├── .gitmodules ├── huggingface-ios-app ├── huggingface-ios-app │ ├── Assets.xcassets │ │ ├── Contents.json │ │ ├── AccentColor.colorset │ │ │ └── Contents.json │ │ └── AppIcon.appiconset │ │ │ └── Contents.json │ ├── ViewController.swift │ ├── Info.plist │ ├── Base.lproj │ │ ├── Main.storyboard │ │ └── LaunchScreen.storyboard │ ├── AppDelegate.swift │ └── SceneDelegate.swift ├── huggingface-ios-app.xcodeproj │ ├── project.xcworkspace │ │ ├── contents.xcworkspacedata │ │ └── xcshareddata │ │ │ └── IDEWorkspaceChecks.plist │ ├── xcuserdata │ │ └── danielvela.xcuserdatad │ │ │ ├── xcschemes │ │ │ └── xcschememanagement.plist │ │ │ └── xcdebugger │ │ │ └── Breakpoints_v2.xcbkptlist │ ├── xcshareddata │ │ └── xcschemes │ │ │ └── huggingface-ios-app.xcscheme │ └── project.pbxproj ├── Sources │ ├── BartModel.swift │ ├── DialogGPTModel.swift │ ├── Utils.swift │ ├── T5SmallModel.swift │ ├── GPT2Model.swift │ ├── Math.swift │ ├── GPT2Tokenizer.swift │ ├── BartTokenizer.swift │ ├── MLMultiArray+Utils.swift │ └── DistilGPT2Tokenizer.swift ├── huggingface-ios-appTests │ └── Distilgpt2Tests.swift └── Resources │ └── models │ └── facebook │ └── blenderbot-400M-distill │ └── merges.txt ├── .gitignore ├── try.py ├── create-jit.py ├── create-chat-model.py ├── readme.md └── search-models.py /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "exporters"] 2 | path = exporters 3 | url = https://github.com/huggingface/exporters.git 4 | -------------------------------------------------------------------------------- /huggingface-ios-app/huggingface-ios-app/Assets.xcassets/Contents.json: -------------------------------------------------------------------------------- 1 | { 2 | "info" : { 3 | "author" : "xcode", 4 | "version" : 1 5 | } 6 | } 7 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | /models 2 | /exported 3 | /huggingface-ios-app/Resources/models 4 | 5 | ## User settings 6 | xcuserdata/ 7 | 8 | ## Xcode 8 and earlier 9 | *.xcscmblueprint 10 | *.xccheckout 11 | 12 | *.mlpackage 13 | 14 | -------------------------------------------------------------------------------- /huggingface-ios-app/huggingface-ios-app.xcodeproj/project.xcworkspace/contents.xcworkspacedata: -------------------------------------------------------------------------------- 1 | 2 | 4 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /huggingface-ios-app/huggingface-ios-app/Assets.xcassets/AccentColor.colorset/Contents.json: -------------------------------------------------------------------------------- 1 | { 2 | "colors" : [ 3 | { 4 | "idiom" : "universal" 5 | } 6 | ], 7 | "info" : { 8 | "author" : "xcode", 9 | "version" : 1 10 | } 11 | } 12 | -------------------------------------------------------------------------------- /huggingface-ios-app/huggingface-ios-app/Assets.xcassets/AppIcon.appiconset/Contents.json: -------------------------------------------------------------------------------- 1 | { 2 | "images" : [ 3 | { 4 | "idiom" : "universal", 5 | "platform" : "ios", 6 | "size" : "1024x1024" 7 | } 8 | ], 9 | "info" : { 10 | "author" : "xcode", 11 | "version" : 1 12 | } 13 | } 14 | -------------------------------------------------------------------------------- /huggingface-ios-app/huggingface-ios-app.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | IDEDidComputeMac32BitWarning 6 | 7 | 8 | 9 | -------------------------------------------------------------------------------- /huggingface-ios-app/huggingface-ios-app/ViewController.swift: -------------------------------------------------------------------------------- 1 | // 2 | // ViewController.swift 3 | // huggingface-ios-app 4 | // 5 | // Created by Daniel Vela on 10/5/23. 6 | // 7 | 8 | import UIKit 9 | 10 | class ViewController: UIViewController { 11 | 12 | override func viewDidLoad() { 13 | super.viewDidLoad() 14 | // Do any additional setup after loading the view. 15 | } 16 | 17 | 18 | } 19 | 20 | -------------------------------------------------------------------------------- /huggingface-ios-app/huggingface-ios-app/Info.plist: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | UIApplicationSceneManifest 6 | 7 | UIApplicationSupportsMultipleScenes 8 | 9 | UISceneConfigurations 10 | 11 | UIWindowSceneSessionRoleApplication 12 | 13 | 14 | UISceneConfigurationName 15 | Default Configuration 16 | UISceneDelegateClassName 17 | $(PRODUCT_MODULE_NAME).SceneDelegate 18 | UISceneStoryboardFile 19 | Main 20 | 21 | 22 | 23 | 24 | 25 | 26 | -------------------------------------------------------------------------------- /huggingface-ios-app/huggingface-ios-app.xcodeproj/xcuserdata/danielvela.xcuserdatad/xcschemes/xcschememanagement.plist: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | SchemeUserState 6 | 7 | huggingface-ios-app.xcscheme_^#shared#^_ 8 | 9 | orderHint 10 | 0 11 | 12 | 13 | SuppressBuildableAutocreation 14 | 15 | 0BB76E862A0B9E6C000D9187 16 | 17 | primary 18 | 19 | 20 | 0BB76E9C2A0B9E6D000D9187 21 | 22 | primary 23 | 24 | 25 | 0BB76EA62A0B9E6D000D9187 26 | 27 | primary 28 | 29 | 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /try.py: -------------------------------------------------------------------------------- 1 | from transformers import BlenderbotTokenizer 2 | from transformers import BlenderbotForConditionalGeneration 3 | 4 | def get_models(): 5 | # pytorch keeps an internal state of the conversation 6 | model_name = "facebook/blenderbot-400M-distill" 7 | # model_name = "facebook/blenderbot-1B-distill" 8 | # model_name = "facebook/blenderbot-90M" 9 | tokenizer = BlenderbotTokenizer.from_pretrained(model_name, cache_dir="./models") 10 | model = BlenderbotForConditionalGeneration.from_pretrained(model_name) 11 | return tokenizer, model 12 | 13 | def generate_answer(): 14 | tokenizer, model = get_models() 15 | print(tokenizer(" Hello world")["input_ids"]) 16 | # Ask user for input from command line 17 | user_message = input(">> ") 18 | inputs = tokenizer(user_message, return_tensors="pt") 19 | result = model.generate(**inputs) 20 | message_bot = tokenizer.decode( 21 | result[0], skip_special_tokens=True 22 | ) # .replace("", "").replace("", "") 23 | 24 | print(message_bot) 25 | 26 | 27 | print("Talk to the bot") 28 | 29 | while True: 30 | generate_answer() -------------------------------------------------------------------------------- /create-jit.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from transformers import AutoTokenizer 3 | from transformers import AutoModelForSeq2SeqLM, AutoModelForCausalLM, GPT2LMHeadModel, AutoModel 4 | 5 | 6 | # Load "microsoft/DialoGPT-small" 7 | # torch_model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small", torchscript=True) 8 | torch_model = AutoModelForCausalLM.from_pretrained("facebook/blenderbot_small-90M", torchscript=True) 9 | # Set the model in evaluation mode. 10 | torch_model.eval() 11 | 12 | # Trace the model with random data. 13 | # tensor of random ints 14 | example_input = torch.randint(0, 100, (1,128), dtype=torch.int64) 15 | print(example_input.shape) 16 | print(example_input) 17 | traced_model = torch.jit.trace(torch_model, example_input) 18 | out = traced_model(example_input) 19 | print(out[0].shape) 20 | 21 | 22 | 23 | import coremltools as ct 24 | import coremltools.models.datatypes as datatypes 25 | sequence_length = 64 26 | array_input = datatypes.Array(sequence_length) 27 | 28 | # Define inputs and outputs for the Core ML model. 