lora['r'], + lora_alpha=config.vision_lora['lora_alpha'], + target_modules=config.vision_lora['layer'], + lora_dropout=config.vision_lora['dp'], + task_type="CAUSAL_LM", + ) + peft_model = get_peft_model(self.model, vision_lora_config, adapter_name="vision") + self.config.vision_lora['r'] = config.vision_lora['r'] + self.config.vision_lora['lora_alpha'] = config.vision_lora['lora_alpha'] + self.config.vision_lora['layer'] = config.vision_lora['layer'] + self.config.vision_lora['dp'] = config.vision_lora['dp'] + + assert getattr(config, "speech_lora", None) is not None + speech_lora_config = LoraConfig( + r=config.speech_lora['r'], + lora_alpha=config.speech_lora['lora_alpha'], + target_modules=config.speech_lora['layer'], + lora_dropout=config.speech_lora['dp'], + task_type="CAUSAL_LM", + ) + peft_model.base_model.active_adapter.append("speech") + peft_model.add_adapter("speech", speech_lora_config) + self.config.speech_lora['r'] = config.speech_lora['r'] + self.config.speech_lora['lora_alpha'] = config.speech_lora['lora_alpha'] + self.config.speech_lora['layer'] = config.speech_lora['layer'] + self.config.speech_lora['dp'] = config.speech_lora['dp'] + + def set_lora_adapter(self, adapter_name) -> None: + from peft.tuners.lora.layer import LoraLayer + for module in self.modules(): + if isinstance(module, LoraLayer): + if module.merged: + warnings.warn("Adapter cannot be set when the model is merged. Unmerging the model first.") + module.unmerge() + module.set_adapter(adapter_name) + module._disable_adapters = False + + def unset_lora_adapter(self) -> None: + # Ref: peft/tuners/tuners_utils.py - enable_adapters() + # Ref: peft/tuners/lora/layer.py + from peft.tuners.lora.layer import LoraLayer + for module in self.modules(): + if isinstance(module, LoraLayer): + # disable grads on all adapter layers + # TODO weijian: may use enable_adapters() instead + for layer_name in module.adapter_layer_names: + layer = getattr(module, layer_name) + layer.requires_grad_(False) + module._disable_adapters = True + + # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings + def get_input_embeddings(self): + return self.model.embed_tokens + + # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings + def get_output_embeddings(self): + return self.lm_head + + # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder + def set_decoder(self, decoder): + self.model = decoder + + # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder + def get_decoder(self): + return self.model + + # Ignore copy + @add_start_docstrings_to_model_forward(PHI4MM_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + input_image_embeds: Optional[torch.FloatTensor] = None, + image_sizes: Optional[torch.LongTensor] = None, + image_attention_mask=None, + input_audio_embeds: Optional[torch.FloatTensor] = None, + audio_embed_sizes=None, + audio_attention_mask=None, + input_mode=None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + num_logits_to_keep: int = 0, + ) -> Union[Tuple, CausalLMOutputWithPast]: + 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]`. + + num_logits_to_keep (`int`, *optional*): + Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all + `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that + token can save memory, which becomes pretty significant for long sequences or large vocabulary size. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, Phi4MMForCausalLM + + >>> model = Phi4MMForCausalLM.from_pretrained("TBA") + >>> tokenizer = AutoTokenizer.from_pretrained("TBA") + + >>> prompt = "This is an example script ." + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + 'This is an example script .\n Certainly! Below is a sample script that demonstrates a simple task, such as calculating the sum' + ```""" + if ( + use_cache + and self.config.rope_scaling + and cache_position is not None + and cache_position[0] == self.config.original_max_position_embeddings + ): + logger.warning( + f"If you are not using the generate method, you may encounter nonsensical outputs after the {self.config.original_max_position_embeddings}th token, as the KV cache needs to be recomputed." + ) + + 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 + + if isinstance(input_mode, torch.Tensor): + assert len(input_mode) == 1 + input_mode = input_mode[0].item() + input_mode = InputMode(input_mode) + + if input_mode in [InputMode.VISION_SPEECH, InputMode.VISION]: + self.set_lora_adapter('vision') + audio_projection_mode = 'vision' + elif input_mode == InputMode.SPEECH: + self.set_lora_adapter('speech') + audio_projection_mode = 'speech' + elif input_mode == InputMode.LANGUAGE: + self.unset_lora_adapter() + audio_projection_mode = 'speech' + else: + raise ValueError(f"Invalid input_mode: {input_mode}") + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + input_image_embeds=input_image_embeds, + image_sizes=image_sizes, + image_attention_mask=image_attention_mask, + input_audio_embeds=input_audio_embeds, + audio_embed_sizes=audio_embed_sizes, + audio_attention_mask=audio_attention_mask, + audio_projection_mode=audio_projection_mode, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits, labels, self.vocab_size) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + input_image_embeds=None, + image_sizes=None, + image_attention_mask=None, + input_audio_embeds=None, + audio_embed_sizes=None, + audio_attention_mask=None, + input_mode=None, + cache_position=None, + position_ids=None, + use_cache=True, + num_logits_to_keep=None, + **kwargs + ): + # Overwritten -- this model may need to switch between short and long rope, invalidating the cache in the + # process + + # When the first time input length reached long and short factor switching point, enforce re-compute cache + # It will cause downside of slower at this single token position, however, better than current failure. + if ( + past_key_values + and self.config.rope_scaling + and input_ids.shape[1] >= self.config.original_max_position_embeddings + 1 + ): + past_length = cache_position[0] + if past_length <= self.config.original_max_position_embeddings: + past_key_values = None + + model_inputs = super().prepare_inputs_for_generation( + input_ids=input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + input_image_embeds=input_image_embeds, + image_sizes=image_sizes, + image_attention_mask=image_attention_mask, + input_audio_embeds=input_audio_embeds, + audio_embed_sizes=audio_embed_sizes, + audio_attention_mask=audio_attention_mask, + input_mode=input_mode, + cache_position=cache_position, + position_ids=position_ids, + use_cache=use_cache, + num_logits_to_keep=num_logits_to_keep, + **kwargs, + ) + return model_inputs + + +@add_start_docstrings( + """ + The [`Phi4MMModel`] with a sequence classification head on top (linear layer). + + [`Phi4MMForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + PHI4MM_START_DOCSTRING, +) +# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Phi, LLAMA->PHI, self.transformer->self.model, transformer_outputs->model_outputs +class Phi4MMForSequenceClassification(Phi4MMPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = Phi4MMModel(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(PHI4MM_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + model_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = model_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility + sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 + sequence_lengths = sequence_lengths % input_ids.shape[-1] + sequence_lengths = sequence_lengths.to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config) + + if not return_dict: + output = (pooled_logits,) + model_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=model_outputs.past_key_values, + hidden_states=model_outputs.hidden_states, + attentions=model_outputs.attentions, + ) + + +@add_start_docstrings( + """ + [`Phi4MMModel`] with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for + Named-Entity-Recognition (NER) tasks. + """, + PHI4MM_START_DOCSTRING, +) +# Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with Mpt->Phi,MPT->PHI,self.transformer->self.model,transformer_outputs->model_outputs +class Phi4MMForTokenClassification(Phi4MMPreTrainedModel): + def __init__(self, config: Phi4MMConfig): + super().__init__(config) + self.num_labels = config.num_labels + + self.model = Phi4MMModel(config) + if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None: + classifier_dropout = config.classifier_dropout + elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None: + classifier_dropout = config.hidden_dropout + else: + classifier_dropout = 0.1 + self.dropout = nn.Dropout(classifier_dropout) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(PHI4MM_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=TokenClassifierOutput, + config_class=_CONFIG_FOR_DOC, + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + attention_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + **deprecated_arguments, + ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + model_outputs = self.model( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = model_outputs[0] + hidden_states = self.dropout(hidden_states) + logits = self.classifier(hidden_states) + + loss = None + if labels is not None: + # move labels to correct device to enable model parallelism + labels = labels.to(logits.device) + batch_size, seq_length = labels.shape + loss_fct = CrossEntropyLoss() + loss = loss_fct( + logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length) + ) + + if not return_dict: + output = (logits,) + model_outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=model_outputs.hidden_states, + attentions=model_outputs.attentions, + ) + + +AutoConfig.register("phi4mm", Phi4MMConfig) +AutoModelForCausalLM.register(Phi4MMConfig, Phi4MMForCausalLM) +Phi4MMConfig.register_for_auto_class() +Phi4MMForCausalLM.register_for_auto_class("AutoModelForCausalLM") diff --git a/phi_4_mm.tech_report.02252025.pdf b/phi_4_mm.tech_report.02252025.pdf new file mode 100644 index 0000000..1b22e6e --- /dev/null +++ b/phi_4_mm.tech_report.02252025.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a5469d9123cbee2b41729db3217cacfeaa96eaf543868caa2eeec7cf2d24547d +size 5295165 diff --git a/preprocessor_config.json b/preprocessor_config.json new file mode 100644 index 0000000..6dd4622 --- /dev/null +++ b/preprocessor_config.json @@ -0,0 +1,14 @@ +{ + "auto_map": { + "AutoProcessor": "processing_phi4mm.Phi4MMProcessor", + "AutoImageProcessor": "processing_phi4mm.Phi4MMImageProcessor", + "AutoFeatureExtractor": "processing_phi4mm.Phi4MMAudioFeatureExtractor" + }, + "image_processor_type": "Phi4MMImageProcessor", + "processor_class": "Phi4MMProcessor", + "feature_extractor_type": "Phi4MMAudioFeatureExtractor", + "audio_compression_rate": 8, + "audio_downsample_rate": 1, + "audio_feat_stride": 1, + "dynamic_hd": 36 +} diff --git a/processing_phi4mm.py b/processing_phi4mm.py new file mode 100644 index 0000000..c453631 --- /dev/null +++ b/processing_phi4mm.py @@ -0,0 +1,733 @@ +# 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. + +""" +Processor class for Phi4MM +""" +import re +from typing import List, Optional, Tuple, Union +import math +from enum import Enum + +import numpy as np +import scipy +import torch +import torchvision + +from transformers import AutoFeatureExtractor, AutoImageProcessor +from transformers.feature_extraction_sequence_utils import SequenceFeatureExtractor +from transformers.image_processing_utils import BaseImageProcessor, BatchFeature +from transformers.image_utils import ( + ImageInput, + make_list_of_images, + valid_images, +) +from transformers.processing_utils import ProcessorMixin +from transformers.tokenization_utils_base import PaddingStrategy, TextInput, TruncationStrategy +from transformers.utils import TensorType, logging +from torch.nn.utils.rnn import pad_sequence + + +logger = logging.get_logger(__name__) + +# Special tokens +_COMPATIBLE_IMAGE_SPECIAL_TOKEN_PATTERN = r'<\|image_\d+\|>' # For backward compatibility +_COMPATIBLE_AUDIO_SPECIAL_TOKEN_PATTERN = r'<\|audio_\d+\|>' # For backward compatibility +_IMAGE_SPECIAL_TOKEN = '<|endoftext10|>' +_AUDIO_SPECIAL_TOKEN = '<|endoftext11|>' +_IMAGE_SPECIAL_TOKEN_ID = 200010 # '<|endoftext10|>', or we can better name it (in `tokenizer_config.json`) +_AUDIO_SPECIAL_TOKEN_ID = 200011 # '<|endoftext11|>' + + +class InputMode(Enum): + LANGUAGE = 0 + VISION = 1 + SPEECH = 2 + VISION_SPEECH = 3 + + +class Phi4MMImageProcessor(BaseImageProcessor): + r""" + Constructs a Phi4MM image processor. + """ + model_input_names = ["input_image_embeds", "image_sizes", "image_attention_mask"] + + def __init__( + self, + dynamic_hd, + **kwargs, + ) -> None: + super().__init__(**kwargs) + self.dynamic_hd = dynamic_hd + + def find_closest_aspect_ratio(self, aspect_ratio, target_ratios, width, height, image_size): + best_ratio_diff = float('inf') + best_ratio = (1, 1) + area = width * height + for ratio in target_ratios: + target_aspect_ratio = ratio[0] / ratio[1] + ratio_diff = abs(aspect_ratio - target_aspect_ratio) + if ratio_diff < best_ratio_diff: + best_ratio_diff = ratio_diff + best_ratio = ratio + elif ratio_diff == best_ratio_diff: + if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: + best_ratio = ratio + return best_ratio + + def dynamic_preprocess(self, image, min_num=1, max_num=12, image_size=384, mask_size=27, use_thumbnail=True): + orig_width, orig_height = image.size + + w_crop_num = math.ceil(orig_width/float(image_size)) + h_crop_num = math.ceil(orig_height/float(image_size)) + if w_crop_num * h_crop_num > max_num: + + aspect_ratio = orig_width / orig_height + + # calculate the existing image aspect ratio + target_ratios = set( + (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if + i * j <= max_num and i * j >= min_num) + target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) + + # find the closest aspect ratio to the target + target_aspect_ratio = self.find_closest_aspect_ratio( + aspect_ratio, target_ratios, orig_width, orig_height, image_size) + + # calculate the target width and height + target_width = image_size * target_aspect_ratio[0] + target_height = image_size * target_aspect_ratio[1] + else: + target_width = image_size * w_crop_num + target_height = image_size * h_crop_num + target_aspect_ratio = (w_crop_num, h_crop_num) + + # Calculate the ratio + ratio_width = target_width / orig_width + ratio_height = target_height / orig_height + if ratio_width < ratio_height: + new_size = (target_width, int(orig_height * ratio_width)) + padding_width = 0 + padding_height = target_height - int(orig_height * ratio_width) + else: + new_size = (int(orig_width * ratio_height), target_height) + padding_width = target_width - int(orig_width * ratio_height) + padding_height = 0 + + attention_mask = torch.ones((int(mask_size*target_aspect_ratio[1]), int(mask_size*target_aspect_ratio[0]))) + if padding_width >= 14: + attention_mask[:, -math.floor(padding_width/14):] = 0 + if padding_height >= 14: + attention_mask[-math.floor(padding_height/14):,:] = 0 + assert attention_mask.sum() > 0 + + if min(new_size[1], target_height) < 10 or min(new_size[0], target_width) < 10: + raise ValueError(f'the aspect ratio is very extreme {new_size}') + + image = torchvision.transforms.functional.resize(image, [new_size[1], new_size[0]],) + + resized_img = torchvision.transforms.functional.pad(image, [0, 0, padding_width, padding_height], fill=[255,255,255]) + + return resized_img, attention_mask + + def pad_to_max_num_crops(self, images, max_crops=5): + """ + images: B x 3 x H x W, B<=max_crops + """ + B, _, H, W = images.shape + if B < max_crops: + pad = torch.zeros(max_crops - B, 3, H, W, dtype=images.dtype, device=images.device) + images = torch.cat([images, pad], dim=0) + return images + + def pad_mask_to_max_num_crops(self, masks, max_crops=5): + B, H, W = masks.shape + if B < max_crops: + pad = torch.ones(max_crops - B, H, W, dtype=masks.dtype, device=masks.device) + masks = torch.cat([masks, pad], dim=0) + return masks + + def preprocess( + self, + images: ImageInput, + return_tensors: Optional[Union[str, TensorType]] = None, + ): + """ + Args: + images (`ImageInput`): + Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If + passing in images with pixel values between 0 and 1, set `do_rescale=False`. + return_tensors (`str` or `TensorType`, *optional*): + The type of tensors to return. Can be one of: + - Unset: Return a list of `np.ndarray`. + - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. + - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. + - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. + - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. + """ + images = make_list_of_images(images) + + if not valid_images(images): + raise ValueError( + "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " + "torch.Tensor, tf.Tensor or jax.ndarray." + ) + + # Basic settings. + img_processor = torchvision.transforms.Compose([ + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize( + (0.5, 0.5, 0.5), + (0.5, 0.5, 0.5) + ), + ]) + dyhd_base_resolution = 448 + + # Dynamic HD + base_resolution = dyhd_base_resolution + images = [image.convert('RGB') for image in images] + # cover 384 and 448 resolution + mask_resolution = base_resolution // 14 + elems, image_attention_masks = [], [] + for im in images: + elem, attention_mask = self.dynamic_preprocess(im, max_num=self.dynamic_hd, image_size=base_resolution, mask_size=mask_resolution) + elems.append(elem) + image_attention_masks.append(attention_mask) + hd_images = [img_processor(im) for im in elems] + global_image = [torch.nn.functional.interpolate(im.unsqueeze(0).float(), size=(base_resolution, base_resolution), mode='bicubic',).to(im.dtype) for im in hd_images] + shapes = [[im.size(1), im.size(2)] for im in hd_images] + mask_shapes = [[mask.size(0), mask.size(1)] for mask in image_attention_masks] + global_attention_mask = [torch.ones((1, mask_resolution, mask_resolution)) for _ in hd_images] + hd_images_reshape = [im.reshape(1, 3, + h//base_resolution, + base_resolution, + w//base_resolution, + base_resolution + ).permute(0,2,4,1,3,5).reshape(-1, 3, base_resolution, base_resolution).contiguous() for im, (h, w) in zip(hd_images, shapes)] + attention_masks_reshape = [mask.reshape(1, + h//mask_resolution, + mask_resolution, + w//mask_resolution, + mask_resolution + ).permute(0,1,3,2,4).reshape(-1, mask_resolution, mask_resolution).contiguous() for mask, (h, w) in zip(image_attention_masks, mask_shapes)] + downsample_attention_masks = [mask[:,0::2,0::2].reshape(1, + h//mask_resolution, + w//mask_resolution, + mask_resolution//2+mask_resolution%2, + mask_resolution//2+mask_resolution%2 + ).permute(0,1,3,2,4) for mask, (h,w) in zip(attention_masks_reshape, mask_shapes)] + downsample_attention_masks = [mask.reshape(mask.size(1)*mask.size(2), mask.size(3)*mask.size(4))for mask in downsample_attention_masks] + num_img_tokens = [256 + 1 + int(mask.sum().item()) + int(mask[:,0].sum().item()) + 16 for mask in downsample_attention_masks] + + hd_images_reshape = [torch.cat([_global_image] + [_im], dim=0) for _global_image, _im in zip(global_image, hd_images_reshape)] + hd_masks_reshape = [torch.cat([_global_mask] + [_mask], dim=0) for _global_mask, _mask in zip(global_attention_mask, attention_masks_reshape)] + max_crops = max([img.size(0) for img in hd_images_reshape]) + image_transformed = [self.pad_to_max_num_crops(im, max_crops) for im in hd_images_reshape] + image_transformed = torch.stack(image_transformed, dim=0) + mask_transformed = [self.pad_mask_to_max_num_crops(mask, max_crops) for mask in hd_masks_reshape] + mask_transformed = torch.stack(mask_transformed, dim=0) + + returned_input_image_embeds = image_transformed + returned_image_sizes = torch.tensor(shapes, dtype=torch.long) + returned_image_attention_mask = mask_transformed + returned_num_img_tokens = num_img_tokens + + data = { + "input_image_embeds": returned_input_image_embeds, + "image_sizes": returned_image_sizes, + "image_attention_mask": returned_image_attention_mask, + "num_img_tokens": returned_num_img_tokens, + } + + return BatchFeature(data=data, tensor_type=return_tensors) + + +AudioInput = Tuple[Union[np.ndarray, torch.Tensor], int] +AudioInputs = List[AudioInput] + + +def speechlib_mel(sample_rate, n_fft, n_mels, fmin=None, fmax=None): + """Create a Mel filter-bank the same as SpeechLib FbankFC. + + Args: + sample_rate (int): Sample rate in Hz. number > 0 [scalar] + n_fft (int): FFT size. int > 0 [scalar] + n_mel (int): Mel filter size. int > 0 [scalar] + fmin (float): lowest frequency (in Hz). If None use 0.0. + float >= 0 [scalar] + fmax: highest frequency (in Hz). If None use sample_rate / 2. + float >= 0 [scalar] + + Returns + out (numpy.ndarray): Mel transform matrix + [shape=(n_mels, 1 + n_fft/2)] + """ + + bank_width = int(n_fft // 2 + 1) + if fmax is None: + fmax = sample_rate / 2 + if fmin is None: + fmin = 0 + assert fmin >= 0, "fmin cannot be negtive" + assert fmin < fmax <= sample_rate / 2, "fmax must be between (fmin, samplerate / 2]" + + def mel(f): + return 1127.0 * np.log(1.0 + f / 700.0) + + def bin2mel(fft_bin): + return 1127.0 * np.log(1.0 + fft_bin * sample_rate / (n_fft * 700.0)) + + def f2bin(f): + return int((f * n_fft / sample_rate) + 0.5) + + # Spec 1: FFT bin range [f2bin(fmin) + 1, f2bin(fmax) - 1] + klo = f2bin(fmin) + 1 + khi = f2bin(fmax) + + khi = max(khi, klo) + + # Spec 2: SpeechLib uses trianges in Mel space + mlo = mel(fmin) + mhi = mel(fmax) + m_centers = np.linspace(mlo, mhi, n_mels + 2) + ms = (mhi - mlo) / (n_mels + 1) + + matrix = np.zeros((n_mels, bank_width), dtype=np.float32) + for m in range(0, n_mels): + left = m_centers[m] + center = m_centers[m + 1] + right = m_centers[m + 2] + for fft_bin in range(klo, khi): + mbin = bin2mel(fft_bin) + if left < mbin < right: + matrix[m, fft_bin] = 1.0 - abs(center - mbin) / ms + + return matrix + + +class Phi4MMAudioFeatureExtractor(SequenceFeatureExtractor): + model_input_names = ["input_audio_embeds", "audio_embed_sizes", "audio_attention_mask"] + + def __init__(self, audio_compression_rate, audio_downsample_rate, audio_feat_stride, **kwargs): + feature_size = 80 + sampling_rate = 16000 + padding_value = 0.0 + super().__init__(feature_size, sampling_rate, padding_value, **kwargs) + + self.compression_rate = audio_compression_rate + self.qformer_compression_rate = audio_downsample_rate + self.feat_stride = audio_feat_stride + + self._eightk_method = "fillzero" + self._mel = speechlib_mel(16000, 512, 80, fmin=None, fmax=7690).T + + self._hamming400 = np.hamming(400) # for 16k audio + self._hamming200 = np.hamming(200) # for 8k audio + + def duration_to_frames(self, duration): + """duration in s, estimated frames""" + frame_rate = 10 + + num_frames = duration * 1000 // frame_rate + return num_frames + + def __call__( + self, + audios: List[AudioInput], + return_tensors: Optional[Union[str, TensorType]] = None, + ): + # Ref: https://github.com/huggingface/transformers/blob/v4.47.0/src/transformers/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.py#L161 + returned_input_audio_embeds = [] + returned_audio_embed_sizes = [] + audio_frames_list = [] + + for audio_data, sample_rate in audios: + audio_embeds = self._extract_features(audio_data, sample_rate) + audio_frames = len(audio_embeds) * self.feat_stride + audio_embed_size = self._compute_audio_embed_size(audio_frames) + + returned_input_audio_embeds.append(torch.tensor(audio_embeds)) + returned_audio_embed_sizes.append(torch.tensor(audio_embed_size).long()) + audio_frames_list.append(audio_frames) + + returned_input_audio_embeds = pad_sequence( + returned_input_audio_embeds, batch_first=True + ) + returned_audio_embed_sizes = torch.stack(returned_audio_embed_sizes, dim=0) + audio_frames = torch.tensor(audio_frames_list) + returned_audio_attention_mask = torch.arange(0, audio_frames.max()).unsqueeze(0) < audio_frames.unsqueeze(1) if len(audios) > 1 else None + + data = { + "input_audio_embeds": returned_input_audio_embeds, + "audio_embed_sizes": returned_audio_embed_sizes, + } + if returned_audio_attention_mask is not None: + data["audio_attention_mask"] = returned_audio_attention_mask + + return BatchFeature(data=data, tensor_type=return_tensors) + + def _extract_spectrogram(self, wav, fs): + """Extract spectrogram features from waveform. + Args: + wav (1D array): waveform of the input + fs (int): sampling rate of the waveform, 16000 or 8000. + If fs=8000, the waveform will be resampled to 16000Hz. + Output: + log_fbank (2D array): a TxD matrix of log Mel filterbank features. + D=80, and T is the number of frames. + """ + if wav.ndim > 1: + wav = np.squeeze(wav) + + # by default, we extract the mean if stereo + if len(wav.shape) == 2: + wav = wav.mean(1) + + # Resample to 16000 or 8000 if needed + if fs > 16000: + wav = scipy.signal.resample_poly(wav, 1, fs // 16000) + fs = 16000 + elif 8000 < fs < 16000: + wav = scipy.signal.resample_poly(wav, 1, fs // 8000) + fs = 8000 + elif fs < 8000: + raise RuntimeError(f"Unsupported sample rate {fs}") + + if fs == 8000: + if self._eightk_method == "resample": + # Input audio is 8 kHz. Convert to 16 kHz before feature + # extraction + wav = scipy.signal.resample_poly(wav, 2, 1) + fs = 16000 + # Do nothing here for fillzero method + elif fs != 16000: + # Input audio is not a supported sample rate. + raise RuntimeError(f"Input data using an unsupported sample rate: {fs}") + + preemphasis = 0.97 + + if fs == 8000: + n_fft = 256 + win_length = 200 + hop_length = 80 + fft_window = self._hamming200 + elif fs == 16000: + n_fft = 512 + win_length = 400 + hop_length = 160 + fft_window = self._hamming400 + + # Spec 1: SpeechLib cut remaining sample insufficient for a hop + n_batch = (wav.shape[0] - win_length) // hop_length + 1 + # Here we don't use stride_tricks since the input array may not satisfy + # memory layout requirement and we need writeable output + # Here we only use list of views before copy to desination + # so it is more efficient than broadcasting + y_frames = np.array( + [wav[_stride : _stride + win_length] for _stride in range(0, hop_length * n_batch, hop_length)], + dtype=np.float32, + ) + + # Spec 2: SpeechLib applies preemphasis within each batch + y_frames_prev = np.roll(y_frames, 1, axis=1) + y_frames_prev[:, 0] = y_frames_prev[:, 1] + y_frames = (y_frames - preemphasis * y_frames_prev) * 32768 + + S = np.fft.rfft(fft_window * y_frames, n=n_fft, axis=1).astype(np.complex64) + + if fs == 8000: + # Need to pad the output to look like 16 kHz data but with zeros in + # the 4 to 8 kHz bins. + frames, bins = S.shape + padarray = np.zeros((frames, bins)) + S = np.concatenate((S[:, 0:-1], padarray), axis=1) # Nyquist bin gets set to zero + + spec = np.abs(S).astype(np.float32) + return spec + + def _extract_features(self, wav, fs): + """Extract log filterbank features from waveform. + Args: + wav (1D array): waveform of the input + fs (int): sampling rate of the waveform, 16000 or 8000. + If fs=8000, the waveform will be resampled to 16000Hz. + Output: + log_fbank (2D array): a TxD matrix of log Mel filterbank features. + D=80, and T is the number of frames. + """ + spec = self._extract_spectrogram(wav, fs) + spec_power = spec**2 + + fbank_power = np.clip(spec_power.dot(self._mel), 1.0, None) + log_fbank = np.log(fbank_power).astype(np.float32) + + return log_fbank + + def _compute_audio_embed_size(self, audio_frames): + integer = audio_frames // self.compression_rate + remainder = audio_frames % self.compression_rate + + result = integer if remainder == 0 else integer + 1 + + integer = result // self.qformer_compression_rate + remainder = result % self.qformer_compression_rate + result = integer if remainder == 0 else integer + 1 # qformer compression + + return result + + +class Phi4MMProcessor(ProcessorMixin): + r""" + Constructs a Phi4MM processor which raps an image processor, a audio processor, and a GPT tokenizer into a single processor. + + [`Phi4MMProcessor`] offers all the functionalities of [`Phi4MMImageProcessor`] and [`GPT2Tokenizer`]. See the + [`~Phi4MMProcessor.__call__`] and [`~Phi4MMProcessor.decode`] for more information. + + Args: + image_processor ([`Phi4MMImageProcessor`], *optional*): + The image processor is a required input. + tokenizer ([`GPT2Tokenizer`], *optional*): + The tokenizer is a required input. + """ + + attributes = ["image_processor", "audio_processor", "tokenizer"] + tokenizer_class = "GPT2TokenizerFast" + image_processor_class = "AutoImageProcessor" # Phi4MMImageProcessor will be registered later + audio_processor_class = "AutoFeatureExtractor" # Phi4MMAudioFeatureExtractor will be registered later + + def __init__(self, image_processor, audio_processor, tokenizer): + self.image_processor = image_processor + self.audio_processor = audio_processor + self.tokenizer = tokenizer + + def __call__( + self, + text: Union[TextInput, List[TextInput]], + images: Optional[ImageInput] = None, + audios: Optional[AudioInputs] = None, + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Optional[Union[bool, str, TruncationStrategy]] = None, + max_length=None, + return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH, + ) -> BatchFeature: + """ + Main method to prepare for the model one or several sequences(s) and image(s). This method forards the `text` + and `kwargs` arguments to GPT2Tokenizer's [`~GPT2Tokenizer.__call__`] if `text` is not `None` to encode + the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to + Phi4MMImageProcessor's [`~Phi4MMImageProcessor.__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. Both channels-first and channels-last formats are supported. + 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. + - **input_image_embeds** -- Pixel values to be fed to a model. + - **image_sizes** -- List of tuples specifying the size of each image in `input_image_embeds`. + - **image_attention_mask** -- List of attention masks for each image in `input_image_embeds`. + - **input_audio_embeds** -- Audio embeddings to be fed to a model. + - **audio_embed_sizes** -- List of integers specifying the size of each audio in `input_audio_embeds`. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model. + """ + image_inputs = self.image_processor(images, return_tensors=return_tensors) if images is not None else {} + audio_inputs = self.audio_processor(audios, return_tensors=return_tensors) if audios is not None else {} + inputs = self._convert_images_audios_text_to_inputs( + image_inputs, + audio_inputs, + text, + padding=padding, + truncation=truncation, + max_length=max_length, + return_tensors=return_tensors, + ) + + # idenfity the input mode + if len(image_inputs) > 0 and len(audio_inputs) > 0: + input_mode = InputMode.VISION_SPEECH + elif len(image_inputs) > 0: + input_mode = InputMode.VISION + elif len(audio_inputs) > 0: + input_mode = InputMode.SPEECH + else: + input_mode = InputMode.LANGUAGE + inputs["input_mode"] = torch.tensor([input_mode.value], dtype=torch.long) + + return inputs + + @property + def special_image_token_id(self): + return self.tokenizer.convert_tokens_to_ids(self.special_image_token) + + def get_special_image_token_id(self): + return self.tokenizer.convert_tokens_to_ids(self.special_image_token) + + @property + def chat_template(self): + return self.tokenizer.chat_template + + def _convert_images_audios_text_to_inputs( + self, images, audios, text, padding=False, truncation=None, max_length=None, return_tensors=None + ): + # prepare image id to image input ids + if len(images) > 0: + input_image_embeds = images["input_image_embeds"] + image_sizes = images["image_sizes"] + image_attention_mask = images["image_attention_mask"] + num_img_tokens = images['num_img_tokens'] + else: + input_image_embeds = torch.tensor([]) + image_sizes = torch.tensor([]) + image_attention_mask = torch.tensor([]) + num_img_tokens = [] + + # prepare audio id to audio input ids + if len(audios) > 0: + input_audio_embeds = audios["input_audio_embeds"] + audio_embed_sizes = audios["audio_embed_sizes"] + audio_attention_mask = audios.get("audio_attention_mask", None) + else: + input_audio_embeds = torch.tensor([]) + audio_embed_sizes = torch.tensor([]) + audio_attention_mask = None + + # Replace certain special tokens for compatibility + # Ref: https://stackoverflow.com/questions/11475885/python-replace-regex + if isinstance(text, str): + text = [text] + assert isinstance(text, list) + processed_text = [re.sub(_COMPATIBLE_IMAGE_SPECIAL_TOKEN_PATTERN, _IMAGE_SPECIAL_TOKEN, t) for t in text] + processed_text = [re.sub(_COMPATIBLE_AUDIO_SPECIAL_TOKEN_PATTERN, _AUDIO_SPECIAL_TOKEN, t) for t in processed_text] + + input_ids_list = [self.tokenizer(t).input_ids for t in processed_text] + + img_cnt, audio_cnt = 0, 0 # only needed for later assertion + image_token_count_iter = iter(num_img_tokens) + audio_embed_size_iter = iter(audio_embed_sizes.tolist()) + new_input_ids_list = [] + for input_ids in input_ids_list: + i = 0 + while i < len(input_ids): + token_id = input_ids[i] + if token_id == _AUDIO_SPECIAL_TOKEN_ID: + token_count = next(audio_embed_size_iter) + audio_cnt += 1 + elif token_id == _IMAGE_SPECIAL_TOKEN_ID: + token_count = next(image_token_count_iter) + img_cnt += 1 + else: + i += 1 + continue + tokens = [token_id] * token_count + input_ids = input_ids[:i] + tokens + input_ids[i + 1:] + i += token_count + input_ids = torch.tensor(input_ids, dtype=torch.long) + new_input_ids_list.append(input_ids) + lengths = torch.tensor([len(input_ids) for input_ids in new_input_ids_list]) + max_len = lengths.max() + input_ids = input_ids.new_full((len(new_input_ids_list), max_len), self.tokenizer.pad_token_id) + # batched inference requires left padding + for i in range(len(new_input_ids_list)): + input_ids[i, max_len - len(new_input_ids_list[i]):] = new_input_ids_list[i] + + # If the below assertion fails, it might be that input pure-text + # messages contain image/audio special tokens literally + # (<|endoftext10|>, <|endoftext11|>). + assert ( + img_cnt == len(num_img_tokens) + ), ( + f"Number of image tokens in prompt_token_ids ({img_cnt}) " + f"does not match number of images ({len(num_img_tokens)})" + ) + assert ( + audio_cnt == len(audio_embed_sizes) + ), ( + f"Number of audio tokens in prompt_token_ids ({audio_cnt}) " + f"does not match number of audios ({len(audio_embed_sizes)})" + ) + + # prepare attention mask + seq_range = torch.arange(max_len - 1, -1, -1) + attention_mask = seq_range.unsqueeze(0) < lengths.unsqueeze(1) + + # prepare batch feature + data = { + "input_ids": input_ids, + "input_image_embeds": input_image_embeds, + "image_sizes": image_sizes, + "image_attention_mask": image_attention_mask, + "input_audio_embeds": input_audio_embeds, + "audio_embed_sizes": audio_embed_sizes, + "audio_attention_mask": audio_attention_mask, + "attention_mask": attention_mask, + } + + return BatchFeature( + data=data + ) + + # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama + def batch_decode(self, *args, **kwargs): + """ + This method forwards all its arguments to GPT2Tokenizer'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->Llama + def decode(self, *args, **kwargs): + """ + This method forwards all its arguments to GPT2Tokenizer'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 + def model_input_names(self): + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + audio_processor_input_names = self.audio_processor.model_input_names + return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names + audio_processor_input_names)) + + +AutoImageProcessor.register("Phi4MMImageProcessor", Phi4MMImageProcessor) +AutoFeatureExtractor.register("Phi4MMAudioFeatureExtractor", Phi4MMAudioFeatureExtractor) diff --git a/processor_config.json b/processor_config.json new file mode 100644 index 0000000..e30d7f7 --- /dev/null +++ b/processor_config.json @@ -0,0 +1,6 @@ +{ + "auto_map": { + "AutoProcessor": "processing_phi4mm.Phi4MMProcessor" + }, + "processor_class": "Phi4MMProcessor" +} diff --git a/sample_finetune_speech.py b/sample_finetune_speech.py new file mode 100644 index 0000000..32650b5 --- /dev/null +++ b/sample_finetune_speech.py @@ -0,0 +1,478 @@ +""" +finetune Phi-4-multimodal-instruct on an speech task + +scipy==1.15.1 +peft==0.13.2 +backoff==2.2.1 +transformers==4.46.1 +accelerate==1.3.0 +""" + +import argparse +import json +import os +from pathlib import Path + +import torch +import sacrebleu +from accelerate import Accelerator +from accelerate.utils import gather_object +from datasets import load_dataset +from torch.utils.data import Dataset +from tqdm import tqdm +from transformers import ( + AutoModelForCausalLM, + AutoProcessor, + BatchFeature, + Trainer, + TrainingArguments, + StoppingCriteria, + StoppingCriteriaList, +) + + +INSTSRUCTION = { + "en_zh-CN": "Translate the audio to Mandarin.", + "en_id": "Translate the audio to Indonesian.", + "en_sl": "Translate the audio to Slovenian.", +} +TOKENIZER = { + "en_zh-CN": "zh", + "en_ja": "ja-mecab", +} +ANSWER_SUFFIX = "<|end|><|endoftext|>" +_IGNORE_INDEX = -100 +_TRAIN_SIZE = 50000 +_EVAL_SIZE = 200 + +class MultipleTokenBatchStoppingCriteria(StoppingCriteria): + """Stopping criteria capable of receiving multiple stop-tokens and handling batched inputs.""" + + def __init__(self, stop_tokens: torch.LongTensor, batch_size: int = 1) -> None: + """Initialize the multiple token batch stopping criteria. + + Args: + stop_tokens: Stop-tokens. + batch_size: Batch size. + + """ + + self.stop_tokens = stop_tokens + self.max_stop_tokens = stop_tokens.shape[-1] + self.stop_tokens_idx = torch.zeros(batch_size, dtype=torch.long, device=stop_tokens.device) + + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: + # Only gather the maximum number of inputs compatible with stop tokens + # and checks whether generated inputs are equal to `stop_tokens` + generated_inputs = torch.eq(input_ids[:, -self.max_stop_tokens :].unsqueeze(1), self.stop_tokens) + equal_generated_inputs = torch.all(generated_inputs, dim=2) + + # Mark the position where a stop token has been produced for each input in the batch, + # but only if the corresponding entry is not already set + sequence_idx = torch.any(equal_generated_inputs, dim=1) + sequence_set_mask = self.stop_tokens_idx == 0 + self.stop_tokens_idx[sequence_idx & sequence_set_mask] = input_ids.shape[-1] + + return torch.all(self.stop_tokens_idx) + +class CoVoSTDataset(Dataset): + def __init__(self, processor, data_dir, split, + lang="en_zh-CN", rank=0, world_size=1): + + self.data = load_dataset("facebook/covost2", + lang, + data_dir=data_dir, + split=split, + trust_remote_code=True + ) + self.training = "train" in split + self.processor = processor + self.instruction = INSTSRUCTION[lang] + + if world_size > 1: + self.data = self.data.shard(world_size, rank) + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + """ + {'client_id': '0013037a1d45cc33460806cc3f8ecee9d536c45639ba4cbbf1564f1c051f53ff3c9f89ef2f1bf04badf55b3a2e7654c086f903681a7b6299616cff6f67598eff', + 'file': '{data_dir}/clips/common_voice_en_699711.mp3', + 'audio': {'path': '{data_dir}/clips/common_voice_en_699711.mp3', + 'array': array([-1.28056854e-09, -1.74622983e-09, -1.16415322e-10, ..., + 3.92560651e-10, 6.62794264e-10, -3.89536581e-09]), + 'sampling_rate': 16000}, + 'sentence': '"She\'ll be all right."', + 'translation': '她会没事的。', + 'id': 'common_voice_en_699711'} + """ + data = self.data[idx] + user_message = { + 'role': 'user', + 'content': '<|audio_1|>\n' + self.instruction, + } + prompt = self.processor.tokenizer.apply_chat_template( + [user_message], tokenize=False, add_generation_prompt=True + ) + inputs = self.processor(text=prompt, audios=[(data["audio"]["array"], data["audio"]["sampling_rate"])], return_tensors='pt') + + answer = f"{data['translation']}{ANSWER_SUFFIX}" + answer_ids = self.processor.tokenizer(answer, return_tensors='pt').input_ids + if self.training: + input_ids = torch.cat([inputs.input_ids, answer_ids], dim=1) + labels = torch.full_like(input_ids, _IGNORE_INDEX) + labels[:, -answer_ids.shape[1] :] = answer_ids + else: + input_ids = inputs.input_ids + labels = answer_ids + + return { + 'input_ids': input_ids, + 'labels': labels, + 'input_audio_embeds': inputs.input_audio_embeds, + 'audio_embed_sizes': inputs.audio_embed_sizes, + } + +def pad_sequence(sequences, padding_side='right', padding_value=0): + """ + Pad a list of sequences to the same length. + sequences: list of tensors in [seq_len, *] shape + """ + assert padding_side in ['right', 'left'] + max_size = sequences[0].size() + trailing_dims = max_size[1:] + max_len = max(len(seq) for seq in sequences) + batch_size = len(sequences) + output = sequences[0].new_full((batch_size, max_len) + trailing_dims, padding_value) + for i, seq in enumerate(sequences): + length = seq.size(0) + if padding_side == 'right': + output.data[i, :length] = seq + else: + output.data[i, -length:] = seq + return output + + +def cat_with_pad(tensors, dim, padding_value=0): + """ + cat along dim, while pad to max for all other dims + """ + ndim = tensors[0].dim() + assert all( + t.dim() == ndim for t in tensors[1:] + ), 'All tensors must have the same number of dimensions' + + out_size = [max(t.shape[i] for t in tensors) for i in range(ndim)] + out_size[dim] = sum(t.shape[dim] for t in tensors) + output = tensors[0].new_full(out_size, padding_value) + + index = 0 + for t in tensors: + # Create a slice list where every dimension except dim is full slice + slices = [slice(0, t.shape[d]) for d in range(ndim)] + # Update only the concat dimension slice + slices[dim] = slice(index, index + t.shape[dim]) + + output[slices] = t + index += t.shape[dim] + + return output + + +def covost_collate_fn(batch): + input_ids_list = [] + labels_list = [] + input_audio_embeds_list = [] + audio_embed_sizes_list = [] + audio_attention_mask_list = [] + for inputs in batch: + input_ids_list.append(inputs['input_ids'][0]) + labels_list.append(inputs['labels'][0]) + input_audio_embeds_list.append(inputs['input_audio_embeds']) + audio_embed_sizes_list.append(inputs['audio_embed_sizes']) + audio_attention_mask_list.append( + inputs['input_audio_embeds'].new_full((inputs['input_audio_embeds'].size(1),), True, dtype=torch.bool) + ) + + try: + input_ids = pad_sequence(input_ids_list, padding_side='left', padding_value=0) + labels = pad_sequence(labels_list, padding_side='left', padding_value=0) + audio_attention_mask = ( + pad_sequence(audio_attention_mask_list, padding_side='right', padding_value=False) + if len(audio_attention_mask_list) > 1 + else None + ) + except Exception as e: + print(e) + print(input_ids_list) + print(labels_list) + raise + attention_mask = (input_ids != 0).long() + input_audio_embeds = cat_with_pad(input_audio_embeds_list, dim=0) + audio_embed_sizes = torch.cat(audio_embed_sizes_list) + + return BatchFeature( + { + 'input_ids': input_ids, + 'labels': labels, + 'attention_mask': attention_mask, + 'input_audio_embeds': input_audio_embeds, + 'audio_embed_sizes': audio_embed_sizes, + 'audio_attention_mask': audio_attention_mask, + 'input_mode': 2, # speech mode + } + ) + + + +def create_model(model_name_or_path, use_flash_attention=False): + model = AutoModelForCausalLM.from_pretrained( + model_name_or_path, + torch_dtype=torch.bfloat16 if use_flash_attention else torch.float32, + _attn_implementation='flash_attention_2' if use_flash_attention else 'sdpa', + trust_remote_code=True, + ).to('cuda') + + return model + + +@torch.no_grad() +def evaluate( + model, processor, eval_dataset, save_path=None, disable_tqdm=False, eval_batch_size=1 +): + rank = int(os.environ.get('RANK', 0)) + local_rank = int(os.environ.get('LOCAL_RANK', 0)) + + model.eval() + all_generated_texts = [] + all_labels = [] + + eval_dataloader = torch.utils.data.DataLoader( + eval_dataset, + batch_size=eval_batch_size, + collate_fn=covost_collate_fn, + shuffle=False, + drop_last=False, + num_workers=8, + prefetch_factor=2, + pin_memory=True, + ) + stop_tokens = ["<|end|>", processor.tokenizer.eos_token] + stop_tokens_ids = processor.tokenizer(stop_tokens, add_special_tokens=False, padding="longest", return_tensors="pt")["input_ids"] + stop_tokens_ids = stop_tokens_ids.to(f'cuda:{local_rank}') + + for inputs in tqdm( + eval_dataloader, disable=(rank != 0) or disable_tqdm, desc='running eval' + ): + stopping_criteria=StoppingCriteriaList([MultipleTokenBatchStoppingCriteria(stop_tokens_ids, batch_size=inputs.input_ids.size(0))]) + inputs = inputs.to(f'cuda:{local_rank}') + generated_ids = model.generate( + **inputs, eos_token_id=processor.tokenizer.eos_token_id, max_new_tokens=64, + stopping_criteria=stopping_criteria, + ) + + stop_tokens_idx = stopping_criteria[0].stop_tokens_idx.reshape(inputs.input_ids.size(0), -1)[:, 0] + + stop_tokens_idx = torch.where( + stop_tokens_idx > 0, + stop_tokens_idx - stop_tokens_ids.shape[-1], + generated_ids.shape[-1], + ) + generated_text = [ + processor.decode(_pred_ids[inputs["input_ids"].shape[1] : _stop_tokens_idx], skip_special_tokens=True, clean_up_tokenization_spaces=False) + for _pred_ids, _stop_tokens_idx in zip(generated_ids, stop_tokens_idx) + ] + all_generated_texts.extend(generated_text) + labels = [processor.decode(_label_ids[_label_ids != 0]).rstrip(ANSWER_SUFFIX) for _label_ids in inputs["labels"]] + all_labels.extend(labels) + + all_generated_texts = gather_object(all_generated_texts) + all_labels = gather_object(all_labels) + + if rank == 0: + assert len(all_generated_texts) == len(all_labels) + bleu = sacrebleu.corpus_bleu(all_generated_texts, [all_labels]) + print(bleu) + if save_path: + with open(save_path, 'w') as f: + save_dict = { + 'all_generated_texts': all_generated_texts, + 'all_labels': all_labels, + 'score': bleu.score, + } + json.dump(save_dict, f) + + return bleu.score + return None + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + '--model_name_or_path', + type=str, + default='microsoft/Phi-4-multimodal-instruct', + help='Model name or path to load from', + ) + parser.add_argument( + "--common_voice_dir", + type=str, + default="CommonVoice/EN", + help="Unzipped Common Voice Audio dataset directory, refer to https://commonvoice.mozilla.org/en/datasets, version 4.0", + ) + parser.add_argument( + "--lang", + type=str, + default="en_sl", + help="Language pair for translation.", + ) + parser.add_argument('--use_flash_attention', action='store_true', help='Use Flash Attention') + parser.add_argument('--output_dir', type=str, default='./output/', help='Output directory') + parser.add_argument('--batch_size', type=int, default=128, help='Batch size') + parser.add_argument( + '--batch_size_per_gpu', + type=int, + default=32, + help='Batch size per GPU (adjust this to fit in GPU memory)', + ) + parser.add_argument( + '--num_train_epochs', type=int, default=1, help='Number of training epochs' + ) + parser.add_argument('--learning_rate', type=float, default=4.0e-5, help='Learning rate') + parser.add_argument('--wd', type=float, default=0.01, help='Weight decay') + parser.add_argument('--no-tqdm', dest='tqdm', action='store_false', help='Disable tqdm') + args = parser.parse_args() + + accelerator = Accelerator() + + with accelerator.local_main_process_first(): + processor = AutoProcessor.from_pretrained( + args.model_name_or_path, + trust_remote_code=True, + ) + model = create_model( + args.model_name_or_path, + use_flash_attention=args.use_flash_attention, + ) + + model.set_lora_adapter('speech') + + + rank = int(os.environ.get('RANK', 0)) + world_size = int(os.environ.get('WORLD_SIZE', 1)) + + eval_dataset = CoVoSTDataset(processor, + data_dir=args.common_voice_dir, + split=f'test[:{_EVAL_SIZE}]', + lang=args.lang, + rank=rank, + world_size=world_size) + + train_dataset = CoVoSTDataset(processor, + data_dir=args.common_voice_dir, + split=f'train[:{_TRAIN_SIZE}]', + lang=args.lang) + + num_gpus = accelerator.num_processes + print(f'training on {num_gpus} GPUs') + assert ( + args.batch_size % (num_gpus * args.batch_size_per_gpu) == 0 + ), 'Batch size must be divisible by the number of GPUs' + gradient_accumulation_steps = args.batch_size // (num_gpus * args.batch_size_per_gpu) + + if args.use_flash_attention: + fp16 = False + bf16 = True + else: + fp16 = True + bf16 = False + + # hard coded training args + training_args = TrainingArguments( + num_train_epochs=args.num_train_epochs, + per_device_train_batch_size=args.batch_size_per_gpu, + gradient_checkpointing=True, + gradient_checkpointing_kwargs={'use_reentrant': False}, + gradient_accumulation_steps=gradient_accumulation_steps, + optim='adamw_torch', + adam_beta1=0.9, + adam_beta2=0.95, + adam_epsilon=1e-7, + learning_rate=args.learning_rate, + weight_decay=args.wd, + max_grad_norm=1.0, + lr_scheduler_type='linear', + warmup_steps=50, + logging_steps=10, + output_dir=args.output_dir, + save_strategy='no', + save_total_limit=10, + save_only_model=True, + bf16=bf16, + fp16=fp16, + remove_unused_columns=False, + report_to='none', + deepspeed=None, + disable_tqdm=not args.tqdm, + dataloader_num_workers=4, + ddp_find_unused_parameters=True, # for unused SigLIP layers + ) + + # eval before fine-tuning + out_path = Path(training_args.output_dir) + out_path.mkdir(parents=True, exist_ok=True) + + score = evaluate( + model, + processor, + eval_dataset, + save_path=out_path / 'eval_before.json', + disable_tqdm=not args.tqdm, + eval_batch_size=args.batch_size_per_gpu, + ) + if accelerator.is_main_process: + print(f'BLEU Score before finetuning: {score}') + + trainer = Trainer( + model=model, + args=training_args, + data_collator=covost_collate_fn, + train_dataset=train_dataset, + ) + + trainer.train() + trainer.save_model() + if accelerator.is_main_process: + processor.save_pretrained(training_args.output_dir) + accelerator.wait_for_everyone() + + # eval after fine-tuning (load saved checkpoint) + # first try to clear GPU memory + del model + del trainer + __import__('gc').collect() + torch.cuda.empty_cache() + + # reload the model for inference + model = AutoModelForCausalLM.from_pretrained( + training_args.output_dir, + torch_dtype=torch.bfloat16 if args.use_flash_attention else torch.float32, + trust_remote_code=True, + _attn_implementation='flash_attention_2' if args.use_flash_attention else 'sdpa', + ).to('cuda') + + score = evaluate( + model, + processor, + eval_dataset, + save_path=out_path / 'eval_after.json', + disable_tqdm=not args.tqdm, + eval_batch_size=args.batch_size_per_gpu, + ) + if accelerator.is_main_process: + print(f'BLEU Score after finetuning: {score}') + + +if __name__ == '__main__': + main() diff --git a/sample_finetune_vision.py b/sample_finetune_vision.py new file mode 100644 index 0000000..0b66975 --- /dev/null +++ b/sample_finetune_vision.py @@ -0,0 +1,556 @@ +""" +finetune Phi-4-multimodal-instruct on an image task + +scipy==1.15.1 +peft==0.13.2 +backoff==2.2.1 +transformers==4.47.0 +accelerate==1.3.0 +""" + +import argparse +import json +import os +import tempfile +import zipfile +from pathlib import Path + +import torch +from accelerate import Accelerator +from accelerate.utils import gather_object +from datasets import load_dataset +from huggingface_hub import hf_hub_download +from PIL import Image +from torch.utils.data import Dataset +from tqdm import tqdm +from transformers import ( + AutoModelForCausalLM, + AutoProcessor, + BatchFeature, + Trainer, + TrainingArguments, +) + +DEFAULT_INSTSRUCTION = "Answer with the option's letter from the given choices directly." +_IGNORE_INDEX = -100 +_TRAIN_SIZE = 8000 +_EVAL_SIZE = 500 +_MAX_TRAINING_LENGTH = 8192 + + +class PmcVqaTrainDataset(Dataset): + def __init__(self, processor, data_size, instruction=DEFAULT_INSTSRUCTION): + # Download the file + file_path = hf_hub_download( + repo_id='xmcmic/PMC-VQA', # repository name + filename='images_2.zip', # file to download + repo_type='dataset', # specify it's a dataset repo + ) + + # file_path will be the local path where the file was downloaded + print(f'File downloaded to: {file_path}') + + # unzip to temp folder + self.image_folder = Path(tempfile.mkdtemp()) + with zipfile.ZipFile(file_path, 'r') as zip_ref: + zip_ref.extractall(self.image_folder) + + data_files = { + 'train': 'https://huggingface.co/datasets/xmcmic/PMC-VQA/resolve/main/train_2.csv', + } + split = 'train' if data_size is None else f'train[:{data_size}]' + self.annotations = load_dataset('xmcmic/PMC-VQA', data_files=data_files, split=split) + self.processor = processor + self.instruction = instruction + + def __len__(self): + return len(self.annotations) + + def __getitem__(self, idx): + """ + {'index': 35, + 'Figure_path': 'PMC8253797_Fig4_11.jpg', + 'Caption': 'A slightly altered cell . (c-c‴) A highly altered cell as seen from 4 different angles . Note mitochondria/mitochondrial networks (green), Golgi complexes (red), cell nuclei (light blue) and the cell outline (yellow).', + 'Question': ' What color is used to label the Golgi complexes in the image?', + 'Choice A': ' A: Green ', + 'Choice B': ' B: Red ', + 'Choice C': ' C: Light blue ', + 'Choice D': ' D: Yellow', + 'Answer': 'B', + 'split': 'train'} + """ + annotation = self.annotations[idx] + image = Image.open(self.image_folder / 'figures' / annotation['Figure_path']) + question = annotation['Question'] + choices = [annotation[f'Choice {chr(ord("A") + i)}'] for i in range(4)] + user_message = { + 'role': 'user', + 'content': '<|image_1|>' + '\n'.join([question] + choices + [self.instruction]), + } + prompt = self.processor.tokenizer.apply_chat_template( + [user_message], tokenize=False, add_generation_prompt=True + ) + answer = f'{annotation["Answer"]}<|end|><|endoftext|>' + inputs = self.processor(prompt, images=[image], return_tensors='pt') + + answer_ids = self.processor.tokenizer(answer, return_tensors='pt').input_ids + + input_ids = torch.cat([inputs.input_ids, answer_ids], dim=1) + labels = torch.full_like(input_ids, _IGNORE_INDEX) + labels[:, -answer_ids.shape[1] :] = answer_ids + + if input_ids.size(1) > _MAX_TRAINING_LENGTH: + input_ids = input_ids[:, :_MAX_TRAINING_LENGTH] + labels = labels[:, :_MAX_TRAINING_LENGTH] + if torch.all(labels == _IGNORE_INDEX).item(): + # workaround to make sure loss compute won't fail + labels[:, -1] = self.processor.tokenizer.eos_token_id + + return { + 'input_ids': input_ids, + 'labels': labels, + 'input_image_embeds': inputs.input_image_embeds, + 'image_attention_mask': inputs.image_attention_mask, + 'image_sizes': inputs.image_sizes, + } + + def __del__(self): + __import__('shutil').rmtree(self.image_folder) + + +class PmcVqaEvalDataset(Dataset): + def __init__( + self, processor, data_size, instruction=DEFAULT_INSTSRUCTION, rank=0, world_size=1 + ): + # Download the file + file_path = hf_hub_download( + repo_id='xmcmic/PMC-VQA', # repository name + filename='images_2.zip', # file to download + repo_type='dataset', # specify it's a dataset repo + ) + + # file_path will be the local path where the file was downloaded + print(f'File downloaded to: {file_path}') + + # unzip to temp folder + self.image_folder = Path(tempfile.mkdtemp()) + with zipfile.ZipFile(file_path, 'r') as zip_ref: + zip_ref.extractall(self.image_folder) + + data_files = { + 'test': 'https://huggingface.co/datasets/xmcmic/PMC-VQA/resolve/main/test_2.csv', + } + split = 'test' if data_size is None else f'test[:{data_size}]' + self.annotations = load_dataset( + 'xmcmic/PMC-VQA', data_files=data_files, split=split + ).shard(num_shards=world_size, index=rank) + self.processor = processor + self.instruction = instruction + + def __len__(self): + return len(self.annotations) + + def __getitem__(self, idx): + """ + {'index': 62, + 'Figure_path': 'PMC8253867_Fig2_41.jpg', + 'Caption': 'CT pulmonary angiogram reveals encasement and displacement of the left anterior descending coronary artery ( blue arrows ).', + 'Question': ' What is the name of the artery encased and displaced in the image? ', + 'Choice A': ' A: Right Coronary Artery ', + 'Choice B': ' B: Left Anterior Descending Coronary Artery ', + 'Choice C': ' C: Circumflex Coronary Artery ', + 'Choice D': ' D: Superior Mesenteric Artery ', + 'Answer': 'B', + 'split': 'test'} + """ + annotation = self.annotations[idx] + image = Image.open(self.image_folder / 'figures' / annotation['Figure_path']) + question = annotation['Question'] + choices = [annotation[f'Choice {chr(ord("A") + i)}'] for i in range(4)] + user_message = { + 'role': 'user', + 'content': '<|image_1|>' + '\n'.join([question] + choices + [self.instruction]), + } + prompt = self.processor.tokenizer.apply_chat_template( + [user_message], tokenize=False, add_generation_prompt=True + ) + answer = annotation['Answer'] + inputs = self.processor(prompt, images=[image], return_tensors='pt') + + unique_id = f'{annotation["index"]:010d}' + return { + 'id': unique_id, + 'input_ids': inputs.input_ids, + 'input_image_embeds': inputs.input_image_embeds, + 'image_attention_mask': inputs.image_attention_mask, + 'image_sizes': inputs.image_sizes, + 'answer': answer, + } + + def __del__(self): + __import__('shutil').rmtree(self.image_folder) + + +def pad_sequence(sequences, padding_side='right', padding_value=0): + """ + Pad a list of sequences to the same length. + sequences: list of tensors in [seq_len, *] shape + """ + assert padding_side in ['right', 'left'] + max_size = sequences[0].size() + trailing_dims = max_size[1:] + max_len = max(len(seq) for seq in sequences) + batch_size = len(sequences) + output = sequences[0].new_full((batch_size, max_len) + trailing_dims, padding_value) + for i, seq in enumerate(sequences): + length = seq.size(0) + if padding_side == 'right': + output.data[i, :length] = seq + else: + output.data[i, -length:] = seq + return output + + +def cat_with_pad(tensors, dim, padding_value=0): + """ + cat along dim, while pad to max for all other dims + """ + ndim = tensors[0].dim() + assert all( + t.dim() == ndim for t in tensors[1:] + ), 'All tensors must have the same number of dimensions' + + out_size = [max(t.shape[i] for t in tensors) for i in range(ndim)] + out_size[dim] = sum(t.shape[dim] for t in tensors) + output = tensors[0].new_full(out_size, padding_value) + + index = 0 + for t in tensors: + # Create a slice list where every dimension except dim is full slice + slices = [slice(0, t.shape[d]) for d in range(ndim)] + # Update only the concat dimension slice + slices[dim] = slice(index, index + t.shape[dim]) + + output[slices] = t + index += t.shape[dim] + + return output + + +def pmc_vqa_collate_fn(batch): + input_ids_list = [] + labels_list = [] + input_image_embeds_list = [] + image_attention_mask_list = [] + image_sizes_list = [] + for inputs in batch: + input_ids_list.append(inputs['input_ids'][0]) + labels_list.append(inputs['labels'][0]) + input_image_embeds_list.append(inputs['input_image_embeds']) + image_attention_mask_list.append(inputs['image_attention_mask']) + image_sizes_list.append(inputs['image_sizes']) + + input_ids = pad_sequence(input_ids_list, padding_side='right', padding_value=0) + labels = pad_sequence(labels_list, padding_side='right', padding_value=0) + attention_mask = (input_ids != 0).long() + input_image_embeds = cat_with_pad(input_image_embeds_list, dim=0) + image_attention_mask = cat_with_pad(image_attention_mask_list, dim=0) + image_sizes = torch.cat(image_sizes_list) + + return BatchFeature( + { + 'input_ids': input_ids, + 'labels': labels, + 'attention_mask': attention_mask, + 'input_image_embeds': input_image_embeds, + 'image_attention_mask': image_attention_mask, + 'image_sizes': image_sizes, + 'input_mode': 1, # vision mode + } + ) + + +def pmc_vqa_eval_collate_fn(batch): + input_ids_list = [] + input_image_embeds_list = [] + image_attention_mask_list = [] + image_sizes_list = [] + all_unique_ids = [] + all_answers = [] + for inputs in batch: + input_ids_list.append(inputs['input_ids'][0]) + input_image_embeds_list.append(inputs['input_image_embeds']) + image_attention_mask_list.append(inputs['image_attention_mask']) + image_sizes_list.append(inputs['image_sizes']) + all_unique_ids.append(inputs['id']) + all_answers.append(inputs['answer']) + + input_ids = pad_sequence(input_ids_list, padding_side='left', padding_value=0) + attention_mask = (input_ids != 0).long() + input_image_embeds = cat_with_pad(input_image_embeds_list, dim=0) + image_attention_mask = cat_with_pad(image_attention_mask_list, dim=0) + image_sizes = torch.cat(image_sizes_list) + + return ( + all_unique_ids, + all_answers, + BatchFeature( + { + 'input_ids': input_ids, + 'attention_mask': attention_mask, + 'input_image_embeds': input_image_embeds, + 'image_attention_mask': image_attention_mask, + 'image_sizes': image_sizes, + 'input_mode': 1, # vision mode + } + ), + ) + + +def create_model(model_name_or_path, use_flash_attention=False): + model = AutoModelForCausalLM.from_pretrained( + model_name_or_path, + torch_dtype=torch.bfloat16 if use_flash_attention else torch.float32, + _attn_implementation='flash_attention_2' if use_flash_attention else 'sdpa', + trust_remote_code=True, + ).to('cuda') + # remove parameters irrelevant to vision tasks + del model.model.embed_tokens_extend.audio_embed # remove audio encoder + for layer in model.model.layers: + # remove audio lora + del layer.mlp.down_proj.lora_A.speech + del layer.mlp.down_proj.lora_B.speech + del layer.mlp.gate_up_proj.lora_A.speech + del layer.mlp.gate_up_proj.lora_B.speech + del layer.self_attn.o_proj.lora_A.speech + del layer.self_attn.o_proj.lora_B.speech + del layer.self_attn.qkv_proj.lora_A.speech + del layer.self_attn.qkv_proj.lora_B.speech + + # TODO remove unused vision layers? + + return model + + +@torch.no_grad() +def evaluate( + model, processor, eval_dataset, save_path=None, disable_tqdm=False, eval_batch_size=1 +): + rank = int(os.environ.get('RANK', 0)) + local_rank = int(os.environ.get('LOCAL_RANK', 0)) + + model.eval() + all_answers = [] + all_generated_texts = [] + + eval_dataloader = torch.utils.data.DataLoader( + eval_dataset, + batch_size=eval_batch_size, + collate_fn=pmc_vqa_eval_collate_fn, + shuffle=False, + drop_last=False, + num_workers=4, + prefetch_factor=2, + pin_memory=True, + ) + for ids, answers, inputs in tqdm( + eval_dataloader, disable=(rank != 0) or disable_tqdm, desc='running eval' + ): + all_answers.extend({'id': i, 'answer': a.strip().lower()} for i, a in zip(ids, answers)) + + inputs = inputs.to(f'cuda:{local_rank}') + generated_ids = model.generate( + **inputs, eos_token_id=processor.tokenizer.eos_token_id, max_new_tokens=64 + ) + + input_len = inputs.input_ids.size(1) + generated_texts = processor.batch_decode( + generated_ids[:, input_len:], + skip_special_tokens=True, + clean_up_tokenization_spaces=False, + ) + all_generated_texts.extend( + {'id': i, 'generated_text': g.strip().lower()} for i, g in zip(ids, generated_texts) + ) + + # gather outputs from all ranks + all_answers = gather_object(all_answers) + all_generated_texts = gather_object(all_generated_texts) + + if rank == 0: + assert len(all_answers) == len(all_generated_texts) + acc = sum( + a['answer'] == g['generated_text'] for a, g in zip(all_answers, all_generated_texts) + ) / len(all_answers) + if save_path: + with open(save_path, 'w') as f: + save_dict = { + 'answers_unique': all_answers, + 'generated_texts_unique': all_generated_texts, + 'accuracy': acc, + } + json.dump(save_dict, f) + + return acc + return None + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + '--model_name_or_path', + type=str, + default='microsoft/Phi-4-multimodal-instruct', + help='Model name or path to load from', + ) + parser.add_argument('--use_flash_attention', action='store_true', help='Use Flash Attention') + parser.add_argument('--output_dir', type=str, default='./output/', help='Output directory') + parser.add_argument('--batch_size', type=int, default=16, help='Batch size') + parser.add_argument( + '--batch_size_per_gpu', + type=int, + default=1, + help='Batch size per GPU (adjust this to fit in GPU memory)', + ) + parser.add_argument( + '--dynamic_hd', + type=int, + default=36, + help='Number of maximum image crops', + ) + parser.add_argument( + '--num_train_epochs', type=int, default=1, help='Number of training epochs' + ) + parser.add_argument('--learning_rate', type=float, default=4.0e-5, help='Learning rate') + parser.add_argument('--wd', type=float, default=0.01, help='Weight decay') + parser.add_argument('--no_tqdm', dest='tqdm', action='store_false', help='Disable tqdm') + parser.add_argument('--full_run', action='store_true', help='Run the full training and eval') + args = parser.parse_args() + + accelerator = Accelerator() + + with accelerator.local_main_process_first(): + processor = AutoProcessor.from_pretrained( + args.model_name_or_path, + trust_remote_code=True, + dynamic_hd=args.dynamic_hd, + ) + model = create_model( + args.model_name_or_path, + use_flash_attention=args.use_flash_attention, + ) + # tune vision encoder and lora + model.set_lora_adapter('vision') + for param in model.model.embed_tokens_extend.image_embed.parameters(): + param.requires_grad = True + + rank = int(os.environ.get('RANK', 0)) + world_size = int(os.environ.get('WORLD_SIZE', 1)) + + train_dataset = PmcVqaTrainDataset(processor, data_size=None if args.full_run else _TRAIN_SIZE) + eval_dataset = PmcVqaEvalDataset( + processor, + data_size=None if args.full_run else _EVAL_SIZE, + rank=rank, + world_size=world_size, + ) + + num_gpus = accelerator.num_processes + print(f'training on {num_gpus} GPUs') + assert ( + args.batch_size % (num_gpus * args.batch_size_per_gpu) == 0 + ), 'Batch size must be divisible by the number of GPUs' + gradient_accumulation_steps = args.batch_size // (num_gpus * args.batch_size_per_gpu) + + if args.use_flash_attention: + fp16 = False + bf16 = True + else: + fp16 = True + bf16 = False + + # hard coded training args + training_args = TrainingArguments( + num_train_epochs=args.num_train_epochs, + per_device_train_batch_size=args.batch_size_per_gpu, + gradient_checkpointing=True, + gradient_checkpointing_kwargs={'use_reentrant': False}, + gradient_accumulation_steps=gradient_accumulation_steps, + optim='adamw_torch', + adam_beta1=0.9, + adam_beta2=0.95, + adam_epsilon=1e-7, + learning_rate=args.learning_rate, + weight_decay=args.wd, + max_grad_norm=1.0, + lr_scheduler_type='linear', + warmup_steps=50, + logging_steps=10, + output_dir=args.output_dir, + save_strategy='no', + save_total_limit=10, + save_only_model=True, + bf16=bf16, + fp16=fp16, + remove_unused_columns=False, + report_to='none', + deepspeed=None, + disable_tqdm=not args.tqdm, + dataloader_num_workers=4, + ddp_find_unused_parameters=True, # for unused SigLIP layers + ) + + # eval before fine-tuning + out_path = Path(training_args.output_dir) + out_path.mkdir(parents=True, exist_ok=True) + + acc = evaluate( + model, + processor, + eval_dataset, + save_path=out_path / 'eval_before.json', + disable_tqdm=not args.tqdm, + eval_batch_size=args.batch_size_per_gpu, + ) + if accelerator.is_main_process: + print(f'Accuracy before finetuning: {acc}') + + trainer = Trainer( + model=model, + args=training_args, + data_collator=pmc_vqa_collate_fn, + train_dataset=train_dataset, + ) + trainer.train() + trainer.save_model() + accelerator.wait_for_everyone() + + # eval after fine-tuning (load saved checkpoint) + # first try to clear GPU memory + del model + del trainer + __import__('gc').collect() + torch.cuda.empty_cache() + + # reload the model for inference + model = AutoModelForCausalLM.from_pretrained( + training_args.output_dir, + torch_dtype=torch.bfloat16 if args.use_flash_attention else torch.float32, + trust_remote_code=True, + _attn_implementation='flash_attention_2' if args.use_flash_attention else 'sdpa', + ).to('cuda') + + acc = evaluate( + model, + processor, + eval_dataset, + save_path=out_path / 'eval_after.json', + disable_tqdm=not args.tqdm, + eval_batch_size=args.batch_size_per_gpu, + ) + if accelerator.is_main_process: + print(f'Accuracy after finetuning: {acc}') + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/sample_inference_phi4mm.py b/sample_inference_phi4mm.py new file mode 100644 index 0000000..9e425b0 --- /dev/null +++ b/sample_inference_phi4mm.py @@ -0,0 +1,243 @@ +import os +import requests +import torch +from PIL import Image +import soundfile +from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig + +model_path = './' + +kwargs = {} +kwargs['torch_dtype'] = torch.bfloat16 + +processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) +print(processor.tokenizer) + +model = AutoModelForCausalLM.from_pretrained( + model_path, + trust_remote_code=True, + torch_dtype='auto', + _attn_implementation='flash_attention_2', +).cuda() +print("model.config._attn_implementation:", model.config._attn_implementation) + +generation_config = GenerationConfig.from_pretrained(model_path, 'generation_config.json') + +user_prompt = '<|user|>' +assistant_prompt = '<|assistant|>' +prompt_suffix = '<|end|>' + +#################################################### text-only #################################################### +prompt = f'{user_prompt}what is the answer for 1+1? Explain it.{prompt_suffix}{assistant_prompt}' +print(f'>>> Prompt\n{prompt}') +inputs = processor(prompt, images=None, return_tensors='pt').to('cuda:0') + +generate_ids = model.generate( + **inputs, + max_new_tokens=1000, + generation_config=generation_config, +) +generate_ids = generate_ids[:, inputs['input_ids'].shape[1] :] +response = processor.batch_decode( + generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False +)[0] + +print(f'>>> Response\n{response}') + +#################################################### vision (single-turn) #################################################### +# single-image prompt +prompt = f'{user_prompt}<|image_1|>What is shown in this image?{prompt_suffix}{assistant_prompt}' +url = 'https://www.ilankelman.org/stopsigns/australia.jpg' +print(f'>>> Prompt\n{prompt}') +image = Image.open(requests.get(url, stream=True).raw) +inputs = processor(text=prompt, images=image, return_tensors='pt').to('cuda:0') +generate_ids = model.generate( + **inputs, + max_new_tokens=1000, + generation_config=generation_config, +) +generate_ids = generate_ids[:, inputs['input_ids'].shape[1] :] +response = processor.batch_decode( + generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False +)[0] +print(f'>>> Response\n{response}') + +#################################################### vision (multi-turn) #################################################### +# chat template +chat = [ + {'role': 'user', 'content': f'<|image_1|>What is shown in this image?'}, + { + 'role': 'assistant', + 'content': "The image depicts a street scene with a prominent red stop sign in the foreground. The background showcases a building with traditional Chinese architecture, characterized by its red roof and ornate decorations. There are also several statues of lions, which are common in Chinese culture, positioned in front of the building. The street is lined with various shops and businesses, and there's a car passing by.", + }, + {'role': 'user', 'content': 'What is so special about this image'}, +] +url = 'https://www.ilankelman.org/stopsigns/australia.jpg' +image = Image.open(requests.get(url, stream=True).raw) +prompt = processor.tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) +# need to remove last <|endoftext|> if it is there, which is used for training, not inference. For training, make sure to add <|endoftext|> in the end. +if prompt.endswith('<|endoftext|>'): + prompt = prompt.rstrip('<|endoftext|>') + +print(f'>>> Prompt\n{prompt}') + +inputs = processor(prompt, [image], return_tensors='pt').to('cuda:0') +generate_ids = model.generate( + **inputs, + max_new_tokens=1000, + generation_config=generation_config, +) +generate_ids = generate_ids[:, inputs['input_ids'].shape[1] :] +response = processor.batch_decode( + generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False +)[0] +print(f'>>> Response\n{response}') + +########################### vision (multi-frame) ################################ +images = [] +placeholder = '' +for i in range(1, 5): + url = f'https://image.slidesharecdn.com/azureintroduction-191206101932/75/Introduction-to-Microsoft-Azure-Cloud-{i}-2048.jpg' + images.append(Image.open(requests.get(url, stream=True).raw)) + placeholder += f'<|image_{i}|>' + +messages = [ + {'role': 'user', 'content': placeholder + 'Summarize the deck of slides.'}, +] + +prompt = processor.tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True +) + +print(f'>>> Prompt\n{prompt}') + +inputs = processor(prompt, images, return_tensors='pt').to('cuda:0') + +generation_args = { + 'max_new_tokens': 1000, + 'temperature': 0.0, + 'do_sample': False, +} + +generate_ids = model.generate( + **inputs, **generation_args, generation_config=generation_config, +) + +# remove input tokens +generate_ids = generate_ids[:, inputs['input_ids'].shape[1] :] +response = processor.batch_decode( + generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False +)[0] + +print(response) + +# NOTE: Please prepare the audio file 'examples/what_is_the_traffic_sign_in_the_image.wav' +# and audio file 'examples/what_is_shown_in_this_image.wav' before running the following code +# Basically you can record your own voice for the question "What is the traffic sign in the image?" in "examples/what_is_the_traffic_sign_in_the_image.wav". +# And you can record your own voice for the question "What is shown in this image?" in "examples/what_is_shown_in_this_image.wav". + +AUDIO_FILE_1 = 'examples/what_is_the_traffic_sign_in_the_image.wav' +AUDIO_FILE_2 = 'examples/what_is_shown_in_this_image.wav' + +if not os.path.exists(AUDIO_FILE_1): + raise FileNotFoundError(f'Please prepare the audio file {AUDIO_FILE_1} before running the following code.') +########################## vision-speech ################################ +prompt = f'{user_prompt}<|image_1|><|audio_1|>{prompt_suffix}{assistant_prompt}' +url = 'https://www.ilankelman.org/stopsigns/australia.jpg' +print(f'>>> Prompt\n{prompt}') +image = Image.open(requests.get(url, stream=True).raw) +audio = soundfile.read(AUDIO_FILE_1) +inputs = processor(text=prompt, images=[image], audios=[audio], return_tensors='pt').to('cuda:0') +generate_ids = model.generate( + **inputs, + max_new_tokens=1000, + generation_config=generation_config, +) +generate_ids = generate_ids[:, inputs['input_ids'].shape[1] :] +response = processor.batch_decode( + generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False +)[0] +print(f'>>> Response\n{response}') + +########################## speech only ################################ +speech_prompt = "Based on the attached audio, generate a comprehensive text transcription of the spoken content." +prompt = f'{user_prompt}<|audio_1|>{speech_prompt}{prompt_suffix}{assistant_prompt}' + +print(f'>>> Prompt\n{prompt}') +audio = soundfile.read(AUDIO_FILE_1) +inputs = processor(text=prompt, audios=[audio], return_tensors='pt').to('cuda:0') +generate_ids = model.generate( + **inputs, + max_new_tokens=1000, + generation_config=generation_config, +) +generate_ids = generate_ids[:, inputs['input_ids'].shape[1] :] +response = processor.batch_decode( + generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False +)[0] +print(f'>>> Response\n{response}') + +if not os.path.exists(AUDIO_FILE_2): + raise FileNotFoundError(f'Please prepare the audio file {AUDIO_FILE_2} before running the following code.') +########################### speech only (multi-turn) ################################ +audio_1 = soundfile.read(AUDIO_FILE_2) +audio_2 = soundfile.read(AUDIO_FILE_1) +chat = [ + {'role': 'user', 'content': f'<|audio_1|>Based on the attached audio, generate a comprehensive text transcription of the spoken content.'}, + { + 'role': 'assistant', + 'content': "What is shown in this image.", + }, + {'role': 'user', 'content': f'<|audio_2|>Based on the attached audio, generate a comprehensive text transcription of the spoken content.'}, +] +prompt = processor.tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) +# need to remove last <|endoftext|> if it is there, which is used for training, not inference. For training, make sure to add <|endoftext|> in the end. +if prompt.endswith('<|endoftext|>'): + prompt = prompt.rstrip('<|endoftext|>') + +print(f'>>> Prompt\n{prompt}') + +inputs = processor(text=prompt, audios=[audio_1, audio_2], return_tensors='pt').to('cuda:0') +generate_ids = model.generate( + **inputs, + max_new_tokens=1000, + generation_config=generation_config, +) +generate_ids = generate_ids[:, inputs['input_ids'].shape[1] :] +response = processor.batch_decode( + generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False +)[0] +print(f'>>> Response\n{response}') + +#################################################### vision-speech (multi-turn) #################################################### +# chat template +audio_1 = soundfile.read(AUDIO_FILE_2) +audio_2 = soundfile.read(AUDIO_FILE_1) +chat = [ + {'role': 'user', 'content': f'<|image_1|><|audio_1|>'}, + { + 'role': 'assistant', + 'content': "The image depicts a street scene with a prominent red stop sign in the foreground. The background showcases a building with traditional Chinese architecture, characterized by its red roof and ornate decorations. There are also several statues of lions, which are common in Chinese culture, positioned in front of the building. The street is lined with various shops and businesses, and there's a car passing by.", + }, + {'role': 'user', 'content': f'<|audio_2|>'}, +] +url = 'https://www.ilankelman.org/stopsigns/australia.jpg' +image = Image.open(requests.get(url, stream=True).raw) +prompt = processor.tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) +# need to remove last <|endoftext|> if it is there, which is used for training, not inference. For training, make sure to add <|endoftext|> in the end. +if prompt.endswith('<|endoftext|>'): + prompt = prompt.rstrip('<|endoftext|>') + +print(f'>>> Prompt\n{prompt}') + +inputs = processor(text=prompt, images=[image], audios=[audio_1, audio_2], return_tensors='pt').to('cuda:0') +generate_ids = model.generate( + **inputs, + max_new_tokens=1000, + generation_config=generation_config, +) +generate_ids = generate_ids[:, inputs['input_ids'].shape[1] :] +response = processor.batch_decode( + generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False +)[0] +print(f'>>> Response\n{response}') diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000..330140f --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,24 @@ +{ + "bos_token": { + "content": "<|endoftext|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "<|endoftext|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "<|endoftext|>", + "unk_token": { + "content": "<|endoftext|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + } +} diff --git a/speech-lora/adapter_config.json b/speech-lora/adapter_config.json new file mode 100644 index 0000000..3fb5a8d --- /dev/null +++ b/speech-lora/adapter_config.json @@ -0,0 +1,23 @@ +{ + "auto_mapping": null, + "base_model_name_or_path": "TBA", + "bias": "none", + "fan_in_fan_out": false, + "inference_mode": true, + "init_lora_weights": true, + "layers_pattern": null, + "layers_to_transform": null, + "lora_alpha": 640, + "lora_dropout": 0.01, + "modules_to_save": [], + "peft_type": "LORA", + "r": 320, + "revision": null, + "target_modules": [ + "qkv_proj", + "o_proj", + "gate_up_proj", + "down_proj" + ], + "task_type": "CAUSAL_LM" +} \ No newline at end of file diff --git a/speech-lora/adapter_model.safetensors b/speech-lora/adapter_model.safetensors new file mode 100644 index 0000000..9726355 --- /dev/null +++ b/speech-lora/adapter_model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1c2237461a4d1f9292cd128147bd3f0f70326a48d5d79c8e0f7583b26c095b30 +size 922782296 diff --git a/speech-lora/added_tokens.json b/speech-lora/added_tokens.json new file mode 100644 index 0000000..af52cde --- /dev/null +++ b/speech-lora/added_tokens.json @@ -0,0 +1,12 @@ +{ + "<|/tool_call|>": 200026, + "<|/tool|>": 200024, + "<|assistant|>": 200019, + "<|end|>": 200020, + "<|system|>": 200022, + "<|tag|>": 200028, + "<|tool_call|>": 200025, + "<|tool_response|>": 200027, + "<|tool|>": 200023, + "<|user|>": 200021 +} diff --git a/speech-lora/special_tokens_map.json b/speech-lora/special_tokens_map.json new file mode 100644 index 0000000..330140f --- /dev/null +++ b/speech-lora/special_tokens_map.json @@ -0,0 +1,24 @@ +{ + "bos_token": { + "content": "<|endoftext|>", + "lstrip": false, + "normalized": false, + "rstrip": 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\ No newline at end of file diff --git a/speech_conformer_encoder.py b/speech_conformer_encoder.py new file mode 100644 index 0000000..17c442e --- /dev/null +++ b/speech_conformer_encoder.py @@ -0,0 +1,2906 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +#!