29 | input_shape = ct.Shape(shape=(1, 30 | 3, 31 | ct.RangeDim(lower_bound=25, upper_bound=100, default=45), 32 | ct.RangeDim(lower_bound=25, upper_bound=100, default=45))) 33 | 34 | 35 | # Using image_input in the inputs parameter: 36 | # Convert to Core ML program using the Unified Conversion API. 37 | model = ct.convert( 38 | traced_model, 39 | inputs=[ct.TensorType(shape=example_input.shape, name="input")], 40 | # outputs=[ct.TensorType(name="output")], 41 | convert_to="mlprogram" 42 | ) 43 | 44 | # Save the converted model. 45 | model.save("t5-small.mlpackage") -------------------------------------------------------------------------------- /huggingface-ios-app/Sources/BartModel.swift: -------------------------------------------------------------------------------- 1 | // 2 | // BartModel.swift 3 | // huggingface-ios-app 4 | // 5 | // Created by Daniel Vela on 12/5/23. 6 | // 7 | 8 | import Foundation 9 | import CoreML 10 | 11 | class BartModel { 12 | let encoder: bart_large_encoder 13 | // let decoder: bart_large_decoder 14 | let tokenizer: DistilGPT2Tokenizer 15 | let seqLen = 128 16 | 17 | init() throws { 18 | let modelName = "facebook/bart-large" 19 | self.tokenizer = try DistilGPT2Tokenizer.from_pretrained(modelName) 20 | self.encoder = try bart_large_encoder() 21 | // self.decoder = try bart_large_decoder() 22 | } 23 | 24 | func predict(_ string: String) throws -> String { 25 | let tokens = tokenizer.encode(text: string) 26 | 27 | let maxTokens = (tokens.count > seqLen) 28 | ? Array(tokens[.. 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | -------------------------------------------------------------------------------- /huggingface-ios-app/huggingface-ios-app/Base.lproj/LaunchScreen.storyboard: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | -------------------------------------------------------------------------------- /create-chat-model.py: -------------------------------------------------------------------------------- 1 | from exporters.coreml import export, validate_model_outputs, CoreMLConfig 2 | from exporters.coreml.models import * 3 | from transformers import AutoTokenizer 4 | from transformers import AutoModelForSeq2SeqLM, AutoModelForCausalLM 5 | 6 | 7 | class DialogGPT2CoreMLConfig(GPT2CoreMLConfig): 8 | @property 9 | def inputs(self) -> OrderedDict[str, InputDescription]: 10 | input_descs = super().inputs 11 | input_descs["input_ids"].sequence_length = 64 12 | return input_descs 13 | 14 | feature = "text2text-generation" 15 | # feature = "text-generation" 16 | 17 | models = [ 18 | # ("Alethea/GPT2-chitchat", DialogGPT2CoreMLConfig, AutoModelForCausalLM), 19 | ("microsoft/DialoGPT-small", DialogGPT2CoreMLConfig, AutoModelForCausalLM) 20 | # ("microsoft/DialoGPT-medium", DialogGPT2CoreMLConfig, AutoModelForCausalLM) 21 | # ("microsoft/DialoGPT-large", DialogGPT2CoreMLConfig, AutoModelForCausalLM) 22 | # ("google/flan-t5-small", T5CoreMLConfig, AutoModelForSeq2SeqLM) 23 | ] 24 | 25 | for model_ckpt, config_class, auto_model in models: 26 | print("--------------------------------") 27 | print(" EXPORTING: ", model_ckpt) 28 | base_model = auto_model.from_pretrained( 29 | model_ckpt, torchscript=True 30 | ) 31 | preprocessor = AutoTokenizer.from_pretrained(model_ckpt) 32 | 33 | coreml_config = config_class( 34 | base_model.config, 35 | task=feature, 36 | use_past=False 37 | ) 38 | mlmodel = export( 39 | preprocessor, base_model, coreml_config 40 | ) 41 | validate_model_outputs( 42 | coreml_config, 43 | preprocessor, 44 | base_model, 45 | mlmodel, 46 | coreml_config.atol_for_validation, 47 | ) 48 | mlmodel.save(f"exported/{model_ckpt}.mlpackage") 49 | 50 | print("END --------------------------------") 51 | -------------------------------------------------------------------------------- /huggingface-ios-app/huggingface-ios-app/AppDelegate.swift: -------------------------------------------------------------------------------- 1 | // 2 | // AppDelegate.swift 3 | // huggingface-ios-app 4 | // 5 | // Created by Daniel Vela on 10/5/23. 6 | // 7 | 8 | import UIKit 9 | 10 | @main 11 | class AppDelegate: UIResponder, UIApplicationDelegate { 12 | 13 | 14 | 15 | func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplication.LaunchOptionsKey: Any]?) -> Bool { 16 | // Override point for customization after application launch. 17 | do { 18 | // let model = try T5SmallModel() 19 | // try model.generate(text: "Hola Mundo") 20 | let model = try GPT2Model() 21 | let result = model.generate(text: "Does money buy happiness?", nTokens: 128, callback: { string, double in 22 | // print(string) 23 | }) 24 | print(result) 25 | } catch { 26 | print(error.localizedDescription) 27 | } 28 | return true 29 | } 30 | 31 | // MARK: UISceneSession Lifecycle 32 | 33 | func application(_ application: UIApplication, configurationForConnecting connectingSceneSession: UISceneSession, options: UIScene.ConnectionOptions) -> UISceneConfiguration { 34 | // Called when a new scene session is being created. 35 | // Use this method to select a configuration to create the new scene with. 36 | return UISceneConfiguration(name: "Default Configuration", sessionRole: connectingSceneSession.role) 37 | } 38 | 39 | func application(_ application: UIApplication, didDiscardSceneSessions sceneSessions: Set) { 40 | // Called when the user discards a scene session. 41 | // If any sessions were discarded while the application was not running, this will be called shortly after application:didFinishLaunchingWithOptions. 42 | // Use this method to release any resources that were specific to the discarded scenes, as they will not return. 43 | } 44 | 45 | 46 | } 47 | 48 | -------------------------------------------------------------------------------- /huggingface-ios-app/Sources/DialogGPTModel.swift: -------------------------------------------------------------------------------- 1 | // 2 | // DialogGPTModel.swift 3 | // huggingface-ios-app 4 | // 5 | // Created by Daniel Vela on 14/5/23. 6 | // 7 | 8 | import Foundation 9 | import CoreML 10 | 11 | class DialogGPTModel { 12 | let model: DialoGPT_small 13 | let seqLen = 12 14 | 15 | let eos_token_id = 50256 16 | 17 | init() throws { 18 | self.model = try DialoGPT_small() 19 | } 20 | 21 | func generate(_ string: String) throws -> String { 22 | var tokens = [13921, 1637, 2822, 12157, 30, 50256] 23 | 24 | 25 | for _ in 0..<6 { 26 | var next_token = try predict(tokens) 27 | tokens.append(next_token) 28 | print(String(next_token) + ", ") 29 | } 30 | 31 | return "" 32 | } 33 | 34 | private func predict(_ tokens: [Int]) throws -> Int { 35 | var prev_prob = 0.0 36 | var next_token = 0 37 | let maxTokens = (tokens.count > seqLen) 38 | ? Array(tokens[.. prev_prob { 61 | next_token = tok 62 | prev_prob = prob 63 | } 64 | 65 | } 66 | 67 | return next_token 68 | } 69 | } 70 | -------------------------------------------------------------------------------- /huggingface-ios-app/Sources/Utils.swift: -------------------------------------------------------------------------------- 1 | // 2 | // Utils.swift 3 | // AudioBoloss 4 | // 5 | // Created by Julien Chaumond on 07/01/2019. 6 | // Copyright © 2019 Hugging Face. All rights reserved. 