/usr/bin/env python3 + +# activation_checkpointing.py +"""helper function for activation checkpointing""" + +from typing import Union, Dict, Callable +from functools import partial +from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import ( + checkpoint_wrapper, + offload_wrapper, + CheckpointImpl, +) + + +# utils.py +"""cascade basic blocks""" + +import math +import backoff +import random +import numpy as np +from typing import Optional, Tuple, Union +import torch +from torch import nn +from torch import Tensor +import torch.nn.functional as F + + +# conformer_encoder.py +"""ConformerEncoder Module""" + +from typing import Optional, Tuple, List, Literal +import abc +import math +import numpy as np + +import torch +from torch import nn, Tensor + +from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import CheckpointWrapper +from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel + + +# activation_checkpointing.py +def validate_checkpointing_config(activation_checkpointing): + """validate activation checkpointing configuration""" + if isinstance(activation_checkpointing, str): + assert activation_checkpointing in ( + "", + "checkpoint", + "offload", + ), "activation_checkpointing has to be a dict or a str in ('', 'checkpoint', 'offload')." + elif isinstance(activation_checkpointing, dict): + assert activation_checkpointing.get("module", "transformer") in ( + "transformer", + "attention", + ), "module in activation_checkpointing has to be in ('transformer', 'attention')." + else: + raise ValueError("activation_checkpointing has to be a str or dict.") + + +def embedding_checkpoint_wrapper( + activation_checkpointing: Union[str, Dict], +) -> Callable: + """return encoder embedding activation checkpoint wrapper""" + validate_checkpointing_config(activation_checkpointing) + + if isinstance(activation_checkpointing, str): + if activation_checkpointing: + if activation_checkpointing == "offload": + return offload_wrapper + return partial(checkpoint_wrapper) + return lambda x: x + + if isinstance(activation_checkpointing, dict): + enabled = activation_checkpointing.get("embed", False) + if enabled: + offloading = activation_checkpointing.get("offload", False) + if offloading: + return offload_wrapper + impl = ( + CheckpointImpl.REENTRANT + if activation_checkpointing.get("reentrant", False) + else CheckpointImpl.NO_REENTRANT + ) + return partial(checkpoint_wrapper, checkpoint_impl=impl) + return lambda x: x + raise ValueError("Invalid activation_checkpointing config") + + +def encoder_checkpoint_wrapper( + activation_checkpointing: Union[str, Dict], + layer_cls: type, + idx: int = 0, +) -> Callable: + """return encoder activation checkpoint wrapper""" + validate_checkpointing_config(activation_checkpointing) + + if isinstance(activation_checkpointing, str): + if activation_checkpointing: + if activation_checkpointing == "offload": + return offload_wrapper + return partial(checkpoint_wrapper) + return lambda x: x + + if isinstance(activation_checkpointing, dict): + target_layer_cls = activation_checkpointing.get("module", "transformer") + if target_layer_cls.lower() == "transformer": + target_layer_cls = ( + "EncoderLayer", + "ConformerEncoderLayer", + ) + elif target_layer_cls.lower() == "attention": + target_layer_cls = ("MultiHeadedAttention", "MultiHeadAttention") + checkpointing_interval = activation_checkpointing.get("interval", 1) + offloading = activation_checkpointing.get("offload", False) + impl = ( + CheckpointImpl.REENTRANT + if activation_checkpointing.get("reentrant", True) + else CheckpointImpl.NO_REENTRANT + ) + + if idx % checkpointing_interval == 0 and layer_cls.__name__ in target_layer_cls: + if offloading: + return offload_wrapper + return partial(checkpoint_wrapper, checkpoint_impl=impl) + return lambda x: x + + raise ValueError("Invalid activation_checkpointing config") + + +def attn_checkpointing(activation_checkpointing: Union[str, Dict], i) -> Union[str, Dict]: + """return activation checkpointing config for attention layer""" + if isinstance(activation_checkpointing, str): + return "" + + if isinstance(activation_checkpointing, dict): + target_layer_cls = activation_checkpointing.get("module", "transformer") + checkpointing_interval = activation_checkpointing.get("interval", 1) + if target_layer_cls == "attention" and i % checkpointing_interval == 0: + return activation_checkpointing + return "" + + raise ValueError("Invalid activation_checkpointing config") + + +# utils.py +class Block(nn.Module): + """Block abstract module""" + + def __init__(self, input_size, output_size): + super().__init__() + self.input_size = input_size + self.output_size = output_size + +def get_activation(name="relu"): + """Select an activation function by name + + Args: + name: str + activation function name, + one of ["relu", "gelu", "swish", "sigmoid"], + default "relu". + """ + name = name.lower() + if name == "relu": + return nn.ReLU(inplace=True) + if name == "gelu": + return nn.GELU() + if name == "swish": + return Swish() + if name == "sigmoid": + return torch.nn.Sigmoid() + return nn.Identity() + +def adaptive_enc_mask(x_len, chunk_start_idx, left_window=0, right_window=0): + """ + The function is very important for Transformer Transducer Streaming mode + Args: + xs_len (int): sequence length + chunk_start_idx (list): first idx of each chunk, such as [0,18,36,48]. It also supports adaptive chunk size [0,10,15,45] + left_window (int): how many left chunks can be seen + right_window (int): how many right chunks can be seen. It is used for chunk overlap model. + Returns: + mask (torch.Tensor): a mask tensor for streaming model + Torch 1.0.1 + tensor([[1., 1., 0., 0.], + [0., 1., 1., 0.], + [0., 0., 1., 1.]]) + Torch 1.4.1 + tensor([[True., True., False., False.], + [False., True., True., False.], + [False., False., True., True.]]) + """ + chunk_start_idx = torch.Tensor( + chunk_start_idx + ).long() # first idx of each chunk, such as [0,18,36,48]. + start_pad = torch.nn.functional.pad( + chunk_start_idx, (1, 0) + ) # append 0 to the beginning, so it becomes [0, 0, 18, 36, 48] + end_pad = torch.nn.functional.pad( + chunk_start_idx, (0, 1), value=x_len + ) # append x_len to the end, so it becomes [0,18,36,48, x_len] + seq_range = torch.arange(0, x_len).unsqueeze(-1) # seq_range size: [x_len, 1] + idx = ((seq_range < end_pad) & (seq_range >= start_pad)).nonzero()[:, 1] # idx size: [x_len] + boundary = end_pad[idx] # boundary size: [x_len] + seq_range_expand = ( + torch.arange(0, x_len).unsqueeze(0).expand(x_len, -1) + ) # seq_range_expand size [x_len, x_len] + idx_left = idx - left_window + idx_left[idx_left < 0] = 0 + boundary_left = start_pad[idx_left] + mask_left = seq_range_expand >= boundary_left.unsqueeze(-1) + idx_right = idx + right_window + idx_right[idx_right > len(chunk_start_idx)] = len(chunk_start_idx) + boundary_right = end_pad[idx_right] + mask_right = seq_range_expand < boundary_right.unsqueeze(-1) + return mask_left & mask_right + +class Swish(nn.Module): + """Implement Swish activation module. + From https://arxiv.org/pdf/2005.03191.pdf + + """ + + def __init__(self) -> None: + super().__init__() + self.act_fn = nn.Sigmoid() + + def forward(self, x: Tensor) -> Tensor: + """Apply Swish function + + Args: + x: torch.Tensor + Input. + """ + return x * self.act_fn(x) + +class GLU(nn.Module): + """Implement Gated Linear Unit (GLU) module""" + + def __init__(self, dim: int = -1, act_name: str = "sigmoid") -> None: + super().__init__() + self.dim = dim + self.act_name = act_name.lower() + + if self.act_name == "relu": + self.act_fn = nn.ReLU(inplace=True) + elif self.act_name == "gelu": + self.act_fn = nn.GELU() + elif self.act_name == "swish": + self.act_fn = Swish() + elif self.act_name == "sigmoid": + self.act_fn = nn.Sigmoid() + else: + self.act_fn = nn.Identity() + + def forward(self, x: Tensor) -> Tensor: + """GLU forward + Apply Swish function on the first half of input matrices + with sigmoid of the second half. + + Args: + x: torch.Tensor + Input. + + """ + half_x, gate = x.chunk(2, dim=self.dim) + return half_x * self.act_fn(gate) + +# TODO: Abdel, this can be improved using GLU module +class GLUPointWiseConv(nn.Module): + """GLUPointWiseConv module + used for conformer architecture, + for more details see: + https://arxiv.org/pdf/2005.08100v1.pdf + + Args: + input_dim: int + input channel size. + output_dim: int + output channel size. + kernel_size: int + kernel size + glu_type: str, optional + activation function one of + ["sigmoid", "relu", "gelu"] + default "sigmoid". + bias_in_glu: bool, optional + use addtive bias in glu + causal: bool, optional + if set to True, padding is set to the half of + kernel size, ie, convolution can't see future frames. + default False. + + """ + + def __init__( + self, input_dim, output_dim, kernel_size, glu_type="sigmoid", bias_in_glu=True, causal=False + ): + super().__init__() + + self.glu_type = glu_type + self.output_dim = output_dim + self.bias_in_glu = bias_in_glu + if causal: + self.ext_pw_conv_1d = nn.Conv1d( + input_dim, output_dim * 2, kernel_size, 1, padding=(kernel_size - 1) + ) + else: + self.ext_pw_conv_1d = nn.Conv1d( + input_dim, output_dim * 2, kernel_size, 1, padding=(kernel_size - 1) // 2 + ) + + if glu_type == "sigmoid": + self.glu_act = nn.Sigmoid() + elif glu_type == "relu": + self.glu_act = nn.ReLU() + elif glu_type == "gelu": + self.glu_act = nn.GELU() + elif glu_type == "swish": + self.glu_act = Swish() + else: + raise ValueError(f"Unsupported activation type {self.glu_act}") + + if bias_in_glu: + self.b1 = nn.Parameter(torch.zeros(1, output_dim, 1)) + self.b2 = nn.Parameter(torch.zeros(1, output_dim, 1)) + + def forward(self, x): + """ + Args: + x: torch.Tensor + input tensor + """ + # to be consistent with GLULinear, we assume the input always has the #channel (#dim) in the last dimension of the tensor, so need to switch the dimension first for 1D-Conv case + x = x.permute([0, 2, 1]) + x = self.ext_pw_conv_1d(x) + if self.glu_type == "bilinear": + if self.bias_in_glu: + x = (x[:, 0 : self.output_dim, :] + self.b1) * ( + x[:, self.output_dim : self.output_dim * 2, :] + self.b2 + ) + else: + x = (x[:, 0 : self.output_dim, :]) * ( + x[:, self.output_dim : self.output_dim * 2, :] + ) + else: + if self.bias_in_glu: + x = (x[:, 0 : self.output_dim, :] + self.b1) * self.glu_act( + x[:, self.output_dim : self.output_dim * 2, :] + self.b2 + ) + else: + x = (x[:, 0 : self.output_dim, :]) * self.glu_act( + x[:, self.output_dim : self.output_dim * 2, :] + ) + + x = x.permute([0, 2, 1]) + return x + + +class DepthWiseSeperableConv1d(nn.Module): + """DepthWiseSeperableConv1d module used in Convnet module + for the conformer, for more details see: + https://arxiv.org/pdf/2005.08100v1.pdf + + Args: + input_dim: int + input channel size. + depthwise_seperable_out_channel: int + if set different to 0, the number of depthwise_seperable_out_channel + will be used as a channel_out of the second conv1d layer. + otherwise, it equal to 0, the second conv1d layer is skipped. + kernel_size: int + kernel_size + depthwise_multiplier: int + number of input_dim channels duplication. this value + will be used to compute the hidden channels of the Conv1D. + padding: int, optional + padding for the conv1d, + default: 0. + + """ + + def __init__( + self, + input_dim, + depthwise_seperable_out_channel, + kernel_size, + depthwise_multiplier, + padding=0, + ): + super().__init__() + + self.dw_conv = nn.Conv1d( + input_dim, + input_dim * depthwise_multiplier, + kernel_size, + 1, + padding=padding, + groups=input_dim, + ) + + if depthwise_seperable_out_channel != 0: + self.pw_conv = nn.Conv1d( + input_dim * depthwise_multiplier, depthwise_seperable_out_channel, 1, 1, 0 + ) + else: + self.pw_conv = nn.Identity() + self.depthwise_seperable_out_channel = depthwise_seperable_out_channel + + def forward(self, x): + """ + + Args: + x: torch.Tensor + input tensor + """ + x = self.dw_conv(x) + if self.depthwise_seperable_out_channel != 0: + x = self.pw_conv(x) + return x + + +class ConvModule(nn.Module): + """ConvModule Module for the conformer block. + for more details see: + https://arxiv.org/pdf/2005.08100v1.pdf + + Args: + input_dim: int + input channel size. + ext_pw_out_channel: int + if > 0, ext_pw_out_channel is a dim channel size + for the last pointwise conv after swish activation. + depthwise_seperable_out_channel: int + if set different to 0, the number of depthwise_seperable_out_channel + will be used as a channel_out of the second conv1d layer. + otherwise, it equal to 0, the second conv1d layer is skipped. + ext_pw_kernel_size: int + kernel size of the conv pointwise of the conformer. + kernel_size: int + kernel size. + depthwise_multiplier: int + number of input_dim channels duplication. this value + will be used to compute the hidden channels of the Conv1D. + dropout_rate: float + dropout rate. + causal: bool, optional + if set to True, convolution have no access + to future frames. default False. + batch_norm: bool, optional + if set to True, apply batchnorm before activation. + default False + chunk_se: int, optional + 0 for offline SE. + 1 for streaming SE, where mean is computed + by accumulated history until current chunk_se. + 2 for streaming SE, where mean is computed + by only the current chunk. + chunk_size: int, optional + chunk size for cnn. default 18 + activation: str, optional + activation function used in ConvModule, + default: "relu". + glu_type: str, optional + activation function used for the glu, + default: "sigmoid". + bias_in_glu: bool, optional + if set to True, use additive bias in the weight module + before GLU. + linear_glu_in_convm: bool, optional + if set to True, use GLULinear module, + otherwise, used GLUPointWiseConv module. + default to False. + export: bool, optional, + if set to True, padding is equal to 0. This is for inference, + or onnx export. Typically this is set by the export program or + the decoder program, and it isn't present in your config file. + default False + """ + + def __init__( + self, + input_dim, + ext_pw_out_channel, + depthwise_seperable_out_channel, + ext_pw_kernel_size, + kernel_size, + depthwise_multiplier, + dropout_rate, + causal=False, + batch_norm=False, + chunk_se=0, + chunk_size=18, + activation="relu", + glu_type="sigmoid", + bias_in_glu=True, + linear_glu_in_convm=False, + export=False, + ): + super().__init__() + self.layer_norm = nn.LayerNorm(input_dim) + self.input_dim = input_dim + self.ext_pw_out_channel = ext_pw_out_channel + self.ext_pw_kernel_size = ext_pw_kernel_size + self.depthwise_seperable_out_channel = depthwise_seperable_out_channel + self.glu_type = glu_type + self.bias_in_glu = bias_in_glu + self.linear_glu_in_convm = linear_glu_in_convm + self.causal = causal + + self._add_ext_pw_layer() + + self.batch_norm = batch_norm + self.kernel_size = kernel_size + + if batch_norm: + self.bn_layer = nn.BatchNorm1d(input_dim) + + self.act = get_activation(activation) + self.dropout = nn.Dropout(dropout_rate) + self.export = export + + if causal: + if export: # Inference only. + padding = 0 # A cache is concatenated to the left. No padding in the kernel. + else: + # Training only. Padding will be added symmetrically on both sides. + # After convolution, clip off kernel_size-1 points on the right. + padding = kernel_size - 1 + else: + padding = (kernel_size - 1) // 2 + + self.dw_sep_conv_1d = DepthWiseSeperableConv1d( + input_dim, + depthwise_seperable_out_channel, + kernel_size, + depthwise_multiplier, + padding=padding, + ) + + if depthwise_seperable_out_channel != 0: + if input_dim != depthwise_seperable_out_channel: + self.ln2 = nn.Linear(depthwise_seperable_out_channel, input_dim) + else: + if depthwise_multiplier != 1: + self.ln2 = nn.Linear(input_dim * depthwise_multiplier, input_dim) + + def _add_ext_pw_layer(self): + """ + This function is an extension of __init__ function + and dedicated to the convolution module creation + of the conformer. + """ + self.ln1 = self.glu = self.bn_layer = self.ext_pw_conv_1d = nn.Identity() # jit hacks. + self.squeeze_excitation = nn.Identity() # jit. + self.apply_ln1 = self.fix_len1 = False # jit. + + if self.ext_pw_out_channel != 0: + if self.causal: + self.ext_pw_conv_1d = nn.Conv1d( + self.input_dim, + self.ext_pw_out_channel, + self.ext_pw_kernel_size, + 1, + padding=(self.ext_pw_kernel_size - 1), + ) + if self.ext_pw_kernel_size > 1: + self.fix_len1 = True + else: + self.fix_len1 = False + else: + self.ext_pw_conv_1d = nn.Conv1d( + self.input_dim, + self.ext_pw_out_channel, + self.ext_pw_kernel_size, + 1, + padding=(self.ext_pw_kernel_size - 1) // 2, + ) + self.fix_len1 = False + + if self.linear_glu_in_convm: + self.glu = GLULinear( + self.input_dim, self.ext_pw_out_channel, self.glu_type, self.bias_in_glu + ) + else: + self.glu = GLUPointWiseConv( + self.input_dim, + self.ext_pw_out_channel, + self.ext_pw_kernel_size, + self.glu_type, + self.bias_in_glu, + self.causal, + ) + + if self.input_dim != self.ext_pw_out_channel: + self.apply_ln1 = True + self.ln1 = nn.Linear(self.ext_pw_out_channel, self.input_dim) + else: + self.apply_ln1 = False + else: + self.pw_conv_simplify_w = torch.nn.Parameter(torch.ones(3)) + self.pw_conv_simplify_b = torch.nn.Parameter(torch.zeros(3)) + + def forward(self, x): + """ConvModule Forward. + + Args: + x: torch.Tensor + input tensor. + """ + x = self.layer_norm(x) + + if self.ext_pw_out_channel != 0: + x = self.glu(x) + if self.causal and self.ext_pw_kernel_size > 1: + x = x[:, : -(self.ext_pw_kernel_size - 1), :] + if self.apply_ln1: + x = self.ln1(x) + else: + x_0 = x * self.pw_conv_simplify_w[0] + self.pw_conv_simplify_b[0] + x_1 = x * self.pw_conv_simplify_w[1] + self.pw_conv_simplify_b[1] + x = x_0 + x_1 + + x = x.permute([0, 2, 1]) + + x = self.dw_sep_conv_1d(x) + if self.causal and self.kernel_size > 1: + x = x[:, :, : -(self.kernel_size - 1)] + if hasattr(self, "ln2"): + x = x.permute([0, 2, 1]) + x = self.ln2(x) + x = x.permute([0, 2, 1]) + if self.batch_norm: + x = self.bn_layer(x) + x = self.act(x) + + if self.ext_pw_out_channel != 0: + x = self.ext_pw_conv_1d(x) + if self.fix_len1: + x = x[:, :, : -(self.ext_pw_kernel_size - 1)] + + if self.apply_ln1: + x = x.permute([0, 2, 1]) + x = self.ln1(x) + x = x.permute([0, 2, 1]) + + x = x.permute([0, 2, 1]) + else: + x = x.unsqueeze(1).permute([0, 1, 3, 2]) + x = x * self.pw_conv_simplify_w[2] + self.pw_conv_simplify_b[2] + x = x.squeeze(1) + + x = self.dropout(x) + return x + +class GLULinear(nn.Module): + """Linear + GLU module + + Args: + input_dim: int + input size + output_dim: int + output size. + glu_type: + activation function name used in glu module. + default "sigmoid" (swish function). + bias_in_glu: bool, optional + If True, the addtive bias is added. Default False. + """ + + def __init__( + self, + input_dim, + output_dim, + glu_type="sigmoid", + bias_in_glu=True, + ): + super().__init__() + self.linear = nn.Linear(input_dim, output_dim * 2, bias_in_glu) + self.glu_act = GLU(-1, glu_type) + + def forward(self, x): + """GLULinear forward + + Args: + x: torch.Tensor + inpute tensor. + """ + x = self.linear(x) + return self.glu_act(x) + +class FeedForward(nn.Module): + """FeedForward Module. + For more details see Conformer paper: + https://arxiv.org/pdf/2005.08100.pdf + + Args: + d_model: int + input size. + d_inner: int + output size. + dropout_rate: float, + dropout rate. + activation: str, + activation function name, + one of ["relu", "swish", "sigmoid"], + sigmoid activation is only used with "glu_in_fnn=True", + default "sigmoid". + bias_in_glu: bool, optional + """ + + def __init__( + self, + d_model, + d_inner, + dropout_rate, + activation="sigmoid", + bias_in_glu=True, + ): + super().__init__() + self.d_model = d_model + self.d_inner = d_inner + + self.layer_norm = nn.LayerNorm(d_model) + module = GLULinear(d_model, d_inner, activation, bias_in_glu) + self.net = nn.Sequential( + module, + nn.Dropout(dropout_rate), + nn.Linear(d_inner, d_model), + nn.Dropout(dropout_rate), + ) + + def forward(self, x): + """FeedForward forward function. + + Args: + x: torch.Tensor + input tensor. + """ + out = self.net(self.layer_norm(x)) + + return out + +#### positional encoding starts here +def _pre_hook( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs +): + """Perform pre-hook in load_state_dict for backward compatibility. + + Note: + We saved self.pe until v.0.5.2 but we have omitted it later. + Therefore, we remove the item "pe" from `state_dict` for backward compatibility. + + """ + k = prefix + "pe" + if k in state_dict: + state_dict.pop(k) + +class T5RelativeAttentionLogitBias(nn.Module): + """ + This module implements the relative position bias described in Section 2.1 of + the T5 paper: https://arxiv.org/pdf/1910.10683.pdf + + The Huggingface implementation is used as a reference + https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/t5/modeling_t5.py#L435 + + Modifies attention as Q*K^T + B, where B is a learned scalar bias based on relative position + of the query and key. It is HxNxN, where H is the number of heads, N is the sequence length. + + I've made these modifications to the original T5 bias: + - Skipping of the bucketing step. Original T5 bias converted rel position distances into + logarithmically increasing buckets. This is supposed to help with length generalization. + - I just directly use rel position index as bias values, as we don't need length + generalization (40s max is good enough for ASR encoder), and it keeps ONNX export simple. + - I've also extended it so that biases can be asymmetric, the default implementation treats + L->R and R->L the same. Asymmetric was found to yield better results in my experiments. + + Args: + num_heads: int + Number of attention heads + num_buckets: int + Number of buckets to use for relative attention bias. This is the size of the learnable + bias parameter. Bucketing is not yet supported, so this defaults to -1 which means + no bucketing is used (max_distance determines size of bias param). + max_distance: int + Maximum distance to use for relative attention bias. With num_buckets=-1, this directly + controls the max size of the bias parameter. When num_buckets > 0 is supported, this + will control the maximum distance for logarithmic bucketing after which all positions + are in the same bucket. + symmetric: bool + Whether to use symmetric or asymmetric biases. symmetric=False uses 2x number of bias + params to distinguish L->R from R->L. This was found to be better for the encoder. + """ + + def __init__(self, num_heads, num_buckets=-1, max_distance=1000, symmetric=False): + super().__init__() + self.num_heads = num_heads + self.num_buckets = num_buckets + self.max_distance = max_distance + self.symmetric = symmetric + self._skip_bucketing = self.num_buckets < 0 + if self._skip_bucketing: + self.num_buckets = max_distance + else: + raise NotImplementedError("T5 attention bias with bucketed positions is not yet tested") + if not self.symmetric: + self.num_buckets *= 2 + self.bias_values = nn.Embedding(self.num_buckets, self.num_heads) + + def forward(self, x): + # instantiate bias compatible with shape of x + maxpos = x.size(1) + context_position = torch.arange(maxpos, device=x.device, dtype=torch.long)[:, None] + memory_position = torch.arange(maxpos, device=x.device, dtype=torch.long)[None, :] + relative_position = memory_position - context_position + # clipping to a maximum distance using ops that play well with ONNX export + relative_position = relative_position.masked_fill( + relative_position < -self.max_distance, -self.max_distance + ) + relative_position = relative_position.masked_fill( + relative_position > self.max_distance - 1, self.max_distance - 1 + ) + + # mapping from relative position to index in the bias parameter + if self._skip_bucketing: + bias_idx = relative_position + else: + bias_idx = self._bucket_relative_position(relative_position) + if self.symmetric: + bias_idx = bias_idx.abs() + else: + bias_idx += self.num_buckets // 2 + + t5_rel_att_bias = self.bias_values(bias_idx) # [L, L, H] + t5_rel_att_bias = t5_rel_att_bias.permute(2, 0, 1).unsqueeze(0) # [1, H, L, L] + + return t5_rel_att_bias + + def _bucket_relative_position(self, relative_position): + # this is a placeholder (isn't tested, likely buggy) using HuggingFace implem as a reference + # this also needs to be extended to support asymmetric +/- ve positions + relative_buckets = 0 + if not self.causal: + num_buckets //= 2 + relative_buckets += (relative_position > 0).to(torch.long) * num_buckets + relative_position = torch.abs(relative_position) + else: + relative_position = -torch.min(relative_position, torch.zeros_like(relative_position)) + # now relative_position is in the range [0, inf) + + # half of the buckets are for exact increments in positions + max_exact = num_buckets // 2 + is_small = relative_position < max_exact + + # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance + relative_position_if_large = max_exact + ( + torch.log(relative_position.float() / max_exact) + / math.log(self.max_distance / max_exact) + * (num_buckets - max_exact) + ).to(torch.long) + relative_position_if_large = torch.min( + relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1) + ) + + relative_buckets += torch.where(is_small, relative_position, relative_position_if_large) + return relative_buckets + +class AbsolutePositionalEncoding(nn.Module): + """Absolute Positional encoding module. + This module implement Absolute sinusoidal positional encoding + from: https://arxiv.org/pdf/1706.03762.pdf + + Args: + d_model: int + Input embedding size. + dropout_rate: float + dropout rate + max_len: int, optional + Maximum input length sequence, Default 5000 + + """ + + def __init__(self, d_model, dropout_rate, max_len=5000): + """Construct an PositionalEncoding object.""" + super().__init__() + self.d_model = d_model + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + self._register_load_state_dict_pre_hook(_pre_hook) + + def extend_pe(self, x): + """Reset the positional encodings. + + Args: + x: torch.Tensor + """ + if self.pe is not None: + if self.pe.size(1) >= x.size(1): + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + pe = torch.zeros(x.size(1), self.d_model) + position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, self.d_model, 2, dtype=torch.float32) + * -(math.log(10000.0) / self.d_model) + ) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + pe = pe.unsqueeze(0) + self.pe = pe.to(device=x.device, dtype=x.dtype) + + def forward(self, x: torch.Tensor): + """Add positional encoding. + + Args: + x: torch.Tensor + Input tensor. shape is (batch, time, ...) + + Returns: + torch.Tensor: Encoded tensor. Its shape is (batch, time, ...) + + """ + self.extend_pe(x) + x = x * self.xscale + self.pe[:, : x.size(1)] + return self.dropout(x) + +#### forward embedding layers starts here + +@backoff.on_exception(backoff.expo, Exception, max_tries=10) +def np_loadtxt_with_retry(filepath): + """np.loadtxt with retry + + Args: + filepath: str + file path to the numpy array. + """ + result = np.loadtxt(filepath, dtype="f") + return result + +class MeanVarianceNormLayer(nn.Module): + """Mean/variance normalization layer. + + Will substract mean and multiply input by inverted standard deviation. + Typically used as a very first layer in a model. + + Args: + input_size: int + layer input size. + """ + + def __init__(self, input_size): + super().__init__() + self.input_size = input_size + self.register_buffer("global_mean", torch.zeros(input_size)) + self.register_buffer("global_invstd", torch.ones(input_size)) + self.global_mean: Optional[Tensor] + self.global_invstd: Optional[Tensor] + + def forward(self, input_: Tensor) -> Tensor: + """MeanVarianceNormLayer Forward + + Args: + input_: torch.Tensor + input tensor. + """ + return (input_ - self.global_mean) * self.global_invstd + + def load_mean_invstd(self, mean_file, invstd_file, cuside_features=False): + """Load feature mean and variance used for normalization. + + Args: + mean_file: str + path to the feature mean statistics file. + invstd_file: str + path to the features inverted standard deviation + statistics file. + cuside_features: bool + Boolean that indicates CUSIDE is being used. + The statistics of CUSIDE features are copied + from the normal features + """ + self.global_mean.data = torch.from_numpy(np_loadtxt_with_retry(mean_file)) + self.global_invstd.data = torch.from_numpy(np_loadtxt_with_retry(invstd_file)) + + if cuside_features: + self.global_mean.data = torch.cat((self.global_mean.data, self.global_mean.data), 0) + self.global_invstd.data = torch.cat( + (self.global_invstd.data, self.global_invstd.data), 0 + ) + +class CausalConv1D(nn.Conv1d): + """ + A causal version of nn.Conv1d where each step would have limited access to locations on its right or left + All arguments are the same as nn.Conv1d except padding. + + If padding is set None, then paddings are set automatically to make it a causal convolution where each location would not see any steps on its right. + + If padding is set as a list (size of 2), then padding[0] would be used as left padding and padding[1] as right padding. + It would make it possible to control the number of steps to be accessible on the right and left. + This mode is not supported when stride > 1. padding[0]+padding[1] should be equal to (kernel_size - 1). + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + padding: Union[str, int] = 0, + dilation: int = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = "zeros", + device=None, + dtype=None, + ) -> None: + self.cache_drop_size = None + if padding is None: + self._left_padding = kernel_size - 1 + self._right_padding = stride - 1 + else: + if stride != 1 and padding != kernel_size - 1: + raise ValueError("No striding allowed for non-symmetric convolutions!") + if isinstance(padding, int): + self._left_padding = padding + self._right_padding = padding + elif ( + isinstance(padding, list) + and len(padding) == 2 + and padding[0] + padding[1] == kernel_size - 1 + ): + self._left_padding = padding[0] + self._right_padding = padding[1] + else: + raise ValueError(f"Invalid padding param: {padding}!") + + self._max_cache_len = self._left_padding + + super().__init__( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=0, + dilation=dilation, + groups=groups, + bias=bias, + padding_mode=padding_mode, + device=device, + dtype=dtype, + ) + + def update_cache(self, x, cache=None): + if cache is None: + new_x = F.pad(x, pad=(self._left_padding, self._right_padding)) + next_cache = cache + else: + new_x = F.pad(x, pad=(0, self._right_padding)) + new_x = torch.cat([cache, new_x], dim=-1) + if self.cache_drop_size > 0: + next_cache = new_x[:, :, : -self.cache_drop_size] + else: + next_cache = new_x + next_cache = next_cache[:, :, -cache.size(-1) :] + return new_x, next_cache + + def forward(self, x, cache=None): + x, cache = self.update_cache(x, cache=cache) + x = super().forward(x) + if cache is None: + return x + else: + return x, cache + + +class CausalConv2D(nn.Conv2d): + """ + A causal version of nn.Conv2d where each location in the 2D matrix would have no access to locations on its right or down + All arguments are the same as nn.Conv2d except padding which should be set as None + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + padding: Union[str, int] = 0, + dilation: int = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = "zeros", + device=None, + dtype=None, + ) -> None: + if padding is not None: + raise ValueError("Argument padding should be set to None for CausalConv2D.") + self._left_padding = kernel_size - 1 + self._right_padding = stride - 1 + + padding = 0 + super().__init__( + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation, + groups, + bias, + padding_mode, + device, + dtype, + ) + + def forward( + self, + x, + ): + if self.training: + x = F.pad( + x, + pad=( + self._left_padding, + self._right_padding, + self._left_padding, + self._right_padding, + ), + ) + else: + x = F.pad( + x, + pad=(self._left_padding, self._right_padding, 0, 0), + ) + x = super().forward(x) + return x + + +class NemoConvSubsampling(torch.nn.Module): + """Convlutional subsampling module, taken from NeMo ASR + (https://github.com/NVIDIA/NeMo/blob/b367413645d5c72db3c2c96e46e95a34501479cf/nemo/collections/asr/parts/submodules/subsampling.py) + + Striding Subsampling: "Speech-Transformer: A No-Recurrence Sequence-to-Sequence Model for + Speech Recognition" by Linhao Dong et al. (https://ieeexplore.ieee.org/document/8462506) + + + Compared with the EncoderConv2D (`input_layer: custom`), this is a much simplified approach, + and uses no LayerNorm and far fewer Conv2Ds. Moreover, depthwise convolutions are used to reduce + FLOPs, but the first layer is kept as a regular convolution so as not to degrade accuracy. + + `Striding` and `dw_striding` are the same except that the latter uses depthwise convolutions + after the first layer, whereas the former does not. + + Args: + subsampling_factor (int): Time reduction factor + feat_in (int): size of the input features + feat_out (int): size of the output features + subsampling (str): The subsampling technique, choose from + {"striding", "dw-striding", "striding_conv1d", "dw_striding_conv1d"} + conv_channels (int): Number of channels for the convolution layers, default is 256. + subsampling_conv_chunking_factor (int): Input chunking factor which can be -1 (no chunking) + 1 (auto) or a power of 2. Default is 1 + activation (Module): activation function, default is nn.ReLU() + is_causal (bool): whether to use causal Conv1/2D, where each step will have limited access + to locations on its right or left + """ + + def __init__( + self, + feat_in, + feat_out, + subsampling_factor=4, + subsampling="dw_striding", + conv_channels=256, + subsampling_conv_chunking_factor=1, + activation=nn.ReLU(), + is_causal=False, + ): + super().__init__() + self._subsampling = subsampling + self._conv_channels = conv_channels + self._feat_in = feat_in + self._feat_out = feat_out + + if subsampling_factor % 2 != 0: + raise ValueError("Sampling factor should be a multiply of 2!") + self._sampling_num = int(math.log(subsampling_factor, 2)) + self.subsampling_factor = subsampling_factor + self.is_causal = is_causal + self.subsampling_causal_cond = subsampling in ("dw_striding", "striding", "striding_conv1d") + + if ( + subsampling_conv_chunking_factor != -1 + and subsampling_conv_chunking_factor != 1 + and subsampling_conv_chunking_factor % 2 != 0 + ): + raise ValueError("subsampling_conv_chunking_factor should be -1, 1, or a power of 2") + self.subsampling_conv_chunking_factor = subsampling_conv_chunking_factor + + in_channels = 1 + layers = [] + + if subsampling == "dw_striding": + self._stride = 2 + self._kernel_size = 3 + self._ceil_mode = False + + if self.is_causal: + self._left_padding = self._kernel_size - 1 + self._right_padding = self._stride - 1 + self._max_cache_len = subsampling_factor + 1 + else: + self._left_padding = (self._kernel_size - 1) // 2 + self._right_padding = (self._kernel_size - 1) // 2 + self._max_cache_len = 0 + + # Layer 1 + if self.is_causal: + layers.append( + CausalConv2D( + in_channels=in_channels, + out_channels=conv_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=None, + ) + ) + else: + layers.append( + torch.nn.Conv2d( + in_channels=in_channels, + out_channels=conv_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=self._left_padding, + ) + ) + in_channels = conv_channels + layers.append(activation) + + for i in range(self._sampling_num - 1): + if self.is_causal: + layers.append( + CausalConv2D( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=None, + groups=in_channels, + ) + ) + else: + layers.append( + torch.nn.Conv2d( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=self._left_padding, + groups=in_channels, + ) + ) + + layers.append( + torch.nn.Conv2d( + in_channels=in_channels, + out_channels=conv_channels, + kernel_size=1, + stride=1, + padding=0, + groups=1, + ) + ) + layers.append(activation) + in_channels = conv_channels + + elif subsampling == "striding": + self._stride = 2 + self._kernel_size = 3 + self._ceil_mode = False + + if self.is_causal: + self._left_padding = self._kernel_size - 1 + self._right_padding = self._stride - 1 + self._max_cache_len = subsampling_factor + 1 + else: + self._left_padding = (self._kernel_size - 1) // 2 + self._right_padding = (self._kernel_size - 1) // 2 + self._max_cache_len = 0 + + for i in range(self._sampling_num): + if self.is_causal: + layers.append( + CausalConv2D( + in_channels=in_channels, + out_channels=conv_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=None, + ) + ) + else: + layers.append( + torch.nn.Conv2d( + in_channels=in_channels, + out_channels=conv_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=self._left_padding, + ) + ) + layers.append(activation) + in_channels = conv_channels + + elif subsampling == "striding_conv1d": + in_channels = feat_in + + self._stride = 2 + self._kernel_size = 5 + self._ceil_mode = False + + if self.is_causal: + self._left_padding = self._kernel_size - 1 + self._right_padding = self._stride - 1 + self._max_cache_len = subsampling_factor + 1 + else: + self._left_padding = (self._kernel_size - 1) // 2 + self._right_padding = (self._kernel_size - 1) // 2 + self._max_cache_len = 0 + + for i in range(self._sampling_num): + if self.is_causal: + layers.append( + CausalConv1D( + in_channels=in_channels, + out_channels=feat_out if self._sampling_num == i + 1 else conv_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=None, + ) + ) + else: + layers.append( + torch.nn.Conv1d( + in_channels=in_channels, + out_channels=feat_out if self._sampling_num == i + 1 else conv_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=self._left_padding, + ) + ) + layers.append(activation) + in_channels = conv_channels + + elif subsampling == "dw_striding_conv1d": + in_channels = feat_in + + self._stride = 2 + self._kernel_size = 5 + self._ceil_mode = False + + self._left_padding = (self._kernel_size - 1) // 2 + self._right_padding = (self._kernel_size - 1) // 2 + + # Layer 1 + layers.extend( + [ + torch.nn.Conv1d( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=self._left_padding, + groups=in_channels, + ), + torch.nn.Conv1d( + in_channels=in_channels, + out_channels=feat_out if self._sampling_num == 1 else conv_channels, + kernel_size=1, + stride=1, + padding=0, + groups=1, + ), + ] + ) + in_channels = conv_channels + layers.append(activation) + + for i in range(self._sampling_num - 1): + layers.extend( + [ + torch.nn.Conv1d( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=self._left_padding, + groups=in_channels, + ), + torch.nn.Conv1d( + in_channels=in_channels, + out_channels=feat_out if self._sampling_num == i + 2 else conv_channels, + kernel_size=1, + stride=1, + padding=0, + groups=1, + ), + ] + ) + layers.append(activation) + in_channels = conv_channels + + else: + raise ValueError(f"Not valid sub-sampling: {subsampling}!") + + if subsampling in ["dw_striding", "striding"]: + in_length = torch.tensor(feat_in, dtype=torch.float) + out_length = calc_length( + lengths=in_length, + all_paddings=self._left_padding + self._right_padding, + kernel_size=self._kernel_size, + stride=self._stride, + ceil_mode=self._ceil_mode, + repeat_num=self._sampling_num, + ) + self.out = torch.nn.Linear(conv_channels * int(out_length), feat_out) + self.conv2d_subsampling = True + elif subsampling in ["striding_conv1d", "dw_striding_conv1d"]: + self.out = None + self.conv2d_subsampling = False + else: + raise ValueError(f"Not valid sub-sampling: {subsampling}!") + + self.conv = torch.nn.Sequential(*layers) + + def get_sampling_frames(self): + return [1, self.subsampling_factor] + + def get_streaming_cache_size(self): + return [0, self.subsampling_factor + 1] + + def forward(self, x, mask): + """ + Forward method for NeMo subsampling. + + Args: + x[Batch, Time, Filters]: torch.Tensor + input tensor + x_mask: torch.Tensor + input mask + + Returns: + x: torch.Tensor + Resulting tensor from subsampling (B, T // time_reduction_factor, feat_out) + pad_mask: torch.Tensor + tensor of padded hidden state sequences (B, 1, T // time_reduction_factor) + """ + # Unsqueeze Channel Axis + if self.conv2d_subsampling: + x = x.unsqueeze(1) + # Transpose to Channel First mode + else: + x = x.transpose(1, 2) + + # split inputs if chunking_factor is set + if self.subsampling_conv_chunking_factor != -1 and self.conv2d_subsampling: + if self.subsampling_conv_chunking_factor == 1: + # if subsampling_conv_chunking_factor is 1, we split only if needed + # avoiding a bug / feature limiting indexing of tensors to 2**31 + # see https://github.com/pytorch/pytorch/issues/80020 + x_ceil = 2**31 / self._conv_channels * self._stride * self._stride + if torch.numel(x) > x_ceil: + need_to_split = True + else: + need_to_split = False + else: + # if subsampling_conv_chunking_factor > 1 we always split + need_to_split = True + + if need_to_split: + x, success = self.conv_split_by_batch(x) + if not success: # if unable to split by batch, try by channel + if self._subsampling == "dw_striding": + x = self.conv_split_by_channel(x) + else: + x = self.conv(x) # try anyway + else: + x = self.conv(x) + else: + x = self.conv(x) + + # Flatten Channel and Frequency Axes + if self.conv2d_subsampling: + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).reshape(b, t, -1)) + # Transpose to Channel Last mode + else: + x = x.transpose(1, 2) + + if mask is None: + return x, None + + max_audio_length = x.shape[1] + feature_lens = mask.sum(1) + padding_length = torch.ceil(feature_lens / self.subsampling_factor) + if self.is_causal and self.subsampling_causal_cond: + feature_lens_remainder = feature_lens % self.subsampling_factor + padding_length[feature_lens_remainder != 1] += 1 + pad_mask = ( + torch.arange(0, max_audio_length, device=x.device).expand(padding_length.size(0), -1) + < padding_length.unsqueeze(1) + ) + return x, pad_mask.unsqueeze(1) + + def reset_parameters(self): + # initialize weights + if self._subsampling == "dw_striding": + with torch.no_grad(): + # init conv + scale = 1.0 / self._kernel_size + dw_max = (self._kernel_size**2) ** -0.5 + pw_max = self._conv_channels**-0.5 + + torch.nn.init.uniform_(self.conv[0].weight, -scale, scale) + torch.nn.init.uniform_(self.conv[0].bias, -scale, scale) + + for idx in range(2, len(self.conv), 3): + torch.nn.init.uniform_(self.conv[idx].weight, -dw_max, dw_max) + torch.nn.init.uniform_(self.conv[idx].bias, -dw_max, dw_max) + torch.nn.init.uniform_(self.conv[idx + 1].weight, -pw_max, pw_max) + torch.nn.init.uniform_(self.conv[idx + 1].bias, -pw_max, pw_max) + + # init fc (80 * 64 = 5120 from https://github.com/kssteven418/Squeezeformer/blob/13c97d6cf92f2844d2cb3142b4c5bfa9ad1a8951/src/models/conformer_encoder.py#L487 + fc_scale = (self._feat_out * self._feat_in / self._sampling_num) ** -0.5 + torch.nn.init.uniform_(self.out.weight, -fc_scale, fc_scale) + torch.nn.init.uniform_(self.out.bias, -fc_scale, fc_scale) + + def conv_split_by_batch(self, x): + """Tries to split input by batch, run conv and concat results""" + b, _, _, _ = x.size() + if b == 1: # can't split if batch size is 1 + return x, False + + if self.subsampling_conv_chunking_factor > 1: + cf = self.subsampling_conv_chunking_factor + else: + # avoiding a bug / feature limiting indexing of tensors to 2**31 + # see https://github.com/pytorch/pytorch/issues/80020 + x_ceil = 2**31 / self._conv_channels * self._stride * self._stride + p = math.ceil(math.log(torch.numel(x) / x_ceil, 2)) + cf = 2**p + + new_batch_size = b // cf + if new_batch_size == 0: # input is too big + return x, False + + return torch.cat([self.conv(chunk) for chunk in torch.split(x, new_batch_size, 0)]), True + + def conv_split_by_channel(self, x): + """For dw convs, tries to split input by time, run conv and concat results""" + x = self.conv[0](x) # full conv2D + x = self.conv[1](x) # activation + + for i in range(self._sampling_num - 1): + _, c, t, _ = x.size() + + if self.subsampling_conv_chunking_factor > 1: + cf = self.subsampling_conv_chunking_factor + else: + # avoiding a bug / feature limiting indexing of tensors to 2**31 + # see https://github.com/pytorch/pytorch/issues/80020 + p = math.ceil(math.log(torch.numel(x) / 2**31, 2)) + cf = 2**p + + new_c = int(c // cf) + if new_c == 0: + new_c = 1 + + new_t = int(t // cf) + if new_t == 0: + new_t = 1 + + x = self.channel_chunked_conv(self.conv[i * 3 + 2], new_c, x) # conv2D, depthwise + + # splitting pointwise convs by time + x = torch.cat( + [self.conv[i * 3 + 3](chunk) for chunk in torch.split(x, new_t, 2)], 2 + ) # conv2D, pointwise + x = self.conv[i * 3 + 4](x) # activation + return x + + def channel_chunked_conv(self, conv, chunk_size, x): + """Performs channel chunked convolution""" + + ind = 0 + out_chunks = [] + for chunk in torch.split(x, chunk_size, 1): + step = chunk.size()[1] + + if self.is_causal: + chunk = nn.functional.pad( + chunk, + pad=( + self._kernel_size - 1, + self._stride - 1, + self._kernel_size - 1, + self._stride - 1, + ), + ) + ch_out = nn.functional.conv2d( + chunk, + conv.weight[ind : ind + step, :, :, :], + bias=conv.bias[ind : ind + step], + stride=self._stride, + padding=0, + groups=step, + ) + else: + ch_out = nn.functional.conv2d( + chunk, + conv.weight[ind : ind + step, :, :, :], + bias=conv.bias[ind : ind + step], + stride=self._stride, + padding=self._left_padding, + groups=step, + ) + out_chunks.append(ch_out) + ind += step + + return torch.cat(out_chunks, 1) + + def change_subsampling_conv_chunking_factor(self, subsampling_conv_chunking_factor: int): + if ( + subsampling_conv_chunking_factor != -1 + and subsampling_conv_chunking_factor != 1 + and subsampling_conv_chunking_factor % 2 != 0 + ): + raise ValueError("subsampling_conv_chunking_factor should be -1, 1, or a power of 2") + self.subsampling_conv_chunking_factor = subsampling_conv_chunking_factor + + +def calc_length(lengths, all_paddings, kernel_size, stride, ceil_mode, repeat_num=1): + """Calculates the output length of a Tensor passed through a convolution or max pooling layer""" + add_pad: float = all_paddings - kernel_size + one: float = 1.0 + for i in range(repeat_num): + lengths = torch.div(lengths.to(dtype=torch.float) + add_pad, stride) + one + if ceil_mode: + lengths = torch.ceil(lengths) + else: + lengths = torch.floor(lengths) + return lengths.to(dtype=torch.int) + +#### multihead attention starts here +class AttModule(nn.Module): + """Attention abstraction module""" + + def __init__(self): + super().__init__() + self.export_mode = False + + def set_export(self, mode=True): + """set the export mode""" + self.export_mode = mode + + def forward( + self, + x: Tensor, + memory: Optional[Tensor] = None, + pos_emb: Optional[Tensor] = None, + att_mask: Optional[Tensor] = None, + ) -> Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]: + """AttModule forward + + Args: + x: torch.Tensor + input tensor. + memory: torch.Tensor, optional + memory tensor. + pos_emb: torch.Tensor, optional + positional encoder embedding. + att_mask: torch.Tensor, optional + attention mask tensor. + """ + return x, memory, pos_emb, att_mask + + +class AttBlock(Block, AttModule): + """Attention Block module to support both Attention and Block module.""" + + def memory_dims(self, max_len=False): + """memory dimensions""" + return (1, self.input_size) + +def masked_softmax( + scores, + mask: Optional[Tensor], +): + if mask is not None: + mask = mask.unsqueeze(1).eq(0) # (batch, 1, time1, time2) + scores = scores.masked_fill(mask, -torch.inf) + attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0) # (batch, head, time1, time2) + else: + attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + return attn + + +class MultiHeadedAttention(nn.Module): + """Multi-Head Attention layer with optional relative position embedding and GLU. + + Args: + n_head: int + the number of heads. + n_feat: int + input size features. + dropout_rate: float + dropout rate. + use_LN: bool + apply layer norm or not + dropout_at_output: bool + whether to apply dropout at output + attention_inner_dim: int, optional + the attention dimension used in the class, + it can be different from the input dimension n_feat. + default: -1 (equal to n_feat). + use_pt_scaled_dot_product_attention: bool, optional + if set True, use pytorch scaled dot product attention in training. NOTE: this will NOT + be used in ONNX decoding due to a lack of support. In that case, we use the original + attention implementation, which shows no regression. + default: False. + n_value: int, optional + if set to values other than -1, use a different dimension for value. With the default value (i.e. -1), it is backward compatible. + group_size: int, optional. must divide `n_head` + if group_size > 1: GQA + if group_size = 1: MHA + if group_size = n_head: MQA + """ + + inv_sqrt_d_k: torch.jit.Final[float] + h: torch.jit.Final[int] + h_k: torch.jit.Final[int] + g: torch.jit.Final[int] + + def __init__( + self, + n_head, + n_feat, + dropout_rate, + attention_inner_dim=-1, + glu_type="swish", + bias_in_glu=True, + use_pt_scaled_dot_product_attention=False, + n_value=-1, + group_size: int = 1, + ): + super().