7 | // 8 | 9 | import Foundation 10 | 11 | struct Utils { 12 | /// Time a block in ms 13 | static func time(label: String, _ block: () -> T) -> T { 14 | let startTime = CFAbsoluteTimeGetCurrent() 15 | let result = block() 16 | let diff = (CFAbsoluteTimeGetCurrent() - startTime) * 1_000 17 | print("[\(label)] \(diff)ms") 18 | return result 19 | } 20 | 21 | /// Time a block in seconds and return (output, time) 22 | static func time(_ block: () -> T) -> (T, Double) { 23 | let startTime = CFAbsoluteTimeGetCurrent() 24 | let result = block() 25 | let diff = CFAbsoluteTimeGetCurrent() - startTime 26 | return (result, diff) 27 | } 28 | 29 | /// Return unix timestamp in ms 30 | static func dateNow() -> Int64 { 31 | // Use `Int` when we don't support 32-bits devices/OSes anymore. 32 | // Int crashes on iPhone 5c. 33 | return Int64(Date().timeIntervalSince1970 * 1000) 34 | } 35 | 36 | /// Clamp a val to [min, max] 37 | static func clamp(_ val: T, _ vmin: T, _ vmax: T) -> T { 38 | return min(max(vmin, val), vmax) 39 | } 40 | 41 | /// Fake func that can throw. 42 | static func fakeThrowable(_ input: T) throws -> T { 43 | return input 44 | } 45 | 46 | /// Substring 47 | static func substr(_ s: String, _ r: Range) -> String? { 48 | let stringCount = s.count 49 | if stringCount < r.upperBound || stringCount < r.lowerBound { 50 | return nil 51 | } 52 | let startIndex = s.index(s.startIndex, offsetBy: r.lowerBound) 53 | let endIndex = s.index(startIndex, offsetBy: r.upperBound - r.lowerBound) 54 | return String(s[startIndex..(_ dict: Dictionary) -> Dictionary { 59 | var inverted: [V: K] = [:] 60 | for (k, v) in dict { 61 | inverted[v] = k 62 | } 63 | return inverted 64 | } 65 | } 66 | 67 | -------------------------------------------------------------------------------- /huggingface-ios-app/huggingface-ios-app.xcodeproj/xcuserdata/danielvela.xcuserdatad/xcdebugger/Breakpoints_v2.xcbkptlist: -------------------------------------------------------------------------------- 1 | 2 | 6 | 7 | 9 | 21 | 22 | 23 | 25 | 37 | 38 | 39 | 41 | 53 | 54 | 55 | 56 | 57 | -------------------------------------------------------------------------------- /huggingface-ios-app/huggingface-ios-app/SceneDelegate.swift: -------------------------------------------------------------------------------- 1 | // 2 | // SceneDelegate.swift 3 | // huggingface-ios-app 4 | // 5 | // Created by Daniel Vela on 10/5/23. 6 | // 7 | 8 | import UIKit 9 | 10 | class SceneDelegate: UIResponder, UIWindowSceneDelegate { 11 | 12 | var window: UIWindow? 13 | 14 | 15 | func scene(_ scene: UIScene, willConnectTo session: UISceneSession, options connectionOptions: UIScene.ConnectionOptions) { 16 | // Use this method to optionally configure and attach the UIWindow `window` to the provided UIWindowScene `scene`. 17 | // If using a storyboard, the `window` property will automatically be initialized and attached to the scene. 18 | // This delegate does not imply the connecting scene or session are new (see `application:configurationForConnectingSceneSession` instead). 19 | guard let _ = (scene as? UIWindowScene) else { return } 20 | } 21 | 22 | func sceneDidDisconnect(_ scene: UIScene) { 23 | // Called as the scene is being released by the system. 24 | // This occurs shortly after the scene enters the background, or when its session is discarded. 25 | // Release any resources associated with this scene that can be re-created the next time the scene connects. 26 | // The scene may re-connect later, as its session was not necessarily discarded (see `application:didDiscardSceneSessions` instead). 27 | } 28 | 29 | func sceneDidBecomeActive(_ scene: UIScene) { 30 | // Called when the scene has moved from an inactive state to an active state. 31 | // Use this method to restart any tasks that were paused (or not yet started) when the scene was inactive. 32 | } 33 | 34 | func sceneWillResignActive(_ scene: UIScene) { 35 | // Called when the scene will move from an active state to an inactive state. 36 | // This may occur due to temporary interruptions (ex. an incoming phone call). 37 | } 38 | 39 | func sceneWillEnterForeground(_ scene: UIScene) { 40 | // Called as the scene transitions from the background to the foreground. 41 | // Use this method to undo the changes made on entering the background. 42 | } 43 | 44 | func sceneDidEnterBackground(_ scene: UIScene) { 45 | // Called as the scene transitions from the foreground to the background. 46 | // Use this method to save data, release shared resources, and store enough scene-specific state information 47 | // to restore the scene back to its current state. 48 | } 49 | 50 | 51 | } 52 | 53 | -------------------------------------------------------------------------------- /readme.md: -------------------------------------------------------------------------------- 1 | # Huggingface to CoreML 2 | 3 | This repository contains a script and information to convert a Huggingface models to CoreML. 4 | 5 | ## Info 6 | - Model to transform: [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small?text=Hi.) Try it by executing in command line `$ python3 create-chat-model.py` 7 | - Also create this [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small?text=Hi.) 8 | 9 | ### Links 10 | - [HuggingFace](https://huggingface.co/) 11 | - [HuggingFace Transformers](https://huggingface.co/transformers/) 12 | - [HuggingFace Core ML Models](https://huggingface.co/coreml#models) 13 | - [Using Stable Diffusion with Core ML on Apple Silicon](https://huggingface.co/blog/diffusers-coreml) 14 | - [Export Hugging Face models to Core ML and TensorFlow Lite](https://github.com/huggingface/exporters) 15 | - [Swift Core ML implementations of Transformers: GPT-2, DistilGPT-2, BERT, DistilBERT, more coming soon!](https://github.com/huggingface/swift-coreml-transformers) 16 | - [Figuring out the shape of a Transformer Model To translate it to a coreML model](https://developer.apple.com/forums/thread/682408) 17 | - [Core ML Stable Diffusion](https://github.com/apple/ml-stable-diffusion) 18 | - [Deploying Transformers on the Apple Neural Engine](https://machinelearning.apple.com/research/neural-engine-transformers) 19 | - [Swift app demonstrating Core ML Stable Diffusion](https://github.com/huggingface/swift-coreml-diffusers) 20 | - [Apple Core ML](https://developer.apple.com/documentation/coreml) 21 | -[BenderbotTokenizer doc](https://huggingface.co/docs/transformers/model_doc/blenderbot#transformers.BlenderbotTokenizer) 22 | - [BenderbotTokenizer implementation](https://github.com/huggingface/transformers/blob/3335724376319a0c453049d0cd883504f530ff52/src/transformers/models/blenderbot/tokenization_blenderbot.py#L4) 23 | - https://huggingface.co/microsoft/DialoGPT-small?text=Hi. 24 | - [Converting from PyTorch](https://coremltools.readme.io/docs/pytorch-conversion) 25 | 26 | ## Initial steps 27 | 28 | 0. Clone repo: `$ git clone https://github.com/madcato/huggingface-coreml.git` 29 | 1. Execute chatbot to try chatbot (optional), `$ python3 try.py` 30 | 2. Install huggingface exporters submodule, 31 | `$ git submodule init && git submodule update` 32 | 3. `$ cd exporters && pip3 install -e . && cd ..` 33 | 4. `python3 export` 34 | 35 | The last step will create two `mlpackage` files in the `exported` folder: one for decoder and another for decoder part of the model. These files can be opened with Xcode and the model can be tested in the playground or project. 36 | 37 | ### Try 38 | 39 | - `$ python3 -m exporters.coreml --model=t5-small --feature=text2text-generation exported` 40 | - `$ python3 -m exporters.coreml --model=distilgpt2 --feature=text2text-generation exported` 41 | 42 | ## Exporters features 43 | 44 | - 'feature-extraction', 45 | - 'feature-extraction-with-past', 46 | - 'fill-mask', 47 | - 'image-classification', 48 | - 'masked-im', 49 | - 'multiple-choice', 50 | - 'next-sentence-prediction', 51 | - 'object-detection', 52 | - 'question-answering', 53 | - 'semantic-segmentation', 54 | - 'text-classification', 55 | - 'text-generation', 56 | - 'text-generation-with-past', 57 | - 'text2text-generation', 58 | - 'token-classification', 59 | - 'sequence-classification', 60 | - 'causal-lm', 61 | - 'causal-lm-with-past', 62 | - 'seq2seq-lm', 63 | - 'seq2seq-lm-with-past', 64 | - 'speech2seq-lm', 65 | - 'speech2seq-lm-with-past', 66 | - 'masked-lm', 67 | - 'vision2seq-lm', 68 | - 'default', 69 | - 'default-with-past', 70 | - 'automatic-speech-recognition', 71 | - 'ctc' 72 | -------------------------------------------------------------------------------- /huggingface-ios-app/Sources/T5SmallModel.swift: -------------------------------------------------------------------------------- 1 | // 2 | // T5SmallModel.swift 3 | // huggingface-ios-app 4 | // 5 | // Created by Daniel Vela on 13/5/23. 6 | // 7 | 8 | import Foundation 9 | import CoreML 10 | 11 | class T5SmallModel { 12 | let encoder: encoder_t5_small 13 | let decoder: decoder_t5_small 14 | let seqLen = 64 15 | 16 | init() throws { 17 | self.encoder = try encoder_t5_small() 18 | self.decoder = try decoder_t5_small() 19 | } 20 | 21 | enum DecodingStrategy { 22 | /// At each time step, we select the most likely next token 23 | case greedy 24 | /// Sample only from the top-k most-probable tokens (k is a hyper-parameter). 25 | case topK(Int) 26 | /// Sample from the top tokens with a cumulative probability just above a threshold (nucleus/top-p). 27 | case topP(Double) 28 | } 29 | 30 | private let strategy = DecodingStrategy.greedy 31 | 32 | func predict(_ tokens: [Int]) throws -> Int { 33 | // let tokens = tokenizer.encode(text: string) 34 | // let tokens = [1] + tokens_0 + [2] 35 | // let tokens = [8774, 296, 149, 33, 25, 58, 1, 0 , 0, 0, 0, 0, 0,0, 0,0 ,0, 0,0 ,0, 0, 0, 0,0,0,0,0,0,0,0,0,0,0,0] 36 | 37 | 38 | let maxTokens = (tokens.count > seqLen) 39 | ? Array(tokens[.. String { 78 | // var tokens = tokenizer.encode(text: text) 79 | var tokens = [363, 31, 7, 39, 564, 58, 1] 80 | var newTokens: [Int] = [] 81 | for i in 0.. seqLen) 60 | ? Array(tokens[.. 2 | 5 | 8 | 9 | 15 | 21 | 22 | 23 | 24 | 25 | 31 | 32 | 34 | 40 | 41 | 42 | 45 | 51 | 52 | 53 | 54 | 55 | 65 | 67 | 73 | 74 | 75 | 76 | 82 | 84 | 90 | 91 | 92 | 93 | 95 | 96 | 99 | 100 | 101 | -------------------------------------------------------------------------------- /search-models.py: -------------------------------------------------------------------------------- 1 | from exporters.coreml import export, validate_model_outputs, CoreMLConfig 2 | from exporters.coreml.models import * 3 | from transformers import AutoTokenizer 4 | from transformers import AutoModelForSeq2SeqLM, AutoModelForCausalLM, GPT2Model 5 | 6 | feature = "text2text-generation" 7 | 8 | models = [ 9 | # ("npc-engine/exported-bart-light-gail-chatbot", BartCoreMLConfig), # not working 10 | # ("shahules786/Safetybot-T5-base", T5CoreMLConfig), 11 | # ("osanseviero/t5-finetuned-test", T5CoreMLConfig), 12 | # ("BlackSamorez/rudialogpt3_medium_based_on_gpt2_2ch", GPT2CoreMLConfig), # Error coniguracion 13 | # ("gorkemgoknar/gpt2chatbotenglish", GPT2CoreMLConfig), # Error coniguracion 14 | # ("Alethea/GPT2-chitchat", GPT2CoreMLConfig), # Error coniguracion 15 | # ("thu-coai/CDial-GPT2_LCCC-base", GPT2CoreMLConfig), # Error modelo 16 | # ("robinhad/gpt2-uk-conversational", GPT2CoreMLConfig), # Error coniguracion 17 | # ("AriakimTaiyo/gpt2-chat", GPT2CoreMLConfig), # Error coniguracion 18 | # ("Vaibhav-rm/GPT2-Shri-v1", GPT2CoreMLConfig), # Error coniguracion 19 | # ("LrxLcs/GPT2-V2", GPT2CoreMLConfig), # Error coniguracion 20 | # ("LrxLcs/GPT2-Test", GPT2CoreMLConfig), # Error coniguracion 21 | # ("huolongguo10/CDial-GPT2-LCCC-Base-copy", GPT2CoreMLConfig), # Error coniguracion 22 | # ("h2oai/h2ogpt-oasst1-512-12b", GPT2CoreMLConfig), # Error coniguracion 23 | # ("distilgpt2", GPT2CoreMLConfig), # Error coniguracion 24 | ("microsoft/DialoGPT-small", GPT2CoreMLConfig), # Error coniguracion 25 | # ("gorkemgoknar/gpt2chatbotenglish", GPT2CoreMLConfig) 26 | ("distilgpt2", GPT2CoreMLConfig), 27 | ("ethzanalytics/distilgpt2-tiny-conversational", GPT2CoreMLConfig) 28 | ] 29 | 30 | for model_ckpt, config_class in models: 31 | print("--------------------------------") 32 | print(" EXPORTING: ", model_ckpt) 33 | base_model = GPT2Model.from_pretrained( 34 | model_ckpt, torchscript=True 35 | ) 36 | preprocessor = AutoTokenizer.from_pretrained(model_ckpt) 37 | 38 | 39 | print(" ENCODER --------------------------------") 40 | coreml_config = config_class( 41 | base_model.config, 42 | task="text2text-generation", 43 | use_past=False, 44 | # seq2seq="encoder" 45 | ) 46 | mlmodel = export( 47 | preprocessor, base_model, coreml_config 48 | ) 49 | # validate_model_outputs( 50 | # coreml_config, 51 | # preprocessor, 52 | # base_model, 53 | # mlmodel, 54 | # coreml_config.atol_for_validation, 55 | # ) 56 | mlmodel.save(f"exported/{model_ckpt}.mlpackage") 57 | 58 | # print(" DECODER --------------------------------") 59 | # coreml_config = config_class( 60 | # base_model.config, 61 | # task="text2text-generation", 62 | # use_past=False, 63 | # seq2seq="decoder" 64 | # ) 65 | # mlmodel = export( 66 | # preprocessor, base_model, coreml_config 67 | # ) 68 | # validate_model_outputs( 69 | # coreml_config, 70 | # preprocessor, 71 | # base_model, 72 | # mlmodel, 73 | # coreml_config.atol_for_validation, 74 | # ) 75 | # mlmodel.save(f"exported/{model_ckpt}_decoder.mlpackage") 76 | print("END --------------------------------") 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | # # Encoder 92 | # coreml_config = BlenderbotSmallCoreMLConfig( 93 | # base_model.config, 94 | # task="text2text-generation", 95 | # use_past=False, 96 | # seq2seq="encoder" 97 | # ) 98 | # mlmodel = export( 99 | # preprocessor, base_model, coreml_config 100 | # ) 101 | 102 | # mlmodel.save(f"exported/{model_ckpt}_encoder.mlpackage") 103 | 104 | # # Decoder 105 | # coreml_config = BlenderbotSmallCoreMLConfig( 106 | # base_model.config, 107 | # task="text2text-generation", 108 | # use_past=False, 109 | # seq2seq="decoder" 110 | # ) 111 | # mlmodel = export( 112 | # preprocessor, base_model, coreml_config 113 | # ) 114 | 115 | # mlmodel.save(f"exported/{model_ckpt}_decoder.mlpackage") -------------------------------------------------------------------------------- /huggingface-ios-app/Sources/GPT2Model.swift: -------------------------------------------------------------------------------- 1 | // 2 | // GPT2Model.swift 3 | // huggingface-ios-app 4 | // 5 | // Created by Daniel Vela on 14/5/23. 6 | // 7 | 8 | import Foundation 9 | import CoreML 10 | 11 | final class GPT2Model { 12 | private let model: DialoGPT_small 13 | private let tokenizer: GPT2Tokenizer 14 | private let seqLen = 64 15 | private let eos_token_id = 50256 16 | 17 | init() throws { 18 | let modelName = "distilgpt2" 19 | self.tokenizer = try GPT2Tokenizer.from_pretrained(modelName) 20 | self.model = try DialoGPT_small() 21 | } 22 | 23 | enum DecodingStrategy { 24 | /// At each time step, we select the most likely next token 25 | case greedy 26 | /// Sample only from the top-k most-probable tokens (k is a hyper-parameter). 