__init__() + if n_value == -1: + n_value = n_feat + if attention_inner_dim == -1: + attention_inner_dim = n_feat + assert attention_inner_dim % n_head == 0 + + # We assume d_v always equals d_k + self.d_k = attention_inner_dim // n_head + self.inv_sqrt_d_k = 1.0 / math.sqrt(self.d_k) + self.h = n_head + assert n_head % group_size == 0, "group_size must divide n_head" + self.g = group_size + self.h_k = n_head // group_size + + self.linear_q = nn.Linear(n_feat, attention_inner_dim) + self.linear_k = nn.Linear(n_feat, attention_inner_dim // group_size) + self.linear_v = nn.Linear(n_value, attention_inner_dim // group_size) + self.linear_out = nn.Linear(attention_inner_dim // group_size, n_value) + + self.attn = torch.jit.Attribute(None, Optional[Tensor]) + self.dropout = nn.Dropout(p=dropout_rate) + self.dropout_rate = dropout_rate + self.use_pt_scaled_dot_product_attention = use_pt_scaled_dot_product_attention + + if use_pt_scaled_dot_product_attention and group_size > 1: + raise ValueError("Cannot use PT Scaled Attention with GQA") + + # Torchscript eager quantization. Note that these functions below are + # NOOPs and have very little impact on performance unless quantization is + # enabled. + self.quant_q = torch.ao.quantization.QuantStub() + self.quant_x = torch.ao.quantization.QuantStub() + self.dequant = torch.ao.quantization.DeQuantStub() + self.ffunc = torch.ao.nn.quantized.FloatFunctional() + + def forward( + self, + query: Tensor, + key: Tensor, + value: Tensor, + pos_k: Tensor, + pos_v: Tensor, + mask: Optional[Tensor], + relative_attention_bias: Optional[Tensor] = None, + ): + """Compute 'Scaled Dot Product Attention'. + + Args: + query: torch.Tensor + query tensor (batch, time1, size) + key: torch.Tensor + key tensor (batch, time2, size) + value: torch.Tensor + value tensor (batch, time1, size) + pos_k: torch.Tensor + key tensor used for relative positional embedding. + pos_v: torch.Tensor + value tensor used for relative positional embedding. + mask: torch.Tensor + mask tensor (batch, time1, time2) + relative_attention_bias: torch.Tensor + bias added to attention logits w.r.t. relative positions (1, n_head, time1, time2) + """ + n_batch = query.size(0) + + q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) # (b, t, d) + k = self.linear_k(key).view(n_batch, -1, self.h_k, self.d_k) # (b, t, d) + v = self.linear_v(value).view(n_batch, -1, self.h_k, self.d_k) + q = ( + q.transpose(1, 2) + if self.use_pt_scaled_dot_product_attention and not torch.jit.is_scripting() + else q.transpose(1, 2) * self.inv_sqrt_d_k + ) + k = k.transpose(1, 2) # (batch, head_k, time2, d_k) + v = v.transpose(1, 2) # (batch, head_k, time2, d_k) + + if self.use_pt_scaled_dot_product_attention and not torch.jit.is_scripting(): + attn_mask = None + if mask is not None: + mask = mask.unsqueeze(1) + if relative_attention_bias is not None: + attn_mask = mask + relative_attention_bias + else: + attn_mask = mask + if mask.dtype != q.dtype: + attn_mask = attn_mask.to(q.dtype) + + with torch.backends.cuda.sdp_kernel( + enable_flash=True, enable_math=True, enable_mem_efficient=True + ): + x = torch.nn.functional.scaled_dot_product_attention( + q, + k, + v, + attn_mask=attn_mask, + dropout_p=self.dropout_rate, + ) + else: + if self.h != self.h_k: + q = q.reshape(n_batch, self.g, self.h_k, -1, self.d_k) + A = torch.einsum("b g h t d, b h s d -> b h t s", q, k) + else: + A = torch.matmul(q, k.transpose(-2, -1)) + if pos_k is not None: + if self.h != self.h_k: + B = torch.einsum("b g h t d, t s d -> b h t s", q, pos_k) + else: + reshape_q = ( + q.contiguous().view(n_batch * self.h, -1, self.d_k).transpose(0, 1) + ) # (t1,nh,dk) + B = torch.matmul(reshape_q, pos_k.transpose(-2, -1)) # pos_k: (t1,dk,t2) + B = B.transpose(0, 1).view(n_batch, self.h, pos_k.size(0), pos_k.size(1)) + scores = A + B + else: + scores = A + + if relative_attention_bias is not None: + scores = scores + relative_attention_bias + + attn = masked_softmax(scores, mask) # (batch, head, time1, time2) + + self.attn = attn + + p_attn = self.dropout(attn) + x = torch.matmul(p_attn.to(v.dtype), v) # (batch, head, time1, d_k) + if pos_v is not None: + reshape_attn = ( + p_attn.contiguous() + .view(n_batch * self.h, pos_v.size(0), pos_v.size(1)) + .transpose(0, 1) + ) # (t1, bh, t2) + + attn_v = ( + torch.matmul(reshape_attn, pos_v) + .transpose(0, 1) + .contiguous() + .view(n_batch, self.h, pos_v.size(0), self.d_k) + ) + x = x + attn_v + x = ( + x.transpose(1, 2).contiguous().view(n_batch, -1, self.h_k * self.d_k) + ) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + +def unfold_tensor(xs_pad, max_seq_len): + """ + For a given tensor with shape of (N, T, D), if sequence length T is longer than max_seq_len, + this function unfold it to a (NT', max_seq_len, D) where T' is T // max_seq_len. + Args: + xs_pad: N, T, D + """ + _, _, D = xs_pad.shape + xs_pad = xs_pad.transpose(-1, -2) # convert to N, D, T + # N x D x 1 x T => N x (D x max_seq_len) x T' + xs_pad = F.unfold( + xs_pad[..., None, :], + kernel_size=(1, max_seq_len), + stride=(1, max_seq_len), + ) + + new_bsz, _, slen = xs_pad.shape + # N x D x max_seq_len x T' + xs_pad = xs_pad.view(new_bsz, -1, max_seq_len, slen) + # N x T' x max_seq_len x D + xs_pad = xs_pad.permute(0, 3, 2, 1).contiguous() + # NT' x max_seq_len x D + xs_pad = xs_pad.view(-1, max_seq_len, D) + return xs_pad + +# conformer_encoder.py +class MultiSequential(torch.nn.Sequential): + """Multi-input multi-output torch.nn.Sequential""" + + @torch.jit.ignore + def forward(self, *args): + """Forward method implementation.""" + for m in self: + args = m(*args) + return args + +def repeat(repeat_num, module_gen_fn): + """repeat module N times + + :param int repeat_num: repeat time + :param function module_gen_fn: function to generate module + :return: repeated modules + :rtype: MultiSequential + """ + return MultiSequential(*[module_gen_fn(i) for i in range(repeat_num)]) + +class ConformerEncoderLayer(nn.Module): + """ConformerEncoder Layer module. + for more details see conformer paper: + https://arxiv.org/abs/2005.08100 + This module implement the Conformer block layer. + + Args: + d_model: int + attention dim. + ext_pw_out_channel: int + if > 0, ext_pw_out_channel is a dim channel size + for the last pointwise conv after swish activation. + depthwise_seperable_out_channel: int + if set different to 0, the number of depthwise_seperable_out_channel + will be used as a channel_out of the second conv1d layer. + otherwise, it equal to 0, the second conv1d layer is skipped. + depthwise_multiplier: int + number of input_dim channels duplication. this value + will be used to compute the hidden channels of the Conv1D. + n_head: int + the number of heads for multihead attention module. + d_ffn: int + output size of the feed_forward blocks. + ext_pw_kernel_size: int + kernel size of the conv pointwise of the conformer. + kernel_size: int + kernel size. + dropout_rate: float + dropout rate. + causal: bool, optional + if set to True, convolution have no access + to future frames. default False. + batch_norm: bool, optional + if set to True, apply batchnorm before activation + in ConvModule layer of the conformer. + default False + activation: str, optional + activation function name, + one of ["relu", "swish", "sigmoid"], + sigmoid activation is only used with "glu_in_fnn=True", + default "relu". + chunk_se: int, optional + 0 for offline SE. + 1 for streaming SE, where mean is computed + by accumulated history until current chunk_se. + 2 for streaming SE, where mean is computed + by only the current chunk. + default 0. + chunk_size: int, optional + chunk_size for cnn. default 18 + conv_activation: str, optional + activation function used in ConvModule part + of the conformer, default "relu". + conv_glu_type: str, optional + activation function used for the glu inside + the ConvModule part of the conformer. + default: "sigmoid". + bias_in_glu: bool, optional + if set to True, use additive bias in the weight module + before GLU. + linear_glu_in_convm: bool, optional + if set to True, use GLULinear module, + otherwise, used GLUPointWiseConv module. + default to False. + attention_innner_dim: int, otional + if equal to -1, attention dim for linears k/q/v is + equal to d_model. otherwise attention_innner_dim is used. + default -1. + attention_glu_type: str, optional + activation function for glu used in the multihead attention, + default "swish". + activation_checkpointing: str, optional + a dictionarry of {"module","interval","offload"}, where + "module": str + accept ["transformer", "attention"] to select + which module should do activation checkpointing. + "interval": int, default 1, + interval of applying activation checkpointing, + interval = 1 means that we apply checkpointing + on every layer (if activation), otherwise, + we apply it every x interval. + "offload": bool, default False, + if set to True, we offload activation to cpu and + reload it during backward, otherwise, + we recalculate activation in backward. + default "". + export: bool, optional + if set to True, it remove the padding from convolutional layers + and allow the onnx conversion for inference. + default False. + use_pt_scaled_dot_product_attention: bool, optional + if set to True, use pytorch's scaled dot product attention implementation in training. + attn_group_sizes: int, optional + the number of groups to use for attention, default 1 (Multi-Head Attention), + 1 = typical Multi-Head Attention, + 1 < attn_group_sizes < attention_heads = Grouped-Query Attention + attn_group_sizes = attenion_heads = Multi-Query Attention + """ + + def __init__( + self, + d_model=512, + ext_pw_out_channel=0, + depthwise_seperable_out_channel=256, + depthwise_multiplier=1, + n_head=4, + d_ffn=2048, + ext_pw_kernel_size=1, + kernel_size=3, + dropout_rate=0.1, + causal=False, + batch_norm=False, + activation="relu", + chunk_se=0, + chunk_size=18, + conv_activation="relu", + conv_glu_type="sigmoid", + bias_in_glu=True, + linear_glu_in_convm=False, + attention_innner_dim=-1, + attention_glu_type="swish", + activation_checkpointing="", + export=False, + use_pt_scaled_dot_product_attention=False, + attn_group_sizes: int = 1, + ): + super().__init__() + + self.feed_forward_in = FeedForward( + d_model=d_model, + d_inner=d_ffn, + dropout_rate=dropout_rate, + activation=activation, + bias_in_glu=bias_in_glu, + ) + + self.self_attn = encoder_checkpoint_wrapper( + activation_checkpointing, + MultiHeadedAttention, + )( + MultiHeadedAttention( + n_head, + d_model, + dropout_rate, + attention_innner_dim, + attention_glu_type, + bias_in_glu, + use_pt_scaled_dot_product_attention=use_pt_scaled_dot_product_attention, + group_size=attn_group_sizes, + ) + ) + self.conv = ConvModule( + d_model, + ext_pw_out_channel, + depthwise_seperable_out_channel, + ext_pw_kernel_size, + kernel_size, + depthwise_multiplier, + dropout_rate, + causal, + batch_norm, + chunk_se, + chunk_size, + conv_activation, + conv_glu_type, + bias_in_glu, + linear_glu_in_convm, + export=export, + ) + + self.feed_forward_out = FeedForward( + d_model=d_model, + d_inner=d_ffn, + dropout_rate=dropout_rate, + activation=activation, + bias_in_glu=bias_in_glu, + ) + + self.layer_norm_att = nn.LayerNorm(d_model) + self.layer_norm = nn.LayerNorm(d_model) + + def forward( + self, + x, + pos_k, + pos_v, + mask, + relative_attention_bias: Optional[Tensor] = None, + ): + """ConformerEncoder forward. + + Args: + x: torch.Tensor + input feature of shape (batch, max_time_in, size) + pos_k: torch.Tensor + positional key embedding. + mask: torch.Tensor + mask for x (batch, max_time_in) + relative_attention_bias: Optional[torch.Tensor] + bias added to attention logits w.r.t. relative positions (1, n_head, time1, time2) + """ + x = x + 0.5 * self.feed_forward_in(x) + norm_x = self.layer_norm_att(x) + + x = x + self.self_attn( + norm_x, + norm_x, + norm_x, + pos_k, + pos_v, + mask, + relative_attention_bias=relative_attention_bias, + ) + x = x + self.conv(x) + x = x + 0.5 * self.feed_forward_out(x) + + out = self.layer_norm(x) + + return out, pos_k, pos_v, mask + +class TransformerEncoderBase(abc.ABC, nn.Module): + """The Base class for Transformer based encoders + + Please set causal = True in streaming model + Args: + input_size: int + input feature dimension. + chunk_size: int, list(int) + Number of frames for each chunk + This variable can take 2 forms: + int: Used for inference, or single chunk size training + list(int) : Used only for variable chunk size training + Some examples for the 2 cases: + chunk_size = 12 + chunk_size = [6, 8, 12, 24] + left_chunk: int, list(int) + Number of chunks used for masking in streaming mode. + This variable can take 2 forms: + int: Used for inference, or single chunk size training + list(int) : Used only for variable chunk size training. When + chunk_size is a list, left_chunk must be a list with same length. + Some examples for the 2 cases: + left_chunk = 6 + left_chunk = [12, 9, 6, 3] + attention_dim: int, optional + attention dimension. default 256. + attention_heads: int, optional + the number of heads. default 4 + input_layer: str, optional + input layer type before Conformer, + one of ["linear", "conv2d", "custom", "vgg2l", "embed"], + default "conv2d" + cnn_out: int, optional + the number of CNN channels before Conformer. + default -1. + cnn_layer_norm: bool, optional + layer norm between Conformer and the first CNN. + default False. + time_reduction: int, optional + time reduction factor + default 4 + dropout_rate: float, optional + dropout rate. default 0.1 + padding_idx: int, optional + padding index for input_layer=embed + default -1 + relative_attention_bias_args: dict, optional + use more efficient scalar bias-based relative multihead attention (Q*K^T + B) + implemented in cmb.basics.embedding.[T5/ALiBi]RelativeAttentionLogitBias + usage: relative_attention_bias_args={"type": t5/alibi} + additional method-specific arguments can be provided (see transformer_base.py) + positional_dropout_rate: float, optional + dropout rate after positional encoding. default 0.0 + nemo_conv_settings: dict, optional + A dictionary of settings for NeMo Subsampling. + default None + conv2d_extra_padding: str, optional + Add extra padding in conv2d subsampling layers. Choices are + (feat, feat_time, none, True). + if True or feat_time, the extra padding is added into non full + supraframe utts in batch. + Default: none + attention_group_size: int, optional + the number of groups to use for attention, default 1 (Multi-Head Attention), + 1 = typical Multi-Head Attention, + 1 < attention_group_size < attention_heads = Grouped-Query Attention + attention_group_size = attenion_heads = Multi-Query Attention + """ + + def __init__( + self, + input_size, + chunk_size, + left_chunk, + attention_dim=256, + attention_heads=4, + input_layer="nemo_conv", + cnn_out=-1, + cnn_layer_norm=False, + time_reduction=4, + dropout_rate=0.0, + padding_idx=-1, + relative_attention_bias_args=None, + positional_dropout_rate=0.0, + nemo_conv_settings=None, + conv2d_extra_padding: Literal["feat", "feat_time", "none", True] = "none", + attention_group_size=1, + encoder_embedding_config=None, + ): + super().__init__() + self.input_size = input_size + self.input_layer = input_layer + self.chunk_size = chunk_size + self.left_chunk = left_chunk + self.attention_dim = attention_dim + self.num_heads = attention_heads + self.attention_group_size = attention_group_size + self.time_reduction = time_reduction + self.nemo_conv_settings = nemo_conv_settings + self.encoder_embedding_config = encoder_embedding_config + + if self.input_layer == "nemo_conv": + default_nemo_conv_settings = { + "subsampling": "dw_striding", + "subsampling_factor": self.time_reduction, + "feat_in": input_size, + "feat_out": attention_dim, + "conv_channels": 256, + "subsampling_conv_chunking_factor": 1, + "activation": nn.ReLU(), + "is_causal": False, + } + # Override any of the defaults with the incoming, user settings + if nemo_conv_settings: + default_nemo_conv_settings.update(nemo_conv_settings) + for i in ["subsampling_factor", "feat_in", "feat_out"]: + assert ( + i not in nemo_conv_settings + ), "{i} should be specified outside of the NeMo dictionary" + + self.embed = NemoConvSubsampling( + **default_nemo_conv_settings, + ) + else: + raise ValueError("unknown input_layer: " + input_layer) + + self.pos_emb = AbsolutePositionalEncoding(attention_dim, positional_dropout_rate) + + self.relative_attention_bias_type = ( + relative_attention_bias_args.get("type") if relative_attention_bias_args else None + ) + if self.relative_attention_bias_type == "t5": + assert ( + self.num_heads % self.attention_group_size == 0 + ), "attention_group_size must divide n_head" + self.relative_attention_bias_layer = T5RelativeAttentionLogitBias( + self.num_heads // self.attention_group_size, + max_distance=relative_attention_bias_args.get("t5_bias_max_distance", 1000), + symmetric=relative_attention_bias_args.get("t5_bias_symmetric", False), + ) + else: + raise NotImplementedError + + + def post_init(self, init_model_config): + + pretrained_speech_encoder_path = init_model_config.get('pretrained_speech_encoder_path', None) + if pretrained_speech_encoder_path: + model_state = torch.load(pretrained_speech_encoder_path, map_location="cpu") + encoder_state_dict = {} + for k, v in model_state.items(): + if "encoder." in k: + tmp_k = k.replace("encoder.", "") + encoder_state_dict[tmp_k] = v + + if hasattr(self, "encoder_embedding"): + del self.encoder_embedding + self.load_state_dict(encoder_state_dict) + + if not hasattr(self, "encoder_embedding"): + self.encoder_embedding = MeanVarianceNormLayer(self.encoder_embedding_config["input_size"]) + + mean_file = init_model_config.get('mean_file', None) + invstd_file = init_model_config.get('invstd_file', None) + if mean_file is not None and invstd_file is not None: + self.encoder_embedding.load_mean_invstd(mean_file, invstd_file) + + def compute_lens_change(self, feature_lens): + """feature_lens: int + return updated feature lens. + + This used to return a different lambda function for each case that computed + the right thing. That does not work within Torchscript. If you really + need this to be faster, create nn.Module()-s for all the cases and return + one of them. Torchscript does support that. + """ + if self.input_layer == "nemo_conv": + # Handle the special causal case + subsampling_causal_cond = self.nemo_conv_settings.get("subsampling", "dw_striding") in [ + "dw_striding", + "striding", + "striding_conv1d", + ] + is_causal = self.nemo_conv_settings.get("is_causal", False) + if is_causal and subsampling_causal_cond: + lens_change = ( + torch.ceil(feature_lens / self.time_reduction).long() + if isinstance(feature_lens, Tensor) + else math.ceil(feature_lens / self.time_reduction) + ) + feature_lens_remainder = feature_lens % self.time_reduction + if isinstance(feature_lens, Tensor): + lens_change[feature_lens_remainder != 1] += 1 + elif feature_lens_remainder != 1: + lens_change += 1 + return lens_change + ceil_func = math.ceil if isinstance(feature_lens, int) else torch.ceil + return ceil_func(feature_lens / self.time_reduction) + + @abc.abstractmethod + def forward(self): + """Abstract forward method implementation.""" + + def _chunk_size_selection(self, chunk_size=None, left_chunk=None): + """If chunk size is a list, we will randomly select a chunk size.""" + + if chunk_size is None: + chunk_size = self.chunk_size + if left_chunk is None: + left_chunk = self.left_chunk + if isinstance(chunk_size, list): + # Variable chunk size during training + chunk_size_index = int(torch.randint(low=0, high=len(chunk_size), size=(1,))) + chunk_size_train_eff = chunk_size[chunk_size_index] + if not isinstance(left_chunk, list): + raise ValueError("Since chunk_size is a list, left_chunk must be a list") + if len(left_chunk) != len(chunk_size): + raise ValueError( + "The length of left_chunk must be the same as length of chunk_size." + ) + left_chunk_train_eff = left_chunk[chunk_size_index] + else: + chunk_size_train_eff = chunk_size + left_chunk_train_eff = left_chunk + + return chunk_size_train_eff, left_chunk_train_eff + + def _get_embed_class(self, embed): + # pylint: disable=protected-access + is_embed_using_act_chkpt = isinstance(embed, CheckpointWrapper) + is_embed_fsdp_wrapped = isinstance(embed, FullyShardedDataParallel) + embed_class = embed + if is_embed_using_act_chkpt: + embed_class = embed._checkpoint_wrapped_module + if is_embed_fsdp_wrapped: + embed_class = embed.module + return embed_class + + def _forward_embeddings_core(self, input_tensor, masks): + embed_class = self._get_embed_class(self.embed) + assert isinstance(embed_class, NemoConvSubsampling) + input_tensor, masks = self.embed(input_tensor, masks) + return input_tensor, masks + + def _position_embedding(self, input_tensor): + pos_k = None + pos_v = None + if self.relative_attention_bias_layer is None: + input_tensor = self.pos_emb(input_tensor) # default to add abs sinusoid embedding + return pos_k, pos_v + + def _streaming_mask(self, seq_len, batch_size, chunk_size, left_chunk): + chunk_size_train_eff, left_chunk_train_eff = self._chunk_size_selection( + chunk_size, left_chunk + ) + + # Create mask matrix for streaming + # S stores start index. if chunksize is 18, s is [0,18,36,....] + chunk_start_idx = np.arange(0, seq_len, chunk_size_train_eff) + # avoid randomness when run evaluation or decoding + if self.training and np.random.rand() > 0.5: + # Either first or last chunk is not complete. + # If only the last one is not complete, EOS is not effective + chunk_start_idx = seq_len - chunk_start_idx + chunk_start_idx = chunk_start_idx[::-1] + chunk_start_idx = chunk_start_idx[:-1] + chunk_start_idx = np.insert(chunk_start_idx, 0, 0) + + enc_streaming_mask = ( + adaptive_enc_mask(seq_len, chunk_start_idx, left_window=left_chunk_train_eff) + .unsqueeze(0) + .expand([batch_size, -1, -1]) + ) + return enc_streaming_mask + + def forward_embeddings(self, xs_pad, masks, chunk_size_nc=None, left_chunk_nc=None): + """Forwarding the inputs through the top embedding layers + + Args: + xs_pad: torch.Tensor + input tensor + masks: torch.Tensor + input mask + chunk_size_nc: (optional, default is None) chunk size for non-causal layers + left_chunk_nc: (optional, default is None) # of left chunks for non-causal layers + """ + # pylint: disable=R0915 + # get new lens. + seq_len = int(self.compute_lens_change(xs_pad.shape[1])) + if seq_len <= 0: + raise ValueError( + f"""The squence length after time reduction is invalid: {seq_len}. + Your input feature is too short. Consider filtering out the very + short sentence from data loader""", + ) + + batch_size = xs_pad.shape[0] + + enc_streaming_mask = self._streaming_mask( + seq_len, batch_size, self.chunk_size, self.left_chunk + ) + + if xs_pad.is_cuda: + enc_streaming_mask = enc_streaming_mask.cuda() + xs_pad = xs_pad.cuda() + + input_tensor = xs_pad + input_tensor, masks = self._forward_embeddings_core(input_tensor, masks) + + streaming_mask = enc_streaming_mask + if streaming_mask is not None and masks is not None: + hs_mask = masks & streaming_mask + elif masks is not None: + hs_mask = masks + else: + hs_mask = streaming_mask + + if chunk_size_nc is not None: + enc_streaming_mask_nc = self._streaming_mask( + seq_len, batch_size, chunk_size_nc, left_chunk_nc + ) + if xs_pad.is_cuda: + enc_streaming_mask_nc = enc_streaming_mask_nc.cuda() + if masks is not None: + hs_mask_nc = masks & enc_streaming_mask_nc + else: + hs_mask_nc = enc_streaming_mask_nc + else: + hs_mask_nc = None + + pos_k, pos_v = self._position_embedding(input_tensor) + + if chunk_size_nc is None: + return input_tensor, pos_k, pos_v, hs_mask, masks + return input_tensor, pos_k, pos_v, hs_mask, masks, hs_mask_nc + + def get_offset(self): + """Returns offset used when retaining inputs for decoding. + + This is essentially, how many additional frames have to be added to + the front-end CNN input to ensure it can produce a single output. + So if the "padding" parameter is 0, typically offset will be > 0. + """ + return get_offset(self.input_layer, self.time_reduction) + + +def get_offset(input_layer: str, time_reduction: int): + """Get an offset. We will use the offset for determining #frames of a subsampled feature. + + Args: + input_layer (str): Type of an input layer + time_reduction (int): time reduction factor for downsampling a feature + Returns: + int: offset + """ + if input_layer in ("conv2d", "nemo_conv") and time_reduction == 4: + return 3 + if input_layer in ("conv2d",) and time_reduction == 6: + return 1 + if input_layer in ("conv2d", "nemo_conv") and time_reduction == 8: + return 7 + return 0 + + +class ConformerEncoder(TransformerEncoderBase): + """ConformerEncoder module. + see original paper for more details: + https://arxiv.org/abs/2005.08100 + + Please set causal = True in streaming model + Args: + input_size: int + input feature dimension. + chunk_size: int, list(int) + Number of frames for each chunk + This variable can take 2 forms: + int: Used for inference, or single chunk size training + list(int) : Used only for variable chunk size training + Some examples for the 2 cases: + chunk_size = 12 + chunk_size = [6, 8, 12, 24] + left_chunk: int, list(int) + Number of chunks used for masking in streaming mode. + This variable can take 2 forms: + int: Used for inference, or single chunk size training + list(int) : Used only for variable chunk size training. When + chunk_size is a list, left_chunk must be a list with same length. + Some examples for the 2 cases: + left_chunk = 6 + left_chunk = [12, 9, 6, 3] + left_chunk: int + number of chunks used for masking in streaming mode. + num_lang: int + This parameter is used to store the number of languages in the lang_dict, + only used for multiseed/multilingual models. default None. + attention_dim: int, optional + attention dimension. default 256. + attention_heads: int, optional + the number of heads. default 4 + linear_units: + the number of units of position-wise feed forward. + default 2048 + num_block: + number of Transformer layer. default 6 + dropout_rate: float, optional + dropout rate. default 0.1 + input_layer: str, optional + input layer type before Conformer, + one of ["linear", "conv2d", "custom", "vgg2l", "embed"], + default "conv2d" + causal: bool, optional + if set to True, convolution have no access + to future frames. default False. + batch_norm: bool, optional + if set to True, apply batchnorm before activation + in ConvModule layer of the conformer. + default False + cnn_out: int, optional + the number of CNN channels before Conformer. + default -1. + cnn_layer_norm: bool, optional + layer norm between Conformer and the first CNN. + default False. + ext_pw_out_channel: int, optional + the number of channel for CNN + before depthwise_seperable_CNN. + If 0 then use linear. default 0. + ext_pw_kernel_size: int, optional + kernel size of N before depthwise_seperable_CNN. + only work for ext_pw_out_channel > 0. + default 1 + depthwise_seperable_out_channel: int, optional + the number of channel for + depthwise_seperable_CNN. + default 256. + depthwise_multiplier: int, optional + the number of multiplier for + depthwise_seperable_CNN. + default 1. + chunk_se: int, optional + 0 for offline SE. + 1 for streaming SE, where mean is computed + by accumulated history until current chunk_se. + 2 for streaming SE, where mean is computed + by only the current chunk. + default 0. + kernel_size: int, optional + the number of kernels for depthwise_seperable_CNN. + default 3. + activation: str, optional + FeedForward block activation. + one of ["relu", "swish", "sigmoid"] + default "relu". + conv_activation: str, optional + activation function used in ConvModule part + of the conformer, default "relu". + conv_glu_type: str, otional + activation used use glu in depthwise_seperable_CNN, + default "sigmoid" + bias_in_glu: bool, optional + if set to True, use additive bias in the weight module + before GLU. default True + linear_glu_in_convm: bool, optional + if set to True, use GLULinear module, + otherwise, used GLUPointWiseConv module. + default to False. + attention_glu_type: str + only work for glu_in_attention !