27 | case topK(Int) 28 | /// Sample from the top tokens with a cumulative probability just above a threshold (nucleus/top-p). 29 | case topP(Double) 30 | } 31 | 32 | private let strategy = DecodingStrategy.topK(2) 33 | 34 | /// Main prediction loop: 35 | /// Predict next token from array of previous tokens. 36 | /// - featurization 37 | /// - model inference 38 | /// - Decoding according to the model's `strategy` 39 | func predict(tokens: [Int]) -> Int { 40 | let maxTokens = (tokens.count > seqLen) 41 | ? Array(tokens[.. Void)?) -> String { 95 | var tokens = tokenizer.encode(text: text) + [eos_token_id] 96 | var newTokens: [Int] = [] 97 | for i in 0..", i, nextToken, tokens.count) 108 | callback?( 109 | tokenizer.decode(tokens: newTokens), time 110 | ) 111 | } 112 | return tokenizer.decode(tokens: newTokens) 113 | } 114 | } 115 | -------------------------------------------------------------------------------- /huggingface-ios-app/Sources/Math.swift: -------------------------------------------------------------------------------- 1 | // 2 | // Math.swift 3 | // CoreMLBert 4 | // 5 | // Created by Julien Chaumond on 27/06/2019. 6 | // Copyright © 2019 Hugging Face. All rights reserved. 7 | // 8 | 9 | import Foundation 10 | import Accelerate 11 | import CoreML 12 | 13 | /// 14 | /// From M.I. Hollemans 15 | /// 16 | /// https://github.com/hollance/CoreMLHelpers 17 | /// 18 | struct Math { 19 | 20 | /** 21 | Returns the index and value of the largest element in the array. 22 | 23 | - Parameters: 24 | - ptr: Pointer to the first element in memory. 25 | - count: How many elements to look at. 26 | - stride: The distance between two elements in memory. 27 | */ 28 | static func argmax(_ ptr: UnsafePointer, count: Int, stride: Int = 1) -> (Int, Float) { 29 | var maxValue: Float = 0 30 | var maxIndex: vDSP_Length = 0 31 | vDSP_maxvi(ptr, vDSP_Stride(stride), &maxValue, &maxIndex, vDSP_Length(count)) 32 | return (Int(maxIndex), maxValue) 33 | } 34 | 35 | /** 36 | Returns the index and value of the largest element in the array. 37 | - Parameters: 38 | - ptr: Pointer to the first element in memory. 39 | - count: How many elements to look at. 40 | - stride: The distance between two elements in memory. 41 | */ 42 | static func argmax(_ ptr: UnsafePointer, count: Int, stride: Int = 1) -> (Int, Double) { 43 | var maxValue: Double = 0 44 | var maxIndex: vDSP_Length = 0 45 | vDSP_maxviD(ptr, vDSP_Stride(stride), &maxValue, &maxIndex, vDSP_Length(count)) 46 | return (Int(maxIndex), maxValue) 47 | } 48 | 49 | 50 | /// MLMultiArray helper. 51 | /// Works in our specific use case. 52 | static func argmax(_ multiArray: MLMultiArray) -> (Int, Double) { 53 | assert(multiArray.dataType == .double) 54 | let ptr = UnsafeMutablePointer(OpaquePointer(multiArray.dataPointer)) 55 | return Math.argmax(ptr, count: multiArray.count) 56 | } 57 | 58 | /// MLMultiArray helper. 59 | /// Works in our specific use case. 60 | static func argmax32(_ multiArray: MLMultiArray) -> (Int, Float) { 61 | assert(multiArray.dataType == .float32) 62 | let ptr = UnsafeMutablePointer(OpaquePointer(multiArray.dataPointer)) 63 | let count = multiArray.count 64 | var maxValue: Float = 0 65 | var maxIndex: vDSP_Length = 0 66 | vDSP_maxvi(ptr, vDSP_Stride(1), &maxValue, &maxIndex, vDSP_Length(count)) 67 | return (Int(maxIndex), maxValue) 68 | } 69 | 70 | /// Top-K. 71 | /// Select the k most-probable elements indices from `arr` 72 | /// and return both the indices (from the original array) 73 | /// and their softmaxed probabilities. 74 | /// 75 | static func topK(arr: [Float], k: Int) -> (indexes: [Int], probs: [Float]) { 76 | let x = Array(arr.enumerated().map { ($0, $1) } 77 | .sorted(by: { a, b -> Bool in a.1 > b.1 }) 78 | .prefix(through: min(k, arr.count) - 1)) 79 | let indexes = x.map { $0.0 } 80 | let logits = x.map { Float($0.1) } 81 | let probs = softmax(logits) 82 | return (indexes: indexes, probs: probs) 83 | } 84 | 85 | /// Multinomial sampling from an array of probs. Works well with topK 86 | static func sample(indexes: [Int], probs: [Float]) -> Int { 87 | let i = randomNumber(probabilities: probs) 88 | return indexes[i] 89 | } 90 | 91 | /** 92 | Computes the "softmax" function over an array. 93 | Based on code from https://github.com/nikolaypavlov/MLPNeuralNet/ 94 | This is what softmax looks like in "pseudocode" (actually using Python 95 | and numpy): 96 | x -= np.max(x) 97 | exp_scores = np.exp(x) 98 | softmax = exp_scores / np.sum(exp_scores) 99 | First we shift the values of x so that the highest value in the array is 0. 100 | This ensures numerical stability with the exponents, so they don't blow up. 101 | */ 102 | static func softmax(_ x: [Float]) -> [Float] { 103 | var x = x 104 | let len = vDSP_Length(x.count) 105 | 106 | // Find the maximum value in the input array. 107 | var max: Float = 0 108 | vDSP_maxv(x, 1, &max, len) 109 | 110 | // Subtract the maximum from all the elements in the array. 111 | // Now the highest value in the array is 0. 112 | max = -max 113 | vDSP_vsadd(x, 1, &max, &x, 1, len) 114 | 115 | // Exponentiate all the elements in the array. 116 | var count = Int32(x.count) 117 | vvexpf(&x, x, &count) 118 | 119 | // Compute the sum of all exponentiated values. 120 | var sum: Float = 0 121 | vDSP_sve(x, 1, &sum, len) 122 | 123 | // Divide each element by the sum. This normalizes the array contents 124 | // so that they all add up to 1. 125 | vDSP_vsdiv(x, 1, &sum, &x, 1, len) 126 | 127 | return x 128 | } 129 | 130 | /// Multinomial sampling 131 | /// 132 | /// From https://stackoverflow.com/questions/30309556/generate-random-numbers-with-a-given-distribution 133 | /// 134 | static func randomNumber(probabilities: [Float]) -> Int { 135 | // Sum of all probabilities (so that we don't have to require that the sum is 1.0): 136 | let sum = probabilities.reduce(0, +) 137 | // Random number in the range 0.0 <= rnd < sum : 138 | let rnd = sum * Float(arc4random_uniform(UInt32.max)) / Float(UInt32.max) 139 | // Find the first interval of accumulated probabilities into which `rnd` falls: 140 | var accum: Float = 0.0 141 | for (i, p) in probabilities.enumerated() { 142 | accum += p 143 | if rnd < accum { 144 | return i 145 | } 146 | } 147 | // This point might be reached due to floating point inaccuracies: 148 | return (probabilities.count - 1) 149 | } 150 | } 151 | -------------------------------------------------------------------------------- /huggingface-ios-app/Sources/GPT2Tokenizer.swift: -------------------------------------------------------------------------------- 1 | // 2 | // GPT2Tokenizer.swift 3 | // CoreMLBert 4 | // 5 | // Created by Julien Chaumond on 18/07/2019. 6 | // Copyright © 2019 Hugging Face. All rights reserved. 7 | // 8 | 9 | import Foundation 10 | 11 | fileprivate struct BytePair: Hashable { 12 | let a: String 13 | let b: String 14 | init(_ a: String, _ b: String) { 15 | self.a = a 16 | self.b = b 17 | } 18 | init(tuple: [String]) { 19 | self.a = tuple[0] 20 | self.b = tuple[1] 21 | } 22 | 23 | static func == (lhs: BytePair, rhs: BytePair) -> Bool { 24 | return lhs.a == rhs.a && lhs.b == rhs.b 25 | } 26 | func hash(into hasher: inout Hasher) { 27 | hasher.combine(a) 28 | hasher.combine(b) 29 | } 30 | } 31 | 32 | fileprivate extension String { 33 | func ranges(of string: String, options: CompareOptions = .regularExpression) -> [Range] { 34 | var result: [Range] = [] 35 | var start = startIndex 36 | while let range = range(of: string, options: options, range: start.. 