=0 + default "swish". + export: bool, optional + if set to True, it remove the padding from convolutional layers + and allow the onnx conversion for inference. + default False. + activation_checkpointing: str, optional + a dictionarry of {"module","interval","offload"}, where + "module": str + accept ["transformer", "attention"] to select + which module should do activation checkpointing. + "interval": int, default 1, + interval of applying activation checkpointing, + interval = 1 means that we apply checkpointing + on every layer (if activation), otherwise, + we apply it every x interval. + "offload": bool, default False, + if set to True, we offload activation to cpu and + reload it during backward, otherwise, + we recalculate activation in backward. + default "". + extra_layer_output_idx: int + the layer index to be exposed. + relative_attention_bias_args: dict, optional + use more efficient scalar bias-based relative multihead attention (Q*K^T + B) + implemented in cmb.basics.embedding.[T5/ALiBi]RelativeAttentionLogitBias + usage: relative_attention_bias_args={"type": t5/alibi} + additional method-specific arguments can be provided (see transformer_base.py) + time_reduction: int optional + time reduction factor + default 4 + use_pt_scaled_dot_product_attention: whether to use pytorch scaled dot product attention + in training. + Default: False + nemo_conv_settings: dict, optional + A dictionary of settings for NeMo Subsampling. + default: None + usage: nemo_conv_settings= + { + "subsampling": + dw_striding/striding/dw_striding_conv1d/striding_conv1d, + "conv_channels": int, + "subsampling_conv_chunking_factor": int, + "is_causal": True/False + } + conv2d_extra_padding: str, optional + Add extra padding in conv2d subsampling layers. Choices are + (feat, feat_time, none, True) + Default: none + replication_pad_for_subsample_embedding: For batched-streaming decoding, use + "replication" padding for the cache at start of utterance. + Default: False + attention_group_size: int, optional + the number of groups to use for attention, default 1 (Multi-Head Attention), + 1 = typical Multi-Head Attention, + 1 < attention_group_size < attention_heads = Grouped-Query Attention + attention_group_size = attenion_heads = Multi-Query Attention + """ + + extra_multi_layer_output_idxs: List[int] + + def __init__( # pylint: disable-all + self, + input_size, + chunk_size, + left_chunk, + num_lang=None, + attention_dim=256, + attention_heads=4, + linear_units=2048, + num_blocks=6, + dropout_rate=0.1, + input_layer="nemo_conv", + causal=True, + batch_norm=False, + cnn_out=-1, + cnn_layer_norm=False, + ext_pw_out_channel=0, + ext_pw_kernel_size=1, + depthwise_seperable_out_channel=256, + depthwise_multiplier=1, + chunk_se=0, + kernel_size=3, + activation="relu", + conv_activation="relu", + conv_glu_type="sigmoid", + bias_in_glu=True, + linear_glu_in_convm=False, + attention_glu_type="swish", + export=False, + extra_layer_output_idx=-1, + extra_multi_layer_output_idxs=[], + activation_checkpointing="", + relative_attention_bias_args=None, + time_reduction=4, + use_pt_scaled_dot_product_attention=False, + nemo_conv_settings=None, + conv2d_extra_padding: Literal["feat", "feat_time", "none", True] = "none", + replication_pad_for_subsample_embedding=False, + attention_group_size=1, + encoder_embedding_config=None, + ): + super().__init__( + input_size, + chunk_size, + left_chunk, + attention_dim, + attention_heads, + input_layer, + cnn_out, + cnn_layer_norm, + time_reduction, + dropout_rate=dropout_rate, + relative_attention_bias_args=relative_attention_bias_args, + positional_dropout_rate=0.0, + nemo_conv_settings=nemo_conv_settings, + conv2d_extra_padding=conv2d_extra_padding, + attention_group_size=attention_group_size, + encoder_embedding_config=encoder_embedding_config, + ) + self.num_blocks = num_blocks + self.num_lang = num_lang + self.kernel_size = kernel_size + self.embed = embedding_checkpoint_wrapper(activation_checkpointing)(self.embed) + self.replication_pad_for_subsample_embedding: bool = replication_pad_for_subsample_embedding + assert self.num_heads % attention_group_size == 0, "attention_group_size must divide n_head" + self.num_heads_k = self.num_heads // attention_group_size + + self.encoders = repeat( + num_blocks, + lambda i: encoder_checkpoint_wrapper( + activation_checkpointing, ConformerEncoderLayer, i + )( + ConformerEncoderLayer( + d_model=attention_dim, + ext_pw_out_channel=ext_pw_out_channel, + depthwise_seperable_out_channel=depthwise_seperable_out_channel, + depthwise_multiplier=depthwise_multiplier, + n_head=attention_heads, + d_ffn=linear_units, + ext_pw_kernel_size=ext_pw_kernel_size, + kernel_size=kernel_size, + dropout_rate=dropout_rate, + causal=causal, + batch_norm=batch_norm, + activation=activation, + chunk_se=chunk_se, + chunk_size=chunk_size, + conv_activation=conv_activation, + conv_glu_type=conv_glu_type, + bias_in_glu=bias_in_glu, + linear_glu_in_convm=linear_glu_in_convm, + attention_glu_type=attention_glu_type, + activation_checkpointing=attn_checkpointing(activation_checkpointing, i), + export=export, + use_pt_scaled_dot_product_attention=use_pt_scaled_dot_product_attention, + attn_group_sizes=attention_group_size, + ) + ), + ) + self.extra_layer_output_idx = extra_layer_output_idx + self.extra_multi_layer_output_idxs = extra_multi_layer_output_idxs + # Make a zeros scalar we can use in get_initial_state to determine + # the device and the needed dtype: + self.register_buffer("dev_type", torch.zeros(()), persistent=False) + + def init_relative_attention_bias(self, input_tensor): + if self.relative_attention_bias_layer: + return self.relative_attention_bias_layer(input_tensor) + + def calculate_hs_mask(self, xs_pad, device, mask): + max_audio_length = xs_pad.shape[1] + batch_size = xs_pad.shape[0] + enc_streaming_mask = self._streaming_mask( + max_audio_length, batch_size, self.chunk_size, self.left_chunk + ) + enc_streaming_mask = enc_streaming_mask.to(device) + if mask is None: + return enc_streaming_mask + + feature_lens = mask.sum(1) + padding_length = feature_lens + pad_mask = ( + torch.arange(0, max_audio_length, device=device).expand(padding_length.size(0), -1) + < padding_length.unsqueeze(1) + ) + pad_mask = pad_mask.unsqueeze(1) + pad_mask = pad_mask & enc_streaming_mask + return pad_mask + + @torch.jit.ignore + def forward(self, xs_pad, masks): + """Conformer Forward function + + Args: + xs_pad: torch.Tensor + input tensor + masks: torch.Tensor + post-embedding input lengths + """ + xs_pad = self.encoder_embedding(xs_pad) + input_tensor, pos_k, pos_v, hs_mask, masks = self.forward_embeddings(xs_pad, masks) + + unfolded = False + ori_bz, seq_len, D = input_tensor.shape + max_seq_len = 500 #maxium position for absolute positional encoding + if seq_len > max_seq_len: + # audio sequence is longer than max_seq_len, unfold it into chunks of max_seq_len + unfolded = True + # the unfold op will drop residual frames, pad it to the multiple of max_seq_len + if seq_len % max_seq_len > 0: + chunk_pad_size = max_seq_len - (seq_len % max_seq_len) + else: + chunk_pad_size = 0 + if chunk_pad_size > 0: + input_tensor_pad = F.pad(input_tensor, (0, 0, 0, chunk_pad_size), "constant", 0) + input_tensor = input_tensor_pad.to(input_tensor.device) + + input_tensor = unfold_tensor(input_tensor, max_seq_len) + if masks is not None: + # revise hs_mask here because the previous calculated hs_mask did not consider extra pad + subsampled_pad_mask = masks.squeeze(1) # [bz, subsampled_unmask_seq_len] + extra_padded_subsamlped_pad_mask = F.pad(subsampled_pad_mask, (0, chunk_pad_size), "constant", False) # extra padding to the pad mask + extra_padded_subsamlped_pad_mask = extra_padded_subsamlped_pad_mask.unsqueeze(-1).float() + masks_unfold = unfold_tensor(extra_padded_subsamlped_pad_mask, max_seq_len) # unfold the pad mask like we did to the input tensor + masks_unfold = masks_unfold.squeeze(-1).bool() # unfold op does not support bool tensor + else: + masks_unfold = None + hs_mask = self.calculate_hs_mask(input_tensor, input_tensor.device, masks_unfold) # calculate hs_mask based on the unfolded pad mask + layer_emb = None + + relative_attention_bias = self.init_relative_attention_bias(input_tensor) + + _simplified_path = ( + self.extra_layer_output_idx == -1 + and relative_attention_bias is None + ) + + if _simplified_path: + input_tensor, *_ = self.encoders(input_tensor, pos_k, pos_v, hs_mask) + else: + for i, layer in enumerate(self.encoders): + input_tensor, _, _, _ = layer( + input_tensor, + pos_k, + pos_v, + hs_mask, + relative_attention_bias=relative_attention_bias, + ) + + if i == self.extra_layer_output_idx: + layer_emb = input_tensor + if unfolded: + embed_dim = input_tensor.shape[-1] + input_tensor = input_tensor.reshape(ori_bz, -1, embed_dim) + # if we ever padded before unfolding, we need to remove the padding + if chunk_pad_size > 0: + input_tensor = input_tensor[:, :-chunk_pad_size, :] + return input_tensor, masks #, layer_emb + + def gradient_checkpointing_enable(self): + pass diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..feafd52 --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c1b9f641d4f8b7247b8d5007dd3b6a9f6a87cb5123134fe0d326f14d10c0585 +size 15524479 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000..eb04aec --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,125 @@ +{ + "add_prefix_space": false, + "added_tokens_decoder": { + "200010": { + "content": "<|endoftext10|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "200011": { + "content": "<|endoftext11|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "199999": { + "content": "<|endoftext|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "200018": { + "content": "<|endofprompt|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "200019": { + "content": "<|assistant|>", + "lstrip": false, + "normalized": false, + "rstrip": true, + "single_word": false, + "special": true + }, + "200020": { + "content": "<|end|>", + "lstrip": false, + "normalized": false, + "rstrip": true, + "single_word": false, + "special": true + }, + "200021": { + "content": "<|user|>", + "lstrip": false, + "normalized": false, + "rstrip": true, + "single_word": false, + "special": true + }, + "200022": { + "content": "<|system|>", + "lstrip": false, + "normalized": false, + "rstrip": true, + "single_word": false, + "special": true + }, + "200023": { + "content": "<|tool|>", + "lstrip": false, + "normalized": false, + "rstrip": true, + "single_word": false, + "special": false + }, + "200024": { + "content": "<|/tool|>", + "lstrip": false, + "normalized": false, + "rstrip": true, + "single_word": false, + "special": false + }, + "200025": { + "content": "<|tool_call|>", + "lstrip": false, + "normalized": false, + "rstrip": true, + "single_word": false, + "special": false + }, + "200026": { + "content": "<|/tool_call|>", + "lstrip": false, + "normalized": false, + "rstrip": true, + "single_word": false, + "special": false + }, + "200027": { + "content": "<|tool_response|>", + "lstrip": false, + "normalized": false, + "rstrip": true, + "single_word": false, + "special": false + }, + "200028": { + "content": "<|tag|>", + "lstrip": false, + "normalized": false, + "rstrip": true, + "single_word": false, + "special": true + } + }, + "bos_token": "<|endoftext|>", + "chat_template": "{% for message in messages %}{% if message['role'] == 'system' and 'tools' in message and message['tools'] is not none %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|tool|>' + message['tools'] + '<|/tool|>' + '<|end|>' }}{% else %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|end|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>' }}{% else %}{{ eos_token }}{% endif %}", + "clean_up_tokenization_spaces": false, + "eos_token": "<|endoftext|>", + "model_max_length": 131072, + "pad_token": "<|endoftext|>", + "tokenizer_class": "GPT2TokenizerFast", + "unk_token": "<|endoftext|>" +} diff --git a/vision-lora/adapter_config.json b/vision-lora/adapter_config.json new file mode 100644 index 0000000..72c07d6 --- /dev/null +++ b/vision-lora/adapter_config.json @@ -0,0 +1,23 @@ +{ + "auto_mapping": null, + "base_model_name_or_path": "TBA", + "bias": "none", + "fan_in_fan_out": false, + 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\ No newline at end of file diff --git a/vision_siglip_navit.py b/vision_siglip_navit.py new file mode 100644 index 0000000..e3da680 --- /dev/null +++ b/vision_siglip_navit.py @@ -0,0 +1,1717 @@ +# coding=utf-8 +# Copyright 2024 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. +""" Siglip model configuration""" + +import os +from typing import Union + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +SIGLIP_PRETRAINED_CONFIG_ARCHIVE_MAP = { + "google/siglip-base-patch16-224": "https://huggingface.co/google/siglip-base-patch16-224/resolve/main/config.json", +} + + +class SiglipTextConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`SiglipTextModel`]. It is used to instantiate a + Siglip text encoder according to the specified arguments, defining the model architecture. Instantiating a + configuration with the defaults will yield a similar configuration to that of the text encoder of the Siglip + [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) 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 32000): + Vocabulary size of the Siglip text model. Defines the number of different tokens that can be represented by + the `inputs_ids` passed when calling [`SiglipModel`]. + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + max_position_embeddings (`int`, *optional*, defaults to 64): + 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). + hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. + layer_norm_eps (`float`, *optional*, defaults to 1e-06): + The epsilon used by the layer normalization layers. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + pad_token_id (`int`, *optional*, defaults to 1): + The id of the padding token in the vocabulary. + bos_token_id (`int`, *optional*, defaults to 49406): + The id of the beginning-of-sequence token in the vocabulary. + eos_token_id (`int`, *optional*, defaults to 49407): + The id of the end-of-sequence token in the vocabulary. + Example: + ```python + >>> from transformers import SiglipTextConfig, SiglipTextModel + >>> # Initializing a SiglipTextConfig with google/siglip-base-patch16-224 style configuration + >>> configuration = SiglipTextConfig() + >>> # Initializing a SiglipTextModel (with random weights) from the google/siglip-base-patch16-224 style configuration + >>> model = SiglipTextModel(configuration) + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "siglip_text_model" + + def __init__( + self, + vocab_size=32000, + hidden_size=768, + intermediate_size=3072, + num_hidden_layers=12, + num_attention_heads=12, + max_position_embeddings=64, + hidden_act="gelu_pytorch_tanh", + layer_norm_eps=1e-6, + attention_dropout=0.0, + # This differs from `CLIPTokenizer`'s default and from openai/siglip + # See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538 + pad_token_id=1, + bos_token_id=49406, + eos_token_id=49407, + _flash_attn_2_enabled=True, + **kwargs, + ): + super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) + + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.max_position_embeddings = max_position_embeddings + self.layer_norm_eps = layer_norm_eps + self.hidden_act = hidden_act + self.attention_dropout = attention_dropout + self._flash_attn_2_enabled = _flash_attn_2_enabled + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": + cls._set_token_in_kwargs(kwargs) + + config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) + + # get the text config dict if we are loading from SiglipConfig + if config_dict.get("model_type") == "siglip": + config_dict = config_dict["text_config"] + + if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: + logger.warning( + f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " + f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." + ) + + return cls.from_dict(config_dict, **kwargs) + + +class SiglipVisionConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a + Siglip vision encoder according to the specified arguments, defining the model architecture. Instantiating a + configuration with the defaults will yield a similar configuration to that of the vision encoder of the Siglip + [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture. + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + Args: + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + num_channels (`int`, *optional*, defaults to 3): + Number of channels in the input images. + image_size (`int`, *optional*, defaults to 224): + The size (resolution) of each image. + patch_size (`int`, *optional*, defaults to 16): + The size (resolution) of each patch. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported. + layer_norm_eps (`float`, *optional*, defaults to 1e-06): + The epsilon used by the layer normalization layers. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + Example: + ```python + >>> from transformers import SiglipVisionConfig, SiglipVisionModel + >>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration + >>> configuration = SiglipVisionConfig() + >>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration + >>> model = SiglipVisionModel(configuration) + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "siglip_vision_model" + + def __init__( + self, + hidden_size=768, + intermediate_size=3072, + num_hidden_layers=12, + num_attention_heads=12, + num_channels=3, + image_size=224, + patch_size=16, + hidden_act="gelu_pytorch_tanh", + layer_norm_eps=1e-6, + attention_dropout=0.0, + _flash_attn_2_enabled=True, + **kwargs, + ): + super().__init__(**kwargs) + + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.num_channels = num_channels + self.patch_size = patch_size + self.image_size = image_size + self.attention_dropout = attention_dropout + self.layer_norm_eps = layer_norm_eps + self.hidden_act = hidden_act + self._flash_attn_2_enabled = _flash_attn_2_enabled + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": + cls._set_token_in_kwargs(kwargs) + + config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) + + # get the vision config dict if we are loading from SiglipConfig + if config_dict.get("model_type") == "siglip": + config_dict = config_dict["vision_config"] + + if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: + logger.warning( + f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " + f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." + ) + + return cls.from_dict(config_dict, **kwargs) + + +class SiglipConfig(PretrainedConfig): + r""" + [`SiglipConfig`] is the configuration class to store the configuration of a [`SiglipModel`]. It is used to + instantiate a Siglip model according to the specified arguments, defining the text model and vision model configs. + Instantiating a configuration with the defaults will yield a similar configuration to that of the Siglip + [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture. + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + Args: + text_config (`dict`, *optional*): + Dictionary of configuration options used to initialize [`SiglipTextConfig`]. + vision_config (`dict`, *optional*): + Dictionary of configuration options used to initialize [`SiglipVisionConfig`]. + kwargs (*optional*): + Dictionary of keyword arguments. + Example: + ```python + >>> from transformers import SiglipConfig, SiglipModel + >>> # Initializing a SiglipConfig with google/siglip-base-patch16-224 style configuration + >>> configuration = SiglipConfig() + >>> # Initializing a SiglipModel (with random weights) from the google/siglip-base-patch16-224 style configuration + >>> model = SiglipModel(configuration) + >>> # Accessing the model configuration + >>> configuration = model.config + >>> # We can also initialize a SiglipConfig from a SiglipTextConfig and a SiglipVisionConfig + >>> from transformers import SiglipTextConfig, SiglipVisionConfig + >>> # Initializing a SiglipText and SiglipVision configuration + >>> config_text = SiglipTextConfig() + >>> config_vision = SiglipVisionConfig() + >>> config = SiglipConfig.from_text_vision_configs(config_text, config_vision) + ```""" + + model_type = "siglip" + + def __init__(self, text_config=None, vision_config=None, **kwargs): + super().__init__(**kwargs) + + if text_config is None: + text_config = {} + logger.info("`text_config` is `None`. Initializing the `SiglipTextConfig` with default values.") + + if vision_config is None: + vision_config = {} + logger.info("`vision_config` is `None`. initializing the `SiglipVisionConfig` with default values.") + + self.text_config = SiglipTextConfig(**text_config) + self.vision_config = SiglipVisionConfig(**vision_config) + + self.initializer_factor = 1.0 + + @classmethod + def from_text_vision_configs(cls, text_config: SiglipTextConfig, vision_config: SiglipVisionConfig, **kwargs): + r""" + Instantiate a [`SiglipConfig`] (or a derived class) from siglip text model configuration and siglip vision + model configuration. + Returns: + [`SiglipConfig`]: An instance of a configuration object + """ + + return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs) + +# coding=utf-8 +# Copyright 2024 Google AI and The HuggingFace 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 Siglip model.""" + + +import math +import warnings +from dataclasses import dataclass +from typing import Any, Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn.init import _calculate_fan_in_and_fan_out + +from transformers.activations import ACT2FN +from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask +from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling +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, +) + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "google/siglip-base-patch16-224" + +SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "google/siglip-base-patch16-224", + # See all SigLIP models at https://huggingface.co/models?filter=siglip +] + +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.torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + +def _trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn( + "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2, + ) + + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + if tensor.dtype in [torch.float16, torch.bfloat16]: + # The `erfinv_` op is not (yet?) defined in float16+cpu, bfloat16+gpu + og_dtype = tensor.dtype + tensor = tensor.to(torch.float32) + tensor.erfinv_() + tensor = tensor.to(og_dtype) + else: + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.0)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + if tensor.dtype == torch.float16: + # The `clamp_` op is not (yet?) defined in float16+cpu + tensor = tensor.to(torch.float32) + tensor.clamp_(min=a, max=b) + tensor = tensor.to(torch.float16) + else: + tensor.clamp_(min=a, max=b) + + +def trunc_normal_tf_( + tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0 +) -> torch.Tensor: + """Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \\leq \text{mean} \\leq b`. + NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the + bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0 + and the result is subsquently scaled and shifted by the mean and std args. + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + """ + with torch.no_grad(): + _trunc_normal_(tensor, 0, 1.0, a, b) + tensor.mul_(std).add_(mean) + + +def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"): + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) + if mode == "fan_in": + denom = fan_in + elif mode == "fan_out": + denom = fan_out + elif mode == "fan_avg": + denom = (fan_in + fan_out) / 2 + + variance = scale / denom + + if distribution == "truncated_normal": + # constant is stddev of standard normal truncated to (-2, 2) + trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978) + elif distribution == "normal": + with torch.no_grad(): + tensor.normal_(std=math.sqrt(variance)) + elif distribution == "uniform": + bound = math.sqrt(3 * variance) + with torch.no_grad(): + tensor.uniform_(-bound, bound) + else: + raise ValueError(f"invalid distribution {distribution}") + + +def lecun_normal_(tensor): + variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal") + + +def default_flax_embed_init(tensor): + variance_scaling_(tensor, mode="fan_in", distribution="normal") + + +@dataclass +# Copied from transformers.models.clip.modeling_clip.CLIPVisionModelOutput with CLIP->Siglip +class SiglipVisionModelOutput(ModelOutput): + """ + Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states. + Args: + image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): + The image embeddings obtained by applying the projection layer to the pooler_output. + 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 model. + 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 model at the output of each layer plus the optional initial embedding outputs. + 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 after the attention softmax, used to compute the weighted average in the self-attention + heads. + """ + + image_embeds: Optional[torch.FloatTensor] = None + last_hidden_state: torch.FloatTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +@dataclass +# Copied from transformers.models.clip.modeling_clip.CLIPTextModelOutput with CLIP->Siglip +class SiglipTextModelOutput(ModelOutput): + """ + Base class for text model's outputs that also contains a pooling of the last hidden states. + Args: + text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): + The text embeddings obtained by applying the projection layer to the pooler_output. + 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 model. + 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 model at the output of each layer plus the optional initial embedding outputs. + 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 after the attention softmax, used to compute the weighted average in the self-attention + heads. + """ + + text_embeds: Optional[torch.FloatTensor] = None + last_hidden_state: torch.FloatTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +@dataclass +# Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->Siglip +class SiglipOutput(ModelOutput): + """ + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): + Contrastive loss for image-text similarity. + logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): + The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text + similarity scores. + logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): + The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image + similarity scores. + text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): + The text embeddings obtained by applying the projection layer to the pooled output of [`SiglipTextModel`]. + image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): + The image embeddings obtained by applying the projection layer to the pooled output of [`SiglipVisionModel`]. + text_model_output(`BaseModelOutputWithPooling`): + The output of the [`SiglipTextModel`]. + vision_model_output(`BaseModelOutputWithPooling`): + The output of the [`SiglipVisionModel`]. + """ + + loss: Optional[torch.FloatTensor] = None + logits_per_image: torch.FloatTensor = None + logits_per_text: torch.FloatTensor = None + text_embeds: torch.FloatTensor = None + image_embeds: torch.FloatTensor = None + text_model_output: BaseModelOutputWithPooling = None + vision_model_output: BaseModelOutputWithPooling = None + + def to_tuple(self) -> Tuple[Any]: + return tuple( + self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() + for k in self.keys() + ) + + +class SiglipVisionEmbeddings(nn.Module): + def __init__(self, config: SiglipVisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.image_size = config.image_size + self.patch_size = config.patch_size + + self.patch_embedding = nn.Conv2d( + in_channels=config.num_channels, + out_channels=self.embed_dim, + kernel_size=self.patch_size, + stride=self.patch_size, + padding="valid", + ) + + self.num_patches_per_side = self.image_size // self.patch_size + self.num_patches = self.num_patches_per_side**2 + self.num_positions = self.num_patches + self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) + + def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.BoolTensor) -> torch.Tensor: + batch_size = pixel_values.size(0) + + patch_embeds = self.patch_embedding(pixel_values) + embeddings = patch_embeds.flatten(2).transpose(1, 2) + + max_im_h, max_im_w = pixel_values.size(2), pixel_values.size(3) + max_nb_patches_h, max_nb_patches_w = max_im_h // self.patch_size, max_im_w // self.patch_size + boundaries = torch.arange(1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side) + position_ids = torch.full( + size=( + batch_size, + max_nb_patches_h * max_nb_patches_w, + ), + fill_value=0, + ) + + for batch_idx, p_attn_mask in enumerate(patch_attention_mask): + nb_patches_h = p_attn_mask[:, 0].sum() + nb_patches_w = p_attn_mask[0].sum() + + fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h) + fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w) + + bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True) + bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True) + + pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w).flatten() + position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids + + position_ids = position_ids.to(self.position_embedding.weight.device) + + embeddings = embeddings + self.position_embedding(position_ids) + return embeddings + + +# Copied from transformers.models.clip.modeling_clip.CLIPTextEmbeddings with CLIP->Siglip +class SiglipTextEmbeddings(nn.Module): + def __init__(self, config: SiglipTextConfig): + super().__init__() + embed_dim = config.hidden_size + + self.token_embedding = nn.Embedding(config.vocab_size, embed_dim) + self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False + ) + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + ) -> torch.Tensor: + seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2] + + if position_ids is None: + position_ids = self.position_ids[:, :seq_length] + + if inputs_embeds is None: + inputs_embeds = self.token_embedding(input_ids) + + position_embeddings = self.position_embedding(position_ids) + embeddings = inputs_embeds + position_embeddings + + return embeddings + + +class SiglipAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + # Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__ + def __init__(self, config): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.scale = self.head_dim**-0.5 + self.dropout = config.attention_dropout + + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + batch_size, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2) + + k_v_seq_len = key_states.shape[-2] + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale + + if attn_weights.size() != (batch_size, self.num_heads, q_len, k_v_seq_len): + raise ValueError( + f"Attention weights should be of size {(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len): + raise ValueError( + f"Attention mask should be of size {(batch_size, 1, q_len, k_v_seq_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (batch_size, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(batch_size, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +class SiglipFlashAttention2(SiglipAttention): + """ + Llama flash attention module. This module inherits from `LlamaAttention` 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. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.is_causal = False # Hack to make sure we don't use a causal mask + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + output_attentions = False + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + # Flash attention requires the input to have the shape + # batch_size x seq_length x head_dim x hidden_dim + # therefore we just need to keep the original shape + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + # cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + # query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + # if past_key_value is not None: + # cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + # key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache + # to be able to avoid many of these transpose/reshape/view. + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + dropout_rate = self.dropout if self.training else 0.0 + + # 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( + "The input hidden states seems to be silently casted in float32, this might be related to 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=dropout_rate + ) + + attn_output = attn_output.reshape(bsz, q_len, self.embed_dim).contiguous() + attn_output = self.out_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights + + 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 (`int`, *optional*): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + """ + + # 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 + + 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), + ) + + +# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip +class SiglipMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->Siglip +class SiglipEncoderLayer(nn.Module): + def __init__(self, config: SiglipConfig): + super().__init__() + self.embed_dim = config.hidden_size + self.self_attn = ( + SiglipAttention(config) + if not getattr(config, "_flash_attn_2_enabled", False) + else SiglipFlashAttention2(config) + ) + self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.mlp = SiglipMLP(config) + self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + output_attentions: Optional[bool] = False, + ) -> Tuple[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 shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values. + output_attentions (`bool`, *optional*, defaults to `False`): + 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 = self.layer_norm1(hidden_states) + hidden_states, attn_weights = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +class SiglipPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = SiglipConfig + base_model_prefix = "siglip" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + """Initialize the weights""" + + if isinstance(module, SiglipVisionEmbeddings): + width = ( + self.config.vision_config.hidden_size + if isinstance(self.config, SiglipConfig) + else self.config.hidden_size + ) + nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width)) + elif isinstance(module, nn.Embedding): + default_flax_embed_init(module.weight) + elif isinstance(module, SiglipAttention): + nn.init.normal_(module.q_proj.weight) + nn.init.normal_(module.k_proj.weight) + nn.init.normal_(module.v_proj.weight) + nn.init.normal_(module.out_proj.weight) + nn.init.zeros_(module.q_proj.bias) + nn.init.zeros_(module.k_proj.bias) + nn.init.zeros_(module.v_proj.bias) + nn.init.zeros_(module.out_proj.bias) + elif isinstance(module, SiglipMLP): + nn.init.normal_(module.fc1.weight) + nn.init.normal_(module.fc2.weight) + nn.init.normal_(module.fc1.bias, std=1e-6) + nn.init.normal_(module.fc2.bias, std=1e-6) + elif isinstance(module, SiglipMultiheadAttentionPoolingHead): + nn.init.normal_(module.probe.data) + nn.init.normal_(module.attention.in_proj_weight.data) + nn.init.zeros_(module.attention.in_proj_bias.data) + elif isinstance(module, SiglipModel): + logit_scale_init = torch.tensor(0.0) + module.logit_scale.data.fill_(logit_scale_init) + module.logit_bias.data.zero_() + elif isinstance(module, (nn.Linear, nn.Conv2d)): + lecun_normal_(module.weight) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +SIGLIP_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 ([`SiglipConfig`]): 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. +""" + +SIGLIP_TEXT_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) + 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) + 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.max_position_embeddings - 1]`. + [What are position IDs?](../glossary#position-ids) + 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. +""" + +SIGLIP_VISION_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using + [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. + 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. +""" + +SIGLIP_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) + 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) + 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.max_position_embeddings - 1]`. + [What are position IDs?](../glossary#position-ids) + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using + [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. + return_loss (`bool`, *optional*): + Whether or not to return the contrastive loss. + 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. +""" + + +# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->Siglip +class SiglipEncoder(nn.Module): + """ + Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a + [`SiglipEncoderLayer`]. + Args: + config: SiglipConfig + """ + + def __init__(self, config: SiglipConfig): + super().__init__() + self.config = config + self.layers = nn.ModuleList([SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + # Ignore copy + def forward( + self, + inputs_embeds, + attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutput]: + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + 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. + 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) + 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 + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + hidden_states = inputs_embeds + for encoder_layer in self.layers: + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + encoder_layer.__call__, + hidden_states, + attention_mask, + output_attentions, + ) + else: + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + 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 SiglipTextTransformer(nn.Module): + def __init__(self, config: SiglipTextConfig): + super().__init__() + self.config = config + embed_dim = config.hidden_size + self.embeddings = SiglipTextEmbeddings(config) + self.encoder = SiglipEncoder(config) + self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + + self.head = nn.Linear(embed_dim, embed_dim) + + @add_start_docstrings_to_model_forward(SIGLIP_TEXT_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipTextConfig) + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPooling]: + r""" + Returns: + """ + 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 + + if input_ids is None: + raise ValueError("You have to specify input_ids") + + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + + hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids) + + # note: SigLIP's text model does not use a causal mask, unlike the original CLIP model. + # expand attention_mask + if attention_mask is not None: + # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len] + attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype) + + encoder_outputs = self.encoder( + inputs_embeds=hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + last_hidden_state = encoder_outputs[0] + last_hidden_state = self.final_layer_norm(last_hidden_state) + + # Assuming "sticky" EOS tokenization, last token is always EOS. + pooled_output = last_hidden_state[:, -1, :] + pooled_output = self.head(pooled_output) + + if not return_dict: + return (last_hidden_state, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@add_start_docstrings( + """The text model from SigLIP without any head or projection on top.""", + SIGLIP_START_DOCSTRING, +) +class SiglipTextModel(SiglipPreTrainedModel): + config_class = SiglipTextConfig + + _no_split_modules = ["SiglipTextEmbeddings", "SiglipEncoderLayer"] + + def __init__(self, config: SiglipTextConfig): + super().__init__(config) + self.text_model = SiglipTextTransformer(config) + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.text_model.embeddings.token_embedding + + def set_input_embeddings(self, value): + self.text_model.embeddings.token_embedding = value + + @add_start_docstrings_to_model_forward(SIGLIP_TEXT_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipTextConfig) + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPooling]: + r""" + Returns: + Examples: + ```python + >>> from transformers import AutoTokenizer, SiglipTextModel + >>> model = SiglipTextModel.from_pretrained("google/siglip-base-patch16-224") + >>> tokenizer = AutoTokenizer.from_pretrained("google/siglip-base-patch16-224") + >>> # important: make sure to set padding="max_length" as that's how the model was trained + >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt") + >>> outputs = model(**inputs) + >>> last_hidden_state = outputs.last_hidden_state + >>> pooled_output = outputs.pooler_output # pooled (EOS token) states + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + return self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + +class SiglipVisionTransformer(nn.Module): + def __init__(self, config: SiglipVisionConfig): + super().__init__() + self.config = config + embed_dim = config.hidden_size + + self.embeddings = SiglipVisionEmbeddings(config) + self.encoder = SiglipEncoder(config) + self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + self.head = SiglipMultiheadAttentionPoolingHead(config) + + @add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipVisionConfig) + def forward( + self, + pixel_values, + patch_attention_mask: Optional[torch.BoolTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPooling]: + r""" + Returns: + """ + 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 + + batch_size = pixel_values.size(0) + if patch_attention_mask is None: + patch_attention_mask = torch.ones( + size=( + batch_size, + pixel_values.size(2) // self.config.patch_size, + pixel_values.size(3) // self.config.patch_size, + ), + dtype=torch.bool, + device=pixel_values.device, + ) + + hidden_states = self.embeddings(pixel_values=pixel_values, patch_attention_mask=patch_attention_mask) + + patch_attention_mask = patch_attention_mask.view(batch_size, -1) + # The call to `_upad_input` in `_flash_attention_forward` is expensive + # So when the `patch_attention_mask` is full of 1s (i.e. attending to the whole sequence), + # avoiding passing the attention_mask, which is equivalent to attending to the full sequence + if not torch.any(~patch_attention_mask): + attention_mask=None + else: + attention_mask = ( + _prepare_4d_attention_mask(patch_attention_mask, hidden_states.dtype) + if not self.config._flash_attn_2_enabled + else patch_attention_mask + ) + + encoder_outputs = self.encoder( + inputs_embeds=hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + last_hidden_state = encoder_outputs[0] + last_hidden_state = self.post_layernorm(last_hidden_state) + + pooled_output = self.head( + hidden_state=last_hidden_state, + attention_mask=patch_attention_mask, + ) + + if not return_dict: + return (last_hidden_state, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +class SiglipMultiheadAttentionPoolingHead(nn.Module): + """Multihead Attention Pooling.""" + + def __init__(self, config: SiglipVisionConfig): + super().__init__() + + self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size)) + self.attention = torch.nn.MultiheadAttention(config.hidden_size, config.num_attention_heads, batch_first=True) + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.mlp = SiglipMLP(config) + + def forward(self, hidden_state, attention_mask): + batch_size = hidden_state.shape[0] + probe = self.probe.repeat(batch_size, 1, 1) + + hidden_state = self.attention( + query=probe, key=hidden_state, value=hidden_state, key_padding_mask=~attention_mask + )[0] + + residual = hidden_state + hidden_state = self.layernorm(hidden_state) + hidden_state = residual + self.mlp(hidden_state) + + return hidden_state[:, 0] + + +@add_start_docstrings( + """The vision model from SigLIP without any head or projection on top.""", + SIGLIP_START_DOCSTRING, +) +class SiglipVisionModel(SiglipPreTrainedModel): + config_class = SiglipVisionConfig + main_input_name = "pixel_values" + + def __init__(self, config: SiglipVisionConfig): + super().__init__(config) + + self.vision_model = SiglipVisionTransformer(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.vision_model.embeddings.patch_embedding + + @add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipVisionConfig) + def forward( + self, + pixel_values, + patch_attention_mask: Optional[torch.BoolTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPooling]: + r""" + Returns: + Examples: + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import AutoProcessor, SiglipVisionModel + >>> model = SiglipVisionModel.from_pretrained("google/siglip-base-patch16-224") + >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224") + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + >>> inputs = processor(images=image, return_tensors="pt") + >>> outputs = model(**inputs) + >>> last_hidden_state = outputs.last_hidden_state + >>> pooled_output = outputs.pooler_output # pooled features + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + return self.vision_model( + pixel_values=pixel_values, + patch_attention_mask=patch_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + +@add_start_docstrings(SIGLIP_START_DOCSTRING) +class SiglipModel(SiglipPreTrainedModel): + config_class = SiglipConfig + + def __init__(self, config: SiglipConfig): + super().__init__(config) + + if not isinstance(config.text_config, SiglipTextConfig): + raise ValueError( + "config.text_config is expected to be of type SiglipTextConfig but is of type" + f" {type(config.text_config)}." + ) + + if not isinstance(config.vision_config, SiglipVisionConfig): + raise ValueError( + "config.vision_config is expected to be of type SiglipVisionConfig but is of type" + f" {type(config.vision_config)}." + ) + + text_config = config.text_config + vision_config = config.vision_config + + self.text_model = SiglipTextTransformer(text_config) + self.vision_model = SiglipVisionTransformer(vision_config) + + self.logit_scale = nn.Parameter(torch.randn(1)) + self.logit_bias = nn.Parameter(torch.randn(1)) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(SIGLIP_TEXT_INPUTS_DOCSTRING) + def get_text_features( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> torch.FloatTensor: + r""" + Returns: + text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by + applying the projection layer to the pooled output of [`SiglipTextModel`]. + Examples: + ```python + >>> from transformers import AutoTokenizer, AutoModel + >>> import torch + >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224") + >>> tokenizer = AutoTokenizer.from_pretrained("google/siglip-base-patch16-224") + >>> # important: make sure to set padding="max_length" as that's how the model was trained + >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt") + >>> with torch.no_grad(): + ... text_features = model.get_text_features(**inputs) + ```""" + # Use SigLIP model's config for some fields (if specified) instead of those of vision & text components. + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + text_outputs = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + pooled_output = text_outputs[1] + + return pooled_output + + @add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING) + def get_image_features( + self, + pixel_values: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> torch.FloatTensor: + r""" + Returns: + image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by + applying the projection layer to the pooled output of [`SiglipVisionModel`]. + Examples: + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import AutoProcessor, AutoModel + >>> import torch + >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224") + >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224") + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + >>> inputs = processor(images=image, return_tensors="pt") + >>> with torch.no_grad(): + ... image_features = model.get_image_features(**inputs) + ```""" + # Use SiglipModel's config for some fields (if specified) instead of those of vision & text components. + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + vision_outputs = self.vision_model( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + pooled_output = vision_outputs[1] + + return pooled_output + + @add_start_docstrings_to_model_forward(SIGLIP_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=SiglipOutput, config_class=SiglipConfig) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + pixel_values: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + return_loss: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SiglipOutput]: + r""" + Returns: + Examples: + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import AutoProcessor, AutoModel + >>> import torch + >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224") + >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224") + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + >>> texts = ["a photo of 2 cats", "a photo of 2 dogs"] + >>> # important: we pass `padding=max_length` since the model was trained with this + >>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt") + >>> with torch.no_grad(): + ... outputs = model(**inputs) + >>> logits_per_image = outputs.logits_per_image + >>> probs = torch.sigmoid(logits_per_image) # these are the probabilities + >>> print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'") + 31.9% that image 0 is 'a photo of 2 cats' + ```""" + # Use SigLIP model's config for some fields (if specified) instead of those of vision & text components. + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + vision_outputs = self.vision_model( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + text_outputs = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + image_embeds = vision_outputs[1] + text_embeds = text_outputs[1] + + # normalized features + image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True) + text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True) + + # cosine similarity as logits + logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * self.logit_scale.exp() + self.logit_bias + logits_per_image = logits_per_text.t() + + loss = None + if return_loss: + raise NotImplementedError("SigLIP loss to be implemented") + + if not return_dict: + output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) + return ((loss,) + output) if loss is not None else output + + return SiglipOutput( + loss=loss, + logits_per_image=logits_per_image, + logits_per_text=logits_per_text, + text_embeds=text_embeds, + image_embeds=image_embeds, + text_model_output=text_outputs, + vision_model_output=vision_outputs, + ) + + +def get_siglip_vision_model(_flash_attn_2_enabled=True, **kwargs): + siglip_vision_config = { + "hidden_size": 1152, + "image_size": 448, + "intermediate_size": 4304, + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14, + } + + model_config = SiglipVisionConfig(**siglip_vision_config, _flash_attn_2_enabled=_flash_attn_2_enabled, **kwargs) + + vision_model = SiglipVisionModel(model_config).vision_model + + return vision_model diff --git a/vocab.json b/vocab.json new file mode 100644 index 0000000..ea953a4 --- /dev/null +++ b/vocab.json @@ -0,0 +1 @@ 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