49 | private let encoder: [String: Int] 50 | private let decoder: [Int: String] 51 | 52 | init(urlMerges: URL, urlVocab: URL ) throws { 53 | let bpeMergesTxt = try! String(contentsOf: urlMerges) 54 | let arr = bpeMergesTxt.split(separator: "\n").map { String($0) } 55 | var bpeRanks: Dictionary = [:] 56 | for i in 1.. GPT2Tokenizer { 73 | guard let urlMerges = Bundle.main.url(forResource: model_name + "/merges", withExtension: "txt"), 74 | let urlVocab = Bundle.main.url(forResource: model_name + "/vocab", withExtension: "json") else { 75 | throw TokenizerError.invalidModelName(model_name) 76 | } 77 | 78 | return try GPT2Tokenizer(urlMerges: urlMerges, urlVocab: urlVocab) 79 | } 80 | 81 | func byteEncode(text: String) -> [String] { 82 | let RE = #"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"# 83 | let tokens = text.ranges(of: RE).map { String(text[$0]) } 84 | return tokens.map { (token) -> String in 85 | return Array(token.utf8).map { byteEncoder[$0]! }.joined() 86 | } 87 | } 88 | 89 | private func getPairs(word: [String]) -> Set { 90 | var s = Set() 91 | for i in 0.. String { 102 | if token.count <= 1 { 103 | return token 104 | } 105 | 106 | var word = Array(token).map { String($0) } 107 | var pairs = Array(getPairs(word: word)) 108 | 109 | while true { 110 | let bigrams = pairs.filter { (bp) -> Bool in bpeRanks[bp] != nil } 111 | if bigrams.count == 0 { 112 | break 113 | } 114 | let bigram = bigrams.min { (bp1, bp2) -> Bool in 115 | return bpeRanks[bp1]! < bpeRanks[bp2]! 116 | }! 117 | let first = bigram.a 118 | let second = bigram.b 119 | var newWord: [String] = [] 120 | var i = 0 121 | while i < word.count { 122 | if let j = word[i.. [String] { 149 | var tokens: [String] = [] 150 | for token in self.byteEncode(text: text) { 151 | let xx = self.bpe(token: token).split(separator: " ").map { String($0) } 152 | tokens.append(contentsOf: xx) 153 | } 154 | return tokens 155 | } 156 | 157 | /// Main entry point 158 | func encode(text: String) -> [Int] { 159 | return tokenize(text: text).map { encoder[$0]! } 160 | } 161 | 162 | /// Decode 163 | func decode(tokens: [Int]) -> String { 164 | let text = tokens.map { decoder[$0] ?? "X" }.joined(separator: "") 165 | let utfCodepoints = text.map { byteDecoder[String($0)]! } 166 | return String(decoding: utfCodepoints, as: UTF8.self) 167 | } 168 | } 169 | -------------------------------------------------------------------------------- /huggingface-ios-app/Sources/BartTokenizer.swift: -------------------------------------------------------------------------------- 1 | // 2 | // BartTokenizer.swift 3 | // huggingface-ios-app 4 | // 5 | // Created by Daniel Vela on 12/5/23. 6 | // 7 | 8 | import Foundation 9 | 10 | fileprivate struct BytePair: Hashable { 11 | let a: String 12 | let b: String 13 | init(_ a: String, _ b: String) { 14 | self.a = a 15 | self.b = b 16 | } 17 | init(tuple: [String]) { 18 | self.a = tuple[0] 19 | self.b = tuple[1] 20 | } 21 | 22 | static func == (lhs: BytePair, rhs: BytePair) -> Bool { 23 | return lhs.a == rhs.a && lhs.b == rhs.b 24 | } 25 | func hash(into hasher: inout Hasher) { 26 | hasher.combine(a) 27 | hasher.combine(b) 28 | } 29 | } 30 | 31 | fileprivate extension String { 32 | func ranges(of string: String, options: CompareOptions = .regularExpression) -> [Range] { 33 | var result: [Range] = [] 34 | var start = startIndex 35 | while let range = range(of: string, options: options, range: start.. 45 | private let encoder: [String: Int] 46 | private let decoder: [Int: String] 47 | 48 | init(urlMerges: URL, urlVocab: URL) throws { 49 | let bpeMergesTxt = try String(contentsOf: urlMerges) 50 | let arr = bpeMergesTxt.split(separator: "\n").map { String($0) } 51 | var bpeRanks: Dictionary = [:] 52 | for i in 1.. BartTokenizer { 69 | guard let urlMerges = Bundle.main.url(forResource: model_name + "/merges", withExtension: "txt"), 70 | let urlVocab = Bundle.main.url(forResource: model_name + "/vocab", withExtension: "json") else { 71 | throw TokenizerError.invalidModelName(model_name) 72 | } 73 | 74 | return try BartTokenizer(urlMerges: urlMerges, urlVocab: urlVocab) 75 | } 76 | 77 | func byteEncode(text: String) -> [String] { 78 | let range = NSRange(location: 0, length: text.utf16.count) 79 | let pattern = #"\S+\n?"# 80 | let regex = try! NSRegularExpression(pattern: pattern) 81 | let tokens = regex.matches(in: text, range: range).map { String(text[Range($0.range, in: text)!]) }.map { $0.lowercased() } 82 | return tokens.map { (token) -> String in 83 | return Array(token.utf8).map { byteEncoder[$0]! }.joined() 84 | } 85 | } 86 | 87 | private func getPairs(word: [String]) -> Set { 88 | var s = Set() 89 | for i in 0.. String { 100 | var token = token.replacingOccurrences(of: "([.,!?()])", with: " $1", options: .regularExpression) 101 | token = token.replacingOccurrences(of: "(')", with: " $1 ", options: .regularExpression) 102 | token = token.replacingOccurrences(of: "\\s{2,}", with: " ", options: .regularExpression) 103 | if token.contains("\n") { 104 | token = token.replacingOccurrences(of: "\n", with: " __newln__") 105 | } 106 | 107 | let tokens = token.split(separator: " ") 108 | var words = [String]() 109 | for token in tokens { 110 | guard !token.isEmpty else { 111 | continue 112 | } 113 | var token = token.lowercased() 114 | var word = Array(token).map { String($0) } 115 | word = Array(word[0.."] 116 | var pairs = getPairs(word: word) 117 | 118 | if pairs.isEmpty { 119 | words.append(token) 120 | continue 121 | } 122 | 123 | while true { 124 | let bigrams = pairs.filter { (bp) -> Bool in bpeRanks[bp] != nil } 125 | if bigrams.count == 0 { 126 | break 127 | } 128 | let bigram = bigrams.min { (bp1, bp2) -> Bool in 129 | return bpeRanks[bp1]! < bpeRanks[bp2]! 130 | }! 131 | let first = bigram.a 132 | let second = bigram.b 133 | var newWord = [String]() 134 | var i = 0 135 | 136 | while i < word.count { 137 | if let j = word[i.. [String] { 165 | var tokens: [String] = [] 166 | for token in self.byteEncode(text: text) { 167 | let xx = self.bpe(token: token).split(separator: " ").map { String($0) } 168 | tokens.append(contentsOf: xx) 169 | } 170 | return tokens 171 | } 172 | 173 | /// Main entry point 174 | func encode(text: String) -> [Int] { 175 | return tokenize(text: text).map { encoder[$0]! } 176 | } 177 | 178 | /// Decode 179 | func decode(tokens: [Int]) -> String { 180 | let text = tokens.map { decoder[$0]! }.joined(separator: "") 181 | let utfCodepoints = text.map { byteDecoder[String($0)]! } 182 | return String(decoding: utfCodepoints, as: UTF8.self) 183 | } 184 | } 185 | -------------------------------------------------------------------------------- /huggingface-ios-app/Sources/MLMultiArray+Utils.swift: -------------------------------------------------------------------------------- 1 | // 2 | // MLMultiArray+Utils.swift 3 | // CoreMLBert 4 | // 5 | // Created by Julien Chaumond on 27/06/2019. 6 | // Copyright © 2019 Hugging Face. All rights reserved. 7 | // 8 | 9 | import Foundation 10 | import CoreML 11 | 12 | extension MLMultiArray { 13 | /// All values will be stored in the last dimension of the MLMultiArray (default is dims=1) 14 | static func from(_ arr: [Int], dims: Int = 1) -> MLMultiArray { 15 | var shape = Array(repeating: 1, count: dims) 16 | shape[shape.count - 1] = arr.count 17 | /// Examples: 18 | /// dims=1 : [arr.count] 19 | /// dims=2 : [1, arr.count] 20 | /// 21 | let o = try! MLMultiArray(shape: shape as [NSNumber], dataType: .int32) 22 | let ptr = UnsafeMutablePointer(OpaquePointer(o.dataPointer)) 23 | for (i, item) in arr.enumerated() { 24 | ptr[i] = Int32(item) 25 | } 26 | return o 27 | } 28 | 29 | /// This will concatenate all dimensions into one one-dim array. 30 | static func toIntArray(_ o: MLMultiArray) -> [Int] { 31 | var arr = Array(repeating: 0, count: o.count) 32 | let ptr = UnsafeMutablePointer(OpaquePointer(o.dataPointer)) 33 | for i in 0.. [Double] { 41 | var arr: [Double] = Array(repeating: 0, count: o.count) 42 | let ptr = UnsafeMutablePointer(OpaquePointer(o.dataPointer)) 43 | for i in 0.. [Float32] { 51 | var arr: [Float32] = Array(repeating: 0, count: o.count) 52 | let ptr = UnsafeMutablePointer(OpaquePointer(o.dataPointer)) 53 | for i in 0.. MLMultiArray { 71 | let arr = try! MLMultiArray(shape: shape as [NSNumber], dataType: .double) 72 | let ptr = UnsafeMutablePointer(OpaquePointer(arr.dataPointer)) 73 | for i in 0.., indexing: [Indexing]) -> MLMultiArray { 93 | let ranges = indexing.enumerated().map { (i, indexing) in 94 | switch indexing { 95 | case .select(let select): 96 | return select.. MLMultiArray { 109 | assert( 110 | indexing.count == o.shape.count 111 | ) 112 | assert( 113 | indexing.filter { $0 == Indexing.slice }.count == 1 114 | ) 115 | var selectDims: [Int: Int] = [:] 116 | for (i, idx) in indexing.enumerated() { 117 | if case .select(let select) = idx { 118 | selectDims[i] = select 119 | } 120 | } 121 | return slice( 122 | o, 123 | sliceDim: indexing.firstIndex { $0 == Indexing.slice }!, 124 | selectDims: selectDims 125 | ) 126 | } 127 | 128 | /// Slice an array according to a list, according to `sliceDim` (which dimension to slice on) 129 | /// and a dictionary of `dim` to `index`. 130 | /// 131 | /// You must select all other dimensions than the slice dimension (cf. the assert). 132 | static func slice(_ o: MLMultiArray, sliceDim: Int, selectDims: [Int: Int]) -> MLMultiArray { 133 | assert( 134 | selectDims.count + 1 == o.shape.count 135 | ) 136 | var shape: [NSNumber] = Array(repeating: 1, count: o.shape.count) 137 | shape[sliceDim] = o.shape[sliceDim] 138 | /// print("About to slice ndarray of shape \(o.shape) into ndarray of shape \(shape)") 139 | let arr = try! MLMultiArray(shape: shape, dataType: .double) 140 | 141 | /// let srcPtr = UnsafeMutablePointer(OpaquePointer(o.dataPointer)) 142 | /// TODO: use srcPtr instead of array subscripting. 143 | let dstPtr = UnsafeMutablePointer(OpaquePointer(arr.dataPointer)) 144 | for i in 0.. String { 171 | func indent(_ x: Int) -> String { 172 | return String(repeating: " ", count: x) 173 | } 174 | 175 | // This function is called recursively for every dimension. 176 | // Add an entry for this dimension to the end of the array. 177 | var indices = indices + [0] 178 | 179 | let d = indices.count - 1 // the current dimension 180 | let N = shape[d].intValue // how many elements in this dimension 181 | var s = "[" 182 | if indices.count < shape.count { // not last dimension yet? 183 | for i in 0.. Bool { 28 | return lhs.a == rhs.a && lhs.b == rhs.b 29 | } 30 | func hash(into hasher: inout Hasher) { 31 | hasher.combine(a) 32 | hasher.combine(b) 33 | } 34 | } 35 | 36 | fileprivate extension String { 37 | func ranges(of string: String, options: CompareOptions = .regularExpression) -> [Range] { 38 | var result: [Range] = [] 39 | var start = startIndex 40 | while let range = range(of: string, options: options, range: start.. 51 | private let encoder: [String: Int] 52 | private let decoder: [Int: String] 53 | 54 | init(urlMerges: URL, urlVocab: URL) throws { 55 | let bpeMergesTxt = try String(contentsOf: urlMerges) 56 | let arr = bpeMergesTxt.split(separator: "\n").map { String($0) } 57 | var bpeRanks: Dictionary = [:] 58 | for i in 1.. DistilGPT2Tokenizer { 75 | guard let urlMerges = Bundle.main.url(forResource: model_name + "/merges", withExtension: "txt"), 76 | let urlVocab = Bundle.main.url(forResource: model_name + "/vocab", withExtension: "json") else { 77 | throw TokenizerError.invalidModelName(model_name) 78 | } 79 | 80 | return try DistilGPT2Tokenizer(urlMerges: urlMerges, urlVocab: urlVocab) 81 | } 82 | 83 | func byteEncode(text: String) -> [String] { 84 | let RE = #"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"# 85 | let tokens = text.ranges(of: RE).map { String(text[$0]) } 86 | return tokens.map { (token) -> String in 87 | return Array(token.utf8).map { byteEncoder[$0]! }.joined() 88 | } 89 | } 90 | 91 | private func getPairs(word: [String]) -> Set { 92 | var s = Set() 93 | for i in 0.. String { 104 | if token.count <= 1 { 105 | return token 106 | } 107 | 108 | var word = Array(token).map { String($0) } 109 | var pairs = Array(getPairs(word: word)) 110 | 111 | while true { 112 | let bigrams = pairs.filter { (bp) -> Bool in bpeRanks[bp] != nil } 113 | if bigrams.count == 0 { 114 | break 115 | } 116 | let bigram = bigrams.min { (bp1, bp2) -> Bool in 117 | return bpeRanks[bp1]! < bpeRanks[bp2]! 118 | }! 119 | let first = bigram.a 120 | let second = bigram.b 121 | var newWord: [String] = [] 122 | var i = 0 123 | while i < word.count { 124 | if let j = word[i.. [String] { 151 | var tokens: [String] = [] 152 | for token in self.byteEncode(text: text) { 153 | let xx = self.bpe(token: token).split(separator: " ").map { String($0) } 154 | tokens.append(contentsOf: xx) 155 | } 156 | return tokens 157 | } 158 | 159 | /// Main entry point 160 | func encode(text: String) -> [Int] { 161 | return tokenize(text: text).map { encoder[$0]! } 162 | } 163 | 164 | /// Decode 165 | func decode(tokens: [Int]) -> String { 166 | let text = tokens.map { decoder[$0]! }.joined(separator: "") 167 | let utfCodepoints = text.map { byteDecoder[String($0)]! } 168 | return String(decoding: utfCodepoints, as: UTF8.self) 169 | } 170 | } 171 | 172 | let byteEncoder: Dictionary = [ 173 | 33: "!", 174 | 34: "\"", 175 | 35: "#", 176 | 36: "$", 177 | 37: "%", 178 | 38: "&", 179 | 39: "'", 180 | 40: "(", 181 | 41: ")", 182 | 42: "*", 183 | 43: "+", 184 | 44: ",", 185 | 45: "-", 186 | 46: ".", 187 | 47: "/", 188 | 48: "0", 189 | 49: "1", 190 | 50: "2", 191 | 51: "3", 192 | 52: "4", 193 | 53: "5", 194 | 54: "6", 195 | 55: "7", 196 | 56: "8", 197 | 57: "9", 198 | 58: ":", 199 | 59: ";", 200 | 60: "<", 201 | 61: "=", 202 | 62: ">", 203 | 63: "?", 204 | 64: "@", 205 | 65: "A", 206 | 66: "B", 207 | 67: "C", 208 | 68: "D", 209 | 69: "E", 210 | 70: "F", 211 | 71: "G", 212 | 72: "H", 213 | 73: "I", 214 | 74: "J", 215 | 75: "K", 216 | 76: "L", 217 | 77: "M", 218 | 78: "N", 219 | 79: "O", 220 | 80: "P", 221 | 81: "Q", 222 | 82: "R", 223 | 83: "S", 224 | 84: "T", 225 | 85: "U", 226 | 86: "V", 227 | 87: "W", 228 | 88: "X", 229 | 89: "Y", 230 | 90: "Z", 231 | 91: "[", 232 | 92: "\\", 233 | 93: "]", 234 | 94: "^", 235 | 95: "_", 236 | 96: "`", 237 | 97: "a", 238 | 98: "b", 239 | 99: "c", 240 | 100: "d", 241 | 101: "e", 242 | 102: "f", 243 | 103: "g", 244 | 104: "h", 245 | 105: "i", 246 | 106: "j", 247 | 107: "k", 248 | 108: "l", 249 | 109: "m", 250 | 110: "n", 251 | 111: "o", 252 | 112: "p", 253 | 113: "q", 254 | 114: "r", 255 | 115: "s", 256 | 116: "t", 257 | 117: "u", 258 | 118: "v", 259 | 119: "w", 260 | 120: "x", 261 | 121: "y", 262 | 122: "z", 263 | 123: "{", 264 | 124: "|", 265 | 125: "}", 266 | 126: "~", 267 | 161: "\u{00a1}", 268 | 162: "\u{00a2}", 269 | 163: "\u{00a3}", 270 | 164: "\u{00a4}", 271 | 165: "\u{00a5}", 272 | 166: "\u{00a6}", 273 | 167: "\u{00a7}", 274 | 168: "\u{00a8}", 275 | 169: "\u{00a9}", 276 | 170: "\u{00aa}", 277 | 171: "\u{00ab}", 278 | 172: "\u{00ac}", 279 | 174: "\u{00ae}", 280 | 175: "\u{00af}", 281 | 176: "\u{00b0}", 282 | 177: "\u{00b1}", 283 | 178: "\u{00b2}", 284 | 179: "\u{00b3}", 285 | 180: "\u{00b4}", 286 | 181: "\u{00b5}", 287 | 182: "\u{00b6}", 288 | 183: "\u{00b7}", 289 | 184: "\u{00b8}", 290 | 185: "\u{00b9}", 291 | 186: "\u{00ba}", 292 | 187: "\u{00bb}", 293 | 188: "\u{00bc}", 294 | 189: "\u{00bd}", 295 | 190: "\u{00be}", 296 | 191: "\u{00bf}", 297 | 192: "\u{00c0}", 298 | 193: "\u{00c1}", 299 | 194: "\u{00c2}", 300 | 195: "\u{00c3}", 301 | 196: "\u{00c4}", 302 | 197: "\u{00c5}", 303 | 198: "\u{00c6}", 304 | 199: "\u{00c7}", 305 | 200: "\u{00c8}", 306 | 201: "\u{00c9}", 307 | 202: "\u{00ca}", 308 | 203: "\u{00cb}", 309 | 204: "\u{00cc}", 310 | 205: "\u{00cd}", 311 | 206: "\u{00ce}", 312 | 207: "\u{00cf}", 313 | 208: "\u{00d0}", 314 | 209: "\u{00d1}", 315 | 210: "\u{00d2}", 316 | 211: "\u{00d3}", 317 | 212: "\u{00d4}", 318 | 213: "\u{00d5}", 319 | 214: "\u{00d6}", 320 | 215: "\u{00d7}", 321 | 216: "\u{00d8}", 322 | 217: "\u{00d9}", 323 | 218: "\u{00da}", 324 | 219: "\u{00db}", 325 | 220: "\u{00dc}", 326 | 221: "\u{00dd}", 327 | 222: "\u{00de}", 328 | 223: "\u{00df}", 329 | 224: "\u{00e0}", 330 | 225: "\u{00e1}", 331 | 226: "\u{00e2}", 332 | 227: "\u{00e3}", 333 | 228: "\u{00e4}", 334 | 229: "\u{00e5}", 335 | 230: "\u{00e6}", 336 | 231: "\u{00e7}", 337 | 232: "\u{00e8}", 338 | 233: "\u{00e9}", 339 | 234: "\u{00ea}", 340 | 235: "\u{00eb}", 341 | 236: "\u{00ec}", 342 | 237: "\u{00ed}", 343 | 238: "\u{00ee}", 344 | 239: "\u{00ef}", 345 | 240: "\u{00f0}", 346 | 241: "\u{00f1}", 347 | 242: "\u{00f2}", 348 | 243: "\u{00f3}", 349 | 244: "\u{00f4}", 350 | 245: "\u{00f5}", 351 | 246: "\u{00f6}", 352 | 247: "\u{00f7}", 353 | 248: "\u{00f8}", 354 | 249: "\u{00f9}", 355 | 250: "\u{00fa}", 356 | 251: "\u{00fb}", 357 | 252: "\u{00fc}", 358 | 253: "\u{00fd}", 359 | 254: "\u{00fe}", 360 | 255: "\u{00ff}", 361 | 0: "\u{0100}", 362 | 1: "\u{0101}", 363 | 2: "\u{0102}", 364 | 3: "\u{0103}", 365 | 4: "\u{0104}", 366 | 5: "\u{0105}", 367 | 6: "\u{0106}", 368 | 7: "\u{0107}", 369 | 8: "\u{0108}", 370 | 9: "\u{0109}", 371 | 10: "\u{010a}", 372 | 11: "\u{010b}", 373 | 12: "\u{010c}", 374 | 13: "\u{010d}", 375 | 14: "\u{010e}", 376 | 15: "\u{010f}", 377 | 16: "\u{0110}", 378 | 17: "\u{0111}", 379 | 18: "\u{0112}", 380 | 19: "\u{0113}", 381 | 20: "\u{0114}", 382 | 21: "\u{0115}", 383 | 22: "\u{0116}", 384 | 23: "\u{0117}", 385 | 24: "\u{0118}", 386 | 25: "\u{0119}", 387 | 26: "\u{011a}", 388 | 27: "\u{011b}", 389 | 28: "\u{011c}", 390 | 29: "\u{011d}", 391 | 30: "\u{011e}", 392 | 31: "\u{011f}", 393 | 32: "\u{0120}", 394 | 127: "\u{0121}", 395 | 128: "\u{0122}", 396 | 129: "\u{0123}", 397 | 130: "\u{0124}", 398 | 131: "\u{0125}", 399 | 132: "\u{0126}", 400 | 133: "\u{0127}", 401 | 134: "\u{0128}", 402 | 135: "\u{0129}", 403 | 136: "\u{012a}", 404 | 137: "\u{012b}", 405 | 138: "\u{012c}", 406 | 139: "\u{012d}", 407 | 140: "\u{012e}", 408 | 141: "\u{012f}", 409 | 142: "\u{0130}", 410 | 143: "\u{0131}", 411 | 144: "\u{0132}", 412 | 145: "\u{0133}", 413 | 146: "\u{0134}", 414 | 147: "\u{0135}", 415 | 148: "\u{0136}", 416 | 149: "\u{0137}", 417 | 150: "\u{0138}", 418 | 151: "\u{0139}", 419 | 152: "\u{013a}", 420 | 153: "\u{013b}", 421 | 154: "\u{013c}", 422 | 155: "\u{013d}", 423 | 156: "\u{013e}", 424 | 157: "\u{013f}", 425 | 158: "\u{0140}", 426 | 159: "\u{0141}", 427 | 160: "\u{0142}", 428 | 173: "\u{0143}", 429 | ] 430 | 431 | let byteDecoder = Utils.invert(byteEncoder) 432 | -------------------------------------------------------------------------------- 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130 | Ġ B 131 | Ġs e 132 | Ġs o 133 | Ġm y 134 | Ġc an 135 | Ġ M 136 | Ġli ke 137 | Ġs h 138 | Ġ or 139 | Ġn e 140 | u r 141 | Ġa t 142 | Ġj ust 143 | Ġa b 144 | Ġ C 145 | Ġg et 146 | an t 147 | on e 148 | r i 149 | g e 150 | i l 151 | p p 152 | Ġyou r 153 | u n 154 | er s 155 | Ġ D 156 | ha n 157 | Ġp l 158 | k ing 159 | Ġ if 160 | Ġw ould 161 | Ġm a 162 | es t 163 | Ġg o 164 | h ing 165 | Ġa l 166 | am e 167 | n t 168 | Ġa ll 169 | Ġc om 170 | Ġ N 171 | a u 172 | i ght 173 | Ġe x 174 | es s 175 | Ġc on 176 | Ġw he 177 | a b 178 | Ġ P 179 | Ġs u 180 | Ġ Y 181 | e m 182 | Ġu p 183 | Ġw or 184 | Ġd on 185 | Ġf r 186 | Ġ out 187 | ver y 188 | r a 189 | e op 190 | ĠI t 191 | o l 192 | Ġp ro 193 | eop le 194 | Ġ ( 195 | i g 196 | Ġab out 197 | Ġs ome 198 | u m 199 | u l 200 | u s 201 | Ġan y 202 | at ion 203 | Ġd e 204 | Ġw hat 205 | in k 206 | c t 207 | Ġp eople 208 | at e 209 | Ġ O 210 | Ġ " 211 | â Ģ 212 | ar t 213 | Ġl e 214 | ' m 215 | Ġ E 216 | a st 217 | is t 218 | Ġ v 219 | i ve 220 | Ġfr om 221 | ac k 222 | Ġ F 223 | Ġ G 224 | Ġp o 225 | Ġon e 226 | c h 227 | q u 228 | Ġt r 229 | e l 230 | Ġk n 231 | Ġ L 232 | Ġthe m 233 | oo d 234 | a in 235 | Ġ 1 236 | v en 237 | a nd 238 | Ġmo re 239 | au se 240 | Ġ R 241 | in d 242 | Ġw ill 243 | Ġthe re 244 | ou gh 245 | Ġth ink 246 | Ġs p 247 | Ġbe c 248 | Ġt im 249 | k s 250 | ou r 251 | Ġkn ow 252 | or t 253 | âĢ Ļ 254 | Ġwh o 255 | Ġwhe n 256 | Ġf e 257 | ar d 258 | id e 259 | i e 260 | o re 261 | ec t 262 | e nd 263 | Ġpl ay 264 | Ġha s 265 | t her 266 | Ġn o 267 | Ġp re 268 | Ġl o 269 | .. . 270 | ou nd 271 | it y 272 | Ġbec ause 273 | Ġu s 274 | t h 275 | Ġ 2 276 | Ġw ant 277 | a re 278 | s s 279 | Ġin t 280 | on g 281 | e re 282 | Ġh ow 283 | uc h 284 | Ġt han 285 | ' re 286 | e ar 287 | Ġthe ir 288 | Ġd id 289 | Ġre ally 290 | Ġtim e 291 | ĠY ou 292 | ou s 293 | s o 294 | in e 295 | ac t 296 | a g 297 | i es 298 | Ġh is 299 | ic e 300 | Ġha d 301 | Ġth ing 302 | Ġo ther 303 | Ġe very 304 | Ġb y 305 | u re 306 | 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Ġincl ude 3927 | Ġn or 3928 | r ate 3929 | ĠE ither 3930 | ĠHope fully 3931 | Ġas pect 3932 | Ġg ender 3933 | ack s 3934 | ĠK ore 3935 | ĠT y 3936 | Ġeduc ation 3937 | Ġrequ est 3938 | ar m 3939 | Ġtal ked 3940 | Ġpre m 3941 | Ġy a 3942 | Ġn ational 3943 | Ġsome what 3944 | Ġfeel ings 3945 | Ġper cent 3946 | Ġb reat 3947 | Ġadd ing 3948 | Ġ: / 3949 | Ġcr im 3950 | Ġign or 3951 | Ġfol ks 3952 | ens ion 3953 | A L 3954 | Ġproduc ts 3955 | Ġt ip 3956 | Ġmo ves 3957 | F L 3958 | Ġdirect ion 3959 | Ġattack s 3960 | Ġpiss ed 3961 | k in 3962 | i ers 3963 | Ġboo st 3964 | o h 3965 | Ġvict im 3966 | Ġyour s 3967 | ĠC ons 3968 | Ġdem ocr 3969 | t ion 3970 | Ġinfl u 3971 | Ġsc ared 3972 | ĠN et 3973 | Ġlet s 3974 | Ġstr ange 3975 | sp ect 3976 | Ġsa le 3977 | Ġpl ot 3978 | ĠB ro 3979 | Ġexc use 3980 | ess ion 3981 | ĠS y 3982 | ĠBas ically 3983 | Ġsupp ose 3984 | Ġf ile 3985 | Ġ ^ 3986 | ĠA T 3987 | Ġprofess ional 3988 | Ġsn ow 3989 | arb age 3990 | v iously 3991 | Ġbl ind 3992 | Ġexc lus 3993 | 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